|Gautam, Sagar - University Of Missouri|
|Costello, Christine - University Of Missouri|
|Thompson, Allen - University Of Missouri|
|Svoma, Bohumil - University Of Missouri|
Submitted to: Annual International SWAT Conference
Publication Type: Abstract Only
Publication Acceptance Date: 5/4/2017
Publication Date: 6/28/2017
Citation: Gautam, S., Costello, C., Baffaut, C., Thompson, A., Svoma, B.M., Sadler, E.J. 2017. Predicting drought in an agricultural watershed given climate variability [abstract]. Annual International SWAT Conference. p. 67.
Technical Abstract: Changes in the future hydrologic cycle due to changes in temperature (T) and precipitation (P) are likely to be associated with increases in hydrologic extremes. This study evaluates the impacts of climate variability on drought using the Soil and Water Assessment Tool (SWAT) in Goodwater Creek Experimental Watershed (73 km2) in Missouri, USA. The Standardized Precipitation Index, Standardized Streamflow Index, and Z-score based soil moisture index were computed for historical data to quantify meteorological, hydrological and agricultural drought, respectively. The physical values, (e.g., mm precipitation) that corresponded to droughts during the historic period were used to evaluate future drought frequency. The frequency of future drought was predicted using twelve T & P datasets from the Coupled Model Intercomparison Project Phase 5 for the four relative concentration pathways (RCP 2.6, 4.5, 6.0, and 8.5). These data were statistically downscaled at the watershed scale to a finer resolution. After calibration of the SWAT model based on historical data (1993-2010), simulations were run for the future (until 2075) using these climate data. SWAT-simulated streamflow and soil moisture were used to compute drought indices on a monthly basis. Results showed that droughts will occur more frequently in the future than they have during the historic period for the majority of climate model outputs under all four emission pathways. Multiple indices were analyzed to better understand how future hydrologic changes present risk to different sectors, i.e., water managers and farmers.