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

2019 ◽  
Vol 20 (3) ◽  
pp. 357-378 ◽  
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.

scholarly journals Combined assimilation of streamflow and snow water equivalent for mid-term ensemble streamflow forecasts in snow-dominated regions

2016 ◽  
Author(s):  
Jean M. Bergeron ◽  
Mélanie Trudel ◽  
Robert Leconte
Keyword(s):  
Data Assimilation ◽  
State Vector ◽  
Snow Water Equivalent ◽  
Synthetic Data ◽  
Hydrologic Model ◽  
Open Loop ◽  
The State ◽  
Wide Range ◽  
Snow Water ◽  
The Impact

Abstract. The potential of data assimilation for hydrologic predictions has been demonstrated in many research studies. Watersheds over which multiple observation types are available can potentially further benefit from data assimilation by having multiple updated states from which hydrologic predictions can be generated. However, the magnitude and time span of the impact of the assimilation of an observation varies according not only to its type, but also to the variables included in the state vector. This study examines the impact of multivariate synthetic data assimilation using the Ensemble Kalman Filter (EnKF) into the spatially distributed hydrologic model CEQUEAU for the mountainous Nechako River located in British-Columbia, Canada. Synthetic data includes daily snow cover area (SCA), daily measurements of snow water equivalent (SWE) at three different locations and daily streamflow data at the watershed outlet. Results show a large variability of the continuous rank probability skill score over a wide range of prediction horizons (days to weeks) depending on the state vector configuration and the type of observations assimilated. Overall, the variables most closely linearly linked to the observations are the ones worth considering adding to the state vector. The performance of the assimilation of basin-wide SCA, which does not have a decent proxy among potential state variables, does not surpass the open loop for any of the simulated variables. However, the assimilation of streamflow offers major improvements steadily throughout the year, but mainly over the short-term (up to 5 days) forecast horizons, while the impact of the assimilation of SWE gains more importance during the snowmelt period over the mid-term (up to 50 days) forecast horizon compared with open loop. The combined assimilation of streamflow and SWE performs better than its individual counterparts, offering improvements over all forecast horizons considered and throughout the whole year, including the critical period of snowmelt. This highlights the potential benefit of using multivariate data assimilation for streamflow predictions in snow-dominated regions.

Avalanche Fatalities during Atmospheric River Events in the Western United States

2017 ◽  
Vol 18 (5) ◽  
pp. 1359-1374 ◽  
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.

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

2008 ◽  
Vol 9 (6) ◽  
pp. 1416-1426 ◽  
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.

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

2021 ◽  
Author(s):  
Abby C. Lute ◽  
John Abatzoglou ◽  
Timothy Link
Keyword(s):  
Climate Change ◽  
United States ◽  
Spatial Resolution ◽  
Snow Water Equivalent ◽  
High Spatial Resolution ◽  
Western United States ◽  
Climate Data ◽  
Western Us ◽  
Snow Water ◽  
Snow Model

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.

scholarly journals Combined assimilation of streamflow and snow water equivalent for mid-term ensemble streamflow forecasts in snow-dominated regions

2016 ◽  
Vol 20 (10) ◽  
pp. 4375-4389 ◽  
Author(s):  
Jean M. Bergeron ◽  
Mélanie Trudel ◽  
Robert Leconte
Keyword(s):  
Data Assimilation ◽  
State Vector ◽  
Snow Water Equivalent ◽  
Synthetic Data ◽  
Hydrologic Model ◽  
Open Loop ◽  
The State ◽  
Wide Range ◽  
Snow Water ◽  
The Impact

Abstract. The potential of data assimilation for hydrologic predictions has been demonstrated in many research studies. Watersheds over which multiple observation types are available can potentially further benefit from data assimilation by having multiple updated states from which hydrologic predictions can be generated. However, the magnitude and time span of the impact of the assimilation of an observation varies according not only to its type, but also to the variables included in the state vector. This study examines the impact of multivariate synthetic data assimilation using the ensemble Kalman filter (EnKF) into the spatially distributed hydrologic model CEQUEAU for the mountainous Nechako River located in British Columbia, Canada. Synthetic data include daily snow cover area (SCA), daily measurements of snow water equivalent (SWE) at three different locations and daily streamflow data at the watershed outlet. Results show a large variability of the continuous rank probability skill score over a wide range of prediction horizons (days to weeks) depending on the state vector configuration and the type of observations assimilated. Overall, the variables most closely linearly linked to the observations are the ones worth considering adding to the state vector due to the limitations imposed by the EnKF. The performance of the assimilation of basin-wide SCA, which does not have a decent proxy among potential state variables, does not surpass the open loop for any of the simulated variables. However, the assimilation of streamflow offers major improvements steadily throughout the year, but mainly over the short-term (up to 5 days) forecast horizons, while the impact of the assimilation of SWE gains more importance during the snowmelt period over the mid-term (up to 50 days) forecast horizon compared with open loop. The combined assimilation of streamflow and SWE performs better than their individual counterparts, offering improvements over all forecast horizons considered and throughout the whole year, including the critical period of snowmelt. This highlights the potential benefit of using multivariate data assimilation for streamflow predictions in snow-dominated regions.

scholarly journals Why Do Global Reanalyses and Land Data Assimilation Products Underestimate Snow Water Equivalent?

2016 ◽  
Vol 17 (11) ◽  
pp. 2743-2761 ◽  
Author(s):  
Patrick D. Broxton ◽  
Xubin Zeng ◽  
Nicholas Dawson
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Snow Water Equivalent ◽  
Primary Reason ◽  
Snow Water ◽  
Land Data Assimilation ◽  
Freezing Temperatures ◽  
Data Assimilation System ◽  
Assimilation System ◽  
Snow Thickness

Abstract There is a large uncertainty of snow water equivalent (SWE) in reanalyses and the Global Land Data Assimilation System (GLDAS), but the primary reason for this uncertainty remains unclear. Here several reanalysis products and GLDAS with different land models are evaluated and the primary reason for their deficiencies are identified using two high-resolution SWE datasets, including the Snow Data Assimilation System product and a new dataset for SWE and snowfall for the conterminous United States (CONUS) that is based on PRISM precipitation and temperature data and constrained with thousands of point snow observations of snowfall and snow thickness. The reanalyses and GLDAS products substantially underestimate SWE in the CONUS compared to the high-resolution SWE data. This occurs irrespective of biases in atmospheric forcing information or differences in model resolution. Furthermore, reanalysis and GLDAS products that predict more snow ablation at near-freezing temperatures have larger underestimates of SWE. Since many of the products do not assimilate information about SWE and snow thickness, this indicates a problem with the implementation of land models and pinpoints the need to improve the treatment of snow ablation in these systems, especially at near-freezing temperatures.

Seasonal Cycle Shifts in Hydroclimatology over the Western United States

2005 ◽  
Vol 18 (2) ◽  
pp. 372-384 ◽  
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.

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

2019 ◽  
Vol 58 (1) ◽  
pp. 131-143 ◽  
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.

scholarly journals Generating Observation-Based Snow Depletion Curves for Use in Snow Cover Data Assimilation

Geosciences ◽  
2018 ◽  
Vol 8 (12) ◽  
pp. 484 ◽  
Author(s):  
Kristi Arsenault ◽  
Paul Houser
Keyword(s):  
Data Assimilation ◽  
Snow Cover ◽  
Land Surface ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Land Surface Model ◽  
Open Loop ◽  
Surface Model ◽  
Snow Water ◽  
Logarithmic Functions

Snow depletion curves (SDC) are functions that are used to show the relationship between snow covered area and snow depth or water equivalent. Previous snow cover data assimilation (DA) studies have used theoretical SDC models as observation operators to map snow depth to snow cover fraction (SCF). In this study, a new approach is introduced that uses snow water equivalent (SWE) observations and satellite-based SCF retrievals to derive SDC relationships for use in an Ensemble Kalman filter (EnKF) to assimilate snow cover estimates. A histogram analysis is used to bin the SWE observations, which the corresponding SCF observations are then averaged within, helping to constrain the amount of data dispersion across different temporal and regional conditions. Logarithmic functions are linearly regressed with the binned average values, for two U.S. mountainous states: Colorado and Washington. The SDC-based logarithmic functions are used as EnKF observation operators, and the satellite-based SCF estimates are assimilated into a land surface model. Assimilating satellite-based SCF estimates with the observation-based SDC shows a reduction in SWE-related RMSE values compared to the model-based SDC functions. In addition, observation-based SDC functions were derived for different intra-annual and physiographic conditions, and landcover and elevation bands. Lower SWE-based RMSE values are also found with many of these categorical observation-based SDC EnKF experiments. All assimilation experiments perform better than the open-loop runs, except for the Washington region’s 2004–2005 snow season, which was a major drought year that was difficult to capture with the ensembles and observations.

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