developing a web-based forecasting tool for nutrient management
TRANSCRIPT
The Fertilizer Forecaster:guiding short-term decisions in nutrient management
Project Director’s MeetingOctober 12, 2016
United States Department of Agriculture
National Instituteof Foodand Agriculture
This project was supported by Agriculture and Food Research Initiative Competitive Grant number 2012-67019-1929 from the USDA National Institute of Food and Agriculture.
Anthony Buda, Peter Kleinman, Ray Bryant, and Gordon FolmarUSDA Agricultural Research Service
Patrick Drohan, Lauren Vitko, Doug Miller, and Stephen Crawford
Penn State University
Seann Reed and Peter AhnertNOAA NWS Middle Atlantic River Forecast Center
• Applying fertilizers and manures at the wrong time increases the risk of surface water contamination.
CDT/Nabil K. Mark
Thursday, Feb. 12, 2009Thousands of fish killed - Owner blames manure runoff from farm
Centre Daily Times
• Site assessment tools are currently seasonal (e.g., P Index), but daily recommendations would be helpful.
0
4
8
12
16
2 days 9 days
Dissolved reactive P in runoff (mg/L)
Time since surface application
no dairy manure
20 kg P/ha (P based)
70 kg P/ha (N based)
Daily decision making in nutrient management
Work with a project advisory team to develop web-based forecasting tool
Fertilizer Forecaster – when and where to apply fertilizers and manures
Evaluate three runoff forecasting models (Easton et al., in review with JEQ)
Test web-based system to identify when and where to apply fertilizers and manures
Allegheny Plateau
Piedmont
Coastal Plain
Ridge &Valley
Project watershedsAnderson Creek Watershed
Anderson Creek Spring Creek Watershed / Rock Springs
Spring Creek
Mahantango Creek Watershed
Mahantango Creek
Conewago Creek Watershed
Conewago Creek
Sacramento (SAC) Soil Moisture Accounting (SMA) model
Mahantango Creek Experimental Watershed
WE-38
Surface runoff observed (cfs)
SAC-SMA interflow +
surface runoff (cfs)
Interflow and surface runoff time series deemed best predictors of surface runoff occurrence in small headwater basins like WE-38. 0 20 40 60 80 100120140160180200
0
50
100
150
200
r2 = 0.62
Evaluated NOAA’s gridded (2×2 km) SAC-HT model forrunoff prediction in small basins
Sacramento (SAC) Soil Moisture Accounting (SMA) model
• , where
• SAC-SMA expresses soil moisture as a saturation ratio
θ = volumetric water contentθr = permanent wilting pointθs = porosity
Saturation ratios predicted by the SAC-SMA model are a good proxy for surface (i.e., top 25 cm) moisture conditions in the WE-38 watershed.
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
Mea
n vo
lum
etric
wat
er
cont
ent (
m3 m
-3)
SAC-SMA saturation ratio
Vol. soil moisture = 0.17 (saturation ratio) + 0.15r2 = 0.70; p < 0.001
Volumetric water content (top 25 cm) versus SAC-SMA saturation ratios (top 25 cm)
Assessed modeled vs. measured moisture patternsSAC-HT accurately predicts surface soil moisture in WE-38
Developed basin-scale runoff risk thresholds factoring in antecedent moisture and runoff contributing areas
Low Risk
SAC-SMA saturation ratio < 0.6
SAC-SMA runoff coefficient < 0.02
Moderate Risk
SAC-SMA saturation ratio > 0.6
SAC-SMA runoff coefficient 0.02 0.2
High Risk
SAC-SMA saturation ratio > 0.6
SAC-SMA runoff coefficient > 0.2
Representing field-scale runoff risksimplicity versus accuracy
Simplicity
Fixed-width buffers are simple, but they do not represent the reality of variable source area hydrology on the ground.
Fixed width buffer
Fixed width buffers simple, but not necessarily accurate
Accuracy
Variable source areas
Runoff contributing areas vary in size and shape, and field-scale tools should attempt to capture these dynamics.
Variable width buffers difficult to map, but more realistic
Representing field-scale runoff riskalternatives to fixed-width stream buffers
Depth to Water Indexleast cost elevation difference to
nearest stream
0 0.1 0.25 0.5 1 5
Depth to Water Index (m)
Topographic Wetness Indexnatural logarithm of contributing
area divided by slope
Dry WetTopographic Wetness Index
1.9 13.46.2 8.4
Representing field-scale runoff riskalternatives to fixed-width stream buffers
Topographic Wetness Indexnatural logarithm of contributing
area divided by slope
Depth to Water Indexleast cost elevation difference to
nearest stream
0 0.1 0.25 0.5 1 5
Depth to Water Index (m)Dry Wet
Topographic Wetness Index
1.9 13.46.2 8.4
Runoff depth (mm)
WE-38 Watershed(7.3 km2)
Precipitation depth (mm)
÷
Runoff coefficient
Mapping more realistic runoff contributing areasan approach combining runoff coefficients and wetness indices
October 27-29, 200320 mm
October 27-29, 200366 mm
October 27-29, 20030.3
Mattern Watershed (11 ha)
A practical example in the Mattern WatershedOctober 27-29, 2003; predicted runoff coefficient = 0.3
Topographic Wetness Index
0 2 4 6 8 10 12 14 160%
20%
40%
60%
80%
100%
Topographic Wetness Index
Perc
ent o
f w
ater
shed
are
a
Depth to Water Index
0 50 100 150 200 2500%
20%
40%
60%
80%
100%
Depth to Water Index (m)
Perc
ent o
f w
ater
shed
are
a
Map all TWIs > 7.5
Map all DTWs < 6.5
Saturated area observed
Saturated areapredicted
(runoff contributing area)
YES NO
YES
NO
True positive (TP) False positive (FP)
False negative (FN) True negative (TN)
Which index is better?comparing observed versus predicted runoff contributing areas
Cohen’s kappa () = ( TP + TN) − ( T̂P + ̂TN )m − (̂ TP − T̂N )
+
actual agreement expected agreement
TP + FP + FN + TNm =
Saturated area observed
YES NO
YES
NO
True positive (TP) False positive (FP)
False negative (FN) True negative (TN)
Which index is better?comparing observed versus predicted runoff contributing areas
Cohen’s kappa () – used in past studies of spatial saturation patterns
accounts for agreement due to random chance
ranges from - (no skill) to +1 (perfect skill)
Saturated areapredicted
(runoff contributing area)
Which index is better?comparing observed versus predicted runoff contributing areas
Pred
icte
d
YES NO
YES
NO
53 1,512
187 3,244
Observed
Cohen’s kappa () = -0.02Agreement = none
Pred
icte
d
YES NO
YES
NO
229 833
11 3,923
Observed
Cohen’s kappa () = 0.30Agreement = fair
Depth to Water IndexTopographic Wetness Index
Wet boot Wet bootMapped saturated area Mapped saturated area
Generate 5,000 random points
Generate 5,000 random points
Low
MediumHigh
Runoff risk
A hypothetical runoff risk forecast showing a low to moderate runoff risk for the 88 2×2 km forecast cells that make up the Mahantango Creek Watershed.
Forecast areas of runoff generation
The zoomed in view would show the extent of the moderate runoff risk buffer, defining areas expected to be hydrologically connected to the stream.
Summary and next stepsSoil moisture and runoff contributing area thresholds express runoff risk in terms of variable source area hydrology.
Downscaled contributing area maps require further evaluation to assure they accurately portray runoff risk at the sub-field scale.
Basin- and field-scale risk thresholds will be integrated into the Fertilizer Forecaster and tested in real time as well as with hindcasting methods.