Future Changes in Snowpack, Snowmelt, and Runoff Potential Extremes Over North America

Cho, Eunsang; McCrary, Rachel; Jacobs, Jennifer M.

doi:10.1029/2021gl094985

Cited by 20 publications

(11 citation statements)

References 64 publications

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“…Also, the impact of seasonal snowpack on extreme events such as snowmelt and rain-on-snow floods (Davenport et al, 2020;Li et al, 2019;Musselman et al, 2018), drought (Huning and AghaKouchak, 2020), and wildfires (Westerling et al, 2006) are of considerable interest to the water resources planners and decision-makers. Despite being a vital component of water balances and extreme events, estimating the spatiotemporal change in snow water storage referred to as snow water equivalent (SWE), remains a significant challenge for the snow hydrology community (Cho et al, 2021b;Dozier et al, 2016;Kim et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Precipitation biases and snow physics limitations drive the uncertainties in macroscale modeled snow water equivalent

Cho

Vuyovich

Kumar

et al. 2022

Hydrol. Earth Syst. Sci.

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Abstract. Seasonal snow is an essential component of regional and global water and energy cycles, particularly in snow-dominant regions that rely on snowmelt for water resources. Land surface models (LSMs) are a common approach for developing spatially and temporally complete estimates of snow water equivalent (SWE) and hydrologic variables at a large scale. However, the accuracy of the LSM-based SWE outputs is limited and unclear by mixed factors such as uncertainties in the meteorological boundary conditions and the model physics. In this study, we assess the SWE, snowfall, precipitation, and air temperature products from a 12-member ensemble – with four LSMs and three meteorological forcings – using automated SWE, precipitation, and temperature observations from 809 Snowpack Telemetry stations over the western US. Results show that the mean annual maximum LSM SWE is underestimated by 268 mm. The timing of peak SWE from the LSMs is on average 36 d earlier than that of the observations. By the date of peak SWE, winter accumulated precipitation is underestimated (forcings mean: 485 mm vs. stations: 690 mm). In addition, the precipitation partitioning physics generates different snowfall estimates by an average of 113 mm with the same forcing data. Even though there are widespread cold biases (up to 3 ∘C) in the temperature forcings, larger ablations and lower ratios of SWE to total precipitation are found even in the accumulation period, indicating that melting physics in LSMs drives some SWE uncertainties. Based on the principal component analysis, we find that precipitation bias and partitioning methods have a large contribution to the first principal component, which accounts for about half of the total variance. The results provide insights into prioritizing strategies to improve SWE estimates from LSMs for hydrologic applications.

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Section: Introductionmentioning

confidence: 99%

Precipitation biases and snow physics limitations drive the uncertainties in macroscale modeled snow water equivalent

Cho

Vuyovich

Kumar

et al. 2022

Hydrol. Earth Syst. Sci.

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“…Atmospheric river‐fed storms are projected to increase in frequency and magnitude in the 21st century at the expense of low to moderate frontal storms (Gershunov et al., 2019; Swain et al., 2018), further increasing precipitation variability (Swain et al., 2018). As temperature continues to rise, less precipitation will be stored as snowpack (Siirila‐Woodburn et al., 2021), more will be delivered through extreme precipitation events (Payne et al., 2020), and the relationship between precipitation and streamflow throughout the year will become more complex (Cho et al., 2021; Davenport et al., 2020; Huang et al., 2020; Rhoades et al., 2021). The transformation of California's water cycle appears imminent, as the state already experiences events with high rain to snow ratios and rain‐on‐snow events, resulting in nonlinear watershed responses (Davenport et al., 2020; Henn et al., 2020).…”

Section: Discussionmentioning

confidence: 99%

The Unprecedented Character of California's 20th Century Enhanced Hydroclimatic Variability in a 600‐Year Context

Zamora-Reyes

Broadman

Bigio

et al. 2022

Geophysical Research Letters

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Recently, year‐to‐year swings in California winter precipitation extremes have resulted in drought, wildfires, and floods causing billions of dollars in damage. These recent precipitation swings represent an increasing trend in variability of California's hydroclimate over the past decades. Here, we put this trend in a longer‐term context using tree‐ring‐based precipitation, streamflow, and snow water equivalent reconstructions. We show that the statewide rise in hydroclimate variability in the 20th century is driven by an increasing trend in the magnitude of wet extremes. A prior period of strong variability in the 16th century, in contrast, is related to an increasing trend in the magnitude of dry extremes. Our results are consistent with climate model simulations that suggest an increasingly volatile future for California's hydroclimate and highlight the importance of collaboration between scientists and water resource managers to incorporate this increased variability into their decision‐making and planning, acknowledging higher risks for compound events.

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“…Observations of trends to date and modeling of future land-climate interactions demonstrate the threat of climate change to all of the world's major mountain water towers: the Western US (e.g. Marshall et al 2019, Cho et al 2021, Rhoades et al 2018, 2022, the Andes (Bozkurt et al 2018, Rhoades et al 2022, the European Alps (e.g. Coppola et al 2018), the Pan-Tibetan Highlands (e.g.…”

Section: Introductionmentioning

confidence: 99%

Evolution of global snow drought characteristics from 1850 to 2100

Cowherd

Leung

Girotto

2023

Environ. Res. Lett.

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Seasonal snow is an integral part of the global water supply and storage system. Snow droughts impact ecological, agricultural, and urban systems by altering the amount and timing of meltwater delivery. These droughts are characterized by a lack of on-the-ground snow (snow water equivalent, SWE) that can be caused by low total precipitation (dry drought) or low proportion of precipitation falling as snowfall (warm drought), often combined with an early melt. The Standardized SWE Index (SWEI) ranks the current status of SWE for a given location compared to a baseline condition and identifies the existence, but not the cause, of snow drought. In this work, we use estimates of SWE, temperature, and precipitation from nine Coupled Model Intercomparison Project phase 6 (CMIP6) models to quantify the frequency, severity, and type of snow droughts globally for historical and future scenarios.Compared to a historical baseline (1850--1900) total snow drought frequency more than doubles under SSP2-4.5 and SSP5-8.5; all of the increase in snow drought frequency comes from an increase in warm droughts.The probability distribution of future SWEI in major snowy basins around the world are likely to be centered on more negative values, which corresponds to more severe drought and, with only moderate changes in distribution spread, more frequent drought. CMIP6 simulations pinpoint snow drought as an emerging global threat to water resources and highlight the need to explore higher resolution future models that better capture complex mountain topography, wildland fires, and snow-forest interactions.

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Future Changes in Snowpack, Snowmelt, and Runoff Potential Extremes Over North America

Cited by 20 publications

References 64 publications

Precipitation biases and snow physics limitations drive the uncertainties in macroscale modeled snow water equivalent

Precipitation biases and snow physics limitations drive the uncertainties in macroscale modeled snow water equivalent

The Unprecedented Character of California's 20th Century Enhanced Hydroclimatic Variability in a 600‐Year Context

Evolution of global snow drought characteristics from 1850 to 2100

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