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Title: Sensitivity and model reduction of simulated snow processes: Contrasting observational and parameter uncertainty to improve prediction

Abstract

The hydrology of high-elevation, mountainous regions is poorly represented in Earth Systems Models (ESMs), yet these ecosystems play an important role in the storage and land-atmosphere exchange of water. As much of the western United States’ water comes from water stored in the snowpack (snow water equivalent, SWE), model representation of these regions is important. This study assesses how uncertainty in both model parameters and forcing affect simulated snow processes through sensitivity analysis (active subspaces) on model inputs (meteorological forcing and model input parameters) for a widely used snow model. Observations from an AmeriFlux tower at the Niwot Ridge research site are used to force an integrated, single-column hydrologic model, ParFlow-CLM. This study finds that trees can mute the effects of snow albedo causing the evergreen needleleaf scenarios to be sensitive primarily to hydrologic forcing while bare ground simulations are more sensitive to the snow parameters. The bare ground scenarios are most sensitive overall. Both forcing and model input parameters are important for obtaining accurate hydrologic model results.

Authors:
; ; ; ;
Publication Date:
Research Org.:
Colorado School of Mines, Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
OSTI Identifier:
1577227
Alternate Identifier(s):
OSTI ID: 1803002
Grant/Contract Number:  
SC0016491
Resource Type:
Published Article
Journal Name:
Advances in Water Resources
Additional Journal Information:
Journal Name: Advances in Water Resources Journal Volume: 135 Journal Issue: C; Journal ID: ISSN 0309-1708
Publisher:
Elsevier
Country of Publication:
United Kingdom
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; Water resources; Hydrologic modeling; Sensitivity analysis; Snow water equivalent; Active subspaces

Citation Formats

Ryken, Anna, Bearup, Lindsay A., Jefferson, Jennifer L., Constantine, Paul, and Maxwell, Reed M. Sensitivity and model reduction of simulated snow processes: Contrasting observational and parameter uncertainty to improve prediction. United Kingdom: N. p., 2020. Web. doi:10.1016/j.advwatres.2019.103473.
Ryken, Anna, Bearup, Lindsay A., Jefferson, Jennifer L., Constantine, Paul, & Maxwell, Reed M. Sensitivity and model reduction of simulated snow processes: Contrasting observational and parameter uncertainty to improve prediction. United Kingdom. https://doi.org/10.1016/j.advwatres.2019.103473
Ryken, Anna, Bearup, Lindsay A., Jefferson, Jennifer L., Constantine, Paul, and Maxwell, Reed M. Wed . "Sensitivity and model reduction of simulated snow processes: Contrasting observational and parameter uncertainty to improve prediction". United Kingdom. https://doi.org/10.1016/j.advwatres.2019.103473.
@article{osti_1577227,
title = {Sensitivity and model reduction of simulated snow processes: Contrasting observational and parameter uncertainty to improve prediction},
author = {Ryken, Anna and Bearup, Lindsay A. and Jefferson, Jennifer L. and Constantine, Paul and Maxwell, Reed M.},
abstractNote = {The hydrology of high-elevation, mountainous regions is poorly represented in Earth Systems Models (ESMs), yet these ecosystems play an important role in the storage and land-atmosphere exchange of water. As much of the western United States’ water comes from water stored in the snowpack (snow water equivalent, SWE), model representation of these regions is important. This study assesses how uncertainty in both model parameters and forcing affect simulated snow processes through sensitivity analysis (active subspaces) on model inputs (meteorological forcing and model input parameters) for a widely used snow model. Observations from an AmeriFlux tower at the Niwot Ridge research site are used to force an integrated, single-column hydrologic model, ParFlow-CLM. This study finds that trees can mute the effects of snow albedo causing the evergreen needleleaf scenarios to be sensitive primarily to hydrologic forcing while bare ground simulations are more sensitive to the snow parameters. The bare ground scenarios are most sensitive overall. Both forcing and model input parameters are important for obtaining accurate hydrologic model results.},
doi = {10.1016/j.advwatres.2019.103473},
journal = {Advances in Water Resources},
number = C,
volume = 135,
place = {United Kingdom},
year = {2020},
month = {1}
}

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