Articles | Volume 26, issue 22
https://doi.org/10.5194/hess-26-5721-2022
https://doi.org/10.5194/hess-26-5721-2022
Research article
 | 
14 Nov 2022
Research article |  | 14 Nov 2022

Precipitation biases and snow physics limitations drive the uncertainties in macroscale modeled snow water equivalent

Eunsang Cho, Carrie M. Vuyovich, Sujay V. Kumar, Melissa L. Wrzesien, Rhae Sung Kim, and Jennifer M. Jacobs

Data sets

Data and code for: Precipitation Biases and Snow Physics Limitations Drive the Uncertainties in Macroscale Modeled Snow Water Equivalent Eunsang Cho, Carrie M. Vuyovich, Sujay V. Kumar, Melissa L. Wrzesien, Rhae Sung Kim, & Jennifer M. Jacobs https://doi.org/10.5281/zenodo.7158404

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Short summary
While land surface models are a common approach for estimating macroscale snow water equivalent (SWE), the SWE accuracy is often limited by uncertainties in model physics and forcing inputs. In this study, we found large underestimations of modeled SWE compared to observations. Precipitation forcings and melting physics limitations dominantly contribute to the SWE underestimations. Results provide insights into prioritizing strategies to improve the SWE simulations for hydrologic applications.