Date of Award
Master of Science in Agricultural and Biological Engineering
Agricultural and Biological Engineering
Bernard A. Engel
Bernard A. Engel
Committee Member 1
Dennis C. Flanagan
Committee Member 2
Jane R. Frankenberger
This study was conducted to develop a simplified method of obtaining future climate data inputs for natural resource models and apply that method to three locations within the continental United States to assess the effect of climate change on soil erosion, runoff, and fire risk. A method was developed for quickly obtaining future climate data over a wide range of scenarios, General Circulation Models (GCMs), and timescales from the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) and Fifth Assessment Report (AR5) model families using the MarkSim® DSSAT Weather Generator and a Microsoft Excel VBA Macro, the final result being a properly formatted parameter file which can be used by CLIGEN (CLImate GENerator) within the Water Erosion Prediction Project (WEPP) model. By using software which already exists on most computers and not requiring climatological or modeling knowledge to operate, the method herein for creating WEPP climate input files is fast and simple, requiring as little as 15 minutes.^ At the first site, analysis of a small (6.7 acre, 2.71 ha) field site monitored as part of the USDA-ARS Conservation Effects Assessment Project in NE Indiana was conducted to determine the effect of climate change on agricultural resources. Precipitation, runoff, soil erosion, and crop growth were modeled using WEPP and the four Representative Concentration Pathway (RCP) scenarios used with the CMIP5 model family from the IPCC 5AR to determine the effectiveness of common agricultural Best Management Practices (BMPs) under predicted climate change. Although precipitation is predicted here to increase by 2100, sediment loss and runoff will decrease due to a reduction of concrete frost conditions during late winter. However, an increase in the amount of precipitation falling in spring and earlier soybean senescence was predicted to lead to increased soil loss in early spring and fall.^ At the second site, a small agricultural hillslope managed by the USDA-ARS in the Southern Coastal Plain of the United States was modeled using WEPP under current and future climates to assess the effect of predicted future climate change on soil erosion, runoff, and BMP effectiveness. Future climate data was similar to that used at the first site. Predicted climatic shift caused soil loss and runoff to be reduced in the first three months of the year, while late fall and early winter months had increases in predicted soil loss and runoff. Increased temperatures were predicted to cause winter cover crops to grow faster, unhindered by frost in winter. Soil loss increased when cotton senesced earlier under warmer temperatures. Early season water deficits and higher evapotranspiration also increased irrigation demands in the growing season. The combination of no-till, rye cover crop, and riparian buffer increased in effectiveness into the future, while all other management systems had either similar or slightly reduced effectiveness under predicted future climate. ^ At the third site, the Blackwood Creek watershed, a tributary of Lake Tahoe in California, was assessed for potential changes in climate and fire risk under 21st century climates projected by the IPCC AR5. While total precipitation varied by decade, the portion of precipitation falling as snow decreased by as much as 26%, and projected air temperatures increased by as much as 3.4°C by 2090. Total soil water (TSW) predictions by WEPP indicated that fire ignition in the Sierra Nevada region from 1984-2013 coincided with simulated minimum TSW. Risk categories based on simulated TSW changed under projected future climate, with an increase in the number of high risk days defined by TSWs less than 40 mm. Simulated TSW in the Blackwood Creek watershed at the time of historic fires in the region also indicated that the Keetch-Byram Drought Index (KBDI) was correlated to TSW (R2 = 0.59) when KBDI was less than 500.
Trotochaud, Joseph, "Climate change impact assessments using the Water Erosion Prediction Project model" (2015). Open Access Theses. 622.