Estimating the spatial distribution of snow water equivalent in the world's mountains

Dozier, Jeff; Bair, Edward H.; Davis, Robert E.

doi:10.1002/wat2.1140

Cited by 208 publications

(212 citation statements)

References 84 publications

(124 reference statements)

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“…Potential sources of uncertainty include unobservable F SCA during cloudy periods, reflectance bias at higher sensor zenith angles (Dozier et al, 2008), melt out date (Slater et al, 2013), snow albedo, and wind speed. However, we assert that all of these sources of error have been vetted and addressed in depth (e.g., Dozier et al, 2016;Molotch et al, 2010;Rittger et al, 2016). The comparison with in situ measurements from the region are unbiased and have very low error when uncertainty is accounted for (Sect.…”

Section: Resultsmentioning

confidence: 76%

Using machine learning for real-time estimates of snow water equivalent in the watersheds of Afghanistan

et al. 2018

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Abstract. In the mountains, snowmelt often provides most of the runoff. Operational estimates use imagery from optical and passive microwave sensors, but each has its limitations. An accurate approach, which we validate in Afghanistan and the Sierra Nevada USA, reconstructs spatially distributed snow water equivalent (SWE) by calculating snowmelt backward from a remotely sensed date of disappearance. However, reconstructed SWE estimates are available only retrospectively; they do not provide a forecast. To estimate SWE throughout the snowmelt season, we consider physiographic and remotely sensed information as predictors and reconstructed SWE as the target. The period of analysis matches the AMSR-E radiometer's lifetime from 2003 to 2011, for the months of April through June. The spatial resolution of the predictions is 3.125 km, to match the resolution of a microwave brightness temperature product. Two machine learning techniques -bagged regression trees and feed-forward neural networks -produced similar mean results, with 0-14 % bias and 46-48 mm RMSE on average. Nash-Sutcliffe efficiencies averaged 0.68 for all years. Daily SWE climatology and fractional snow-covered area are the most important predictors. We conclude that these methods can accurately estimate SWE during the snow season in remote mountains, and thereby provide an independent estimate to forecast runoff and validate other methods to assess the snow resource.

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Section: Resultsmentioning

confidence: 76%

Using machine learning for real-time estimates of snow water equivalent in the watersheds of Afghanistan

et al. 2018

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“…For the year 2016, we complement the MODIS fSCA retrievals with aggregated 20 m resolution retrievals from the Sentinel-2A mission (Drusch et al, 2012). fSCA estimates are derived from the Level 1C orthorectified top of the atmosphere reflectance product, with cloud-free scenes manually selected.…”

Section: Sentinel-2mentioning

confidence: 99%

“…With its high albedo and large water-holding capacity, snow is a modulator of the global radiation balance and hydrological cycle, making it one of the drivers of the atmospheric circulation and the associated climate (Andreadis and Lettenmaier, 2006;Liston, 1999). Since the snow water equivalent (SWE) can exhibit considerable variability over small distances (Clark et al, 2011), mapping the SWE distribution remains a difficult task (Dozier et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites

et al. 2018

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Abstract. With its high albedo, low thermal conductivity and large water storing capacity, snow strongly modulates the surface energy and water balance, which makes it a critical factor in mid-to high-latitude and mountain environments. However, estimating the snow water equivalent (SWE) is challenging in remote-sensing applications already at medium spatial resolutions of 1 km. We present an ensemble-based data assimilation framework that estimates the peak subgrid SWE distribution (SSD) at the 1 km scale by assimilating fractional snow-covered area (fSCA) satellite retrievals in a simple snow model forced by downscaled reanalysis data. The basic idea is to relate the timing of the snow cover depletion (accessible from satellite products) to the peak SSD. Peak subgrid SWE is assumed to be lognormally distributed, which can be translated to a modeled time series of fSCA through the snow model. Assimilation of satellite-derived fSCA facilitates the estimation of the peak SSD, while taking into account uncertainties in both the model and the assimilated data sets. As an extension to previous studies, our method makes use of the novel (to snow data assimilation) ensemble smoother with multiple data assimilation (ES-MDA) scheme combined with analytical Gaussian anamorphosis to assimilate time series of Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-2 fSCA retrievals. The scheme is applied to Arctic sites near Ny-Ålesund (79 • N, Svalbard, Norway) where field measurements of fSCA and SWE distributions are available. The method is able to successfully recover accurate estimates of peak SSD on most of the occasions considered. Through the ES-MDA assimilation, the root-mean-square error (RMSE) for the fSCA, peak mean SWE and peak subgrid coefficient of variation is improved by around 75, 60 and 20 %, respectively, when compared to the prior, yielding RMSEs of 0.01, 0.09 m water equivalent (w.e.) and 0.13, respectively. The ES-MDA either outperforms or at least nearly matches the performance of other ensemble-based batch smoother schemes with regards to various evaluation metrics. Given the modularity of the method, it could prove valuable for a range of satellite-era hydrometeorological reanalyses.

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“…Currently remote-sensing techniques can only reliably pro-vide information about snow cover based on observations in the visible spectrum (Dietz et al, 2012). Current spaceborne sensors do not provide accurate data on snow water equivalent (SWE) and/or snow depth (SD) in mountainous regions (Dozier et al, 2016). Microwave imaging has a coarse resolution (grid cell size: ∼ 25 km), so does not characterize snowpack variability in the Mediterranean mountains, which have a high spatial heterogeneity not captured with this resolution.…”

Section: Introductionmentioning

confidence: 99%

Daily gridded datasets of snow depth and snow water equivalent for the Iberian Peninsula from 1980 to 2014

Alonso‐González

López‐Moreno

Gascoin

et al. 2018

Earth Syst. Sci. Data

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Abstract. We present snow observations and a validated daily gridded snowpack dataset that was simulated from downscaled reanalysis of data for the Iberian Peninsula. The Iberian Peninsula has long-lasting seasonal snowpacks in its different mountain ranges, and winter snowfall occurs in most of its area. However, there are only limited direct observations of snow depth (SD) and snow water equivalent (SWE), making it difficult to analyze snow dynamics and the spatiotemporal patterns of snowfall. We used meteorological data from downscaled reanalyses as input of a physically based snow energy balance model to simulate SWE and SD over the Iberian Peninsula from 1980 to 2014. More specifically, the ERA-Interim reanalysis was downscaled to 10 km × 10 km resolution using the Weather Research and Forecasting (WRF) model. The WRF outputs were used directly, or as input to other submodels, to obtain data needed to drive the Factorial Snow Model (FSM). We used lapse rate coefficients and hygrobarometric adjustments to simulate snow series at 100 m elevations bands for each 10 km × 10 km grid cell in the Iberian Peninsula. The snow series were validated using data from MODIS satellite sensor and ground observations. The overall simulated snow series accurately reproduced the interannual variability of snowpack and the spatial variability of snow accumulation and melting, even in very complex topographic terrains. Thus, the presented dataset may be useful for many applications, including land management, hydrometeorological studies, phenology of flora and fauna, winter tourism, and risk management. The data presented here are freely available for download from Zenodo (https://doi.org/10.5281/zenodo.854618). This paper fully describes the work flow, data validation, uncertainty assessment, and possible applications and limitations of the database.

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Estimating the spatial distribution of snow water equivalent in the world's mountains

Cited by 208 publications

References 84 publications

Using machine learning for real-time estimates of snow water equivalent in the watersheds of Afghanistan

Using machine learning for real-time estimates of snow water equivalent in the watersheds of Afghanistan

Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites

Daily gridded datasets of snow depth and snow water equivalent for the Iberian Peninsula from 1980 to 2014

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