Modeling Spatial Distribution of Snow Water Equivalent by Combining Meteorological and Satellite Data with Lidar Maps

Journal Article · 30 September 2022 · Artificial Intelligence for the Earth Systems

DOI: https://doi.org/10.1175/AIES-D-22-0010.1 · OSTI ID:1900297

Mital, Utkarsh  ^[1]; Dwivedi, Dipankar ^[1]; Özgen-Xian, Ilhan ^[2]; Brown, James B. ^[3]; Steefel, Carl I. ^[1]

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1. a Energy Geosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California
2. a Energy Geosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, b Institute of Geoecology, Technische Universität Braunschweig, Braunschweig, Germany
3. c Environmental Genomics and System Biology, Lawrence Berkeley National Laboratory, Berkeley, California, d Department of Statistics, University of California, Berkeley, Berkeley, California

Abstract An accurate characterization of the water content of snowpack, or snow water equivalent (SWE), is necessary to quantify water availability and constrain hydrologic and land surface models. Recently, airborne observations (e.g., lidar) have emerged as a promising method to accurately quantify SWE at high resolutions (scales of ∼100 m and finer). However, the frequency of these observations is very low, typically once or twice per season in the Rocky Mountains of Colorado. Here, we present a machine learning framework that is based on random forests to model temporally sparse lidar-derived SWE, enabling estimation of SWE at unmapped time points. We approximated the physical processes governing snow accumulation and melt as well as snow characteristics by obtaining 15 different variables from gridded estimates of precipitation, temperature, surface reflectance, elevation, and canopy. Results showed that, in the Rocky Mountains of Colorado, our framework is capable of modeling SWE with a higher accuracy when compared with estimates generated by the Snow Data Assimilation System (SNODAS). The mean value of the coefficient of determination R ² using our approach was 0.57, and the root-mean-square error (RMSE) was 13 cm, which was a significant improvement over SNODAS (mean R ² = 0.13; RMSE = 20 cm). We explored the relative importance of the input variables and observed that, at the spatial resolution of 800 m, meteorological variables are more important drivers of predictive accuracy than surface variables that characterize the properties of snow on the ground. This research provides a framework to expand the applicability of lidar-derived SWE to unmapped time points. Significance Statement Snowpack is the main source of freshwater for close to 2 billion people globally and needs to be estimated accurately. Mountainous snowpack is highly variable and is challenging to quantify. Recently, lidar technology has been employed to observe snow in great detail, but it is costly and can only be used sparingly. To counter that, we use machine learning to estimate snowpack when lidar data are not available. We approximate the processes that govern snowpack by incorporating meteorological and satellite data. We found that variables associated with precipitation and temperature have more predictive power than variables that characterize snowpack properties. Our work helps to improve snowpack estimation, which is critical for sustainable management of water resources.

• Research Organization: Lawrence Berkeley National Laboratory, Berkeley, CA (United States)
• Sponsoring Organization: USDOE; USDOE Office of Science (SC), Biological and Environmental Research (BER). Earth and Environmental Systems Science Division
• Grant/Contract Number: AC02-05CH11231
• OSTI ID: 1900297
• Journal Information: Artificial Intelligence for the Earth Systems, Journal Name: Artificial Intelligence for the Earth Systems Journal Issue: 4 Vol. 1; ISSN 2769-7525
• Publisher: American Meteorological SocietyCopyright Statement
• Country of Publication: United States
• Language: English