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Pflug et al. (2025) -- Process based and machine learning model outputs
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Created: Dec 30, 2024 at 7:16 p.m.
Last updated: Dec 30, 2024 at 7:58 p.m.
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Abstract

Snow reanalyses that combine process-based models and remote sensing observations of snow provide estimates of seasonal snow water equivalent (SWE) evolution that surpass the accuracies of traditional modeling approaches. However, snow reanalyses are only available over smaller subregions, and sometimes use computationally expensive modeling approaches. We investigate whether 1 km-resolution and daily SWE from a popular reanalysis could be learned by connecting only the most-trusted meteorological fields (multidecadal precipitation patterns and daily air temperature) and remotely sensed snow cover using a deep learning model. Relative to point observations of SWE evolution in the western United States, the lightweight deep learning model was able to reproduce the spatial and temporal evolution estimated by the snow reanalysis. Further, we found that the deep learning model could be trained in the western United States and then reused to estimate SWE evolution in the European Alps, demonstrating a high average coefficient of correlation (0.81) and low peak-SWE bias (< 1%) versus point estimates of SWE. SWE from the deep learning model also outperformed SWE estimates from physically based land surface simulations, capturing elevation-driven impacts on SWE spatial heterogeneity and interannual differences in seasonal SWE magnitudes important for water resources, climate regulation, and local ecology. This study demonstrates how deep learning approaches could be used to mine connections between daily SWE evolution, snow cover remote sensing, and limited meteorological information to generate and expand the geographical extent of fine-resolution historical snow estimates in complex terrain.

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Content

README.txt 

In situ SWE measurements, and the overlapping SWE estimates for process based and artificial intelligence models. Data here corresponds with the data from Pflug et al. (202X): Lightweight and Regionally Tranferrable Snow Water Equivalent Estimation Using a Long Short-Term Memory Network

Authors: Justin M. Pflug, Sujay V. Kumar, Dorothy K. Hall, George A. Riggs, Goutam Konapala, Kristen M. Whitney, Melissa L. Wrzesien, Wanshu Nie, Ziheng Sun, Kristi R. Arsenault

Zipped files contain directories of CSV files for the Western United States (WUS.tar.gz) and European Alps (Alps.tar.gz) Snow Water Equivalent, in meters. Each file contains a daily data across a 330-day period, starting September 1 of the year prior to the water year (e.g., water-year 2010 begins 1 September 2009). File contents include the in situ SWE measurements [In_situ], SWE estimates from process-based simulations overlapping the in situ locations [Noah-MP and ERA5-Land], and SWE estimates made at 1 km resolution using a long short-term memory network [LSTM and LSTM_melt-corrected]. Western US files also contain data from a historical snow reanalysis (Fang et al. 2022). Readers are referenced to Pflug et al. (202X) for more information.

File naming conventions: ----.csv
	ID: Station ID. Western US stations are all SNOTEL observations (start with 'SNOTEL:')
	LON: Longitude in degrees West (Western US) or East (Alps) [reff: WGS84]
	LAT: Latitude in degrees North [reff: WGS84]
	WY: Water year.

Fang, Y., Liu, Y., Margulis, S.A., 2022. A western United States snow reanalysis dataset over the Landsat era from water years 1985 to 2021. Sci Data 9, 677. https://doi.org/10.1038/s41597-022-01768-7
Pflug, J.M., Kumar, S.V., Hall, D.K., Riggs, G.A., Konapala, G., Whitney, K.M., Wrzesien, M.L., Nie, W., Sun, Z., Arsenault, K.R., 202X. Lightweight and regionally transferrable snow water equivalent estimation using a long short-term memory network [in review]

How to Cite

Pflug, J. M. (2024). Pflug et al. (2025) -- Process based and machine learning model outputs, HydroShare, http://www.hydroshare.org/resource/5f13dd29cebe42f08a1a67539899bcb2

This resource is shared under the Creative Commons Attribution CC BY.

 http://creativecommons.org/licenses/by/4.0/
CC-BY

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