Estimating the distribution of snow water equivalent and snow extent beneath cloud cover in the Salt–Verde River basin, Arizona

Molotch, N. P.; Fassnacht, S. R.; Bales, R. C.; Helfrich, S.

doi:10.1002/hyp.1408

Cited by 57 publications

(49 citation statements)

References 17 publications

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“…For example, McGuire et al (2005) and Andreadis and Lettenmaier (2006) reported only being able to use SCA images for days where cloud obscuration was less than 20 % of the grid cell, while Rodell and Houser (2004) used 6 % as the threshold for minimum visibility. Consequently, several investigations into techniques for removing cloud cover from SCA images have been reported (e.g., Lichtenegger et al, 1981;Seidel et al, 1983;Molotch et al, 2004;Parajka and Blöschl, 2008;Gafurov and Bárdossy, 2009).…”

Section: The Problem Of Cloud Obscurationmentioning

confidence: 99%

“…Lichtenegger et al (1981) and Seidel et al (1983) used elevation, slope, exposure, and brightness information from a digital terrain model (DTM) to extrapolate snow cover from cloud-free to cloud-covered areas in digital Landsat multispectral scanner data, assuming that for each elevation zone, the regions with equivalent exposure and slope angle carry the same amount of snow. Molotch et al (2004) filtered NOHRSC SCA maps (based on AVHRR/GOES data) using gridded positive accumulated degree days (ADD) and AVHRR-derived binary SCA to obtain a threshold for defining snow cover in the Salt and Verde rivers in Arizona. They reported temperature data to be helpful for estimating snow extent beneath clouds and to thereby improve spatial and temporal continuity of SCA and SWE products.…”

Section: The Problem Of Cloud Obscurationmentioning

confidence: 99%

See 1 more Smart Citation

Reducing cloud obscuration of MODIS snow cover area products by combining spatio-temporal techniques with a probability of snow approach

López-Burgos

Gupta

Clark

2013

Hydrol. Earth Syst. Sci.

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Abstract. Satellite remote sensing can be used to investigate spatially distributed hydrological states for use in modeling, assessment, and management. However, in the visual wavelengths, cloud cover can often obscure significant portions of the images. This study develops a rule-based, multistep method for removing clouds from MODIS snow cover area (SCA) images. The methods used include combining images from more than one satellite, time interpolation, spatial interpolation, and estimation of the probability of snow occurrence based on topographic information. Applied over the upper Salt River basin in Arizona, the method reduced the degree of cloud obscuration by 93.8 %, while maintaining a similar degree of image accuracy to that of the original images.

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Section: The Problem Of Cloud Obscurationmentioning

confidence: 99%

Section: The Problem Of Cloud Obscurationmentioning

confidence: 99%

Reducing cloud obscuration of MODIS snow cover area products by combining spatio-temporal techniques with a probability of snow approach

López-Burgos

Gupta

Clark

2013

Hydrol. Earth Syst. Sci.

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“…Modeling techniques include statistical approaches, such as Carroll and Cressie (1996), Elder et al (1998), Erxleben et al (2002), Anderton et al (2004), Molotch et al (2004), Dressler et al (2006), López-Moreno and Nogués-Bravo (2006), Skaugen (2007), Bavera et al (2014), and conceptual, or physically based models -e.g., Lehning et al (2006;. These works have improved our knowledge about, e.g., the relevance of single forcings in determining the distribution of snow on complex terrains (Anderton et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Using a fixed-wing UAS to map snow depth distribution: an evaluation at peak accumulation

et al. 2016

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Abstract. We investigate snow depth distribution at peak accumulation over a small Alpine area ( ∼ 0.3 km 2 ) using photogrammetry-based surveys with a fixed-wing unmanned aerial system (UAS). These devices are growing in popularity as inexpensive alternatives to existing techniques within the field of remote sensing, but the assessment of their performance in Alpine areas to map snow depth distribution is still an open issue. Moreover, several existing attempts to map snow depth using UASs have used multi-rotor systems, since they guarantee higher stability than fixed-wing systems. We designed two field campaigns: during the first survey, performed at the beginning of the accumulation season, the digital elevation model of the ground was obtained. A second survey, at peak accumulation, enabled us to estimate the snow depth distribution as a difference with respect to the previous aerial survey. Moreover, the spatial integration of UAS snow depth measurements enabled us to estimate the snow volume accumulated over the area. On the same day, we collected 12 probe measurements of snow depth at random positions within the case study to perform a preliminary evaluation of UAS-based snow depth. Results reveal that UAS estimations of point snow depth present an average difference with reference to manual measurements equal to −0.073 m and a RMSE equal to 0.14 m. We have also explored how some basic snow depth statistics (e.g., mean, standard deviation, minima and maxima) change with sampling resolution (from 5 cm up to ∼ 100 m): for this case study, snow depth standard deviation (hence coefficient of variation) increases with decreasing cell size, but it stabilizes for resolutions smaller than 1 m. This provides a possible indication of sampling resolution in similar conditions.

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“…Shimamura et al (2003) estimated SWE in a catchment by combining satellite-derived SCA with in-situ data. Molotch et al (2004) and Kazama et al (2008) successfully combined the satellite-derived SCA data with distributed snowmelt models to estimate the spatial SWE during the snowmelt season. In most research, an uncertain parameter is assumed to be constant over the catchment or other large area.…”

Section: Introductionmentioning

confidence: 99%

Spatial snowfall distribution in mountainous areas estimated with a snow model and satellite remote sensing

Asaoka

Kominami

2012

Hydrological Research Letters

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Abstract:We describe an approach for reconstructing snowfall that combines satellite observations of the snow disappearance date (SDD) with a snow model for two mountainous areas in Japan having distinct snow climatology. This approach allows assessment of the distribution of snow and topographical effects on snowfall within a catchment. We also evaluated how the reconstructed snowfall affects the catchment snow hydrology.Validation at observation sites demonstrated that a combination of the snow model and snowfall reconstructions successfully estimated the seasonal changes of snow water equivalent (SWE). In Japan, the dependence of snowfall on elevation is stronger in mountainous areas along the coast on the Sea of Japan side of Japan's central mountain spine (where snowfall triples with every 1000 m increase in elevation; C sf = 0.002 m −1 ) compared with inland locations of the same region (where snowfall doubles with every 1000 m increase; C sf = 0.001 m −1). Moreover, the reconstructed snowfall improved the estimation of maximum catchment SWE. Maximum total SWE, estimated with reconstructed snowfall, was 3.8 × 10 8 m 3 in Kurobe catchment (along the coast on the Sea of Japan side of Japan's central mountain spine), while that, estimated by convectional method with the spatially-constant C sf = 0.001 m −1 , was 2.0 × 10 8 m 3 . As a result, estimations of the snow disappearance date and of the catchment snowmelt were also improved. These results suggest that it is useful to estimate the spatial snow distribution, especially where steep topography causes large gradients of snowfall amount with respect to elevation.

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Estimating the distribution of snow water equivalent and snow extent beneath cloud cover in the Salt–Verde River basin, Arizona

Cited by 57 publications

References 17 publications

Reducing cloud obscuration of MODIS snow cover area products by combining spatio-temporal techniques with a probability of snow approach

Reducing cloud obscuration of MODIS snow cover area products by combining spatio-temporal techniques with a probability of snow approach

Using a fixed-wing UAS to map snow depth distribution: an evaluation at peak accumulation

Spatial snowfall distribution in mountainous areas estimated with a snow model and satellite remote sensing

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