Simulating critical source areas
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2017 SWCS Annual Conference July 30-August 2, 2017 Monona Terrace Convention Center
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Simulating critical source areas
- 1. Simulating critical source areas across scales using watershed models Margaret McCahon Kalcic (kalcic.4@osu.edu), Rebecca Logsdon Muenich, Yu-Chen Wang, Anna Apostel, Awoke Teshager, Jay Martin, Donald Scavia Ohio State University and University of Michigan SWCS International Annul Conference, Field to Watershed Session Monday, July 31, 2017
- 2. Cover crop Grassed waterway Wetland Watershed modeling with SWAT “The objective of the SWAT model is to predict the effect of management decisions on water, sediment, nutrient and pesticide yields with reasonable accuracy on large, ungaged river basins.” Acronyms: CSA Critical Source Area TP Total Phosphorus DRP Dissolved Reactive Phosphorus SWAT Soil and Water Assessment Tool
- 3. Two case studies 1. Maumee case study Multiple model comparison in the Maumee River watershed Simulated sub-watershed CSAs before vs. after improved agricultural management 2. Raisin case study Sensitivity of field-level CSAs to management assumptions in the River Raisin watershed Baseline calibrated model vs. farmer surveys Maumee Raisin
- 4. 1. Maumee case study Multiple watershed modeling teams: Multiple models guide strategies for agricultural nutrient reductions. 2017. Frontiers in Ecology and the Environment. Funding from the Erb Foundation and Ohio Department of Higher Education Rem Confesor Jay Martin Noel Aloysius Jeffrey Kast Marie Gildow Anna Apostel Don Scavia Margaret Kalcic Rebecca Muenich Yu-Chen Wang Awoke Teshager Joe DePinto Todd Redder Chelsie Boles Haw Yen Jeff Arnold Mike White Dale Robertson SPARROW model (SPAtially Referenced Regressions On Watershed attributes) Richard Becker In only one of the two studies: SWAT models (Soil and Water Assessment Tool)
- 5. 1. Maumee case study Before Previous Project After Current Project Point source inputs Updated to monthly average Updated to monthly, included combined sewer overflows Climate inputs Updated Updated Farm management inputs/assumptions Unchanged 1. Updated existing BMPs (cover crops, buffer strips, no-tillage, fertilizer placement) 2. County-specific manure and fertilizer application rates Data sources: CEAP surveys, Robyn Wilson surveys, Fertilizer Institute Data, Ag. Census Calibration and years Unchanged (run 2005-2014) Recalibrated to flow, sediment, and nutrients near outlet, 2005-2015
- 6. 6 PreviousProjectCurrentProject USGS - SPARROWUMOSULTHU Estimated TP Delivery to Lake Erie (average annual 2005-2015) Lowest quantile Highest quantile 1. Maumee case study
- 7. 7 PreviousProjectCurrentProject Estimated DRP Delivery to Lake Erie (average annual 2005-2015) Lowest quantile Highest quantile UMOSULTHU 1. Maumee case study
- 8. 8 0 1 2 4 5 3 0 1 2 4 3 Number of watershed models in agreement about P critical source areas* TP DRP * CSAs are sub- watersheds contributing the 20% highest area- weighted P load. Estimated P Delivery to Lake Erie (multi-model agreement) PreviousProjectCurrentProject 1. Maumee case study
- 9. 1. Maumee case study Findings about sub-watershed CSAs: CSAs differed among multiple models Each modeler set different farm management assumptions CSAs changed with improved management assumptions county-level fertilizer and manure applications
- 10. 2. Raisin case study SWAT model calibrated to flow and nutrients near outlet Used in a Pay-for-Performance study enrolling farmers in conservation for TP reduction Comparison of field-level outputs for: A. Baseline calibrated model B. Model modified to incorporate farmer survey data
- 11. Farm-level information Field-level information 2. Raisin case study Surveyed farmers about management practices
- 12. Farm size (ha) 2. Raisin case study Estimated TP Export from Fields Each figure show’s one farmer’s fields
- 13. Farm size (ha) 2. Raisin case study Estimated DRP Export from Fields Each figure show’s one farmer’s fields
- 14. 2. Raisin case study Findings about field-scale CSAs: CSAs differed considerably between baseline and farm survey Calibration farm management assumptions vs. surveyed In most cases P losses were greater from farm surveys
- 15. Moving forward What can we say about the confidence of simulated CSAs? Depends on confidence of farm management data at field scale Useful for regional planning? How does uncertainty due to farm management assumptions compare to other aspects of model? (parameter, structural) Other forms of uncertainty are more readily quantified and reported Creating online tools where farmers can input management Funding from Erb Family Foundation, the Ohio Department of Higher Education, and the Great Lakes Restoration Initiative
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- Session 3. Field to Watershed: Connecting Local Scale Influence to Larger Scale Significance Conservation efforts can be performed at different temporal and spatial scales. Complex management strategies such as nutrient trading can have a tremendous impact on water quality within and beyond a specific watershed, but a simple grassed waterway can also have a positive impact in the overall water quality of an area. This specialty track will explore scale issues of conservation practices and their impacts. Case studies that highlight the connection between local to watershed or regional scale are encouraged. Title: Simulating critical source areas across scales using watershed models Authors: Margaret Kalcic, Rebecca Muenich, Yu-Chen Wang, Anna Apostel, Awoke Teshager, Jay Martin, Donald Scavia Abstract: Watershed models are powerful tools for simulating water quality impacts of agricultural practices at large and small scales. The Soil and Water Assessment Tool (SWAT) can estimate critical source areas—either fields or subwatersheds—contributing nutrients and sediments to streams, rivers, and lakes. Yet the validity of these estimates depends on detailed input data on land management and validation against measured data at comparable scales to the critical source areas. Over time we have improved land management assumptions in SWAT models for major watersheds draining to Lake Erie’s western basin. We are analyzing the changes to predicted critical source areas at a field and subwatershed level, and testing the sensitivity of the model to improved land management inputs. We also compared field-level results from the calibrated model to those of the model with data from farm surveys, and find that fields simulated with surveyed land management practices have far greater variability in nutrient export than subwatersheds. This suggests field-level targeting of conservation should yield greater water quality improvement at the watershed outlet than a focus on the subwatersheds with highest simulated loading.