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Demonstrating a Hybrid Machine Learning Approach for Snow Characteristic Estimation Throughout the Western United States.

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Title: Demonstrating a Hybrid Machine Learning Approach for Snow Characteristic Estimation Throughout the Western United States.
Authors: Steele, Hannah1 (AUTHOR) steeleha@oregonstate.edu, Small, Eric E.2 (AUTHOR), Raleigh, Mark S.1 (AUTHOR)
Source: Water Resources Research. Jun2024, Vol. 60 Issue 6, p1-17. 17p.
Subjects: Standard deviations, Machine learning, Snow cover
Geographic Terms: United States
Abstract: Snow is a critical component of global climate and provides water resources to over 1 billion people worldwide. Yet current measurement methods and modeling techniques lack the ability to fully capture snow characteristics such as snow water equivalent (SWE) and density across variable landscapes. In recent years, physics‐informed machine learning (ML) methods have demonstrated promise for combining data‐driven learning and physical information. However, this capability has not been widely explored within snow hydrology. Here, we develop a "hybrid" model that applies ML informed by outputs from a physical model and assess whether it provides more accurate estimations of SWE and snow density. We trained and evaluated models at 49 SNOw TELemetry locations spanning a range of snow climates in the western US using 9 years of daily data. The research addressed two questions. In the first, the performance of the hybrid model was compared against a plain neural network (long short‐term memory, Long‐Short Term Memory), a high‐quality physical model, and a statistical snow density model. The second question focused on how regionally trained hybrid models compared to a westwide model as well as their transferability between multiple snow regions. The results showed that combining physical information and ML reduced SWE Root Mean Square Error by 35% compared to a physical model and 51% compared to a neural network. Additionally, regional training only provided minimal benefits compared with a westwide model. These findings indicate that a hybrid approach can yield more accurate snowpack characterization than either physical snow models or ML alone. Plain Language Summary: Seasonal mountain snow provides important water resources for communities throughout the western United States and other global regions. Measuring this snow is difficult due to the large area and variable terrain found in most mountain ranges. This study uses a specialized computer model to produce better estimates of important snowpack information such as snow water equivalent (SWE) and snow density. The computer model combines a neural network (a type of model meant to mimic the human brain) with snowpack estimates from a mathematical model based on physics. When used together, this study found that this "hybrid" approach of the computer and mathematical models was better than other traditional ways of estimating SWE and snow density. Key Points: A hybrid approach combining machine learning and physical model output was developed and trained over a wide range of snow conditionsThe hybrid model estimated snow density and snow water equivalent with improved accuracy relative to data‐driven and physical modelsLocal training of the hybrid model did not improve snow estimates over a model trained at all study sites across the western United States [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:Snow is a critical component of global climate and provides water resources to over 1 billion people worldwide. Yet current measurement methods and modeling techniques lack the ability to fully capture snow characteristics such as snow water equivalent (SWE) and density across variable landscapes. In recent years, physics‐informed machine learning (ML) methods have demonstrated promise for combining data‐driven learning and physical information. However, this capability has not been widely explored within snow hydrology. Here, we develop a "hybrid" model that applies ML informed by outputs from a physical model and assess whether it provides more accurate estimations of SWE and snow density. We trained and evaluated models at 49 SNOw TELemetry locations spanning a range of snow climates in the western US using 9 years of daily data. The research addressed two questions. In the first, the performance of the hybrid model was compared against a plain neural network (long short‐term memory, Long‐Short Term Memory), a high‐quality physical model, and a statistical snow density model. The second question focused on how regionally trained hybrid models compared to a westwide model as well as their transferability between multiple snow regions. The results showed that combining physical information and ML reduced SWE Root Mean Square Error by 35% compared to a physical model and 51% compared to a neural network. Additionally, regional training only provided minimal benefits compared with a westwide model. These findings indicate that a hybrid approach can yield more accurate snowpack characterization than either physical snow models or ML alone. Plain Language Summary: Seasonal mountain snow provides important water resources for communities throughout the western United States and other global regions. Measuring this snow is difficult due to the large area and variable terrain found in most mountain ranges. This study uses a specialized computer model to produce better estimates of important snowpack information such as snow water equivalent (SWE) and snow density. The computer model combines a neural network (a type of model meant to mimic the human brain) with snowpack estimates from a mathematical model based on physics. When used together, this study found that this "hybrid" approach of the computer and mathematical models was better than other traditional ways of estimating SWE and snow density. Key Points: A hybrid approach combining machine learning and physical model output was developed and trained over a wide range of snow conditionsThe hybrid model estimated snow density and snow water equivalent with improved accuracy relative to data‐driven and physical modelsLocal training of the hybrid model did not improve snow estimates over a model trained at all study sites across the western United States [ABSTRACT FROM AUTHOR]
ISSN:00431397
DOI:10.1029/2023WR035805