Leveraging Next‐Generation Satellite Remote Sensing‐Based Snow Data to Improve Seasonal Water Supply Predictions in a Practical Machine Learning‐Driven River Forecast System
Abstract Seasonal predictions of spring‐summer river flow volume (water supply forecasts, WSFs) are foundational to western US water management. We test a new space‐based remote sensing product, spatially and temporally complete (STC) MODSCAG fractional snow‐covered area (fSCA), as input for the Nat...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-04-01
|
| Series: | Water Resources Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2023WR035785 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850115164091711488 |
|---|---|
| author | Sean W. Fleming Karl Rittger Catalina M. Oaida Taglialatela Indrani Graczyk |
| author_facet | Sean W. Fleming Karl Rittger Catalina M. Oaida Taglialatela Indrani Graczyk |
| author_sort | Sean W. Fleming |
| collection | DOAJ |
| description | Abstract Seasonal predictions of spring‐summer river flow volume (water supply forecasts, WSFs) are foundational to western US water management. We test a new space‐based remote sensing product, spatially and temporally complete (STC) MODSCAG fractional snow‐covered area (fSCA), as input for the Natural Resources Conservation Service (NRCS) operational US West‐wide WSF system. fSCA data were considered alongside traditional SNOTEL predictors, in both statistical and AI‐based NRCS operational hydrologic models, throughout the forecast season, in four test watersheds (Walker, Wind, Piedra, and Gila Rivers in California, Wyoming, Colorado, and New Mexico). Outcomes from over 200 WSF models suggest fSCA‐enabled accuracy gains are most consistent and explainable for short‐lead, late‐season forecasts (roughly 10%–25% improvements, typically), which in operational practice can be challenging as snowlines rise above in situ measurement sites. Gains are roughly proportional to how thoroughly spring‐summer runoff is dominated by snowmelt, and how poorly in situ networks monitor late‐season snowpack. fSCA also improved accuracy for long‐lead, early‐season forecasts, which are similarly problematic in WSF practice, but not for WSFs issued around the time of peak snow accumulation, when in situ measurements reasonably characterize mountain snowpack available for upcoming spring‐summer snowmelt. The AI‐based hydrologic model generally outperformed the statistical model and, in some cases, better‐capitalized on satellite remote sensing. Additionally, preliminary analyses suggest reasonable WSF skill in many cases using fSCA as the sole predictor, potentially useful in sparsely monitored regions; and that combining satellite and in situ products in data‐driven hydrologic models using genetic algorithm‐based predictor selection could help guide new SNOTEL site selection. |
| format | Article |
| id | doaj-art-e50197681b944db0a650ffc9332f8c2d |
| institution | OA Journals |
| issn | 0043-1397 1944-7973 |
| language | English |
| publishDate | 2024-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Water Resources Research |
| spelling | doaj-art-e50197681b944db0a650ffc9332f8c2d2025-08-20T02:36:39ZengWileyWater Resources Research0043-13971944-79732024-04-01604n/an/a10.1029/2023WR035785Leveraging Next‐Generation Satellite Remote Sensing‐Based Snow Data to Improve Seasonal Water Supply Predictions in a Practical Machine Learning‐Driven River Forecast SystemSean W. Fleming0Karl Rittger1Catalina M. Oaida Taglialatela2Indrani Graczyk3US Department of Agriculture Natural Resources Conservation Service Oregon Snow Survey Data Collection Office Portland OR USAInstitute for Artic and Alpine Research University of Colorado Boulder CO USAJet Propulsion Laboratory Applied Science Systems Engineering Group California Institute of Technology Pasadena CA USAJet Propulsion Laboratory NASA Western Water Applications Office California Institute of Technology Pasadena CA USAAbstract Seasonal predictions of spring‐summer river flow volume (water supply forecasts, WSFs) are foundational to western US water management. We test a new space‐based remote sensing product, spatially and temporally complete (STC) MODSCAG fractional snow‐covered area (fSCA), as input for the Natural Resources Conservation Service (NRCS) operational US West‐wide WSF system. fSCA data were considered alongside traditional SNOTEL predictors, in both statistical and AI‐based NRCS operational hydrologic models, throughout the forecast season, in four test watersheds (Walker, Wind, Piedra, and Gila Rivers in California, Wyoming, Colorado, and New Mexico). Outcomes from over 200 WSF models suggest fSCA‐enabled accuracy gains are most consistent and explainable for short‐lead, late‐season forecasts (roughly 10%–25% improvements, typically), which in operational practice can be challenging as snowlines rise above in situ measurement sites. Gains are roughly proportional to how thoroughly spring‐summer runoff is dominated by snowmelt, and how poorly in situ networks monitor late‐season snowpack. fSCA also improved accuracy for long‐lead, early‐season forecasts, which are similarly problematic in WSF practice, but not for WSFs issued around the time of peak snow accumulation, when in situ measurements reasonably characterize mountain snowpack available for upcoming spring‐summer snowmelt. The AI‐based hydrologic model generally outperformed the statistical model and, in some cases, better‐capitalized on satellite remote sensing. Additionally, preliminary analyses suggest reasonable WSF skill in many cases using fSCA as the sole predictor, potentially useful in sparsely monitored regions; and that combining satellite and in situ products in data‐driven hydrologic models using genetic algorithm‐based predictor selection could help guide new SNOTEL site selection.https://doi.org/10.1029/2023WR035785remote sensingstreamflow modelingsnow monitoringwater supplywestern USoperational forecasting |
| spellingShingle | Sean W. Fleming Karl Rittger Catalina M. Oaida Taglialatela Indrani Graczyk Leveraging Next‐Generation Satellite Remote Sensing‐Based Snow Data to Improve Seasonal Water Supply Predictions in a Practical Machine Learning‐Driven River Forecast System Water Resources Research remote sensing streamflow modeling snow monitoring water supply western US operational forecasting |
| title | Leveraging Next‐Generation Satellite Remote Sensing‐Based Snow Data to Improve Seasonal Water Supply Predictions in a Practical Machine Learning‐Driven River Forecast System |
| title_full | Leveraging Next‐Generation Satellite Remote Sensing‐Based Snow Data to Improve Seasonal Water Supply Predictions in a Practical Machine Learning‐Driven River Forecast System |
| title_fullStr | Leveraging Next‐Generation Satellite Remote Sensing‐Based Snow Data to Improve Seasonal Water Supply Predictions in a Practical Machine Learning‐Driven River Forecast System |
| title_full_unstemmed | Leveraging Next‐Generation Satellite Remote Sensing‐Based Snow Data to Improve Seasonal Water Supply Predictions in a Practical Machine Learning‐Driven River Forecast System |
| title_short | Leveraging Next‐Generation Satellite Remote Sensing‐Based Snow Data to Improve Seasonal Water Supply Predictions in a Practical Machine Learning‐Driven River Forecast System |
| title_sort | leveraging next generation satellite remote sensing based snow data to improve seasonal water supply predictions in a practical machine learning driven river forecast system |
| topic | remote sensing streamflow modeling snow monitoring water supply western US operational forecasting |
| url | https://doi.org/10.1029/2023WR035785 |
| work_keys_str_mv | AT seanwfleming leveragingnextgenerationsatelliteremotesensingbasedsnowdatatoimproveseasonalwatersupplypredictionsinapracticalmachinelearningdrivenriverforecastsystem AT karlrittger leveragingnextgenerationsatelliteremotesensingbasedsnowdatatoimproveseasonalwatersupplypredictionsinapracticalmachinelearningdrivenriverforecastsystem AT catalinamoaidataglialatela leveragingnextgenerationsatelliteremotesensingbasedsnowdatatoimproveseasonalwatersupplypredictionsinapracticalmachinelearningdrivenriverforecastsystem AT indranigraczyk leveragingnextgenerationsatelliteremotesensingbasedsnowdatatoimproveseasonalwatersupplypredictionsinapracticalmachinelearningdrivenriverforecastsystem |