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.

Thank you

Partners

STATE CONSERVATION COMMISSI ON

Middle Atlantic River Forecast

Center