In this study we construct and compare three different deep learning (DL) models for estimating daily snow water equivalent (SWE) from high‐resolution gridded meteorological fields over the Rocky Mountain region. To train the DL models, Snow Telemetry (SNOTEL) station‐based SWE observations are used as the prediction target. All DL models produce higher median Nash‐Sutcliffe Efficiency (NSE) values than a conceptual SWE model and interpolated gridded data sets, although mean squared errors also tend to be higher. Sensitivity of the SWE prediction to the model's input variables is analyzed using an explainable artificial intelligence (XAI) method, yielding insight into the physical relationships learned by the models. This method reveals the dominant role precipitation and temperature play in snowpack dynamics. In applying our models to estimate SWE throughout the Rocky Mountains, an extrapolation problem arises since the statistical properties of SWE (e.g., annual maximum) and geographical properties of individual grid points (e.g., elevation) differ from the training data. This problem is solved by normalizing the SWE with its historical maximum value to alleviate extrapolation for all tested DL models. Our work shows that the DL models are promising tools for estimating SWE, and sufficiently capture relevant physical relationships to make them useful for spatial and temporal extrapolation of SWE values.
Duan, Shiheng, et al. "Using Temporal Deep Learning Models to Estimate Daily Snow Water Equivalent Over the Rocky Mountains." Water Resources Research, vol. 60, no. 4, Apr. 2024. https://doi.org/10.1029/2023WR035009
Duan, Shiheng, Ullrich, Paul, Risser, Mark, & Rhoades, Alan (2024). Using Temporal Deep Learning Models to Estimate Daily Snow Water Equivalent Over the Rocky Mountains. Water Resources Research, 60(4). https://doi.org/10.1029/2023WR035009
Duan, Shiheng, Ullrich, Paul, Risser, Mark, et al., "Using Temporal Deep Learning Models to Estimate Daily Snow Water Equivalent Over the Rocky Mountains," Water Resources Research 60, no. 4 (2024), https://doi.org/10.1029/2023WR035009
@article{osti_2337973,
author = {Duan, Shiheng and Ullrich, Paul and Risser, Mark and Rhoades, Alan},
title = {Using Temporal Deep Learning Models to Estimate Daily Snow Water Equivalent Over the Rocky Mountains},
annote = {Abstract In this study we construct and compare three different deep learning (DL) models for estimating daily snow water equivalent (SWE) from high‐resolution gridded meteorological fields over the Rocky Mountain region. To train the DL models, Snow Telemetry (SNOTEL) station‐based SWE observations are used as the prediction target. All DL models produce higher median Nash‐Sutcliffe Efficiency (NSE) values than a conceptual SWE model and interpolated gridded data sets, although mean squared errors also tend to be higher. Sensitivity of the SWE prediction to the model's input variables is analyzed using an explainable artificial intelligence (XAI) method, yielding insight into the physical relationships learned by the models. This method reveals the dominant role precipitation and temperature play in snowpack dynamics. In applying our models to estimate SWE throughout the Rocky Mountains, an extrapolation problem arises since the statistical properties of SWE (e.g., annual maximum) and geographical properties of individual grid points (e.g., elevation) differ from the training data. This problem is solved by normalizing the SWE with its historical maximum value to alleviate extrapolation for all tested DL models. Our work shows that the DL models are promising tools for estimating SWE, and sufficiently capture relevant physical relationships to make them useful for spatial and temporal extrapolation of SWE values.},
doi = {10.1029/2023WR035009},
url = {https://www.osti.gov/biblio/2337973},
journal = {Water Resources Research},
issn = {ISSN 0043-1397},
number = {4},
volume = {60},
place = {United States},
publisher = {American Geophysical Union (AGU)},
year = {2024},
month = {04}}
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 379, Issue 2194https://doi.org/10.1098/rsta.2020.0093