Pflug et al. (2023) -- Model configuration and outputs


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Owners: Justin M Pflug
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Created: Jul 12, 2023 at 8:03 p.m.
Last updated: Jul 12, 2023 at 11:05 p.m.
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Content types: Multidimensional Content 
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Abstract

Snow is a vital component of the global land surface energy and water budget. In this study, we investigate the how synthetic observations of snow water equivalent (SWE) representative of a synthetic aperture radar remote sensing platform could improve spatiotemporal estimates of snowpack. We use an Observation System Simulation Experiment, specifically investigating how much snow simulated using popular models and forcing data could be improved by assimilating synthetic observations of SWE. We focus this study across a 24°-by-37° domain in the Western United States and Canada, simulating snow at approximately 250 m resolution and hourly timesteps in water-year 2019. We perform two data assimilation experiments, including: 1) a simulation excluding synthetic observations in forests where canopies obstruct remote sensing retrievals, and 2) a simulation inferring snow distribution in forested grid cells using synthetic observations from nearby canopy-free grid cells. Results found that assimilating synthetic SWE observations improved average SWE biases at peak snowpack timing in shrub, grass, crop, bare-ground, and wetland land cover types from 14%, to within 1%. However, forested grid cells contained a disproportionate amount of SWE volume. In forests, SWE mean absolute errors at peak snowpack were 111 mm, and average SWE biases were on the order of 150%. Here, the data assimilation approach that estimated forest SWE using observations from the nearest canopy-free grid cells substantially improved these SWE biases (18%) and the SWE mean absolute error (27 mm). Data assimilation also improved estimates of the temporal evolution of both SWE, even in spring snowmelt periods when melting snow and high snow liquid water content block the synthetic SWE retrievals. In fact, in the Upper Colorado River basin, melt-season SWE biases were improved from 63% to within 1%, and the Nash Sutcliffe Efficiency of runoff improved from –2.59 to 0.22. These results demonstrate the value of a snow-focused globally relevant remote sensing platform, and data assimilation for improving the characterization of SWE and associated water availability.

Coverage

Spatial

Coordinate System/Geographic Projection:
WGS 84 EPSG:4326
Coordinate Units:
Decimal degrees
Place/Area Name:
Western United States
North Latitude
52.0000°
East Longitude
-104.0000°
South Latitude
31.0000°
West Longitude
-125.0000°

Temporal

Start Date: 10/01/2018
End Date: 09/01/2019
Leaflet Map data © OpenStreetMap contributors

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Data Services

The following web services are available for data contained in this resource. Geospatial Feature and Raster data are made available via Open Geospatial Consortium Web Services. The provided links can be copied and pasted into GIS software to access these data. Multidimensional NetCDF data are made available via a THREDDS Data Server using remote data access protocols such as OPeNDAP. Other data services may be made available in the future to support additional data types.

How to Cite

Pflug, J. M. (2023). Pflug et al. (2023) -- Model configuration and outputs, HydroShare, http://www.hydroshare.org/resource/e0ad80f818bf4062a335e9e0d7362834

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

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

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