SnowClim v1.0: High-resolution snow model and data for the western United States

Abstract. Seasonal snowpack dynamics shape the biophysical and societal characteristics of many global regions. However, snowpack accumulation and duration have generally declined in recent decades largely due to anthropogenic climate change. Mechanistic understanding of snowpack spatiotemporal heterogeneity and climate change impacts will benefit from snow data products that are based on physical principles, that are simulated at high spatial resolution, and that cover large geographic domains. Existing datasets do not meet these requirements, hindering our ability to understand both contemporary and changing snow regimes and to develop adaptation strategies in regions where snowpack patterns and processes are important components of Earth systems. We developed a computationally efficient physics-based snow model, SnowClim, that can be run in the cloud. The model was evaluated and calibrated at Snowpack Telemetry sites across the western United States (US), achieving a site-median root mean square error for daily snow water equivalent of 62 mm, bias in peak snow water equivalent of −9.6 mm, and bias in snow duration of 1.2 days when run hourly. Positive biases were found at sites with mean winter temperature above freezing where the estimation of precipitation phase is prone to errors. The model was applied to the western US using newly developed forcing data created by statistically downscaling pre-industrial, historical, and pseudo-global warming climate data from the Weather Research and Forecasting (WRF) model. The resulting product is the SnowClim dataset, a suite of summary climate and snow metrics for the western US at 210 m spatial resolution (Lute et al., 2021). The physical basis, large extent, and high spatial resolution of this dataset will enable novel analyses of changing hydroclimate and its implications for natural and human systems.

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Avalanche Fatalities during Atmospheric River Events in the Western United States

Journal of Hydrometeorology ◽

10.1175/jhm-d-16-0219.1 ◽

2017 ◽

Vol 18 (5) ◽

pp. 1359-1374 ◽

Cited By ~ 18

Author(s):

Benjamin J. Hatchett ◽

Susan Burak ◽

Jonathan J. Rutz ◽

Nina S. Oakley ◽

Edward H. Bair ◽

...

Keyword(s):

United States ◽

Water Vapor ◽

Snow Water Equivalent ◽

Vapor Transport ◽

Western United States ◽

Snow Avalanche ◽

Water Vapor Transport ◽

Atmospheric Rivers ◽

Avalanche Forecasting ◽

Snow Water

Abstract The occurrence of atmospheric rivers (ARs) in association with avalanche fatalities is evaluated in the conterminous western United States between 1998 and 2014 using archived avalanche reports, atmospheric reanalysis products, an existing AR catalog, and weather station observations. AR conditions were present during or preceding 105 unique avalanche incidents resulting in 123 fatalities, thus comprising 31% of western U.S. avalanche fatalities. Coastal snow avalanche climates had the highest percentage of avalanche fatalities coinciding with AR conditions (31%–65%), followed by intermountain (25%–46%) and continental snow avalanche climates (<25%). Ratios of avalanche deaths during AR conditions to total AR days increased with distance from the coast. Frequent heavy to extreme precipitation (85th–99th percentile) during ARs favored critical snowpack loading rates with mean snow water equivalent increases of 46 mm. Results demonstrate that there exists regional consistency between snow avalanche climates, derived AR contributions to cool season precipitation, and percentages of avalanche fatalities during ARs. The intensity of water vapor transport and topographic corridors favoring inland water vapor transport may be used to help identify periods of increased avalanche hazard in intermountain and continental snow avalanche climates prior to AR landfall. Several recently developed AR forecast tools applicable to avalanche forecasting are highlighted.

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Spatiotemporal Characteristics of Snowpack Density in the Mountainous Regions of the Western United States

Journal of Hydrometeorology ◽

10.1175/2008jhm981.1 ◽

2008 ◽

Vol 9 (6) ◽

pp. 1416-1426 ◽

Cited By ~ 45

Author(s):

Naoki Mizukami ◽

Sanja Perica

Keyword(s):

United States ◽

Snow Depth ◽

Snow Water Equivalent ◽

Winter Season ◽

Western United States ◽

Densification Rate ◽

Snow Density ◽

Mountainous Regions ◽

Spatiotemporal Characteristics ◽

Snow Water

Abstract Snow density is calculated as a ratio of snow water equivalent to snow depth. Until the late 1990s, there were no continuous simultaneous measurements of snow water equivalent and snow depth covering large areas. Because of that, spatiotemporal characteristics of snowpack density could not be well described. Since then, the Natural Resources Conservation Service (NRCS) has been collecting both types of data daily throughout the winter season at snowpack telemetry (SNOTEL) sites located in the mountainous areas of the western United States. This new dataset provided an opportunity to examine the spatiotemporal characteristics of snowpack density. The analysis of approximately seven years of data showed that at a given location and throughout the winter season, year-to-year snowpack density changes are significantly smaller than corresponding snow depth and snow water equivalent changes. As a result, reliable climatological estimates of snow density could be obtained from relatively short records. Snow density magnitudes and densification rates (i.e., rates at which snow densities change in time) were found to be location dependent. During early and midwinter, the densification rate is correlated with density. Starting in early or mid-March, however, snowpack density increases by approximately 2.0 kg m−3 day−1 regardless of location. Cluster analysis was used to obtain qualitative information on spatial patterns of snowpack density and densification rates. Four clusters were identified, each with a distinct density magnitude and densification rate. The most significant physiographic factor that discriminates between clusters was proximity to a large water body. Within individual mountain ranges, snowpack density characteristics were primarily dependent on elevation.

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Seasonal Cycle Shifts in Hydroclimatology over the Western United States

Journal of Climate ◽

10.1175/jcli-3272.1 ◽

2005 ◽

Vol 18 (2) ◽

pp. 372-384 ◽

Cited By ~ 331

Author(s):

Satish Kumar Regonda ◽

Balaji Rajagopalan ◽

Martyn Clark ◽

John Pitlick

Keyword(s):

United States ◽

Pacific Northwest ◽

Snow Water Equivalent ◽

Warming Trend ◽

Winter Precipitation ◽

Western United States ◽

Temperature And Precipitation ◽

The Pacific ◽

Snow Water ◽

Northwest Region

Abstract Analyses of streamflow, snow mass temperature, and precipitation in snowmelt-dominated river basins in the western United States indicate an advance in the timing of peak spring season flows over the past 50 years. Warm temperature spells in spring have occurred much earlier in recent years, which explains in part the trend in the timing of the spring peak flow. In addition, a decrease in snow water equivalent and a general increase in winter precipitation are evident for many stations in the western United States. It appears that in recent decades more of the precipitation is coming as rain rather than snow. The trends are strongest at lower elevations and in the Pacific Northwest region, where winter temperatures are closer to the melting point; it appears that in this region in particular, modest shifts in temperature are capable of forcing large shifts in basin hydrologic response. It is speculated that these trends could be potentially a manifestation of the general global warming trend in recent decades and also due to enhanced ENSO activity. The observed trends in hydroclimatology over the western United States can have significant impacts on water resources planning and management.

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High-spatial-resolution monthly temperature and precipitation dataset for China for 1901–2017

10.5194/essd-2019-83 ◽

2019 ◽

Cited By ~ 1

Author(s):

Shouzhang Peng ◽

Yongxia Ding ◽

Zhi Li

Keyword(s):

Climate Change ◽

Spatial Resolution ◽

Time Series Data ◽

High Spatial Resolution ◽

Series Data ◽

Climatic Research Unit ◽

Climate Data ◽

Large Area ◽

Mountainous Regions ◽

Weather Stations

Abstract. High-spatial-resolution and long-term climate data are highly desirable for understanding climate-related natural processes. China covers a large area with a low density of weather stations in some regions, especially mountainous regions. This study describes a high-spatial-resolution (0.5’, ∼1 km) dataset of monthly temperatures (minimum, maximum, and mean TMPs) and precipitation (PRE) for the main land area of China for the period 1901–2017. The dataset was spatially downscaled from raw 30’ climatic research unit (CRU) time series data and validated using data from 745 weather stations across China. Compared to raw CRU data of low spatial resolution, the mean absolute error decreased by 0.56 °C for the TMPs and 10.1 % for PRE, the root-mean-square error decreased by 0.65 °C for the TMPs and 11.6 % for PRE, and the Nash–Sutcliffe efficiency coefficients increased from 0.83 to 0.95 for the TMPs and from 0.63 to 0.76 for PRE. Indirect validations from site-scale observations indicated that the dataset captured the climatology well, as well as the annual and seasonal monotonic trends in each climatic variable considered. We concluded that the new high-spatial-resolution dataset is sufficiently reliable for use in investigation of climate change across China. This dataset will be useful in investigations related to climate change across China. The dataset presented in this article is published in the Network Common Data Form (NetCDF) at https://doi.org/10.5281/zenodo.3114194 for precipitation (Peng, 2019a) and https://doi.org/10.5281/zenodo.3185722 for temperatures (Peng, 2019b). The dataset includes 156 NetCDF files compressed with zip format and one user guidance text file.

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Reconstruction of daily gridded snow water equivalent product for the Pan-Arctic region based on a ridge regression machine learning approach

10.5194/essd-2021-344 ◽

2021 ◽

Author(s):

Donghang Shao ◽

Hongyi Li ◽

Jian Wang ◽

Xiaohua Hao ◽

Tao Che ◽

...

Keyword(s):

Climate Change ◽

Machine Learning ◽

Regression Model ◽

Spatial Resolution ◽

Ridge Regression ◽

Snow Water Equivalent ◽

Learning Algorithm ◽

Arctic Region ◽

Machine Learning Algorithm ◽

Snow Water

Abstract. Snow water equivalent is an important parameter of the surface hydrological and climate systems, and it has a profound impact on Arctic amplification and climate change. However, there are great differences among existing snow water equivalent products. In the Pan-Arctic region, the existing snow water equivalent products are limited time span and limited spatial coverage, and the spatial resolution is coarse, which greatly limits the application of snow water equivalent data in cryosphere change and climate change studies. In this study, utilizing the ridge regression model (RRM) of a machine learning algorithm, we integrated various existing snow water equivalent (SWE) products to generate a spatiotemporally seamless and high-precision RRM SWE product. The results show that it is feasible to utilize a ridge regression model based on a machine learning algorithm to prepare snow water equivalent products on a global scale. We evaluated the accuracy of the RRM SWE product using Global Historical Climatology Network (GHCN) data and Russian snow survey data. The MAE, RMSE, R, and R2; between the RRM SWE products and observed snow water equivalents are 0.24, 30.29 mm, 0.87, and 0.76, respectively. The accuracy of the RRM SWE dataset is improved by 24 %, 25 %, 32 %, 7 %, and 10 % compared with the original AMSR-E/AMSR2 snow water equivalent dataset, ERA-Interim SWE dataset, Global Land Data Assimilation System (GLDAS) SWE dataset, GlobSnow SWE dataset, and ERA5-land SWE dataset, respectively, and it has a higher spatial resolution. The RRM SWE product production method does not rely too much on an independent snow water equivalent product, it makes full use of the advantages of each snow water equivalent dataset, and it considers the altitude factor. The average MAE of RRM SWE product at different altitude intervals is 0.24 and the average RMSE is 23.55 mm, this method has good stability, it is extremely suitable for the production of snow datasets with large spatial scales, and it can be easily extended to the preparation of other snow datasets. The RRM SWE product is expected to provide more accurate snow water equivalent data for the hydrological model and climate model and provide data support for cryosphere change and climate change studies. The RRM SWE product is available from the ‘A Big Earth Data Platform for Three Poles’ (http://dx.doi.org/10.11888/Snow.tpdc.271556) (Li et al., 2021).

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A New Method to Characterize Changes in the Seasonal Cycle of Snowpack

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-18-0150.1 ◽

2019 ◽

Vol 58 (1) ◽

pp. 131-143 ◽

Cited By ~ 3

Author(s):

Amato T. Evan

Keyword(s):

United States ◽

Annual Cycle ◽

Snow Water Equivalent ◽

Winter Season ◽

New Method ◽

Western United States ◽

Gaussian Shape ◽

Annual Cycles ◽

Snow Water ◽

High Elevations

AbstractIn the western United States, water stored as mountain snowpack is a large percentage of the total water needed to meet the region’s demands, and it is likely that, as the planet continues to warm, mountain snowpack will decline. However, detecting such trends in the observational record is challenging because snowpack is highly variable in both space and time. Here, a method for characterizing mountain snowpack is developed that is based on fitting observed annual cycles of snow water equivalent (SWE) to a gamma-distribution probability density function. A new method for spatially interpolating the distribution’s fitting parameters to create a gridded climatology of SWE is also presented. Analysis of these data shows robust trends in the shape of the annual cycle of snowpack in the western United States. Over the 1982–2017 water years, the annual cycle of snowpack is becoming narrower and more Gaussian. A narrowing of the annual cycle corresponds to a shrinking of the length of the winter season, primarily because snowpack melting is commencing earlier in the water year. Because the annual cycle of snowpack at high elevations tends to be more skewed than at lower elevations, a more Gaussian shape suggests that snowpack is becoming more characteristic of that at lower elevations. Although no robust downward trends in annual-mean SWE are found, robust trends in the shape of the SWE annual cycle have implications for regional water resources.

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A High-Resolution Data Assimilation Framework for Snow Water Equivalent Estimation across the Western United States and Validation with the Airborne Snow Observatory

Journal of Hydrometeorology ◽

10.1175/jhm-d-18-0009.1 ◽

2019 ◽

Vol 20 (3) ◽

pp. 357-378 ◽

Cited By ~ 4

Author(s):

Catalina M. Oaida ◽

John T. Reager ◽

Konstantinos M. Andreadis ◽

Cédric H. David ◽

Steve R. Levoe ◽

...

Keyword(s):

United States ◽

High Resolution ◽

Data Assimilation ◽

Snow Water Equivalent ◽

High Elevation ◽

Open Loop ◽

Western United States ◽

Olympic Peninsula ◽

Depletion Curve ◽

Snow Water

Abstract Numerical simulations of snow water equivalent (SWE) in mountain systems can be biased, and few SWE observations have existed over large domains. New approaches for measuring SWE, like NASA’s ultra-high-resolution Airborne Snow Observatory (ASO), offer an opportunity to improve model estimates by providing a high-quality validation target. In this study, a computationally efficient snow data assimilation (DA) approach over the western United States at 1.75-km spatial resolution for water years (WYs) 2001–17 is presented. A local ensemble transform Kalman filter implemented as a batch smoother is used with the VIC hydrology model to assimilate the remotely sensed daily MODIS fractional snow-covered area (SCA). Validation of the high-resolution SWE estimates is done against ASO SWE data in the Tuolumne basin (California), Uncompahgre basin (Colorado), and Olympic Peninsula (Washington). Results indicate good performance in dry years and during melt, with DA reducing Tuolumne basin-average SWE percent differences from −68%, −92%, and −84% in open loop to 0.6%, 25%, and 3% after DA for WYs 2013–15, respectively, for ASO dates and spatial extent. DA also improved SWE percent difference over the Uncompahgre basin (−84% open loop, −65% DA) and Olympic Peninsula (26% open loop, −0.2% DA). However, in anomalously wet years DA underestimates SWE, likely due to an inadequate snow depletion curve parameterization. Despite potential shortcomings due to VIC model setup (e.g., water balance mode) or parameterization (snow depletion curve), the DA framework implemented in this study shows promise in overcoming some of these limitations and improving estimated SWE, in particular during drier years or at higher elevations, when most in situ observations cannot capture high-elevation snowpack due to lack of stations there.

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