Iowa Daily Erosion Project 2: Real time soil and water resource inventory
Brian K. Gelder, PhDAssociate Scientist
Iowa State UniversityD. James, D. Herzmann, R. Cruse, J. Laflen, W.
Kraszewski, J. Opsomer, D. Flanagan, J. Frankenberger
Iowa Daily Erosion Project
• Our mission:To help farmers, land managers, and the public better understand the dynamics and magnitude of runoff and soil erosion through daily estimation of these processes on agricultural areas and dissemination of the estimates via the web
These estimates are made using WEPP, the Water Erosion Prediction Project model, a mechanistic model designed for agricultural and forest plots.
• Township level data– Soils– Slopes– Manage
ment
NEXRAD Precipdata
Iowa Daily Erosion Project 1
• Estimating– Sheet and rill erosion – Soil moisture – Runoff– Rainfall
• On 18,000 hillslopes– 6‐20 per township
• Weather from NEXRAD/ Iowa Mesonet
• Slope, soils, and management information from 1997 USDA Natural Resource Inventory (NRI)– Extrapolated to present
• Estimates aggregated to township level
• Debuted in 2003
Iowa Daily Erosion Project 2
• IDEP continues to function, but– Rotations are out of date (1994‐97)– Hillslopes are simple, uniform slope– Actual locations not known
• Impossible to assess current accuracy• Township structure not ideal for water issues
• Research in remote sensing makes it possible to generate all model inputs– Run at HUC12 watershed scale on approximately 250 flowpaths per watershed
• 1700 HUC12 watersheds, each approximately 100 km2
NEXRAD Precip
LiDARSlopes
SSURGO Soils
Remote Management
Iowa Daily Erosion Project 2
• New Interface– Similar to Google Earth, Bing Maps
• Selectable basemaps, overlays, data layers
• Transition to remotely sensed inputs– Rainfall – 1x1 km 5 minute NEXRAD
• Upgraded from 4x4 km 15 minute NEXRAD– Management – Remote sensing of crops/residue cover– Hillslope profile – LiDAR derived – Soils – SSURGO derived
• Enables field and watershed monitoring comparisons for estimating model accuracy and precision– Eventually stream delivery and channel erosion
IDEP 2 Rainfall
• Upgrade to Level II NEXRAD radar data from the NWS and UI– 1 x 1 km rainfall – Every 5 minutes
• Previously– 4 x 4 km rainfall– Every 15 minutes
• Improved wind, temp, and solar spatial resolution
IDEP 2 Management• Estimates are currently made on
agricultural land parcels greater than 10 acres
• To accurately delineate agricultural land we began with pre‐2008 publically available USDA FSA Common Land Units
• A CLU has– Permanent, contiguous boundary– Common land cover &
management– Common owner– Common producer
• Does not represent crop boundaries
• Aerially truthed to 2009 field boundaries
IDEP 2 Management
• To obtain crop rotations we use USDA NASS Cropland Data Layer– Annual map of crop cover
– Based on Landsat & other images
– Does not delineate common management practices
2011
20092008
2010
20132012
IDEP 2 Management
2011
20092008
2010
20132012
For each 2009 field boundary the majority land cover and fraction is calculated for each year from 2008‐2013 and the pattern is assigned to one of nine major rotation types and extrapolated to the current year.
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IDEP 2 Management
2008‐2013 Crop Rotations
IDEP 2 Management• Remotely sensed residue
cover estimates will come from Landsat TM and ETM+ sensor
• Soil and residue reflect differently in middle infrared
• Index relationship can define tillage intensity
• Index uses crop cover from previous step to improve estimates
IDEP 2 Management
Post Fall Residue Polygons NDTI = Band 5 – Band 7Band 5 + Band 7
IDEP 2 Management
• Determining residue cover (RC)– Identify and download all imagery
from the Landsat archive over Iowa from October 15 to June 15 from 2000‐01 to 2011‐12
– Use Fmask, an automated process for classifying imagery into snow, cloud, cloud shadow, water, and clear sky pixels
– Visually inspect Fmask results to find any missed clouds and create additional cloud/shadow masks if needed
– Calculate Normalized Difference Tillage Index (NDTI) value to estimate RC
IDEP 2 Management• Steps to determine residue cover (RC)
– Utilize date, crop, growing crop (and potentially soil) adjusted NDTI values to calculate residue cover
• 100% NDTI value decreases with time• 100% decreases from corn ‐> beans• 0% ‐ consistent across date & crop• Growing crop increases values• No till class may be needed
– Utilizing all available imagery minimum residue cover for each field is estimated in two time periods
• After fall tillage (before March 15)• After planting (before June 15)
– Based on residue cover estimates, corn and soybean tillage practices are assigned to 1 of 4 possible regimes
• No‐till• Mulch‐till (2 levels) • Conventional tillage
y = 507.95x - 23.333R² = 0.8063
0
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0.000 0.050 0.100 0.150 0.200
AVG
_RC
PLA
N
NDTI_Corn
y = 165.95x + 0.0596R² = 0.1142
0
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0.000 0.050 0.100 0.150 0.200
AVG
_RC
PLA
N
NDTI_Soybean
IDEP 2 Hillslope Profiles
• LiDAR topographic data can better predict water flow across the landscape but LiDAR is not perfect –– Doesn’t always flow
• Roads/railroads create digital dams
• Areas of no returns (water, heavy trees) create errors
– Enforcement is needed to get water to the channels or depressions and not back up in fields
Fill Depth at 3 m Resolution
Best Connection(s)
Remaining Fill Depth
IDEP 2 Hillslopes (Cont.)
• Once surface is enforced further processing begins– Too many flowpaths to run them all
• Random sampling
– Stratify HUC12s into subcatchments
• Using Douglas‐Peuker constant drop stream analysis
• 100‐250 subcatchments per HUC12
– Select 1 ag flowpath per subcatchment
Catchments and Channels
Field Level Flowpaths
Field Level Flowpaths and Soils
HUC12 Framework
IDEP2 Rainfall
IDEP2 Erosion
IDEP2 Runoff
Questions?
• Acknowledgements:– Iowa State University Agronomy Department Endowment
– USDA Agricultural Research Service– Environmental Defense Fund
IDEP 2 Management
June 1 2011 Residue Cover Index