Efficient and Regionally Transferable Snow Water Equivalent Estimation Using a Long Short‐Term Memory Network Journal Article uri icon

Overview

abstract

  • Abstract; Snow reanalyses that combine process based models and remote sensing observations of snow provide estimates of seasonal snow water equivalent (SWE) evolution that surpass the accuracies of traditional modeling approaches. However, snow reanalyses are only available over smaller subregions and sometimes use computationally expensive modeling approaches. We investigate whether 1 km‐resolution and daily SWE from a popular reanalysis could be learned by connecting only trusted meteorological fields (multidecadal precipitation patterns and daily air temperature) and remotely sensed snow cover using a deep learning model. Relative to point observations of SWE evolution in the western United States, the deep learning model was able to reproduce the spatial and temporal evolution estimated by the snow reanalysis. Further, we found that the deep learning model was efficient and could be expanded geographically to estimate SWE evolution in the European Alps, demonstrating a high average coefficient of correlation (0.81) and low peak‐SWE bias (<1%) versus point estimates of SWE in seasonally snowy locations, with no statistical difference between regions at different elevations and with different forest cover. This study demonstrates how deep learning approaches could be used to mine connections between daily SWE evolution, snow cover remote sensing, and limited meteorological information to generate and expand the geographical extent of fine‐resolution historical snow estimates in complex terrains.

publication date

  • December 1, 2025

Date in CU Experts

  • December 26, 2025 8:03 AM

Full Author List

  • Pflug JM; Kumar SV; Hall DK; Riggs GA; Konapala G; Whitney KM; Wrzesien ML; Nie W; Sun Z; Arsenault KR

author count

  • 10

Other Profiles

International Standard Serial Number (ISSN)

  • 2993-5210

Electronic International Standard Serial Number (EISSN)

  • 2993-5210

Additional Document Info

volume

  • 2

issue

  • 4

number

  • e2025JH000593