Master’s Degree Thesis Seminar Agricultural and Biological Engineering Sarah Rutkowski

1. Role of Climate Variability on Subsurface Drainage and Streamflow Patterns in Agricultural Watersheds Master’s Degree Thesis Seminar Agricultural and Biological Engineering Sarah Rutkowski May 11th, 2012

2. Tile Drainage • Subsurface (tile) drains lower high water table levels in poorly drained soils. • Large portions of drained agricultural land in the Midwest were once wetlands (Du et al., 2005) • Numerous impacts on water quality and hydrology Ale and Bowling (2010)

3. Water Quality Impairments • Tile drains facilitate the transport of nutrients to surface water • Nitrate losses at the field scale have been measured by Kladivko et al. (2004): • Annual nitrate losses ranged from 18 to 37 kilograms per hectare • Best Management Practices to reduce nutrient pollution: cover crops, drainage water management, grassed waterways Iowa Natural Resource Conservation Service (2008)

4. Hydrologic Changes in Tile Drained Watersheds • Influences flashiness and flow variability: • Streamflow recession occurs more rapidly as tile drainage extent increases (Ale et al., 2010) • Alters low flow and peak flow • Increasing low flow and decreasing peak flows as the extent of tile drainage increases (Schilling and Libra, 2003) • Kumar et al. (2009) found increasing trends in low, median, and high flow metrics in Indiana. Precipitation highly influences these trends. • Streamflow recession as influenced • by potential tile drained area. Ale and Bowling (2010)

5. Climate Change Effects on Streamflow Hydrology and Water Quality • Precipitation and soil moisture will increase in the winter and spring and decrease in the summer (Wuebbles and Hayhoe, 2004) • Water quality issues arise in the spring, prior to planting: Timing of fertilizer application • Conservation practices such as Drainage Water Management conserve water during dry seasons

6. Tools Available to Estimate Tile Drainage and Water Quality Impacts • Hydrology models are available at varying spatial scales • Evaluate non-point source pollution and hydrologic effects from drainage • Provide drainage volume estimates which can aid decisions made regarding water, nutrients, crop management, etc. • Field Scale Models: • DRAINMOD • Root Zone Water Quality Model (RZWQM) • Watershed Scale Models: • Soil and Water Assessment Tool (SWAT)

7. Variable Infiltration Capacity Model • Many large scale hydrology models still lack a component for climate change analysis in tile drained river basins • Variable Infiltration Capacity (VIC) Model has been used for regional and continental climate change studies • Motivation for updates: • Capable of simulating tile drainage for areas greater than just a single watershed • Could be used to potentially model drainage for the entire Midwest • Quantify drainflow and nitrates to estimate impact to the Hypoxic Zone in the Gulf of Mexico

8. VIC Model Processes • Divides study area into grid cells • Multiple soil layers (usually 3) • Vegetation scheme which varies sub-grid. • Driven by meteorological data (precipitation, wind-speed, temperature) Calculates lateral flow from the bottom soil layer using the baseflow curve (Cherkauer et al., 2003)

9. Model Equations for Drainflow Addition • Arno Baseflow Equation by Todini (1996): Baseflow curve is divided into linear and non-linear baseflow response and is defined by three parameters (WS, Ds, and DSmax) • Maximum baseflow out of the bottom soil layer, DSmax • Baseflow shape changes at the soil moisture threshold, WS • Fraction of the maximum baseflow where response shifts, Ds • Ellipse Equation: Used to adjust the Arno equation to calculate subsurface drainage • Used to solve for new maximum baseflow (DSmax) out of the bottom soil layer

10. Original and Modified Baseflow Curve • New baseflow parameters are calculated based on the original, user defined values • WS’ is calculated first based on drain depth, followed by Ds’ • Maximum baseflow rate DSmax is calculated last using Ds’ and WS’ and the ellipse equation Equilibrium Soil moisture value when water table first rises above the drain depth

11. Calibration: Study Site Data and Setup Water Table and Drainflow Data Southeast Purdue Agricultural Center (SEPAC) Located in Butlerville, Indiana Drain depths at 0.75 meters Observations from plots with drains spaced at distances of 20, 10, and 5 meters West Block East Block Kladivko et al. (2003)

12. Model Input Data • Meteorological forcing file: hourly precipitation, temperature, and wind speed from the SEPAC weather station (Naz, 2006) • Soil physical properties from measurements at SEPAC (Kladivko,1999 ) • Vegetation properties: leaf area indices, root depths: Land Use History of North America (LUHNA) by Cole et al., 1998 • Calibration Parameters: • Baseflow: Ds and DSmax • Water Table: Brook’s and Corey Water Retention Curve EXP andsoil bubbling pressure: BUBBLE • Soil Infiltration Parameter: Bi

13. Model Sensitivity • “One at a Time” sensitivity analysis • Relative Sensitivity of each parameter: y= predicted drainage output x = base parameter value xhighand xlow correspond to the high and low parameter values yhigh and ylow are the corresponding response variable values at the high and low parameter values

14. Relative Sensitivity of Calibration Parameters

15. Calibration Methods • Calibrated using drainage and average water table data between the 1988 and 1990 water years • Simulate observed data from study site using a single grid cell • Compare output to average water table and drainage from the West 20 meter Plot • Nash-Sutcliffe Efficiency (NSE) • Percent Error (PE) • Coefficient of Determination (R2) • Validated the model using drainage data • Water table measurements were not collected after 1990

16. Calibration Results Calibration Period: 1988 to1990 Water Years Drainage Water Table Drainage Statistics NS = .34 PE = 2.10% R2 = .34 Water Table Statistics NS= -.08 PE = -22.7 R2 = .26 __ Simulated Data __ Observed Data __ Depth of Tile Drain (0.75 meters)

17. Drainage Efficiency

18. Conclusions from Field Scale Analysis • VIC model is primarily used for large scale analyses • Water table depths calculated by the model were not as dynamic as the observed data • Evidence of preferential flow in observed data could also account for lower model efficiency • The drainage model predicts total drainage within 21 % of the data between the water years of 1988 and 1994 • Reasonably predicts drainage depths suitable for watershed scale tests

19. Objectives and Hypotheses 1.) Indiana watersheds have experienced higher annual low flows due to increased water storage capacity in the soil from conventional subsurface drainage. 2.) Water conservation from DWM during the growing season will decrease under future climate conditions from the levels seen now.

20. Watershed Scale Study Hypothesis #1 The White River watershed extends across the majority of central Indiana: Delineated upstream of Indianapolis to avoid urban influence

21. Model Setup: Creating Input Files using Spatial Data Watershed Boundary and VIC grid cells NASS Cropland Data Layer Indiana Drainage Guide Recommendations Potentially Tile Drained Land (Ale, 2009) Hypothesis #1

22. Methods • Model simulations from 1930 through 2005 water years • Two Model Scenarios • Drainage Algorithm ON (Calibration Case) • Drainage Algorithm OFF • Most recent 20 years used for calibration and validation • Preliminary soil parameters and constants were taken directly from the field scale calibration • Ds and DSmax were changed during calibration for grid cells containing less than 50% tile drainage. Hypothesis #1

23. Metrics • The following metrics were used to compare the predicted streamflow from each model simulation. • Low Flow: Seven-Day Minimum (Low) Flow • High Flow: Seven-Day Maximum (Peak) Flow • Mean Flow: Mean Annual Flow (MAF) • Streamflow Variability: • Richards-Baker Flashiness Index (RBI) Hypothesis #1

24. Calibration and Validation Results Legend: Model ___ Observed ___ Calibration Statistics: NSE = .45 PE= -11.5 % R2= .59 Validation Statistics: NSE = .70 PE= - 12.8 % R2= .75 Hypothesis #1

25. Comparing Hydrographs between the Model Simulations Model ___ Observed ___ Hypothesis #1

26. Compare Flow Metrics Between the Drainage and No Drainage Model Simulations Seven Day Minimum Flow Mean Annual Flow Seven Day Peak Flow RBI Hypothesis #1

27. Conclusions and Answer to First Hypothesis Hypothesis: Tile drainage systems have increased annual low flow due and streamflow flashiness • Conclusions: • Streamflow flashiness is higher in drained conditions • Peak flows are larger while low flows are reduced

28. Climate Variability Effects: Observed Trends in the White River Watershed • Overall increasing trend in all flow metrics largely due to precipitation • How will precipitation and temperature continue to affect tile drained landscapes in the future? Average Annual Precipitation per 15 Year Time Period Seven Day Low Flow

29. Future Climate Projections Average Annual Precipitation Relative to Historic Period from 1980-2009 Average Annual Temperature Relative to Historic Period from 1980-2009 Future Time Periods Future Time Periods • GFDL Model • Emissions Scenarios: • A2: High Emissions: Best representation of our current GHG trajectory • A1B: Mid to High Emissions: Technological advances will limit some GHG • B1: Conservative Emissions: Future climate with many technological advances

30. Interaction of Conservation Practices with Climate Change • Conservation practices such as drainage water management could be used to mitigate seasonal variability from climate change • Drainage Water Management (DWM) controls the water table height and level of drainage seasonally • How will water conserved by DWM change in future climate conditions? • Hypothesis 2: Water conservation will decrease during the growing season in future climates Hypothesis #2

31. DWM and Conventional Drainage Model Setup • The VIC model was also modified to handle monthly changes in drain depth from DWM • Mimics the effect of raising and lowering the DWM control structure • Two model setups both forced with future climate data for all 3 emissions scenarios: • Agriculturally drained land is using Drainage Water Management (DWM) • Conventional Tile Drainage (using the previous model setup) • Three control heights are used for DWM Case: • Winter: 0.3 meters • April and September: 0.9 meters • Summer (Growing Season): 0.6 meters Hypothesis #2

32. Field and Watershed Scale Hydrographs Grid Cell Flow at Watershed Outlet Legend: DWM _ Conventional _ Hypothesis #2

33. Factor Separations Hypothesis #2 Examine how factors (DWM and future climate) affect streamflow metrics DWM increases low flow in historic and future climate conditions Climate change has a greater impact on streamflow metrics than DWM

34. Annual Water Conservation Broken into 30 Year Time Periods Water Conserved in the soil column is difference between the streamflow from the DWM and conventional drainage simulations. Differences in flow equals the amount of water that remains in the soil column or used as evapotranspiration Net increase in water conservation throughout the 21st century Hypothesis #2

35. Water Conserved by DWM during the Growing Season Growing Season Water Conservation • Decreasing water conservation during the growing season in future time periods • Similar trend in growing season • Evapotranspiration trends are similar: less water availability in the future • DWM case predicts higher ET than conventional drainage Hypothesis #2

36. Conclusions and Answer to Second Hypothesis • Hypothesis: Growing season water conservation will decrease throughout the next century • Conclusions: • Dry summers will decrease water availability, less water to conserve. Growing season ET is also decreasing. • DWM is very effective in the Spring months at maintaining high water table levels. • DWM will become more efficient as precipitation totals increase

37. Overall Conclusions • Hypothesis 1: Subsurface drainage has increased low flows and decreased streamflow flashiness • The model proved that streamflow flashiness is increasing and low flows are reduced • There are increasing low flow trends that are likely due to precipitation • Hypothesis 2: Growing season water conservation will decrease in future climates • DWM is more effective at holding soil moisture during the growing season and will be a valuable practice in future climates • Water Conservation will increase during the spring and periods of high precipitation and decrease throughout the next century during dry seasons

38. Project Improvements • Improve field scale calibration using a more stationary dataset • Reassess whether the correct parameter values were chosen • Select a global climate model that more accurately represents future streamflow in the Upper White River watershed • Decreasing low flow trends in the GFDL model in opposition of the observed data • GFDL model was chosen because it has been used for studies in the Midwest

39. The End Thank you for watching my presentation! Acknowledgments: I’d like to thank my advisors Drs. Keith Cherkauer and Laura Bowling Dr. Eileen Kladivko for providing me with data from SEPAC Srinivasulu Ale for sparking the idea for this research endeavor My friends and family for their support!

41. Validation Hydrographs Simulated Water Table: Validation

43. Monthly Conservation Cycle • Negative Water Conservation: • Considerable losses in April due to lowering DWM boards for Spring Planting • Less noticeable losses in the late growing season (July through August) • Water Conservation during seasons of higher precipitation (December through March) Hypothesis #2