Developing a distributed modeling framework considering the spatiotemporally varying hydrological processes for sub-daily flood forecasting in semi-humid and semi-arid watersheds

Abstract

Abstract The complex and varied climate, short duration and high intensity of rainfall, and complicated subsurface properties of semi-humid and semi-arid watersheds pose challenges for sub-daily flood forecasting. Previous studies revealed that lumped models are insufficient because they do not effectively account for the spatial variability of hydrological processes. Extending the lumped model to a distributed modeling framework is a reliable approach for runoff simulation. However, existing distributed models do not adequately characterize the strong spatiotemporal variability of the sub-daily hydrological processes in semi-humid and semi-arid watersheds. To address the above concerns, a distributed modeling framework was proposed that is extended by lumped models and accounts for the effects of time-varying rainfall intensity and reservoir regulation on hydrological processes. Moreover, the Fourier Amplitude Sensitivity Test (FAST) method is performed to identify the sensitive parameters for efficient calibration. To evaluate the performance of the proposed distributed model, it was tested in eight watersheds. The results indicate that the proposed distributed model simulates sub-daily flood events with mean evaluation metrics of 0.80, 9.2%, 13.0%, and 1.05 for NSE, BIAS, RPE, and PTE, respectively, superior to the lumped model. Furthermore, to further evaluate the difference between the proposed distributed model and the existing distributed models, it was compared with the Variable Infiltration Capacity (VIC) model at various time steps, including 3h, 6h, 12h, and24 h. The proposed distributed model was able to better capture the flooding processes at shorter time steps, especially 3 h. Therefore, it can be considered a practical tool for sub-daily flood forecasting in semi-humid and semi-arid watersheds.