Using Temporal Deep Learning Models to Estimate Daily Snow Water Equivalent Over the Rocky Mountains
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...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-04-01
|
| Series: | Water Resources Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2023WR035009 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849687187891683328 |
|---|---|
| author | Shiheng Duan Paul Ullrich Mark Risser Alan Rhoades |
| author_facet | Shiheng Duan Paul Ullrich Mark Risser Alan Rhoades |
| author_sort | Shiheng Duan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e61f3e59009d4a7d97c577a544b28d59 |
| institution | DOAJ |
| issn | 0043-1397 1944-7973 |
| language | English |
| publishDate | 2024-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Water Resources Research |
| spelling | doaj-art-e61f3e59009d4a7d97c577a544b28d592025-08-20T03:22:22ZengWileyWater Resources Research0043-13971944-79732024-04-01604n/an/a10.1029/2023WR035009Using Temporal Deep Learning Models to Estimate Daily Snow Water Equivalent Over the Rocky MountainsShiheng Duan0Paul Ullrich1Mark Risser2Alan Rhoades3Atmospheric Science Graduate Group University of California Davis CA USAAtmospheric Science Graduate Group University of California Davis CA USALawrence Berkeley National Laboratory Berkeley CA USALawrence Berkeley National Laboratory Berkeley CA USAAbstract 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.https://doi.org/10.1029/2023WR035009snow water equivalent predictiondeep learningextrapolation |
| spellingShingle | Shiheng Duan Paul Ullrich Mark Risser Alan Rhoades Using Temporal Deep Learning Models to Estimate Daily Snow Water Equivalent Over the Rocky Mountains Water Resources Research snow water equivalent prediction deep learning extrapolation |
| title | Using Temporal Deep Learning Models to Estimate Daily Snow Water Equivalent Over the Rocky Mountains |
| title_full | Using Temporal Deep Learning Models to Estimate Daily Snow Water Equivalent Over the Rocky Mountains |
| title_fullStr | Using Temporal Deep Learning Models to Estimate Daily Snow Water Equivalent Over the Rocky Mountains |
| title_full_unstemmed | Using Temporal Deep Learning Models to Estimate Daily Snow Water Equivalent Over the Rocky Mountains |
| title_short | Using Temporal Deep Learning Models to Estimate Daily Snow Water Equivalent Over the Rocky Mountains |
| title_sort | using temporal deep learning models to estimate daily snow water equivalent over the rocky mountains |
| topic | snow water equivalent prediction deep learning extrapolation |
| url | https://doi.org/10.1029/2023WR035009 |
| work_keys_str_mv | AT shihengduan usingtemporaldeeplearningmodelstoestimatedailysnowwaterequivalentovertherockymountains AT paulullrich usingtemporaldeeplearningmodelstoestimatedailysnowwaterequivalentovertherockymountains AT markrisser usingtemporaldeeplearningmodelstoestimatedailysnowwaterequivalentovertherockymountains AT alanrhoades usingtemporaldeeplearningmodelstoestimatedailysnowwaterequivalentovertherockymountains |