Orographic effects on snow deposition patterns in mountainous terrain

Mott, Rebecca; Scipion, Danny; Schneebeli, M.; Dawes, Nicholas; Berne, Alexis; Lehning, Michael

doi:10.1002/2013jd019880

Cited by 105 publications

(138 citation statements)

References 58 publications

(151 reference statements)

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“…Snow depth distribution is usually marked by strong spatial variability at small scales (Grünewald et al, 2010;López Moreno et al, 2013;López-Moreno et al, 2015;Mott et al, 2014) and this hampers our evaluation since coordinates of probe data must be collected with a very high spatial precision due to the spatial resolution we have considered. For this purpose, coordinates were obtained by total station theodolite observations referred to GPS baselines that were surveyed by static approach (40 min sessions).…”

Section: Point Data Collectionmentioning

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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Section: Point Data Collectionmentioning

confidence: 99%

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

et al. 2016

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“…The latter was developed to study multiple subjects such as the impact of climate change on snow cover (Bavay et al, 2009(Bavay et al, , 2013, the effect of wind and topography on snow deposition (Mott and Lehning, 2010;Mott et al, 2014) or the sublimation of drifting snow (Groot Zwaaftink et al, 2013 evolution of the vertical snow profile, as well as the vertical profiles of soil moisture and soil temperature (Bartelt and Lehning, 2002;Lehning et al, 2002b, a). It accounts for the canopy layer (Gouttevin et al, 2015) and can simulate the vertical water transport using either the Richards equation or a simple bucket scheme (Wever et al, 2014(Wever et al, , 2015.…”

Section: Model Descriptionmentioning

confidence: 99%

StreamFlow 1.0: an extension to the spatially distributed snow model Alpine3D for hydrological modelling and deterministic stream temperature prediction

Gallice

Bavay²,

Brauchli

et al. 2016

Geosci. Model Dev.

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Abstract. Climate change is expected to strongly impact the hydrological and thermal regimes of Alpine rivers within the coming decades. In this context, the development of hydrological models accounting for the specific dynamics of Alpine catchments appears as one of the promising approaches to reduce our uncertainty of future mountain hydrology. This paper describes the improvements brought to StreamFlow, an existing model for hydrological and stream temperature prediction built as an external extension to the physically based snow model Alpine3D. StreamFlow's source code has been entirely written anew, taking advantage of object-oriented programming to significantly improve its structure and ease the implementation of future developments. The source code is now publicly available online, along with a complete documentation. A special emphasis has been put on modularity during the re-implementation of StreamFlow, so that many model aspects can be represented using different alternatives. For example, several options are now available to model the advection of water within the stream. This allows for an easy and fast comparison between different approaches and helps in defining more reliable uncertainty estimates of the model forecasts. In particular, a case study in a Swiss Alpine catchment reveals that the stream temperature predictions are particularly sensitive to the approach used to model the temperature of subsurface flow, a fact which has been poorly reported in the literature to date. Based on the case study, StreamFlow is shown to reproduce hourly mean discharge with a Nash-Sutcliffe efficiency (NSE) of 0.82 and hourly mean temperature with a NSE of 0.78.

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“…Snow accumulation across the mountains is primarily influenced by orographic processes, involving feedbacks between atmospheric circulation, terrain and the geomorphic processes of mountain uplift, erosion and glaciation on the Earth's surface (Roe, 2005;Roe and Baker, 2006;Pedersen et al, 2010;Kessler et al, 2006;Stolar et al, 2007;Galewsky, 2009;Mott et al, 2014). Orographic precipitation is well documented and central to determining the amount of snow water equivalent (SWE) in mountainous regions.…”

Section: P B Kirchner Et Al: Lidar Measurement Of Seasonal Snow Acmentioning

confidence: 99%

“…2 and 4). Above 3300 m, the reduced lift over flatter terrain, the exhaustion of precipitable water as storms rise less steeply, and the horizontal displacement of falling snow likely all contribute to declining precipitation at the higher elevations (Mott et al, 2014;Houze Jr., 2012). These processes have been approximated in the Sierra Nevada through simulations based on the convergence of the boundary layer and slope of the local terrain but, until now, have been difficult to observe (Alpert, 1986).…”

Section: Variability Of Orographic Trendsmentioning

confidence: 99%

LiDAR measurement of seasonal snow accumulation along an elevation gradient in the southern Sierra Nevada, California

Kirchner

Bales

Molotch

et al. 2014

Hydrol. Earth Syst. Sci.

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Abstract. We present results from snow-on and snow-off airborne-scanning LiDAR measurements over a 53 km 2 area in the southern Sierra Nevada. We found that snow depth as a function of elevation increased approximately 15 cm per 100 m, until reaching an elevation of 3300 m, where depth sharply decreased at a rate of 48 cm per 100 m. Departures from the 15 cm per 100 m trend, based on 1 m elevation-band means of regression residuals, showed slightly less steep increases below 2050 m; steeper increases between 2050 and 3300 m; and less steep increases above 3300 m. Although the study area is partly forested, only measurements in open areas were used. Below approximately 2050 m elevation, ablation and rainfall are the primary causes of departure from the orographic trend. From 2050 to 3300 m, greater snow depths than predicted were found on the steeper terrain of the northwest and the less steep northeast-facing slopes, suggesting that ablation, aspect, slope and wind redistribution all play a role in local snow-depth variability. At elevations above 3300 m, orographic processes mask the effect of wind deposition when averaging over large areas. Also, terrain in this basin becomes less steep above 3300 m. This suggests a reduction in precipitation from upslope lifting and/or the exhaustion of precipitable water from ascending air masses. Our results suggest a cumulative precipitation lapse rate for the 2100-3300 m range of about 6 cm per 100 m elevation for the accumulation period of 3 December 2009 to 23 March 2010. This is a higher gradient than the widely used PRISM (Parameter-elevation Relationships on Independent Slopes Model) precipitation products, but similar to that from reconstruction of snowmelt amounts from satellite snow-cover data. Our findings provide a unique characterization of the consistent, steep average increase in precipitation with elevation in snow-dominated terrain, using high-resolution, highly accurate data and highlighs the importance of solar radiation, wind redistribution and mid-winter melt with regard to snow distribution.

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Orographic effects on snow deposition patterns in mountainous terrain

Cited by 105 publications

References 58 publications

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

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

StreamFlow 1.0: an extension to the spatially distributed snow model Alpine3D for hydrological modelling and deterministic stream temperature prediction

LiDAR measurement of seasonal snow accumulation along an elevation gradient in the southern Sierra Nevada, California

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