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Publication Date
1 January 2022

An Improved Zhang's Dynamic Water Balance Model Using Budyko-Based Snow Representation for Better Streamflow Predictions

Dynamic water balance model using Budyko-based snow representation.
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A snow module is developed in this study to improve the model's applicability to the CONUS. The snow module is designed based on the concept of Budyko's framework to preserve the spirit and parameter parsimony that ZDWBM has achieved. The augmented model with snow module shows a significant improvement in simulating monthly streamflow, especially for snow-affected regions. However, we find the augmented model produces a seasonal bias in the simulated streamflow, requiring an additional improvement strategy. We assume the model's seasonal bias is attributable to the time-invariant model parameters inaccurately representing the time-varying catchment characteristics. In order to minimize the seasonal bias, the snow augmented model is calibrated with monthly parameters (ZDWBM-msnow). After applying monthly parameterization to the snow augmented model, it becomes suitable for more than 90% of the total catchments, and the seasonal bias diminishes.


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 the 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. Our analyses suggest that ZDWBM-msnow could be used to estimate monthly runoff for ungauged basins across the continental United States.


This study explores the limitations of an existing dynamic water balance model (ZDWBM) proposed by L. Zhang et al. (2008). It provides measures to overcome those limits based on more than 1,200 unmodified basins across the CONUS with observation data of 31 years on average. Zhang's dynamic water balance model (ZDWBM) describes the monthly water balance of a catchment by partitioning the hydrological process based on Budyko's framework. 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.

Point of Contact
Naresh Devineni
City College of New York (CUNY)
Funding Program Area(s)