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Developing a Web-based Forecasting Tool for Nutrient Management
This project developed runoff forecasting models and thresholds to guide short-term nutrient management decisions. It tested the Sacramento Soil Moisture Accounting model and found it could predict runoff occurrence and soil moisture levels. Based on field data, it established thresholds for low, moderate, and high runoff risk based on soil saturation levels and forecasted runoff amounts. Initial tests found the thresholds had predictive skill. The project aims to create a web tool to provide watershed-scale and local-scale runoff risk forecasts to inform nutrient application decisions. Continued validation of the soil moisture and runoff models is needed.
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Developing a Web-based Forecasting Tool for Nutrient Management
- 1. The Fertilizer Forecaster: guiding short‐term decisions in nutrient management Project Director’s Meeting July29, 2015 United States Department of Agriculture National Institute of Food and 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 Folmar USDA Agricultural Research Service Patrick Drohan, Jasmeet Lamba, Lauren Vitko, Doug Miller, Brian Bills, and Paul Knight Penn State University Seann Reed and Peter Ahnert NOAA NWS Middle Atlantic River Forecast Center
- 2. • Applying fertilizers and manures at the wrong time increases the risk of surface water contamination. CDT/Nabil K. Mark Thursday, Feb. 12, 2009 Thousands 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. Daily decision making in nutrient management
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- 4. Allegheny Plateau Piedmont Coastal Plain Ridge & Valley Project watersheds Anderson CreekWatershed Anderson Creek Spring Creek Watershed / Rock Springs Spring Creek Mahantango Creek Watershed Mahantango Creek Conewago Creek Watershed Conewago Creek
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- 7. 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 WE‐38. 0 50 100 150 200 050 100 150 200 r2 = 0.62 Adapting NOAA’s SAC‐SMA model to runoff prediction in small basins
- 8. & Runoff contributing areas vs. Runoff No runoff 0.0 0.2 0.4 0.6 0.8 1.0 0 0.10.2 0.3 0.4 0.5 Moisture thresholds Runoff coefficient Vol. soil moisture (m3 m‐3) θ 0.36 m3 m‐3 Large stormSmall storm Creating meaningful runoff risk thresholds moving beyond simple runoff occurrence forecasts
- 9. Sacramento (SAC) Soil Moisture Accounting (SMA) model •Saturation ratio = θ − θr θs − θr , where •SAC‐SMA expresses soil moisture as a saturation ratio θ = volumetric water content θr = permanent wilting point θs = 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 0.1 0.2 0.3 0.4 0.5 00.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Mean volumetric water content (m3m‐3) SAC‐SMA saturation ratio Vol. soil moisture = 0.17 (saturation ratio) + 0.15 r2 = 0.70; p < 0.001 Volumetric water content (top 25 cm) versus SAC‐SMA saturation ratios (top 25 cm) Does SAC‐SMA capture daily saturation patterns? comparing modeled and measured moisture in WE‐38
- 10. Runoff volume (m3) WE‐38 Watershed (7.3 km2) Gburek et al.,1999; 2002 Precipitation depth (m) ÷ Runoff contributing area (m2) Calculating runoff contributing areas borrowing a concept from the Pennsylvania Phosphorus Index
- 11. Gburek et al.,1999; 2002 Runoff contributing area (m2) Contributing distance (m) Stream length (m) ÷ WE‐38 stream length = 11,250 m WE‐38 Watershed (7.3 km2) Runoff contributing distances derived from contributing area and stream length
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- 14. Upslope areas increasingly likely to be hydrologically connected Saturation excess region 0 10 20 30 40 50 60 0 100 200300 400 500 600 Setting a saturation excess runoff threshold slope fields likely to connect at contributing distances > 150 m SAC‐SMA runoff versus runoff contributing distance r2 = 0.70 150 17.5
- 15. Runoff risk thresholds proposed decision rules for runoff risk Low Risk SAC‐SMA saturation ratio < 0.6 SAC‐SMA interflow + surface runoff 0 cfs Moderate Risk SAC‐SMA saturation ratio > 0.6 SAC‐SMA interflow + surface runoff > 0 cfsand < 17.5 cfs High Risk SAC‐SMA saturation ratio > 0.6 SAC‐SMA interflow + surface
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- 18. The Gerrity skill score for a 3 3 verification table •Ranges from ‐∞ (no skill) to 1 (perfect skill) • Gives more credit for correct forecasts of rarer events and less credit for correct forecasts of common events Observed risk LOW MOD HIGH LOW MOD HIGH Forecast risk (a) Joint probability distribution p (f1, o1) p (f1, o2) p (f1, o3) p (f2, o1) p (f2, o2) p (f2, o3) p (f3, o1) p (f3, o2) p (f3, o3) (b) Scoring weights Observed risk LOW MOD HIGH s 1, 1 s 1, 2 s 1, 3 s 2, 1 s 2, 2 s 2, 3 s 3, 1 s 3, 2 s 3, 3 Are these thresholds reasonable? assessing model skill for the WE‐38 watershed Wilks, 2011
- 19. Are these thresholds reasonable? assessing model skill for the WE‐38 watershed Assessed forecast skill using three years (2010 to 2012) of daily stormflow (determined by hydrograph separation) and soil moisture data from the WE‐38 watershed. Observed risk LOW MOD HIGH LOW MOD HIGH Forecast risk (a) Joint probability distribution(b) Scoring weights Observed risk LOW MOD HIGH 0.46 ‐0.50 ‐1.00 ‐0.50 0.55 0.05 ‐1.00 0.05 52.13 225 74 0 101 181 1 0 38 5 Total (n) = 625 0.36 0.12 0.00 0.16 0.29 0.00 0.00 0.06 0.01 Probability 0.52 0.47 0.01 Wilks, 2011
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