A Framework for assessing the performance of DWM at large Scale

1. MOHAMED A. YOUSSEF and R. WAYNE SKAGGS By A Framework for assessing the performance of DWM at large Scale

2. Most field experiments, conducting a side-by-side comparison between managed and unmanaged drainage systems, document reductions in annual drain flow and N drainage loss by a range of 20% to 60%. • This wide range is expected as the performance of DWM is affected by many factors including: • weather (precipitation, temperature, and other factors affecting ET), • soil (hydraulic conductivity, texture, presence of shallow impermeable layer, SOM,…), • farming practices (cropping system, nutrient management, tillage and residue management, cover crops,…), and • drainage system design (drain depth and spacing, degree of surface drainage) introduction

3. The performance of DWM measured in a field experiment represents the performance of the practice for a specific set of site conditions. {S1, S2, ..., Si,… Sn} = {P1, P2, …, Pi, …Pn} introduction where Pi is the performance of DWM for the site conditions Si. • Thus, each experiment assessing the performance of DWM only represents one realization (Si, Pi) of the population of the performance of DWM for all possible site conditions, n.

4. It is practically impossible to conduct a large enough number of experiments to define the relationship P(S), or the performance as a function of the site conditions. • Experiments are usually conducted for a relatively short period ( 3 to 5 years) and thus cannot assess the performance of the practice over the long term. • Thus, there is a need for a tool that extrapolates the results of field experiments and assesses the performance of DWM for different site conditions. introduction

5. A conceptual representation of a tool for assessing the performance of DWM. DWM Assessment tool • Site Conditions • Weather • Soil • Farming practices • Drainage system • Reduction in drain flow • Reduction in N drainage losses DWM Assessment Tool

6. It is computer modeling based tool. DWM Assessment tool • Site Conditions • Weather • Soil • Farming practices • Drainage system • Reduction in drain flow • Reduction in N drainage losses DRAINMOD DRAINMOD-NII • Both DRAINMOD and DRAINMOD-NII models have been successfully tested using data sets from several Midwestern States (Illinois, Indiana, Minnesota, Iowa)

7. Development: • Select a set of benchmark soils, common farming practices, long-term weather records covering the geographic region of interest. • Use DRAINMOD to simulate the hydrology and DRAINMOD-NII to simulate N dynamics for each unique combination of soil type-farming practices-weather record. • Conduct the simulations for common drain depths and spacings and for both managed and unmanaged scenarios. • Determine the reduction in drain flow and N drainage losses caused by DWM for all simulated site conditions. • For each site conditions, reductions can be estimated for dry, normal, and wet years. DWM Assessment tool: APPROACH 1

8. Application: • Identify the conditions of the site of interest. • Select the reductions in drain flow and N drainage losses predicted by the models for the conditions closest to the site of interest. • Advantages • Simplest (the user of the tool does not need to run the models), • least input requirements. • Suitable for large scale assessment of DWM (how much reduction in N losses is expected if DWM is implemented on drained lands within a specific watershed, river basin, a state or the entire Midwest) • Disadvantages • least accurate predictions (compared with Approaches 2 and 3). • Less reliable for making site specific predictions that are accurate enough to be used with an incentive program. DWM Assessment tool: APPROACH 1

9. Development: • Select a set of benchmark soils, common farming practices, long-term weather records covering the geographic region of interest. • Use DRAINMOD to simulate the hydrology and DRAINMOD-NII to simulate N dynamics for each unique combination of soil type-farming practices-weather record. • Determine the annual flow-weighted nitrate-N concentration for all simulated site conditions. • For each site conditions, determine nitrate-N concentrations for dry, normal, and wet years. DWM Assessment tool: APPROACH 2

10. Application: • Identify the conditions of the site of interest and prepare site specific inputs for DRAINMOD. • Run DRAINMOD for the site of interest under the managed and unmanaged scenarios and calculate reductions in drain flow • Select the flow-weighted nitrate concentration predicted by DRAINMOD-NII for the conditions closest to the site of interest. • Calculate reduction in N mass loss DWM Assessment tool: APPROACH 2

11. Advantages • Medium level of complexity (the user of the tool must know how to run the hydrologic model DRAINMOD), • Medium level of input requirements (hydrologic inputs for DRAINMOD). • Compared to approach 1, this approach can make predictions that better represent the local site conditions, including the year to year variability caused by precipitation. • Model predictions should be accurate enough to be used with an incentive program. (A hypothesis that needs to be tested) • Disadvantages • More difficult than approach 1. DWM Assessment tool: APPROACH 2

12. Drainmod/drainmod-nii application to assess dwm across the midwest

13. Thorp et al. (2007, 2009) evaluated and compared the performance of the RZWQM/DSSAT and DRAINMOD/DRAINMOD-N II models using ten years of measured hydrologic, water quality and crop yield data for a corn-soybean agricultural system on silty clay loam/clay loam soils in central Iowa, U.S. (42.2° N, 93.6° W). • We used DRAINMOD/DRAINMOD-NII models, calibrated for the Iowa corn-soybean production system, to simulate the performance of DWM as affected by differences in climatic conditions, crop planting and harvesting dates, and N fertilization rates across the U.S. Midwest. • Weconducted the simulations using 25-years of historical climate data for 48 locations across the Midwest. MODELING APPROACH

14. Historical climate data were obtained from the National Solar Radiation Database. • The simulated drainage system consists of subsurface drains 145 cm deep, spaced 27.4 m apart. • The simulated cropping system includes corn, planted in even years, and soybean, planted in odd years. • Planting and harvesting dates were based on the state and county level National Agricultural Statistics Service (NASS) data(USDA, 2007). MODELING APPROACH

15. Nitrogen fertilization was applied seven days before planting at rates equal to 5-yr (2001-2006) avg., state level N fertilizer application rates. • DRAINMOD/DRAINMOD-N II simulated the hydrology, C and N dynamics, and crop yield for each of the 48 locations under both conventional drainage (CVD) and DWM. • The weir settings for the DWM scenario are 30 cm (off growing) season, 60 cm (in growing season), drain depth for 7 wks (planting) and 3 wks (harvesting). MODELING APPROACH

16. Predicted effects of DWM ON hydrology

17. DWM was most effective in reducing drain flow at the south and southeast locationsand least effective at the North and Northwest locations. • The predicted fate of the water that did not pass through the drainage system because of implementing DWM varied across the region. • The large reductions in drain flow associated with implementing DWM in the south and southeast locations resulted in a substantial increase in surface runoff and only a modest increase in vertical seepage. • In the north and northwest locations where DWM is relatively less effective (drain flow reductions < 20%), the water that did not pass through the drainage system because of DWM was primarily lost through ET. DWM EFFECTS ON HYDROLOGY

18. DWM EFFECTS ON N dynamics

19. Similar to the hydrology, DWM was most effective in reducing N drainage losses at the south and southeast locations and least effective at the north and northwest locations. • Model predictions support the hypothesis that DWM increases the anaerobic conditions in the soil profile, which promotes denitrification and reduces N leaching losses. • The small N concentrations in runoff water explain the modest increase in mass loss of N via surface runoff despite the large increase in surface runoff induced by DWM. • The increase in denitrification rates caused by DWM resulted in a decrease in the concentration of NO3-N in groundwater, and a corresponding reduction in NO3-N mass losses via vertical seepage despite the modest increase in vertical seepage flux. DWM EFFECTS ON n dynamics

20. DWM did not affect N plant uptake and net N mineralization. • Model predictions also support the hypothesis that DWM does not significantly change nitrogen concentration in drainage water and thus the percent reduction in N drainage loss can be approximated by the percent reduction in drain flow. • The predicted N change in N cycling caused by DWM indicates that the modest changes in N concentration in drainage water do not necessarily mean that the practice have a little impact on N dynamics in the system DWM EFFECTS ON n dynamics

21. Questions?