Assessing Mountains as Natural Reservoirs with a Multi-Metric Framework

TitleAssessing Mountains as Natural Reservoirs with a Multi-Metric Framework
Publication TypeJournal Article
Year of Publication2018
AuthorsRhoades, Alan M., Jones Andrew D., and Ullrich Paul A.
JournalEarth's Future
Volume6
Number9
Pages1221-1241
Date Published08/2018
Abstract / Summary

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 multi‐metric model evaluation frameworks informed by stakeholder interactions are critical. In this study, we present one such multi‐metric framework to evaluate snowpack datasets in the California Sierra Nevada: the snow water equivalent (SWE) triangle. SWE triangle metrics help to describe snowpack characteristics associated with total water volume build‐up, 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 multi‐metric evaluation framework, we evaluate several publicly available snow products including the Sierra Nevada Snow Reanalysis (SNSR), Livneh (L15), and the North American Land Data Assimilation System version 2 (NLDAS‐2) datasets. We then evaluate regional climate model skill within the North American Coordinated Regional Climate Downscaling Experiment (NA‐CORDEX). All datasets 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.

URLhttp://doi.org/10.1002/2017EF000789
DOI10.1002/2017EF000789
Journal: Earth's Future
Year of Publication: 2018
Volume: 6
Number: 9
Pages: 1221-1241
Date Published: 08/2018

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 multi‐metric model evaluation frameworks informed by stakeholder interactions are critical. In this study, we present one such multi‐metric framework to evaluate snowpack datasets in the California Sierra Nevada: the snow water equivalent (SWE) triangle. SWE triangle metrics help to describe snowpack characteristics associated with total water volume build‐up, 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 multi‐metric evaluation framework, we evaluate several publicly available snow products including the Sierra Nevada Snow Reanalysis (SNSR), Livneh (L15), and the North American Land Data Assimilation System version 2 (NLDAS‐2) datasets. We then evaluate regional climate model skill within the North American Coordinated Regional Climate Downscaling Experiment (NA‐CORDEX). All datasets 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
Citation:
Rhoades, AM, AD Jones, and PA Ullrich.  2018.  "Assessing Mountains as Natural Reservoirs with a Multi-Metric Framework."  Earth's Future 6(9): 1221-1241.  https://doi.org/10.1002/2017EF000789.