A New Estimate of North American Mountain Snow Accumulation From Regional Climate Model Simulations

Wrzesien, Melissa L.; Durand, Michael; Pavelsky, T.; Kapnick, Sarah B.; Zhang, Yu; Guo, Junyi; Shum, C. K.

doi:10.1002/2017gl076664

Cited by 60 publications

(75 citation statements)

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“…While the classification scheme proposed by Harpold et al () is straightforward and easy to use, it does not account for the co‐occurrence of warm and dry conditions, which have been shown to result in significantly more severe summer low flow periods than only dry conditions alone (Dierauer et al, ). Additionally, it does not account for spatial and temporal variations in the timing of peak SWE, which varies substantially between and within mountain ranges (Wrzesien et al, ).…”

Section: Methodsmentioning

confidence: 99%

Snow Drought Risk and Susceptibility in the Western United States and Southwestern Canada

Dierauer

Allen

Whitfield

2019

Water Resources Research

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In western North America (WNA), mountain snowpack supplies much of the water used for irrigation, municipal, and industrial uses. Thus, snow droughts (a lack of snow accumulation in winter) can have drastic ecological and socioeconomic impacts. In this study, the historical (1951–2013) frequency, severity, and risk (frequency × severity) of dry, warm, and warm and dry snow droughts are quantified at the grid‐cell and ecoregion scale for snow‐dominated regions in the western United States and southwestern Canada (sWNA). Based on multiple linear regression analysis, relationships between mean winter temperature, snow drought risk, and snow water equivalent sensitivity are explored. Piecewise linear regression is used to identify temperature thresholds for mapping temperature‐related snow drought susceptibility. Results highlight spatial differences in snow drought regimes across sWNA and reveal that temperature thresholds exist at −3.1 °C (±0.3 °C) and 1.4 °C (±0.3 °C), above which the warm snow drought risk increases more rapidly. Approximately 3% of the nonglaciated snow storage in this region has high susceptibility to temperature‐related snow drought, representing 11 km3 of water, or approximately one third the capacity of Lake Mead. Under a +2 °C climate scenario, an additional 8% (28 km3) of this snow storage volume will transition to high susceptibility.

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Section: Methodsmentioning

confidence: 99%

Snow Drought Risk and Susceptibility in the Western United States and Southwestern Canada

Dierauer

Allen

Whitfield

2019

Water Resources Research

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Section: Introductionmentioning

confidence: 99%

“…Whereas Wrzesien et al () identified a bias in current estimates of SWS over mountain ranges, the primary goal in this paper is to assess whether the role of precipitation is correctly estimated during the cool season (October through March) across entire watersheds. To achieve this objective, we compare representative‐year wintertime precipitation and SWS estimates from existing global data sets to the WRF simulations performed by Wrzesien et al (). Past studies cited above suggest that WRF produces reasonable results in mountain environments; though model evaluation against gridded SWE (Qian et al, ; Wrzesien et al, ), precipitation (Hughes et al, ), or even terrestrial water storage estimates from the Gravity Recovery and Climate Experiment satellite (Frappart et al, ; Wrzesien et al, ) is possible, no true validation data set exists.…”

Section: Introductionmentioning

confidence: 99%

“…In mountain regions, global data sets can perform poorly (Snauffer et al, ), perhaps underestimating peak snow accumulation by up to 90% in the Sierra Nevada (Wrzesien et al, ). To refine mountain SWE estimation, Wrzesien et al () used WRF to develop a new representative climatology of snow water storage (SWS) across all North American mountains by simulating one average water year for individual mountain ranges, and their results suggested that global data sets underestimate SWS in North America by ~35%. Biases are particularly likely in mountainous regions, where SWS estimates from the Canadian Sea Ice and Snow Evolution (Mudryk & Derksen, ) ensemble global data product is 66% lower than the high‐resolution WRF estimate.…”

Section: Introductionmentioning

confidence: 99%

“…To achieve this objective, we compare representative‐year wintertime precipitation and SWS estimates from existing global data sets to the WRF simulations performed by Wrzesien et al (). Past studies cited above suggest that WRF produces reasonable results in mountain environments; though model evaluation against gridded SWE (Qian et al, ; Wrzesien et al, ), precipitation (Hughes et al, ), or even terrestrial water storage estimates from the Gravity Recovery and Climate Experiment satellite (Frappart et al, ; Wrzesien et al, ) is possible, no true validation data set exists. As such, in this paper, we assess only whether global data sets are consistent with WRF simulations rather than performing a formal validation.…”

Section: Introductionmentioning

confidence: 99%

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A Reassessment of North American River Basin Cool‐Season Precipitation: Developments From a New Mountain Climatology Data Set

Wrzesien

Durand

Pavelsky

2019

Water Resources Research

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Characterizing hydrological processes on large scales is challenging due to limitations of observational networks, remoting sensing platforms, and modeling techniques. Water balances have larger uncertainties in mountain regions, where orographic processes produce high spatial variability in precipitation patterns and snow accumulation. Recent work suggests current water budgets underestimate mountain snow water storage, perhaps indicating biases in modeled precipitation. We assess whether global hydroclimate data sets underestimate precipitation for six North American watersheds, ranging from 3–70% mountainous. By selecting a single representative year for each watershed, we compare relatively high‐resolution precipitation estimates from the Weather Research and Forecasting (WRF) regional climate model with four global products: Modern‐Era Retrospective Analysis for Research and Applications, version 2, the Global Land Data Assimilation System, the Global Precipitation Climatology Project, and the Climate Research Unit's climate data set. Comparisons to WRF precipitation suggest that observation‐based gridded data products do not produce reasonable estimates of watershed‐scale cool‐season precipitation, underestimating by 1–69%. The Global Precipitation Climatology Project and the Climate Research Unit data set have average biases of −26% and −38%, respectively. The Modern‐Era Retrospective Analysis for Research and Applications version 2 and the Global Land Data Assimilation System show smaller underestimates relative to WRF (−17% and −21%, respectively), with nearly all mean bias from the mountains (underestimated by 27% and 39%) rather than the topographically simpler lowlands (underestimated by 5% and 2%). We suggest global products fail to capture orographic enhancement of precipitation, resulting in large underestimates of precipitation, snowfall, and snow water storage in mountains of selected North American watersheds, which highlights the need for more accurate precipitation estimates to accurately assess spatiotemporal variations in the water cycle.

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Snowpack relative permittivity and density derived from near‐coincident lidar and ground‐penetrating radar

Bonnell,

McGrath,

Hedrick

et al. 2023

Hydrological Processes

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Depth‐based and radar‐based remote sensing methods (e.g., lidar, synthetic aperture radar) are promising approaches for remotely measuring snow water equivalent (SWE) at high spatial resolution. These approaches require snow density estimates, obtained from in‐situ measurements or density models, to calculate SWE. However, in‐situ measurements are operationally limited, and few density models have seen extensive evaluation. Here, we combine near‐coincident, lidar‐measured snow depths with ground‐penetrating radar (GPR) two‐way travel times (twt) of snowpack thickness to derive >20 km of relative permittivity estimates from nine dry and two wet snow surveys at Grand Mesa, Cameron Pass, and Ranch Creek, Colorado. We tested three equations for converting dry snow relative permittivity to snow density and found the Kovacs et al. (1995) equation to yield the best comparison with in‐situ measurements (RMSE = 54 kg m−3). Variogram analyses revealed a 19 m median correlation length for relative permittivity and snow density in dry snow, which increased to >30 m in wet conditions. We compared derived densities with estimated densities from several empirical models, the Snow Data Assimilation System (SNODAS), and the physically based iSnobal model. Estimated and derived densities were combined with snow depths and twt to evaluate density model performance within SWE remote sensing methods. The Jonas et al. (2009) empirical model yielded the most accurate SWE from lidar snow depths (RMSE = 51 mm), whereas SNODAS yielded the most accurate SWE from GPR twt (RMSE = 41 mm). Densities from both models generated SWE estimates within ±10% of derived SWE when SWE averaged >400 mm, however, model uncertainty increased to >20% when SWE averaged <300 mm. The development and refinement of density models, particularly in lower SWE conditions, is a high priority to fully realize the potential of SWE remote sensing methods.

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A New Estimate of North American Mountain Snow Accumulation From Regional Climate Model Simulations

Cited by 60 publications

References 61 publications

Snow Drought Risk and Susceptibility in the Western United States and Southwestern Canada

Snow Drought Risk and Susceptibility in the Western United States and Southwestern Canada

A Reassessment of North American River Basin Cool‐Season Precipitation: Developments From a New Mountain Climatology Data Set

Snowpack relative permittivity and density derived from near‐coincident lidar and ground‐penetrating radar

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