Independent evaluation of the SNODAS snow depth product using regional-scale lidar-derived measurements

Hedrick, A. R.; Marshall, Hans‐Peter; Winstral, A. H.; Elder, Kelly; Yueh, Simon; Cline, D.

doi:10.5194/tc-9-13-2015

Cited by 44 publications

(30 citation statements)

References 42 publications

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“…5). This follows previous reports that without sufficient data, estimates of snow depth under these conditions can be difficult and error prone due to the underlying variance in elevation within grid boundaries [Hedrick et al, 2015]. Clow et al [2012] showed that for over-forested regions of the Colorado Rockies, SNODAS estimates of snow depth accounted for as much as 72% of the variance line (1-km resolution) in forested areas, but SNODAS was only able to account for 16% of snow-depth variance in areas above the treeline.…”

Section: Snow Depthsupporting

confidence: 85%

Technical report: The design and evaluation of a basin‐scale wireless sensor network for mountain hydrology

Zhang

Glaser

Bales

et al. 2017

Water Resources Research

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A network of sensors for spatially representative water‐balance measurements was developed and deployed across the 2000 km2 snow‐dominated portion of the upper American River basin, primarily to measure changes in snowpack and soil‐water storage, air temperature, and humidity. This wireless sensor network (WSN) consists of 14 sensor clusters, each with 10 measurement nodes that were strategically placed within a 1 km2 area, across different elevations, aspects, slopes, and canopy covers. Compared to existing operational sensor installations, the WSN reduces hydrologic uncertainty in at least three ways. First, redundant measurements improved estimation of lapse rates for air and dew‐point temperature. Second, distributed measurements captured local variability and constrained uncertainty in air and dew‐point temperature, snow accumulation, and derived hydrologic attributes important for modeling and prediction. Third, the distributed relative‐humidity measurements offer a unique capability to monitor upper‐basin patterns in dew‐point temperature and characterize elevation gradient of water vapor‐pressure deficit across steep, variable topography. Network statistics during the first year of operation demonstrated that the WSN was robust for cold, wet, and windy conditions in the basin. The electronic technology used in the WSN‐reduced adverse effects, such as high current consumption, multipath signal fading, and clock drift, seen in previous remote WSNs.

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Section: Snow Depthsupporting

confidence: 85%

Technical report: The design and evaluation of a basin‐scale wireless sensor network for mountain hydrology

Zhang

Glaser

Bales

et al. 2017

Water Resources Research

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“…Although SNODAS assimilates MODIS imagery into the model, it does not appear to capture the finer elevation patterns we found using the MOD10A product (Fig. 7), which is consistent with challenges reported by other SNODAS verification efforts in complex terrain (Clow et al, 2012;Hedrick et al, 2015). The Great Basin shows tremendous sensitivity to snow ephemerality from topography and elevation 15 and thus, represents an area where improvements in the physically-based modeling of shallow snow and rain-snow transition elevations will be critical to predicting snow water resources under a variable and changing climate.…”

supporting

confidence: 78%

“…11). Blowing snow sublimation was not the dominant cause of snow ephemerality in the Great Basin for any year, but its known that SNODAS struggles to represent wind redistribution of snow Clow et al (2012);Hedrick et al (2015). The mechanisms causing snow ephemerality that can be inferred from the SNODAS model have important implications for water availability in the Great Basin, but we lack confidence in the model fidelity in these shallow snowpacks.…”

mentioning

confidence: 97%

Now You See It Now You Don’t: A Case Study of Ephemeral Snowpacks in the Great Basin U.S.A.

Petersky

Harpold

2018

Preprint

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Ephemeral snowpacks, or those that routinely experience accumulation and ablation at the same time and persist for <60 days, are challenging to observe and model. Using 328 site years from the Great Basin, we show that ephemeral snowmelt delivers water earlier than seasonal snowmelt. For example, we found that day of peak soil moisture preceded day of last snowmelt in the Great Basin by 79 days for shallow soil moisture in ephemeral snowmelt compared to 5 days for seasonal snowmelt. To 5 understand Great Basin snow distribution, we used moderate resolution imaging spectroradiometer (MODIS) and Snow Data Assimilation System (SNODAS) data from water years 2005-2014 to map snow extent. During this time period snowpack was highly variable. The maximum seasonal snow cover was 64 % in 2010 and the minimum was 24 % in 2014. We found that elevation had a strong control on snow ephemerality, and nearly all snowpacks over 2500 m were seasonal. Snowpacks were more likely to be ephemeral on south facing slopes than north facing slopes at elevations above 2500 m. Additionally, we 10 used SNODAS-derived estimates of solid and liquid precipitation, melt, sublimation, and blowing snow sublimation to define snow ephemerality mechanisms. In warm years, the Great Basin shifts to ephemerally dominant as the rain-snow transition increases in elevation. Given that snow ephemerality is expected to increase as a consequence of climate change, we put forward several challenges and recommendations to bolster physics based modeling of ephemeral snow such as better metrics for snow ephemerality and more ground-based observations. 15 IntroductionSeasonal snowmelt supplies water to one-sixth of the world's population, which supports one-fourth of the global economy (Barnett et al., 2005;Sturm et al., 2017). Seasonal snowpack provides predictable melt timing and volumes in the spring, which influences streamflow timing, surface water and groundwater availability (Berghuijs et al., 2014;Jasechko et al., 2014;Stewart et al., 2005). Reliable spring snowmelt also provides a strong control on vegetation phenology and productivity in many 20 ecosystems (Parida and Buermann, 2014;Trujillo et al., 2012). Despite the importance of seasonal snow to water supplies, much of the world's snow is ephemeral, which means it melts and sublimates throughout the snow cover season instead of having one consistent period of snowmelt. Even small shifts from seasonal to ephemeral snowpack due to regional warming could disrupt 1 Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-749 Manuscript under review for journal Hydrol. Earth Syst. Sci. Discussion started: 8 January 2018 c Author(s) 2018. CC BY 4.0 License. snowmelt timing in ways that could alter summer productivity, soil temperature, and soil moisture regimes Harpold and Molotch, 2015;Jefferson, 2011;Parida and Buermann, 2014;Regonda et al., 2005;Stielstra et al., 2015;Trujillo et al., 2012). A shift from seasonal to ephemeral snowpacks will also have negative implications for the wi...

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“…Existing techniques include terrestrial or airborne laser scanning (e.g., Hopkinson et al, 2004;Deems et al, 2006Deems et al, , 2013Prokop et al, 2008;Dadic et al, 2010;Grünewald et al, 2010Grünewald et al, , 2013Lehning et al, 2011;Hopkinson et al, 2012;Grünewald and Lehning, 2015;Hedrick et al, 2015), SAR (synthetic aperture radar, Luzi et al, 2009), aerial photography (Blöschl and Kirnbauer, 1992;König and Sturm, 1998;Worby et al, 2008), time-lapse photography (Farinotti et al, 2010), and optical and micro-wave data from satellite platforms (Parajka and Blöschl, 2006;Dietz et al, 2012). The good performance of these methods has been widely discussed, but survey expenses are still a constraint (Hood and Hayashi, 2010).…”

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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Independent evaluation of the SNODAS snow depth product using regional-scale lidar-derived measurements

Cited by 44 publications

References 42 publications

Technical report: The design and evaluation of a basin‐scale wireless sensor network for mountain hydrology

Technical report: The design and evaluation of a basin‐scale wireless sensor network for mountain hydrology

Now You See It Now You Don’t: A Case Study of Ephemeral Snowpacks in the Great Basin U.S.A.

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

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