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Integrating Meteorological Forcings and Satellite-Derived Observations to Emulate Snow Water Equivalent in the Colorado Rocky Mountains Using a Spatiotemporal Deep Learning Model

Year: 2026
Type: article
Abstract: Accurate estimates of snow water equivalent (SWE) are critical for hydrologic forecasting and water resource management in snow-dominated regions. While in situ observations, such as those from the U.S. Department of Agriculture Snowpack Telemetry (SNOTEL) network, provide direct measurements, their sparse spatial coverage limits their broader applicability. Numerical models such as Noah LSM with multiparameterization options (Noah-MP) provide spatially continuous SWE estimates but are computationally demanding. This study develops a deep learning–based emulator to reproduce SWE simulated by Noah-MP using a convolutional long short-term memory (ConvLSTM) network trained on meteorological forcings from the Weather Research and Forecasting Model and augmented with static physiographic variables and remotely sensed snow cover and leaf area index data. Incorporating spatial coupling substantially improves performance relative to a pixelwise long short-term memory baseline, increasing the fraction of grid cells with nonnegative Nash–Sutcliffe efficiency (NSE) by 15%–27% during the test period. The emulator reproduces large-scale SWE patterns consistent with both Noah-MP and Snow Data Assimilation System (SNODAS) and maintains competitive NSE and root-mean-square error performance relative to SNOTEL observations. Seasonal sensitivity experiments yield high R 2 values up to 0.96 during accumulation and 0.97 during melt. Once trained, the emulator generates SWE predictions faster than the parent physics-based simulation, enabling rapid, high-resolution estimates that support real-time monitoring and ensemble forecasting. Significance Statement Communities across the western United States rely on mountain snow as a significant source of freshwater, yet it remains difficult to track how much snow is stored at any given time. Ground measurements are sparse, and the numerical models used to estimate snowpack can take hours to run. We show that a data-driven approach can mimic the behavior of these complex models while producing results almost instantly. By capturing how weather and landscape features shape snowpack across the Colorado Rockies, the system achieves performance comparable to that of a widely used numerical model, with improved agreement with ground measurements at many locations. This capability provides a faster, more practical tool for water planning, drought monitoring, and spring runoff forecasting.
DOI: 10.1175/jhm-d-25-0208.1
Language: English
Open Access status: green
OpenAlex ID: W7171531640
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Cites: 114
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Related to: 10