A Dynamic Calibration Approach to Estimate Hydrological Parameters for Future Scenarios

Abstract Submission: Climate change is altering the dynamics of hydrologic systems, leading to increased uncertainty in water resources management. In areas such as the Upper and Middle Rio Grande Basin, shifts in precipitation, snowmelt patterns, and rising temperatures are expected to disrupt water availability. Accurately forecasting future hydrological behavior is crucial, yet traditional models based on stationary assumptions struggle to capture these evolving dynamics. This study aims to estimate and project calibration parameters in the upper and middle Rio Grande basin using static and dynamic calibration techniques in the Soil and Water Assessment Tool (SWAT) model. Historical hydrometeorological data from 1970 to 2020 were used to simulate hydrologic processes in the basin. Dynamic calibration adjusts model parameters over nine overlapping 10-year periods to account for climate and environmental changes over time. In contrast, static calibration was applied to the entire 50-year dataset. Analysis of trends in the calibrated parameters allows them to be extrapolated to predict future behavior of parameters and streamflow under changing climate conditions. The effectiveness of each calibration method is assessed using several metrics, including the Nash-Sutcliffe Efficiency (NSE), the coefficient of determination (R²), and the root mean square error (RMSE). This dynamic calibration approach provided improved predictive capabilities for streamflow prediction, offering critical information for water resources management in a non-stationary climate.
Keywords: Hydrological modeling, dynamic calibration, Rio Grande Basin.

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