minnesota river basin turbidity tmdl and lake pepin ... · table 3-15. water quality monitoring...

Minnesota River Basin Turbidity TMDL and Lake Pepin Excessive

Nutrient TMDL Model Calibration and Validation Report

Prepared for:

Minnesota Pollution Control Agency St. Paul, MN

Prepared by:

June 5, 2009

3200 Chapel Hill-Nelson Hwy, Suite 105 • PO Box 14409 Research Triangle Park, NC 27709

Tel 919-485-8278 • Fax 919-485-8280

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Typewritten Text

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Minnesota River Model Calibration June 5, 2009

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Table of Contents

List of Tables ............................................................................................................................. iv

List of Figures ............................................................................................................................. vi

1 Introduction........................................................................................................................... 1-1 1.1 Earlier Minnesota River Model................................................................................................................ 1-5 1.2 Approach for Updating Model ................................................................................................................. 1-5

2 Model Quality Objectives..................................................................................................... 2-1 2.1 Objectives of Model Calibration Activities.............................................................................................. 2-1 2.2 Model Calibration and Validation Procedures ......................................................................................... 2-2

2.2.1 Calibration/Validation of Flow.......................................................................................................... 2-2 2.2.2 Calibration/Validation of Water Quality............................................................................................ 2-3

3 Data Assembly...................................................................................................................... 3-1 3.1 Meteorological Data................................................................................................................................. 3-1

3.1.1 Data Sources ...................................................................................................................................... 3-1 3.1.2 Patching of Missing Precipitation Data ............................................................................................. 3-5 3.1.3 Air Temperature................................................................................................................................. 3-7 3.1.4 Dewpoint Temperature ...................................................................................................................... 3-8 3.1.5 Solar Radiation, Wind, and Cloud Cover .......................................................................................... 3-8 3.1.6 Potential Evapotranspiration.............................................................................................................3-12

3.2 Land Use .................................................................................................................................................3-13 3.2.1 Land Use for 2000 ............................................................................................................................3-13 3.2.2 Manure Application Areas................................................................................................................3-14 3.2.3 Conservation Tillage.........................................................................................................................3-14 3.2.4 Conservation Reserves......................................................................................................................3-15

3.3 Soils and Particle Size Distribution.........................................................................................................3-15 3.4 Reach Characteristics ..............................................................................................................................3-18

3.4.1 Use of HEC Flood Elevation Models ...............................................................................................3-18 3.4.2 Revisions to Reach Network.............................................................................................................3-22 3.4.3 Chippewa River Linkage ..................................................................................................................3-24

3.5 Point Sources...........................................................................................................................................3-26 3.5.1 Point Source Missing Data Patching Assumptions...........................................................................3-30 3.5.2 Stabilization Ponds ...........................................................................................................................3-31


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3.6 Atmospheric Deposition..........................................................................................................................3-32 3.7 Observed Flow and Water Quality..........................................................................................................3-32

3.7.1 Flow Gaging .....................................................................................................................................3-32 3.7.2 Water Quality Observations..............................................................................................................3-35

3.8 Secondary Data .......................................................................................................................................3-38 3.8.1 Sediment Source Attribution: River Corridor vs. Upland Sources ...................................................3-38 3.8.2 Hydrology and Erosion from Manured Lands ..................................................................................3-39 3.8.3 Tile Drain Transport .........................................................................................................................3-41 3.8.4 Small-Scale Watershed Models ........................................................................................................3-43

4 Hydrologic Recalibration and Validation............................................................................. 4-1 4.1 Approach.................................................................................................................................................. 4-1 4.2 Parameter Specification ........................................................................................................................... 4-1

4.2.1 Hydrologic Parameters ...................................................................................................................... 4-1 4.2.2 Tile Drain Simulation ........................................................................................................................ 4-5

4.3 Calibration................................................................................................................................................ 4-7 4.4 Validation................................................................................................................................................4-10 4.5 Water Balance .........................................................................................................................................4-10

5 Sediment Recalibration and Validation ................................................................................ 5-1 5.1 Approach.................................................................................................................................................. 5-1

5.1.1 Sheet and Rill versus Gully Erosion .................................................................................................. 5-1 5.1.2 Sediment Transport in Tile Drainage................................................................................................. 5-3 5.1.3 Representation of Bank and Bluff Erosion ........................................................................................ 5-4 5.1.4 Reconciling Source Data with Modeling ........................................................................................... 5-5

5.2 Calibration................................................................................................................................................ 5-7 5.2.1 Sediment Parameter Calibration ........................................................................................................ 5-7 5.2.2 Sediment Calibration and Validation Results ...................................................................................5-17

6 Nutrient Recalibration and Validation.................................................................................. 6-1 6.1 Approach.................................................................................................................................................. 6-1

6.1.1 Inorganic Phosphorus ........................................................................................................................ 6-1 6.1.2 Nitrate (plus Nitrite) Nitrogen ........................................................................................................... 6-8 6.1.3 Ammonia Nitrogen ...........................................................................................................................6-11 6.1.4 Organic Matter Loading....................................................................................................................6-17 6.1.5 Nutrient Bed Concentrations.............................................................................................................6-19

6.2 Nutrient Calibration and Validation........................................................................................................6-19 6.2.1 Total Phosphorus Calibration and Validation...................................................................................6-19


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6.2.2 Total Nitrogen Calibration and Validation .......................................................................................6-26

7 Uncertainty and Sensitivity Analysis for Calibrated Models ............................................... 7-1 7.1 Uncertainty Analysis................................................................................................................................ 7-1 7.2 Sensitivity Analysis.................................................................................................................................. 7-4

8 References ........................................................................................................................... 8-1

Appendix A. Model Land Use by Major Watershed ............................................................. A-1

Appendix B. Hydrologic Calibration Results .........................................................................B-1

Appendix C. Hydrologic Validation Results ..........................................................................C-1

Appendix D. Calibration and Validation for Total Suspended Sediment .............................. D-1

Appendix E. Calibration and Validation for Total Phosphorus..............................................E-1

Appendix F. Calibration and Validation for Total Nitrogen ..................................................F-1


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List of Tables Table 1-1. Waterbody Segments Impaired by Turbidity to be Addressed by the

Minnesota River Model ...................................................................................................... 1-4 Table 2-1. Tolerance Targets for Hydrologic Simulation .................................................................... 2-3 Table 2-2. Calibration Targets for Water Quality ................................................................................ 2-6 Table 3-1. Original Meteorological Stations Used for HSPF Model ................................................... 3-1 Table 3-2. Discontinued Summary of the Day Stations....................................................................... 3-4 Table 3-3. Hourly Precipitation Index Stations.................................................................................... 3-5 Table 3-4. Surface Airways Stations.................................................................................................... 3-5 Table 3-5. Grouping of Patch and Index Stations for Precipitation ..................................................... 3-6 Table 3-6. Grouping of Patch and Index Stations for Air Temperature ............................................... 3-7 Table 3-7. Stations Used for Potential Evapotranspiration ................................................................ 3-13 Table 3-8. Area-weighted Soil Fractions (Percent)............................................................................ 3-17 Table 3-9. Pervious Land Delivered Sediment Fractionation Factors after Clay Enrichment ........... 3-17 Table 3-10. Subbasins with HEC-RAS Generated FTables................................................................. 3-21 Table 3-11. Revisions to Reach Network ............................................................................................ 3-24 Table 3-12. Point Source Discharges Included in the Minnesota River Model ................................... 3-27 Table 3-13. Stabilization Pond Effluent Concentration Assumptions.................................................. 3-32 Table 3-14. USGS Flow Gages used for Hydrologic Calibration........................................................ 3-33 Table 3-15. Water Quality Monitoring Stations used for Calibration.................................................. 3-37 Table 3-16. Sediment Source Attribution in the Minnesota River Basin............................................. 3-38 Table 3-17. Summary of Multi-year Manure Application Studies on Runoff and Sediment

Reductions ........................................................................................................................ 3-40 Table 4-1. Key Hydrologic Parameters................................................................................................ 4-4 Table 4-2. Hydrologic Calibration Summary, 1993-2006 ................................................................... 4-9 Table 4-3. Hydrologic Validation Summary, 1986-1992................................................................... 4-10 Table 4-4. Simulated Components of Total Flow by Major Watershed, 1986-2006 ......................... 4-11 Table 5-1. Calibrated Upland Sediment Transport Parameters............................................................ 5-8 Table 5-2. Bluff Erosion Contribution Rates to Available Stream Bed Sediment ............................. 5-16 Table 5-3. Calibration Statistics for Suspended Sediment (1993-2006) ............................................ 5-19 Table 5-4. Validation Statistics for Suspended Sediment (1993-2006) ............................................. 5-20 Table 5-5. Sediment Loading Rates (tons/ac/yr) Generated by the Minnesota River Basin

Model for 1993-2006........................................................................................................ 5-22 Table 5-6. Percentage Contributions to Total Sediment Load by Land Use and Basin ..................... 5-22 Table 6-1. Inorganic Phosphorus Potency Factors for Surface Erosion from Agricultural

Land Uses. .......................................................................................................................... 6-4 Table 6-2. Inorganic Phosphorus Potency Factor (#/ton) for Scoured Sediment (Gullies)

and Interflow Sediment ...................................................................................................... 6-5


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Table 6-3. Subsurface Inorganic Phosphorus Concentrations.............................................................. 6-6 Table 6-4. Nitrate Surface Buildup/Washoff Parameters for Agricultural Lands

(pounds/acre/day, 1st of month) ........................................................................................ 6-12 Table 6-5. Subsurface Nitrate Concentrations for Agricultural Lands (mg/L, 1st of month) ............. 6-13 Table 6-6. Ammonia (as N) Parameters for Pervious Land Uses for All Watersheds

(1st of month) .................................................................................................................... 6-15 Table 6-7. Surface Potency Factors for Organic Matter (lbs/ton-sediment) ...................................... 6-18 Table 6-8. Subsurface Concentrations of Organic Matter (mg/L, 1st of month) ................................ 6-18 Table 6-9. Calibration Statistics for Total Phosphorus (1993-2006) ................................................. 6-23 Table 6-10. Validation Statistics for Total Phosphorus (1993-2006)................................................... 6-24 Table 6-11. Total Phosphorus Loading Rates (lbs/ac/yr) Generated by the Minnesota River

Basin Model for 1993-2006.............................................................................................. 6-25 Table 6-12. Percentage Contributions to Total Phosphorus Load by Land Use and Basin.................. 6-25 Table 6-13. Calibration Statistics for Total Nitrogen (1993-2006)...................................................... 6-28 Table 6-14. Validation Statistics for Total Nitrogen (1993-2006) ....................................................... 6-29 Table 6-15. Total Nitrogen Loading Rates (lbs/ac/yr) Generated by the Minnesota River Basin

Model for 1993-2006........................................................................................................ 6-30 Table 6-16. Percentage Contributions to Total Nitrogen Load by Land Use and Basin ...................... 6-30 Table 7-1. Uncertainty Analysis for the Model Validation Period (1986-1992).................................. 7-3 Table 7-2. Normalized Sensitivity Coefficients (for Total Annual Loading Rate from

Agricultural Land in the Watonwan River Watershed) ...................................................... 7-4


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List of Figures Figure 1-1. Base Map of the Minnesota River Watershed .................................................................................. 1-2 Figure 1-2. Segments of the Minnesota River Basin Listed for Turbidity Impairments (MPCA, 2005) ........... 1-3 Figure 3-1. Locations of Weather Stations Used for Meteorological Data ......................................................... 3-3 Figure 3-2. Solar Radiation Time Series at Minneapolis AP, Monthly Values................................................... 3-9 Figure 3-3. Solar Radiation Time Series at Rochester, Monthly Values............................................................. 3-9 Figure 3-4. Solar Radiation Time Series at Sioux Falls AP, Monthly Values....................................................3-10 Figure 3-5. Cloud Cover Time Series at Minneapolis AP, Monthly Values ......................................................3-11 Figure 3-6. Cloud Cover Time Series at Rochester, Monthly Values ................................................................3-11 Figure 3-7. Cloud Cover Time Series at Sioux Falls AP, Monthly Values ........................................................3-12 Figure 3-8. Reach coverage of HEC models ......................................................................................................3-20 Figure 3-9. Revised Mainstem Reach Subbasins ...............................................................................................3-23 Figure 3-10. Chippewa River Diversion Dam and Watson Sag, Watson, Minnesota ..........................................3-25 Figure 3-11. Point Source Discharge Locations...................................................................................................3-29 Figure 3-12. Representation of Discharge from Stabilization Ponds ...................................................................3-31 Figure 3-13. USGS Flow Gage Locations............................................................................................................3-34 Figure 3-14. Water Quality Monitoring Stations used for Calibration.................................................................3-36 Figure 4-1. Distribution of Event Infiltration Rate Indices (INFILT) from Soil Properties ................................ 4-2 Figure 4-2. Spatial Distribution of Measured Soil Available Water Capacity .................................................... 4-3 Figure 4-3. Distribution of Lower Zone Soil Storage Capacity .......................................................................... 4-4 Figure 4-4. Tile Drain Density Spatial Distribution. ........................................................................................... 4-6 Figure 4-5. Mean daily flow: Model vs. USGS 05330000, Minnesota River near Jordan, MN ......................... 4-8 Figure 4-6. Water Balance Components, 1986-2006 .........................................................................................4-12 Figure 5-1. Conceptual Representation of Stream Sediment Processing ............................................................ 5-6 Figure 5-2. Observed and Predicted Daily Average TSS, Watonwan River at Garden City, 2000-2006 ........... 5-9 Figure 5-3. Scatterplot of Simulated vs. Observed TSS Load, Watonwan River at Garden City, 1993-2006 ...5-10 Figure 5-4. Power Plot of Observed and Predicted TSS Load vs. Flow, Watonwan River at Garden City,

1993-2006........................................................................................................................................5-10 Figure 5-5. Power Plot of Simulated and Observed TSS Concentrations vs. Flow, Watonwan River at

Garden City, 1993-2006 ..................................................................................................................5-11 Figure 5-6. Predicted Sediment Size Fractions vs. Flow, Watonwan River at Garden City, 1993-2006 ...........5-12 Figure 5-7. Relationship of Shear Stress (τ) to Flow, Watonwan River Reach 733...........................................5-12 Figure 5-8. Active Bed Storage of Sediment in Watonwan Reach 733 .............................................................5-13 Figure 5-9. Active Bed Storage of Sediment in Watonwan Reach 731 (Bluff Area).........................................5-13 Figure 5-10. TSS Concentration Errors (Simulated minus Observed) versus Flow, Watonwan River at

Garden City, 1993-2006 ..................................................................................................................5-14 Figure 5-11. TSS Concentration Errors (Simulated minus Observed) versus Month, Watonwan River at

Garden City, 1993-2006 ..................................................................................................................5-15


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Figure 5-12. Observed and Simulated TSS for the Validation Period (1986-1992), Watonwan River at Garden City..................................................................................................................................5-15

Figure 5-13. Source Attribution of Sediment Loads, 1993-2006 .........................................................................5-17 Figure 5-14. Comparison of SSC (USGS) and TSS (MPCA, MCES) Measurements, Minnesota River

at Jordan...........................................................................................................................................5-18 Figure 6-1. Correlation of Total Phosphorus Concentration with Flow, Minnesota River at Jordan .................6-21 Figure 6-2. Distribution of Total Phosphorus Simulation Errors versus Flow and Month,

Minnesota River at Jordan ...............................................................................................................6-21 Figure 6-3. Example Calibration Plot for Total Phosphorus, Minnesota River at Jordan ..................................6-22 Figure 6-4. Example of Total Nitrogen Calibration, Watonwan River at Garden City......................................6-26 Figure 6-5. Prediction Error (Simulated minus Observed) for Total Nitrogen, Watonwan River

at Garden City..................................................................................................................................6-27


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1-1

1 Introduction The Minnesota River begins at Big Stone Lake on the South Dakota border, and from there flows 335 miles southeast to Mankato and then northeast to join the Mississippi at Fort Snelling (Figure 1-1). The watershed covers 16,770 square miles. Most of the watershed was originally native prairie and pothole wetlands. Now it is part of the corn belt, with the majority of the land area converted to corn-soybean rotation and other types of agriculture. For many parts of the watershed conversion to agriculture required enhancement of drainage through ditches and subsurface tile drains.

Many portions of the Minnesota River drainage exhibit high levels of turbidity. Turbidity of water is a measure of light scattering, and is caused by suspended and dissolved matter, such as clay, silt, organic matter, algae, and stains due to dissolved organic compounds. In the Minnesota River, increased turbidity is primarily due to total suspended solids (TSS). TSS is the concentration of suspended material in the water as measured by the dry weight of solids filtered out of a known volume of water. TSS can include sand, silt, clay, plant fibers, algae, and other organic material. Inorganic sediment in water typically makes up most of the TSS and turbidity under conditions when turbidity is elevated; however, organic matter can be the dominant contributor to turbidity at other times, particularly under low flow conditions.

Turbidity limits light penetration and inhibits healthy plant growth on the river bottom. With elevated turbidity, aquatic organisms may have trouble finding food, gill function may be affected, and elevated amounts of sediment associated with turbidity can cause spawning areas and other habitat to be covered. To address these problems, Minnesota water quality regulations establish a limit or water quality standard for turbidity. In class 2B waters (such as the Minnesota River), the water quality standard for turbidity is 25 NTUs. “NTUs” stands for Nephelometric Turbidity Units, which is a standardized measure of light scattering in a water sample.

Eighteen waterbody segments in the Minnesota River basin are listed as impaired by turbidity and will require the development of TMDLs (Figure 1-2 and Table 1-1).

The same loading of sediment that causes increased turbidity in the Minnesota River also affects water quality downstream, particularly in Lake Pepin, an impoundment of the Mississippi River. The Minnesota River is estimated to contribute about 80 to 90 percent of the sediment load to Lake Pepin (Kelley et al., 2006). Lake Pepin is subject to accelerated infilling due to upstream sediment loads along with turbidity problems. Sediment-associated nutrients, such as phosphorus, move along with the sediment. At low flows, these increased nutrient loads accelerate algal growth in Lake Pepin.


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Figure 1-1. Base Map of the Minnesota River Watershed


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Figure 1-2. Segments of the Minnesota River Basin Listed for Turbidity Impairments

(MPCA, 2005)


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Table 1-1. Waterbody Segments Impaired by Turbidity to be Addressed by the Minnesota River Model

Reach

Minnesota River; Chippewa R to Stoney Run Cr 07020004-501

Yellow Medicine River; Spring Cr to Minnesota R 07020004-502

Minnesota River; Timms Cr to Redwood R 07020004-509

Minnesota River; Minnesota Falls Dam to Hazel Cr 07020004-515

Hawk Creek; Spring Cr to Minnesota R 07020004-587

Chippewa River; Watson Sag Diversion to Minnesota R 07020005-501

Redwood River; Ramsey Cr to Minnesota R 07020006-501

Minnesota River; Shahaska Cr to Rogers Cr 07020007-501

Minnesota River; Blue Earth R to Shahaska Cr 07020007-502

Minnesota River; Cottonwood R to Little Cottonwood R 07020007-503

Minnesota River; Swan Lk Outlet to Minneopa Cr 07020007-505

Minnesota River; Beaver Cr to Birch Coulee 07020007-514

Cottonwood River; JD #30 to Minnesota R 07020008-501

Blue Earth River; Le Sueur R to Minnesota R 07020009-501

Blue Earth River; Rapidan Dam to Le Sueur R 07020009-509

Watonwan River; Perch Cr to Blue Earth R 07020010-501

Le Sueur River; Maple R to Blue Earth R 07020011-501

Minnesota River; Rush R to High Island Cr 07020012-503

The turbidity impairment listings are based on both direct observations of turbidity and measurements of total suspended solids (TSS). Where insufficient turbidity measurements are available, TSS is used as a surrogate based on strong observed correlation between TSS and turbidity.

In addition to turbidity, the model updates are designed to provide support to the Lake Pepin nutrient TMDL by evaluating the export of nutrients (particularly phosphorus) from the Minnesota River above Jordan, and ultimately to the Mississippi River and Lake Pepin.


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1.1 THE 2002 MINNESOTA RIVER MODEL Starting in 2000 and concluding in 2002, Tetra Tech (2002) completed the development and calibration of comprehensive HSPF watershed and receiving-water models for the entire Minnesota River basin between Lac qui Parle and Jordan, MN, building upon an earlier set of models created and partially calibrated by MPCA. The Minnesota River Basin Model consists of 10 linked HSPF models, addressing eight major subwatersheds plus the Middle Minnesota and Lower Minnesota River mainstem to Jordan, MN. The focus of the 2002 modeling effort was on support for the DO/BOD TMDL for the Lower Minnesota River, which required accurate simulation of nutrient loading and algal response in addition to DO and BOD. Development of the model first required calibration of hydrology and sediment transport, as the movement of water and sediment drive the movement of other constituents.

A detailed simulation model thus already exists for sediment in the basin. However, the previous modeling system had some limitations for sediment simulation to support TMDL allocations for turbidity. Most notably, the model was calibrated to existing data (through 1992), which are primarily at the large watershed scale. This makes it difficult to validate model performance for loading from individual source areas and land uses, because only the net downstream effects (which combine loading and channel processes) are observed. In addition, reviewers have noted possible discrepancies between simulated and observed sediment loads, which may reflect either shortcomings in the model or the effects of grab sampling that are unrepresentative of the cross-sectional average concentration. Channel hydraulics during storm events are of particular importance for the mobilization and transport of sediment, and may need to be refined. In addition, the model is based on 1990 land use, and needed to be updated to reflect current land use and management practices.

1.2 APPROACH FOR UPDATING MODEL The earlier Minnesota River Basin Model was developed and calibrated using land use data from 1989-90 and meteorological time series for 1986 to 1992. The model was subsequently updated to reflect changes in agricultural land use and major wastewater discharges through 2000; however, it has not been implemented for meteorology past 1992, nor has it incorporated a complete representation of more recent land uses.

Tetra Tech’s approach began with updating the model simulation period and underlying data coverages through 2006. This provides a significantly longer period for model testing. A number of specific enhancements were made to better refine the suspended sediment calibration, most significantly including the use of existing flood models to improve estimates of channel shear stress and better characterization of river corridor sources. Additional information from new smaller-scale modeling efforts is also incorporated. These efforts have resulted in the creation of an improved model of demonstrated predictive ability that meets MPCA’s needs for completing the turbidity and nutrient TMDLs.

The revised model has been recalibrated for flow, sediment, total phosphorus, and total nitrogen. In addition, the model continues to predict a full suite of other components, including BOD/DO, algae, and fecal coliform bacteria, based on the parameters established for the previous version of the model. However, quantitative recalibration for these other parameters was not addressed in the present work effort.


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2-1

2 Model Quality Objectives Environmental simulation models are simplified mathematical representations of complex real world systems. Models cannot accurately depict the multitude of processes occurring at all physical and temporal scales – especially in a system as large and complex as the Minnesota River basin. Models can, however, make use of known interrelationships among variables to predict how a given quantity or variable would change in response to a change in an interdependent variable or forcing function. In this way, models can be useful frameworks for investigations of how a system would likely respond to a perturbation from its current state. To provide a credible basis for prediction and the evaluation of mitigation options, the ability of the model to represent real world conditions must be demonstrated through a process of model calibration and validation.

USEPA (2002) recommends following a systematic planning process to define quality objectives and performance criteria. For modeling projects, systematic planning identifies the expected outcome of the modeling, its technical goals, cost and schedule, and the criteria for determining whether the inputs and outputs of the various intermediate stages of the project, as well as the project’s final product, are acceptable.

The primary objective of this work is to support MPCA’s development of turbidity TMDLs for the Minnesota River basin. The work also supports MPCA’s analysis of phosphorus loading to Lake Pepin, for which the Minnesota River represents a major source. The ultimate quality objectives for this project are thus to provide accurate and defensible estimates of (1) the frequency distribution of turbidity in listed segments of the Minnesota River basin and (2) phosphorus loading leaving the downstream end of the model domain and potentially available for delivery to Lake Pepin. To accomplish these objectives, the model must simulate the components of turbidity (predominantly suspended sediment) and the factors that determine the transport of sediment and nutrients. As stated in MPCA (2006), “The basic objective for an impairment analysis is to understand the cause-and-effect relationships governing water quality such that management alternatives can be explored that will bring the water quality back into compliance. Water quality monitoring is necessary to define existing conditions, but provides little predictive capability. Employing an analytical tool such as a water quality model helps both to provide an understanding of the complex cause-and-effect relationships currently affecting the impairment and to provide a capability for extrapolating predictions of water quality over space and time. A modeling analysis can also be used to understand and project the consequences of alternative management and planning activities. Models can significantly improve the informational background on which decisions are based and substantially reduce the cost of managing water resources.”

2.1 OBJECTIVES OF MODEL CALIBRATION ACTIVITIES The principal study questions for this project address the occurrence of turbidity in excess of water quality standards in the Minnesota River basin and the export of phosphorus from the Minnesota River basin. To develop a TMDL, the model needs to show the response of the system to future changes in land use and management practices. The general objective for model calibration is to create a reliable predictive tool that can be used to evaluate such responses. To ensure reliable tools, the model will be subjected to an iterative process of calibration and validation.

Calibration consists of the process of adjusting model parameters to provide a match to observed conditions. Calibration is necessary because of the semi-empirical nature of water quality models. Although these models are formulated from mass balance principles, most of the kinetic descriptions in the models are empirically derived. These empirical derivations contain a number of coefficients that are usually determined by calibration to data collected in the waterbody of interest.


2-2

Calibration tunes the models to represent conditions appropriate to the waterbody and watershed under study. However, calibration alone is not sufficient to assess the predictive capability of the model, or to determine whether the model developed via calibration contains a valid representation of cause and effect relationships. To help determine the adequacy of the calibration and to evaluate the uncertainty associated with the calibration, the model is subjected to a validation step. In the validation step, the model is applied to a set of data independent from that used in calibration.

For the Minnesota River basin, an earlier HSPF model was developed, calibrated, and validated by Tetra Tech (2002). The previous model simulates flow, sediment, nutrients, algae, dissolved oxygen, oxygen demand, and bacteria, but has been calibrated only through 1992. In addition, the focus of the previous application was on dissolved oxygen and nutrients, rather than sediment. Nonetheless, the prior effort provides an important starting point for creation of an improved model to address turbidity and phosphorus loading issues under current conditions.

While the model developers have striven to achieve the highest quality of fit possible during calibration and validation, the decision purposes of the models must be kept in mind. Specifically, the models will be used to evaluate whether or not management objectives for suspended sediment, turbidity, and phosphorus loading will be met under a variety of future conditions, and to provide a basis for comparison among management alternatives. While some degree of uncertainty in model predictions is unavoidable; the calibration needs to demonstrate that the importance of different sources of loading is fairly and accurately evaluated while avoiding simulation bias – a systematic deviation between model predictions and observations – to the extent practicable.

2.2 MODEL CALIBRATION AND VALIDATION PROCEDURES

2.2.1 Calibration/Validation of Flow The HSPF watershed model contains components to address runoff and constituent loading from pervious land surfaces (PERLNDs), runoff and constituent loading from impervious land surfaces (IMPLNDs), and flow of water and transport/transformation of chemical constituents in stream reaches (RCHRESs). Primary external forcing is provided by the specification of meteorological time series. The model operates on a lumped basis within subwatersheds. Upland responses within a subwatershed are simulated on a per-acre basis and converted to net loads on linkage to stream reaches. Within each subwatershed, the upland areas are separated into multiple land use categories.

Hydrologic response itself is not a direct indicator associated with the principal study questions, but adequate hydrologic calibration provides the necessary basis for the water quality calibration. Indeed, the accuracy of the water quality simulation is to a large extent constrained to be no better than the accuracy of the hydrologic calibration.

The water balance represented by the model was not expected to change significantly from the previous successful calibration in response to the additional work requested under the present assignment – except that simulation of storm flow peaks would potentially be improved through incorporation of HEC results in the FTables. However, the extension of the model through 2006 does provide 14 additional years for calibration and validation. Accordingly, the targets for hydrologic calibration remained the same as were specified for the previous model application, and the approach to hydrologic re-calibration followed the recommendations contained in the HSPEXP expert system (Lumb et al., 1994) and USEPA technical modeling guidance for HSPF (USEPA, 1999).

In the present effort, the model is extended for the period 1993-2006. The additional simulation period provides ample data for reevaluation of the calibration and validation of flow simulation. The revised model incorporates a shift in land use in the middle 1990s. Therefore, a refinement of the simulation of


2-3

flow contained in the previous model is conducted on the period from 1993-2006. Modifications to the calibration of the previous model are tested through validation reapplication to the 1986-1992 time period.

Calibration of the HSPF model is a sequential process, beginning with hydrology, followed by the movement of sediment, and chemical water quality. Hydrologic calibration uses the standard operating procedures for the model described in Donigian et al. (1984), Lumb et al. (1994), USEPA (1999), and USEPA (2006). The modeling QAPP proposed tolerance targets for the flow simulation, summarized in the first five lines of Table 2-1. These targets should be met in both the calibration period (1993-2006) and validation period (1986-1992), using the same set of parameters.

Table 2-1. Tolerance Targets for Hydrologic Simulation

Model Component

Existing Minnesota River Basin Model

(1986-1992) Acceptable Tolerance

1. Error in total volume -2.2% - +3.1% ± 10%

2. Error in 50% lowest flows -1.7% - +9.6% ± 10%

3. Error in 10% highest flows -2.7% - +7.9% ± 15%

4. Error in storm volume -13.9% - +3.8% ± 15%

5. Seasonal volume error 2.6% - 37.5% ± 10%

6. Winter volume error Not previously reported ± 30%

7. Spring volume error Not previously reported ± 30%

8. Summer volume error Not previously reported ± 30%

9. Fall volume error Not previously reported ± 30%

The seasonal volume error referred to in Table 2-1 and the QAPP is that proposed by Lumb et al. (1994), which is calculated as the average summer error minus the average winter error. Tetra Tech now believes that this measure has somewhat limited usefulness as a calibration target because (1) it can provide apparently good results when there are large, but compensating, errors in summer and winter estimates, (2) it ignores spring and fall simulation errors, and (3) dependence on winter errors can be problematic in Minnesota climate, where ice often interferes with accurate flow gaging. Therefore, Tetra Tech proposed evaluation on the basis of average volume error in each season individually (items 6 through 9) rather than through the seasonal volume error (item 5). Nonetheless, the seasonal volume error is presented for consistency with the QAPP.

It is important to clarify that the tolerance ranges are intended to be applied to mean values, and that individual events or observations may show larger differences and still be acceptable (Donigian, 2000).

Additional statistics, such as the coefficient of model fit efficiency (Nash-Sutcliffe coefficient), which measures the predictive ability of the model for individual daily flows, are also very useful in evaluating model hydrologic calibration and are reported, although specific targets were not defined in the QAPP.

2.2.2 Calibration/Validation of Water Quality Unlike flow, water quality parameters are not observed continuously. The calibration must therefore rely on comparison of continuous model output to point-in-time-and-space observations. This creates a situation in which it is not possible to fully separate error in the model from variability inherent in the observations. For example, a model could provide an accurate representation of an event mean or daily


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average concentration in a reach, but an individual observation at one time and one point in a reach itself may differ significantly from the average. For this reason, it is important to use statistical tests of equivalence that are relevant to the principal study questions in the evaluation of the water quality calibration.

A focus of the present effort is the improvement of model fit for suspended sediment. USEPA (2006) recently released BASINS Technical Note 8 on Sediment Parameter and Calibration Guidance for HSPF. In addition to appropriate ranges of parameter values, this contains as an appendix a paper by Donigian and Love presented at the Water Environment Federation TMDL 2003 Conference entitled “Sediment Calibration Procedures and Guidelines for Watershed Modeling.” Together, these comprise the most detailed and up-to-date guidance on use of HSPF for sediment simulation. The recommended calibration procedure is summarized as typically including the following components:

1. Estimating target (or expected) sediment loading rates from the landscape, often as a function of topography, land use, and management practices

2. Calibrating the model loading rates to the target rates

3. Adjusting scour, deposition and transport parameters for the stream channel to mimic expected behavior of the streams/waterbodies

4. Analyzing sediment bed behavior (i.e., bed depths) and transport in each channel reach as compared to field observations

5. Analyzing overall sediment budgets for the land and stream contributions, along with stream aggrading and degrading behavior throughout the stream network

6. Comparing simulated and observed sediment concentrations, including particle size distribution information, and load information where available

7. Repeating steps 1 through 6 as needed to develop a reasonable overall representation of sediment sources, delivery, and transport throughout the watershed system

The nutrient simulation depends on both the hydrologic and sediment calibration, along with the specification of loading from point sources. Errors and uncertainties in the hydrology and sediment simulation will propagate into the nutrient simulation. Perhaps more importantly, the lack of detailed time series for loading from point sources intrinsically limits the accuracy of the nutrient simulation, as was demonstrated in the previous model application.

For both sediment and nutrients it is unreasonable to propose that the model predict all temporal variations in load. Unmonitored changes in point source loading, loading due to bluff failures, variations in tillage, fertilization, and harvest activities on local farms, and precipitation events that are not adequately represented by the available gage network will all result in unavoidable deviations between the model predictions and observations. The model should, however, provide an accurate representation of long-term and seasonal trends in concentration and load, and correctly represent the relationship between flow and load.

For sediment, an issue of particular concern is that the majority of available water column measurements represent total suspended solids (TSS) rather than suspended sediment concentrations (SSC). MPCA and Metropolitan Council Environmental Services (MCES) have primarily reported TSS, while the U.S. Geological Survey (USGS) has primarily reported SSC, at least in later data. These two measures are often taken as equivalent, but represent different analytical methods (Gray et al., 2000). SSC measurements determine all the sediment within a water sample, either by filtration or evaporation. The TSS measurements use a predetermined volume (typically 0.1 L) from the original water sample obtained while the sample is being mixed with a magnetic stirrer. The aliquot is then passed through a filter, which is dried and weighed. The most fundamental difference between the two measurements is that the TSS method involves withdrawing a small subsample by pipette. If a sample contains a significant percentage


2-5

of sand-size material, then stirring the sample before sub-sampling will rarely produce an aliquot representative of the total sediment concentration in the sample, due to the rapid settling properties of sand-size material. A subsample drawn from the middle of the original sample may underestimate total sediment concentrations, but a subsample drawn from the lower part of the sample may overestimate the sand content. In addition, the pipette used for subsampling may clog when larger size material is present.

Field sampling procedures can also have a significant impact on reported sediment concentrations. USGS typically uses an equal-width-increment or equal-discharge-increment approach to obtain a cross-sectional composite sample representative of transport at a sampling station. TSS samples are obtained in a variety of ways, but often represent the results of a single grab obtained by submerging an open bottle from a bridge or other convenient location, which may not be representative of the width-average transport. In deeper stream segments, sampling may underestimate total sediment movement, the variable which is predicted by the model, because they omit bedload.

On the whole, TSS samples tend to be biased low relative to SSC, with greater bias as sand content increases. The results are not consistently predictable, however, and individual TSS samples may be significantly lower or higher than paired SSC measurements (Gray et al., 2000).

For these reasons it is important to use statistical tests of equivalence between observed and simulated concentrations, rather than relying on a pre-specified model tolerance on difference in concentrations.

As specified in the modeling QAPP, the model objective for comparing paired observed and simulated sediment concentration values is to minimize the discrepancy, without specifying an absolute tolerance on the difference. Instead of pre-specifying an absolute tolerance, two-sample t-tests are applied on the differences in mean concentration and mean load, with a target that there should be less than an 80 percent probability that the means differ (i.e., a probability value greater than 0.20). Higher probability values are desirable – and were obtained in some watersheds in the previous model – but are not always achievable. A problem with the t-test is that the test is on a null hypothesis that the mean difference is exactly equal to zero, not whether the difference is significant. Therefore, a low value on the t-test (rejection of the null hypothesis) is considered of practical significance only when the mean difference is greater than 10 percent. To ensure that bias relative to flow regime is not present, an additional test is performed on the equality of transport curve slopes (log-log regression of observed versus predicted load), along with graphical analysis of the distribution of error relative to flow and season. Total sediment loads predicted by the model are also compared to empirical estimates obtained using FLUX analyses. (The FLUX comparison is given somewhat less weight than the t-tests on load proposed in the QAPP because the FLUX, which are themselves based on the grab sample data, may be subject to extrapolation error during unmonitored periods.)

Small sample sizes also present a problem for comparison, particularly when they cover only a few events. An arbitrary minimum of 20 samples is imposed for evaluation of the statistical tests. (The largest data sets have up to 985 samples for sediment and 667 samples for nutrients during the calibration period.)

Similar issues affect the comparison of model predictions and observed data for nutrients. Most of the nutrient data are from grab samples, representing a point in space and time. These point measurements will naturally be more variable than model predictions of daily average concentrations integrated over a model reach. In addition, the quality of the simulation of total phosphorus is largely dependent on the quality of the sediment simulation. Draft tolerance objectives for total nitrogen and total phosphorus were specified based on HSPF guidance and experience with the previous version of the model, with the assumption that significant improvements in fit could be obtained from the planned updates to the model.

The targets for the water quality calibration and validation are summarized in Table 2-2. This table does not cover aspects of the previous model calibration, such as temperature simulation, that are not expected to change significantly as a result of the work proposed in this work order. As with flow, the tolerance


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ranges are intended to be applied to mean values, and individual events or observations may show larger differences and still be acceptable (Donigian, 2000).

Table 2-2. Calibration Targets for Water Quality

Model Component Existing

Minnesota River Basin Model Target

SEDIMENT

Error in concentration -215.7% - 48.9% Minimize

Paired t-test p value, concentration 0.006 – 0.81 > 0.20

Paired t-test p value, load 0.001 – 0.93 > 0.20

Difference in transport curve slope 7 % - 24 % < 20 %

TOTAL NITROGEN

Error in concentration -34.8% - +13.2% ± 25%

Paired t-test p value, concentration 0.05 – 0.85 > 0.20

Paired t-test p value, load 0.05 – 0.71 > 0.20

Difference in transport curve slope Not reported < 20 %

TOTAL PHOSPHORUS

Error in concentration -100 % - +9.2% ± 25%

Paired t-test p value, concentration <0.01 – 0.55 > 0.20

Paired t-test p value, load <0.01 – 0.72 > 0.20

Difference in transport curve slope Not reported < 20 %

The various measures should be considered together in a weight of evidence analysis. It should be emphasized that the targets shown in Table 2-2 are desired goals that may or may not be achievable. If a target is not met despite all reasonable efforts this does not imply that the model is unacceptable for its intended uses, but does require a thorough discussion of the problem, an evaluation of potential effects on model uses, and potential corrective actions that could be employed to improve model performance (e.g., additional data collection or modification of model code).


3-1

3 Data Assembly

3.1 METEOROLOGICAL DATA The previous HSPF model application, which provides simulations covering 1986-1992, is driven by precipitation records from 43 weather stations coupled with other meteorological time series from a smaller subset of stations. An update of the model simulation period through 2006 required that existing meteorological time series be extended. The MetAdapt and WDMUtil tools were used to process and append the previous weather time series.

3.1.1 Data Sources For the previous application, 41 Summary of the Day (SOD) stations, part of a cooperative network associated with the National Climatic Data Center, and four Surface Airways (SA) stations associated with the National Weather Service were used to develop the meteorological time series. These 45 stations were used either directly in the modeling, or as index stations to append, patch, and/or generate a series (Table 3-1 and Figure 3-1). Not all of these stations have remained operational, while other useful stations have come on line. Tetra Tech surveyed the availability and completeness of weather data for 1993 to 2006, and then compiled and patched time series from the selected stations using the MetADAPT program. Also, due to differences in data processing during this update, three of the original SOD stations used for data patching were not necessary (Artichoke Lake, Chaska, and Farmington 3 NW). Additional stations were added to fill discontinued time series, and to improve the precipitation patching process.

Table 3-1. Original Meteorological Stations Used for HSPF Model

SOD Cooperative ID SA WBAN Name Elevation (ft, NGVD) Comment

215435 14922 Minneapolis AP (MSP) 872

397667 14944 Sioux Falls AP (FSD) 1,428

- 14925 Rochester 1,304

- 14926 St. Cloud 1,018

138026 - Swea City 4 W 1,239

138270 - Titonka 1,170

210112 - Alexandria Chandler FL 1,416

210287 - Artichoke Lake 1,075

210667 - Benson 1,040

210852 - Blue Earth 1,065

210981 - Bricelyn 1,170

211263 - Canby 1,243

211465 - Chaska 720 Gaps begin in 2000

212038 - Dawson 1,055


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SOD Cooperative ID SA WBAN Name Elevation (ft, NGVD) Comment

212698 - Fairmont 1,187

212737 - Farmington 3 NW 980

213076 - Gaylord 1,018

213174 - Glenwood 2 WNW 1,198

214176 - Jordan 1S 930

214546 - Lamberton SW Exp Stn 1,144

214641 - LeCenter 1,070 Ends in 1991

214994 - Madison Sewage Plant 1,080

215073 - Mankato 850

215204 - Marshall 1,152

215400 - Milan 1 NW 1,020

215482 - Minneota 1,170

215563 - Montevideo 1 SW 985

215638 - Morris WC Exp Stn 1,140

215842 - New London 1,240

215892 - New Ulm 2S 799 Discontinued

216152 - Olivia 3 SE 1,100

216839 - Redwood Falls 1,044

217326 - St James Filt Plant 1,100

217405 - St Peter 2 SW 850

217907 - Springfield 1 NW 1,066

218025 - Stewart 1,040 Ends in 2003

218323 - Tracy 1,403

218429 - Tyler 1,735

218520 - Vesta 1,080

218692 - Waseca Exp Station 1,153

218808 - Wells 1,197

219000 - Willmar Radio KWLM 1,120 Discontinued

219033 - Windom 1,375

219046 - Winnebago 1,110

219208 - Young America 1,020 Ends in 1995


3-3

Figure 3-1. Locations of Weather Stations Used for Meteorological Data


3-4

3.1.1.1 Summary of the Day Weather Stations Multiple sources were used to gather precipitation and air temperature data for the SOD stations. These included the following.

EarthInfo (1993-2004)

Minnesota State Climatologist (2005-2006)

Idaho State Climatologist (2005-2006)

South Dakota Climatologist (2005-2006)

These data were processed, assigned time stamps, and appended to create the necessary record for each respective station. Flag information was preserved during this step and this process was performed for each rainfall and air temperature record.

There were six stations that were either discontinued prior to 1993 or ended in 1993 or later. Those stations with summary comments regarding how they were addressed are presented in Table 3-2. This applied to precipitation for all six stations and air temperature if applicable.

Table 3-2. Discontinued Summary of the Day Stations

Discontinued Stations Station Used to Append

Cooperative ID Name

Elevation (ft, NGVD)

Data Ends

Cooperative ID Name

Elevation (ft, NGVD)

General Location from

Discontinued Station

214641 LeCenter 1,070 1991 215571 Montgomery 1,100 5.9 mi NE

215892 New Ulm

2S 799 1992 215887 New Ulm

2SE 890

0 minutes latitude, 2 minutes longitude

218025 Stewart 1,040 2003 213962 Hutchinson

1N 1,095 12 mi N

219000

Willmar Radio KWLM 1,120 1992 219004 Willmar RTC 1,100

1 minute latitude, 1 minute longitude

219208 Young

America 1,020 1995 219085 Winsted 1,030 14.6 mi NE

Hourly precipitation (HP) stations were used as index stations in the process of patching and disaggregating the SOD rainfall records (Table 3-3). The hourly rainfall records came from two sources: 1) EarthInfo CD set and 2) NCDC web site. The stations that ended in 2004 were retrieved from the EarthInfo CD set and processed with MetAdapt. They were manually appended by using an ASCII editor to include missing flags for 2005-2006. The two airport stations were similarly processed. However, for 2005-2006, they were appended with the hourly rain record available from the NCDC web site.


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Table 3-3. Hourly Precipitation Index Stations

HP Cooperative ID Name Data Ends

MN3311 Granite Falls 2004

MN4453 Lakefield 2 NE 2004

MN4721 Le Sueur 2004

MN5435 Minneapolis AP (MSP) 2006

MN7602 Sherburn 3 WSW 2004

MN8323 Tracy 2004

SD1076 Brookings 2 NE 2004

SD7667 Sioux Falls AP (FSD) 2006

3.1.1.2 Surface Airways Weather Stations SA stations are major weather data collection stations generally located at airports. In addition to precipitation, parameters such as wind, relative humidity, and dew point temperature are typically collected on an hourly basis. Four surface airways stations were utilized for this work (Table 3-4). The data were retrieved from one or more of the following sources: EarthInfo CD set, NCDC web site, and Midwest Regional Climate Center (MRCC).

Table 3-4. Surface Airways Stations

SA WBAN Name Airport Code

14922 Minneapolis AP MSP

14925 Rochester RST

14926 St. Cloud STC

14944 Sioux Falls AP FSD

3.1.2 Patching of Missing Precipitation Data The MetAdapt tool, using the Normal Ratio Method patching routine, was used to patch precipitation time series from daily SOD stations and disaggregate into hourly time series using HP index stations (Table 3-5). Since airport stations are typically better maintained, at least one index station was always assigned from an airport.

The precipitation patching for the past work (1986 – 1992) was performed by first addressing the missing data in the patch record by using SOD stations as index. Next, the HP stations were used as a disaggregating template only. The model extension through 2006 took advantage of the MetAdapt tool to streamline the weather data processing and to use hourly weather stations for both patching missing data and to provide a disaggregation template.


3-6

Table 3-5. Grouping of Patch and Index Stations for Precipitation

Patch Station SOD Cooperative ID Patch Station Name Index HP Station(s)

215435 Minneapolis AP (MSP) MN5435

397667 Sioux Falls AP (FSD) SD7667, MN4453

138026 Swea City 4 W SD7667, MN7602, MN4453

138270 Titonka SD7667, MN7602, MN4453

210112 Alexandria Chandler FL MN5435, MN3311, MN8323

210667 Benson SD7667, MN3311, MN8323

210852 Blue Earth MN5435, MN7602, MN4453

210981 Bricelyn MN5435, MN7602, MN4453

211263 Candy SD7667, SD1076, MN8323

212038 Dawson SD7667, MN3311, MN8323

212698 Fairmont MN5435, MN7602, MN4453

213076 Gaylord MN5435, SD7667, MN5987

213174 Glenwood 2 WNW MN5435, MN3311, MN8323

214176 Jordan 1S MN5435, MN4721, MN4453

214546 Lamberton SW Exp Stn SD7667, MN8323, MN4453

214641 LeCenter MN5435, MN4721, MN4453

215073 Mankato MN5435, MN4721, MN4453

215204 Marshall SD7667, MN8323, MN3311

215400 Milan 1 NW SD7667, MN3311, MN8323

215482 Minneota SD7667, MN8323, MN3311

215563 Montevideo 1 SW SD7667, MN3311, MN8323

215638 Morris WC Exp Stn SD7667, MN3311, MN8323

215842 New London MN5435, MN3311, MN8323

215892 New Ulm 2SE MN5435, MN4721, MN4453

216152 Olivia 3 SE MN5435, MN3311, MN8323

216839 Redwood Falls MN5435, MN3311, MN8323

217326 St James Filt Plant MN5435, MN7602, MN4453

217405 St Peter 2 SW MN5435, MN4721, MN4453

217907 Springfield 1 NW MN5435, MN8323, MN4453

218025 Stewart MN5435, MN4721, MN4453

218323 Tracy SD7667, MN8323, MN4453


3-7

Patch Station SOD Cooperative ID Patch Station Name Index HP Station(s)

218429 Tyler SD7667, MN8323, SD1076

218520 Vesta SD7667, MN8323, MN3311

218692 Waseca Exp Station MN5435, MN5987, MN4453

218808 Wells MN5435, MN4721, MN4453

219000 Willmar MN5435, MN3311, MN8323

219033 Windom SD7667, MN4453, MN8323

219046 Winnebago MN5435, MN7602, MN4453

219208 Young America MN5435, MN4721, MN4453

3.1.3 Air Temperature Hourly air temperature records were developed from the daily maximum and minimum observations at select SOD stations. First, neighboring SOD stations were used as index stations to address missing periods. Then, an hourly template developed from an actual hourly observation record of air temperature was used to create the calculated hourly air temperature series at the respective patched station. Table 3-6 presents each patched station and its respective index station.

Table 3-6. Grouping of Patch and Index Stations for Air Temperature

Station Cooperative ID Station Name

Hourly SA Station for Developing

Hourly Template

SOD Index Station for Patching Missing

Records

210667 Benson 14944 215400, 215638

211263 Canby 14944 215563, 215204

212698 Fairmont 14925 138026, 219046

213076 Gaylord 14922 217405, 215073

213174 Glenwood 2 WNW 14922 210112, 215638

214176 Jordan 1S 14922 215073, 217405

214546 Lamberton SW Exp Stn 14944 211263, 217907

215073 Mankato 14922 218692, 217405

215204 Marshall 14944 218323, 214546

215563 Montevideo 1 SW 14944 215400, 211263

215892 New Ulm 2SE 14922 217405, 216152

216152 Olivia 3 SE 14922 215842, 211263

216835 Redwood Falls 14922 216152, 217907

217326 St James Filt Plant 14925 212698, 219046


3-8

Station Cooperative ID Station Name

Hourly SA Station for Developing

Hourly Template

SOD Index Station for Patching Missing

Records

217405 St Peter 2 SW 14922 215073, 213076

217907 Springfield 1 NW 14922 214546, 219033

218025 Stewart 14922 216152, 213076

218323 Tracy 14944 215204, 214546

218692 Waseca Exp Station 14925 215073, 218808

219000 Wilmar 14922 215842, 216152

219033 Windom 14944 214546, 217907

219046 Winnebago 14925 138026, 218808

3.1.4 Dewpoint Temperature Most Summary-of-the-Day stations do not report dewpoint, so the dewpoint temperature series were developed in WDMUtil. They were calculated on a daily basis by assuming the minimum daily temperature at the stations in Table 3-6 were equivalent to the daily dewpoint temperature. Dewpoint time series were developed for the same set of stations used for hourly temperature.

3.1.5 Solar Radiation, Wind, and Cloud Cover Calculated ground level solar radiation series were obtained from the Midwest Regional Climate Center (MRCC) for three SA stations: Minneapolis, Rochester, and Sioux Falls. The series for the Minneapolis station appeared inconsistent when compared to the other two stations (Figure 3-2 through Figure 3-4), particularly beginning in 1997. The cause of this discrepancy could not be determined, although it may be due to differences in measurement methods. Therefore, data from Minneapolis during the period 1997-2006 were replaced using data collected at Rochester, geographically closer to Minneapolis. Solar radiation data provided by MPCA for the earlier model were consistent with MRCC data for 1986-1990, but not for 1991-1992. Data from 1991-1992 in the previous model were replaced using MRCC data in all three solar time series. The daily values were disaggregated to hourly values using WDMUtil.

Solar radiation data at Rochester and Sioux Falls appear to show an upward trend beginning the early 1990s, which may reflect differences in estimation methods. This is important, as solar radiation and wind movement are used to calculate potential evapotranspiration, which is one of the key forcing functions for the hydrologic model.


3-9

Monthly sum of solar radiation at Minneapolis AP

0

2,000

4,000

6,000

8,000

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14,000

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20,000

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lang

leys

Figure 3-2. Solar Radiation Time Series at Minneapolis AP, Monthly Values

Monthly sum of solar radiation at Rochester

0

5,000

10,000

15,000

20,000

25,000

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lang

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Figure 3-3. Solar Radiation Time Series at Rochester, Monthly Values


3-10

Monthly sum of solar radiation at Sioux Falls AP

0

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lang

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Figure 3-4. Solar Radiation Time Series at Sioux Falls AP, Monthly Values

Hourly wind speed data were obtained from EarthInfo through 2004 and then appended with observations from NCDC web site.

The Weather Service stopped reporting cloud cover at most stations in 1993. After 1993, cloud cover was estimated on a daily basis. The calculated MRCC incident solar radiation was used with calculated cloudless solar radiation to estimate the cloud cover using the following equation from Eagleson (1970):

Daily cloud cover = (Sobs / Ssr1 – 1) / 0.078

Where: Sobs = MRCC calculated solar

Ssr1 = Hamon calculated cloudless solar radiation as a function of latitude and time of year

The results of this approach to estimating cloud cover are presented in Figure 3-5 through Figure 3-7. The range of values is larger and the estimated, annual cover is less for the more recent period (1993 – 2006) compared to the earlier period (1986 – 1992). Cloud cover is used for the heat balance portion of the model. However, since the model is not particularly sensitive to cloud cover, no adjustments were made to the series.

As with solar data, cloud cover values for Minneapolis were not used after 1997 (cloud cover is estimated from solar radiation). Rochester cloud cover was used from 1997 through 2006 for the Minneapolis cloud time series.


3-11

Average monthly cloud cover at Minneapolis AP

0

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84

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tent

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Figure 3-5. Cloud Cover Time Series at Minneapolis AP, Monthly Values

Average monthly cloud cover at Rochester

0

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tent

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Figure 3-6. Cloud Cover Time Series at Rochester, Monthly Values


3-12

Average monthly cloud cover at Sioux Falls AP

0

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1019

84

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Figure 3-7. Cloud Cover Time Series at Sioux Falls AP, Monthly Values

3.1.6 Potential Evapotranspiration The potential evapotranspiration (PET) calculations were performed in WDMUtil using the Penman method (1948), which estimates daily pan evaporation in inches after Kohler et al. (1955). Daily inputs of maximum and minimum air temperature (F), average dewpoint (F), wind movement (miles/day), and solar radiation (langleys/day) are used for the calculations. Wind movement (typically measured at a height of 30 feet) was adjusted to be representative of wind movement at two feet. Table 3-7 lists the SOD stations used for PET in the models and the SA stations used for wind movement and solar radiation.

To maintain consistency between PET in the prior and updated time series, PET time series were recalculated for the original 1986 through 1992 time period. For 1986 through 1990, there were minor differences between the original PET time series calculated by MPCA and those generated by WDMUtil. The differences were more noticeable on a daily basis, but monthly and annual sums of PET varied little between the two data sources. The reason for the variation is unknown, but is likely due to a different software package used to calculate PET, or at least an earlier version of WDMUtil. However, the 1991 through 1992 PET estimates differ substantially between the two sources, due to the use of the MRCC solar radiation data for the recalculated time series.

Changes in the way solar radiation has been estimated over time led to inconsistencies in the PET time series. This issue needed to be addressed during hydrologic calibration.


3-13

Table 3-7. Stations Used for Potential Evapotranspiration

Station for Potential Evapotranspiration Calculation & Source Station for

Air/Dewpoint Temperature Station for Wind Movement and Solar Radiation

SOD Coop ID SOD Name SA WBAN SA Name

211263 Canby 14944 Sioux Falls AP

212698 Fairmont 14925 Rochester

215073 Mankato 14925 Rochester

215204 Marshall 14944 Sioux Falls AP

215563 Montevideo 1 SW 14944 Sioux Falls AP

215892 New Ulm 2SE 14922 Minneapolis AP

216839 Redwood Falls 14944 Sioux Falls AP

217907 Springfield 1 NW 14944 Sioux Falls AP

217326 St James Filt Plant 14925 Rochester

217405 St Peter 2 SW 14922 Minneapolis AP

219000 Willmar 14922 Minneapolis AP

219033 Windom 14925 Rochester

219046 Winnebago 14925 Rochester

3.2 LAND USE

3.2.1 Land Use for 2000 The previous model was developed using 1989 land cover from the Land Management Information Center (LMIC). Recently, the University of Minnesota Remote Sensing and Geospatial Laboratory (http://www.land.umn.edu/index.htm) released a statewide 2000 land cover classification. This coverage is also accompanied by an analysis of impervious cover. The analysis subsequently was used as the basis for the Minnesota portion of the National Land Cover Database (NLCD). Tetra Tech used the NLCD coverage (which includes the portions of the basin in Iowa) to represent near-current land use and cover.

During the period from 1989 to 2000, several types of land use change occurred in the basin. Major changes include expansion of some urban/suburban areas and retirement of some farmland – both of which are captured by the satellite data. During the same time period changes in agricultural practices occurred, including changes in the amount of land in conservation tillage and conservation reserves in the Conservation Reserve Enhancement Program (CREP) and Conservation Reserve Program (CRP) programs. The previous modeling effort included an analysis of conservation tillage and CREP/CRP reserves circa 2000.

Land use for aggregated, model categories is summarized by major watershed in Appendix A. Overall, there were increases in conservation tillage, urban lands, and wetlands. Forest land and conventionally-tilled cropland decreased over the decade between 1990 and 2000.


3-14

In addition to an expected increase in urban land, the 2000 coverage shows a significant decrease in forest land accompanied by an increase in wetlands. (Note that some of the urban increase may be due to changes in the way in which the NLCD defines urban/developed land.) Most residual forest land in the watershed lies along perennial streams, and it appears the shift from forest to forested wetlands is largely an artifact of the land use classification methodology. Fortunately these land uses contribute only a small portion of total land area within the basin and both have low rates of pollutant loading. However, differences in hydrology between forest and wetland covers may have an impact on hydrologic simulation.

3.2.2 Manure Application Areas Agricultural lands to which manure is applied are treated separately in the model because rates of nitrogen and phosphorus application in these areas have historically tended to exceed application rates of commercial fertilizers on fields. In addition, the application of manure can change the soil structure and hydrologic response of the soil (see discussion in Section 3.8.2).

A variety of methods have been used to estimate manure application areas. The earliest version of the model used extrapolation from a detailed survey of animal operations and manure application in the Blue Earth watershed. During the creation of the year 2000 scenario for the DO TMDL, these assumptions were revisited. MPCA then created a comprehensive spreadsheet of county-level animal information, incorporating assumptions on manure production, percent of manure field applied by animal type, and the resulting agronomic application area (Gervino, 2002). The probable application area within any single year was then estimated by MPCA as one-fourth of the agronomic application area. The average application area is assumed to remain the same from year to year, although the specific fields receiving manure may change.

The current version of the model uses the county-level estimates of manure application area developed by MPCA for year 2000 conditions. A similar analysis provides manure application areas for 1990 conditions. The information on animal numbers is available only at the county level, and thus does not provide direct estimates of the area receiving manure application in individual model subbasins. County-level fractions were therefore used to create a weighted average fraction for each model subbasin, based on an overlay of counties and subbasin boundaries.

3.2.3 Conservation Tillage The model simulates cropland in two categories: conventional tillage and conservation tillage. These two categories are not explicitly identified in the land use coverages, but must be distinguished due to their different hydrologic and pollutant load generation characteristics.

The typical cropping practice is corn-soybean rotation, although other tilled crops, such as sugar beets, are also present. Conservation tillage involves a variety of practices, including no-till and mulch tillage. The most important distinction for the uses of the model is between practices that achieve high residue cover and those that do not. Transect tillage surveys are conducted every two years for most counties, and year 2000 results were selected for consistency with the calibration period land use coverage. The fraction in conservation tillage was then defined as the fraction of land in corn-soybean rotation that met greater than 30 percent crop residue (or greater than 15 percent when surveyed following soybeans). For the previous generation of the model (Tetra Tech, 2002) an analysis of the year 2000 transect tillage surveys was performed by James Klang of MPCA. In February 2008, MPCA provided an updated summary of the transect tillage surveys. The earlier summary analysis appears to be generally consistent with these new data, and so was used in the model.

The transect tillage surveys are available only at the county level, and thus do not provide direct estimates of the extent of conservation tillage in individual model subbasins. County-level fractions were therefore


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used to create a weighted average fraction for each model subbasin based on an overlay of counties and subbasin boundaries. These fractions were then applied to apportion the agricultural land in a subwatershed between conventional and conservation tillage.

The amounts of land in conservation tillage varies from year to year, as does the amount of acreage in corn, soybeans, and other crops. However, because detailed crop information is considered proprietary data, only the county-level summaries are publicly available and more precise specification is not feasible.

3.2.4 Conservation Reserves A variety of conservation programs have diverted farmland to conservation. Conservation Reserve Program (CRP), Conservation Reserve Enhancement Program (CREP), and Reinvest In Minnesota (RIM) have active 10-15 year conversions from row cropping to native grass establishments, wetlands or woods. Current acreage by practice were available in a tabulated format from the USDA - FSA and the State Board of Water and Soil Resources (BWSR). Conservation Reserve Enhancement Program (CREP) had a longer-term lands conversion into native prairie grass, wetlands or woods and was also tracked by BWSR.

The land area in CRP/CREP/RIM varies from year to year, but conditions for 2000 are used in the model for consistency with the land use coverage. In scenarios developed with the previous application of the model, land in conservation reserves as of 2000 was estimated based on county-level summaries of the CRP, CREP, and RIM programs. This step is no longer necessary for year 2000 conditions, as the land use coverage directly identifies land that has been converted to grassland, wetlands, or woods.

3.3 SOILS AND PARTICLE SIZE DISTRIBUTION Soil properties are used to establish a variety of parameters in the model (see, for example, Section 4.2.1). The basic soils coverage has not changed from the earlier model (Tetra Tech, 2002); however, additional information has been extracted on particle size distributions.

As currently implemented, the model assumes the same fractional representation of sand, silt, and clay components in source loads from all watersheds. Better representation will be important to accurate allocation of turbidity sources. Some information on soil particle size distribution is readily available from the STATSGO and SSURGO soil coverages. While particle size in intact soils is not a direct measure of particle size distribution in sediment delivered to streams (and HSPF simulates sediment as a single, undifferentiated component on uplands), better estimates of the size distribution of delivered sediments could be provided through consideration of the soil data and literature information on fines enrichment.

Area weighted fractions of sand, silt, and clay were generated from STATSGO data. Data only from the top soil layer was used and area averaged by subwatershed. While percent clay is available directly from the STATSGO database, percent silt and sand were derived by difference from the percent passing a #200 sieve.

HSPF simulates sediment delivery from the land surface in a single class; this is partitioned at the edge of the reach into sand, silt, and clay fractions. The fractional distribution in part reflects the parent soils in a watershed; however, it is also strongly affected by transport processes. Specifically, the fine fraction is enriched relative to the total sediment load.

Because fine particles are more easily transported, enrichment of fines takes place both in the process of pickup of detached soil particles from the land surface and during transport in ephemeral streams. The coarser sand fraction is less likely to be moved in the first place, and is more likely to be deposited out in


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first-order and ephemeral streams before reaching modeled stream reaches. The changes in particle-size distribution can be highly significant. For instance, Dong et al. (1983) conducted a detailed study of a 21 km2 agricultural watershed in southeastern Wisconsin where the soil was 24 percent sand, 49 percent silt, and 27 percent clay and found that suspended sediment samples near the outlet of the subbasin during runoff events consisted of 9 percent silt and 91 percent clay, with minimal sand.

A variety of empirical methods have been suggested to calculate clay enrichment ratios (summarized in Novotny and Olem, 1994). However, these are of somewhat limited use for model parameterization for several reasons:

1. The estimates typically do not include movement in bedload, which is where the majority of the sand fraction will be transported.

2. Clay enrichment will vary dynamically as a function of the energy of an individual washoff event.

3. Published estimates typically incorporate deposition loss in streams of a high enough order such that reach sediment deposition is directly simulated in the HSPF model.

4. Even when the majority of the land areas contribute only fine sediment to the stream network due to enrichment during overland transport there will be some areas immediately adjacent to streams that contribute a much higher percentage of coarse material.

As a result, the specification of fractionation of eroded material in HSPF is an imprecise art, subject to revision during model calibration. USEPA (2006) recommends only that “the fractions should reflect the relative percent of the surface material…available for erosion in the surrounding watershed, but should also include an enrichment factor of silt and clay to represent the likelihood of these finer materials reaching the channel.”

In the relatively flat topography of the Minnesota River basin, much of the sand eroded from upland pervious land surfaces is trapped prior to reaching streams, while the sand component within streams is primarily derived from bank erosion or bluff collapse. The previous application of the Minnesota River model assumed fractionation of sediment from pervious lands of approximately 5 percent sand, 65 percent silt, and 40 percent clay. This provided reasonable results, but likely does not sufficiently account for clay enrichment, as the average soil composition in the model area is 28 percent sand, 46 percent silt, and 26 percent clay – for a clay enrichment ratio of 1.5. Clay enrichment ratios are typically found to be in the range of 1.2 to 1.5 at the plot scale, but should be greater at the watershed scale to account for trapping of coarser material in additional overland transport and in first-order streams.

To maintain general consistency with the previous model we have assumed that the sand fraction from upland erosion should be 5 percent on average, but will vary with soil composition in the individual watershed. A clay enrichment factor of 2 is assumed. The area-weighted soil fractions by watershed are shown in Table 3-8. Fractionation using the above assumptions is shown in Table 3-9.


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Table 3-8. Area-weighted Soil Fractions (Percent)

Watershed Sand Silt Clay

Lower 34 42 25

Middle 29 45 26

Le Sueur 24 45 31

Blue Earth 26 46 27

Watonwan 30 45 26

Cottonwood 28 45 27

Redwood 28 47 26

Yellow 25 48 27

Hawk 23 49 28

Chippewa 33 45 23

Table 3-9. Pervious Land Delivered Sediment Fractionation Factors after Clay Enrichment

Watershed Sand Silt Clay

Lower 0.060 0.448 0.492

Middle 0.052 0.430 0.518

Le Sueur 0.043 0.344 0.613

Blue Earth 0.047 0.409 0.544

Watonwan 0.053 0.429 0.518

Cottonwood 0.050 0.417 0.533

Redwood 0.049 0.434 0.516

Yellow 0.045 0.414 0.541

Hawk 0.042 0.408 0.551

Chippewa 0.059 0.490 0.451

Fractionation of solids also must be supplied for runoff from impervious land. Particle size distribution on streets is typically much coarser than native soil, with less than 10 percent in silt and clay (Sartor et al., 1974); however, the fine fraction can be greater on other types of urban impervious surfaces, such as roofs. Enrichment still occurs, with less transport of larger solids particles, although it is much less significant than for pervious land because of the typically high delivery ratio from urban surfaces. In the absence of additional detailed information, urban surfaces are assumed to deliver approximately equal fractions of sand, silt, and clay (33 percent, 33 percent, 34 percent).

For sediment delivery through tile drains, deposition due to ponding at the inlet caused decreases in total solids concentrations of 50 to 90 percent according to Ginting et al. (2000 and 2001) and Oolman and Wilson (2003). This phenomenon is already accounted for in the model, with the fraction set at the lower


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end (50 percent) to partially account for the potential scour of sediment at tile drain outlets. Snowmelt runoff results in smaller decreases. In Ginting et al. (2000), the particle size distribution for runoff entering the tile inlets was as follows: Sand – 0 percent, Silt – 5 to 11 percent, Clay – 89 to 95 percent. We have therefore assigned midpoints of these ranges: 8 percent silt and 92 percent clay.

3.4 REACH CHARACTERISTICS The earlier model was composed of 75 HSPF subbasins. Stream reaches generally coincide with the subbasins, with only a single main reach for each subbasin represented in the model. In several cases, there are additional reaches added (without separate specification of watershed area) to represent the hydrologic behavior of lakes and reservoirs.

3.4.1 Use of HEC Flood Elevation Models Movement of sediment in stream networks, including transport, scour, and deposition rates, is determined by flow energy. HSPF does not directly solve hydraulic equations for flow routing, but rather specifies information on the relationship between stage, discharge, and geometry through Functional Tables (FTables). The calculation of boundary shear stress from the FTable information is a key component of the simulation of sediment transport.

Information contained in the FTables is developed outside the HSPF model. This information was subject to considerable uncertainty in the previous models. For the mainstem segments, FTable information was extrapolated from stage-discharge records available at USGS gage stations. No documentation has been located on the creation of FTables for the pre-existing tributary models, which were not modified during the last round of model updates. Thus, none of the FTables in the previous models are known to have been rigorously created from hydraulic models. This constitutes a major source of uncertainty in the application of the models for sediment transport and turbidity analysis.

For many parts of the Minnesota River and tributaries, hydraulic models have been created in connection with Federal Emergency Management Agency (FEMA) flood studies. This is of particular importance for a sediment TMDL because the hydrology and instream hydraulics as represented by the model directly influence the erosion and sedimentation processes—which will drive the sediment generation and transport relationships used for the TMDL. Existing hydraulic models that describe stream geometry can be used to more accurately represent the discharge-storage-surface area relationships of modeled stream reaches such that they can be incorporated into the HSPF model.

The October 2001 Section 22 Study (St Paul District ACOE, 2001) primarily describes how various sources of hydrologic data were evaluated to develop a consistent set of frequency distributions for discharge and elevation for the main stem of the Minnesota River from Ortonville to its confluence with the Mississippi River at Mendota Heights. In particular, elevation-frequency and discharge-frequency relationships were developed for Big Stone Lake, the Highway 75 Reservoir, Marsh Lake Dam, and Lac qui Parle Dam. The primary source of data for these relationships was the St. Paul District of the U.S. Army Corps of Engineers (USACE) Water Control Section. Discharge-frequency relationships were developed for the Minnesota River at: Ortonville, Montevideo, Mankato, Jordan, downstream of Montevideo and upstream of Mankato, downstream of Mankato and upstream of Jordan, downstream of Jordan to the mouth, and for the Mississippi River at St. Paul. USGS streamflow gages served as the primary source of data for these relationships.

3.4.1.1 HEC-RAS Modeling HEC-RAS is a one-dimensional hydraulic model of water flowing through natural channels. Capable of modeling complex stream networks, dendritic systems or a single river reach, HEC-RAS is typically used


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for channel flow analysis and floodplain determination. HEC-RAS applications provide an excellent basis for creating the FTables at selected points within a stream network. The accuracy of the generated FTable is dependent upon the spacing and number of HEC-RAS cross sections throughout a stream network, as well as the accuracy of the measured flows used to correlate river stage to discharge. HEC-RAS can interpolate between cross sections if the gaps are relatively small, but large gaps can eliminate the usefulness of disconnected upstream sections for F-table generation. If several measured flows are provided with a HEC-RAS model (e.g., flows from the 10-, 50-, 100-, and 500-year return periods), the HSPF modeler can interpolate additional flows using percent differences in order to complete enough points in an FTable. As previously mentioned, data from adjacent stream gages can also be used to establish flow profiles in HEC-RAS for a particular reach.

The available HEC models from the FEMA flood studies were assembled and analyzed to determine their extents, connectivity, and relevance to the Minnesota River HSPF model. A HEC model was considered relevant to a subbasin if it extended at a minimum from the downstream end to the subbasin’s centroid, in addition to having downstream connectivity to the mainstem or other HEC models. HEC models were provided in both HEC-RAS and HEC-2 formats; in the case of the later, the HEC-2 model files were converted into a HEC-RAS format. GIS coverage of the cross sections was provided for the HEC-RAS models, but spatial extents for the HEC-2 models required further analysis and measurement within ArcGIS to determine their coverage and relevance to the HSPF model (Figure 3-7). HEC-generated FTables were incorporated for 32 subbasins (Figure 3-8). Based on significant changes in channel morphology as indicated by the HEC flood models, several of the model reaches were eventually subdivided into smaller segments and assigned individual FTables (see Section 3.4.2). Unfortunately, HEC models were not available for portions of the hydrologic network, notably including the Blue Earth and Watonwan River systems.


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Figure 3-8. Reach coverage of HEC models


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Table 3-10. Subbasins with HEC-RAS Generated FTables

Sub-basin

Major River Name River Station

Modeled Length (mi)

# of Cross Sections HEC-RAS Model

401 Lower MN R. 63-20 17.7 43 Tt_LMR_Merged_Comm.prj




301 Lower MN R. 247.4-212 11.5 16 Tt_LMR_Merged_Comm.prj

302 Lower MN R. 300-247.4 17.4 19 Tt_LMR_Merged_Comm.prj

505 Middle MN R. 844071-737616 20.1 48 MNriver_combo.prj


306 Middle MN R. 191.46-900373 12.6 14 MNriver_combo.prj

307 Middle MN R. 208.93-191.46 17.5 35 MNriver_combo.prj

389 Middle MN R. 225.1-208.93 16 40 MNriver_combo.prj

390 Middle MN R. 19-225.1 13.9 43 MNriver_combo.prj


313 Middle MN R.. 81-70 5.1 11 MNriver_combo.prj

311 Middle MN R.. 111-81 11.1 33 MNriver_combo.prj

312 Upper MN R. 285.6-111 14.4 35 MNriver_combo.prj

600 Le Sueur R. 25-1 5.9 33 Le Sueur.prj

620 Le Sueur R. 77-25 13.8 60 Le Sueur.prj

750 Cottonwood R. 40-0 41.0 159 Tt_750_Lower.prj

752 Cottonwood R. 65-40 27.2 88 Tt_752_Upper.prj

753 Cottonwood R. 79-65 11.9 50 Tt_753_Upper.prj

754 Cottonwood R. 120-92.1 25.9 83 Tt_754.prj

755 Cottonwood R. 92.1-79 12.9 50 Tt_755.prj

781 Redwood R. 25.1-1 21.2 121 rwriver_combo.prj

784 Redwood R. 56.1-25.1 25.6 97 rwriver_combo.prj

803 Redwood R. 132-56.1 35.4 177 rwriver_combo.prj

855 Yellow Medicine 30-10 13.7 25 Yelmedr_all_Tt.prj



901 Hawk Creek 15.27-0 15.2 30 hawkcreek.prj

919 Lower Chippewa 66972.9-15 12.4 68 ChipRiver_combo.prj

920 Lower Chippewa 102173.5-66972.9 6.6 28 ChipRiver_combo.prj


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To use HEC-RAS to generate FTables, additional flow profiles were created for every flow change point along a modeled reach in order to account for lower flows and improve FTable accuracy. Many of the provided HEC models already contained several observed flow profiles for various flood return periods (e.g., 10-, 50-, 100-, and 500-yr storms); however, more flow profiles were needed to create an FTable. As a result, Tetra Tech calculated the mean percent change between every flow change point along the reach from the provided flow profiles. Tetra Tech subsequently assigned between 9 and 11 flow profiles (ranging between base flow and the 500-yr event peak flow) to the most upstream cross section. Finally, downstream flows were calculated for each flow change point and flow profile using the mean percent flow change values.

For each flow profile, HEC-RAS models provide the following water surface profile outputs for FTable generation:

• Q Total – total flow in cross section (cfs)

• Length Wt – weighted cross section reach length based on flow distribution (ft)

• Max Chl Dpth – maximum main channel depth (ft)

• SA Total – cumulative surface area for entire cross section from the bottom of the reach (acres)

• Volume – cumulative volume of water in the direction of computation (acre-ft)

Each point (or flow profile) representing the discharge-storage-surface area relationship by computed FTable is thus a weighted average of channel stage and discharge that is based on the weighted cross section reach length within the entire modeled reach. Also included for each flow profile in the FTable are the cumulative surface area and water volume between the reaches’ upstream and downstream cross section.

3.4.2 Revisions to Reach Network As previously mentioned, Tetra Tech used the water surface profiles from the HEC-RAS models to further subdivide the model reaches according to significant changes in channel morphology. These changes include noticeable inflections in energy grade lines due to bed slope changes or flow control structures. After review of the HEC-RAS models, Tetra Tech decided to separate four basins along the Minnesota River mainstem into more refined subbasins with FTables that better estimate hydrology and sediment transport (Table 3-11 and Figure 3-9). After subdividing the reaches along the main stem, the subbasins were subsequently delineated within the original basin using DEM data. Two additional routing reaches and FTables were created for the Chippewa and Yellow Medicine River tributaries to represent downstream segments with increased gradient. Subbasin areas were not delineated for the new routing reaches on the Chippewa and Yellow Medicine rivers; all land area is simulated as draining to the original reach, but flow from this reach is then transported through the new routing reach.


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Figure 3-9. Revised Mainstem Reach Subbasins


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Table 3-11. Revisions to Reach Network

Old Basin

New Subbasins/

Reach

Modeled Stream

Length (mi) Major River Downstream Location

310 16.5 Minnesota Downstream of Hawk Creek confluence

311 11.1 Minnesota Downstream of Stoney Run Creek confluence

312 14.4 Minnesota Downstream of Chippewa River confluence 910

313 5.1 Minnesota Head of Granville Falls

389 16 Minnesota Downstream of Beaver Creek confluence 890

390 13.9 Minnesota Downstream of Sacred Heart Creek confluence

306 12.6 Minnesota Upstream of Fort Ridgely Creek confluence 507

307 17.5 Minnesota Downstream of Wabasha Creek confluence

301 11.5 Minnesota Downstream of Swan Lake Outlet 502

302 17.4 Minnesota Upstream of Blue Earth River confluence

860 38.3 Yellow-Medicine Upstream of Henley Falls 8601

855 13.7 Yellow-Medicine Confluence with Minnesota River

920 6.6 Chippewa Inlet to Watson-Sag high flow bypass channel 9201

919 12.4 Chippewa Confluence with the Minnesota River 1 Subdivided into routing reaches within the HSPF model. FTables were created from HEC-RAS but land areas were

not delineated for the new reaches.

3.4.3 Chippewa River Linkage In the previous model, the Chippewa River was simulated separately and not linked directly to the remainder of the Minnesota River system. The reason for this is that flow in the Chippewa is split at Watson Sag, upstream of Montevideo, with a portion of the flow diverted through Watson Sag into Lac qui Parle (Figure 3-10).


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Figure 3-10. Chippewa River Diversion Dam and Watson Sag, Watson, Minnesota

In the revised version of the model the Chippewa flows not diverted through Watson Sag are directly linked to the remainder of the model. To do this, it is necessary to estimate the fractions of flow at the Chippewa Dam at Watson.

The Corps of Engineers maintains records of flows released downstream through Chippewa Dam, with some missing data. Missing data were filled by extrapolating the measured flow on the Chippewa River at Milan to Watson, estimating the diversion flow according to operating rules (USACE St. Paul, 1995), and subtracting the diversion flow from the estimated total flow.


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In the model, both outputs at Chippewa Dam are specified as demand time series. To ensure that the partitioning is consistent with the model simulation, the demand series are specified as follows:

1. The model is calibrated for flows on the Chippewa River at the Milan gage.

2. Output from the model is tabulated at Watson, above Chippewa Dam.

3. If the modeled flow at Watson is less than the reported or estimated release downstream, the release is adjusted, creating the demand time series for flow downstream.

4. The modeled flow minus the releases downstream becomes the demand time series for diversion through Watson Sag (exiting the model).

3.5 POINT SOURCES Point source databases for years 1993 to 2007 were provided by MPCA and used to update the point source model files. These data were downloaded and assembled from either the EPA PCS database or the Minnesota “Delta” database. There were numerous occasions for which reported data was missing, or not recorded, for a particular point source for one or more model parameter (Flow, BOD, Fecal Coliform, Total Ammonia Nitrogen(TN), Total Phosphorus(TP), and TSS). Flow data was most often reported as “calendar month average,” BOD as “BOD Carbonaceous 05 Day 20C,” Fecal Coliform as “Fecal Coliform, MPN or Membrane Filter 44.5C,” TN as “Nitrogen, Ammonia, Total (as N),” TP as “Phosphorus, Total (as P),” and TSS as “Solids, Total Suspended.” Also, there were instances where a reported data point was suspected to be in error. Assumptions used to populate missing reported data follows in Section 3.5.1. Sixty-three point sources were used in the modeling efforts (Figure 3-11 and Table 3-13).

In addition, smaller treatment ponds were generally modeled in aggregate. Implementation of methods for modeling these ponds are described in Section 3.5.2. MPCA is currently in the process of assembling daily effluent flow data for the 17 largest facilities. While these data were not used in the model calibration, they will be incorporated into model scenario applications. The impact on model calibration is expected to be small.


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Table 3-12. Point Source Discharges Included in the Minnesota River Model

Facility Name

NPDES Permit

Number Model Watershed

Design Flow

(MGD)

1993-2006 Average

Flow (MGD)

NPDES Class County

Blue Earth MN0020532 Blue Earth 0.98 0.64 Minor Faribault Darling International MN0002313 Blue Earth 0.06 0.09 Minor Faribault Fairmont MN0030112 Blue Earth 3.90 1.61 Major Martin Fairmont Foods of Minn., Inc. MN0001996 Blue Earth 0.20 3.12 Minor Martin Trimont MN0022071 Blue Earth 0.35 0.11 Minor Martin Welcome MN0021296 Blue Earth 0.26 0.09 Minor Martin Winnebago MN0025267 Blue Earth 1.70 0.44 Minor Faribault Benson MN0020036 Chippewa 0.99 0.41 Minor Swift Evansville MN0023329 Chippewa 0.10 0.06 Minor Douglas Hancock MN0023582 Chippewa 0.14 0.16 Minor Stevens Kensington MN0021598 Chippewa 0.08 0.06 Minor Douglas Kerkhoven MN0020583 Chippewa 0.15 0.12 Minor Swift Montevideo MN0020133 Chippewa 3.00 0.95 Major Chippewa Starbuck MN0021415 Chippewa 0.28 0.21 Minor Pope Watson MN0022144 Chippewa 0.03 0.03 Minor Chippewa Springfield MN0024953 Cottonwood 0.78 0.33 Minor Brown Storden MN0052248 Cottonwood 0.05 0.02 Minor Cottonwood Wabasso MN0025151 Cottonwood 0.11 0.07 Minor Redwood Walnut Grove MN0021776 Cottonwood 0.20 0.12 Minor Redwood Wanda MN0020524 Cottonwood 0.03 0.04 Minor Redwood Clara City MN0023035 Hawk Creek 0.46 0.17 Minor Chippewa Maynard MN0056588 Hawk Creek 0.48 0.06 Minor Chippewa Willmar MN0025259 Hawk Creek 5.04 3.53 Major Kandiyohi Amboy MN0022624 Le Sueur 0.29 0.12 Minor Blue Earth Madison Lake MN0040789 Le Sueur 0.13 0.08 Minor Blue Earth New Richland MN0021032 Le Sueur 0.60 0.15 Minor Waseca Saint Clair MN0024716 Le Sueur 0.21 0.08 Minor Blue Earth Waldorf MN0021849 Le Sueur 0.10 0.06 Minor Waseca Waseca MN0020796 Le Sueur 3.50 1.46 Major Waseca Arlington MN0020834 Lower Mainstem 0.67 0.39 Minor Sibley Belle Plaine MN0022772 Lower Mainstem 0.54 0.48 Minor Scott Dairy Farmers of America - Winthrop MN0003671 Lower Mainstem 0.24 0.08 Minor Sibley Henderson MN0023621 Lower Mainstem 0.18 0.17 Minor Sibley Lafayette MN0023876 Lower Mainstem 0.10 0.08 Minor Nicollet LeCenter MN0023931 Lower Mainstem 0.82 0.28 Minor Le Sueur Le Sueur MN0022152 Lower Mainstem 1.66 1.89 (Major) Le Sueur Milton G. Waldbaum MN0060798 Lower Mainstem 0.40 0.25 Minor Sibley Norwood (Young America) MN0024392 Lower Mainstem 0.91 0.53 Minor Carver CHS Oilseed Processing - Mankato MN0001228 Middle Mainstem 3.80 3.32 Minor Blue Earth Franklin MN0021083 Middle Mainstem 0.12 0.06 Minor Renville


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Facility Name

NPDES Permit

Number Model Watershed

Design Flow

(MGD)

1993-2006 Average

Flow (MGD)

NPDES Class County

Granite Falls MN0021211 Middle Mainstem 1.11 0.50 Minor Chippewa Hillcrest Health Care MN0041696 Middle Mainstem 0.01 0.01 Minor Blue Earth Lake Crystal MN0055981 Middle Mainstem 0.59 0.26 Minor Blue Earth Mankato MN0030171 Middle Mainstem 11.25 6.90 Major Blue Earth Morgan MN0020443 Middle Mainstem 0.36 0.14 Minor Redwood Morton MN0051292 Middle Mainstem 0.13 0.13 Minor Renville New Ulm MN0030066 Middle Mainstem 6.77 2.50 Major Brown Olivia MN0020907 Middle Mainstem 0.55 0.31 Minor Renville Redwood Falls MN0020401 Middle Mainstem 0.80 0.82 Minor Redwood Renville MN0020737 Middle Mainstem 0.19 0.38 Minor Renville Sacred Heart MN0024708 Middle Mainstem 0.24 0.09 Minor Renville Saint Peter MN0022535 Middle Mainstem 4.00 8.77 Major Nicollet Southern Minn. Beet Sugar MN0040665 Middle Mainstem 3.00 0.36 Major Renville Saint George MN0064785 Middle Mainstem 0.01 0.01 Minor Sibley Wis-pak of Mankato MN0063029 Middle Mainstem 0.26 0.18 Minor Nicollet Xcel – Minn. Valley Plant MN0000906 Middle Mainstem 7.40 0.09 Minor Chippewa Marshall MN0022179 Redwood 4.50 2.80 Major Lyon Minn. Corn Processors (ADM) MN0057037 Redwood 2.60 0.95 Major Lyon Comfrey MN0021687 Watonwan 0.05 0.03 Minor Brown Madelia MN0024040 Watonwan 1.31 0.69 Major Watonwan Saint James MN0024759 Watonwan 2.96 1.09 Major Watonwan Truman MN0021652 Watonwan 0.78 0.17 Minor Martin Vernon Center MN0030490 Watonwan 0.06 0.05 Minor Blue Earth


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Figure 3-11. Point Source Discharge Locations


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3.5.1 Point Source Missing Data Patching Assumptions Missing point source data were patched for all constituents used by the model regardless of whether or not that constituent was a focus of the present model recalibration effort. Thus, constituents such as fecal coliform were updated as well as solids and nutrients to provide a basis for other future applications of the model.

3.5.1.1 Flow, Biological Oxygen Demand (BOD), and Total Suspended Solids (TSS) For instances where data were missing for three or less months the average of the preceding and anteceding reported values were used to populate the missing data fields. In cases where the missing data were for greater than a three month time period, the average of all reported values for that specific month was used in place of the data gap. If a particular reported value appeared to be a magnitude of ten more than all other reported values the number was simply changed to the correct magnitude. When the reported value appeared to be unrealistic even if the magnitude was shifted then the missing data rules mentioned above were applied.

3.5.1.2 Fecal Coliform When fecal coliform data were missing in months that were reported in other years then the average of all reported values for that specific month was used in place of the data gap. For the months where no data was reported at all (mostly winter) then the value of 1,000,000 was entered. This assumptions was used for all point sources except Montevideo where fecal coliform data was available for the whole year starting in 1995. The same methods used for flow and BOD data to address suspect data entry errors (i.e., magnitude shifts) were applied to fecal coliform data sets except for the specific application of the fecal coliform missing data methodology.

3.5.1.3 Total Ammonia Nitrogen (TN) If TN data were missing in months that had reported values for that month in other years then the average of all reported values for that specific month was used in place of the data gap. This was used even in instances where data were missing for long periods of time (e.g., for periods from 1986 until ~2003). For point sources that had very few reported values the average of all reported values was entered for months that were known to have discharges (e.g., Fairmont Foods of MN, Inc.). In cases where the data from 1986 to 1993 were not reported the values were extrapolated backwards in time from the data for the time period from 1993 to 2007 (important note: this extrapolation could include any missing or suspect data that had been added/changed in the 1993 to 2007 period). The same methods used for flow, BOD, and fecal coliform data to address suspect data entry errors (i.e., magnitude shifts) were applied to TN data sets except for the specific application of the TN missing data methodology.

3.5.1.4 Total Phosphorus (TP) When TP data were missing for three or less months the average of the preceding and antecedent reported values were used to populate the missing data fields. For large time periods of missing data that existed between 1986 and 2007—usually because they were not required to be recorded/reported until recent years—a series of assumptions provided by MPCA were used to fill the large gaps:

1. For secondary wastewater treatment plant the average discharge concentration of TP was assumed to be 3.58 mg/L.

2. For advanced secondary wastewater treatment plant the average discharge concentration of TP was assumed to be 2.78 mg/L.


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3. For stabilization ponds, the monthly average discharge concentration of TP was assumed to be 2.7 mg/L for the spring months, and 0.79 for the fall months.

4. If the average of reported TP values for a point source was greater than the assumed concentrations in assumptions 1-3 then the average of all reported values were used instead.

The same methods used for flow, BOD, fecal coliform, and TN data to address suspect data entry errors (i.e., magnitude shifts) were applied to TP data sets except for the specific application of the TP missing data methodology.

3.5.2 Stabilization Ponds Many smaller communities in Minnesota treat waste only through use of stabilization or aeration ponds. During periods of discharge, these wastewater stabilization ponds have the potential to contribute significant amounts of nutrients and other pollutants to receiving waters. Stabilization ponds were represented in the previous HPSF modeling work and the methodology was replicated for the updated model, but all of the discharge volume data and some of the pollutant concentrations were updated.

Stabilization ponds are permitted to discharge during two time periods, April 1 to June 15 and September 15 to December 15. Typically the operators perform two discharges of 7 to 10 days each per period, and the discharge events occur near the middle of the periods. However, at any one time not all ponds are discharging. For the purposes of the model, the spring and fall unit discharges from ponds were represented by a statistical, semi-circular distribution apportioned over the permitted discharge window (Figure 3-12). The unit discharge volume, divided equally between the spring and fall discharge periods, is then multiplied by the annual flow volume of all ponds discharging to a given stream reach. However, the pollutant concentrations, and therefore the loads, were varied between the spring and fall periods, reflecting distinct differences in spring and fall effluent quality. The stabilization pond discharges were numerous and not included individually in the model. Instead, the pond dischargers were aggregated by sub-watershed into a single “surrogate” discharge to include their impacts on hydrology and water quality.

Figure 3-12. Representation of Discharge from Stabilization Ponds


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Annual discharge was estimated from point source monitoring data provided by MPCA. For pollutant discharge, the following parameters were accounted for: organic P, ortho-P, nitrate, ammonia, organic N, ultimate BOD, TSS (silt and clay fractions) and fecal coliform bacteria. Monitoring data were used to estimate the spring and fall concentrations of total phosphorus, BOD, TSS, and fecal coliform bacteria. The ratio of organic P to ortho-P was taken from the earlier effluent concentration assumptions, which were based on an MPCA study (Helgen, 1992). Monitoring data for nitrate, ammonia, and organic N were not available, so the values reported in Helgen were used for nitrogen species. Five-day BOD reported in the monitoring data was converted to ultimate BOD with a conversion factor of 2.28. Reported TSS was assumed to apportioned to 5 percent silt and 95 percent clay based on best professional judgment. Concentration assumptions for the spring and fall time periods are shown in Table 3-13.

Table 3-13. Stabilization Pond Effluent Concentration Assumptions

Parameter Units Spring Fall

Organic Phosphorus mg/L 0.6512 0.2145

Ortho-phosphate as P mg/L 0.9488 1.0855

NO3-N mg/L 0.05 0.008

NH3-N mg/L 5.8 0.07

Organic N mg/L 9 4.5

Ultimate CBOD mg/L 17.1 10.26

TSS silt mg/L 1.26 1.08

TSS clay mg/L 23.94 20.52

FC #/100mL 13.1 19

3.6 ATMOSPHERIC DEPOSITION Atmospheric deposition contributes nutrients directly to water surfaces and also influences buildup of nutrients on the land surface. The latter process is most important in the Minnesota River basin as the area in open water surfaces is small. Atmospheric deposition is considered significant for inorganic nitrogen and is included in the model. Wet atmospheric deposition data was retrieved from the station at Lamberton (MN27). Due to the lack of data within the watershed, dry atmospheric deposition data was retrieved from EPA CASTNET web site for a site in Wisconsin, PRK-134. Monthly concentration values for ammonium (NH4-N) and nitrate (NO3-N) were developed for 1993 through 2006.

Atmospheric deposition is not simulated for phosphorus because surface loads of phosphorus are simulated via a sediment potency approach, rather than by a buildup-washoff formulation.

3.7 OBSERVED FLOW AND WATER QUALITY

3.7.1 Flow Gaging Stream flow gaging provides the essential information for calibration of hydrologic models. Stream gages in the watershed have been operated by the USGS since as early as the early 1900s. Most major rivers the flow to the Minnesota River contain a USGS flow gage near there mouth. Three gage locations along the Minnesota River mainstem were selected for the hydrologic calibration. These stations are


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located at or near Jordan, Mankato, and Morton. All of the gage station data used for the hydrologic calibration is summarized in Table 3-14 with locations shown in Figure 3-13.

The flow gaging records are also stored in the WDMs and are used both to test model performance and to drive the upstream boundary condition for the simulation model below Lac qui Parle. These records were updated through 2006.

Data quality is an important issue for stream gage records. With most every gaging station, flood peaks that exceed the range for which velocities have been measured (or those that disable the stage recorder) are often estimated with considerable uncertainty (Table 3-14). The gage stations utilized for the Minnesota River model calibration are located at large, stable river segments that are often protected by levees, etc. According to the USGS records, the stations located along the main stem are susceptible to obstruction from sediment deposition.

Table 3-14. USGS Flow Gages used for Hydrologic Calibration

USGS ID Name Resolution Start Date Stop Date Notes

05320000 Blue Earth River near Rapidan, MN Mean Daily Flow 1/1/1986 12/31/2006

05304500 Chippewa River near Milan, MN Mean Daily Flow 1/1/1986 12/31/2006

Dec. 06’ missing due to ice

05317000 Cottonwood River near New Ulm, MN Mean Daily Flow 1/1/1986 12/31/2006


05330000 Minnesota River near Jordan, MN Mean Daily Flow 1/1/1986 12/31/2006


05320500 Le Sueur River near Rapidan, MN Mean Daily Flow 1/1/1986 12/31/2006


05325000 Minnesota River at Mankato, MN Mean Daily Flow 1/1/1986 12/31/2006


05316580 Minnesota River at Morton, MN Mean Daily Flow 10/1/2000 12/31/2006

05316500 Redwood River near Redwood Falls, MN Mean Daily Flow 1/1/1986 12/31/2006


05319500 Watonwan River near Garden City, MN Mean Daily Flow 1/1/1986 12/31/2006


05313500 Yellow Medicine River near Granite Falls, MN

Mean Daily Flow 1/1/1986 12/31/2006



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Figure 3-13. USGS Flow Gage Locations


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3.7.2 Water Quality Observations Multiple agencies have collected water quality monitoring data within the Minnesota River watershed during the model calibration period. Although inconsistency of sample collection and analysis protocols, analyzed constituents, and reported units exists among the different agencies, the majority of the observation sites contained sufficient data for suspended sediment and nutrient calibration following data processing and qualifying by Tetra Tech.

Monitoring data was provided or downloaded from several sources. Much of the water quality data was collected by the Minnesota Pollution Control Agency (MPCA) through the Minnesota Milestone Site River Monitoring Project or the Clean Water Partnership (CWP). The MPCA took over water quality monitoring of Minnesota’s streams in 1967 when it was created. The current sites and sampling rotations monitored through the Milestone Program were selected in 1994 based on the amount of quality of previous data. The CWP monitoring program came into existence around the late 1980s in an effort to help local units of government address non-point sources of water pollution. The CWP provides funding for local water quality projects in two phases; the first of which involves setting up monitoring and data collection stations with assistance from the MPCA. All of the data collected by the MPCA from both programs goes into a national database (STORET) maintained by the U.S. EPA.

Three other sources of water quality monitoring data that Tetra Tech used for model calibration are the U.S. Geological Survey (USGS) and Minnesota State University (MSU), which performed monitoring through funding from the MPCA and MCES. Many of the USGS gage locations used for the hydrologic calibration also contained water quality monitoring data. In addition to providing wastewater services to the seven-county metropolitan area of Minneapolis/St. Paul, MCES also performs water resource monitoring of lakes and rivers throughout the metropolitan area. As a result, Tetra Tech utilized much of the MCES data along the Minnesota River at Jordan, Mankato, and St. Peter, and at the Blue Earth and Le Sueur Rivers near Rapidan.

Data from 1993 to present were assembled by MPCA to supplement the database established for the earlier version of the model. MPCA provided initial QA review, which included the removal of five composite samples with extremely high sediment concentrations obtained in the Cottonwood in April-June 2005 that are believed to reflect improper performance of the automated compositor. The monitoring data provided by MPCA required some additional processing and filtering prior to model calibration. This included removing sample blanks, averaging replicates from same day grab samples, calculating total nitrogen (TN) from reported nitrate/nitrite and TKN concentrations, and quantifying samples below detection limits. Only replicate samples from the same agency were averaged. In the case where composite sample values were reported, Tetra Tech used the median date within the range for calibration. Figure 3-14 shows the locations of the monitoring stations used for water quality calibration and Table 3-15 lists each stations monitoring agency, date range of reported samples, and sample size for each constituent (TSS, TN, TP). Note that the non-USGS location ID’s refer to the STORET database.


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Figure 3-14. Water Quality Monitoring Stations used for Calibration


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Table 3-15. Water Quality Monitoring Stations used for Calibration

1 USGS data provided as suspended sediment (SSED) and MN state agency data provided as TSS

Monitoring Location Agency ID Start Stop

TSS/SSEDSamples1

TP Samples

TN Samples

MPCA S000-134 2/4/1986 10/5/2006 128 129 29 Blue Earth – Mankato USGS 05322000 8/29/1989 8/26/1997 136 174 91

MCES BR-12 9/15/1998 12/14/2004 176 178 179 Blue Earth – Rapidan MSU S001-231 1/11/2005 9/26/2006 57 58 35

MPCA BP2 4/9/1996 11/19/1996 28 4 28 Blue Earth – Good Thunder USGS 05318290 3/15/1991 7/25/1991 17 17 16

MPCA unknown 10/8/1987 9/19/1994 54 54 54 Chippewa – Montevideo

USGS 05305400 8/15/1989 6/18/1992 3 5 5

CWP S001-918 4/14/1997 9/26/2006 156 157 16

MPCA S000-139 2/4/1986 10/5/2006 130 132 156 Cottonwood – New Ulm

USGS 05317000 3/21/1989 10/11/2006 16 13 14

MCES Le Sueur 1.3 9/15/1998 12/14/2004 196 253 195

MSU S000-340 1/11/2005 9/26/2006 57 57 28 Le Sueur – Rapidan

USGS 05320500 4/20/1989 10/13/2006 41 37 36

MCES MN Riv 39.4 1/24/1986 12/15/2006 770 572 544 Minnesota –Jordan USGS 05330000 1/16/1986 10/17/2006 79 92 84

USGS 05325000 4/9/1986 9/28/2005 1,540 93 30 Minnesota – Mankato USGS 05325050 8/29/1989 8/26/1992 29 30 30

MCES MN Riv 89.7 9/15/1998 12/14/2004 181 8 0 Minnesota – St. Peter MPCA S000-041 1/19/1993 9/21/2006 57 41 17

MSU S004-130 1/11/2005 9/26/2006 62 42 34 Minnesota – St. Peter USGS 05325300 8/30/1989 8/26/1992 6 7 7

CWP S001-679 6/20/1990 9/26/2006 173 174 33

MPCA S000-299 2/5/1986 10/30/2006 131 129 81 Redwood – Redwood Falls

USGS 05316500 5/4/1989 6/5/1991 23 8 9

CWP S000-163 5/18/2000 9/19/2006 251 252 211

MPCA S000-163 10/7/1987 9/21/2006 105 107 60 Watonwan – Garden City

USGS 05319500 8/25/1989 10/13/2006 26 24 24

MPCA S000-159 10/8/1987 10/30/2006 104 106 64 Yellow-Medicine – Granite Falls USGS 05313510 8/17/1989 6/5/1991 5 5 5


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3.8 SECONDARY DATA

3.8.1 Sediment Source Attribution: River Corridor vs. Upland Sources Sediment sources to rivers include upland erosion and channel degradation. Upland erosion may be further divided into sheer and rill erosion and gully erosion. Sheet and rill erosion addresses movement by overland flow of sediment detached by raindrop impact, tillage operations, and other input. Gully erosion occurs where concentrated flows scour the soil matrix, which is also a function of overland flow depth.

In the Minnesota River basin another important source of sediment contribution to the stream network is the failure and sloughing of bluffs and near-channel gullies – especially important where tributaries enter the channel of the old glacial river Warren. In some areas, bluffs may reach over 250 feet in height, and loading from these bluffs is determined more by geologic processes than channel shear stress. Sediment load generated by bluff failures may enter streams directly, or be deposited on the flood plain where it is available for scour by subsequent high-flow events.

This problem was addressed in the earlier Minnesota River Basin Model by use of Special Action routines that periodically add sediment to the beds of selected river reaches. This sediment then becomes available for scour when shear stress reaches critical levels. The approach is believed to provide a reasonable approximation of the average impact of bluff loading, and was constrained to reproduce estimates of net bluff contribution provided by Dr. Mulla of the University of Minnesota.

Subsequently, several studies have addressed the issue of sediment contribution from bluff erosion in the Blue Earth watershed, believed to be a major contributor to total sediment load in the Minnesota River. Sekely et al. (2002) conducted surveys of streambanks in the Blue Earth River from 1997 to 2000 to determine annual rates of streambank slumping and developed erosion rate constants for surveyed sites. Thoma et al. (2005) demonstrated use of airborne LIDAR to estimate mass wasting over 56 km of the Blue Earth River in 2001 and 2002. In addition to gross erosion rates from bluffs, Sekely et al. report estimates of the particle size distribution of slumped material. MPCA is currently funding radionuclide studies through the Science Museum of Minnesota that have the potential to establish the age of sediment contributions to the system. These studies could be used to refine the model specification of bluff erosion contributions.

MPCA has funded a series of studies to better constrain sources of sediment loading using radioisotope techniques. Most importantly, short-lived isotopes derived from the atmosphere are indicative offloading of near surface sediment by (primarily) sheet and rill erosion. The results of these studies have not yet been published; however MPCA provided the modeling team with a summary of sediment source attribution by major watershed (Table 3-16).

Table 3-16. Sediment Source Attribution in the Minnesota River Basin

Field-

Derived Streambank/ Bed-Derived

Ravine-Derived

Direct Bluff-Derived

Minnesota R. at Jordan 25% 25% 40% 10%

Minnesota R. at Mankato 25% 30% 40% 5%

Le Sueur and Blue Earth 20% 25% 45% 10%

Watonwan River 35% 30% 30% 5%

Chippewa, Yellow Medicine, Redwood, Cottonwood, and Hawk Creek 35% 25% 30% 10%


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3.8.2 Hydrology and Erosion from Manured Lands Research conducted by Gilley and Risse (2000) suggests that manure application changes rates of runoff and sediment detachment. A literature review by MPCA (2005) also provided a summary of studies on the beneficial effects of manure application. This phenomenon is not included in the prior model and may be important for source attribution, although it is unlikely to have much effect on overall model predictions. Additional literature sources are reviewed and an appropriate modeling solution was implemented.

3.8.2.1 Organic Matter and Soil Aggregation Changes in cropland runoff characteristics due to manure application may be attributable to increases in soil organic matter and related physical properties such as soil aggregation. Organic matter is considered a major binding agent responsible for stabilizing aggregates, the basic unit of soil structure (Mikha and Rice, 2004).

Several authors have found increased aggregation in soils receiving manure applications. Jiao et al. (2005) reported that application of 30 and 45 Mg/ha of cattle manure produced more large water-stable aggregates than inorganic fertilizers in a sandy loam soil under corn production. A 24-year application of beef cattle manure on a clay loam in southern Alberta increased the number of large macropores, associated with improved aggregation, in three of four treatments (Miller et al., 2002). In another study, improved aggregation (and reduced soil loss) associated with manure application was attributed to elevations in labile carbohydrate fraction of total C, cation exchange capacity, and calcium (McDowell and Sharpley, 2003). Mikha and Rice (2004) attributed increases in large soil aggregates to the binding effect of available carbon in a manured corn field.

3.8.2.2 Runoff and Soil Loss Soil aggregation is a key physical property of the soil impacting erosivity. Increased amounts of water-stable aggregates promote infiltration, porosity, and water holding capacity contributing to reductions in runoff and sediment loss from manure-amended crop fields (Gilley et al., 2002).

Gilley and Risse (2000) reviewed eight studies of solid manure application (dairy and beef). Annual application rates ranged from 11 to 45 Mg/ha on soil characterized as loamy sand to silt clay loams. Regression equations were developed to predict runoff and soil loss based on manure application rates. Soil loss and runoff exhibited a negative linear relationship with manure application rate. Slope length had a negative effect on soil loss, as expected, and therefore tended to minimize the positive effect of manure for long slope lengths. Table 3-17 provides findings from Gilley and Risse (2000) and five additional studies from the recent literature of multi-year application of manure.

A wide range of reductions have been found for runoff (15 to 94 percent) and sediment (2 to 86 percent) in the literature. In some cases, reductions in the mean values occurred, but were not significant due to large variability in results.

Several studies suggest that application of liquid manure sources, including those containing little residue (e.g., swine) can also be effective in reducing runoff and soil loss. High applications rates of liquid manure presented in Table 3-17 may not be directly comparable to solid manure application rates in Gilley and Risse (2000). Liquid swine manure in the studies cited was 98 percent moisture. The dairy manure ranged from approximately 80 percent to 90 percent. The liquid rates adjusted for moisture content fall in the range of about 1 to 18 Mg/ha on a solids basis, with the lowest rates associated with swine manure.


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Table 3-17. Summary of Multi-year Manure Application Studies on Runoff and Sediment Reductions

Location Reference Soil

Texture

Manure Application

Rate (Mg/ha) Manure

Type

Soil Loss Reduction

(%)

Runoff Reduction

(%)

Arlington and Madison, WI 1

Bundy et al. (2001) Silt loam 90 Liquid

dairy 87 2 80 2

Monmouth, IL 1 Daverede et al. (2004) Silt loam 93 Liquid

swine 57 --

Morris, MN 1 Gessel et al. (2004)

Clay loam 74 Liquid

swine 52 39

Various

Gilley and Risse (2000) review

Loamy sand to silty clay loam

11 to 45 Beef and dairy 15 to 65 2 to 62

Arlington, WI 1 Grande et al. (2005) Silt loam 106 Liquid

dairy 84 to 94 3 75 to 86 3

Central Pennsylvania 1

McDowell and Sharpley (2003)

Silt loam (100 to 200 kg P/ha)

Dairy and poultry 20 to 60 --

1 only significant results reported in the table 2 results for chisel plow tillage 3 spring application/spring runoff of low cut silage Note: liquid manure application is reported on a wet basis

3.8.2.3 Manure Application History and Timing There are examples of reductions in runoff and soil loss from both single and multi-year applications. Ginting et al. (1998) studied one-time application of solid manure to a loam in Morris, Minnesota in 1992 that did not have a manure history. Results from the following year for conventional till corn suggested a 50 percent and 21 percent decrease in soil loss and runoff, respectively, for beef manure application rates of 56 Mg/ha. Grande et al. (2005) found a decrease in spring runoff and sediment concentration from a short term application of liquid dairy manure to a silt loam in Wisconsin. Sediment load also increased during spring runoff from 84 to 94 percent for spring applied manure. Manure application had a significant effect on sediment loss from corn grain plots in one of two springs (spring applied). A one-year application by Bundy et al. (2001) at 72 Mg/ha of dairy manure resulted in runoff and sediment load reductions (52 percent) on conventional till plots (silt loam) during fall runoff. Long term applications (5 years) resulted in similar reductions.

Gessel et al. (2004) studied fall applications of liquid swine manure, finding significant reductions in runoff at twice agronomic rates for P in Minnesota in the fall, but not during the spring. A study by Grande et al. (2005) attributed the absence of a manure effect in fall runoff to the dominating effect of corn residue. Both spring and fall applications had an effect during spring runoff from grain corn. Spring application studies reported by Bundy et al. (2001) resulted in a significant difference only during fall runoff for 1 year applications, though, the absence of an effect may be related to timing of application. In this case, application of liquid manure occurred in the same month as the runoff simulation. While residues contained in some solid manures such as straw and sawdust bedding would tend to provide a


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protective mulch layer upon application, the beneficial impacts of liquid manures likely rests with the organic matter interaction with the soil, a process that is not instantaneous.

The review by Gilley and Risse (2000) did not include two earlier studies conducted by Gilley and others in 1998 and 1999. Gilley at al. (1999) conducted a study of long-term manure application (17 Mg/ha d.b.) to sandy loam soils near Mitchell, Nebraska. While reductions in erosion did occur (4 to 12 percent), the differences were not statistically significant. Several proposed explanations for the lack of response included a potential artifact of the testing methodology, the sandy texture of the soil, the manure application rate, and the organic matter content. Since soil and application characteristics were within the range contained in the studies reviewed by Gilley and Risse (2000), the method of removing soils samples from the field and sieving onto a soil pan, may have reduced soil aggregation, muting the effect of the manure. Gilley and Eghball (1998) failed to show an effect from a one-time application at rates of 12 and 48 Mg/ha to sorghum and wheat residue. In this case, the manure was applied by hand just before the rainfall simulation. The lack of response to treatment in this case is consistent with a study by Bundy et al. (2001) that found no effect in the same month of the application to plots without an application history.

Finally, in two Minnesota studies of solid beef manure (11-22 Mg/ha) and liquid swine manure (2.7 Mg/ha d.b.) applied to the same clay loam plots, no significant differences according to nutrient source were found (manure versus fertilizer) in flow or total solids loading after eight years of application (Zhao et al., 2001; Thoma et al., 2005). The authors suggest that native properties of the soil (high organic matter and clay content) and associated aggregation levels could help explain the lack of treatment response.

3.8.2.4 Model Representation of Manured Land Reports in the literature provide a strong body of evidence that animal manure application to cropland often results in runoff (2 to 86 percent) and sediment (15 to 94 percent) reductions. These effects are most likely related to the binding effect of organic matter resulting in greater soil aggregates, however, the presence of residues in solid manures may have an additional positive effect in some cases. Strong patterns according to application history and timing, manure type, or soil texture did not emerge. The studies reviewed here included both surface-applied and incorporation methods of application.

It should be noted that there were some studies or years within studies where a positive effect of manure application was not demonstrated. Possible reasons for the lack of response were discussed, though none provide clear explanations in each case. It does appear that the application history needs to be longer than a season to realize noteworthy benefits in most cases.

Application of animal manure in the Minnesota River Basin occurs in all seasons, though primarily in fall and spring (Mulla et al., 2001). Manure from several sources including swine, dairy and beef is applied primarily to corn and soybean crops using both broadcast and incorporation methods.

Within the model, manured lands are represented as having a larger capacity for water storage in the upper soil zone (UZSN) than other agricultural lands. As is shown in Section 4.5, this increased storage capacity has a significant impact on surface runoff and sediment transport. The resulting simulation is generally in agreement with the results of studies on manured land cited in this section.

3.8.3 Tile Drain Transport As in the previous model, tile drains are simulated as interflow in the HSPF model (see Section 4.2.2). The simulation of sediment transport through surface inlets and tile drainage was refined for the revised model. Surface inlets are suspected to be a source of sediment in the drainage system, though their locations and densities throughout the basin are not well known. The following summary provides


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findings from the literature regarding the potential effects of surface inlets with a primary focus on sediment.

3.8.3.1 Surface Inlets in the Minnesota River Basin Soils of till plains and glacial lake beds are poorly drained and must be tiled for agricultural operations. Surface inlets are widely used in south-central Minnesota as a component of tile drainage systems due to the tendency for ponding in small, landscape depressions common throughout the region. The most common type of surface inlet in the region is a flush pipe inlet, usually comprised of a 6 to 8 diameter concrete or steel riser set flush with the field surface, covered with a grate, and connected to subsurface tile drains (Oolman and Wilson, 2003).

Limited data are available to characterize the locations and densities of surface inlets and subsurface tile drainage. The best data appears to be from the MNRAP studies of the early nineties. Data on drain tile inlets (called intakes in the study) was collected and summarized in one of the MNRAP studies (Mueller and Wehrenberg, 1993). Data collected from 37 minor subwatersheds covering 433 mi2 (77 percent cropland), showed densities ranging from less than 1 to 40 inlets per mi2 of cropland (average was 11). At 11 inlets per mi2 of cropland and 9,610 mi2 of cropland, there could be more than 100,000 inlets in the model study area.

3.8.3.2 Effects of Surface Inlets While inlets allow timely planting and reduce flood damages to crops, they also provide a direct pathway for runoff to reach surface waters. There are few studies in the literature concerning the effects of surface inlets and pollutant transport. Tile drainage in general (via subsurface lines) has been more widely studied. Without surface inlets, subsurface tile lines are expected to export only small amounts of sediment and phosphorus as a result of filtering within the soil profile.

Ginting et al. (2000) studied two subwatersheds, 44 and 42 ha in size, in the Watonwan watershed of south central Minnesota to evaluate the quantity and quality of runoff and pollutant losses through three surface inlets. The study found an average total solids (TS) load of 44 kg/ha/yr and 1,256 kg/yr/inlet. Flow weighted TS concentrations in snowmelt runoff ranged from 0.1 to 0.4 g/L versus 0.2 to 13.9 g/L in rainfall runoff. Snowmelt runoff contributed 34 to 45 percent of the TS load to surface inlets. Particulate solids entering the inlets were predominately less than 2µm in size.

During large runoff events, pollutant losses are reduced due to ponding within depressions when the tile system capacity is exceeded. Deposition due to ponding at the inlet caused decreases in total solids concentrations of 59 to 90 percent according to Ginting et al. (2000 and 2001). Incoming concentrations of TS at 1.7 to 2.3 g/L decreased to 0.3 to 0.7 g/L after 2 hours of ponding. Particulate P followed the same pattern. Prolonged ponding caused dissolved P to increase due to either dissolution of particulate P or desorption from soil.

Zhao et al. (2001) conducted a study of tile drainage and surface inlets near Lamberton, MN on ridge till (RT) and moldboard plowed (MP) plots (9.9m x 18.2m). Sediment loading through surface inlets for urea-applied, MP plots was 846 kg/ha or 15 kg/inlet for one simulated spring rainfall (~3 in). Sediment loading from RT plots was 344 kg/ha through surface inlets (6 kg/inlet). Subsurface tile drainage contributed 16 to 73 kg/ha of sediment, for MP and RT plots, representing 2 and 18 percent of the combined surface and subsurface load. The authors suggested that the increase in subsurface sediment with RT plots was caused by preferential flow paths beneath the RT plots. Flow weighted sediment concentrations in surface runoff for MP was 3.2 g/L versus 1.4 g/L for RT.

Thoma et al. (2005) studied TS and nutrient loss on the same plots as Zhao et al. (2001). On average 20 percent of rainfall and snow water equivalent was lost to surface inlet and tile flow combined. Just more


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than a quarter of the combined flow was contributed by surface inlets. For the two full years that the study was conducted, TS loading in surface inlet flow was 1,799 kg/ha/yr or 32 kg/yr/inlet. Rainfall during 2000 and 2001 averaged 770 mm compared to the 30-year average at Lamberton of 670 mm (average rainfall in Ginting et al., 2000 was 470 mm). The flow weighted concentration of TS in surface inlet flow was 3.9 g/L averaged over all treatments. Snowmelt accounted for 11 percent of annual sediment load losses to surface inlets.

Oolman and Wilson (2003) used simulation models (WEPP, BASIN, and GRASSF) to study sediment control practices for surface inlets in Minnesota. The suite of routines, named DROPLETS, is described in detail by Wilson et al. (1997). The study also simulated two different tillage schemes: conventional and no till. For conventional tillage, the effluent sediment load for a flush pipe open tile intake was 30 to 50 percent of the total influent load. Runoff to depressions was 8 to 10 percent clay (soils were silty clay loam and clay loam).

Finally, a MNRAP modeling study was conducted by SCS (1993) to assess the impacts of surface inlets with the AGNPS model. Four scenarios were modeled: inlet, inlet with deposition, surface ditches, and created wetlands. Results suggested that while 20 percent of the watershed drained through surface inlets, sediment loading through surface inlets represented 35 percent of the load to the watershed outlet. In the scenario where nearly 50 percent of sediment draining to the inlet was deposited, inlet sediment loading represented 20 percent of the watershed outlet loading.

3.8.3.3 Tile Drain Transport Simulation in the Model Several trends are evident from this review. Sediment in surface runoff draining to depressions is reduced by 30 to 90 percent prior to export through inlets. Particle loss to inlets is predominately clay-sized, as larger particles will likely settle out in transit to and after entering depressions. Also, snowmelt runoff contributes less than 50 percent of the sediment load to inlets due to absence of rainfall detachment of sediment particles. In Ginting et al. (2000), the particle size distribution for runoff entering the tile inlets 89 to 95 percent clay, 5 to 11 percent silt, and zero sand.

Though data from the MNRAP studies provide a starting point, information on the number of tile intakes per unit area for region appears to be limited. Further, there is little information on the area of cropland draining to surface inlets. Therefore, the approach in the previous model was continued to represent tile drain inflow via the interflow inflow (INTFW) factor, as describe in Section 4.2.2.

3.8.4 Small-Scale Watershed Models The HSPF model for the Minnesota River is intended to be used as a basin-scale model that can produce realistic estimates of load allocations at the scale of major subwatersheds. Dalzell et al. (2004) note: “While HSPF can be accurately calibrated to existing watershed scale water quality data, because it is not designed to explicitly account for agricultural management practices, its ability to accurately simulate the effects of changes in agricultural management practices on water quality is subject to large uncertainties.” The basin-scale model should be adjusted or tuned to replicate the results of local-scale models that are sufficiently well calibrated. In addition, allocations derived from HSPF at the major watershed scale should be partitioned back to individual source areas using local-scale watershed models whenever possible.

While the earlier Minnesota River Basin Model incorporates information from a number of smaller-scale modeling efforts, there are a number of more recent monitoring and calibrated modeling efforts at the local-scale that have not been incorporated into the previous model. This information (if judged to be of adequate quality) can be used to refine model representation of sediment transport at the local scale.


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3.8.4.1 Small-Scale Modeling in the Previous HSPF Application A limited number of small-scale studies were available when the Minnesota River basin model was refined in 2002. These included:

• USGS HSPF modeling of the Heron Lake basin (just south of the Minnesota River drainage and heavily tiled) was used as a reference for the hydrologic calibration (Jones and Winterstein, 2000).

• Bank and bluff sediment concentrations were constrained to match estimates provided by Dr. David Mulla (unpublished at the time).

• Simulation of field-scale nutrient export relied heavily on several MRAP studies, including SCS (1993), Payne (1994), and Mulla et al. (2001). In particular, SCS (1993) reports results of AGNPS/GLEAMS modeling undertaken by Pete Cooper.

• Unpublished results of ADAPT modeling.

3.8.4.2 Other Recent Small-Scale Modeling Studies A number of additional local-scale modeling studies are available for parts of the Minnesota River basin. Those modeling efforts that address local-scale hydrology and sediment transport provide important information to the basin-scale HSPF model. A number of additional modeling studies focus on nutrient transport, and are of less direct relevance to the turbidity model effort – except insofar as they may aid in refining the simulation of the algal component of turbidity.

Most of the local-scale efforts have involved the ADAPT model, a field-scale water table management simulation model that integrates GLEAMS and DRAINMOD routines. While the model has a sophisticated representation of subsurface flow, it represents surface runoff and erosion using the relatively simple SCS curve number and Universal Soil Loss Equation approaches, coupled with a sediment delivery ratio.

Studies Predicting Flow and Sediment

Dalzell et al. (2004) reported calibration of the ADAPT model to monthly data for 1994-1996 from Sand Creek, a tributary of the Lower Minnesota River. The Sand Creek watershed has an area of 650 km2, with about 63 percent of the area in row crop agriculture and 30 percent of the area utilizing subsurface tile drainage. The hydrologic calibration is fair (R2=0.75), although snowmelt appears to be poorly predicted. Mean monthly flow was underpredicted by 6 percent. The sediment calibration is less precise. At the monthly scale, the model predicted 69 percent of the variability in observed sediment yield at the mouth of Sand Creek. The model underpredicted mean monthly sediment losses by 10 percent.

Boody and Krinke (2001) used ADAPT to estimate 50-year average edge-of-field sediment losses for the Chippewa River watershed (losses are broken down by land use). These were extended to estimates of annual sediment delivery using a delivery ratio method. No calibration to observations in the Chippewa is documented in the report, thought the report says that calibration was conducting using monitoring data such as research conducted for the Sustainable Farming Systems Project.

Oolman and Wilson (2003) used simulation models to study sediment control practices for surface inlets in Minnesota, as noted in Section 3.8.3.2. The purpose of the study was to compare scenarios and no comparison to existing data was attempted.

As part of the statewide phosphorus assessment, Mulla and Gowda (2004) calculated erosion potential using the Universal Soil Loss Equation, then assumed that loading to streams occurs primarily from land within 100 m of surface waterbodies. The USLE measures erosion potential, and this simplified approach does not directly calculate sediment transport. Because the extent of the flowing stream network varies


3-45

with hydrologic condition, the approach) does not provide time series output, only ranges of long-term average loading rates across different antecedent moisture conditions. The method is also unable to directly incorporate the effects of surface and subsurface tile drain inlets, which may transport sediment from distances much further than 100 m from streams. While useful for statewide assessment, the simplified approach taken in this work appears to be of limited value for improving the Minnesota River basin model.

Gowda and Mulla (2005) studied a tile drained corn field covering 32-ha in Blue Earth County for one year (1998). Application and calibration of the ADAPT model resulted in poor results for one monitoring location, but better results were obtained for a more sloping drainage area. Results from the latter location included R2 values of 0.73, 0.81, and 0.57 for flow, sediment, and phosphorus, respectively. This is of limited applicability to basin-scale modeling given small size of the study area, particularly the portion that performed satisfactorily, compared to the basin scale of the HSPF model.

Gowda and Mulla (2006) applied ADAPT to a 3,856-ha portion of the High Island Creek watershed (Lower Minnesota River). The model was calibrated to data from April-September, 2001 and April-June, 2002. Results show close agreement with measured flow, sediment, N and P. Though flow was over-predicted by 24 percent for the monthly mean (0.37 cms), sediment was within 2 percent of the monthly observed mean. Per-hectare losses of sediment, nitrogen, and phosphorus were 0.08 tons, 28.67 kg, and 0.64 kg, respectively, for the calibration period. The model was subsequently used to evaluate alternative tillage and nutrient management scenarios.

Studies Predicting Flow and Nutrients

Zhao et al. (2000) used the DRAINMOD-N model to evaluate the long-term impact of N application rate and subsurface drain spacing on corn yield and nitrate losses on experimental plots in Lamberton, MN. Petrolia et al. (2005) used ADAPT to evaluate 30-year nitrate export in the Highwater Creek/Dutch Charlie Creek (133,058 ac) and Sleepy Eye Creek (174,180 ac) minor watersheds within the Cottonwood River major watershed, though calibration comparisons are not presented.

Nangia et al. (2005a) used ADAPT to predict nitrogen export from a subwatershed in Seven Mile Creek. 4029 ha (9,957 ac) subwatershed of Seven Mile Creek. Calibration and validation data from 2000 through 2004 were used. Davis et al. (2000) applied ADAPT to tile drained plots near Waseca, Minnesota in the Le Sueur watershed to evaluate nitrate losses.

Gowda et al. (2007) describe application of the ADAPT model to the Sand and Bevens Creek watersheds for nitrogen TMDL development. The model for Sand Creek is the same one described in Dalzell et al. (2004), however, the focus is on nitrate in the current manuscript. The models explained 70 and 67 percent of the variability in nitrate loss for the Sand and Bevens Creek watersheds, respectively. The difference in mean monthly nitrate loss was 3 and 15 percent.

Four additional papers were reviewed and were excluded from consideration because no calibration information was provided, predictions were not particularly useful for our study, or the study was conducted on small individual fields or plots. There were Updegraff et al. (2004), Nangia et al. (2005b), Sands et al. (2003), and Nangia et al. (2005c).

Other Studies

The University of Minnesota National Center for Earth-Surface Dynamics is leading a study sponsored by MPCA to develop an integrated sediment budget for the Le Sueur River basin. The sediment budget will be based on two parallel approaches: 1) a cosmogenic and fallout radionuclide budget to apportion sediment by sources, and 2) a physical sediment budget utilizing in-field measurements, sediment gaging stations, and air photo analyses to determine sediment flux from the uplands through the watershed, including flux into and out of storage. The study is ongoing and results were not available in time for the model.


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The University of Minnesota is also engaged in the Ravines, Bluffs, and Streambanks study. This project, sponsored by the MPCA, will develop an inventory of ravines, bluffs, and streambanks, utilizing various GIS coverages. Field visits will be used to verify these methods. Ultimately, the project will develop sediment loading rates from these three sources for the Minnesota River basin. This study is also ongoing, and results were not available in time for the model.


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4 Hydrologic Recalibration and Validation

4.1 APPROACH The previous implementation of the model (Tetra Tech, 2002) provided a good fit to hydrology throughout the basin for 1986-1992 conditions. For the present implementation the model was extended through 2006. Model FTable revisions, as described in Section 3.4, affect both the calibration and validation period. In addition, some corrections to the meteorological data and point source forcing were made for the earlier time period.

The first step in checking hydrologic performance was examination of model fit, using existing parameters, for the 1993-2006 time period. Initial applications showed a significantly poorer quality of fit for this time period than for the earlier time period. This prompted an extensive recalibration effort, using 1993-2006 for calibration and 1986-1992 for validation. Recalibration trials were attempted both manually and using the automated PEST optimization software (Watermark Numerical Computing, 2002). It did not, however, initially appear to be possible to obtain a single set of hydrologic parameters that performed equally well on the 1993-2006 and 1986-1992 time periods.

Diagnosis of this problem focused first on land use, as there appeared to be some significant differences in the way that wetland and forest were identified between the 1989 and 2000 land coverages (see Section 3.2). Model sensitivity analyses showed, however, that this could not realistically account for the observed discrepancies. Further examination focused on the meteorological data. As noted above in Section 3.1.5, there are apparent significant changes in the solar radiation data that drive calculation of PET after 1993, leading to apparently higher PET values for the later period. Recognizing this fact, it was determined that the existing set of hydrologic parameters would provide an excellent fit to the full period of data if a different PET pan factor was assigned to pre- and post-1993 simulations. Therefore, the model was reverted to the earlier set of parameters and calibrated first by adjusting the pan factor. At the same time, evapotranspiration from active groundwater (AGWETP) was restricted to the wetland land use, as is recommended in HSPF guidance (USEPA, 1999). With these changes, only minor modifications to the existing parameter set was needed to obtain a good fit to the 1993-2006 simulation period. Performance of the revised parameters was then successfully validated on the 1986-1992 observations.

4.2 PARAMETER SPECIFICATION Specification of hydrologic parameters largely follows the approach set forth in Tetra Tech (2002). Some modifications were required, as described in the following sections.

4.2.1 Hydrologic Parameters A number of the parameters used in HSPF reflect properties of the soils. These parameters can be derived from, or related to, reported soil characteristics. This approach has two important advantages: First, it reduces the number of unconstrained or “free” parameters that must be addressed in calibration. Second, it helps to ensure that variability in parameter values between basins is systematic and based on physical evidence.

For the simulation of hydrology, parameters for infiltration rate index (INFILT; in/hr) and nominal lower zone soil storage (LZSN; in) can be related to soil parameters. Infiltration estimates in soils coverages are based on ring infiltrometers under dry conditions, and do not reflect actual infiltration rates during storm events, when surface sealing may occur. Further, the INFILT parameter used in HSPF is not a direct


4-2

measure of infiltration rate, but rather an infiltration index, which cannot be measured directly. Based on the 2002 calibration, effective infiltration rates during events appear to be on the order of 1/40 to 1/50 of reported infiltrometer infiltration rates reported in the SSURGO soil coverage vertically averaged over the soil profile. The 1/50 ratio appears to be applicable to basins in which the clay fraction of surface soils is on the order of 31 percent (e.g., Le Sueur), while the 1/40 ratio is applicable at a clay fraction of about 25 percent. Based on a linear interpolation, the proposed ratio is 1/(41 + (Clay%-25.7)*2.59). The resulting distribution of average INFILT values across basins is shown in Figure 4-1. This figure shows whole basin averages; in basins where soil properties vary significantly across the basin, estimates can be applied at the sub-basin level.

Figure 4-1. Initial Distribution of Watershed Average Event Infiltration Rate Indices (INFILT) Interpreted from SSURGO Soil Properties

The INFILT values shown in Figure 4-1 were then adjusted during calibration, with the only significant changes in the final model being a reduction in INFILT for Chippewa (from 0.093 to 0.075) and an increase for Le Sueur (from 0.020 to 0.024).

The LZSN parameter is also an index, rather than a measurable physical property, but is related to soil available water capacity. Estimates of depth-integrated available water capacity obtained from the soils spatial coverage are shown in Figure 4-2.

0

0.02

0.04

0.06

0.08

0.1

Infil

tratio

n (in

/hr)

ChippewaHawk-Yellow

Redwood

CottonwoodWatonwan

Blue Earth

Le SueurMiddle Minnesota

Lower Minnesota


4-3

Figure 4-2. Spatial Distribution of Measured Soil Available Water Capacity Note: Summarized from SSURGO soil properties.

Calibrated values for total soil water capacity (LZSN plus UZSN) are consistent with reported soil available water capacity at a mean rooting depth of approximately 87-100 cm. This is assumed to constitute the sum of upper and lower zone soil storages in natural areas. For agricultural areas, the smaller upper zone storage is assumed to be a result of tillage practices during the growing season, and is therefore assigned a fixed value across all basins. The calibrated values of lower zone storage capacity (LZSN), as basin averages, are shown in Figure 4-3. Several basins with strong spatial soil contrasts are assigned separate values for different portions. Tiling is assumed not to affect the nominal soil storage capacities; rather, it affects the rates of inflow and outflow for these storage pools.


4-4

0

1

2

3

4

5

6

7

Blue Earth

Chippew

a

Cottonw

ood

Hawk

Lower

LeSueu

r

Upper L

eSueur

Middle

Minnes

ota

Lower

Minneso

ta

Redwood

Lower

Watonwan

Upper W

atonwan

Upper Y

ellow

Med

icine

Lower

Yellow

Med

icine

LZS

N (i

n)

Figure 4-3. Distribution of Calibrated Values for Lower Zone Soil Storage Capacity

The most important parameters controlling the general simulation of hydrologic response are, in order of importance: infiltration rate (INFILT); pan ratio, relating actual evapotranspiration from the land surface to measured pan evaporation (also incorporating any spatial differences between the land area and the measurement gage); lower zone soil nominal moisture storage (LZSN), lower zone evapotranspiration parameter (LZETP); groundwater recession coefficient (AGWRC), and interflow inflow parameter (INTFW). These parameters provide the major controls on the partitioning of rainfall among direct overland runoff, tile drainage, soil moisture storage, evapotranspiration, and groundwater. Values or ranges for each major watershed are summarized in Table 4-1. Multiple other parameters also have smaller impacts on the timing and distribution of flow, and may be examined in the model input files.

Table 4-1. Key Hydrologic Parameters

Watershed INFILT (in/hr) Pan Factor (1986/1993) LZSN (in) LZETP

AGWRC (per day) INTFW

Blue Earth 0.031 0.74/0.61 5.23 0.935 2.5 – 6.0

Chippewa 0.075 0.80/0.68 4.30 0.975 1.0

Cottonwood 0.045 0.72/0.62 5.00 0.940 2.5 – 3.4

Hawk 0.0282 0.80/0.69 6.29 0.950 2.0 – 3.0

Le Sueur 0.016– 0.024 0.77/0.65 5.2 – 5.4 0.930 2.5 – 6.0

Middle Minnesota 0.050 0.78/0.65 5.23 0.930 2.5 – 3.4

Lower Minnesota 0.061 0.74/0.61 5.23 0.905 2.5 – 3.4

Redwood 0.043 0.77/0.68 4.71 0.910 2.5 – 3.4

Watonwan 0.04 – 0.12 0.77/0.67 4.8 – 4.9 0.940 2.5 – 4.0

Yellow Medicine 0.02 – 0.0314 0.80/0.69 5.86 – 6.2

Monthly pattern by land use specified for all basins.

0.94/0.95 2.0 – 3.4


4-5

Of the key parameters described above, three (INFILT, LZSN, and INTFW) are specified based on spatial characteristics. LZETP is assumed to be a characteristic of the cover type and condition, and is varied monthly and by land use, but held to a constant pattern across all watersheds. Only the pan ratio and AGWRC were treated as free inter-basin calibration factors; however, variability among basins was held to the minimum necessary to achieve a reasonable hydrologic calibration.

4.2.2 Tile Drain Simulation Land use in the Minnesota River basin is primarily agriculture (typically in corn/soybean rotation). A dominant characteristic of the basin is the presence of extensive tile drainage, as many of the soils are naturally poorly drained. Installation of tile drainage has converted what were predominantly glacial plain outwash depressional wetlands into productive farmland. The presence of tile drains, which include both surface and subsurface inlets, has radically altered the natural hydrology of the area. Surface inlet tile drains, in particular, may also play a significant role in the transport of sediment and pollutants from agricultural land to the river.

It is not feasible to simulate individual tile drain systems at the large basin scale. Further, neither the location nor the total density of tile drainage is known throughout the basin: In most areas, only the public tile drains and ditches are documented in spatial coverages, and the extent of private tile drains is known only for limited areas.

The HSPF model does not contain any routines for the explicit representation of tile drains. An approach to represent the effects of tile drainage was developed for the 2002 model and forms the basis of the current application. Specifically, tile drainage is incorporated into the HSPF representation of interflow.

In typical applications of HSPF, surface runoff represents the quick flow storm response, interflow an intermediate time-scale hydrologic response, and groundwater discharge the base flow hydrologic response. In such applications, interflow represents lateral movement of water through the shallow soil profile.

At a gross or basin scale, the net effect of tile drainage is to move water relatively rapidly out of surface storage without direct surface drainage. Accordingly, it is to be expected that tile drainage is best represented in HSPF as an interflow component, with a response time that is somewhat slower than direct surface runoff, but quicker than groundwater discharge, represented by a relatively fast recession coefficient. In fact, tile drainage constitutes a range of different hydrologic response times. The fraction of the net discharge from tile drains that comes from surface inlets is a rapid-response component. However, tile drain outflow also contains a slower component that consists of subsurface flow that has percolated through the upper soil layers and into the drains through lateral soil flow. As a result, the net tile drainage can, in HSPF, be expected to be represented as a combination of interflow and groundwater discharge.

There are some limitations to the representation of tile drainage as interflow in HSPF. Within HSPF, the rate of interflow inflow is determined in relation to infiltration. First, the infiltration capacity, IBAR (in/hr), is determined as

IBAR = (INFILT / (LZS/LZSN)INFEXP) · INFFAC

where INFILT is the infiltration rate parameter (in/hr), LZS is the current lower soil zone storage (in), LZSN is the nominal lower soil zone storage capacity, and INFFAC is a factor calculated by the model to adjust for frozen ground surface. The sum of interflow plus infiltration (IIBAR) is then determined via a ratio to IBAR:

IIBAR = IBAR * INTFW * 2.0 (LZS/LZSN)


4-6

where INTFW is the interflow inflow rate parameter (dimensionless). Thus, in HSPF the rate of interflow discharge depends on the extent to which the lower soil zone capacity is filled. In reality, tile drain discharge will depend on the capacity of the tiling and the hydraulic head at the tile outlet. Actual tile drainage has an upper limit determined by pipe capacity, regardless of the extent to which infiltration has filled the lower soil zone capacity. As a result, HSPF simulations that obtain a good general representation of tile drainage discharge under normal conditions are likely to over-estimate interflow discharge during large precipitation events with dry antecedent conditions. This effect is indeed evident in HSPF simulations of the Minnesota River basin.

Available data do not allow direct determination of the interflow inflow parameter to represent tile drainage; instead, this parameter must be determined through calibration. The MRAP studies of selected sub-watersheds suggest that there is generally a decreasing trend of intensity of tile drain density from the southeast (Le Sueur, Blue Earth) to the northwest portions (Yellow Medicine, Chippewa) of the basin (Figure 4-4). Basins to the southeast have tile densities on the order of 0.03 km/ac, while those to the northwest have tile densities less than 0.01 km/ac. While the sample of basins in MRAP is small, this trend coincides with changes in soil type and other anecdotal information. Accordingly, the interflow inflow parameter is expected to be higher in the southeast, and lower in the northwest portions of the Minnesota River basin.

Figure 4-4. Tile Drain Density Spatial Distribution.


4-7

The HSPF representation of interflow differs sufficiently from the actual physics of tile drainage that the INTFW parameter cannot be estimated from first principles or soil properties. Instead, it must be determined through calibration. As noted above, the interflow portion of the hydrograph depends on INTFW, INFILT, and LZSN simultaneously. The latter two parameters are determined from soil properties (see below), leaving INTFW as a calibration parameter.

USGS HSPF modeling of the Heron Lake basin (just south of the Minnesota River drainage and heavily tiled) used a value of INTFW of 3.4 to obtain a good fit to flows at multiple gages (Jones and Winterstein, 2000). Calibration efforts in the Minnesota River basin reveal that this value of INTFW is also appropriate for the areas near the Heron Lake basin, such as Watonwan River. We found, however, that slightly lower interflow inflow (INTFW=3) provided a better fit in the months of January through March, when ice and snow may impede inflow to tile drains. In the Le Sueur River basin, which has, on average, the highest clay content of soils and low infiltration rates, a higher value of INTFW is appropriate, with a value of 4 during the spring and summer providing a better fit. Lower values of INTFW (as low as about 1.1 in the Chippewa River basin) can then be specified for the basins with less dense tile drainage.

Information provided by Dr. Bruce Wilson (conference call of April 17, 2001) indicates that the main lines of tile drainage were sized to achieve a specified drainage coefficient, typically equal to ¼ in of water per day in the drains installed in the 1920s, and ½ in of water per day in more recent tile drains. His research also indicates that the majority of drainage in many of these watersheds occurs via the tile drains. This conclusion is also confirmed by geochemical and isotopic tracing of water in the Cobb River and Blue Earth River watersheds reported by Magner and Alexander (2002), who concluded “that 90 percent of riverine water was influenced by subsurface tile-drained row crop agriculture,” while regional groundwater discharge comprised less than 10 percent of the total flow.

For the reasons discussed above, the HSPF model cannot be expected to neatly partition all flow that actually moves through tile drains into the interflow compartment: the quickest responding portion of this flow (surface inlets with short piped runs) will appear as surface runoff, while the slowest responding portion (subsurface inlets with long piped runs) will appear as groundwater discharge; however, the major portion of the storm response should be simulated in the interflow compartment.

The calibrated models represent interflow as ranging from 8 percent (Chippewa River) to 46 percent (Le Sueur River) of the total flow generation from the land surface over the 1986-2006 period of simulation. (See Section 4.5 for the simulated water balance components.) Direct surface runoff that does not pass through soils or tile drains constitutes a small portion of the annual flow, but may predominate during occasional high flow events. The maximum interflow discharge rates simulated by the model were approximately 0.12 in/day in the Watonwan basin and 0.25 in/day in the Le Sueur basin. These rates are aggregates over the whole basin, including both untiled portions of agricultural fields and non-agricultural land uses, and appear to be generally consistent with maximum drainage coefficients of 0.25 to 0.5 in/day for portions of fields served directly by tile inlets.

4.3 CALIBRATION Hydrologic re-calibration used the new (1993-2006) period of simulation. After the revisions to the PET pan coefficient and a few other changes noted above, the model provides an excellent visual fit to gaged flows. An example for the downstream gage on the Minnesota River at Jordan is shown in Figure 4-5.


4-8

0100002000030000400005000060000700008000090000

100000

Jan-93 Jul-94 Jan-96 Jul-97 Jan-99 Jul-00 Jan-02 Jul-03 Jan-05 Jul-06

Date

Flow

(cfs

)

0

2

4

6

8

10

12

14

Dai

ly R

ainf

all (

in)

Avg Monthly Rainfall (in)Avg Observed Flow (1/1/1993 to 11/30/2006 )Avg Modeled Flow (Same Period)

Figure 4-5. Mean daily flow: Model vs. USGS 05330000, Minnesota River near Jordan, MN

Hydrologic calibration was assessed using a standard set of plots and statistical measures produced by Tetra Tech’s HydroCal spreadsheet analysis system. For each of the streamflow gages identified in Section 3.7.1, the following output is produced:

• Time series plot of daily simulated and observed flows

• Time series plot of monthly simulated and observed flows

• Regression of simulated on observed monthly flows

• Time series balance between observed and simulated monthly flows

• Regression of simulated on observed annual flows

• Comparison of simulated and observed flows by month

• Comparison of medians and interquartile ranges by month

• Tabulation of mean, median, 25th percentile, and 75th percentile daily flows by month

• Flow duration curve comparison

• Comparison of flow accumulation over time

• Table of summary statistics on daily flows

The last item presented for each watershed is the table of summary statistics. This reports the information specified in the QAPP for tolerance targets for hydrologic simulation (see Table 2-1), as well as other statistics, such as the Nash-Sutcliffe coefficient. The detailed calibration statistics and graphical comparisons are provided in Appendix B. A summary of key results is in Table 4-2.

As noted in Section 2.2.1, the seasonal calibration target was revised from the original HSPEXP specification to individual targets for each season (shown in the detailed information in Appendix B). To maintain consistency with the QAPP, the “seasonal” error in the sense used by HSPEXP (summer error minus winter error) is also reported in Table 4-2.

Each calibration gage summarized in Appendix B has eight target metrics for hydrologic calibration (total volume, 50% low, 10% high, and four seasonal volume errors), for a total of eighty statistics. QAPP tolerances are met for 79 out of the 80 measures. The exception is for the error in 50 percent low flows in the Minnesota River at Morton (+13.64 percent), which is just outside of the acceptable tolerance range – although the magnitude of the error is quite small (0.43 versus 0.37 in/yr). For this station there are only seven years of data available. The other graphs and statistics, as well as the model validation application,


4-9

also suggest that model performance is somewhat suspect for Yellow Medicine, most likely due to precipitation records that are not fully representative of inputs to the basin.

The general quality of the fit to daily flows can be summarized through the Nash-Sutcliffe coefficient of model fit efficiency (E). This parameter ranges from minus infinity to 1, with higher values indicating better fit, and is formed as the ratio of the mean square error to the variance in the observed data, subtracted from unity. A value of 0 implies that the observed mean is as good a predictor as the model. Values close to 1 are thus desirable. It should be recalled, however, that the Nash-Sutcliffe coefficient is based on matched daily records, and does not account for phase errors. It is also subject to leverage by outliers. Thus, if a large flow is estimated with the right magnitude, but off by one day, this can substantially degrade the Nash-Sutcliffe coefficient, even though annual sums and flow duration percentiles are unaffected. In general, a value of E greater than 0.7 is taken as indication of a strong hydrologic calibration. The calculated values of E are shown in Table 4-2. They tend to increase downstream, and are less than 0.7 for Chippewa, Yellow Medicine, and Le Sueur. Problems with the Yellow Medicine calibration have been mentioned above. In the case of Le Sueur and Chippewa rivers, the watershed is dominated by several large lakes for which the morphometry and outlet characteristics are not well known, likely introducing some phase errors into the simulation.

Table 4-2. Hydrologic Calibration Summary, 1993-2006

Location Nash-Sutcliffe

E on Daily Flow Total Volume

Error Error in 50%

Lowest Flows Error in 10%

Highest Flows

“Seasonal” Error (summer minus winter)

Chippewa 0.586 3.57% -8.52% 9.54% -11.62%

Yellow Medicine 0.285 -9.88% -7.33% -3.83% 14.80%

Redwood 0.747 -1.14% 3.34% 3.91% -11.36%

Cottonwood 0.774 4.65% -6.55% 4.42% 4.36%

Watonwan 0.745 -6.12% 0.16% -6.24% 3.85%

Le Sueur 0.606 -6.91% -4.58% -2.85% 6.58%

Blue Earth 0.797 -5.82% -9.06% 1.70% 15.23%

Minnesota River – Morton

0.921 5.29% 13.64% 1.76% -1.20%

Minnesota River – Mankato

0.895 -0.36% -1.92% 0.86% 6.46%

Minnesota River – Jordan

0.905 -1.68% -9.62% 0.03% 5.02%

Note: Results for Minnesota River at Morton are for 2000-2006.


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4.4 VALIDATION Following calibration, the model was reapplied to the 1986-1992 period as a validation test (Table 4-3). As the focus of the model is on representation of current conditions, fit for the validation period is of somewhat less importance than the calibration period. In addition, there is considerable uncertainty in the representation of point source flows for the validation period, as most of the DMRs prior to 1992 are lost, which potentially affects the low flow calibration.

Detailed calibration statistics and graphical comparisons are provided in Appendix C. The revised model generally meets the accuracy targets specified in the QAPP, with four exceptions out of 72 tests (the Morton gage was not operational in this period): The 50 percent low flows are over-predicted by more than 10 percent in the Chippewa, Le Sueur, Blue Earth, and Minnesota rivers at Mankato. Over-prediction of low flows in the Chippewa and Le Sueur rivers might again be related to lakes in the basin, while the over-prediction of low flows in the Blue Earth River at Rapidan may reflect the effect of Rapidan Dam. The over-prediction of low flows in the Minnesota River at Mankato is likely a direct result of the Le Sueur and Blue Earth simulations.

Table 4-3. Hydrologic Validation Summary, 1986-1992

Location Nash-Sutcliffe

E on Daily Flow Total Volume

Error Error in 50%

Lowest Flows Error in 10%

Highest Flows

“Seasonal” Error (summer minus winter)

Chippewa -0.277 6.39% 15.28% 9.08% -7.53%

Yellow Medicine 0.647 -6.73% 6.54% 1.67% -3.23%

Redwood 0.736 2.18% 4.62% 6.25% -17.13%

Cottonwood 0.729 -4.84% 5.49% -5.91% 5.22%

Watonwan 0.807 -4.38% 7.23% -8.81% 24.51%

Le Sueur 0.616 -4.74% 16.70% -3.34% 4.50%

Blue Earth 0.711 -6.78% 10.26% -6.09% 9.03%

Minnesota River – Morton

No Data

Minnesota River – Mankato

0.856 -0.21% 11.16% 0.53% 6.08%

Minnesota River – Jordan

0.836 -1.24% 3.85% 0.48% 8.44%

4.5 WATER BALANCE A key requirement for a water quality model is proper attribution of the different components of the water balance. That is, the model needs to not just reproduce the observed stream flow, but also needs to reproduce the pathways by which precipitation becomes stream flow or is evaporated or transpired. Different pathways of water movement to streams – whether by surface runoff, subsurface seepage, or tile drain flow – have very different implications for the movement of sediment and associated pollutants.


4-11

The water balance partitioning of incoming precipitation on major watersheds of the Minnesota River basin is summarized in Figure 4-6. As is expected, the majority of the precipitation that falls on the watershed is returned to the atmosphere via evapotranspiration (ET). Of the remaining components, interflow primarily represents tile drain transport in the basin.

The relative importance of surface runoff and interflow vary in accordance with soil infiltration capacity and the density of tile drains in different watersheds in the basin, with the lowest values in the sandy soils of the Chippewa and the highest values in the glacial lake sediments of the Le Sueur River. In addition, the precipitation amounts increase from west to east across the basin. The total water balance is summarized in Table 4-4. Fractional components of runoff to streams in each basin are summarized in Table 4-5.

Table 4-4. Water Balance by Major Watershed, 1986-2005 (in/yr)

Major Watershed

Precipitation & Point Sources

Evapo-transpiration

Surface Runoff Interflow Groundwater

Chippewa 27.42 0.31 0.38 4.09 22.65

Yellow Medicine

26.17 0.61 1.28 1.89 22.40

Hawk Creek 27.44 0.78 1.57 2.74 22.35

Redwood 27.79 0.50 1.66 3.59 22.05

Cottonwood 28.14 0.33 1.72 4.10 22.00

Watonwan 29.73 0.32 1.69 4.58 23.15

Le Sueur 32.67 1.26 3.88 3.34 24.20

Blue Earth 31.13 0.80 3.22 4.17 22.95

Table 4-5. Simulated Components of Total Flow by Major Watershed, 1986-2005

Major Watershed Surface Runoff Interflow Groundwater

Chippewa 6.42% 7.98% 85.59%

Yellow Medicine 16.05% 33.82% 50.13%

Hawk Creek 15.34% 30.78% 53.88%

Redwood 8.64% 28.91% 62.45%

Cottonwood 5.36% 27.99% 66.64%

Watonwan 4.87% 25.61% 69.52%

Le Sueur 14.82% 45.75% 39.43%

Blue Earth 9.78% 39.30% 50.92%


4-12

Chippewa

Ground Water, 4.09, 15%

ET, 22.85, 83%

Surface Runoff, 0.31, 1%

Interf low , 0.38, 1%

Yellow Medicine


ET, 22.15, 85%


Interflow , 1.44, 6%

Hawk Creek


ET, 21.95, 81%

Surface Runoff , 0.78, 3%


Redwood



ET, 21.70, 79%


Cottonwood


ET, 21.80, 78%



WatonwanInterflow , 1.67, 6%Surface Runoff,

0.32, 1%

ET, 22.95, 78%


Le Sueur



ET, 23.75, 74%


Blue Earth


ET, 23.70, 74%

Surface Runoff, 0.80, 3% Interf low ,

3.35, 10%

Chippewa


ET, 22.85, 83%



Yellow Medicine


ET, 22.15, 85%


Interflow , 1.44, 6%

Hawk Creek


ET, 21.95, 81%

Surface Runoff , 0.78, 3%


Redwood



ET, 21.70, 79%


Cottonwood


ET, 21.80, 78%



WatonwanInterflow , 1.67, 6%Surface Runoff,

0.32, 1%

ET, 22.95, 78%


Le Sueur



ET, 23.75, 74%


Blue Earth


ET, 23.70, 74%

Surface Runoff, 0.80, 3% Interf low ,

3.35, 10%

Figure 4-6. Water Balance Components Summary (in/yr and percentage)


4-13

The model also reproduces the expected hydrologic behavior of manured land, as described in Section 3.8.2. Manured land has higher upper zone soil storage capacity than conventionally tilled land, which diverts some of the precipitation which would otherwise runoff to evaporation, infiltration, and interflow. The reduction in direct overland surface runoff relative to conventional tillage varies by basin, and ranges from a low of 35.7 percent in Le Sueur to a high of 73.8 percent in the Chippewa River basin. The exact result varies in accordance with other hydrologic parameters, as well as the distribution of manured and conventional tillage relative to different rainfall stations within a basin. The reduction in surface flow results in a corresponding reduction in sediment transport capacity.


4-14

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5-1

5 Sediment Recalibration and Validation

5.1 APPROACH Sediment transport in the earlier Minnesota River Basin Model was calibrated primarily to instream observations of total suspended sediment; however, this approach does not guarantee a unique solution, as observed sediment concentrations may arise in a variety of ways, including washoff from the land surface, ravine erosion, scour from the channel bed, and collapse of banks and bluffs. To improve the sediment calibration and achieve a more defensible attribution to sources, a multiple constraint approach was followed, consistent with USEPA (2006) guidance. This included the following components, described in further detail in the following sections:

1. Sheet and rill sediment loss from the land surface was estimated based on analogy to the Universal Soil Loss Equation (USLE), consistent with the previous application of the model. However, the parameter controlling delivery of eroded sediment to the reach network was adjusted during calibration to match radioisotopic evidence on the fraction of sediment derived from surface (“field”) sources in recent contact with the atmosphere.

2. Upland ravine (gully) erosion was added to the model and adjusted to replicate MPCA’s interpretation of the evidence on the fraction of sediment derived from gully sources (see Table 3-16).

3. Sediment delivery through tile drains was simulated consistent with the approach developed for the previous implementation of the model (Tetra Tech, 2002).

4. Simulation of reach hydraulics and shear stress was improved through incorporation of information contained in HEC-RAS flood models. Stream bed scour and deposition was then examined on a reach by reach basis to ensure consistency with expected behavior of stream segments.

5. Additional contributions from bluff collapse were simulated in the same manner as the earlier model (Tetra Tech, 2002). Specifically, the pseudo-random process of bluff collapse is approximated as adding mobile sediment to the stream bed at a constant rate. This sediment is then available for scour and transport during high flow events. Critical shear stress parameters for sediment resuspension were then adjusted in reaches receiving bluff input to approximate available evidence on the sediment load attributed to bluff and bank collapse.

6. In addition to concentration, the model is calibrated to load. This is done both by evaluating both apparent load (concentration times flow) and estimates of integrated or total load over the period of simulation produced through use of the USACE FLUX procedure.

5.1.1 Sheet and Rill versus Ravine Erosion Erosion from the land surface occurs in two ways: as sheet and rill erosion, in which sediment is detached from the soil matrix by raindrop impact or by tillage, and as ravine erosion, in which concentrated flow leads to mass wasting. Both processes are known to be important in the Minnesota River watershed.

The previous model (Tetra Tech, 2002) implemented only the sheet and rill erosion component, as information on ravine erosion was lacking. This component is handled in a manner analogous to the previous model, and is described first.

The HSPF model does not directly use the Universal Soil Loss Equation (USLE) for sediment simulation. However, the representation is similar and some of the parameters used in HSPF can be derived from


5-2

USLE factors. The SSURGO database provides a number of USLE parameter estimates by soil type, and these can be used to set initial parameter values – ensuring relative consistency between the HSPF and USLE approaches.

HSPF erosion parameters for pervious land uses were estimated based on a theoretical relationship between HSPF algorithms and documented soil parameters, ensuring consistency in relative estimates of erosion based on soil type and cover. HSPF calculates the detachment rate of sediment by rainfall (in tons/acre) as

JRERPKRERSMPFCOVERDET •⋅⋅−= )1(

where P is precipitation in inches. Actual sediment storage available for transport (DETS) is a function of accumulation over time and the reincorporation rate, AFFIX. The equation for DET is formally similar to the USLE equation,

RE · K · LS · C · P.

USLE predicts sediment loss from one or a series of events at the field scale, and thus incorporates local transport as well as sediment detachment. For a large event with a significant antecedent dry period, it is reasonable to assume that DET≈DETS if AFFIX is greater than zero. Further, during a large event, sediment yield at the field scale is assumed to be limited by supply, rather than transport capacity. Under those conditions, the USLE yield from an event should approximate DET in HSPF.

With these assumptions, the HSPF variable SMPF may be taken as fully analogous to the USLE P factor. The complement of COVER is equivalent to the USLE C factor (i.e., (1 - COVER) = C). This leaves the following equivalence:

LSKREPKRER JRER ⋅⋅=⋅ , or

81.1PLSKREKRER ⋅⋅=

The empirical equation of Richardson et al. (1983) as further tested by Haith and Merrill (1987) gives an expression for RE (in units of MJ-mm/ha-h) in terms of precipitation:

81.16.64 RaRE t ⋅⋅=

where R is precipitation in cm and at is an empirical factor that varies by location and season. As shown in Haith et al. (1992), the expression for RE can be re-expressed in units of tonnes/ha as:

81.16.64132.0 RaRE t ⋅⋅⋅=

This relationship suggests that the HSPF exponent on precipitation, JRER, should be set to 1.81.

The remainder of the terms in the calculation of RE must be subsumed into the KRER term of HSPF, with a units conversion. Writing RE in terms of tons/acre and using precipitation in inches:

[ ] )/24.2(/)/1()/54.2()(6.64132.0)/( 81.181.1 hatonnesactonincminPaactonsRE t ⋅⋅⋅⋅⋅=

For southwest Minnesota (USLE Region 12), at is estimated at 0.10 for October through March and 0.26 for April through September, with an average of 0.18 Selker et al., 1990). (As HSPF does not implement KRER on a seasonal basis, the average value is most relevant.) Simplifying,

RE (tons/ac) = 3.7032 · R · (P)1.81.

The power term for precipitation can then be eliminated from the equation for KRER, leaving the following expression (English units) in terms of the USLE K factor:


5-3

LSKKRER ⋅⋅= 7032.3

The K factor is available directly from soil surveys, while the LS factor can be estimated from slope, using the expression of Wischmeier and Smith (1978):

( ) ( )065.0sin56.4sin41.65045.0 2 ++⋅= kkbLLS θθ , where

θ = tan-1 (S/100), S is the slope in percent, L is the slope length, and b takes the following values: 0.5 for S ≥ 5, 0.4 for 3.5 ≤ S < 5, 0.3 for 1 ≤ S < 3, and 0.2 for S < 1.

This approach establishes initial values for KRER that are consistent with USLE information. The actual loading of sediment from the land surface depends on the amount of detached sediment available for transport and the transport capacity, which is controlled, as a function of runoff depth, by parameters KSER and JSER. Specifically, the amount of sediment transported from the land to the water is the smaller of the detached sediment storage (DETS) and the transport capacity, STCAP, where STCAP is estimated as a function of KSER times surface outflow (inches per model interflow) raised to the JSER power. The JSER parameter determines the shape of the response about an outflow of 1 inch per interval (for this model the interval is 1 hour). Because most storm events produce less than 1 inch per hour of runoff, increased values of JSER lead to decreased transport for all but the most extreme storm events.

The representation of ravine erosion is necessarily simplified in a spatially lumped model. HSPF simulates transport of sediment due to ravine incision in a manner similar to that used for sheet and rill erosion, controlled by parameters KGER and JGER, which serve the same roles as KSER and JSER. The difference is that the sediment is not supply-limited. Simulation of ravine loading with a relatively large value of JSER (e.g., range of 3-5) results in a situation in which loading from ravines is small until runoff reaches 1 in/hr, then increases rapidly.

5.1.2 Sediment Transport in Tile Drainage Tile drainage plays a significant role in the transport of sediment and agricultural pollutants from agricultural land to waterbodies in the Minnesota River basin. Tile drains with surface inlets provide a direct pathway for transport of material that, under natural hydrologic conditions, would be filtered through subsurface material. Subsurface drain inlets may also incorporate sediment from the soil matrix, particularly fine sediment.

In a typical HSPF simulation, the energy available to move sediment off the land is calculated from the depth of surface flow. It is thus not surprising that the original HSPF simulations tended to provide a poor fit to total suspended sediment observations—with a total sediment yield that was either drastically low or mistimed relative to observations—because the sediment simulation was constrained to follow the pattern of predicted surface runoff. A more realistic simulation requires representation of transport through tile drains. This can be accomplished by associating sediment and pollutant loads with the interflow outflow component of the model.

As designed, HSPF does not simulate sediment transport in interflow, while dissolved constituents in interflow may be represented by user-assigned concentration values. This approach is not adequate to simulate sediment and sediment-associated pollutant delivery through tile drainage. Fortunately, the modular structure of HSPF allows operation on internal time series in the SPECIAL ACTIONS block. For both sediment and pollutants, the following procedure is used:

1. Associate a sediment load with interflow outflow. This is represented as a function of the interflow inflow, current detached sediment storage on the land surface, and the parameters controlling the surface washoff of detached sediment. Thus, the capacity for interflow transport of sediment is assumed to be related to the capacity for transport in surface flow. Specifically, the amount of sediment that can be transported into the tile drainage system is estimated as


5-4

IIN = (IFWI/SURO) · WSSD · KI

where IIN is the rate of sediment transport into the tile drainage system (mass per area), (IFWI/SURO) is the ratio of interflow inflow to surface runoff (both expressed as depth), WSSD is the amount of sediment (mass per area) transported by surface runoff, and KI is an adjustment factor that reflects potential losses to settling near tile drain inlets. A value of KI of 0.5 (50 percent) appears to provide reasonable results for the calibration. Greater sediment losses could be specified to represent the effects of BMPs on sediment entry into the tile drain system.

2. Correct detached sediment storage on the land surface to reflect losses due to interflow inflow.

3. Release the sediment stored in the interflow system using an exponential decay representation. A removal rate (RIN) of 0.5 percent per hour was used in the 2002 implementation of the model; however, additional calibration efforts indicate that a value of RIN of 1 percent per hour provides a better fit to observations. Discharge of the sediment in interflow storage is allowed only when outflow of water from interflow is occurring.

The representation of the interflow sediment component is thus related to hydrology and transport capacity, but is essentially empirical in nature. Including a tile drainage sediment component results in a sediment pollutograph that falls off more gradually (has a longer tail) than would be expected from event-based surface washoff alone, with the two new parameters (KI and RIN) controlling the shape of the pollutograph. The shape of the pollutograph obtained with the interflow sediment component described above appears to provide a much better fit to observed sediment concentrations in the Minnesota River basin than can be obtained from a standard HSPF application in which sediment washoff is simulated only during surface runoff events.

As described in Section 3.8.3, tile drain intakes have an effect on the sediment size fractionation of transported solids, shifting the load toward fines. In accordance with that research, the sediment delivered via tile drains is apportioned as 8 percent silt and 92 percent clay.

5.1.3 Representation of Bank and Bluff Erosion The topography of the Minnesota River basin is characterized by areas of high bluffs, particularly where tributary streams enter the former floodplain channel of the glacial river Warren, now occupied by the Minnesota River mainstem. In many of these areas, intermittent failure of bluffs along streams has been identified as a major contributor to total sediment load. In addition, bank erosion contributes sediment load to all stream segments.

Obtaining an accurate sediment simulation requires consideration of the bank and bluff erosion components. However, it is not possible to fully and explicitly represent bank and bluff erosion in the HSPF model, particularly in the calibration phase. HSPF is not a detailed hydraulic/channel erosion model. Further, major bluff failure events are by nature episodic, and their timing is generally neither documented nor predictable.

Nonetheless, bank and bluff erosion can be partially represented in the model, and their long-term impact fully represented. The approach taken to represent bank and bluff erosion builds on the previous implementation of the model: Conceptually, bank and bluff erosion contributions may be separated into two classes:

Small-scale scour during high flow events (“bank erosion,” for simplicity) that are more or less tied to instream shear stresses, and catastrophic stability failure events (“bluff erosion,” for simplicity) that may be unrelated to instream shear stresses.

“Bank” Erosion: The first component is already implicitly represented in the model. That is, the model provides a one-dimensional representation of stream reaches that incorporates scour (and associated


5-5

increases in sediment load) from the stream “bed.” In the one-dimensional representation this also implicitly includes flow-related mobilization of sediment from stream banks – subject to fines depletion and armoring considerations similar to those in the streambed when viewed at a large spatial scale. The major difference is that the sediment available from stream banks is not constrained to the amount of sediment initially present or deposited to the streambed. For the purposes of simulation, this can be represented by ensuring that the initial stream bed depth is specified sufficiently great (in areas with bank erosion problems) that the bed sediment is not depleted during the course of the simulation.

“Bluff” Erosion: Bluff erosion is conceived as sediment input that is not readily predictable from stream shear stress, but is episodic in nature and more closely related to land use and water table elevation in the surrounding land. When a bluff face collapses the event does not translate directly into a pulse of suspended sediment (although a pulse will likely occur). Instead, most of the sediment mass involved in the collapse is converted to bed sediment, which may subsequently be scoured during high flows.

Conceptually, a bluff erosion event can be represented in the HSPF model by using the Special Actions routines to implement a step increase in volume of the bed in the three sediment size classes. The long term average impact of bluff erosion is that the volume of bed sediment potentially available for scour is higher than would otherwise be expected.

Accordingly, the bluff erosion component is simulated as a (long-term) average replenishment of the bed sediment available for scour in those stream reaches where topography suggests bluff erosion is a significant component of the long-term sediment load. This approach yields an increased volume of sediment within the stream channel that is available for scour when scouring flows occur. One result of this approach is that reductions in stream hydrograph peaks will reduce both bank and bluff load contributions to suspended sediment.

The present model refines the representation of bank and bluff erosion in two ways. First, the use of HEC-RAS models to refine the specification of FTables improves the simulation of shear stress in the channel. Second, new information compiled by MPCA allows calibration of the apparent fraction of sediment load derived from different sources.

For the western watersheds, bluff loading is simulated as occurring only in the most downstream segment, where the tributary enters the glacial river Warren valley. For Blue Earth and Le Sueur rivers, smaller amounts of bluff loading are also assigned upstream of the terminal segment consistent with the topography of these rivers.

5.1.4 Reconciling Source Data with Modeling An analysis of the relative fractions contributed by different sediment sources is complicated by the fact that upland sediment load may deposit in the stream bed and later scour and move downstream. Similarly, material eroded from the stream bank may deposit and later remobilize. A unique set of upland loading rates, bed erosion rates, and downstream sediment transport measures is thus not readily interpretable from the model output. At one extreme, all the sediment scoured from the stream bed could consist of re-entrained upland sediment, with the net contribution of “old” stream bank sediments equal to zero. At the other extreme, the same model output could be consistent with the sediment observed at the outlet being derived only from bank erosion, if all the upland sediment settled out and was replaced by “old” bed sediment generated by channel meander. While neither extreme is likely to be true, the ratio of old to new sediment is not directly extractable from the model because individual sediment particles are not tracked as they move in and out of bed storage.

Consider a case in which there is an external (upland) sediment load of X and a bank and bluff erosion load of B. The processes can be conceptually represented by a simple box model (Figure 5-1).


5-6

Temporary BedStorage

Stream Bank and Bluff Sources

UplandLoads

X

Xg Xgr

X(1-g)

Bgr BgB

B(1-g)

Temporary BedStorage

Stream Bank and Bluff Sources

UplandLoads

X

Xg Xgr

X(1-g)

Bgr BgB

B(1-g)

Figure 5-1. Conceptual Representation of Stream Sediment Processing

For an external sediment load X, a fraction g goes into temporary bed storage. A fraction of this (r) is in turn resuspended and transported downstream as Xgr. Similarly, erosion of established stream banks and bluffs yields a total load B. This is assumed to be subject to the same physical processes as the upland load, X: A fraction g goes into temporary storage, of which a further fraction r is transported downstream. (The factor r may be thought of as a recycle rate. The total sediment load transported downstream, Y, is then:

( ) ( )grgBXY +−+= 1 .

The model output provides information on both gross bed scour (GS, resuspension flux only) and net bed scour (NS, balance of scour and deposition). Two additional equations can be written for GS and NS based on the simple box model:

( ) ( ).1 ggrBggrXNSBgrBXgrGS

−++−=++=

Given X, this appears to yield three equations in three unknowns. However, the system of equations is indeterminate, as the output, Y, is simply equal to the net scour (NS) + X. Therefore, there is not a unique solution unless additional constraints are imposed regarding the recycle rate, r.

If r is assumed known, then the equation for GS can be solved to yield

grXgrGSB

+−

=1

.

Substituting the solution for B into the equation for NS and rearranging then yields a solution for g:

GSGSrNSrXNSGSg

+−+−

= .

This in turn may be used to find the solution for B, conditional on r. Fortunately, the solution is not highly sensitive to the selected value of r.

The upland loading component, X consists of both radiologically “new” sediment derived from sheet and rill erosion and “old” sediment derived from ravines. Both components are assumed to have an equal probability of passing through the network. The fraction of the upland loading component that is


5-7

transmitted is (1 – g + gr). Therefore, the downstream output fraction attributable to sheet and rill erosion (WSHSD) is

YgrgWSHSD +−

⋅1

.

The output fraction attributable to gullying can be evaluated similarly.

The bank and bluff loading component, B, includes both bank erosion and contributions from bluff collapse. These components are not separable radiologically, but some independent estimates are available for the gross load from bluffs. Therefore, the bank erosion fraction (not attributable to temporary storage of upland sediment) can be evaluated as B less the loading due to bluffs.

5.2 CALIBRATION

5.2.1 Sediment Parameter Calibration Calibration for sediment ultimately attempts to match instream observed suspended sediment concentrations. However, a large number of processes and parameters are involved, including sediment generation on the uplands, sediment transport from uplands to streams, scour and deposition in the channels, and loading from additional sources such as bluff collapse. To cope with the potential over-parameterization of the model, the following approach was used:

• Generation of detached sediment storage is assumed known, using relationships to USLE parameters to determine raindrop detachment and information previously incorporated into the model to specify tillage detachment.

• The exponent for transport by sheet and rill erosion (JSER) is assumed to be fixed at 2.0 as recommended in USEPA (2006)

• The coefficient in detached sediment transport (KSER) and the parameters controlling ravine erosion (KGER and JGER) are taken as calibration parameters to achieve the relative balance between surface and gully loading of sediment identified by MPCA.

• The sediment balance in each model reach was examined to ensure qualitatively reasonable representations. In general, headwater reaches were assumed to be mildly net degradational and should be, over the long term, close to steady state, except for occasional degradation during large storm events. While there are not observations of sand, silt, and clay fractions of suspended sediment in the reaches, all three components are adjusted during calibration to retain a balance in bed composition and reflect their different transport characteristics (with silt scouring after clay, and sand responding continuously to flow while more cohesive sediments do not scour until a critical shear threshold is reached).

• Reaches that traverse the bluff area at the margins of the glacial river Warren valley may experience significant scour, but are eventually replenished by bluff and bank collapse.

• Contribution rates from bluff collapse and instream scour and deposition parameters are adjusted to match instream concentrations and calculated loads, while maintaining the source attribution fractions reported by MPCA.

5.2.1.1 Upland Sediment Parameters The rate of sediment detachment by rainfall is simulated by analogy to the USLE, as described above, while detachment via tillage operations is specified in HSPF SPECIAL ACTIONS. The exponent for


5-8

transport of detached sediment (JSER) is also fixed based on theoretical considerations. The remaining parameters controlling sheet and rill sediment transport (KSER) and ravine loading (KGER and JGER) are then adjusted during calibration to obtain a match to the relative proportion of surface sediment loading while maintaining an overall sediment mass balance. The calibrated parameter values are shown in Table 5-1

Table 5-1. Calibrated Upland Sediment Transport Parameters

Watershed KSER KGER JGER

Blue Earth 0.280 1.80 4.00

Chippewa 0.380 0.59 4.50

Cottonwood 0.500 4.50 3.80

Le Sueur 0.110 1.00 4.50

Lower MN 0.110 0.75 4.50

Middle MN 0.110 0.70 4.50

Redwood 0.140 0.26 4.60

Watonwan 0.306 2.28 4.00

Yellow Medicine/Hawk 0.110/0.170 0.99 3.50

5.2.1.2 Reach Sediment Parameters Complex cycles of deposition and scour occur in stream reaches, determined by the shear stress exerted on the bed material and the external sediment supply. The model simulates deposition and scour (aggradation and deposition) of silt and clay in stream reaches based on exerted shear stress relative to critical shear stresses for deposition and scour (τCD and τCS) for each sediment size class, particle deposition velocities, and a limiting maximum potential rate of scour (W, lb/ft2/d). The parameters τCD, τCS, and W are site-specific and vary by reach. (HSPF is a spatially lumped model, with one-dimensional representation of reaches. Further, the exerted shear stress, based on simulation of reach-average conditions, varies continuously based on local characteristics of the channel. Thus a single set of parameters will not adequately represent the behavior of bed sediment in all reaches.)

For the non-cohesive, sand fraction of sediment, HSPF provides several options, including the Toffaletti method, the Colby method, and a simplified exponential relationship to flow. Sufficient information is not available to implement the first two options, which additionally can cause stability problems in the model, so the third approach is used. In this approach sand transport capacity is a function of KSAND · AVVELE EXPSND, where AVVELE is the average velocity and KSAND and EXPSND are user-specified parameters.

The sediment calibration process is illustrated in detail for the Watonwan River below.

5.2.1.3 Sediment Calibration Example for Watonwan River The Watonwan River has been monitored for many years at Garden City, MN. The majority of the observations for solids are TSS measurements, although a few USGS observations during the validation period are SSC. The observations may thus incorporate a significant amount of analytical variability, as described in Section 2.2.2.


5-9

Model calibration begins (and often ends) with a visual comparison of time series. Such comparisons are important for the Minnesota River models, but graphical comparison is only one among many analyses and constraints used in the calibration. Figure 5-2 shows the comparison of simulated and observed concentrations for the latter half of the calibration period.

Watonwan River - Garden City2000-2006

0.1

1

10

100

1000

10000

2000 2001 2002 2003 2004 2005 2006Year

TSS,

mg/

L

Simulated Observed

Figure 5-2. Observed and Predicted Daily Average TSS, Watonwan River at Garden City,

2000-2006

General trends seen in the data are reproduced in the simulated time series, but some points are missed. Average error in concentration is -2.6 mg/L and the median error is 1.9 mg/L, indicating low overall bias. Some peaks are underestimated, for example 6/9/2004, when a concentration of 2,130 mg/L was reported. This sample was taken on the rising limb of a moderately sized (but not extreme) runoff event. TSS observations in this range are subject to high levels of uncertainty and concentrations can vary rapidly over the course of a day during the rising limb of the hydrograph; however, the main culprit here is likely the hydrologic simulation. Gaged daily flow on this date was 1,947 cfs, but the model only simulates 990 cfs.

Prediction of both concentration and load is important to uses of the model. Analysis in terms of load weights the calibration toward concentrations at higher flows. The quality of fit for loads may be strongly determined by a few observations at very high flows, so this type of calibration metric can be subject to considerable uncertainty. A scatterplot of simulated load versus same day load estimated from point-in-time sampling shows reasonable agreement (Figure 5-3). A power plot of load versus flow (Figure 5-4) confirms that the model is reproducing load-flow relationships well.


5-10


0.01

0.1

1

10

100

1000

10000

100000

0.1 1 10 100 1000 10000 100000

Observed TSS (tons/day)

Sim

ulat

ed T

SS (t

ons/

day)

Paired data Equal f it

Figure 5-3. Scatterplot of Simulated vs. Observed TSS Load, Watonwan River at Garden City,

1993-2006


0.01

0.1

1

10

100

1000

10000

100000

1000000

1 10 100 1000 10000 100000

Flow, cfs

TSS

Load

, ton

s/da

y

Simulated Observed Pow er (Simulated) Pow er (Observed)

Figure 5-4. Power Plot of Observed and Predicted TSS Load vs. Flow, Watonwan River at

Garden City, 1993-2006


5-11

A plot of concentration versus flow (Figure 5-5) reveals some of the sources of uncertainty. Specifically, there is a tendency for some observations obtained at high flows to be over-predicted.


1

10

100

1000

10000

1 10 100 1000 10000

Flow, cfs

TSS,

mg/

L

Simulated Observed

Figure 5-5. Power Plot of Simulated and Observed TSS Concentrations vs. Flow, Watonwan

River at Garden City, 1993-2006

Monitoring information is not available on sand, silt, and clay fractions, but the predicted loads can be separated into the three size fractions (Figure 5-6). For each of the size fractions there is a baseline curve, which represents channel scouring, and a scatter of points above that curve, which represents excess loading from the land surface during runoff events. The major inflection points in the concentration versus flow relationship for cohesive sediment (silt, at about 425 cfs, and clay, at about 200 cfs) represent the point at which scour of the corresponding portions of the bed material begins, controlled by the critical shear stress (τCS) parameters. Bed scour of non-cohesive sand, in contrast, follows a smooth exponential relationship.


5-12


0

10

20

30

40

50

60

70

80

90

100

0.1 1 10 100 1000 10000

Flow, cfs

TSS,

mg/

LSAND SILT CLAY

Figure 5-6. Predicted Sediment Size Fractions vs. Flow, Watonwan River at Garden City,

1993-2006

Note: Vertical axis truncated

The inflection points can be moved by changing the specification of critical shear stress to obtain a better match to observations. Inference on appropriate values is obtained by reference to a plot of predicted shear stress (τ) versus flow on a reach-by-reach basis (Figure 5-7).

RCH733

0

0.05

0.1

0.15

0.2

0.25

0.3

1 10 100 1000 10000

Flow (cfs)

Shea

r Str

ess

(lb/ft

2 )

Figure 5-7. Relationship of Shear Stress (τ) to Flow, Watonwan River Reach 733


5-13

The relationship is not unimodal. This reflects the fact that the river overflows its banks onto the floodplain above a flow of about 2,000 cfs, at which point average velocities decrease, and, as a result, the estimated average shear stress. This points out a weakness of a one-dimensional reach model, as the velocities and shears within the channel may continue to increase over this flow range. The effect becomes even more pronounced in the mainstem of the Minnesota River downstream of Mankato, where a wide flood plain is accessed.

Critical shear stress parameters are set on a reach-by-reach basis so that free-flowing reaches upstream of bluff areas are mildly degradational over time (Figure 5-8).

Sand

R733: 93'-06'

0

5000

10000

15000

20000

25000

30000

35000

10/23/1992 7/20/1995 4/15/1998 1/9/2001 10/6/2003 7/2/2006

Time

San

d S

tora

ge (t

ons)

SiltR733: 93'-06'

0

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15000

20000

25000

30000

35000

10/23/1992 7/20/1995 4/15/1998 1/9/2001 10/6/2003 7/2/2006

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rage

(ton

s)

ClayR733: 93'-06'

0

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30000

35000

10/23/1992 7/20/1995 4/15/1998 1/9/2001 10/6/2003 7/2/2006

Time

Cla

y S

tora

ge (t

ons)

Figure 5-8. Active Bed Storage of Sediment in Watonwan Reach 733

In contrast, the reach passing through the bluff area is set so that it has much stronger scour potential to account for bluff contribution to suspended sediment, but the mobile sediment in the stream bed is continually replaced by bluff collapse processes (Figure 5-9). In this plot, sediment stored in the bed is rapidly eroded during higher flow events, but is also rapidly replaced by input from the bluffs during lower flow periods.

Sand

R731: 93'-06'

0

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10/23/1992 7/20/1995 4/15/1998 1/9/2001 10/6/2003 7/2/2006

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d S

tora

ge (t

ons)

SiltR731: 93'-06'

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10/23/1992 7/20/1995 4/15/1998 1/9/2001 10/6/2003 7/2/2006

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rage

(ton

s)

ClayR731: 93'-06'

0

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40000

45000

10/23/1992 7/20/1995 4/15/1998 1/9/2001 10/6/2003 7/2/2006

Time

Cla

y S

tora

ge (t

ons)

Figure 5-9. Active Bed Storage of Sediment in Watonwan Reach 731 (Bluff Area)

The simulation of load sources is constrained to be in approximate agreement with the source attribution information supplied by MPCA. In this case, the tabulated loads are 34 percent field, 33 percent gully/ravine, and 34 percent bank and bluff erosion, in close agreement with the MPCA estimates of 35, 30, and 35 percent.

Tabulation of statistics shows a close agreement of observed and predicted concentrations (average error of -2.6 percent). The load average error is higher at 33 percent, but this error is driven by a few extreme events. Most loads are simulated well (median error 0.32 percent), and the t-test on paired loads is passed (p value 0.24) and the transport slope is close (error of 0.5 percent). Simulated loads are 23.6 percent


5-14

higher than estimated loads from FLUX analysis of the monitoring data, consistent with the average error on the load and again driven by a few high flow events.

Plotting concentration errors (simulated minus observed) versus flow shows a fairly even distribution, with a few outliers (Figure 5-10). However, simulated concentrations appear to over-predict observations (positive errors) at both very low flows and very high flows. The over-predictions at low flow are in part driven by point source loading, but also include the receding tail of discharges from tile drains. Over-prediction at the highest flows is mostly due to predicted gully erosion. While the gully transport parameter could be changed to reduce this error, doing so would result in deviation from the MPCA estimate of gully contribution to sediment loads in the Watonwan River. It is further likely that TSS concentrations at very high flows have considerable uncertainty and may be biased low due to high sand content.

Watonwan River - Garden City 1993-2006

-2500.0

-2000.0

-1500.0

-1000.0

-500.0

0.0

500.0

1000.0

1500.0

1 10 100 1000 10000

Flow, cfs

TSS

Con

cent

ratio

n Er

ror,

mg/

L

Figure 5-10. TSS Concentration Errors (Simulated minus Observed) versus Flow,

Watonwan River at Garden City, 1993-2006

Plotting concentration errors versus month provides further insights (Figure 5-11). This suggests that concentrations tend to be underestimated in May and overestimated in August and October. Additional fine-tuning of monthly cover factors might help address this; however, some important factors, such as channel critical shear stresses, cannot be varied on a monthly basis, and, in any case, a large portion of the error that is present is likely due to uncertainties in the hydrologic simulation.


5-15


-2500

-2000

-1500

-1000

-500

0

500

1000

0 2 4 6 8 10 12

Month

TSS

Con

cent

ratio

n Er

ror (

mg/

L)

Figure 5-11. TSS Concentration Errors (Simulated minus Observed) versus Month, Watonwan

River at Garden City, 1993-2006

In sum, the model does a reasonable job of predicting the observed concentrations and loads (which are themselves uncertain) while also honoring constraints regarding source attribution and expected channel behavior. Any one component could readily be improved, but at the expense of other aspects of the calibration. Use of multiple constraints should help protect against spurious over-calibration to data.

Finally, the calibrated model is reapplied to the validation period (Figure 5-12).


0.1

1

10

100

1000

10000

1986 1987 1988 1989 1990 1991 1992Year

TSS,

mg/

L

Simulated Observed

Figure 5-12. Observed and Simulated TSS for the Validation Period (1986-1992), Watonwan River

at Garden City

Results for the validation period are generally similar to those obtained for the calibration period, and the target criteria are met. However, there are some differences. For example, average error in concentration has a larger absolute value (-26.6 percent). This could reflect the more limited data available for the calibration period (with much of the data from a few events in 1991), but also may be in part due to the differences in land use specification and greater uncertainty in point source loadings for this earlier period.


5-16

Following calibration and validation, the model performance is deemed acceptable and can be used to develop allocations to individual sediment source classes.

5.2.1.4 Bluff Erosion Rates As described above, the process of bluff collapse in stream segments entering the glacial river Warren valley is simulated as an increase in the bed sediment that is available for transport in stream segments, given sufficient flow and shear stress. Because the timing of collapse events cannot be precisely simulated, this is represented via HSPF Special Actions as a constant rate of addition to the mobile sediment in the stream bed. The amount of added available mobile sediment is not equal to the volume lost in bluff collapse events, because only some of this material will end up in active stream segments. Therefore, the rate of bluff erosion contribution is taken as a calibration parameter, constrained by the MPCA estimates of source rates. Total bluff loading by major watershed is summarized in Table 5-2.

Table 5-2. Bluff Erosion Contribution Rates to Available Stream Bed Sediment

Watershed Bluff Contribution (tons/hr)

Blue Earth River 28.0

Chippewa River 0.1

Cottonwood River 2.1

Hawk Creek 0.97

Le Sueur River 11.2

Lower Minnesota River 0

Middle Minnesota River 0

Redwood River 1.6

Watonwan River 2.1

Yellow Medicine River 1.5

5.2.1.5 Adherence to Source Attribution Information The parameters controlling upland sediment transport to streams and sediment mobilization within stream reaches are constrained to provide agreement with the source attribution data developed by MPCA while also matching observed concentrations. As shown in Figure 5-13, the resulting model is generally in close agreement with specifications. One apparent exception is Hawk Creek; however, there is very limited monitoring on Hawk Creek and the MPCA specifications simply assume it is similar to Yellow Medicine River – which may not be appropriate because there is a significant fraction of urban land in the Hawk Creek drainage around Willmar.


5-17

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

Blue Earth Chippewa Cottonwood Hawk LeSueur Redwood Watonwan Yellow

Field

Field Target

Ravine

Ravine TargetBank & Bluff

Bank/Bluff Target

Figure 5-13. Source Attribution of Sediment Loads, 1993-2006

5.2.2 Sediment Calibration and Validation Results A full set of calibration and validation graphs for each of the major watersheds is provided in Appendix D. Sediment calibration statistics (1993-2006) are summarized in Table 5-3; corresponding results for the validation period (1986-1992) are in Table 5-4. Most, but not all, of the measures achieve the targets specified in Table 2-2; those that do not are highlighted in tan in the tables below. (A gray highlight in the sample count row indicates that there are insufficient data to apply the comparison to targets).

In some cases, it was not possible to achieve individual targets while at the same time honoring the constraints on source attribution provided by MPCA. Occasional failures to meet load targets may be caused by the undue influence of a few high-flow outliers. Another significant issue is associated with the potential biases and imprecision in TSS sampling. This is shown by examination of the sediment monitoring data for the Minnesota River at Jordan, where there a significant number of SSC measurements (USGS) along with TSS measurements from MPCA and MCES (Figure 5-14). Here, the SSC and TSS measurements are often in approximate agreement. However, the TSS measurements are often lower than SSC, and it is only the TSS measurements that fall significantly below the simulation line. Further, these low values are not readily predictable while maintaining consistency with measurements upstream. Because most stations have predominantly TSS data, and because MPCA uses the relationship between TSS and turbidity for regulatory purposes, the model attempts to predict TSS measurements to the extent possible. However, the evident presence of occasional low bias in some TSS samples can have a detrimental effect on model fit statistics.


5-18

Minnesota River - Jordan1993-1999

0.1

1

10

100

1000

10000

1993 1994 1995 1996 1997 1998 1999

Flow, cfs

TSS,

mg/

LSimulated USGS MPCA MCES

Figure 5-14. Comparison of SSC (USGS) and TSS (MPCA, MCES) Measurements, Minnesota

River at Jordan


5-19

Table 5-3. Calibration Statistics for Suspended Sediment (1993-2006)

Chi

ppew

a

Yello

w

Med

icin

e

Red

woo

d

Cot

tonw

ood

Wat

onw

an

Blu

e Ea

rth

Goo

d Th

unde

r

Blu

e Ea

rth

Rap

idan

Le S

ueur

Blu

e Ea

rth

Man

kato

Min

neso

ta

Man

kato

Min

neso

ta

St P

eter

Min

neso

ta

Jord

an

Crit

erio

n

Count 17 68 238 222 322 28 233 257 144 985 300 661 ≥ 20

Conc Ave Error -8.48% 6.16% -6.56% -19.80% -2.63% -13.84% -7.08% -10.63% -30.02% -10.41% -6.54% -8.69% min

Conc Median Error -7.22% 0.00% -14.02% -1.75% 1.89% -8.59% 2.83% 0.92% 8.72% -6.15% -1.23% -0.32%

Load Ave Error -31.41% 45.80% 38.99% 1.83% 32.81% -21.60% 3.38% 24.91% -3.85% 0.41% 11.55% 8.14% min

Load Median Error -11.58% 0.00% -0.23% -0.11% 0.32% -4.80% 0.07% 0.01% 1.68% -1.65% -0.09% -0.04%

Paired t conc 0.77 0.79 0.91 0.51 0.99 0.62 0.39 0.84 0.00 0.00 0.35 0.02 ≥ 0.20

Paired t load 0.25 0.53 0.27 0.78 0.24 0.47 0.80 0.42 0.86 0.94 0.34 0.17 ≥ 0.20

Transport slope -42.18% 10.08% 4.90% -13.88% 0.48% -28.34% -15.42% 3.12% -9.88% 2.96% -6.65% 2.08% ± 20%

FLUX Ave Error 33.3% 14.9% -43.1% 23.6% -4.1% 5.1% 11.4% 3.1%

Notes: Gray shading indicates insufficient samples for analysis. Tan shading indicates measures that do not meet performance targets specified in the QAPP. The paired t-test performance targets apply only when the mean difference is greater than 10 percent.


5-20

Table 5-4. Validation Statistics for Suspended Sediment (1986-1992)

Chi

ppew

a

Yello

w

Med

icin

e

Red

woo

d

Cot

tonw

ood

Wat

onw

an

Blu

e Ea

rth

Goo

d Th

unde

r

Blu

e Ea

rth

Rap

idan

Le S

ueur

Blu

e Ea

rth

Man

kato

Min

neso

ta

Man

kato

Min

neso

ta

St P

eter

Min

neso

ta

Jord

an

Crit

erio

n

Count 40 41 89 75 60 17 0 37 156 584 0 188 ≥ 20

Conc Ave Error -1.82% -10.47% -49.58% -32.65% -26.58% -33.54% No Data -45.76% 9.32% -2.65% No Data 13.12% min

Conc Median Error -9.74% -1.57% 6.92% 8.49% -2.31% -17.41% No Data -18.61% 6.62% -9.39% No Data 4.53%

Load Ave Error -30.50% 0.00% 0.40% -8.64% 48.24% 14.99% No Data -49.02% 6.42% 13.09% No Data 24.05% min

Load Median Error -0.64% -0.01% 0.44% 0.72% 0.00% -13.66% No Data -5.70% 0.38% -0.41% No Data 0.88%

Paired t conc 0.80 0.49 0.08 0.27 0.37 0.20 No Data 0.02 0.32 0.55 No Data 0.08 ≥ 0.20

Paired t load 0.40 1.00 0.73 0.67 0.36 0.55 No Data 0.12 0.49 0.07 No Data 0.02 ≥ 0.20

Transport slope -9.56% 14.44% 10.28% -0.94% -4.24% 23.60% No Data 28.92% 6.69% 20.07% No Data 17.45% ± 20%

FLUX Ave Error -15.9% 14.2% -45.6% -23.9% No Data -4.1% 88.8% No Data 64.8%



5-21

Instances in which targets are not met in Table 5-3 and Table 5-4 are discussed in further detail below. Refer to Appendix D for accompanying graphical summaries.

• Redwood: The t-test on concentration is not met for the validation period. The average error on concentration is large (-49.6 percent), but the median error is small (6.9 percent). Concentration statistics are highly influenced by one very large outlier, on 6/16/1992, when the observed concentration was 3,048 mg/L, but the predicted concentration only 43 mg/L.

• Blue Earth – Good Thunder: The calibration period transport slope does not meet the target, but only a small amount of data is available (28 samples), all from 1996. These samples do not include low flows, so the estimate of transport curve slope may be inaccurate.

• Le Sueur: Targets are met during the calibration period, but both concentrations and loads are low during the validation period, and the targets for both t-tests, as well as the transport curve slope, are not met for validation. The data set for this station during the validation period is quite small (37 observations), and the errors are concentrated in spring and summer of 1991. These errors are likely associated with a poor fit to hydrology for 1991, as high flows during June and July of that year are not replicated by the model.

• Blue Earth – Mankato: The t-test on concentration is not met for the calibration period, although loads are well simulated. The errors are associated with intensive measuring in 1994 and 1995, in which high concentrations (around 1,000 mg/L) were reported in conjunction with moderate flows, but not replicated by the model. Possibly these high concentrations were associated with bluff collapse events.

• Minnesota River at Mankato: Concentrations appear to be underestimated in the calibration period. The t-test on load and transport slope evaluation do not meet targets for the validation period. There is also a large difference between load estimates from the model and FLUX. The error is associated with over-estimation of concentrations during a relatively small number of high flow events. Observations at this station are for SSC by USGS. It may be the case that there are significant differences in channel conditions in the meandering mainstem between the calibration and validation periods, resulting in one set of channel scour characteristics not fitting both time frames. However, the location of this station is somewhat problematic, as it is unclear if full mixing of inflows from the Upper Minnesota and Blue Earth Rivers occurs.

• Minnesota River at Jordan. The t-tests on concentration and load are not met during the validation period. In both cases, the simulated average load and concentration is higher than the observed average. This is again possibly associated with TSS analyses that are biased low relative to true suspended sediment concentrations.

Loading rates generated by the model for the 1993-2006 calibration period are summarized in Table 5-5. The percentage contributions to the total upland load are summarized in 0.


5-22

Table 5-5. Upland Sediment Loading Rates (tons/ac/yr) Generated by the Minnesota River Basin Model for 1993-2006

Basin Conservation

Tillage Conventional

Tillage Forest Manured Cropland Marsh

Grass/ Pasture Urban

Blue Earth 0.330 0.396 0.076 0.166 0.000 0.137 0.235

Chippewa 0.055 0.077 0.007 0.010 0.000 0.006 0.177

Cottonwood 0.125 0.192 0.027 0.027 0.000 0.032 0.198

Hawk 0.055 0.083 0.025 0.008 0.000 0.033 0.061

Le Sueur 0.347 0.389 0.156 0.204 0.000 0.165 0.357

Lower MN 0.067 0.146 0.032 0.052 0.000 0.034 0.201

Middle MN 0.041 0.121 0.025 0.019 0.000 0.022 0.266

Redwood 0.086 0.092 0.039 0.031 0.000 0.059 0.161

Watonwan 0.066 0.126 0.032 0.009 0.000 0.034 0.215

Yellow Medicine 0.093 0.101 0.040 0.027 0.000 0.068 0.094

Table 5-6. Percentage Contributions to Total Upland Sediment Load by Land Use and Basin

Basin Conservation


Tillage Forest Manured Cropland Marsh Pasture Urban

Blue Earth 43.77% 47.32% 0.20% 2.40% 0.00% 1.19% 5.13%

Chippewa 33.05% 47.48% 0.61% 0.70% 0.00% 1.34% 16.82%

Cottonwood 25.80% 63.75% 0.24% 1.42% 0.00% 0.85% 7.94%

Hawk 29.50% 60.09% 0.60% 0.90% 0.00% 1.99% 6.93%

Le Sueur 40.09% 45.78% 0.69% 4.33% 0.00% 1.97% 7.14%

Lower MN 23.64% 55.67% 1.91% 4.15% 0.00% 3.28% 11.35%

Middle MN 13.42% 61.18% 1.01% 1.87% 0.00% 0.97% 21.55%

Redwood 26.92% 50.92% 0.46% 2.53% 0.00% 6.54% 12.64%

Watonwan 20.18% 64.51% 0.35% 0.61% 0.00% 0.52% 13.83%

Yellow Medicine 37.54% 44.79% 0.34% 1.67% 0.00% 9.60% 6.06%


6-1

6 Nutrient Recalibration and Validation

6.1 APPROACH The earlier Minnesota River basin model was calibrated for nutrients, albeit based on the limited time period of 1986-1992. Changes to hydrology and point source loads, as well as the longer calibration period, lead to potential revisions of this calibration. For phosphorus, which is particle reactive, the extensive changes to the sediment calibration in turn require changes to the phosphorus calibration. Nonetheless, the revised nutrient calibration is firmly based on the previous model.

The scope for the recalibration calls for evaluation of the fit for total nitrogen and total phosphorus. The model, however, does not directly simulate total nitrogen and total phosphorus. Rather, the HSPF models created for the Minnesota River basin simulate nutrient loads from the land surface in four categories: “nitrate”-nitrogen (representing both nitrate and nitrite inorganic nitrogen), ammonia-nitrogen (both sorbed and dissolved), “phosphate” (representing total inorganic phosphorus), and organic matter (partitioned at the water’s edge into organic carbon, BOD, organic nitrogen, and organic phosphorus). Parameters for each of these categories are described below.

6.1.1 Inorganic Phosphorus The HSPF simulation of phosphorus differs significantly from the simulation of nitrogen because inorganic phosphorus is strongly particle-reactive. The movement of large loads of inorganic phosphorus (phosphate) is thus to a large extent controlled by the movement of sediment. In addition, phosphate’s strong sorption to soil particles means that phosphate concentrations tend to be more stable over time than nitrogen concentrations, and more strongly reflect the characteristics of native soils. As with nitrogen, organic phosphorus loading is simulated separately as a fraction of the loading of generalized organic matter.

For sites with low sediment yield and during periods without surface runoff, inorganic phosphorus concentrations are often dominated by point sources, but also contain dissolved inorganic phosphorus transported in interflow and groundwater, as well as inorganic phosphorus generated by the decay of organic matter in the stream. Many of the parameters for the current version of the model reflect the earlier calibration effort (Tetra Tech, 2002), the major difference being assignment of a sediment potency factor for scour.

6.1.1.1 Surface Potency for Agricultural Land Uses The HSPF simulation simulates surface washoff of inorganic phosphorus using a potency factor approach. That is, phosphorus load is estimated as a fraction of sediment yield (expressed as a potency factor with units of pounds of phosphate per ton of sediment). Note that because P movement is a function of sediment movement, the sediment delivery ratio is automatically incorporated into the estimate of P loading. Further, management practices that reduce sediment yield (e.g., conservation tillage) are automatically reflected in the phosphorus simulation.

The basic approach to establishing surface potency for phosphate from agricultural lands is to begin with soil test results that reflect the geographic variability of inorganic phosphorus throughout the Minnesota River watershed. Dr. David Mulla provided a database of 22,421 soil test P results analyzed by the University of Minnesota’s soil testing laboratory and arranged by county by county (Birr and Mulla, 2001). The county averages were used to develop area-weighted average soil test P concentrations for each model major watershed.


6-2

The soil test results contain both Bray and Olsen-P measurements. Fang et al. (2001) demonstrate that the Olsen P measurements are the more strongly correlated to extractable soluble reactive P (SRP), with the relationship described by the following regression equation:

Runoff SRP (µg/L) = -63.61 + 9.41 Olsen-P (mg/kg) (r2 = 0.95)

Fang et al. also provide tabular information suggesting that total phosphorus in soil is on the order of 19 times Olsen P. For simulating sediment-associated washoff, the soil total P concentration is most relevant.

It is also clear that washoff of phosphorus exhibits some seasonal patterns in sediment potency as a result of cycles of fertilization, tillage, and cropping, although the seasonality is not as pronounced as for nitrate (Becher et al., 2000). Unlike nitrate, SCS determined that P losses are highly sensitive to the fertilization system, with peaks associated with spring fertilization (typically about April 20) and, where used, fall fertilization (first week in October).

As with nitrate, the seasonal pattern for P losses was estimated using the results of the MRAP AGNPS/GLEAMS modeling. Table V-5 of the MRAP report (SCS, 1993) contains detailed monthly P washoff and sediment yield estimates for corn and soybeans on a CRU-A tillage system, which can be used to estimate a monthly potency factor for the specific field and tillage system studied. We assumed that the average of the corn and soybean results provides a reasonable estimator of the seasonal pattern (but not the absolute magnitude) of the phosphorus potency factor for agriculture throughout the basin. A correction was, however, made to reflect the fact that the CRU-A tillage system does not include fall fertilization, which is practiced on approximately one-third of croplands in the basin. The potency estimate for October was adjusted upward to account for this in the seasonal pattern.

Estimation of monthly phosphate potency factors for sheet and rill erosion thus proceeded as follows:

1. Average soil P for the basin was determined from soil test Olsen-P.

2. Monthly potency factors were estimated by multiplying the average soil P times the ratio of the monthly value to the annual mean value shown in MRAP modeling.

Sediment is also derived from agricultural lands by scour and gullying. Gullies are assumed to penetrate deeper into the subsoil, and are thus less closely tied to surface fertilization and seasonal variability. The potency factor for scoured sediment was assumed constant throughout the year and taken as a calibration parameter for each major watershed. The resulting values are lower than the summer potency factor for surface washoff, as should be expected.

The resulting estimates of P potency thus preserve both the geographic variability in soil P concentrations found across the basin and the seasonal variability associated with typical tillage practices. Resulting potency factors by basin and month are summarized in Table 6-1 and Table 6-2. Conventional and conservation tillage both have the same potency factor; however, phosphorus loading from conservation tillage will be less because the sediment delivery is also reduced. Phosphorus potency for manured land was assumed to be the potency for conventional tillage multiplied by a factor of 5.4, which is the average ratio of P application on manured land relative to P application for conventional tillage determined in the GEIS (Mulla et al., 2001). As with nitrate, a seasonal correction was also applied to reflect the greater application rates in fall for manured land relative to chemical fertilization documented by FANMAP.

6.1.1.2 Interflow Sediment Potency for Agricultural Land A key feature of the Minnesota River HSPF application is the representation of the transport of sediment through surface inlets to tile drains, simulated as an interflow sediment component. As sediment is transported with interflow, phosphorus is also transported associated with this sediment. Accordingly, an interflow sediment potency factor is also needed (Table 6-2).


6-3

Interflow sediment potency is not varied seasonally. This is largely a practical decision, as the representation of sediment transport via surface inlets using HSPF Special Actions does not lend itself easily to seasonally varying parameters. Examination of simulation results suggests that varying interflow potency at a scale similar to the seasonal variation in surface potency for phosphorus would not result in noticeable changes in simulation results.

6.1.1.3 Interflow and Groundwater Concentrations for Agricultural Land Phosphorus is also transported via subsurface flow in dissolved form. The basis for estimating interflow concentrations was the regression equation for SRP from soil test Olsen-P presented by Fang et al. (2001) and summarized in the previous section, which incorporates the geographic variability in phosphorus concentrations found across the basin. The values are relatively high compared to groundwater concentrations because they include the effect of enhanced transport in tile drains.

It is reasonable to assume that dissolved concentrations in interflow are enriched relative to SRP in bulk soils. Examination of monitoring results across multiple basins suggested that an empirical enrichment ratio of 1.5 provides a good approximation of observations. Finally, seasonal patterns of interflow dissolved concentrations were assumed to follow the same seasonal pattern as surface sediment potency.

Groundwater concentrations of P are generally low and appear to exhibit much less seasonal variability than interflow concentrations. Baseflow concentrations in streams suggest that exerted P concentrations in streams of the Minnesota River basin are on the order of 0.05 to 0.15 mg/L. Geographic variability and a damped seasonal variability were represented by setting the monthly groundwater concentration to the average of the monthly SRP estimate (i.e., equal to the interflow concentration without the enrichment factor) and 0.05 mg/L. Somewhat higher groundwater concentrations may be reasonably expected from manured lands. For these lands, the groundwater concentration on a monthly basis was represented as the average of the (enriched) interflow concentration and 0.05 mg/L.

For the current model the parameters estimated in this way were not changed during calibration, with one exception: groundwater concentrations estimated for Redwood River were increased slightly to match those determined for Yellow Medicine River. Model parameters for interflow and groundwater P from agricultural lands are also summarized in Table 6-3. Results for interflow and groundwater in individual basins will be a weighted average of the concentrations specified for tillage with and without manure application, along with other land uses.

6.1.1.4 Partitioning at the Water’s Edge Although HSPF simulates portions of the P load from the land surface as sediment-associated and portions as dissolved, these components are reassigned at the water’s edge. Indeed, it is expected that significant transformations of P take place in transport in first-order streams that are not included within the HSPF model network. For the reaches that are simulated, it is necessary to re-divide the total inorganic phosphorus load into sorbed and sediment-associated components. In addition, the instream model works with three sediment fractions (sand, silt, and clay), whereas the upland model only simulates generalized sediment. Inorganic P in surface washoff is empirically partitioned as 10 percent dissolved, 58 percent associated with silt, and 32 percent associated with clay. The subsurface components of P loading are assigned entirely to the dissolved fraction. Note, however, that reactions within the stream reaches are simulated, and further sorption and desorption in the reaches will occur.


6-4

Table 6-1. Inorganic Phosphorus Potency Factors for Surface Erosion from Agricultural Land Uses.

P Potency for Surface Sheet and Rill Erosion - Conventional and Conservation Tillage (#/ton)

Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec

Blue Earth 0.524 0.655 0.931 0.876 1.00 0.983 1.10 1.04 0.950 0.655 0.720 0.524

Chippewa 0.410 0.512 0.728 0.685 0.850 0.769 0.860 0.815 0.743 0.512 0.564 0.410

Cottonwood 0.339 0.424 0.603 0.567 0.703 0.636 0.712 0.675 0.615 0.424 0.466 0.339

Le Sueur 0.510 0.638 0.907 0.853 1.06 0.958 1.07 1.02 0.925 0.638 0.702 0.510

Lower MN 0.574 0.718 1.02 0.960 1.19 1.08 1.21 1.14 1.04 0.718 0.790 0.574

Middle MN 0.406 0.507 0.721 0.679 0.841 0.761 0.852 0.807 0.736 0.507 0.558 0.406

Redwood 0.315 0.394 0.560 0.527 0.654 0.592 0.662 0.628 0.572 0.394 0.434 0.315

Watonwan 0.422 0.528 0.751 0.706 0.876 0.792 0.886 0.840 0.766 0.528 0.581 0.422

Yellow Medicine/Hawk 0.335 0.419 0.596 0.560 0.695 0.629 0.703 0.667 0.608 0.419 0.461 0.335

P Potency for Surface Sheet and Rill Erosion - Manured Land (#/ton)

Blue Earth 2.83 3.54 5.03 4.73 5.86 5.31 5.94 5.63 5.13 3.54 3.89 2.83

Chippewa 2.21 2.77 3.93 3.70 4.59 4.15 4.64 4.40 4.01 2.77 3.04 2.21

Cottonwood 1.83 2.29 3.25 3.06 3.80 3.44 3.84 3.64 3.32 2.29 2.52 1.83

Le Sueur 2.76 3.44 4.90 4.61 5.71 5.17 5.78 5.48 5.00 3.44 3.79 2.76

Lower MN 3.10 3.88 5.51 5.18 6.43 5.82 6.51 6.17 5.62 3.88 4.26 3.10

Middle MN 2.19 2.74 3.89 3.66 4.54 4.11 4.60 4.36 3.97 2.74 3.01 2.19

Redwood 1.70 2.13 3.03 2.858 3.53 3.20 3.57 3.39 3.09 2.13 2.34 1.70

Watonwan 2.28 2.85 4.05 3.81 4.73 4.28 4.79 4.54 4.13 2.85 3.14 2.28



6-5

Table 6-2. Inorganic Phosphorus Potency Factor (#/ton) for Scoured Sediment (Ravines) and Interflow Sediment

Scoured Sediment Interflow Sediment

Blue Earth 0.300 0.837

Chippewa 0.675 0.655

Cottonwood 0.350 0.542

Le Sueur 0.725 0.816

Lower MN 0.725 0.917

Middle MN 0.500 0.648

Redwood 0.675 0.504

Watonwan 0.675 0.675

Yellow Medicine/Hawk 0.675 0.536


6-6

Table 6-3. Subsurface Inorganic Phosphorus Concentrations

Dissolved P Concentration in Interflow - Conventional and Conservation Tillage (mg/L)

Watershed Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec

Blue Earth 0.135 0.369 0.440 0.426 0.360 0.333 0.363 0.328 0.324 0.169 0.185 0.135

Chippewa 0.093 0.116 0.164 0.155 0.192 0.174 0.194 0.184 0.168 0.116 0.127 0.093

Cottonwood 0.066 0.083 0.118 0.111 0.137 0.124 0.139 0.132 0.120 0.083 0.091 0.066

Le Sueur 0.130 0.162 0.231 0.217 0.269 0.244 0.273 0.258 0.235 0.162 0.179 0.130

Lower MN 0.154 0.192 0.273 0.257 0.318 0.288 0.322 0.306 0.278 0.192 0.211 0.154

Middle MN 0.091 0.114 0.162 0.152 0.189 0.171 0.191 0.181 0.165 0.114 0.125 0.091

Redwood 0.057 0.072 0.102 0.096 0.119 0.108 0.121 0.114 0.104 0.072 0.079 0.057

Watonwan 0.097 0.121 0.173 0.162 0.201 0.182 0.204 0.193 0.176 0.121 0.134 0.097


Dissolved P Concentration in Interflow - Manured Land (mg/L)


Blue Earth 0.728 0.910 1.29 1.22 1.51 1.37 1.53 1.45 1.32 0.910 1.002 0.728

Chippewa 0.500 0.624 0.888 0.835 1.04 0.937 1.05 0.994 0.906 0.624 0.687 0.500

Cottonwood 0.358 0.447 0.636 0.598 0.742 0.671 0.751 0.712 0.649 0.447 0.492 0.358

Le Sueur 0.701 0.877 1.25 1.17 1.45 1.32 1.47 1.40 1.27 0.877 0.964 0.701

Lower MN 0.829 1.04 1.47 1.39 1.72 1.56 1.74 1.65 1.50 1.04 1.14 0.829

Middle MN 0.492 0.614 0.873 0.822 1.02 0.922 1.03 0.978 0.891 0.614 0.676 0.492

Redwood 0.310 0.388 0.551 0.519 0.643 0.582 0.651 0.617 0.562 0.388 0.427 0.310

Watonwan 0.525 0.656 0.932 0.877 1.09 0.984 1.10 1.04 0.951 0.656 0.722 0.525



6-7

Dissolved P Concentration in Groundwater - Conventional and Conservation Tillage (mg/L)

Blue Earth 0.070 0.081 0.105 0.100 0.118 0.109 0.119 0.114 0.106 0.081 0.087 0.070

Chippewa 0.056 0.064 0.080 0.077 0.089 0.083 0.090 0.086 0.081 0.064 0.067 0.056

Cottonwood 0.047 0.053 0.064 0.062 0.071 0.066 0.071 0.069 0.065 0.053 0.055 0.047

Le Sueur 0.068 0.079 0.102 0.097 0.115 0.106 0.116 0.111 0.103 0.079 0.085 0.068

Lower MN 0.076 0.089 0.116 0.111 0.131 0.121 0.132 0.127 0.118 0.089 0.095 0.076

Middle MN 0.055 0.063 0.079 0.076 0.088 0.082 0.089 0.085 0.080 0.063 0.067 0.055

Redwood 0.047 0.052 0.063 0.061 0.070 0.066 0.070 0.068 0.064 0.052 0.055 0.047

Watonwan 0.057 0.065 0.083 0.079 0.092 0.086 0.093 0.089 0.084 0.065 0.070 0.057


Dissolved P Concentration in Groundwater - Manured Land (mg/L)

Blue Earth 0.389 0.480 0.593 0.559 0.688 0.708 0.789 0.750 0.766 0.536 0.587 0.389

Chippewa 0.275 0.337 0.415 0.392 0.480 0.494 0.549 0.522 0.533 0.375 0.410 0.275

Cottonwood 0.204 0.249 0.304 0.288 0.351 0.361 0.400 0.381 0.389 0.276 0.301 0.204

Le Sueur 0.376 0.463 0.572 0.540 0.663 0.683 0.761 0.723 0.738 0.517 0.566 0.376

Lower Minnesota 0.440 0.543 0.672 0.633 0.779 0.803 0.895 0.850 0.868 0.607 0.665 0.440

Middle Minnesota 0.271 0.332 0.408 0.386 0.472 0.486 0.541 0.514 0.525 0.370 0.404 0.271

Redwood 0.180 0.219 0.267 0.253 0.307 0.316 0.350 0.334 0.340 0.243 0.264 0.180

Watonwan 0.287 0.353 0.434 0.410 0.502 0.517 0.576 0.547 0.559 0.393 0.430 0.287



6-8

6.1.2 Nitrate (plus Nitrite) Nitrogen This constituent represents oxidized inorganic nitrogen (nitrate plus nitrite) loads from the land surface. It is represented as a buildup-washoff parameter on the land surface, and is also associated with interflow and groundwater.

MRAP studies of the basin (Payne, 1994) indicate that nitrate loading from agricultural land occurs predominantly via interflow, and exhibits geographic variability, with higher loads in the eastern portion of the basin (Source Region III) and progressively lower loads to the west (Source Regions II and I). For ten minor watersheds subjected to detailed analysis (SCS, 1993) it was estimated that total N loss rates ranged from 4.4 to 14.3 pounds per acre per year, with 50 percent or more of the loading via subsurface pathways. For agricultural lands, total N losses were found to be not very sensitive to fertilization system, but were sensitive to tillage system.

Based on the MRAP studies, it was assumed that surface nitrate loading varies with land use, but not significantly with geographic location in the basin. Interflow nitrate concentrations were varied by both land use and location. Finally, groundwater concentrations of nitrate were assumed to be the same for all agricultural land uses, but varied between watersheds.

6.1.2.1 Surface Buildup-Washoff Parameters for Agricultural Land The surface simulation of nitrate is unchanged from the previous version of the model, and the following description, from Tetra Tech (2002), remains applicable:

The general seasonal pattern for nitrate availability on the land surface for conventional tillage is based on AGNPS/GLEAMS simulation results presented by month in Table V-5 of SCS (1993) for an average corn/soybean rotation on a CRU-A tillage system. Additional backup material for Table V-5 was provided by Pete Cooper of NRCS. This table gives monthly field-edge results for corn and soybeans. These two results were averaged by month to represent an average representative of a corn/soybean two-year rotation for use in the model.

Because the HSPF model simulates nitrate as a buildup/washoff (WSQO) parameter, losses of the sediment-sorbed fraction between the field edge and the stream reaches included within the model are not explicitly simulated. (This contrasts with the simulation of phosphorus as a sediment-associated constituent, for which sediment transport capacity is reflected in the results). Therefore, it is necessary to discount the sediment-associated fraction of the nitrate load. Based on the AGNPS results, 20 percent of the sediment-associated nitrate loading is assumed, on average, to be delivered to higher-order streams.

Once the sediment-associated component was discounted, the remaining nitrate load was converted to a relative monthly load. Specification of a WSQO parameter requires an accumulation rate (ACCUM), accumulation limit (SQOLIM), and depth of runoff resulting in 90 percent removal of the surface storage (WSQOP). The first two parameters may be specified on a monthly basis. WSQOP is often assumed to be approximately 0.5 inches for urban impervious surfaces, based on the National Urban Runoff Program (NURP) and other study results (Novotny and Olem, 1994). Use of a WSQOP value of 0.5 for rural pervious surfaces is not, however, well supported. Indeed, it is reasonable to assume that much higher WSQOP values may be appropriate for rough rural surfaces, where near-complete washoff is difficult to achieve except during extreme flow events. We have therefore assumed WSQOP values in the range of 1.5 to 1.7 inches. In fact, the model is not particularly sensitive to the specification of WSQOP, as long as it is recognized that calibrated values of SQOLIM and ACCUM are derived conditional on the value assumed for WSQOP.

SQOLIM values determine the maximum amount that can be washed off during a large event. We converted the AGNPS results to pounds per inch of runoff, and assumed that the predicted loads (as


6-9

pounds per inch) are representative of approximately 1 inch of runoff. The averaged corn-soybean numbers were then converted to yield an equivalent SQOLIM for 90 percent removal at a WSQOP of 1.5 inches (the conversion results in scaling up by a factor of 1.275).

ACCUM values are called “accumulation rates” in the model, but properly represent the rate at which the available storage approaches the maximum storage (SQOLIM). ACCUM values thus reflect the rates at which nitrate at the surface available for washoff is replenished; they are not a direct measure of fertilizer application. We chose to set ACCUM rates at one-third of SQOLIM, consistent with the original Minnesota River HSPF models. Setting a relatively small ratio of ACCUM/SQOLIM means that recovery of available concentrations at the land surface is assumed to be quick, or, equivalently, that the rate of reincorporation of nitrate into the soil or biotic matter is high.

Conservation tillage buildup-washoff results were also adopted from the MRAP simulations (Table V-5), and developed in a manner similar to those for conventional tillage.

For manured land, the basic monthly buildup-washoff rates for conventional tillage were used, but scaled up by a factor of 1.55, representing the average rate of manure-N application in excess of non-manured fertilization rates reported for south-central Minnesota in the GEIS (Mulla et al., 2001). An additional seasonal adjustment was made to reflect greater manure application in the fall (Sept.-Nov.) and lower applications rates in spring (Mar.-May) relative to standard fertilization schemes, as reported in the FANMAP studies (p. 30). Specifically, spring application rates were scaled down by 15 percent relative to the mean and fall application rates scaled up by 15 percent.

The resulting surface nitrate parameters for agricultural land are summarized in Table 6-4.

6.1.2.2 Interflow and Groundwater Parameters for Agricultural Land In the Minnesota River basin HSPF model interflow provides the primary representation of discharge via tile drainage, and the interflow component is dominated by tile drainage. The slower component of tile drainage, as well as true groundwater transport, is simulated as groundwater. In HSPF, simulation of loading via interflow and groundwater requires specification of concentrations, which may vary by month.

According to Dr. Mulla, subsurface pathways dominate nitrate loading in the watershed, with the biggest loads in late spring and fall. A similar pattern is seen in agricultural watersheds of northeastern Iowa studied as part of the USGS NAWQA program (Becher et al., 2000). In these watersheds, instream concentrations of total N show a peak in May-June, a minimum in September-October, and a secondary peak in November-December. This pattern reflects fertilization in spring, plant sequestration of nitrogen during the growing season, and late fall application of fertilizer to about one third of the row crop fields in the studied watersheds.

Interflow nitrate from agricultural lands clearly requires a seasonal representation. Available modeling studies do not, however, provide clear estimates by month. (For instance, the AGNPS/GLEAMS modeling presented in the MRAP report shows leaching of nitrate through the soil profile, but not delivery to stream by subsurface pathways. Further, tile drainage is not explicitly simulated in AGNPS.)

Because the subsurface components of loading from agricultural land dominate the nitrate seen in streams, the interflow and groundwater parameters are amenable to guided optimization. Further, the contributions via interflow and groundwater are sufficiently independent to be optimized simultaneously. That is, baseflow conditions (with no interflow) determine groundwater contributions, while periods of high interflow discharge predominantly reflect interflow concentrations. Monitoring data collected by MPCA and USGS clearly shows seasonal patterns in subsurface nitrate loading. There are also evident spatial patterns, with lower concentrations found in the basins to the west that receive less annual rainfall.


6-10

This presumably reflects a lower efficiency of leaching nitrate from the soil profile under lower rainfall conditions.

Tetra Tech (2002) established a representation of nitrate concentration in interflow based on using average annual runoff to scale a seasonal pattern to all watersheds. Groundwater concentrations were specified as a seasonal pattern that differed by MRAP source region and was constant across all land uses. The interflow concentrations from conservation tillage were assumed to be 1.2 times those estimated for conventional tillage, consistent with predictions of increased leaching of N under conservation tillage simulated by SCS (1993). Interflow concentrations from manured lands are assumed to equal those from conventional tillage, multiplied by the ratio of typical N application rates on manured lands relative to conventionally fertilized lands of 1.55 (Mulla et al., 2001).

These parameter values from the previous model were taken as a starting point for recalibration of total nitrogen, recognizing that total nitrogen is predominantly in the nitrate form in this landscape. Given the longer time period available for calibration, the monthly values of interflow and groundwater nitrate were varied to achieve a reasonable fit for each major watershed. Final values are shown in Table 6-5.

6.1.2.3 Nitrate Parameters for Other Land Uses Land use in the Minnesota River basin is dominated by agriculture. Loading of nitrate is predominantly determined by agricultural runoff and subsurface flow in combination with loads from permitted point sources (predominantly waste water treatment plants) and individual septic treatment systems (ISTS) that have discharge lines connected to tile drains. It is difficult to discern the instream signal of nitrate loading from other land uses in available monitoring data, and the model of current conditions is not very sensitive to specifications for these land uses.

As a result, the surface and subsurface nitrate parameters for residential/urban, forest, pasture, and wetland land uses were left unchanged from the existing values. For residential/urban land uses, subsurface nitrate parameters were also left at values determined in the earlier phase of modeling. Surface buildup and washoff parameters for pervious urban land were modified based on data presented in Kuo et al. (1988). Kuo gives long term pollutant buildup rates (in kg/ha) for a variety of pervious and impervious land uses in northern Virginia. We selected results for medium density residential land as most likely to be representative of the generalized urban category in the Minnesota River models.

The accumulation rates given by Kuo et al. should not be interpreted as identical to the ACCUM parameter in HSPF, as the long-term accumulation rates implicitly incorporate non-washoff removal (e.g., by plant uptake, volatilization, etc.) The rates given by Kuo were translated to gross accumulation rates as follows:

1. The reported net accumulation rate for total N was used to calculate an accumulation limit (SQOLIM) based on a limiting time for buildup of 21 days (Haith et al., 1992).

2. The SQOLIM value was then converted back to a gross accumulation rate for total N assuming that 90 percent of the limiting storage (for total N) would be reached in 4 days.

3. The total N gross accumulation rates were converted to nitrate and ammonia accumulation rates with an assumption that 90 percent of the total N accumulation would be in the nitrate form.

4. Finally, the accumulation rates and accumulation limits were modified to be more representative of Minnesota weather conditions. Specifically, the literature values were applied directly to April through September. For the colder months, the accumulation rates were (arbitrarily) scaled down, reaching a minimum of 50 percent of the summer rate in January.


6-11

Accumulation rates for urban impervious surfaces were similarly developed from data supplied by Kuo et al. (1988) for medium density residential land uses. The only difference was that the time to reach 90 percent of limiting storage was assumed to be 3 days.

6.1.3 Ammonia Nitrogen Ammonia nitrogen is a minor constituent of the total nitrogen loading for most land uses. It is included as a separate constituent in the model primarily because of the potential for elevated ammonia loading from manure application areas.

In addition to low rates of loading from the land surface, most of the ammonia observed in monitored watersheds of the Minnesota River basin is attributable to wastewater treatment plants and ISTS. Internal generation from the breakdown of organic matter in the stream can also be important. The upshot is that it is not reasonable to attempt to calibrate nonpoint ammonia loads to instream observations.

Given these considerations, ammonia nitrogen parameters were not changed from the previous model application (Tetra Tech, 2002), as shown in Table 6-6.


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Table 6-4. Nitrate Surface Buildup/Washoff Parameters for Agricultural Lands (pounds/acre/day, 1st of month)

NO3-N Surface – Conventional Tillage, All Basins


ACCUM 0.297 0.272 0.455 0.510 1.445 1.498 0.945 0.807 0.935 0.850 0.552 0.595

SQOLIM 0.892 0.816 1.36 1.53 4.33 4.49 2.84 2.42 2.80 2.55 1.66 1.78

NO3-N Surface – Conservation Tillage, All Basins

ACCUM 0.257 0.217 0.357 0.356 0.861 0.864 0.523 0.425 0.492 0.548 0.442 0.514

SQOLIM 0.771 0.651 1.07 1.07 2.58 2.59 1.57 1.28 1.48 1.64 1.32 1.54

NO3-N Surface – Manured Land, All Basins

ACCUM 0.461 0.421 0.668 0.748 2.12 2.32 1.46 1.25 1.55 1.39 0.902 0.922

SQOLIM 1.38 1.26 2.00 2.24 6.36 6.96 4.40 3.75 4.58 4.16 2.70 2.77


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Table 6-5. Subsurface Nitrate Concentrations for Agricultural Lands (mg/L, 1st of month)

NO3-N in Interflow – Conventional Tillage


Blue Earth 27.0 21.0 29.0 27.0 16.0 20.0 23.0 11.0 5.3 6.0 31.0 28.0

Chippewa 0.697 3.49 6.62 10.8 10.4 8.70 6.00 4.79 5.93 6.97 7.67 1.05

Cottonwood 18.0 15.0 13.6 19.1 7.00 10.0 17.6 7.00 14.2 26.0 20.0 15.0

Le Sueur 3.80 12.0 17.0 17.5 23.0 18.0 20.9 12.4 7.30 6.00 19.8 11.0

Lower Minnesota 27.0 21.0 29.0 27.0 20.0 23.0 23.0 13.0 5.30 6.00 31.0 28.0


Redwood 7.00 5.60 23.0 18.2 18.4 16.1 13.8 6.30 7.80 13.2 14.0 5.40

Watonwan 10.0 9.00 17.1 27.9 33.0 30.6 26.0 13.7 17.0 18.0 19.8 7.00


NO3-N in Interflow – Conservation Tillage

Blue Earth 32.4 25.2 34.8 32.4 19.2 24.0 27.6 13.2 6.40 7.20 37.2 33.6

Chippewa 0.837 4.18 7.95 13.0 12.5 10.4 7.20 5.75 7.11 8.37 9.20 1.26

Cottonwood 21.6 18.0 16.3 22.9 8.40 12.0 21.1 8.40 17.0 31.2 24.0 18.0

Le Sueur 4.60 14.4 20.4 21.0 27.6 21.6 25.0 14.8 8.80 7.20 23.8 13.2

Lower Minnesota 32.4 25.2 34.8 32.4 24.0 27.6 27.6 15.6 6.40 7.20 37.3 33.6


Redwood 8.40 6.70 27.6 21.8 22.1 19.3 16.6 7.56 9.36 15.8 16.8 6.50

Watonwan 12.0 10.8 20.5 33.4 39.6 36.8 31.2 16.5 20.4 21.6 23.8 8.40



6-14

NO3-N in Interflow – Manured Land


Blue Earth 41.8 32.6 45.0 41.8 24.8 31.0 35.6 17.0 8.20 9.30 48.0 43.4

Chippewa 1.08 5.40 10.3 16.7 16.1 16.1 9.30 7.43 9.10 10.8 11.9 1.62

Cottonwood 27.9 23.2 21.1 29.6 10.8 15.5 27.3 10.8 22.0 40.3 31.0 23.2

Le Sueur 5.90 18.6 26.3 27.1 35.6 27.9 32.3 19.2 11.3 9.30 30.7 17.0

Lower Minnesota 41.8 32.6 45.0 41.8 31.0 35.6 35.6 20.2 8.20 9.30 48.0 43.4


Redwood 10.8 8.70 35.6 28.2 19.3 25.0 21.4 9.77 12.1 20.5 21.7 8.40

Watonwan 15.5 14.4 26.5 43.2 51.1 47.5 40.3 21.3 26.4 27.9 30.7 10.8


NO3-N in Groundwater – All Agricultural Land Uses (mg/L)

Blue Earth 11.0 10.0 8.47 3.50 6.50 13.5 9.00 3.89 2.22 7.50 11.0 10.0

Chippewa 4.00 4.18 2.33 2.46 2.56 1.64 2.56 4.87 6.71 2.18 1.72 2.02

Cottonwood 2.46 5.16 2.46 2.77 4.93 3.08 4.93 1.23 1.54 2.16 1.23 1.85

Le Sueur 3.00 2.00 5.47 2.61 8.66 8.45 10.6 1.00 0.80 0.50 1.00 4.00

Lower Minnesota 17.5 12.5 10.0 5.5 12.0 12.0 8.00 3.50 1.25 7.00 11.0 16.5


Redwood 4.46 6.16 4.46 3.77 4.93 3.08 4.93 0.73 1.54 3.16 3.23 3.85

Watonwan 2.50 7.00 6.50 2.60 8.70 8.40 10.6 3.90 1.70 7.00 5.00 2.50



6-15

Table 6-6. Ammonia (as N) Parameters for Pervious Land Uses for All Watersheds (1st of month)

NH3–N Accumulation Rates (pounds/acre/day)

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Forest 0.0033 0.0040 0.0050 0.0120 0.0120 0.0120 0.0120 0.0120 0.0120 0.0080 0.0040 0.0033

Conventional Ag. 0.0030 0.0030 0.0050 0.0130 0.0200 0.0200 0.0150 0.0140 0.0130 0.0100 0.0050 0.0030

Conservation Ag. 0.0030 0.0030 0.0050 0.0130 0.0200 0.0200 0.0150 0.0140 0.0130 0.0100 0.0050 0.0030

Pasture 0.0010 0.0020 0.0040 0.0060 0.0060 0.0060 0.0060 0.0060 0.0060 0.0040 0.0020 0.0010

Wetland 0.0065 0.0069 0.0079 0.0098 0.0098 0.0098 0.0098 0.0098 0.0098 0.0079 0.0069 0.0065

Urban 0.0070 0.0080 0.0100 0.0230 0.0230 0.0230 0.0230 0.0230 0.0230 0.0170 0.0080 0.0070

Manured Land 0.0300 0.0300 0.0300 0.2000 0.1500 0.0500 0.0050 0.0050 0.0050 0.0800 0.0800 0.0300

NH3–N Accumulation Limit (pounds)

Forest 0.004 0.005 0.007 0.015 0.015 0.015 0.015 0.015 0.015 0.011 0.005 0.004

Conventional Ag. 0.008 0.008 0.013 0.033 0.051 0.051 0.038 0.036 0.033 0.025 0.013 0.008

Conservation Ag. 0.008 0.008 0.013 0.033 0.051 0.051 0.038 0.036 0.033 0.025 0.013 0.008

Pasture 0.003 0.004 0.008 0.011 0.011 0.011 0.011 0.011 0.011 0.008 0.004 0.003

Wetland 0.026 0.028 0.031 0.039 0.039 0.039 0.039 0.039 0.039 0.031 0.028 0.026

Urban 0.017 0.02 0.025 0.058 0.058 0.058 0.058 0.058 0.058 0.042 0.02 0.017

Manured Land 0.12 0.12 0.12 0.8 0.6 0.2 0.02 0.02 0.02 0.32 0.32 0.12


6-16

NH3–N Interflow Concentration (mg/L)

Forest 0.12 0.12 0.12 0.06 0.06 0.06 0.06 0.06 0.06 0.12 0.12 0.12

Conventional Ag. 0.4 0.4 0.4 0.24 0.24 0.24 0.24 0.24 0.24 0.4 0.4 0.4

Conservation Ag. 0.3 0.3 0.3 0.2 0.2 0.2 0.2 0.2 0.2 0.3 0.3 0.3

Pasture 0.15 0.15 0.15 0.1 0.1 0.1 0.1 0.1 0.1 0.15 0.15 0.15

Wetland 0.3 0.3 0.3 0.24 0.24 0.24 0.24 0.24 0.24 0.3 0.3 0.3

Urban 0.12 0.12 0.12 0.06 0.06 0.06 0.06 0.06 0.06 0.12 0.12 0.12

Manured Land 2.8 2.8 2.8 5.6 5.6 2.8 1.12 1.12 1.12 2.8 2.8 2.8

NH3–N Groundwater Concentration (mg/L)

Forest 0.08 0.08 0.08 0.05 0.05 0.05 0.05 0.05 0.05 0.08 0.08 0.08

Conventional Ag. 0.15 0.15 0.15 0.08 0.08 0.08 0.08 0.08 0.08 0.15 0.15 0.15

Conservation Ag. 0.1 0.1 0.1 0.05 0.05 0.05 0.05 0.05 0.05 0.1 0.1 0.1

Pasture 0.12 0.12 0.12 0.06 0.06 0.06 0.06 0.06 0.06 0.12 0.12 0.12

Wetland 0.2 0.2 0.2 0.16 0.16 0.16 0.16 0.16 0.16 0.2 0.2 0.2

Urban 0.08 0.08 0.08 0.05 0.05 0.05 0.05 0.05 0.05 0.08 0.08 0.08

Manured Land 1.68 1.68 1.68 2.8 2.8 1.12 0.56 0.56 0.56 1.68 1.68 1.68


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6.1.4 Organic Matter Loading The HSPF model applications simulate transport of generic organic matter from the land surface. This generic organic matter is “translated” at the stream edge into equivalent concentrations of organic nitrogen, organic phosphorus, organic carbon, and biochemical oxygen demand. Organic matter loading was calibrated in the previous version of the model (Tetra Tech, 2002). As it tends to play a relatively small role in the observed TN and TP at most stations in the basin, the previously calibrated values were left unchanged, with the exception of minor adjustments to subsurface loads in the Lower Minnesota River area.

6.1.4.1 Organic Nutrients from Agricultural Land While crop residue, leaf litter, etc., contribute to organic matter transport, the bulk of the readily bioavailable fine organic matter load from the land surface is derived from soil organic matter. Therefore, the basis for the organic matter simulation is the soil organic matter content (weighted average by major watershed), and organic matter washoff from the surface is simulated via a sediment potency factor. However, gross organic material load calculations based on soil organic matter leads to consistent over-estimation of the instream organic carbon, nitrogen, phosphorus, and CBODu components. This occurs because the model simulates these constituents only in their dissolved form within the stream reaches. Therefore, the surface washoff component must address only the dissolved and readily desorbable or decomposable components of organic matter, and not large debris or highly refractory compounds. It appears that reducing the potency factor by an empirical factor of 4 relative to the total organic matter content of soil gives reasonable results for croplands. While this factor is empirical, the derivation of potency factors from the organic matter content does preserve what appear to be reasonable geographic variations in loading. The more refractory components washed off the land surface can contribute to dissolved organic matter concentrations instream through specification of benthic release rates, but these are not causally-linked in the model to upland loading rates.

Organic matter potency factors for manured land were set to four times those on conventional cropland, reflecting the greater availability of labile organic material associated with manure application.

Dissolved concentrations of organic matter in interflow and groundwater discharge appear to exhibit a distinct seasonal component, with peaks in the early spring following snowmelt and in late summer to early fall following harvest. No data were located to characterize these components, so concentrations were set via calibration. The calibration was intended to match observed BOD concentrations instream during conditions of baseflow and mixed baseflow and interflow. Organic matter concentrations were then inferred from the assumed BOD content of organic matter. While the resulting concentration estimates are empirical, geographic variability was preserved by assuming a constant seasonal pattern that is scaled from watershed to watershed based on soil organic matter content. The resulting organic matter parameters are shown in Table 6-7 and Table 6-8.

For translating organic matter load to organic nitrogen and organic phosphorus, the stoichiometric ratios for humic acid were used, in which organic carbon = 0.57 x organic matter, organic nitrogen = 0.714 x organic carbon, and organic phosphorus = 0.125 x organic nitrogen.

6.1.4.2 Organic Nutrients from Other Land Uses The same surface potency factor was assumed for all land uses in a watershed. Interflow and groundwater concentrations of labile organic matter from non-agricultural land uses were set to values ranging from 0.5 to 2.5 mg/L, consistent with experience in other modeling exercises. Model results are not sensitive to the specification of these parameters. For impervious urban lands, buildup (ACCUM of


6-18

0.196 pounds per acre per day) and limiting storage (SQOLIM of 2.358 pounds per acre) were set from data in Kuo et al. (1988), as described under the nitrate simulation.

Table 6-7. Surface Potency Factors for Organic Matter (lbs/ton-sediment)

Watershed General Manured Land

Blue Earth 25.75 103

Chippewa 33 132

Cottonwood 25.1 100.4

Le Sueur 35.45 141.8

Lower Minnesota 46.55 186.2

Middle Minnesota 32.7 130.8

Redwood 25.1 100.4

Watonwan 28.2 112.8

Yellow Medicine/Hawk 27.3 109.2

Table 6-8. Subsurface Concentrations of Organic Matter (mg/L, 1st of month)

Organic Matter Concentration in Interflow (mg/L) - Conventional Tillage, Conservation Tillage

Jan Feb Mar Apr May Jun July Aug Sep Oct Nov Dec

Blue Earth 7.3 4.6 11.0 4.6 6.4 11.0 12.8 14.6 16.4 18.3 16.4 9.1

Chippewa 9.4 5.9 14.0 5.9 8.2 14.0 16.4 18.7 21.1 23.4 21.1 11.7

Cottonwood 7.1 4.5 10.7 4.5 6.2 10.7 12.5 14.2 16.0 17.8 16.0 8.9

Le Sueur 10.1 6.3 15.1 6.3 8.8 15.1 17.6 20.1 22.6 25.1 22.6 12.6

Lower Minnesota 13.2 8.3 19.8 8.3 11.6 19.8 33.0 33.0 33.0 42.9 29.7 16.5


Redwood 7.1 4.5 10.7 4.5 6.2 10.7 12.5 14.2 16.0 17.8 16.0 8.9

Watonwan 8.0 5.0 12.0 5.0 7.0 12.0 14.0 16.0 18.0 20.0 18.0 10.0


Organic Matter Concentration in Interflow (mg/L) - Manured Land (LU7)

All Basins 100 100 85 85 85 50 50 50 50 75 115 100


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Organic Matter Concentration in Groundwater - Conventional Tillage, Conservation Tillage

Blue Earth 0.9 0.9 27.4 22.8 22.8 27.4 41.1 41.1 27.4 20.1 13.7 4.6

Chippewa 1.2 1.2 35.1 29.3 29.3 35.1 52.7 52.7 35.1 25.7 17.6 5.9

Cottonwood 0.9 0.9 26.7 22.3 22.3 26.7 40.1 40.1 26.7 19.6 13.4 4.5

Le Sueur 1.3 1.3 37.7 31.4 31.4 37.7 56.6 56.6 37.7 27.7 18.9 6.3

Lower Minnesota 3.3 6.6 66.0 33.0 23.1 33.0 49.5 57.8 49.5 36.3 29.7 16.5


Redwood 0.9 0.9 26.7 22.3 22.3 26.7 40.1 40.1 26.7 19.6 13.4 4.5

Watonwan 1.0 1.0 30.0 25.0 25.0 30.0 45.0 45.0 30.0 22.0 15.0 5.0

Yellow 1.0 1.0 29.0 24.2 24.2 29.0 43.6 43.6 29.0 21.3 14.5 4.8

Concentrations in Groundwater (mg/L) - Manured Land (LU7)

All Basins 40 40 34 34 34 30 25 25 25 30 46 40

6.1.5 Nutrient Bed Concentrations Scour of sediment from the stream bed reintroduces nutrients into the water column. In addition, bed sediments may release dissolved nutrients into the water column, particularly under hypoxic conditions. For a majority of reaches in the Minnesota River model, scoured bed sediments are assumed to have a concentration of 100 mg/kg NH4-N and 250 mg/kg PO4-P on the silt and clay fractions, with lower concentrations on sand. Bed concentrations below major WWTP discharges are set at approximately 2,500 mg/kg PO4-P, with some adjustments during calibration, consistent with the representation of near-field losses of PO4 in these discharges discussed in Section 6.2.1. Dissolved-phase releases from the sediment are assumed to be zero, except for impoundments and the Minnesota River mainstem downstream of Mankato.

6.2 NUTRIENT CALIBRATION AND VALIDATION The earlier Minnesota River basin model provides a credible representation of loading and transport of nutrients; however, the previous calibration is potentially altered by the modifications made to the hydrologic and sediment transport representation in the current update. The scope for the current work calls for updating the calibration of total phosphorus (TP) and total nitrogen (TN). Focus has been placed on the TP simulation, as TP is of direct interest for the Lake Pepin TMDL.

6.2.1 Total Phosphorus Calibration and Validation During calibration, it was quickly determined that total phosphorus observed in stream segments downstream of WWTPs was typically greater than WWTP load during low flow conditions. This suggested that significant near-field retention of phosphorus may be occurring at low flows, via settling to the stream sediment and uptake by macrophytes. In many cases, there appear to be significant macrophyte beds downstream of WWTP outfalls. To address this issue, HSPF GENER statements were used to modify the phosphorus loads from WWTPs. Specifically, the load recorded in the DMR was


6-20

discounted by a factor exp (-β/Q), where Q is the flow on a given day. When Q is large, this discount term approaches 1, indicating no near-field loss. However, at small values of Q the discount term becomes progressively smaller, indicating increased near-field retention. The value of β was adjusted during calibration, but typically resolved to a value in the range of 5 to 10. For example, at a value of β=7.5, the reduction in the receiving stream at a flow of 7.5 cfs would be 63 percent, at a flow of 100 cfs 7.2 percent, and at a flow of 1000 cfs less than 1 percent.

The phosphorus that is retained below WWTPs during low flow conditions is not lost from the system, although HSPF does not account for mass balance in sediment storage. To compensate, phosphorus concentrations in bed sediments were assigned higher values in reaches immediately below WWTP discharges.

Because phosphorus is particle-reactive, phosphorus loading is simulated based on a sediment potency factor (pounds of phosphorus per ton of sediment ) and the quality of the phosphorus calibration is largely dependent on sediment calibration – with the exception that biases in TSS due to under-representation of the sand fraction will generally have only a minor effect on TP, due to the lower affinity of phosphorus for sand.

The major modification to the current simulation is the addition of separate accounting for gully-derived sediment. Therefore, the phosphorus sediment potency factor for gully sediment was taken as the major calibration factor for TP in the current effort, as summarized above in Table 6-2.

Total phosphorus calibration proceeded in a manner analogous to sediment, with an attempt to minimize both errors in concentration and errors in apparent load. The calibration procedure is subject to similar pitfalls as were encountered for sediment: point-in-time grab samples may not be representative of daily average conditions, particularly during storm runoff events, and grab samples may not provide an accurate estimate of the cross-sectional average concentration. In addition, phosphorus concentrations at many locations are strongly influenced by point source discharges, for which, in most cases, only daily average nutrient concentrations are available.

Observed total phosphorus concentrations throughout the Minnesota River system are only weakly correlated to flow, as is shown for example for the large data set in the Minnesota River at Jordan in Figure 6-1, with what appear to be occasional anomalously high observed values. It appears that there is significant random variability in the observed concentrations, although the relative contributions of natural variability and analytical uncertainty are not known.


6-21


0

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1

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2

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100 1000 10000 100000

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TP, m

g/L

Simulated Observed

Figure 6-1. Correlation of Total Phosphorus Concentration with Flow, Minnesota River at Jordan

To address the observed variability and apparent uncertainty, efforts focused on ensuring that simulated total phosphorus was approximately unbiased relative to flow and month (Figure 6-2).

Total Phosphorus Error (Simulated minus Observed) versus Flow

-2

-1.5

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Pre

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Total Phosphorus Error (Simulated minus Observed) versus Month

-2

-1.5

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Figure 6-2. Distribution of Total Phosphorus Simulation Errors versus Flow and Month,

Minnesota River at Jordan

The resulting simulation follows the general trend of observed phosphorus, but does not match all points (see, for example, Figure 6-3).


6-22


0

0.5

1

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2.5

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2000 2001 2002 2003 2004 2005

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TP, m

g/L

Simulated Observed

Figure 6-3. Example Calibration Plot for Total Phosphorus, Minnesota River at Jordan

A complete set of calibration and validation plots for total phosphorus is provided in Appendix D. Summary statistics for the calibration appear in Table 6-9, with validation statistics in Table 6-10. The majority of the calibration targets are met; however, the model does encounter some problems in matching observations for Blue Earth at Mankato and Minnesota River at Mankato. For both of these stations there may be issues with sample location, as Blue Earth at Mankato samples may be influenced by backwater from the Minnesota River mainstem, while Minnesota River at Mankato samples may not always represent complete mixing of the Blue Earth and upstream Minnesota contributions. Interestingly, the signs of the errors for Blue Earth at Mankato are generally opposite of those for the upstream Blue Earth at Rapidan and Le Sueur river stations, while the signs of the errors for Minnesota River at Mankato during the calibration period are opposite of those at St. Peter, a few miles downstream. It appeared that the fit at these two stations could not be improved without degrading the quality of fit downstream at Jordan.


6-23

Table 6-9. Calibration Statistics for Total Phosphorus (1993-2006)

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Count 17 70 226 227 321 0 234 253 143 93 91 657 ≥ 20

Conc Ave Error -0.28% 7.30% -16.37% -14.86% -2.93% No Data 1.71% -23.89% -0.42% 17.85% -12.48% -4.36% ± 25%

Conc Median Error 13.64% 8.01% -4.04% 5.02% 4.17% No Data 4.48% 0.24% 7.80% 9.93% -9.40% 4.93%

Load Ave Error 5.95% -19.89% -24.58% -5.90% -8.38% No Data -5.59% -21.72% 23.84% 29.22% 1.32% -0.75% ± 25%

Load Median Error 6.03% 0.30% -0.42% 0.26% 0.81% No Data -0.08% -0.01% 0.77% 4.78% -2.38% 0.79%

Paired t conc 0.98 0.57 0.83 0.78 1.00 No Data 0.65 0.25 0.94 0.10 0.02 0.11 ≥ 0.20

Paired t load 0.76 0.25 0.39 0.80 0.90 No Data 0.45 0.46 0.05 0.01 0.89 0.83 ≥ 0.20

Transport slope -1.00% -8.55% -14.14% -7.40% -1.14% No Data -2.92% -6.85% 16.50% 23.10% -4.09% -2.07% ± 20%



6-24

Table 6-10. Validation Statistics for Total Phosphorus (1993-2006)

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Count 42 41 81 75 61 17 0 37 160 30 0 219 ≥ 20

Conc Ave Error -35.70% -18.60% -7.57% -23.46% -16.14% 5.82% No Data 20.45% 4.33% -14.78% No Data -7.83% ± 25%

Conc Median Error 1.99% -16.35% -3.72% -3.39% -9.79% -3.79% No Data 13.30% -5.29% -4.88% No Data -0.40%

Load Ave Error -6.11% -6.15% 54.79% 10.02% 12.75% 25.22% No Data 23.06% 46.38% 7.35% No Data -8.29% ± 25%

Load Median Error 0.27% -0.45% -0.83% -0.33% -1.25% -0.45% No Data 0.84% -0.57% -0.14% No Data -0.07%

Paired t conc 0.16 0.14 0.94 0.40 0.74 0.88 No Data 0.48 0.53 0.14 No Data 0.04 ≥ 0.20

Paired t load 0.72 0.53 0.20 0.67 0.57 0.44 No Data 0.46 0.00 0.47 No Data 0.21 ≥ 0.20

Transport slope 23.02% 4.71% 13.40% 11.59% -2.07% 15.72% No Data 7.55% 29.17% 25.35% No Data 0.97% ± 20%



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Loading rates generated by the model for the 1993-2006 calibration period are summarized in Table 6-11.

Table 6-11. Total Phosphorus Loading Rates (lbs/ac/yr) Generated by the Minnesota River Basin Model for 1993-2006

Basin Conservation



Blue Earth 0.528 0.817 0.091 2.206 0.022 0.213 0.555

Chippewa 0.292 0.329 0.022 0.733 0.014 0.065 0.354

Cottonwood 0.312 0.366 0.033 0.900 0.024 0.100 0.386

Hawk 0.207 0.249 0.031 0.549 0.019 0.075 0.497

Le Sueur 0.778 0.879 0.188 2.284 0.032 0.276 0.662

Lower MN 0.577 0.678 0.066 1.794 0.029 0.141 0.582

Middle MN 0.328 0.396 0.044 0.987 0.025 0.097 0.529

Redwood 0.271 0.293 0.045 0.772 0.023 0.110 0.450

Watonwan 0.301 0.358 0.043 0.989 0.016 0.086 0.369

Yellow Medicine

0.196 0.225 0.038 0.487 0.016 0.087 0.327

The percentage contributions to the total upland load are summarized in Table 6-12.

Table 6-12. Percentage Contributions to Total Phosphorus Load by Land Use and Basin

Basin Conservation



Blue Earth 32.77% 45.65% 0.11% 14.85% 0.08% 0.86% 5.67%

Chippewa 36.53% 42.16% 0.38% 10.72% 0.72% 2.90% 7.01%

Cottonwood 25.64% 48.10% 0.11% 18.72% 0.23% 1.06% 6.14%

Hawk 26.92% 43.71% 0.18% 14.25% 0.24% 1.09% 13.60%

Le Sueur 34.66% 39.85% 0.32% 18.67% 0.13% 1.27% 5.10%

Lower MN 30.98% 39.41% 0.59% 21.86% 0.13% 2.04% 4.99%

Middle MN 23.75% 43.72% 0.39% 21.44% 0.37% 0.96% 9.37%

Redwood 23.70% 45.19% 0.15% 17.44% 0.24% 3.42% 9.86%

Watonwan 24.91% 49.77% 0.13% 18.29% 0.14% 0.35% 6.41%

Yellow Medicine 32.33% 41.01% 0.13% 12.49% 0.35% 5.05% 8.63%


6-26

6.2.2 Total Nitrogen Calibration and Validation The focus of the present modeling effort is on sediment/turbidity and phosphorus. It is important to also update the nitrogen simulation due to the interaction of phosphorus and nitrogen through algal growth; however, nitrogen receives somewhat less focus in this effort.

The model simulates a variety of forms of nitrogen: nitrite (NO2), nitrate (NO3), ammonia/ammonium (NH3, NH4

+), and organic nitrogen. The scope of the present effort calls for revisiting and refining the calibration for total nitrogen only, representing the sum of these components. From observed data, total nitrogen is generally calculated as the sum of NO2+NO3-N and total Kjeldahl N, where total Kjeldahl N measures the sum of the unoxidized forms (ammonium and organic nitrogen). Unfortunately, not all samples include both measurements, so the number of available samples for total N is considerably smaller than those for total P.

Nitrogen concentrations in the Minnesota River system show a strong seasonal component, which is predominantly driven by nitrate concentrations in interflow. The model represents the seasonal cycling well, although there are systematic deviations in individual years (see Figure 6-4). Much of the nitrogen load in the waterbodies of the Minnesota River derives from agricultural fertilization, with a dominant spring peak and a smaller fall peak. The model assumes that the seasonal pattern of nitrogen concentrations in interflow is constant from year to year. In fact, the timing, amount, and method of nitrogen fertilization may vary from year to year, accounting for some of the observed variability.


0

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20

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2000 2001 2002 2003 2004 2005 2006

Year

TN, m

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Simulated Observed

Figure 6-4. Example of Total Nitrogen Calibration, Watonwan River at Garden City

Like total phosphorus, calibration for total nitrogen was carried out by attempting to simultaneously match observed concentrations and loads inferred from observations, while minimizing bias relative to flow and month. A complete set of calibration and validation plots for total nitrogen is provided in Appendix F. Summary statistics for the calibration appear in Table 6-13, with validation statistics in Table 6-14. The majority of the calibration targets are met; however, the model again encounters some problems in matching observations for Blue Earth at Mankato. In several cases, the error estimates on


6-27

total nitrogen load fall outside the target bounds of ± 25 percent. This is typically due to errors in concentration at the highest flows (e.g., Figure 6-5). In addition to the issue that high flow grab samples are likely to be unrepresentative of daily average concentrations, there may be issues associated with the model formulation which represents interflow as a fixed concentration: during very wet periods the leachable nitrogen supply may become exhausted, so that the use of a fixed average interflow concentration over-estimates actual load.

-20.0

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Figure 6-5. Prediction Error (Simulated minus Observed) for Total Nitrogen, Watonwan River at

Garden City


6-28

Table 6-13. Calibration Statistics for Total Nitrogen (1993-2006)

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Count 17 23 35 36 234 0 213 223 22 8 51 612 ≥ 20

Conc Ave Error 7.04% -1.78% -18.08% 21.57% 2.67% No Data 2.47% 4.85% 24.66% 26.94% -7.23% 0.05% ± 25%

Conc Median Error 15.27% 0.00% -19.25% 17.77% 0.42% No Data 1.59% 10.71% 20.43% 34.95% -6.50% 3.00%

Load Ave Error 16.90% 14.39% 7.13% 40.33% 22.02% No Data 10.82% 8.63% 46.09% 25.20% 8.54% 10.19% ± 25%

Load Median Error 13.34% 0.00% -1.89% 2.49% 0.07% No Data 0.03% 0.80% 13.96% 32.45% -2.05% 0.46%

Paired t conc 0.95 0.83 0.59 0.45 1.00 No Data 0.28 1.00 0.02 0.01 0.13 0.98 ≥ 0.20

Paired t load 0.55 0.32 0.67 0.25 0.42 No Data 0.00 0.85 0.03 0.03 0.31 0.00 ≥ 0.20

Transport slope 11.08% 2.48% -0.07% 20.04% 9.01% No Data 3.04% -2.10% 9.98% 78.80% 10.85% 0.82% ± 20%



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Table 6-14. Validation Statistics for Total Nitrogen (1993-2006)

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Count 42 41 82 75 61 17 0 36 95 31 7 104 ≥ 20

Conc Ave Error -5.83% 2.26% 0.10% -21.70% -19.26% -28.32% No Data -19.17% -18.70% -18.03% -25.15% -0.02% ± 25%

Conc Median Error 13.11% -11.24% 4.77% -6.93% -1.07% -28.79% No Data -19.59% -12.29% -13.09% -14.33% -1.64%

Load Ave Error 40.11% 59.42% 22.11% -25.21% -7.84% -24.47% No Data -17.24% -22.19% -20.52% -34.52% -8.48% ± 25%

Load Median Error 1.45% -0.07% 0.97% -0.62% -0.04% -9.68% No Data -9.89% -4.47% -2.19% -3.82% -0.17%

Paired t conc 0.91 0.84 1.00 0.41 0.54 0.14 No Data 0.55 0.00 0.01 4.96% 1.00 ≥ 0.20

Paired t load 0.31 0.20 0.47 0.39 0.69 0.43 No Data 0.55 0.00 0.01 14.42% 0.25 ≥ 0.20

Transport slope 29.68% 7.71% 2.58% -3.56% -1.16% -12.15% No Data -12.28% -12.26% -12.74% -1.01% -11.50% ± 20%



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Loading rates generated by the model for the 1993-2006 calibration period are summarized in Table 6-15.

Table 6-15. Total Nitrogen Loading Rates (lbs/ac/yr) Generated by the Minnesota River Basin Model for 1993-2006

Basin Conservation Tillage

Conventional Tillage Forest

Manured Cropland Marsh Pasture Urban

Blue Earth 29.94 25.08 1.08 32.97 1.02 3.44 7.78

Chippewa 6.34 4.69 0.54 5.60 0.45 1.78 5.17

Cottonwood 12.48 10.94 0. 80 14.42 0.76 2.27 5.95

Hawk 6.30 6.97 0.56 8.78 0.62 1.57 6.43

Le Sueur 28.17 27.96 1.68 38.75 1.00 3.64 8.62

Lower MN 24.73 15.08 0.99 13.00 0.90 2.94 8.06

Middle MN 21.09 13.74 0.78 15.35 0.79 2.27 6.93

Redwood 14.08 12.03 0.76 14.21 0.70 2.22 5.42

Watonwan 23.04 28.22 0.82 29.76 0.76 2.42 6.22

Yellow Medicine 5.56 6.88 0.51 8.64 0.32 1.38 4.09

The percentage contributions to the total upland load are summarized in Table 6-16.

Table 6-16. Percentage Contributions to Total Nitrogen Load by Land Use and Basin

Basin Conservation Tillage

Conventional Tillage Forest

Manured Cropland Marsh Pasture Urban

Blue Earth 45.65% 45.88% 0.03% 6.04% 0.09% 0.34% 1.95%

Chippewa 37.64% 48.63% 0.45% 4.19% 0.44% 3.78% 4.87%

Cottonwood 30.11% 56.97% 0.08% 9.14% 0.21% 0.70% 2.78%

Hawk 29.93% 53.92% 0.12% 8.47% 0.29% 0.83% 6.44%

Le Sueur 39.97% 46.49% 0.09% 10.67% 0.13% 0.53% 2.12%

Lower MN 40.71% 48.97% 0.27% 6.50% 0.13% 1.31% 2.12%

Middle MN 32.11% 56.67% 0.14% 7.77% 0.25% 0.47% 2.58%

Redwood 28.93% 58.15% 0.06% 8.27% 0.17% 1.62% 2.79%

Watonwan 32.50% 59.34% 0.04% 5.99% 0.11% 0.17% 1.84%

Yellow Medicine 33.23% 51.44% 0.06% 8.23% 0.25% 2.89% 3.90%


7-1

7 Uncertainty and Sensitivity Analysis for Calibrated Models

7.1 UNCERTAINTY ANALYSIS From a decision context, the primary function of the calibrated water quality models is to predict the response of selected indicators that can be used to assess attainment of management goals under various future scenarios. An important input to the decision-making process is information on the degree of uncertainty that is associated with model predictions. In some cases, the risks or “costs” of exceeding a target value may be substantially greater than the costs of over-protection, creating an asymmetric decision problem in which there is a strong motivation for risk avoidance. Further, if two scenarios produce equivalent predicted results, the scenario that has the smaller uncertainty is often preferable. Typical practice is to include a conservative margin of safety to minimize this risk. However, it is not possible to evaluate how much of a margin of safety is appropriate without information on the uncertainty associated with model predictions. Therefore, an uncertainty analysis of model predictions is essential.

The major sources of model uncertainty include the following:

• Mathematical formulation. A real water system is too complex for a mathematical model to represent all the dynamics, therefore, no matter how sophisticated a mathematical water quality model is, it is based on a simplified mathematical formulation. The simplifications in general neglect processes that are considered to be insignificant, thus the model can capture the general trend of the real system. In other words, a mathematical model is designed to represent the trend, rather than provide exact replication of the real system. Thus, uncertainty exists when those neglected factors start to play some detectable roles.

• Data Uncertainty. Site-specific data are the basis for developing a water quality model for a specific water body. A water quality model requires data from different sources and for a large number of parameters. Many of these data are subjected to either systematic or random errors. Also, data are always limited in both time and space, thus an interpolation method has to be used to represent continuous inputs. In most cases, monitoring data are not available for all the water quality parameters; thus, they have to be derived based on some empirical method. All these can contribute to uncertainty in the model.

• Parameter Specification. In a water quality model, parameters quantify the relationships in the major dynamic processes. The values of parameters are generally obtained through the model calibration process while constrained by a range of reasonable values documented in literature. Due to the sparseness and uncertainty in data used to configure and calibrate a water quality model, the model parameter is also subjected to uncertainty.

As shown through discussions in the preceding chapters, all three sources of model uncertainty are present in the Minnesota River basin models. Results of model validation can be used to provide a direct, integrated measure of the level of uncertainty associated with model predictions outside the calibration time period.

Table 7-1 provides the uncertainty analysis for the model validation period (including only stations with at least 20 observations during the validation period). The table provides the following statistics for each parameter

• RMSE (Root Mean Squared Error): the square root of the average of the squared differences between observed and simulated concentrations.


7-2

• AAE (Average Absolute Error): the average of the absolute value of the differences between observed and simulated concentrations.

• RAE (Relative Absolute Error): the average absolute error expressed as a percentage of the average observed concentration

• NMSE (Normalized Mean Squared Error): the average of the squared differences between observed and simulated concentrations divided by the variance of the observed data. This measure is recommended by CREM (2003) for comparison between fit of different model outputs.

The table also includes the RAE and NMSE for daily flow predictions in the validation period for those stations where the water quality monitoring station and a flow gage are (approximately) co-located.

The NMSEs are of similar magnitude for TSS and TP, reflecting the strong association between these constituents, while the RMSE and AAE are greatest for sediment, due to the larger magnitude of average TSS concentration.

One of the more important sources of uncertainty is the flow simulation – despite the fact that the flow simulation is generally of high quality. The RAE for daily flow is of similar magnitude to the RAEs for nutrients, while the NMSEs for flow are on the order of one-quarter to one-third of those for nutrients, and about one-fourth to one-fifth of those for sediment. The uncertainty in predicting daily flows is largely due to uncertainty in the meteorological forcing, which contributes an irreducible component to overall model uncertainty.

Another source of uncertainty arises from MPCA’s attribution of sediment sources for each watershed (Section 3.8.1). The model calibration was constrained to reproduce these attributed source fractions, on average, but they are themselves uncertain and the subject of ongoing scientific investigations. Somewhat different (and possibly improved) model fit could be attained by revising these assumptions. In addition, an evaluation of the level of uncertainty in source attribution should be conducted to aid in the interpretation of model scenario results.

It should also be recalled that uncertainty is not the same as error, because the observed data themselves are uncertain and the model is not compared to true daily averages. A portion of the uncertainty can be attributed to within-day variability. An analysis of variance components conducted with hourly and daily simulation output at the mouth of the Redwood River indicated that the RMSE associated with within-day variability was about 15 percent of the total RMSE for sediment, and around 5 percent for nutrients. Because within-day variability is likely to be greatest during runoff events when concentrations are high, the RAE will be inflated by more than 15 percent for sediment and more than 5 percent for nutrients.

In sum, the absolute error on model prediction of individual observations averages around 75-100 percent for sediment and around 50 percent for nutrients. This uncertainty derives from all three major sources listed above (mathematical formulation, data uncertainty, and parameter specification). However, a significant portion of the total uncertainty appears to be associated with the uncertainties in the hydrologic simulation that are largely determined by the uncertainty in attributing point rainfall data to watershed scale runoff. Another, perhaps smaller component of the uncertainty is due to unrepresentativeness of grab sample data for estimating reach-averaged, daily concentrations. Some improvement of the model performance by fine-tuning parameter values may be possible, but the sources of data uncertainty provide a kernel of uncertainty in model predictions that may not be further reducible. The sensitivity of the model to perturbations in the value of individual parameters is explored further in the next section.

Minnesota River Model Calibration August 21, 2008

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Table 7-1. Uncertainty Analysis for the Model Validation Period (1986-1992)

Station Sediment (mg/L) Total P (mg/L) Total N (mg/L) Flow (cfs)

RMSE AAE RAE NMSE RMSE AAE RAE NMSE RMSE AAE RAE NMSE RAE NMSE

Blue Earth - Mankato 186.85 109.09 67.5% 1.30 0.17 0.13 63.8% 2.09 4.35 2.86 23.4% 0.39

Chippewa - Montevideo 85.93 53.90 102.0% 1.92 0.58 0.25 62.1% 1.07 3.48 2.12 61.9% 1.97

Cottonwood - New Ulm 345.33 127.20 92.3% 1.15 0.31 0.13 60.8% 1.04 3.82 2.77 48.0% 0.70 47.4% 0.27

Le Sueur - Rapidan 564.30 368.64 67.9% 1.19 0.24 0.20 65.2% 1.64 5.70 4.50 30.9% 0.68 47.2% 0.38

Minnesota - Jordan 92.70 61.87 69.8% 1.01 0.14 0.09 37.6% 0.98 3.07 2.38 39.1% 0.50 29.6% 0.16

Minnesota - Mankato 154.16 88.55 62.1% 1.33 0.14 0.12 46.1% 1.68 4.00 3.17 31.9% 0.42 29.1% 0.14

Redwood - Redwood Falls 392.78 129.10 91.1% 1.01 0.31 0.19 63.8% 1.48 4.53 3.24 57.2% 1.12 54.1% 0.39

Watonwan - Garden City 174.24 90.99 91.4% 1.79 0.22 0.16 54.9% 2.45 4.92 3.65 39.2% 0.56 45.6% 0.35

Yellow Medicine - Granite Falls 72.52 40.45 52.9% 0.33 0.00 0.00 0.00% 0.00 1.88 1.39 52.1% 0.90 51.5% 0.31


7-4

7.2 SENSITIVITY ANALYSIS Tetra Tech also undertook a model sensitivity analysis to evaluate how the uncertainty in the output of the model can be assigned to different sources of uncertainty in model input. Use of sensitivity analysis is recommended in MPCA (2006): “The modeler must identify the input parameters which have a mathematical influence on the value of the output parameter. Once the related input parameters are identified, the modeler must systematically and individually increase and decrease the value of each relevant input parameter. If small changes in the value of an input parameter result in a large change in value of the output parameter, the output parameter is said to be very sensitive to that input parameter. If large changes in the value of an input parameter result in a small change in value of the output parameter, the output parameter is said to be relatively insensitive to that input parameter. The modeler must identify to which input parameters the output parameter displays the greatest sensitivity…”

HSPF is a large and complex model, with many input parameters. It is not feasible to undertake a comprehensive sensitivity analysis of all inputs to and outputs from the Minnesota River basin model; therefore, the focus is placed on key inputs and outputs. The outputs of concern are predictions of flow, sediment, total phosphorus, and total nitrogen. Loading of these parameters is primarily associated with agricultural land uses in this basin. The sensitivity analysis is thus structured to examine factors associated with loading from cropland, taking as a basis a unit area of conventional cropland in the Watonwan River watershed. Sensitivity is expected to be qualitatively similar for other types of cropland (conservation tillage, manure application areas) and other watersheds. Twenty-nine key inputs (parameter values or forcing data) were selected for analysis based on previous experience with the model, and their impact on total annual loading rate was examined.

Results are expressed as normalized sensitivity coefficients, which represent the percentage change in a response variable associated with a 1 percent change in an input variable. Each input was perturbed by plus or minus 10 percent to conduct the analysis. In one case (the groundwater recession constant, AGWRC), the perturbation was conducted in logarithmic space as the parameter is used in an exponential formulation. All other perturbations were performed on an arithmetic basis.

The perturbations included key inputs of precipitation and evapotranspiration rates, along with groups of the most important parameters controlling runoff, sediment transport, and nutrient loading. The resulting normalized sensitivity coefficients are shown in Table 7-2.

Table 7-2. Normalized Sensitivity Coefficients (for Total Annual Loading Rate from Agricultural Land in the Watonwan River Watershed)

Parameter Flow Sediment Total N Total P

Precipitation Rate 3.33% 14.17% 3.59% 8.07%

Potential Evapotranspiration -1.59% -6.81% -1.54% -3.88%

Snow Catch Factor (SNOWCF) 0.44% 0.36% 0.42% 0.38%

Infiltration (INFILT) -0.04% -2.62% -0.31% -1.17%

Groundwater Recession (LN[AGWRC])

0.00% 0.00% 0.00% 0.00%

Lower Zone Soil Storage Capacity (LZSN)

-0.25% 0.48% -0.27% 0.09%

Upper Zone Soil Storage Capacity (UZSN)

-0.15% -1.02% -0.26% -0.55%


7-5

Parameter Flow Sediment Total N Total P

Interflow Inflow (INTFW) 0.01% -2.91% 0.08% -1.24%

Lower Zone Evapotranspiration Potential (LZETP)

-0.34% -2.47% -0.29% -1.27%

Manning's roughness (NSUR) 0.00% -0.89% 0.01% -0.39%

Soil erodibility (KRER) 0.00% 0.00% 0.00% 0.00%

Tillage detachment of sediment (SPECIAL ACTIONS)

0.00% 0.00% 0.00% 0.00%

Sediment washoff transport capacity (KSER)

0.00% 0.24% 0.00% 0.12%

Sediment gully transport capacity (KGER)

0.00% 0.76% 0.00% 0.33%

Erosion Cover (COVER) 0.00% 0.00% 0.00% 0.00%

Tile drain sediment transport 0.00% 0.10% 0.00% 0.00%

NH3 surface (ACCUM, SQOLIM) 0.00% 0.00% 0.01% 0.00%

NO3 surface (ACCUM, SQOLIM) 0.00% 0.00% 0.06% 0.00%

PO4 surface (POTFS, POTFW) 0.00% 0.00% 0.00% 0.40%

Organic Matter surface (ACCUM, SQOLIM)

0.00% 0.00% 0.00% 0.00%

NH3 Interflow (IFLW-CONC) 0.00% 0.00% 0.01% 0.00%

NO3 Interflow (IFLW-CONC) 0.00% 0.00% 0.65% 0.00%

PO4 Interflow (IFLW-CONC) 0.00% 0.00% 0.00% 0.20%

Organic Matter Interflow (IFLW-CONC)

0.00% 0.00% 0.01% 0.07%

NH3 Groundwater (GRND-CONC)

0.00% 0.00% 0.00% 0.00%

NO3 Groundwater (GRND-CONC)

0.00% 0.00% 0.22% 0.00%

PO4 Groundwater (GRND-CONC)

0.00% 0.00% 0.00% 0.12%

Organic Matter Groundwater (GRND-CONC)

0.00% 0.00% 0.05% 0.16%

Tile drain sediment-associated P transport

0.00% 0.00% 0.00% 0.04%

The greatest magnitude normalized sensitivity coefficients for all responses (flow, sediment, and nutrient loading) are associated with precipitation and evapotranspiration rates. The uncertainty inherent in estimating watershed rainfall based on sparse, daily summary precipitation stations has been noted above, as have the uncertainties associated with estimating potential evapotranspiration. These remain fundamental sources of uncertainty in the model; however, uncertainty due to these parameters should have only minor effects on the relative comparison of different management scenarios.


7-6

Model parameters that have an effect on flow also have an effect on pollutant loading, as delivery of pollutants depends on the hydrologic simulation. Conversely, parameters controlling pollutant load generation have no effect on flow, as expected.

For sediment, a key finding of this analysis is that the parameters controlling the supply of detached sediment (KRER, COVER, and tillage sediment detachment) have little or no impact on predicted annual sediment loads. This represents a situation in which sediment delivery to waterbodies is primarily controlled by transport capacity, rather than supply. As a result, the sediment simulation is much more sensitive to the parameters that control sediment overland transport (KSER, KGER) as well as to any changes in hydrology that change the amount of runoff or its partitioning between surface and subsurface pathways.

The sensitivity pattern for phosphorus load generally follow the pattern for sediment, with the addition of the several phosphorus concentration parameters. For nitrogen, a rather different pattern is seen, as nitrogen is not sediment-associated and predominantly moves via subsurface categories. The normalized sensitivity coefficients relative to nitrogen have high magnitudes for interflow and groundwater nitrogen concentrations, while the sensitivity to specification of ammonia and organic matter loads, as well as surface washoff of nitrogen, is much less.

Model predictions will also be affected by instream processes – most importantly, the scour and deposition of sediment. The response, however, depends on the characteristics of individual reaches and is not as readily amenable to a global quantitative evaluation.


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A-1

Appendix A. Model Land Use by Major Watershed Major Watershed Land Use 1990 (acres) 2000 (acres) % Change

FOREST 32,738 8,414 -74%

HIGH TILL CROPLAND 744,099 391,262 -47%

LOW TILL CROPLAND 111,187 409,659 268%

MANURED LAND 38,448 45,750 19%

MARSH/WETLANDS 3,678 25,498 593%

GRASS/PASTURE 31,735 27,517 -13%

URBAN IMPERVIOUS 3,141 7,434 137%

Blue Earth River

URBAN PERVIOUS 17,714 61,932 250%

FOREST 71,744 58,831 -18%



MANURED LAND 46,732 49,508 6%


GRASS/PASTURE 166,423 151,622 -9%


Chippewa River

URBAN PERVIOUS 21,049 58,824 179%

FOREST 24,933 10,522 -58%



MANURED LAND 46,752 63,236 35%


GRASS/PASTURE 46,134 32,211 -30%


Cottonwood River

URBAN PERVIOUS 15,492 42,830 176%


A-2

Major Watershed Land Use 1990 (acres) 2000 (acres) % Change

FOREST 9,330 4,691 -50%



MANURED LAND 15,626 20,908 34%


GRASS/PASTURE 12,475 11,684 -6%


Hawk Creek

URBAN PERVIOUS 7,931 18,161 129%

FOREST 31,911 10,288 -68%



MANURED LAND 35,007 49,049 40%


GRASS/PASTURE 24,518 27,601 13%


Le Sueur River

URBAN PERVIOUS 14,721 41,211 180%

FOREST 47,877 38,770 -19%



MANURED LAND 47,829 52,551 10%


GRASS/PASTURE 27,118 62,564 131%


Lower Minnesota River

URBAN PERVIOUS 14,463 30,172 109%


A-3


FOREST 87,906 48,575 -45%

HIGH TILL CROPLAND 1,000,426 606,279 -39%


MANURED LAND 68,232 119,323 75%

MARSH/WETLANDS 12,735 82,790 550%

GRASS/PASTURE 73,477 54,139 -26%


Middle Minnesota River

URBAN PERVIOUS 35,464 78,205 121%

FOREST 11,565 4,417 -62%



MANURED LAND 20,495 30,262 48%


GRASS/PASTURE 31,998 41,643 30%


Redwood River


FOREST 15,278 6,069 -60%



MANURED LAND 29,353 37,475 28%


GRASS/PASTURE 23,159 8,248 -64%


Watonwan River

URBAN PERVIOUS 10,811 31,225 189%


A-4


FOREST 11,920 3,059 -74%



MANURED LAND 18,783 22,689 21%


GRASS/PASTURE 34,359 51,302 49%

URBAN IMPERVIOUS 937 2,603 178%

Yellow Medicine River



B-1

Appendix B. Hydrologic Calibration Results

B-1. CHIPPEWA RIVER

02000400060008000

100001200014000160001800020000


Date

Flow

(cfs

)

0

2

4

6

8

10

12

14

Dai

ly R

ainf

all (

in)

Avg Daily Rainfall ( in)Avg Observed Flow (1/1/1993 to 11/30/2006 )Avg Modeled Flow (Same Period)

Figure B-1. Mean Daily Flow: Model vs. USGS 05304500 Chippewa River near Milan, MN

0

2000

4000

6000

8000

J-93 J-94 J-96 J-97 J-99 J-00 J-02 J-03 J-05 J-06

Month

Flow

(cfs

)

0

2

4

6

8

10

12

Mon

thly

Rai

nfal

l (in

)


Figure B-2. Mean Monthly Flow: Model vs. USGS 05304500 Chippewa River near Milan, MN


B-2

y = 0.9131x + 81.337R2 = 0.7974

0

2000

4000

6000

8000

0 2000 4000 6000 8000

Average Observed Flow (cfs)

Ave

rage

Mod

eled

Flo

w (c

fs)

Avg Flow (1/1/1993 to 11/30/2006 )Line of Equal ValueBest-Fit Line

0%

10%

20%30%

40%

50%

60%

70%

80%

90%

100%

J-93 J-94 J-96 J-97 J-99 J-00 J-02 J-03 J-05 J-06

Month

Wat

er B

alan

ce (O

bs +

Mod

)

Avg Observed Flow (1/1/1993 to 11/30/2006 )Avg Modeled Flow (1/1/1993 to 11/30/2006 )Line of Equal Value

Figure B-3. Monthly Flow Regression and Temporal Variation: Model vs. USGS 05304500 Chippewa River near Milan, MN

y = 1.0226x + 8.1512R2 = 0.9529

0

500

1000

1500

2000

0 500 1000 1500 2000


Ave

rage

Mod

eled

Flo

w (c

fs)

Avg Flow (1/1/1993 to 11/30/2006)Line of Equal ValueBest-Fit Line

J F M A M J J A S O N D

0

500

1000

1500

2000

1 2 3 4 5 6 7 8 9 10 11 12

Month

Flow

(cfs

)

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Mon

thly

Rai

nfal

l (in

)

Avg Monthly Rainfall (in)Avg Observed Flow (1/1/1993 to 11/30/2006)Avg Modeled Flow (Same Period)

Figure B-4. Seasonal Regression and Temporal Aggregate: Model vs. USGS 05304500 Chippewa River near Milan, MN


B-3


0

500

1000

1500

2000

2500

1 2 3 4 5 6 7 8 9 10 11 12

Month

Flow

(cfs

)

0

1

1

2

2

3

3

4

4

5

Mon

thly

Rai

nfal

l (in

)

Average Monthly Rainfall ( in) Observed (25th, 75th)Median Observed Flow (1/1/1993 to 11/30/2006) Modeled (Median, 25th, 75th)

Figure B-5. Seasonal Medians and Ranges: Model vs. USGS 05304500 Chippewa River near Milan, MN

Table B-1. Seasonal Summary: Model vs. USGS 05304500 Chippewa River near Milan, MN

MEAN MEDIAN 25TH 75TH MEAN MEDIAN 25TH 75TH

Jan 185.62 154.53 104.52 199.79 136.76 151.00 61.83 196.00Feb 208.41 159.04 100.52 221.05 250.81 168.00 103.00 370.00Mar 572.46 293.56 145.03 715.66 715.98 530.50 267.00 1087.50Apr 1900.39 1300.29 738.66 1882.92 1753.29 978.00 651.50 2155.00May 1219.18 1045.23 735.41 1597.85 1417.53 1260.00 829.75 1850.00Jun 932.35 843.19 519.62 1182.76 1143.11 879.00 619.00 1385.00Jul 924.06 706.16 464.35 1040.23 1027.24 673.50 479.00 1097.50Aug 562.91 408.09 227.80 682.15 512.01 341.00 229.00 615.75Sep 350.81 264.56 164.79 482.61 342.80 232.00 144.75 454.50Oct 447.88 308.57 168.29 510.11 415.83 218.50 122.00 464.75Nov 364.48 296.07 180.79 472.85 296.03 271.50 102.00 392.25Dec 276.18 217.05 166.54 329.57 210.59 185.00 97.45 303.00

MONTH OBSERVED FLOW (CFS) MODELED FLOW (CFS)


B-4

0.1

1

10

100

1000

10000

100000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percent of Time that Flow is Equaled or Exceeded

Dai

ly A

vera

ge F

low

(cfs

)Observed Flow Duration (1/1/1993 to 11/30/2006 )Modeled Flow Duration (1/1/1993 to 11/30/2006 )

Figure B-6. Flow Duration: Model vs. USGS 05304500 Chippewa River near Milan, MN

0%

20%

40%

60%

80%

100%

120%


Nor

mal

ized

Flo

w V

olum

e (O

bser

ved

as 1

00%

)

Observed Flow Volume (1/1/1993 to 11/30/2006 )

Modeled Flow Volume (1/1/1993 to 11/30/2006 )

Figure B-7. Flow Accumulation: Model vs. USGS 05304500 Chippewa River near Milan, MN


B-5

Table B-2. Summary Statistics: Model vs. USGS 05304500 Chippewa River near Milan, MN HSPF Simulated Flow Observed Flow Gage

REACH OUTFLOW

13.91-Year Analysis Period: 1/1/1993 - 11/30/2006 Hydrologic Unit Code: 7020005Flow volumes are (inches/year) for upstream drainage area Latitude: 45.108292

Longitude: -95.7989224Drainage Area (sq-mi): 1880

Total Simulated In-stream Flow: 4.98 Total Observed In-stream Flow: 4.81

Total of simulated highest 10% flows: 2.08 Total of Observed highest 10% flows: 1.90Total of Simulated lowest 50% flows: 0.63 Total of Observed Lowest 50% flows: 0.69

Simulated Summer Flow Volume (months 7-9): 1.15 Observed Summer Flow Volume (7-9): 1.13Simulated Fall Flow Volume (months 10-12): 0.55 Observed Fall Flow Volume (10-12): 0.65Simulated Winter Flow Volume (months 1-3): 0.67 Observed Winter Flow Volume (1-3): 0.58Simulated Spring Flow Volume (months 4-6): 2.60 Observed Spring Flow Volume (4-6): 2.44

Total Simulated Storm Volume: 0.98 Total Observed Storm Volume: 0.96Simulated Summer Storm Volume (7-9): 0.25 Observed Summer Storm Volume (7-9): 0.26

Errors (Simulated-Observed) Error Statistics Recommended Criteria

Error in total volume: 3.57 10Error in 50% lowest flows: -8.52 10Error in 10% highest flows: 9.54 15Seasonal volume error - Summer: 2.44 30Seasonal volume error - Fall: -15.06 30Seasonal volume error - Winter: 14.06 30Seasonal volume error - Spring: 6.56 30Error in storm volumes: 2.76 15Error in summer storm volumes: -4.61 50Nash-Sutcliffe Coefficient of Efficiency, E: 0.586 Model accuracy increasesBaseline adjusted coefficient (Garrick), E': 0.488 as E or E' approaches 1.0

USGS 05304500 CHIPPEWA RIVER NEAR MILAN, MN

B-2. YELLOW MEDICINE RIVER

0

2000

4000

6000

8000

10000

12000

14000


Date

Flow

(cfs

)

0

2

4

6

8

10

12

14

Dai

ly R

ainf

all (

in)


Figure B-8. Mean Daily Flow: Model vs. USGS 05313500 Yellow Medicine River near Granite Falls, MN


B-6

0

1000

2000

3000

4000

J-93 J-94 J-96 J-97 J-99 J-00 J-02 J-03 J-05 J-06

Month

Flow

(cfs

)

0

2

4

6

8

10

12

Mon

thly

Rai

nfal

l (in

)


Figure B-9. Mean Monthly Flow: Model vs. USGS 05313500 Yellow Medicine River near Granite Falls, MN

y = 0.724x + 40.604R2 = 0.6359

0

1000

2000

3000

4000

0 1000 2000 3000 4000


Ave

rage

Mod

eled

Flo

w (c

fs)


0%

10%

20%30%

40%

50%

60%

70%

80%

90%

100%

J-93 J-94 J-96 J-97 J-99 J-00 J-02 J-03 J-05 J-06

Month

Wat

er B

alan

ce (O

bs +

Mod

)


Figure B-10. Monthly Flow Regression and Temporal Variation: Model vs. USGS 05313500 Yellow Medicine River near Granite Falls, MN


B-7

y = 0.778x + 27.947R2 = 0.9338

0

200

400

600

800

1000

0 200 400 600 800 1000


Aver

age

Mod

eled

Flo

w (c

fs)



0

200

400

600

800

1000

1 2 3 4 5 6 7 8 9 10 11 12

Month

Flow

(cfs

)

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Mon

thly

Rai

nfal

l (in

)


Figure B-11. Seasonal Regression and Temporal Aggregate: Model vs. USGS 05313500 Yellow Medicine River near Granite Falls, MN


0

200

400

600

800

1000

1200

1 2 3 4 5 6 7 8 9 10 11 12

Month

Flow

(cfs

)

0

1

1

2

2

3

3

4

4

5M

onth

ly R

ainf

all (

in)

Average Monthly Rainfall (in) Observed (25th, 75th)Median Observed Flow (1/1/1993 to 11/30/2006) Modeled (Median, 25th, 75th)

Figure B-12. Seasonal Medians and Ranges: Model vs. USGS 05313500 Yellow Medicine River near Granite Falls, MN


B-8

Table B-3. Seasonal Summary: Model vs. USGS 05313500 Yellow Medicine River near Granite Falls, MN




0.1

1

10

100

1000

10000

100000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Dai

ly A

vera

ge F

low

(cfs

)

Observed Flow Duration (1/1/1993 to 11/30/2006 )

Modeled Flow Duration (1/1/1993 to 11/30/2006 )

Figure B-13. Flow Duration: Model vs. USGS 05313500 Yellow Medicine River near Granite Falls, MN


B-9

0%

20%

40%

60%

80%

100%

120%


Norm

aliz

ed F

low

Vol

ume

(Obs

erve

d as

100

%)



Figure B-14. Flow Accumulation: Model vs. USGS 05313500 Yellow Medicine River near Granite Falls, MN

Table B-4. Summary Statistics: Model vs. USGS 05313500 Yellow Medicine River near Granite Falls, MN

HSPF Simulated Flow Observed Flow Gage

REACH OUTFLOW





Simulated Summer Flow Volume (months 7-9): 0.74 Observed Summer Flow Volume (7-9): 0.71Simulated Fall Flow Volume (months 10-12): 0.34 Observed Fall Flow Volume (10-12): 0.43Simulated W inter Flow Volume (months 1-3): 0.54 Observed Winter Flow Volume (1-3): 0.60Simulated Spring Flow Volume (months 4-6): 2.62 Observed Spring Flow Volume (4-6): 2.97



Error in total volume: -9.88 10Error in 50% lowest flows: -7.33 10Error in 10% highest flows: -3.83 15Seasonal volume error - Summer: 4.37 30Seasonal volume error - Fall: -20.34 30Seasonal volume error - Winter: -10.43 30Seasonal volume error - Spring: -11.68 30Error in storm volumes: 3.62 15Error in summer storm volumes: 28.58 50Nash-Sutcliffe Coefficient of Efficiency, E: 0.285 Model accuracy increasesBaseline adjusted coefficient (Garrick), E': 0.492 as E or E' approaches 1.0

USGS 05313500 YELLOW MEDICINE RIVER NEAR GRANITE FALLS, MN


B-10

B-3. REDWOOD RIVER

0

2000

4000

6000

8000

10000

12000

14000


Date

Flow

(cfs

)

0

2

4

6

8

10

12

14

Dai

ly R

ainf

all (

in)


Figure B-15. Mean Daily Flow: Model vs. USGS 05316500 Redwood River near Redwood Falls, MN

0

1000

2000

3000

4000

J-93 J-94 J-96 J-97 J-99 J-00 J-02 J-03 J-05 J-06

Month

Flow

(cfs

)

0

2

4

6

8

10

12

Mon

thly

Rai

nfal

l (in

)


Figure B-16. Mean Monthly Flow: Model vs. USGS 05316500 Redwood River near Redwood Falls, MN


B-11

y = 0.9885x + 0.65R2 = 0.8929

0

1000

2000

3000

4000

0 1000 2000 3000 4000


Ave

rage

Mod

eled

Flo

w (c

fs)


0%10%20%30%40%50%60%70%80%

90%100%

J-93 J-94 J-96 J-97 J-99 J-00 J-02 J-03 J-05 J-06

Month

Wat

er B

alan

ce (O

bs +

Mod

)


Figure B-17. Monthly Flow Regression and Temporal Variation: Model vs. USGS 05316500 Redwood River near Redwood Falls, MN

y = 1.0945x - 32.285R2 = 0.9696

0

500

1000

1500

0 500 1000 1500


Ave

rage

Mod

eled

Flo

w (c

fs)



0

500

1000

1500

1 2 3 4 5 6 7 8 9 10 11 12

Month

Flow

(cfs

)

0

1

2

3

4

5

6M

onth

ly R

ainf

all (

in)


Figure B-18. Seasonal regression and temporal aggregate: Model vs. USGS 05316500 Redwood River near Redwood Falls, MN


B-12


0

200

400

600

800

1000

1200

1400

1600

1 2 3 4 5 6 7 8 9 10 11 12

Month

Flow

(cfs

)

0

1

2

3

4

5

6

Mon

thly

Rai

nfal

l (in

)


Figure B-19. Seasonal Medians and Ranges: Model vs. USGS 05316500 Redwood River near Redwood Falls, MN


B-13

Table B-5. Seasonal Summary: Model vs. USGS 05316500 Redwood River near Redwood Falls, MN




0.1

1

10

100

1000

10000

100000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Dai

ly A

vera

ge F

low

(cfs

)

Observed Flow Duration (1/1/1993 to 11/30/2006 )Modeled Flow Duration (1/1/1993 to 11/30/2006 )

Figure B-20. Flow Duration: Model vs. USGS 05316500 Redwood River near Redwood Falls, MN


B-14

0%

20%

40%

60%

80%

100%

120%


Nor

mal

ized

Flo

w V

olum

e (O

bser

ved

as 1

00%

)Observed Flow Volume (1/1/1993 to 11/30/2006 )


Figure B-21. Flow Accumulation: Model vs. USGS 05316500 Redwood River near Redwood Falls, MN


B-15

Table B-6. Summary Statistics: Model vs. USGS 05316500 Redwood River near Redwood Falls, MN


REACH OUTFLOW








Error in total volume: -1.14 10Error in 50% lowest flows: 3.34 10Error in 10% highest flows: 3.91 15Seasonal volume error - Summer: -10.03 30Seasonal volume error - Fall: -26.19 30Seasonal volume error - Winter: 1.33 30Seasonal volume error - Spring: 5.08 30Error in storm volumes: 2.39 15Error in summer storm volumes: -0.50 50Nash-Sutcliffe Coefficient of Efficiency, E: 0.747 Model accuracy increasesBaseline adjusted coefficient (Garrick), E': 0.624 as E or E' approaches 1.0

USGS 05316500 REDWOOD RIVER NEAR REDWOOD FALLS, MN

B-4. COTTONWOOD RIVER

0

5000

10000

15000

20000

25000


Date

Flow

(cfs

)

0

2

4

6

8

10

12

14

Dai

ly R

ainf

all (

in)


Figure B-22. Mean Daily Flow: Model vs. USGS 05317000 Cottonwood River near New Ulm, MN


B-16

0

2000

4000

6000

8000

J-93 J-94 J-96 J-97 J-99 J-00 J-02 J-03 J-05 J-06

Month

Flow

(cfs

)

012345678910

Mon

thly

Rai

nfal

l (in

)


Figure B-23. Mean Monthly Flow: Model vs. USGS 05317000 Cottonwood River near New Ulm, MN

y = 0.9676x + 51.449R2 = 0.9168

0

2000

4000

6000

8000

0 2000 4000 6000 8000


Ave

rage

Mod

eled

Flo

w (c

fs)


0%10%20%30%40%50%60%70%80%

90%100%

J-93 J-94 J-96 J-97 J-99 J-00 J-02 J-03 J-05 J-06

Month

Wat

er B

alan

ce (O

bs +

Mod

)


Figure B-24. Monthly Flow Regression and Temporal Variation: Model vs. USGS 05317000 Cottonwood River near New Ulm, MN


B-17

y = 1.0241x + 14.486R2 = 0.9745

0

500

1000

1500

2000

2500

0 500 1000 1500 2000 2500


Ave

rage

Mod

eled

Flo

w (c

fs)



0

500

1000

1500

2000

2500

1 2 3 4 5 6 7 8 9 10 11 12

Month

Flow

(cfs

)

00.51

1.522.53

3.544.55

Mon

thly

Rai

nfal

l (in

)


Figure B-25. Seasonal regression and temporal aggregate: Model vs. USGS 05317000 Cottonwood River near New Ulm, MN


0

500

1000

1500

2000

2500

3000

1 2 3 4 5 6 7 8 9 10 11 12

Month

Flow

(cfs

)

0

11

2

2

3

34

4

5

5M

onth

ly R

ainf

all (

in)


Figure B-26. Seasonal Medians and Ranges: Model vs. USGS 05317000 Cottonwood River near New Ulm, MN


B-18

Table B-7. Seasonal Summary: Model vs. USGS 05317000 Cottonwood River near New Ulm, MN




0.1

1

10

100

1000

10000

100000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Dai

ly A

vera

ge F

low

(cfs

)


Figure B-27. Flow Duration: Model vs. USGS 05317000 Cottonwood River near New Ulm, MN


B-19

0%

20%

40%

60%

80%

100%

120%


Nor

mal

ized

Flo

w V

olum

e (O

bser

ved

as 1

00%

)Observed Flow Volume (1/1/1993 to 11/30/2006 )


Figure B-28. Flow Accumulation: Model vs. USGS 05317000 Cottonwood River near New Ulm, MN

Table B-8. Summary Statistics: Model vs. USGS 05317000 Cottonwood River near New Ulm, MN HSPF Simulated Flow Observed Flow Gage

REACH OUTFLOW








Error in total volume: 4.65 10Error in 50% lowest flows: -6.55 10Error in 10% highest flows: 4.42 15Seasonal volume error - Summer: 7.76 30Seasonal volume error - Fall: -6.77 30Seasonal volume error - Winter: 3.40 30Seasonal volume error - Spring: 5.73 30Error in storm volumes: -2.55 15Error in summer storm volumes: 4.73 50Nash-Sutcliffe Coefficient of Efficiency, E: 0.774 Model accuracy increasesBaseline adjusted coefficient (Garrick), E': 0.608 as E or E' approaches 1.0

USGS 05317000 COTTONWOOD RIVER NEAR NEW ULM, MN


B-20

B-5. WATONWAN RIVER

02000400060008000

10000120001400016000

Jan-86 Jan-87 Jan-88 Jan-89 Jan-90 Jan-91 Jan-92 Jan-93

Date

Flow

(cfs

)

0

2

4

6

8

10

12

14

Dai

ly R

ainf

all (

in)


Figure B-29. Mean Daily Flow: Model vs. USGS 05319500 Watonwan River near Garden City, MN

0

1000

2000

3000

4000

5000

J-93 J-94 J-96 J-97 J-99 J-00 J-02 J-03 J-05 J-06

Month

Flow

(cfs

)

0

2

4

6

8

10

12

Mon

thly

Rai

nfal

l (in

)


Figure B-30. Mean Monthly Flow: Model vs. USGS 05319500 Watonwan River near Garden City, MN


B-21

y = 0.8305x + 53.776R2 = 0.8739

0

1000

2000

3000

4000

5000

0 1000 2000 3000 4000 5000


Ave

rage

Mod

eled

Flo

w (c

fs)


0%10%20%30%40%50%60%70%80%

90%100%

J-93 J-94 J-96 J-97 J-99 J-00 J-02 J-03 J-05 J-06

Month

Wat

er B

alan

ce (O

bs +

Mod

)


Figure B-31. Monthly flow regression and temporal variation: Model vs. USGS 05319500 Watonwan River near Garden City, MN

y = 0.8522x + 42.544R2 = 0.9699

0

500

1000

1500

0 500 1000 1500


Ave

rage

Mod

eled

Flo

w (c

fs)



0

500

1000

1500

1 2 3 4 5 6 7 8 9 10 11 12

Month

Flow

(cfs

)

00.51

1.522.53

3.544.55

Mon

thly

Rai

nfal

l (in

)


Figure B-32. Seasonal Regression and Temporal Aggregate: Model vs. USGS 05319500 Watonwan River near Garden City, MN


B-22


0

500

1000

1500

2000

2500

1 2 3 4 5 6 7 8 9 10 11 12

Month

Flow

(cfs

)

0

11

2

2

3

34

4

5

5

Mon

thly

Rai

nfal

l (in

)


Figure B-33. Seasonal Medians and Ranges: Model vs. USGS 05319500 Watonwan River near Garden City, MN

Table B-9. Seasonal Summary: Model vs. USGS 05319500 Watonwan River near Garden City, MN





B-23

0.1

1

10

100

1000

10000

100000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Dai

ly A

vera

ge F

low

(cfs


Figure B-34. Flow Duration: Model vs. USGS 05319500 Watonwan River near Garden City, MN

0%

20%

40%

60%

80%

100%

120%


Nor

mal

ized

Flo

w V

olum

e (O

bser

ved

as 1

00%

)



Figure B-35. Flow accumulation: Model vs. USGS 05319500 Watonwan River near Garden City, MN


B-24

Table B-10. Summary statistics: Model vs. USGS 05319500 Watonwan River near Garden City, MN


REACH OUTFLOW








Error in total volume: -6.12 10Error in 50% lowest flows: 0.16 10Error in 10% highest flows: -6.24 15Seasonal volume error - Summer: 2.63 30Seasonal volume error - Fall: -9.10 30Seasonal volume error - Winter: -1.22 30Seasonal volume error - Spring: -9.91 30Error in storm volumes: -13.42 15Error in summer storm volumes: -10.64 50Nash-Sutcliffe Coefficient of Efficiency, E: 0.745 Model accuracy increasesBaseline adjusted coefficient (Garrick), E': 0.647 as E or E' approaches 1.0

USGS 05319500 WATONWAN RIVER NEAR GARDEN CITY, MN

B-6. LE SUEUR RIVER

02000400060008000

100001200014000160001800020000


Date

Flow

(cfs

)

0

2

4

6

8

10

12

14

Dai

ly R

ainf

all (

in)


Figure B-36. Mean Daily Flow: Model vs. USGS 05320500 Le Sueur River near Rapidan, MN


B-25

0

2000

4000

6000

8000

J-93 J-94 J-96 J-97 J-99 J-00 J-02 J-03 J-05 J-06

Month

Flow

(cfs

)

0123456789

Mon

thly

Rai

nfal

l (in

)


Figure B-37. Mean Monthly Flow: Model vs. USGS 05320500 Le Sueur River near Rapidan, MN

y = 0.8914x + 31.733R2 = 0.9158

0

2000

4000

6000

8000

0 2000 4000 6000 8000


Ave

rage

Mod

eled

Flo

w (c

fs)


0%10%20%30%40%50%60%70%80%

90%100%

J-93 J-94 J-96 J-97 J-99 J-00 J-02 J-03 J-05 J-06

Month

Wat

er B

alan

ce (O

bs +

Mod

)


Figure B-38. Monthly Flow Regression and Temporal Variation: Model vs. USGS 05320500 Le Sueur River near Rapidan, MN


B-26

y = 0.8632x + 54.372R2 = 0.9709

0

500

1000

1500

2000

2500

0 500 1000 1500 2000 2500


Ave

rage

Mod

eled

Flo

w (c

fs)



0

500

1000

1500

2000

2500

1 2 3 4 5 6 7 8 9 10 11 12

Month

Flow

(cfs

)

0

1

2

3

4

5

6

Mon

thly

Rai

nfal

l (in

)


Figure B-39. Seasonal Regression and Temporal Aggregate: Model vs. USGS 05320500 Le Sueur River near Rapidan, MN


0

500

1000

1500

2000

2500

3000

3500

1 2 3 4 5 6 7 8 9 10 11 12

Month

Flow

(cfs

)

0

1

2

3

4

5

6

Mon

thly

Rai

nfal

l (in

)


Figure B-40. Seasonal Medians and Ranges: Model vs. USGS 05320500 Le Sueur River near Rapidan, MN

Table B-11. Seasonal Summary: Model vs. USGS 05320500 Le Sueur River near Rapidan, MN





B-27

0.1

1

10

100

1000

10000

100000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Dai

ly A

vera

ge F

low

(cfs

)


Figure B-41. Flow Duration: Model vs. USGS 05320500 Le Sueur River near Rapidan, MN

0%

20%

40%

60%

80%

100%

120%


Nor

mal

ized

Flo

w V

olum

e (O

bser

ved

as 1

00%

)



Figure B-42. Flow Accumulation: Model vs. USGS 05320500 Le Sueur River near Rapidan, MN


B-28

Table B-12. Summary Statistics: Model vs. USGS 05320500 Le Sueur River near Rapidan, MN HSPF Simulated Flow Observed Flow Gage

REACH OUTFLOW








Error in total volume: -6.91 10Error in 50% lowest flows: -4.58 10Error in 10% highest flows: -2.85 15Seasonal volume error - Summer: 6.04 30Seasonal volume error - Fall: -10.98 30Seasonal volume error - Winter: -0.54 30Seasonal volume error - Spring: -12.63 30Error in storm volumes: -1.44 15Error in summer storm volumes: 23.86 50Nash-Sutcliffe Coefficient of Efficiency, E: 0.606 Model accuracy increasesBaseline adjusted coefficient (Garrick), E': 0.617 as E or E' approaches 1.0

USGS 05320500 LE SUEUR RIVER NEAR RAPIDAN, MN

B-7. BLUE EARTH RIVER

0

5000

10000

15000

20000

25000


Date

Flow

(cfs

)

0

2

4

6

8

10

12

14

Dai

ly R

ainf

all (

in)


Figure B-43. Mean Daily Flow: Model vs. USGS 05320000 Blue Earth River near Rapidan, MN


B-29

0

5000

10000

15000

J-93 J-94 J-96 J-97 J-99 J-00 J-02 J-03 J-05 J-06

Month

Flow

(cfs

)

0

2

4

6

8

10

12

Mon

thly

Rai

nfal

l (in

)


Figure B-44. Mean Monthly Flow: Model vs. USGS 05320000 Blue Earth River near Rapidan, MN

y = 0.9412x + 0.6524R2 = 0.9157

0

5000

10000

15000

0 5000 10000 15000


Ave

rage

Mod

eled

Flo

w (c

fs)


0%10%20%30%40%50%60%70%80%

90%100%

J-93 J-94 J-96 J-97 J-99 J-00 J-02 J-03 J-05 J-06

Month

Wat

er B

alan

ce (O

bs +

Mod

)


Figure B-45. Monthly Flow Regression and Temporal Variation: Model vs. USGS 05320000 Blue Earth River near Rapidan, MN


B-30

y = 0.916x + 39.656R2 = 0.9733

0

1000

2000

3000

4000

5000

0 1000 2000 3000 4000 5000


Aver

age

Mod

eled

Flo

w (c

fs)



0

1000

2000

3000

4000

5000

1 2 3 4 5 6 7 8 9 10 11 12

Month

Flow

(cfs

)

0

1

2

3

4

5

6

Mon

thly

Rai

nfal

l (in

)


Figure B-46. Seasonal Regression and Temporal Aggregate: Model vs. USGS 05320000 Blue Earth River near Rapidan, MN


0

1000

2000

3000

4000

5000

6000

7000

1 2 3 4 5 6 7 8 9 10 11 12

Month

Flow

(cfs

)

0

1

2

3

4

5

6

Mon

thly

Rai

nfal

l (in

)


Figure B-47. Seasonal Medians and Ranges: Model vs. USGS 05320000 Blue Earth River near Rapidan, MN

Table B-13. Seasonal Summary: Model vs. USGS 05320000 Blue Earth River near Rapidan, MN





B-31

10

100

1000

10000

100000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Dai

ly A

vera

ge F

low

(cfs

)


Figure B-48. Flow Duration: Model vs. USGS 05320000 Blue Earth River near Rapidan, MN

0%

20%

40%

60%

80%

100%

120%


Nor

mal

ized

Flo

w V

olum

e (O

bser

ved

as 1

00%

)



Figure B-49. Flow Accumulation: Model vs. USGS 05320000 Blue Earth River near Rapidan, MN


B-32

Table B-14. Summary Statistics: Model vs. USGS 05320000 Blue Earth River near Rapidan, MN HSPF Simulated Flow Observed Flow Gage

REACH OUTFLOW








Error in total volume: -5.82 10Error in 50% lowest flows: -9.06 10Error in 10% highest flows: 1.70 15Seasonal volume error - Summer: 6.56 30Seasonal volume error - Fall: -20.46 30Seasonal volume error - Winter: -8.67 30Seasonal volume error - Spring: -7.39 30Error in storm volumes: -0.14 15Error in summer storm volumes: 18.86 50Nash-Sutcliffe Coefficient of Efficiency, E: 0.797 Model accuracy increasesBaseline adjusted coefficient (Garrick), E': 0.681 as E or E' approaches 1.0

USGS 05320000 BLUE EARTH RIVER NEAR RAPIDAN, MN

B-8. MINNESOTA RIVER AT MORTON

05000

100001500020000250003000035000400004500050000

Oct-00 Oct-01 Oct-02 Oct-03 Oct-04 Oct-05

Date

Flow

(cfs

)

0

2

4

6

8

10

12

14

Dai

ly R

ainf

all (

in)


Figure B-50. Mean Daily Flow: Model vs. USGS 05316580 Minnesota River at Morton, MN


B-33

0

10000

20000

30000

O-00 O-01 O-02 O-03 O-04 O-05

Month

Flow

(cfs

)

0

1

2

3

4

5

6

7

8

Mon

thly

Rai

nfal

l (in

)


Figure B-51. Mean Monthly Flow: Model vs. USGS 05316580 Minnesota River at Morton, MN

y = 0.9639x + 197.1R2 = 0.9848

0

10000

20000

30000

0 10000 20000 30000


Ave

rage

Mod

eled

Flo

w (c

fs)


0%

10%

20%30%

40%

50%

60%

70%

80%

90%

100%

O-00 O-01 O-02 O-03 O-04 O-05

Month

Wat

er B

alan

ce (O

bs +

Mod

)


Figure B-52. Monthly Flow Regression and Temporal Variation: Model vs. USGS 05316580 Minnesota River at Morton, MN


B-34

y = 0.9821x + 119.86R2 = 0.9926

0

2000

4000

6000

8000

10000

0 2000 4000 6000 8000 10000


Ave

rage

Mod

eled

Flo

w (c

fs)


O N D J F M A M J J A S

0

2000

4000

6000

8000

10000

10 11 12 1 2 3 4 5 6 7 8 9

Month

Flow

(cfs

)

0

1

2

3

4

5

6

Mon

thly

Rai

nfal

l (in

)


Figure B-53. Seasonal Regression and Temporal Aggregate: Model vs. USGS 05316580 Minnesota River at Morton, MN

O N D J F M A M J J A S

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10 11 12 1 2 3 4 5 6 7 8 9

Month

Flow

(cfs

)

0

1

2

3

4

5

6

Mon

thly

Rai

nfal

l (in

)


Figure B-54. Seasonal Medians and Ranges: Model vs. USGS 05316580 Minnesota River at Morton, MN

Table B-15. Seasonal Summary: Model vs. USGS 05316580 Minnesota River at Morton, MN


B-35


Oct 873.73 499.50 158.00 1277.50 1169.80 542.00 253.25 1495.00Nov 966.28 661.50 393.75 1622.50 1040.29 753.00 398.25 1507.50Dec 871.01 642.50 244.00 1160.00 908.89 691.50 348.25 1397.50Jan 670.24 469.00 245.00 745.75 639.31 395.50 332.25 741.25Feb 743.02 436.00 281.00 883.00 968.91 674.00 336.00 1290.00Mar 1756.68 1125.00 729.00 2225.00 1825.00 1295.00 980.00 1752.50Apr 7907.92 4610.00 1635.00 8295.00 7770.06 5035.00 1430.00 7962.50May 5033.59 3895.00 2467.50 5845.00 5635.74 4400.00 2915.00 6365.00Jun 4047.70 3325.00 2187.50 5675.00 4065.36 3235.00 1937.50 5697.50Jul 2208.54 1910.00 1170.00 2820.00 2111.62 1770.00 1152.50 2777.50Aug 834.80 723.50 495.50 1004.25 923.45 657.00 518.00 1100.00Sep 661.74 465.00 303.50 864.75 922.26 562.50 394.00 1250.00


10

100

1000

10000

100000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Dai

ly A

vera

ge F

low

(cfs

)



Figure B-55. Flow Duration: Model vs. USGS 05316580 Minnesota River at Morton, MN


B-36

0%

20%

40%

60%

80%

100%

120%

Oct-00 Oct-01 Oct-02 Oct-03 Oct-04 Oct-05

Norm

aliz

ed F

low

Vol

ume

(Obs

erve

d as

100

%)



Figure B-56. Flow Accumulation: Model vs. USGS 05316580 Minnesota River at Morton, MN

Table B-16. Summary Statistics: Model vs. USGS 05316580 Minnesota River at Morton, MN


REACH OUTFLOW

6-Year Analysis Period: 10/1/2000 - 9/30/2006 Hydrologic Unit Code: 7020007Flow volumes are (inches/year) for upstream drainage area Latitude: 44.5460717







Error in total volume: 5.29 10Error in 50% lowest flows: 13.64 10Error in 10% highest flows: 1.76 15Seasonal volume error - Summer: 6.62 30Seasonal volume error - Fall: 15.13 30Seasonal volume error - Winter: 7.82 30Seasonal volume error - Spring: 2.93 30Error in storm volumes: 8.48 15Error in summer storm volumes: 15.50 50Nash-Sutcliffe Coefficient of Efficiency, E: 0.921 Model accuracy increasesBaseline adjusted coefficient (Garrick), E': 0.762 as E or E' approaches 1.0

USGS 05316580 MINNESOTA RIVER AT MORTON, MN


B-37

B-9. MINNESOTA RIVER AT MANKATO

0100002000030000400005000060000700008000090000


Date

Flow

(cfs

)

0

2

4

6

8

10

12

14

Dai

ly R

ainf

all (

in)


Figure B-57. Mean Daily Flow: Model vs. USGS 05325000 Minnesota River at Mankato, MN

0

20000

40000

60000

J-93 J-94 J-96 J-97 J-99 J-00 J-02 J-03 J-05 J-06

Month

Flow

(cfs

)

0123456789

Mon

thly

Rai

nfal

l (in

)


Figure B-58. Mean Monthly Flow: Model vs. USGS 05325000 Minnesota River at Mankato, MN


B-38

Figure B-59. Monthly Flow Regression and Temporal Variation: Model vs. USGS 05325000 Minnesota River at Mankato, MN

y = 0.9545x + 267.62R2 = 0.992

0

5000

10000

15000

20000

0 5000 10000 15000 20000


Aver

age

Mod

eled

Flo

w (c

fs)



0

5000

10000

15000

20000

1 2 3 4 5 6 7 8 9 10 11 12

Month

Flow

(cfs

)

0

1

2

3

4

5

6M

onth

ly R

ainf

all (

in)


Figure B-60. Seasonal Regression and Temporal Aggregate: Model vs. USGS 05325000 Minnesota River at Mankato, MN

y = 0.9466x + 320.05R2 = 0.9566

0

20000

40000

60000

0 20000 40000 60000


Ave

rage

Mod

eled

Flo

w (c

fs)


0%

10%

20%30%

40%

50%

60%

70%

80%

90%

100%

J-93 J-94 J-96 J-97 J-99 J-00 J-02 J-03 J-05 J-06

Month

Wat

er B

alan

ce (O

bs +

Mod

)



B-39


0

5000

10000

15000

20000

25000

30000

1 2 3 4 5 6 7 8 9 10 11 12

Month

Flow

(cfs

)

0

1

2

3

4

5

6

Mon

thly

Rai

nfal

l (in

)


Figure B-61. Seasonal Medians and Ranges: Model vs. USGS 05325000 Minnesota River at Mankato, MN

Table B-17. Seasonal Summary: Model vs. USGS 05325000 Minnesota River at Mankato, MN





B-40

0.1

1

10

100

1000

10000

100000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Dai

ly A

vera

ge F

low

(cfs


Figure B-62. Flow Duration: Model vs. USGS 05325000 Minnesota River at Mankato, MN

0%

20%

40%

60%

80%

100%

120%


Norm

aliz

ed F

low

Vol

ume

(Obs

erve

d as

100

%)



Figure B-63. Flow Accumulation: Model vs. USGS 05325000 Minnesota River at Mankato, MN


B-41

Table B-18. Summary Statistics: Model vs. USGS 05325000 Minnesota River at Mankato, MN


REACH OUTFLOW








Error in total volume: -0.36 10Error in 50% lowest flows: -1.92 10Error in 10% highest flows: 0.86 15Seasonal volume error - Summer: 6.95 30Seasonal volume error - Fall: -4.72 30Seasonal volume error - Winter: 0.49 30Seasonal volume error - Spring: -2.47 30Error in storm volumes: 4.98 15Error in summer storm volumes: 20.66 50Nash-Sutcliffe Coefficient of Efficiency, E: 0.895 Model accuracy increasesBaseline adjusted coefficient (Garrick), E': 0.766 as E or E' approaches 1.0

USGS 05325000 MINNESOTA RIVER AT MANKATO, MN

B-10. MINNESOTA RIVER AT JORDAN

0100002000030000400005000060000700008000090000

100000


Date

Flow

(cfs

)

0

2

4

6

8

10

12

14

Dai

ly R

ainf

all (

in)


Figure B-64. Mean Daily Flow: Model vs. USGS 05330000 Minnesota River near Jordan, MN


B-42

0

20000

40000

60000

80000

J-93 J-94 J-96 J-97 J-99 J-00 J-02 J-03 J-05 J-06

Month

Flow

(cfs

)

0

2

4

6

8

10

12

Mon

thly

Rai

nfal

l (in

)


Figure B-65. Mean Monthly Flow: Model vs. USGS 05330000 Minnesota River near Jordan, MN

y = 0.9364x + 339.48R2 = 0.9558

0

20000

40000

60000

80000

0 20000 40000 60000 80000


Ave

rage

Mod

eled

Flo

w (c

fs)


0%

10%

20%30%

40%

50%

60%

70%

80%

90%

100%

J-93 J-94 J-96 J-97 J-99 J-00 J-02 J-03 J-05 J-06

Month

Wat

er B

alan

ce (O

bs +

Mod

)


Figure B-66. Monthly Flow Regression and Temporal Variation: Model vs. USGS 05330000 Minnesota River near Jordan, MN


B-43

y = 0.9517x + 225.51R2 = 0.9913

0

5000

10000

15000

20000

25000

0 5000 10000 15000 20000 25000


Aver

age

Mod

eled

Flo

w (c

fs)



0

5000

10000

15000

20000

25000

1 2 3 4 5 6 7 8 9 10 11 12

Month

Flow

(cfs

)

0

1

2

3

4

5

6

Mon

thly

Rai

nfal

l (in

)


Figure B-67. Seasonal Regression and Temporal Aggregate: Model vs. USGS 05330000 Minnesota River near Jordan, MN


0

5000

10000

15000

20000

25000

30000

1 2 3 4 5 6 7 8 9 10 11 12

Month

Flow

(cfs

)

0

1

2

3

4

5

6

Mon

thly

Rai

nfal

l (in

)


Figure B-68. Seasonal Medians and Ranges: Model vs. USGS 05330000 Minnesota River near Jordan, MN

Table B-19. Seasonal Summary: Model vs. USGS 05330000 Minnesota River near Jordan, MN





B-44

100

1000

10000

100000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Dai

ly A

vera

ge F

low

(cfs

)


Figure B-69. Flow Duration: Model vs. USGS 05330000 Minnesota River near Jordan, MN

0%

20%

40%

60%

80%

100%

120%


Norm

aliz

ed F

low

Vol

ume

(Obs

erve

d as

100

%)



Figure B-70. Flow Accumulation: Model vs. USGS 05330000 Minnesota River near Jordan, MN


B-45

Table B-20. Summary Statistics: Model vs. USGS 05330000 Minnesota River near Jordan, MN


REACH OUTFLOW








Error in total volume: -1.68 10Error in 50% lowest flows: -9.62 10Error in 10% highest flows: 0.03 15Seasonal volume error - Summer: 4.59 30Seasonal volume error - Fall: -7.13 30Seasonal volume error - Winter: -0.43 30Seasonal volume error - Spring: -3.36 30Error in storm volumes: 7.42 15Error in summer storm volumes: 21.98 50Nash-Sutcliffe Coefficient of Efficiency, E: 0.905 Model accuracy increasesBaseline adjusted coefficient (Garrick), E': 0.755 as E or E' approaches 1.0

USGS 05330000 MINNESOTA RIVER NEAR JORDAN, MN


B-46

(This page intentionally left blank)


C-1

Appendix C. Hydrologic Validation Results

C-1. CHIPPEWA RIVER

05000

10000150002000025000300003500040000

Jan-86 Jan-87 Jan-88 Jan-89 Jan-90 Jan-91 Jan-92

Date

Flow

(cfs

)

0

2

4

6

8

10

12

14

Dai

ly R

ainf

all (

in)


Figure C-1. Mean Daily Flow: Model vs. USGS 05304500 Chippewa River near Milan, MN

0

1000

2000

3000

4000

J-86 J-87 J-88 J-89 J-90 J-91 J-92

Month

Flow

(cfs

)

0123456789

Mon

thly

Rai

nfal

l (in

)


Figure C-2. Mean Monthly Flow: Model vs. USGS 05304500 Chippewa River near Milan, MN


C-2

y = 0.9816x + 36.601R2 = 0.742

0

1000

2000

3000

4000

0 1000 2000 3000 4000


Ave

rage

Mod

eled

Flo

w (c

fs)


0%

10%

20%30%

40%

50%

60%

70%

80%

90%

100%

J-86 J-87 J-88 J-89 J-90 J-91 J-92

Month

Wat

er B

alan

ce (O

bs +

Mod

)


Figure C-3. Monthly Flow Regression and Temporal Variation: Model vs. USGS 05304500 Chippewa River near Milan, MN

y = 1.2553x - 84.857R2 = 0.9361

0

500

1000

1500

0 500 1000 1500


Ave

rage

Mod

eled

Flo

w (c

fs)



0

500

1000

1500

1 2 3 4 5 6 7 8 9 10 11 12

Month

Flow

(cfs

)

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Mon

thly

Rai

nfal

l (in

)


Figure C-4. Seasonal Regression and Temporal Aggregate: Model vs. USGS 05304500 Chippewa River near Milan, MN


C-3


0

200

400

600

800

1000

1200

1400

1600

1 2 3 4 5 6 7 8 9 10 11 12

Month

Flow

(cfs

)

0

1

1

2

2

3

3

4

4

5

Mon

thly

Rai

nfal

l (in

)


Figure C-5. Seasonal Medians and Ranges: Model vs. USGS 05304500 Chippewa River near Milan, MN

Table C-1. Seasonal Summary: Model vs. USGS 05304500 Chippewa River near Milan, MN





C-4

1

10

100

1000

10000

100000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Dai

ly A

vera

ge F

low

(cfs

)Observed Flow Duration (1/1/1986 to 12/31/1992 )


Figure C-6. Flow Duration: Model vs. USGS 05304500 Chippewa River near Milan, MN

0%

20%

40%

60%

80%

100%

120%


Nor

mal

ized

Flo

w V

olum

e (O

bser

ved

as 1

00%

)



Figure C-7. Flow Accumulation: Model vs. USGS 05304500 Chippewa River near Milan, MN


C-5

Table C-2. Summary Statistics: Model vs. USGS 05304500 Chippewa River near Milan, MN HSPF Simulated Flow Observed Flow Gage

REACH OUTFLOW








Error in total volume: 6.39 10Error in 50% lowest flows: 15.28 10Error in 10% highest flows: 9.08 15Seasonal volume error - Summer: 0.36 30Seasonal volume error - Fall: -23.81 30Seasonal volume error - Winter: 8.19 30Seasonal volume error - Spring: 19.41 30Error in storm volumes: 4.54 15Error in summer storm volumes: 29.84 50Nash-Sutcliffe Coefficient of Efficiency, E: -0.277 Model accuracy increasesBaseline adjusted coefficient (Garrick), E': 0.476 as E or E' approaches 1.0

USGS 05304500 CHIPPEWA RIVER NEAR MILAN, MN


C-6

C-2. YELLOW MEDICINE RIVER

0500

100015002000250030003500400045005000


Date

Flow

(cfs

)

0

2

4

6

8

10

12

14

Dai

ly R

ainf

all (

in)


Figure C-8. Mean Daily Flow: Model vs. USGS 05313500 Yellow Medicine River near Granite Falls, MN

0

500

1000

1500

2000

2500

J-86 J-87 J-88 J-89 J-90 J-91 J-92

Month

Flow

(cfs

)

0

2

4

6

8

10

12

Mon

thly

Rai

nfal

l (in

)


Figure C-9. Mean Monthly Flow: Model vs. USGS 05313500 Yellow Medicine River near Granite Falls, MN


C-7

y = 1.0048x - 11.341R2 = 0.8582

0

500

1000

1500

2000

2500

0 500 1000 1500 2000 2500


Ave

rage

Mod

eled

Flo

w (c

fs)


0%

10%

20%30%

40%

50%

60%

70%

80%

90%

100%

J-86 J-87 J-88 J-89 J-90 J-91 J-92

Month

Wat

er B

alan

ce (O

bs +

Mod

)


Figure C-10. Monthly Flow Regression and Temporal Variation: Model vs. USGS 05313500 Yellow Medicine River near Granite Falls, MN

y = 0.9481x - 2.3042R2 = 0.9228

0

100

200

300

400

500

0 100 200 300 400 500


Ave

rage

Mod

eled

Flo

w (c

fs)



0

100

200

300

400

500

1 2 3 4 5 6 7 8 9 10 11 12

Month

Flow

(cfs

)

0

0.51

1.52

2.53

3.54

4.5

5M

onth

ly R

ainf

all (

in)


Figure C-11. Seasonal Regression and Temporal Aggregate: Model vs. USGS 05313500 Yellow Medicine River near Granite Falls, MN


C-8


0

100

200

300

400

500

600

1 2 3 4 5 6 7 8 9 10 11 12

Month

Flow

(cfs

)

0

1

1

2

2

3

34

4

5

5

Mon

thly

Rai

nfal

l (in

)


Figure C-12. Seasonal Medians and Ranges: Model vs. USGS 05313500 Yellow Medicine River near Granite Falls, MN

Table C-3. Seasonal Summary: Model vs. USGS 05313500 Yellow Medicine River near Granite Falls, MN





C-9

0.1

1

10

100

1000

10000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Dai

ly A

vera

ge F

low

(cfs


Figure C-13. Flow Duration: Model vs. USGS 05313500 Yellow Medicine River near Granite Falls, MN

0%

20%

40%

60%

80%

100%

120%


Norm

aliz

ed F

low

Vol

ume

(Obs

erve

d as

100

%)



Figure C-14. Flow Accumulation: Model vs. USGS 05313500 Yellow Medicine River near Granite Falls, MN


C-10

Table C-4. Summary Statistics: Model vs. USGS 05313500 Yellow Medicine River near Granite Falls, MN


REACH OUTFLOW








Error in total volume: -6.73 10Error in 50% lowest flows: 6.54 10Error in 10% highest flows: 1.67 15Seasonal volume error - Summer: -19.65 30Seasonal volume error - Fall: -11.08 30Seasonal volume error - Winter: -16.42 30Seasonal volume error - Spring: 4.71 30Error in storm volumes: -11.36 15Error in summer storm volumes: -18.31 50Nash-Sutcliffe Coefficient of Efficiency, E: 0.647 Model accuracy increasesBaseline adjusted coefficient (Garrick), E': 0.561 as E or E' approaches 1.0

USGS 05313500 YELLOW MEDICINE RIVER NEAR GRANITE FALLS, MN

C-3. REDWOOD RIVER

0

2000

4000

6000

8000

10000

12000

14000


Date

Flow

(cfs

)

0

2

4

6

8

10

12

14

Dai

ly R

ainf

all (

in)


Figure C-15. Mean Daily Flow: Model vs. USGS 05316500 Redwood River near Redwood Falls, MN


C-11

0

1000

2000

3000

4000

J-86 J-87 J-88 J-89 J-90 J-91 J-92 J-93

Month

Flow

(cfs

)

0

2

4

6

8

10

12

Mon

thly

Rai

nfal

l (in

)


Figure C-16. Mean Monthly Flow: Model vs. USGS 05316500 Redwood River near Redwood Falls, MN

y = 0.997x + 7.1296R2 = 0.8492

0

1000

2000

3000

4000

0 1000 2000 3000 4000


Ave

rage

Mod

eled

Flo

w (c

fs)


0%

10%

20%30%

40%

50%

60%

70%

80%

90%

100%

J-86 J-87 J-88 J-89 J-90 J-91 J-92 J-93

Month

Wat

er B

alan

ce (O

bs +

Mod

)


Figure C-17. Monthly Flow Regression and Temporal Variation: Model vs. USGS 05316500 Redwood River near Redwood Falls, MN


C-12

y = 1.1199x - 26.35R2 = 0.8717

0

200

400

600

800

1000

0 200 400 600 800 1000


Ave

rage

Mod

eled

Flo

w (c

fs)



0

200

400

600

800

1000

1 2 3 4 5 6 7 8 9 10 11 12

Month

Flow

(cfs

)

0

0.51

1.52

2.53

3.54

4.5

5

Mon

thly

Rai

nfal

l (in

)


Figure C-18. Seasonal Regression and Temporal Aggregate: Model vs. USGS 05316500 Redwood River near Redwood Falls, MN


0

100

200

300

400

500

600

700

800

900

1 2 3 4 5 6 7 8 9 10 11 12

Month

Flow

(cfs

)

0

1

1

2

2

3

3

4

4

5

5

Mon

thly

Rai

nfal

l (in

)


Figure C-19. Seasonal Medians and Ranges: Model vs. USGS 05316500 Redwood River near Redwood Falls, MN

Table C-5. Seasonal Summary: Model vs. USGS 05316500 Redwood River near Redwood Falls, MN





C-13

1

10

100

1000

10000

100000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Dai

ly A

vera

ge F

low

(cfs

)


Figure C-20. Flow Duration: Model vs. USGS 05316500 Redwood River near Redwood Falls, MN

0%

20%

40%

60%

80%

100%

120%


Nor

mal

ized

Flo

w V

olum

e (O

bser

ved

as 1

00%

)



Figure C-21. Flow Accumulation: Model vs. USGS 05316500 Redwood River near Redwood Falls, MN


C-14

Table C-6. Summary Statistics: Model vs. USGS 05316500 Redwood River near Redwood Falls, MN


REACH OUTFLOW








Error in total volume: 2.18 10Error in 50% lowest flows: 4.62 10Error in 10% highest flows: 6.25 15Seasonal volume error - Summer: -21.24 30Seasonal volume error - Fall: -25.87 30Seasonal volume error - Winter: -4.11 30Seasonal volume error - Spring: 21.28 30Error in storm volumes: -0.42 15Error in summer storm volumes: -11.10 50Nash-Sutcliffe Coefficient of Efficiency, E: 0.736 Model accuracy increasesBaseline adjusted coefficient (Garrick), E': 0.583 as E or E' approaches 1.0

USGS 05316500 REDWOOD RIVER NEAR REDWOOD FALLS, MN

C-4. COTTONWOOD RIVER

0

5000

10000

15000

20000

25000


Date

Flow

(cfs

)

0

2

4

6

8

10

12

14

Dai

ly R

ainf

all (

in)


Figure C-22. Mean Daily Flow: Model vs. USGS 05317000 Cottonwood River near New Ulm, MN


C-15

0

2000

4000

6000

8000

J-86 J-87 J-88 J-89 J-90 J-91 J-92 J-93

Month

Flow

(cfs

)

012345678910

Mon

thly

Rai

nfal

l (in

)


Figure C-23. Mean Monthly Flow: Model vs. USGS 05317000 Cottonwood River near New Ulm, MN

y = 0.8536x + 43.696R2 = 0.8509

0

1000

2000

3000

4000

0 1000 2000 3000 4000


Ave

rage

Mod

eled

Flo

w (c

fs)


0%

10%

20%30%

40%

50%

60%

70%

80%

90%

100%

J-86 J-87 J-88 J-89 J-90 J-91 J-92

Month

Wat

er B

alan

ce (O

bs +

Mod

)


Figure C-24. Monthly Flow Regression and Temporal Variation: Model vs. USGS 05317000 Cottonwood River near New Ulm, MN


C-16

y = 1.0058x - 24.218R2 = 0.8898

0

200

400

600

800

1000

0 200 400 600 800 1000


Ave

rage

Mod

eled

Flo

w (c

fs)



0

200

400

600

800

1000

1 2 3 4 5 6 7 8 9 10 11 12

Month

Flow

(cfs

)

0

0.51

1.52

2.53

3.54

4.5

5

Mon

thly

Rai

nfal

l (in

)


Figure C-25. Seasonal Regression and Temporal Aggregate: Model vs. USGS 05317000 Cottonwood River near New Ulm, MN


0

200

400

600

800

1000

1200

1400

1600

1800

2000

1 2 3 4 5 6 7 8 9 10 11 12

Month

Flow

(cfs

)

0

1

2

3

4

5

6

Mon

thly

Rai

nfal

l (in

)


Figure C-26. Seasonal Medians and Ranges: Model vs. USGS 05317000 Cottonwood River near New Ulm, MN

Table C-7. Seasonal Summary: Model vs. USGS 05317000 Cottonwood River near New Ulm, MN





C-17

1

10

100

1000

10000

100000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Dai

ly A

vera

ge F

low

(cfs


Figure C-27. Flow Duration: Model vs. USGS 05317000 Cottonwood River near New Ulm, MN

0%

20%

40%

60%

80%

100%

120%


Nor

mal

ized

Flo

w V

olum

e (O

bser

ved

as 1

00%

)



Figure C-28. Flow Accumulation: Model vs. USGS 05317000 Cottonwood River near New Ulm, MN


C-18

Table C-8. Summary Statistics: Model vs. USGS 05317000 Cottonwood River near New Ulm, MN HSPF Simulated Flow Observed Flow Gage

REACH OUTFLOW








Error in total volume: -4.84 10Error in 50% lowest flows: 5.49 10Error in 10% highest flows: -5.91 15Seasonal volume error - Summer: -12.01 30Seasonal volume error - Fall: -19.06 30Seasonal volume error - Winter: -17.23 30Seasonal volume error - Spring: 9.75 30Error in storm volumes: -22.32 15Error in summer storm volumes: -25.92 50Nash-Sutcliffe Coefficient of Efficiency, E: 0.729 Model accuracy increasesBaseline adjusted coefficient (Garrick), E': 0.544 as E or E' approaches 1.0

USGS 05317000 COTTONWOOD RIVER NEAR NEW ULM, MN

C-5. WATONWAN RIVER

02000400060008000

10000120001400016000


Date

Flow

(cfs

)

0

2

4

6

8

10

12

14

Dai

ly R

ainf

all (

in)


Figure C-29. Mean Daily Flow: Model vs. USGS 05319500 Watonwan River near Garden City, MN


C-19

0

1000

2000

3000

4000

5000

J-86 J-87 J-88 J-89 J-90 J-91 J-92 J-93

Month

Flow

(cfs

)

0

2

4

6

8

10

12

Mon

thly

Rai

nfal

l (in

)


Figure C-30. Mean Monthly Flow: Model vs. USGS 05319500 Watonwan River near Garden City, MN

y = 0.8594x + 45.022R2 = 0.8856

0

1000

2000

3000

4000

5000

0 1000 2000 3000 4000 5000


Ave

rage

Mod

eled

Flo

w (c

fs)


0%

10%

20%30%

40%

50%

60%

70%

80%

90%

100%

J-86 J-87 J-88 J-89 J-90 J-91 J-92 J-93

Month

Wat

er B

alan

ce (O

bs +

Mod

)


Figure C-31. Monthly Flow Regression and Temporal Variation: Model vs. USGS 05319500 Watonwan River near Garden City, MN


C-20

y = 0.9625x - 3.215R2 = 0.9241

0

500

1000

1500

0 500 1000 1500


Ave

rage

Mod

eled

Flo

w (c

fs)



0

500

1000

1500

1 2 3 4 5 6 7 8 9 10 11 12

Month

Flow

(cfs

)

0

1

2

3

4

5

6

Mon

thly

Rai

nfal

l (in

)


Figure C-32. Seasonal Regression and Temporal Aggregate: Model vs. USGS 05319500 Watonwan River near Garden City, MN


0

200

400

600

800

1000

1200

1400

1600

1 2 3 4 5 6 7 8 9 10 11 12

Month

Flow

(cfs

)

0

1

2

3

4

5

6

Mon

thly

Rai

nfal

l (in

)


Figure C-33. Seasonal Medians and Ranges: Model vs. USGS 05319500 Watonwan River near Garden City, MN

Table C-9. Seasonal Summary: Model vs. USGS 05319500 Watonwan River near Garden City, MN





C-21

1

10

100

1000

10000

100000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Dai

ly A

vera

ge F

low

(cfs

)


Figure C-34. Flow Duration: Model vs. USGS 05319500 Watonwan River near Garden City, MN

0%

20%

40%

60%

80%

100%

120%


Nor

mal

ized

Flo

w V

olum

e (O

bser

ved

as 1

00%

)



Figure C-35. Flow Accumulation: Model vs. USGS 05319500 Watonwan River near Garden City, MN


C-22

Table C-10. Summary Statistics: Model vs. USGS 05319500 Watonwan River near Garden City, MN


REACH OUTFLOW


Longitude: -94.1955144PET 0.75 Drainage Area (sq-mi): 851






Error in total volume: -4.38 10Error in 50% lowest flows: 7.23 10Error in 10% highest flows: -8.81 15Seasonal volume error - Summer: 6.55 30Seasonal volume error - Fall: -28.79 30Seasonal volume error - Winter: -17.96 30Seasonal volume error - Spring: 0.98 30Error in storm volumes: -11.79 15Error in summer storm volumes: -10.02 50Nash-Sutcliffe Coefficient of Efficiency, E: 0.807 Model accuracy increasesBaseline adjusted coefficient (Garrick), E': 0.647 as E or E' approaches 1.0

USGS 05319500 WATONWAN RIVER NEAR GARDEN CITY, MN

C-6. LE SUEUR RIVER

02000400060008000

100001200014000160001800020000


Date

Flow

(cfs

)

0

2

4

6

8

10

12

14

Dai

ly R

ainf

all (

in)


Figure C-36. Mean Daily Flow: Model vs. USGS 05320500 Le Sueur River near Rapidan, MN


C-23

0

2000

4000

6000

J-86 J-87 J-88 J-89 J-90 J-91 J-92 J-93

Month

Flow

(cfs

)

012345678910

Mon

thly

Rai

nfal

l (in

)


Figure C-37. Mean Monthly Flow: Model vs. USGS 05320500 Le Sueur River near Rapidan, MN

y = 0.9092x + 31.404R2 = 0.8993

0

2000

4000

6000

0 2000 4000 6000


Ave

rage

Mod

eled

Flo

w (c

fs)


0%

10%

20%30%

40%

50%

60%

70%

80%

90%

100%

J-86 J-87 J-88 J-89 J-90 J-91 J-92 J-93

Month

Wat

er B

alan

ce (O

bs +

Mod

)


Figure C-38. Monthly Flow Regression and Temporal Variation: Model vs. USGS 05320500 Le Sueur River near Rapidan, MN


C-24

y = 0.9573x - 2.9739R2 = 0.9463

0

500

1000

1500

2000

0 500 1000 1500 2000


Ave

rage

Mod

eled

Flo

w (c

fs)



0

500

1000

1500

2000

1 2 3 4 5 6 7 8 9 10 11 12

Month

Flow

(cfs

)

0

1

2

3

4

5

6

Mon

thly

Rai

nfal

l (in

)


Figure C-39. Seasonal Regression and Temporal Aggregate: Model vs. USGS 05320500 Le Sueur River near Rapidan, MN


0

500

1000

1500

2000

2500

1 2 3 4 5 6 7 8 9 10 11 12

Month

Flow

(cfs

)

0

1

2

3

4

5

6

Mon

thly

Rai

nfal

l (in

)


Figure C-40. Seasonal Medians and Ranges: Model vs. USGS 05320500 Le Sueur River near Rapidan, MN

Table C-11. Seasonal Summary: Model vs. USGS 05320500 Le Sueur River near Rapidan, MN





C-25

1

10

100

1000

10000

100000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Dai

ly A

vera

ge F

low

(cfs


Figure C-41. Flow Duration: Model vs. USGS 05320500 Le Sueur River near Rapidan, MN

0%

20%

40%

60%

80%

100%

120%


Nor

mal

ized

Flo

w V

olum

e (O

bser

ved

as 1

00%

)



Figure C-42. Flow Accumulation: Model vs. USGS 05320500 Le Sueur River near Rapidan, MN


C-26

Table C-12. Summary Statistics: Model vs. USGS 05320500 Le Sueur River near Rapidan, MN HSPF Simulated Flow Observed Flow Gage

REACH OUTFLOW








Error in total volume: -4.74 10Error in 50% lowest flows: 16.70 10Error in 10% highest flows: -3.34 15Seasonal volume error - Summer: -1.68 30Seasonal volume error - Fall: -18.66 30Seasonal volume error - Winter: -6.17 30Seasonal volume error - Spring: -2.28 30Error in storm volumes: -0.41 15Error in summer storm volumes: 15.76 50Nash-Sutcliffe Coefficient of Efficiency, E: 0.616 Model accuracy increasesBaseline adjusted coefficient (Garrick), E': 0.586 as E or E' approaches 1.0

USGS 05320500 LE SUEUR RIVER NEAR RAPIDAN, MN

C-7. BLUE EARTH RIVER

02000400060008000

1000012000140001600018000


Date

Flow

(cfs

)

0

2

4

6

8

10

12

14

Dai

ly R

ainf

all (

in)


Figure C-43. Mean Daily Flow: Model vs. USGS 05320000 Blue Earth River near Rapidan, MN


C-27

0

2000

4000

6000

8000

J-86 J-87 J-88 J-89 J-90 J-91 J-92

Month

Flow

(cfs

)

0123456789

Mon

thly

Rai

nfal

l (in

)


Figure C-44. Mean Monthly Flow: Model vs. USGS 05320000 Blue Earth River near Rapidan, MN

y = 0.8029x + 144.22R2 = 0.8038

0

2000

4000

6000

8000

0 2000 4000 6000 8000


Ave

rage

Mod

eled

Flo

w (c

fs)


0%

10%

20%30%

40%

50%

60%

70%

80%

90%

100%

J-86 J-87 J-88 J-89 J-90 J-91 J-92

Month

Wat

er B

alan

ce (O

bs +

Mod

)


Figure C-45. Monthly Flow Regression and Temporal Variation: Model vs. USGS 05320000 Blue Earth River near Rapidan, MN


C-28

y = 0.9459x - 15.084R2 = 0.8987

0

500

1000

1500

2000

2500

0 500 1000 1500 2000 2500


Aver

age

Mod

eled

Flo

w (c

fs)



0

500

1000

1500

2000

2500

1 2 3 4 5 6 7 8 9 10 11 12

Month

Flow

(cfs

)

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Mon

thly

Rai

nfal

l (in

)


Figure C-46. Seasonal Regression and Temporal Aggregate: Model vs. USGS 05320000 Blue Earth River near Rapidan, MN


0

500

1000

1500

2000

2500

3000

3500

4000

1 2 3 4 5 6 7 8 9 10 11 12

Month

Flow

(cfs

)

0

1

1

2

2

3

3

4

4

5

Mon

thly

Rai

nfal

l (in

)


Figure C-47. Seasonal Medians and Ranges: Model vs. USGS 05320000 Blue Earth River near Rapidan, MN

Table C-13. Seasonal Summary: Model vs. USGS 05320000 Blue Earth River near Rapidan, MN





C-29

1

10

100

1000

10000

100000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Dai

ly A

vera

ge F

low

(cfs


Figure C-48. Flow Duration: Model vs. USGS 05320000 Blue Earth River near Rapidan, MN

0%

20%

40%

60%

80%

100%

120%


Nor

mal

ized

Flo

w V

olum

e (O

bser

ved

as 1

00%

)



Figure C-49. Flow Accumulation: Model vs. USGS 05320000 Blue Earth River near Rapidan, MN


C-30

Table C-14. Summary Statistics: Model vs. USGS 05320000 Blue Earth River near Rapidan, MN HSPF Simulated Flow Observed Flow Gage

REACH OUTFLOW








Error in total volume: -6.78 10Error in 50% lowest flows: 10.26 10Error in 10% highest flows: -6.09 15Seasonal volume error - Summer: -4.88 30Seasonal volume error - Fall: -25.67 30Seasonal volume error - Winter: -13.91 30Seasonal volume error - Spring: 1.71 30Error in storm volumes: -7.65 15Error in summer storm volumes: -12.30 50Nash-Sutcliffe Coefficient of Efficiency, E: 0.711 Model accuracy increasesBaseline adjusted coefficient (Garrick), E': 0.584 as E or E' approaches 1.0

USGS 05320000 BLUE EARTH RIVER NEAR RAPIDAN, MN

C-8. MINNESOTA RIVER AT MORTON No gage data are available at this station for the validation period.

C-9. MINNESOTA RIVER AT MANKATO

05000

10000150002000025000300003500040000


Date

Flow

(cfs

)

0

2

4

6

8

10

12

14

Dai

ly R

ainf

all (

in)


Figure C-50. Mean Daily Flow: Model vs. USGS 05325000 Minnesota River at Mankato, MN


C-31

0

10000

20000

30000

J-86 J-87 J-88 J-89 J-90 J-91 J-92

Month

Flow

(cfs

)

012345678910

Mon

thly

Rai

nfal

l (in

)


Figure C-51. Mean Monthly Flow: Model vs. USGS 05325000 Minnesota River at Mankato, MN

y = 0.9349x + 279.45R2 = 0.9022

0

10000

20000

30000

0 10000 20000 30000


Ave

rage

Mod

eled

Flo

w (c

fs)


0%

10%

20%30%

40%

50%

60%

70%

80%

90%

100%

J-86 J-87 J-88 J-89 J-90 J-91 J-92

Month

Wat

er B

alan

ce (O

bs +

Mod

)


Figure C-52. Monthly Flow Regression and Temporal Variation: Model vs. USGS 05325000 Minnesota River at Mankato, MN


C-32

y = 1.0559x - 253.48R2 = 0.9513

0

2000

4000

6000

8000

10000

0 2000 4000 6000 8000 10000


Ave

rage

Mod

eled

Flo

w (c

fs)



0

2000

4000

6000

8000

10000

1 2 3 4 5 6 7 8 9 10 11 12

Month

Flow

(cfs

)

0

1

2

3

4

5

6

Mon

thly

Rai

nfal

l (in

)


Figure C-53. Seasonal Regression and Temporal Aggregate: Model vs. USGS 05325000 Minnesota River at Mankato, MN


0

2000

4000

6000

8000

10000

12000

14000

16000

1 2 3 4 5 6 7 8 9 10 11 12

Month

Flow

(cfs

)

0

1

2

3

4

5

6

Mon

thly

Rai

nfal

l (in

)


Figure C-54. Seasonal Medians and Ranges: Model vs. USGS 05325000 Minnesota River at Mankato, MN

Table C-15. Seasonal Summary: Model vs. USGS 05325000 Minnesota River at Mankato, MN





C-33

10

100

1000

10000

100000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Dai

ly A

vera

ge F

low

(cfs

)



Figure C-55. Flow Duration: Model vs. USGS 05325000 Minnesota River at Mankato, MN

0%

20%

40%

60%

80%

100%

120%


Norm

aliz

ed F

low

Vol

ume

(Obs

erve

d as

100

%)



Figure C-56. Flow Accumulation: Model vs. USGS 05325000 Minnesota River at Mankato, MN


C-34

Table C-16. Summary Statistics: Model vs. USGS 05325000 Minnesota River at Mankato, MN


REACH OUTFLOW








Error in total volume: -0.21 10Error in 50% lowest flows: 11.16 10Error in 10% highest flows: 0.53 15Seasonal volume error - Summer: -1.58 30Seasonal volume error - Fall: -17.41 30Seasonal volume error - Winter: -7.66 30Seasonal volume error - Spring: 8.66 30Error in storm volumes: 4.66 15Error in summer storm volumes: 0.62 50Nash-Sutcliffe Coefficient of Efficiency, E: 0.856 Model accuracy increasesBaseline adjusted coefficient (Garrick), E': 0.691 as E or E' approaches 1.0

USGS 05325000 MINNESOTA RIVER AT MANKATO, MN

C-10. MINNESOTA RIVER AT JORDAN

05000

10000150002000025000300003500040000


Date

Flow

(cfs

)

0

2

4

6

8

10

12

14

Dai

ly R

ainf

all (

in)


Figure C-57. Mean Daily Flow: Model vs. USGS 05330000 Minnesota River near Jordan, MN


C-35

0

10000

20000

30000

40000

J-86 J-87 J-88 J-89 J-90 J-91 J-92

Month

Flow

(cfs

)

0123456789

Mon

thly

Rai

nfal

l (in

)


Figure C-58. Mean Monthly Flow: Model vs. USGS 05330000 Minnesota River near Jordan, MN

y = 0.9109x + 383.99R2 = 0.8765

0

10000

20000

30000

40000

0 10000 20000 30000 40000


Ave

rage

Mod

eled

Flo

w (c

fs)


0%

10%

20%30%

40%

50%

60%

70%

80%

90%

100%

J-86 J-87 J-88 J-89 J-90 J-91 J-92

Month

Wat

er B

alan

ce (O

bs +

Mod

)


Figure C-59. Monthly Flow Regression and Temporal Variation: Model vs. USGS 05330000 Minnesota River near Jordan, MN


C-36

y = 1.0571x - 344.69R2 = 0.9419

0

5000

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0 5000 10000 15000


Ave

rage

Mod

eled

Flo

w (c

fs)



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Month

Flow

(cfs

)

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Mon

thly

Rai

nfal

l (in

)


Figure C-60. Seasonal Regression and Temporal Aggregate: Model vs. USGS 05330000 Minnesota River near Jordan, MN


0

2000

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10000

12000

14000

16000

18000

1 2 3 4 5 6 7 8 9 10 11 12

Month

Flow

(cfs

)

0

1

1

2

2

3

3

4

4

5

Mon

thly

Rai

nfal

l (in

)


Figure C-61. Seasonal Medians and Ranges: Model vs. USGS 05330000 Minnesota River near Jordan, MN

Table C-17. Seasonal Summary: Model vs. USGS 05330000 Minnesota River near Jordan, MN





C-37

100

1000

10000

100000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Dai

ly A

vera

ge F

low

(cfs

)



Figure C-62. Flow Duration: Model vs. USGS 05330000 Minnesota River near Jordan, MN

0%

20%

40%

60%

80%

100%

120%


Norm

aliz

ed F

low

Vol

ume

(Obs

erve

d as

100

%)



Figure C-63. Flow Accumulation: Model vs. USGS 05330000 Minnesota River near Jordan, MN


C-38

Table C-18. Summary Statistics: Model vs. USGS 05330000 Minnesota River near Jordan, MN


REACH OUTFLOW








Error in total volume: -1.24 10Error in 50% lowest flows: 3.85 10Error in 10% highest flows: 0.48 15Seasonal volume error - Summer: -1.51 30Seasonal volume error - Fall: -20.33 30Seasonal volume error - Winter: -9.95 30Seasonal volume error - Spring: 8.54 30Error in storm volumes: 8.15 15Error in summer storm volumes: 2.99 50Nash-Sutcliffe Coefficient of Efficiency, E: 0.836 Model accuracy increasesBaseline adjusted coefficient (Garrick), E': 0.681 as E or E' approaches 1.0

USGS 05330000 MINNESOTA RIVER NEAR JORDAN, MN


D-1

Appendix D. Calibration and Validation for Total Suspended Sediment

(Appendix D is provided in a separate file.)


D-2


E-1

Appendix E. Calibration and Validation for Total Phosphorus

(Appendix E is provided in a separate file.)


E-2


F-1

Appendix F. Calibration and Validation for Total Nitrogen (Appendix F is provided in a separate file.)


F-2

minnesota river basin turbidity tmdl and lake pepin ... · table 3-15. water quality monitoring...

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