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Master’s Degree Thesis SeminarAgricultural and Biological Engineering
Sarah RutkowskiMay 11th, 2012
Role of Climate Variability on Subsurface Drainage and Streamflow Patterns in
Agricultural Watersheds
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Ale and Bowling (2010)
Tile Drainage
• Subsurface (tile) drains lower high water table levels in poorly drained soils.
•Large portions of drained agricultural land in the Midwest were once wetlands (Du et al., 2005)
• Numerous impacts on water quality and hydrology
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Water Quality Impairments
Nitrate losses at the field scale have been measured by Kladivko et al. (2004): Annual nitrate losses ranged from
18 to 37 kilograms per hectare Best Management Practices to
reduce nutrient pollution: cover crops, drainage water management, grassed waterways
• Tile drains facilitate the transport of nutrients to surface water
Iowa Natural Resource Conservation Service (2008)
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Hydrologic Changes in Tile Drained Watersheds
Influences flashiness and flow variability: Streamflow recession occurs more
rapidly as tile drainage extent increases (Ale et al., 2010)
Alters low flow and peak flow Increasing low flow and decreasing
peak flows as the extent of tile drainage increases (Schilling and Libra, 2003)
Kumar et al. (2009) found increasing trends in low, median, and high flow metrics in Indiana. Precipitation highly influences these trends.
Ale and Bowling (2010)
Streamflow recession as influenced by potential tile drained area.
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Climate Change Effects on Streamflow Hydrology and Water Quality
Precipitation and soil moisture will increase in the winter and spring and decrease in the summer (Wuebbles and Hayhoe, 2004)
Water quality issues arise in the spring, prior to planting: Timing of fertilizer application
Conservation practices such as Drainage Water Management conserve water during dry seasons
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Tools Available to Estimate Tile Drainage and Water Quality Impacts
Hydrology models are available at varying spatial scales Evaluate non-point source pollution and hydrologic effects from
drainage
Provide drainage volume estimates which can aid decisions made regarding water, nutrients, crop management, etc.
Field Scale Models: DRAINMOD Root Zone Water Quality Model (RZWQM)
Watershed Scale Models: Soil and Water Assessment Tool (SWAT)
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Variable Infiltration Capacity Model Many large scale hydrology models still lack a
component for climate change analysis in tile drained river basins Variable Infiltration Capacity (VIC) Model has been used
for regional and continental climate change studies
Motivation for updates: Capable of simulating tile drainage for areas greater
than just a single watershed Could be used to potentially model drainage for the
entire Midwest Quantify drainflow and nitrates to estimate impact to the
Hypoxic Zone in the Gulf of Mexico
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VIC Model Processes
Divides study area into grid cells Multiple soil layers (usually 3) Vegetation scheme which varies
sub-grid. Driven by meteorological data
(precipitation, wind-speed, temperature)
(Cherkauer et al., 2003)
Calculates lateral flow from the bottom soil layer using
the baseflow curve
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Model Equations for Drainflow Addition
Arno Baseflow Equation by Todini (1996): Baseflow curve is divided into linear and non-linear baseflow response and is defined by three parameters (WS, Ds, and DSmax) Maximum baseflow out of the bottom soil layer, DSmax Baseflow shape changes at the soil moisture threshold, WS Fraction of the maximum baseflow where response shifts, Ds
Ellipse Equation: Used to adjust the Arno equation to calculate subsurface drainage Used to solve for new maximum baseflow (DSmax) out of the
bottom soil layer
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Original and Modified Baseflow Curve
• New baseflow parameters are calculated based on the original, user defined values
•WS’ is calculated first based on drain depth, followed by Ds’
• Maximum baseflow rate DSmax is calculated last using Ds’ and WS’ and the ellipse equation
Equilibrium Soil moisture value when water table first rises above the drain depth
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Calibration: Study Site Data and Setup
Southeast Purdue Agricultural
Center (SEPAC)
Located inButlerville,
Indiana
Drain depths at 0.75 meters
Observations from plots with drains
spaced at distances of 20, 10, and 5 meters
West Block
East Block
Water Table and Drainflow Data
Kladivko et al. (2003)
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Model Input Data
Meteorological forcing file: hourly precipitation, temperature, and wind speed from the SEPAC weather station (Naz, 2006)
Soil physical properties from measurements at SEPAC (Kladivko, 1999 )
Vegetation properties: leaf area indices, root depths: Land Use History of North America (LUHNA) by Cole et al., 1998
Calibration Parameters:Baseflow: Ds and DSmaxWater Table: Brook’s and Corey Water Retention Curve EXP and
soil bubbling pressure: BUBBLESoil Infiltration Parameter: Bi
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Model Sensitivity
“One at a Time” sensitivity analysis Relative Sensitivity of each parameter:
y= predicted drainage output
x = base parameter value
xhigh and xlow correspond to the high and low parameter values
yhigh and ylow are the corresponding response variable values at the high and low parameter values
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Relative Sensitivity of Calibration Parameters
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Calibration Methods
Calibrated using drainage and average water table data between the 1988 and 1990 water years
Simulate observed data from study site using a single grid cell
Compare output to average water table and drainage from the West 20 meter Plot Nash-Sutcliffe Efficiency (NSE) Percent Error (PE) Coefficient of Determination (R2)
Validated the model using drainage data Water table measurements were not collected after 1990
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Calibration ResultsDrainage Water TableCalibration Period:
1988 to1990 Water Years
__ Simulated Data__ Observed Data __ Depth of Tile Drain (0.75 meters)
Drainage StatisticsNS = .34PE = 2.10% R2 = .34
Water Table StatisticsNS= -.08 PE = -22.7 R2 = .26
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Drainage Efficiency
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Conclusions from Field Scale Analysis
VIC model is primarily used for large scale analyses
Water table depths calculated by the model were not as dynamic as the observed data
Evidence of preferential flow in observed data could also account for lower model efficiency
The drainage model predicts total drainage within 21 % of the data between the water years of 1988 and 1994 Reasonably predicts drainage depths suitable for
watershed scale tests
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Objectives and Hypotheses
2.) Water conservation from DWM during the growing season will decrease under future climate conditions from the levels seen now.
1.) Indiana watersheds have experienced higher annual low flows due to increased water storage capacity in the soil from conventional subsurface drainage.
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Watershed Scale Study
The White River watershed extends across the majority of central Indiana: Delineated upstream of Indianapolis to
avoid urban influence
Hypothesis #1
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Model Setup: Creating Input Files using Spatial Data
Hypothesis #1
NASS Cropland Data Layer
Watershed Boundary and VIC grid cells
Potentially Tile Drained Land (Ale, 2009)
Indiana Drainage Guide Recommendations
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Methods
Model simulations from 1930 through 2005 water years
Two Model Scenarios Drainage Algorithm ON (Calibration Case) Drainage Algorithm OFF
Most recent 20 years used for calibration and validation
Preliminary soil parameters and constants were taken directly from the field scale calibration Ds and DSmax were changed during calibration for grid cells
containing less than 50% tile drainage.Hypothesis #1
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Metrics
Hypothesis #1
The following metrics were used to compare the predicted streamflow from each model
simulation.
• Low Flow: Seven-Day Minimum (Low) Flow• High Flow: Seven-Day Maximum (Peak) Flow• Mean Flow: Mean Annual Flow (MAF)
• Streamflow Variability:• Richards-Baker Flashiness Index (RBI)
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Calibration and Validation Results
Hypothesis #1
CalibrationStatistics:NSE = .45 PE= -11.5 % R2= .59
Validation Statistics: NSE = .70 PE= - 12.8 % R2= .75
Legend:
Model ___Observed ___
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Hypothesis #1
Comparing Hydrographs between the Model Simulations
Model ___ Observed ___
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Compare Flow Metrics Between the Drainage and No Drainage
Model Simulations
Hypothesis #1
Mean Annual FlowSeven Day Minimum Flow
Seven Day Peak Flow RBI
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Conclusions and Answer to First Hypothesis
Hypothesis: Tile drainage systems have increased annual low flow due and streamflow
flashiness
Conclusions: Streamflow flashiness is higher in drained
conditions Peak flows are larger while low flows are
reduced
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Climate Variability Effects: Observed Trends in the White River Watershed
Seven Day Low FlowAverage Annual Precipitation per 15
Year Time Period
• Overall increasing trend in all flow metrics largely due to precipitation• How will precipitation and temperature continue to affect tile drained landscapes in the future?
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GFDL Model Emissions Scenarios:
A2: High Emissions: Best representation of our current GHG trajectory A1B: Mid to High Emissions: Technological advances will limit some GHG B1: Conservative Emissions: Future climate with many technological advances
Future Climate Projections
Average Annual Precipitation Relative to Historic Period from 1980-2009
Average Annual Temperature Relative to Historic Period from 1980-2009
Future Time PeriodsFuture Time Periods
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Interaction of Conservation Practices with Climate Change
Conservation practices such as drainage water management could be used to mitigate seasonal variability from climate change Drainage Water Management (DWM) controls the
water table height and level of drainage seasonally
How will water conserved by DWM change in future climate conditions? Hypothesis 2: Water conservation will decrease
during the growing season in future climates
Hypothesis #2
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DWM and Conventional Drainage Model Setup
The VIC model was also modified to handle monthly changes in drain depth from DWM Mimics the effect of raising and lowering the DWM control structure
Two model setups both forced with future climate data for all 3 emissions scenarios: Agriculturally drained land is using Drainage Water Management
(DWM) Conventional Tile Drainage (using the previous model setup)
Three control heights are used for DWM Case: Winter: 0.3 meters April and September: 0.9 meters Summer (Growing Season): 0.6 meters
Hypothesis #2
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Field and Watershed Scale Hydrographs
Hypothesis #2
Grid Cell Flow at Watershed Outlet
Legend:
DWM _Conventiona
l _
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Examine how factors (DWM and future climate) affect streamflow metrics
DWM increases low flow in historic and future climate conditions
Climate change has a greater impact on streamflow metrics than DWM
Factor Separations
Hypothesis #2
1980-2009 2070-20990.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
Moderate Emissions Model Seven Day Minimum Flow
Time Period
Flo
w (
m3/
s)
1980-2009 2070-20990.0
50.0
100.0
150.0
200.0
250.0
300.0
350.0
Moderate Emissions Model Seven Day Peak Flow
Conventional Drainage
Drainage Water Management
Time Period
Flo
w (
cms)
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Annual Water Conservation Broken into 30 Year Time Periods
Hypothesis #2
Water Conserved in the soil column is difference between the streamflow from the DWM and conventional drainage simulations.
Differences in flow equals the amount of water that remains in the soil column or used as evapotranspiration
Net increase in water conservation throughout the 21st century
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Water Conserved by DWM during the Growing Season
• Decreasing water conservation during the growing season in future time periods
• Similar trend in growing season
•Evapotranspiration trends are similar: less water availability in the future
•DWM case predicts higher ET than conventional drainage
Growing Season Water Conservation
Hypothesis #2
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Conclusions and Answer to Second Hypothesis
Hypothesis: Growing season water conservation will decrease throughout the next century
• Conclusions: • Dry summers will decrease water availability, less water
to conserve. Growing season ET is also decreasing.
• DWM is very effective in the Spring months at maintaining high water table levels.
• DWM will become more efficient as precipitation totals increase
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Overall Conclusions
Hypothesis 2: Growing season water conservation will decrease in future climates DWM is more effective at holding soil moisture during the growing season
and will be a valuable practice in future climates Water Conservation will increase during the spring and periods of high
precipitation and decrease throughout the next century during dry seasons
Hypothesis 1: Subsurface drainage has increased low flows and decreased streamflow flashiness The model proved that streamflow flashiness is increasing and low
flows are reduced There are increasing low flow trends that are likely due to
precipitation
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Project Improvements
Improve field scale calibration using a more stationary dataset Reassess whether the correct parameter values were
chosen
Select a global climate model that more accurately represents future streamflow in the Upper White River watershed Decreasing low flow trends in the GFDL model in opposition
of the observed data GFDL model was chosen because it has been used for
studies in the Midwest
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The End
Thank you for watching my presentation!
Acknowledgments:
I’d like to thank my advisors Drs. Keith Cherkauer and Laura Bowling
Dr. Eileen Kladivko for providing me with data from SEPAC
Srinivasulu Ale for sparking the idea for this research endeavor
My friends and family for their support!
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Simulated Water Table: Validation
Validation Hydrographs
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1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
Drainflow Flashiness
Water Year
Fla
sh
ine
ss
In
de
x (
RB
I)
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Monthly Conservation Cycle• Negative Water Conservation:
- Considerable losses in April due to lowering DWM boards for Spring Planting
- Less noticeable losses in the late growing season (July through August)
• Water Conservation during seasons of higher precipitation (December through March)
Hypothesis #2