Assessing Mountains as Natural Reservoirs With a Multimetric Framework. Issue 9 (11th September 2018)

• Record Type: Journal Article
• Authors: Rhoades, Alan M.; Jones, Andrew D.; Ullrich, Paul A.
• Is Part Of: Earth's future. Volume 6:Issue 9(2018)
• Page Start: 1221
• Page End: 1241
• Publication Date: 2018-09-11
• Subjects: mountain snowpack -- regional downscaling -- multimetric evaluation -- uncertainty quantification -- hydroclimatology -- water resources; Environmental sciences -- Periodicals; Environmental sciences; Periodicals; 550
• DOI: 10.1002/2017EF000789
• Languages: English
• ISSNs: 2328-4277

Abstract

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 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. Plain Language Summary: Mountain snowpack is a key natural water reservoir. Due to anthropogenic climate change, this natural reservoir will likely decline over the next century. This decline will be nonlinear in both space and time. As such, climate model estimates will be key in constraining uncertainty surrounding this decline. These virtual laboratories allow us to understand what could be and not just what has been. The utility of climate models is apparent; however, uncertainties surrounding climate model estimates of mountain snowpack need to be understood. This article assesses uncertainties surrounding both observationally based and climate model estimates of mountain snowpack in the California Sierra Nevada. We use a new multimetric evaluation framework called the snow water equivalent (SWE) triangle. This framework elucidates agreement/disagreement in estimates of peak water volume and timing, accumulation, and melt rates, and the lengths of the accumulation and melt seasons. We found that spread across snowpack data sets were partly driven by differences in the accumulation to peak timing phase of the winter season but was dominated by melt season differences. To expand climate model utility for water management applications, more research is needed to understand why models tended to have earlier peak timing and abrupt snowmelt. Key Points: There is a need to evaluate snowpack data sets beyond conventional techniques such as climate average absolute bias and spatial correlations Multimetric evaluations elucidate processes that create disagreement in climate model snowpack estimates A consistent high bias in snowmelt rates across climate models limits their utility for water management applications