Streamflow Drought Interpreted Using SWAT Model Simulations of Past and Future Hydrologic Scenarios: Application to Neches and Trinity River Basins, Texas

In water resources and environmental management, hydrologic indexes are often valued as decision support tools because of their practical interpretability. This is true with the streamflow drought index (SDI), which is considered to be a relevant tool for assessing the availability of water resources at the watershed level. Hence, the future of freshwater resources at the watershed scale could be better understood by achieving a realistic projection of SDI. This study used a process-based watershed modeling approach to describe a framework for SDI projection. Specifically, the Soil and Water Assessment Tool (SWAT) model was used to simulate distinctly two watersheds located in the state of Texas, the Trinity and the Neches River Basins. The SWAT model was calibrated with monthly streamflow data for the period 1990–1995. The model was subsequently validated with two decades of discharge data (1996–2015). The evaluation of the SWAT performance during the calibration and validation stages showed acceptable values of efficiency criteria for both watersheds (i.e., Nash-Sutcliffe efficiency ranging from 0.56 to 0.65; index of agreement from 0.79 to 0.92). The calibrated model was used to simulate runoff for the future period 2041–2070 using inputs retrieved from a future climate scenario. However, the SDI calculation requires knowledge of the probability distribution of cumulative discharge data. A Kolmogorov-Smirnov’s goodness-of-fit analysis was conducted for both observed and simulated cumulative discharges. A lognormal distribution was considered for estimating time series of SDI. For the period 1996–2015, the SDI values recovered from the SWAT simulations matched closely with those derived directly from the observed discharge data ($0.52\le R^2\le 0.91$ for the Neches River, and $0.79\le R^2\le 0.89$ for the Trinity River). This result demonstrated the capacity of the analytical procedure to capture and project realistically SDI signals. However, analysis of the ${\chi }^2$ statistic of the SDI patterns for the past and the future periods did not reveal any significant difference.