An Improved Zhang's Dynamic Water Balance Model Using Budyko‐Based Snow Representation for Better Streamflow Predictions. Issue 1 (5th January 2022) - Hwang, Jeongwoo; Devineni, Naresh
Abstract: Understanding the water balance of a catchment in relation to its regional climate forcings and catchment characteristics is critical for predicting current and future water resources amid changing climate and land cover. This study intends to improve Zhang's monthly water balance model (a physics‐based conceptual hydrologic model) that reflects the physical partitioning process of the hydrological cycle at the basin level based on regional climate and catchment characteristics. The existing model does not include snow process and has confronted evident limitations in snow‐affected areas, 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. We introduce a snow module based on surface energy balance and Budyko‐limits on melting and combine it with the existing water balance equations. Moreover, monthly parameterization is applied to the model to better explain the time‐varying hydrological characteristics of a catchment. The proposed model involves five different monthly parameters, which determine the physical partitioning process of the hydrological cycle, and they are regionally calibrated and validated under Budyko‐type constraints. The model is applied to 1, 210 basins across the continental United States (CONUS), and the simulated streamflow is compared to the observed data. The proposed model significantly outperformed the original model, improving the median NSE by 31% (from 0.51 to 0.67) and increasing the number of catchments with an acceptable NSE by 58%. The spatial variability of the basin characteristics across the CONUS is also investigated based on the calibrated parameters. Key Points: A parsimonious snow module is developed based on Budyko's framework to improve an existing physics‐based dynamic water balance model A monthly parameterization is proposed to reflect the seasonal behavior of catchment characteristics The augmented model that includes snow with monthly parameterization shows significant improvements in simulating monthly streamflow