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这篇文章引入了流量之外的数据进行参数率定，提高了模型模拟精度，并发现了枯水季精度提高更显著。
Enhancing Hydrologic Model Performance in a Data-Scarce Basin Using Satellite-Based Soil Moisture Data
2026
Accurate hydrological modelling is essential for understanding and forecasting water cycle processes, particularly in data-scarce river basins. Advances in satellite remote sensing now provide valuable complementary data sources that can be used to improve distributed hydrological model predictions. This study evaluates the effectiveness of incorporating satellite-derived soil moisture (SM) in calibrating the Variable Infiltration Capacity (VIC) model for the Wardha River Basin (WRB). Calibration was performed using observed streamflow at the basin outlet together with the GLEAM soil moisture product. To fully capture both spatial patterns and temporal dynamics, the calibration framework combined a traditional streamflow-based metric with a bias-insensitive SM metric. Three multivariate calibration strategies integrating SM were tested against the conventional streamflow-only approach. Model performance was assessed at both annual and seasonal scales, including ungauged sites, to evaluate the added value of spatial information from SM. Results demonstrate that incorporating SM data improves the simulation of streamflow, particularly under low-flow conditions, and enhances predictive skill for upstream and tributary sub-basins. These findings underscore the potential of remotely sensed soil moisture for strengthening hydrological modelling in regions where ground observations are limited.