Time-varying parameters of the hydrological simulation model under a changing environment

Abstract

Time-varying parameters of hydrological models play a crucial role in capturing the dynamic nature of hydrological processes and nonpoint source pollution under changing environments. In this study, the SWAT-DynamicParam model was developed by integrating the Soil and Water Assessment Tool (SWAT) model with an ensemble Kalman filter (EnKF) to identify and analyze time-varying parameters. The results showed that the SWAT-DynamicParam framework is capable of effectively identifying multiple time-varying characteristics of parameters. The combination of flow and evapotranspiration (ET) data can accurately identify changes in CANMX (Maximum canopy storage), CN2 (Initial SCS runoff curve number for moisture condition II) and ALPHA_BF (Baseflow alpha factor), with the Relative Absolute Error 5%. Compared with using static parameters, the model simulation effect was improved by more than 30% when using time-varying parameters. In addition, the variation in parameters showed significant spatio-temporal heterogeneity. The change trend of parameters at Weijiabao station showed the largest fluctuation, with a range greater than 100. Temporally, CN2 and ALPHA_BF both reached peak values in 2008 while the trend of CANMX was the opposite. At the monthly scale, the trends of ALPHA_BF and CANMX were similar: Values were at a minimum in April and May, with the range is 2.57 times the minimum value. The CN2′s lowest value was recorded in August, whereas December saw its highest, reaching 82. In summary, the SWAT-DynamicParam model enhanced simulation accuracy by over 30%, demonstrating the pivotal role of accurately identified time-varying parameters in capturing the dynamic nature of hydrological processes and their response to environmental changes.