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An improved Zhang’s Water Balance Model using Budyko-based snow representation and time-varying parameterization for better streamflow predictions

Presentation Date
Wednesday, December 9, 2020 at 4:30pm



Understanding the water balance of a catchment in relation to its regional climate forcings and catchment characteristics is critical for predicting and planning contemporary and future water resources amid the changing climate and landcover. This study, therefore, intends to improve the Zhang’s monthly water balance model to reflect the physical partitioning process of the hydrological cycle at the basin level based on their regional climate and catchment characteristics. The existing model excludes snow components and has been confronting evident limitations in areas affected by snow, which is a critical aspect since snowmelt water has been a significant source of water resources for many regions, especially in the temperate and frigid zones. To this end, we introduce a snow module and combine it with the existing water balance equations. The proposed model involves five different parameters, which determine the physical partitioning process of the hydrological cycle, and they are regionally calibrated under Budyko-type constraints. Each model parameter has a physical interpretation; snow melting efficiency, catchment retention efficiency, evapotranspiration efficiency, groundwater transition rate, and maximum soil moisture capacity. The model is applied to 1211 basins across the contiguous United States and the simulated streamflow is compared to the observed streamflow data. The proposed model significantly outperformed the current model, improving the mean NSE by 16% and increasing the number of stations with an acceptable level of NSE by 37%. Spatial variability of the basin characteristics across the continental United States is also investigated based on the parameter values that are calibrated.