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Title: Assessing Mountains as Natural Reservoirs With a Multimetric Framework

Abstract

Anthropogenic climate change will continue to diminish the unique role that mountains perform as natural reservoirs and alter long-held assumptions of water management. Climate models are important tools to help constrain uncertainty and understand processes that shape this decline. To ensure that climate model estimates provide stakeholder relevant information, the formulation of multimetric model evaluation frameworks informed by stakeholder interactions are critical. In this study, we present one such multimetric framework to evaluate snowpack data sets in the California Sierra Nevada: the snow water equivalent (SWE) triangle. SWE triangle metrics help to describe snowpack characteristics associated with total water volume buildup, peak water availability, and the rate of water release. This approach highlights compensating errors that would not be reflected in conventional large-scale spatiotemporal analysis. To test our multimetric evaluation framework, we evaluate several publicly available snow products including the Sierra Nevada Snow Reanalysis, Livneh (L15), and the North American Land Data Assimilation System version 2 data sets. We then evaluate regional climate model skill within the North American Coordinated Regional Climate Downscaling Experiment. All data sets analyzed show variation across the various SWE triangle metrics, even within observationally constrained snow products. This spread was especially shown in spring seasonmore » melt rates. Melt rate biases were prevalent throughout most regional climate model simulations, regardless of snow accumulation dynamics, and will need to be addressed to improve their utility for water stakeholders.« less

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [2]
  1. Climate and Ecosystem Sciences DivisionLawrence Berkeley National Laboratory Berkeley CA USA
  2. Department of Land, Air, and Water Resources University of California Davis CA USA
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1469711
Alternate Identifier(s):
OSTI ID: 1469712; OSTI ID: 1476600
Grant/Contract Number:  
[SC0016605; AC02-05CH11231]
Resource Type:
Published Article
Journal Name:
Earth's Future
Additional Journal Information:
[Journal Name: Earth's Future Journal Volume: 6 Journal Issue: 9]; Journal ID: ISSN 2328-4277
Publisher:
Wiley Blackwell (John Wiley & Sons)
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES

Citation Formats

Rhoades, Alan M., Jones, Andrew D., and Ullrich, Paul A. Assessing Mountains as Natural Reservoirs With a Multimetric Framework. United States: N. p., 2018. Web. doi:10.1002/2017EF000789.
Rhoades, Alan M., Jones, Andrew D., & Ullrich, Paul A. Assessing Mountains as Natural Reservoirs With a Multimetric Framework. United States. doi:10.1002/2017EF000789.
Rhoades, Alan M., Jones, Andrew D., and Ullrich, Paul A. Tue . "Assessing Mountains as Natural Reservoirs With a Multimetric Framework". United States. doi:10.1002/2017EF000789.
@article{osti_1469711,
title = {Assessing Mountains as Natural Reservoirs With a Multimetric Framework},
author = {Rhoades, Alan M. and Jones, Andrew D. and Ullrich, Paul A.},
abstractNote = {Anthropogenic climate change will continue to diminish the unique role that mountains perform as natural reservoirs and alter long-held assumptions of water management. Climate models are important tools to help constrain uncertainty and understand processes that shape this decline. To ensure that climate model estimates provide stakeholder relevant information, the formulation of multimetric model evaluation frameworks informed by stakeholder interactions are critical. In this study, we present one such multimetric framework to evaluate snowpack data sets in the California Sierra Nevada: the snow water equivalent (SWE) triangle. SWE triangle metrics help to describe snowpack characteristics associated with total water volume buildup, peak water availability, and the rate of water release. This approach highlights compensating errors that would not be reflected in conventional large-scale spatiotemporal analysis. To test our multimetric evaluation framework, we evaluate several publicly available snow products including the Sierra Nevada Snow Reanalysis, Livneh (L15), and the North American Land Data Assimilation System version 2 data sets. We then evaluate regional climate model skill within the North American Coordinated Regional Climate Downscaling Experiment. All data sets analyzed show variation across the various SWE triangle metrics, even within observationally constrained snow products. This spread was especially shown in spring season melt rates. Melt rate biases were prevalent throughout most regional climate model simulations, regardless of snow accumulation dynamics, and will need to be addressed to improve their utility for water stakeholders.},
doi = {10.1002/2017EF000789},
journal = {Earth's Future},
number = [9],
volume = [6],
place = {United States},
year = {2018},
month = {9}
}

Journal Article:
Free Publicly Available Full Text
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DOI: 10.1002/2017EF000789

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Cited by: 2 works
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