A comparison of SNOTEL and AMSR-E snow water equivalent data sets in western US watersheds

Abstract. Gridded snow water equivalent (SWE) data sets are valuable for estimating the snow water resources and verify different model systems, e.g. hydrological, land surface or atmospheric models. However, changing data availability represents a considerable challenge when trying to derive consistent time series for SWE products. In an attempt to improve the product consistency, we first evaluated the differences between two climatologies of SWE grids that were calculated on the basis of data from 110 and 203 stations, respectively. The "shorter" climatology (2001–2009) was produced using 203 stations (map203) and the "longer" one (1971–2009) 110 stations (map110). Relative to map203, map110 underestimated SWE, especially at higher elevations and at the end of the winter season. We tested the potential of quantile mapping to compensate for mapping errors in map110 relative to map203. During a 9 yr calibration period from 2001 to 2009, for which both map203 and map110 were available, the method could successfully refine the spatial and temporal SWE representation in map110 by making seasonal, regional and altitude-related distinctions. Expanding the calibration to the full 39 yr showed that the general underestimation of map110 with respect to map203 could be removed for the whole winter. The calibrated SWE maps fitted the reference (map203) well when averaged over regions and time periods, where the mean error is approximately zero. However, deviations between the calibrated maps and map203 were observed at single grid cells and years. When we looked at three different regions in more detail, we found that the calibration had the largest effect in the region with the highest proportion of catchment areas above 2000 m a.s.l. and that the general underestimation of map110 compared to map203 could be removed for the entire snow season. The added value of the calibrated SWE climatology is illustrated with practical examples: the verification of a hydrological model, the estimation of snow resource anomalies and the predictability of runoff through SWE.

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Large scale snow water equivalent status monitoring: comparison of different snow water products in the upper Colorado Basin

Hydrology and Earth System Sciences ◽

10.5194/hess-17-5127-2013 ◽

2013 ◽

Vol 17 (12) ◽

pp. 5127-5139 ◽

Cited By ~ 6

Author(s):

G. A. Artan ◽

J. P. Verdin ◽

R. Lietzow

Keyword(s):

Energy Balance ◽

Water Content ◽

Snow Water Equivalent ◽

Weather Forecast ◽

Snow Accumulation ◽

Passive Microwave ◽

Data Sets ◽

Snow Water ◽

Trmm Precipitation ◽

Colorado Basin

Abstract. We illustrate the ability to monitor the status of snow water content over large areas by using a spatially distributed snow accumulation and ablation model that uses data from a weather forecast model in the upper Colorado Basin. The model was forced with precipitation fields from the National Weather Service (NWS) Multi-sensor Precipitation Estimator (MPE) and the Tropical Rainfall Measuring Mission (TRMM) data-sets; remaining meteorological model input data were from NOAA's Global Forecast System (GFS) model output fields. The simulated snow water equivalent (SWE) was compared to SWEs from the Snow Data Assimilation System (SNODAS) and SNOwpack TELemetry system (SNOTEL) over a region of the western US that covers parts of the upper Colorado Basin. We also compared the SWE product estimated from the special sensor microwave imager (SSM/I) and scanning multichannel microwave radiometer (SMMR) to the SNODAS and SNOTEL SWE data-sets. Agreement between the spatial distributions of the simulated SWE with MPE data was high with both SNODAS and SNOTEL. Model-simulated SWE with TRMM precipitation and SWE estimated from the passive microwave imagery were not significantly correlated spatially with either SNODAS or the SNOTEL SWE. Average basin-wide SWE simulated with the MPE and the TRMM data were highly correlated with both SNODAS (r = 0.94 and r = 0.64; d.f. = 14 – d.f. = degrees of freedom) and SNOTEL (r = 0.93 and r = 0.68; d.f. = 14). The SWE estimated from the passive microwave imagery was significantly correlated with the SNODAS SWE (r = 0.55, d.f. = 9, p = 0.05) but was not significantly correlated with the SNOTEL-reported SWE values (r = 0.45, d.f. = 9, p = 0.05).The results indicate the applicability of the snow energy balance model for monitoring snow water content at regional scales when coupled with meteorological data of acceptable quality. The two snow water contents from the microwave imagery (SMMR and SSM/I) and the Utah Energy Balance forced with the TRMM precipitation data were found to be unreliable sources for mapping SWE in the study area; both data sets lacked discernible variability of snow water content between sites as seen in the SNOTEL and SNODAS SWE data. This study will contribute to better understanding the adequacy of data from weather forecast models, TRMM, and microwave imagery for monitoring status of the snow water content.

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

10.5194/gmd-2021-407 ◽

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.

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Linkage between ENSO phases and western US snow water equivalent

Atmospheric Research ◽

10.1016/j.atmosres.2019.104827 ◽

2020 ◽

Vol 236 ◽

pp. 104827 ◽

Cited By ~ 6

Author(s):

Balbhadra Thakur ◽

Ajay Kalra ◽

Venkat Lakshmi ◽

Kenneth W. Lamb ◽

William P. Miller ◽

...

Keyword(s):

Snow Water Equivalent ◽

Western Us ◽

Snow Water ◽

Enso Phases

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The Swiss snow climatology as seen by reanalyses and further gridded datasets

10.5194/ems2021-282 ◽

2021 ◽

Author(s):

Monika Goeldi ◽

Stefanie Gubler ◽

Christian Steger ◽

Simon C. Scherrer ◽

Sven Kotlarski

Keyword(s):

Remote Sensing ◽

Snow Cover ◽

Snow Water Equivalent ◽

Spatiotemporal Variability ◽

Data Sets ◽

Data Set ◽

Climate Services ◽

Snow Cover Extent ◽

Snow Water

Snow cover is a key component of alpine environments and knowledge of its spatiotemporal variability, including long-term trends, is vital for a range of dependent systems like winter tourism, hydropower production, etc. Snow cover retreat during the past decades is considered as an important and illustrative indicator of ongoing climate change. As such, the monitoring of surface snow cover and the projection of its future changes play a key role for climate services in alpine regions.In Switzerland, a spatially and temporally consistent snow cover climatology that can serve as a reference for both climate monitoring and for future snow cover projections is currently missing. To assess the value and the potential of currently available long term spatial snow data we compare a range of different gridded snow water equivalent (SWE) datasets for the area of Switzerland, including three reanalysis-based products (COSMO-REA6, ERA5, ERA5-Land). The gridded data sets have a horizontal resolution between 1 and 30 km. The performance of the data sets is assessed by comparing them against three reference data sets with different characteristics (station data, a high-resolution 1km snow model that assimilates snow observations, and an optical remote sensing data set). Four different snow indicators are considered (mean SWE, number of snow days, date of maximum SWE, and snow cover extent) in nine different regions of Switzerland and six elevation classes.The results reveal high temporal correlations between the individual datasets and, in general, a good performance regarding both countrywide and regional estimates of mean SWE. In individual regions, however, larger biases appear. All data sets qualitatively agree on a decreasing trend of mean SWE during the previous decades particularly at low elevations, but substantial differences can exist. Furthermore, all data sets overestimate the snow cover fraction as provided by the remote sensing reference. In general, reanalysis products capture the general characteristics of the Swiss snow climatology but indicate some distinctive deviations &#8211; e.g. like a systematic under- respectively overestimation of the mean snow water equivalent.

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Predicting natural streamflows in regulated snowmelt-driven watersheds using regionalization methods

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-10-9435-2013 ◽

2013 ◽

Vol 10 (7) ◽

pp. 9435-9476

Author(s):

D. Kim ◽

J. Kaluarachchi

Keyword(s):

Snow Water Equivalent ◽

Precipitation Index ◽

Data Availability ◽

Data Sets ◽

Tank Model ◽

Natural Flow ◽

Snow Water ◽

Minimal Data ◽

Canal Networks ◽

Ungauged Watersheds

Abstract. Predicting streamflows in snow-fed watersheds in the Western United States is important for water allocation. Since many of these watersheds are heavily regulated through canal networks and reservoirs, predicting expected natural flows and therefore water availability under limited data is always a challenge. This study investigates the applicability of the flow duration curve (FDC) method for predicting natural flows in gauged and ungauged snow-fed watersheds. Point snow observations, air temperature, precipitation, and snow water equivalent, are used to simulate snowmelt process with SNOW-17 model and extended to streamflow generation by a FDC method with modified current precipitation index. For regulated (ungauged) watersheds, a parametric regional FDC method is applied to reconstruct natural flow. For comparison, a simplified Tank Model is used as well. The proximity regionalization method is used to generate streamflow using the Tank Model in ungauged watersheds. The results show that the FDC method can produce acceptable natural flow estimates in both gauged and ungauged watersheds under data limited conditions. The performance of the FDC method is better in watersheds with relatively low evapotranspiration (ET). Multiple donor data sets including current precipitation index are recommended to reduce uncertainty of the regional FDC method for ungauged watersheds. In spite of its simplicity, the FDC method can perform better than the Tank Model under minimal data availability.

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Implications of model selection: a comparison of publicly available, conterminous US-extent hydrologic component estimates

Hydrology and Earth System Sciences ◽

10.5194/hess-25-1529-2021 ◽

2021 ◽

Vol 25 (3) ◽

pp. 1529-1568

Author(s):

Samuel Saxe ◽

William Farmer ◽

Jessica Driscoll ◽

Terri S. Hogue

Keyword(s):

United States ◽

Remote Sensing ◽

Snow Water Equivalent ◽

Reanalysis Data ◽

Data Sets ◽

Hydrologic Models ◽

Study Results ◽

Hydrologic System ◽

Snow Water ◽

Multimodel Ensembles

Abstract. Spatiotemporally continuous estimates of the hydrologic cycle are often generated through hydrologic modeling, reanalysis, or remote sensing (RS) methods and are commonly applied as a supplement to, or a substitute for, in situ measurements when observational data are sparse or unavailable. This study compares estimates of precipitation (P), actual evapotranspiration (ET), runoff (R), snow water equivalent (SWE), and soil moisture (SM) from 87 unique data sets generated by 47 hydrologic models, reanalysis data sets, and remote sensing products across the conterminous United States (CONUS). Uncertainty between hydrologic component estimates was shown to be high in the western CONUS, with median uncertainty (measured as the coefficient of variation) ranging from 11 % to 21 % for P, 14 % to 26 % for ET, 28 % to 82 % for R, 76 % to 84 % for SWE, and 36 % to 96 % for SM. Uncertainty between estimates was lower in the eastern CONUS, with medians ranging from 5 % to 14 % for P, 13 % to 22 % for ET, 28 % to 82 % for R, 53 % to 63 % for SWE, and 42 % to 83 % for SM. Interannual trends in estimates from 1982 to 2010 show common disagreement in R, SWE, and SM. Correlating fluxes and stores against remote-sensing-derived products show poor overall correlation in the western CONUS for ET and SM estimates. Study results show that disagreement between estimates can be substantial, sometimes exceeding the magnitude of the measurements themselves. The authors conclude that multimodel ensembles are not only useful but are in fact a necessity for accurately representing uncertainty in research results. Spatial biases of model disagreement values in the western United States show that targeted research efforts in arid and semiarid water-limited regions are warranted, with the greatest emphasis on storage and runoff components, to better describe complexities of the terrestrial hydrologic system and reconcile model disagreement.

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Snow water equivalent in the Alps as seen by gridded data sets, CMIP5 and CORDEX climate models

The Cryosphere ◽

10.5194/tc-11-1625-2017 ◽

2017 ◽

Vol 11 (4) ◽

pp. 1625-1645 ◽

Cited By ~ 15

Author(s):

Silvia Terzago ◽

Jost von Hardenberg ◽

Elisa Palazzi ◽

Antonello Provenzale

Keyword(s):

Remote Sensing ◽

Climate Models ◽

Snow Water Equivalent ◽

Regional Climate ◽

Reanalysis Data ◽

Data Sets ◽

Gridded Data ◽

Mountain Areas ◽

Ensemble Mean ◽

Snow Water

Abstract. The estimate of the current and future conditions of snow resources in mountain areas would require reliable, kilometre-resolution, regional-observation-based gridded data sets and climate models capable of properly representing snow processes and snow–climate interactions. At the moment, the development of such tools is hampered by the sparseness of station-based reference observations. In past decades passive microwave remote sensing and reanalysis products have mainly been used to infer information on the snow water equivalent distribution. However, the investigation has usually been limited to flat terrains as the reliability of these products in mountain areas is poorly characterized.This work considers the available snow water equivalent data sets from remote sensing and from reanalyses for the greater Alpine region (GAR), and explores their ability to provide a coherent view of the snow water equivalent distribution and climatology in this area. Further we analyse the simulations from the latest-generation regional and global climate models (RCMs, GCMs), participating in the Coordinated Regional Climate Downscaling Experiment over the European domain (EURO-CORDEX) and in the Fifth Coupled Model Intercomparison Project (CMIP5) respectively. We evaluate their reliability in reproducing the main drivers of snow processes – near-surface air temperature and precipitation – against the observational data set EOBS, and compare the snow water equivalent climatology with the remote sensing and reanalysis data sets previously considered. We critically discuss the model limitations in the historical period and we explore their potential in providing reliable future projections.The results of the analysis show that the time-averaged spatial distribution of snow water equivalent and the amplitude of its annual cycle are reproduced quite differently by the different remote sensing and reanalysis data sets, which in fact exhibit a large spread around the ensemble mean. We find that GCMs at spatial resolutions equal to or finer than 1.25° longitude are in closer agreement with the ensemble mean of satellite and reanalysis products in terms of root mean square error and standard deviation than lower-resolution GCMs. The set of regional climate models from the EURO-CORDEX ensemble provides estimates of snow water equivalent at 0.11° resolution that are locally much larger than those indicated by the gridded data sets, and only in a few cases are these differences smoothed out when snow water equivalent is spatially averaged over the entire Alpine domain. ERA-Interim-driven RCM simulations show an annual snow cycle that is comparable in amplitude to those provided by the reference data sets, while GCM-driven RCMs present a large positive bias. RCMs and higher-resolution GCM simulations are used to provide an estimate of the snow reduction expected by the mid-21st century (RCP 8.5 scenario) compared to the historical climatology, with the main purpose of highlighting the limits of our current knowledge and the need for developing more reliable snow simulations.

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Microwave Measurements of Snowpack Properties

Hydrology Research ◽

10.2166/nh.1981.0012 ◽

1981 ◽

Vol 12 (3) ◽

pp. 143-166 ◽

Cited By ~ 18

Author(s):

W.H. Stiles ◽

F.T. Ulaby ◽

A. Rango

Keyword(s):

Water Content ◽

Liquid Water ◽

Snow Water Equivalent ◽

Liquid Water Content ◽

Microwave Measurements ◽

Data Sets ◽

Microwave Response ◽

Passive Data ◽

Snow Water ◽

Snowpack Properties

Prior microwave measurements of snow water equivalent and liquid water content and conceptualizations of emission and backscattering models are reviewed. The results of an experiment designed to collect simultaneous passive and active microwave data to be used in interpreting and analyzing the sensitivity of the microwave spectrum to changing snowpack properties are reported. Both the scattering coefficient, σ°, and the apparent radiometric temperature, Tap, were found to be sensitive to changes in snow water equivalent and liquid water content. The σ° data exhibit an exponential-like increase with increasing water equivalent, whereas, the Tap data exhibit an exponential-like decrease. For both the active and passive data, the snow water equivalent at which the microwave response begins to saturate decreases as the wavelength decreases. Increasing liquid water in the snowpack causes a decrease in σ° and an increase in the Tap. Diurnal data sets show the greatest σ° and Tap variation in response to snowmelt at 35 and 37 GHz with correspondingly less variation at the lower frequencies. Based on research results to date, immediate formulation of a comprehensive microwave and snow research program is recommended.

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Snowpack Data Sets Put to the Test

Eos ◽

10.1029/2020eo141900 ◽

2020 ◽

Vol 101 ◽

Author(s):

David Shultz

Keyword(s):

Water Management ◽

Snow Water Equivalent ◽

Data Sets ◽

Snow Water

A new study compares the accuracy of three observation-based methods of calculating snow water equivalent, a key component in water management.

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