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Abstract

The heterogeneous mountain snow cover is challenging the eye and the analytical mind of the observer. The snow distribution affects water resources, natural hazards such as avalanches and ecology. While a lot of recent research has helped to better understand this snow distribution and the processes that cause the heterogeneity, it has not yet been possible to predict snow distribution satisfactorily on the basis of terrain parameters alone. We present a model of the mean snow depth in topographic control units as a function of two terrain parameters: the conventional elevation plus a fractal roughness parameter. For this we used a unique data set of high resolution measurements of snow depth from an airborne laser scanner. The model captures the heterogeneous snow distribution by merely analysing the terrain and the mean precipitation. This unusually simple relationship holds for clusters of the snow depths of small topographical units. By applying fractal analysis, we describe the roughness of the terrain and use this parameter for the prediction of snow deposition. Rougher terrain holds less snow than smoother terrain. This finding is important not only for avalanche warning or eco-hydrological applications, but also for reliably predicting how snow water storage may change in the light of the pronounced climate change already ongoing in mountain regions.

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... Results from this study show good model performance at each site, but a global model containing all data sets could only explain 23% (or 30% excluding catchments with glaciers) of the variability. Grünewald et al. (2013) therefore argued that SD and terrain are less universally related than as hypothesized by Lehning et al. (2011), and the application of a global model is limited. The performance of the linear regression model is dependent on the spatial scale, or support, of the data for which the model is developed. ...
... These relative new techniques give reliable, high-quality, high-spatial-resolution, and accurate SD information and thus allow for analyzing the distribution of SD over multiple scales (e.g., Melvold & Skaugen, 2013). Recently AL, terrestrial LiDAR, and digital photogrammetry data have also been used to investigate possible relations between SD and terrain characteristic (e.g., elevation, slope, aspect, Winstral's wind index, and terrain roughness) (e.g., Grünewald et al., 2010;Helbig et al., 2015;Lehning et al., 2011;Veitinger et al., 2014). Lidar SD data have also been used to verify different snow modeling approaches from the relatively simple statistical model to high-resolution dynamical models Trujillo et al., 2007Trujillo et al., , 2009. ...
... It has been a longstanding ambition to model snow variables from topographical and vegetation characteristics of the landscape. Many studies report quite modest correlations between snow and these characteristics (Gisnås et al., 2016;Jost et al., 2007;Lehning et al., 2011), and furthermore, these correlations are dependent on the spatial support of the dependent variable and the independent variables (Erxleben et al., 2002;Jost et al., 2007). For the applied scale in this study, 0.5 km 2 , we find significant correlations between snow statistics, such as the mean and the standard deviation of SD and the statistics (mean and standard deviation) of the terrain parameter Sqs calculated from the 10 × 10-m grid cell values comprised by the 0.5-km 2 grid. ...
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In the mountains of Norway, snow depth (SD) is highly variable due to strong winds and open terrain. To investigate snow conditions on one of Europe's largest mountain plateaus, Hardangervidda, we conducted snow measurement campaigns in spring 2008 and 2009 using airborne lidar scanning at the approximate time of annual snow maximum (mid‐April). From 658 empirical distributions of SD at Hardangervidda, each comprised about 4,000 SD values sampled from a grid cell of 0.5 km², quantitative tests have shown that the gamma distribution is a better fit for SD than the normal and log‐normal distributions. When aggregating snow and terrain data from 10 × 10 m to 0.5 km², we find that the standard deviation of the terrain parameter squared slope, land cover, and the mean SD are highly correlated (0.7, 0.52, and 0.89) to the standard deviation of SD. A model for SD variance is proposed that, in addition to addressing the dependencies between the variability of SD and the terrain characteristics, also takes into account the observed nonlinear relationship between the mean and the standard deviation of SD. When validated against observed SD variance retrieved from the same area, the model explains 81–83% of the observed variance for spatial scales of 0.5 and 5.1 km², which compares favorably to previous models. The model parameters can be determined from a GIS analysis of a detailed digital terrain and land cover model and will hence not increase the number of calibration parameters when implemented in environmental models.
... Higher elevations are usually steeper and exposed to higher wind speeds, resulting in stronger wind-and gravitational-induced snow redistribution from higher to lower elevations (Winstral and Marks, 2002;Bernhardt and Schulz, 2010). Lehning et al. (2011) explained the spatial variability of snow by altitude and land surface roughness, while Grünewald et al. (2014) attributed snow height variability to four topographic parameters: elevation gradient, slope, aspect, and wind sheltering. Note that slope and roughness are highly correlated Lehning et al. (2011). ...
... Lehning et al. (2011) explained the spatial variability of snow by altitude and land surface roughness, while Grünewald et al. (2014) attributed snow height variability to four topographic parameters: elevation gradient, slope, aspect, and wind sheltering. Note that slope and roughness are highly correlated Lehning et al. (2011). The study area has a dominance of steep, rocky exposures above a certain elevation. ...
Article
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The Mountain snowpack stores months of winter precipitation at high elevations, supplying snowmelt to lowland areas in drier seasons for agriculture and human consumption worldwide. Accurate seasonal predictions of the snowpack are thus of great importance, but such forecasts suffer from major challenges such as resolving interactions between forcing variables at high spatial resolutions. To test novel approaches to resolve these processes, seasonal snowpack simulations are run at different grid resolutions (50 m, 100 m, 250 m) and with variable forcing data for the water year 2016/2017. COSMO-1E data is either dynamically downscaled with the High-resolution Intermediate Complexity Atmospheric Research (HICAR) model or statistically downscaled to provide forcing data for snowpack simulations with the Flexible Snowpack Model (FSM2oshd). Simulations covering complex terrain in the Swiss Alps are carried out with the operational settings of the FSM2oshd model or with a model extension including wind- and gravitational-induced snow transport (FSM2trans). The simulated snow height is evaluated against observed snow height collected during LiDAR flights in spring 2017. Observed spatial snow accumulation patterns and snow height distribution are best matched with simulations using dynamically downscaled data and the FSM2trans model extension, indicating the importance of both accurate meteorological forcing data and snow transport schemes. This study demonstrates for the first time the effects of applying dynamical downscaling schemes to snowpack simulations at the seasonal and catchment scale.
... In mountains, the variation of snow depth with elevation can be complex (Pomeroy and Gray, 1995), though it typically increases with elevation (Langbein, 1947;Pomeroy and Brun, 2001;Barry, 2008;Grünewald and Lehning, 2011;Lehning et al., 2011). Vertical snow accumulation gradients in the mountains reflect orographic precipitation processes, time of year (Fitzharris, 1978) and redistribution by wind, vegetation and gravity (Ellis et al., 2010;MacDonald et al., 2010). ...
... While elevation is the major influence on both snow accumulation and melt, other variables affect snow accumulation in mountain basins. Slope, aspect, wind redistribution, forest canopy, and crown density will all affect the snow accumulation gradient Lehning et al., 2011). Wind and gravity in particular play key roles in redistributing snow (Bernhardt et al., 2012;Kerr et al., 2013;Ayala et al., 2014;Musselman et al., 2015;Sommer et al., 2015;Freudiger et al., 2017;Bisht et al., 2018;Vionnet et al., 2020). ...
Article
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Snowmelt contributions to streamflow in mid-latitude mountain basins typically dominate other runoff sources on annual and seasonal timescales. Future increases in temperature and changes in precipitation will affect both snow accumulation and seasonal runoff timing and magnitude, but the underlying and fundamental roles of mountain basin geometry and hypsometry on snowmelt sensitivity have received little attention. To investigate the role of basin geometry in snowmelt sensitivity, a linear snow accumulation model and the Cold Regions Hydrological Modeling (CRHM) platform driven are used to estimate how hypsometry affects basin-wide snow volumes and snowmelt runoff. Area-elevation distributions for fifty basins in western Canada were extracted, normalized according to their elevation statistics, and classified into three clusters that represent top-heavy, middle, and bottom-heavy basins. Prescribed changes in air temperature alter both the snow accumulation gradient and the total snowmelt energy, leading to snowpack volume reductions (10–40%), earlier melt onsets (1–4 weeks) and end of melt season (3 weeks), increases in early spring melt rates and reductions in seasonal areal melt rates (up to 50%). Basin hypsometry controls the magnitude of the basin response. The most sensitive basins are bottom-heavy, and have a greater proportion of their area at low elevations. The least sensitive basins are top-heavy, and have a greater proportion of their area at high elevations. Basins with similar proportional areas at high and low elevations fall in between the others in terms of sensitivity and other metrics. This work provides context for anticipating the impacts of ongoing hydrological change due to climate change, and provides guidance for both monitoring networks and distributed modeling efforts.
... These observations served as the basis to calculate the lapse rate of snow depth (0.15 m w.e. per 100 m −1 ) and the average snow density (350 kg m −3 ). The influence of slope inclination on snow depth was defined, after Kirnbauer et al. (1991) and Lehning et al. (2011), as follows: ...
... Lacking data for several of these factors, we used only elevation and slope as the main parameters determining snow depth. Elevation is widely considered as the main factor controlling snow distribution (Marchand & Killingtveit 2001;Lehning et al. 2011;Grünewald et al. 2014). Grabiec et al. (2011) and Laska et al. (2017) estimated a snow accumulation gradient of ca. ...
Article
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Glaciers draining to the Hornsund basin (southern Spitsbergen, Svalbard) have experienced a significant retreat and mass volume loss over the last decades, increasing the input of freshwater into the fjord. An increase in freshwater input can influence fjord hydrology, hydrodynamics, sediment flux and biota, especially in a changing climate. Here, we describe the sources of freshwater supply to the fjord based on glaciological and meteorological data from the period 2006 to 2015. The average freshwater input from land to the Hornsund bay is calculated as 2517 ± 82 Mt a−1, with main contributions from glacier meltwater runoff (986 Mt a−1; 39%) and frontal ablation of tidewater glaciers (634 Mt a−1; 25%). Tidewater glaciers in Hornsund lose ca. 40% of their mass by frontal ablation. The terminus retreat component accounts for ca. 30% of the mass loss by frontal ablation, but it can vary between 17% and 44% depending on oceanological, meteorological and geomorphological factors. The contribution of the total precipitation over land excluding winter snowfall (520 Mt a−1), total precipitation over the fjord area (180 Mt a−1) and melting of the snow cover over unglaciated areas (197 Mt a−1) to the total freshwater input appear to be small: 21%, 7% and 8%, respectively.
... Snow depth on the ground increases during the accumulation season and starts decreasing after the melt onset. Concurrently, positive correlations between snow depth or SWE and elevation in the Alps are reported by many authors (Bavera and De Michele, 2009;Durand et al., 2009;Lehning et al., 2011;Grünewald et al., 2014). Accordingly, we propose a snow depth model that is linearly dependent on elevation and with time-dependent coefficients. ...
Article
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A climatology of snow water equivalent (SWE) based on data collected at 240 gauging sites was performed for the Italian Alps over the 1967–2020 period, when Enel routinely conducted snow depth and density measurements with homogeneous methods. Six hydrological sub-regions were investigated spanning from the eastern Alps to the western Alps at altitudes ranging from 1000 to 3000 m a.s.l. Measurements were conducted at fixed dates at the beginning of each month from 1 February to 1 June and on 15 April. To our knowledge, this is the most comprehensive and homogeneous dataset of measured snow depth and density for the Italian Alps. Significant decreasing trends over the years at all fixed dates and elevation classes were identified for both snow depth, equal to -0.12 ± 0.06 m per decade, and snow water equivalent, equal to -51 ± 37 mm per decade, on average in the six macro-basins we selected. The analysis of bulk snow density data showed a temporal evolution along the snow accumulation and melt season, but no altitudinal trends were found. A Moving Average and Running Trend Analysis (MARTA triangles), combined with a Pettitt's test change-point detection, highlighted a decreasing change of snow climatology occurring around the end of the 1980s. The comparison with winter temperature and precipitation data from the HISTALP dataset identified a major role played by temperature on the long-term decrease and changing points of snow depth and SWE with respect to precipitation, mainly responsible for its variability. Correlation with climatic indexes indicates significant negative values of the Pearson correlation coefficient with winter North Atlantic Oscillation (NAO) and positive values with winter Western Mediterranean Oscillation (WeMO) for some areas and elevation classes. Results of this climatology are synthesized in a temporal polynomial model that is useful for climatological studies and water resources management in mountain areas.
... In addition, along with the influence of coniferous tree canopy interception, sublimation, longwave emittance, and unloading of snow from the canopy lead to relatively lower snow depths closer to the tree trunk and a gradual increase in snow depth up to a distance coinciding with the canopy crown (Pomeroy and Dion, 1996;Musselman et al., 2008;Revuelto et al., 2015;Zheng et al., 2019), resulting in significant, but somewhat predictable, intra-canopy variability of snow depths in coniferous forests. Existing interpolation techniques such as inverse distance weighting (Burrough, 1986;Guo et al., 2010;Michele et al., 2016), geostatistical methods (Isaaks and Srivastava, 1989;Guo et al., 2010;Mazzotti et al., 2019;Koutantou et al., 2022), regression and treebased methods (Winstral et al., 2002;Jost et al., 2007;López-Moreno et al., 2010;Lehning et al., 2011;Revuelto et al., 2014;Zheng et al., 2018), or a combination of these methods (Erxleben et al., 2002) do not fully address the aforementioned caveats. Koutantou et al. (2022) emphasized the need for a more sophisticated gap-filling algorithm to avoid likely overestimation of under-sampled under-canopy snow depths. ...
... One example is the forced dynamical lifting of air masses, which rises from elevation differences. Indeed, elevation has often been found to be a main explanatory variable for spatial snow depth in mountainous terrain (Seyfried and Wilcox, 1995;Grünewald and Lehning, 2011;Lehning et al., 2011;Kirchner et al., 2014;. At the much smaller spatial scale of ridges and slopes, heterogeneous snowfall deposition acts across ridges and slopes due to near-surface wind-topography-particle interactions in the absence of snow redistribution and sublimation (Föhn and Meister, 1983;Lehning et al., 2008). ...
Article
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One of the primary causes of non-uniform snowfall deposition on the ground in mountainous regions is the preferential deposition of snow, which results from the interaction of near-surface winds with topography and snow particles. However, producing high-resolution snowfall deposition patterns can be computationally expensive due to the need to run full atmospheric models. To address this, we developed two statistical downscaling schemes that can efficiently downscale near-surface, low-resolution snowfall data to fine-scale snow deposition accounting for the effect of preferential deposition in mountainous regions. Our approach relies on a comprehensive, model database generated using 3D wind fields from an atmospheric model and a preferential deposition model on several thousand simulated topographies covering a broad range in terrain characteristics. Both snowfall downscaling schemes rely on fine-scale topographic scaling parameters and low-resolution wind speed as input. While one scheme, referred to as the “wind scheme”, further necessitates fine-scale vertical wind components, a second scheme, referred to as the “aspect scheme”, does not require fine-scale temporal input. We achieve this by additionally downscaling near-surface vertical wind speed solely using topographic scaling parameters and low-resolution wind direction. We assess the performance of our downscaling schemes using an independent subset of the model database on simulated topographies, model data on actual terrain, and spatially measured new snow depth obtained through a photogrammetric drone survey following a snowfall event on previously snow-free ground. While the assessments show that our downscaling schemes perform well (relative errors ≤ ±3% with modeled and ≤ ±6% with measured snowfall deposition), they also demonstrate comparable results to benchmark downscaling models. However, our schemes notably outperform the benchmark models in representing fine-scale patterns. Our downscaling schemes possess several key features, including high computational efficiency, versatility enabled by the comprehensive model database, and independence from fine-scale temporal input data (aspect scheme), indicating their potential for widespread applicability. Therefore, our downscaling schemes for near-surface snowfall and vertical wind speed can be beneficial for various applications at fine grid resolutions such as in atmospheric and climate sciences, snow hydrology, glaciology, remote sensing, and avalanche sciences.
... To address this, sub-grid parameterizations have been proposed to represent snow depth variability at the mountain ridge and slope scale for snow cover models operating at kilometer scales. These approaches have been applied in various applications to improve the representation of snow depth variability (Liston, 2004;Lehning et al., 2011;He and Ohara, 2019). ...
Article
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The seasonal evolution of snow cover has significant impacts on the hydrological cycle and microclimate in mountainous regions. However, snow processes also play a crucial role in triggering alpine mass movements and flooding, posing risks to people and infrastructure. To mitigate these risks, many countries use operational forecast systems for snow distribution and melt. This paper presents the Swiss Operational Snow-hydrological (OSHD) model system, developed to provide daily analysis and forecasts on snow cover dynamics throughout Switzerland. The OSHD system is a sophisticated snow hydrological model designed specifically for the high-alpine terrain of the Swiss Alps. It leverages exceptional station data and high-resolution meteorological forcing data, as well as various reanalysis products to combine snow modeling with advanced data assimilation and meteorological downscaling methods. The system offers models of varying complexity, each tailored to specific modeling strategies and applications. For snowmelt runoff forecasting, monitoring snow water resources, and research-grade purposes, the OSHD system employs physics-based modeling chains. For snow climatological assessments, a conceptual model chain is available. We are pleased to present two comprehensive datasets from the conceptual and physics-based models that cover the entirety of Switzerland. The first dataset comprises a snow water equivalent climatology spanning 1998–2022, with a spatial resolution of 1 km. The second dataset includes snow distribution and snow melt data spanning 2016–2022 at a high spatial resolution of 250 m. To meet the needs of a multi-purpose snow hydrological model framework, the OSHD system employs various strategies for process representation and sub-grid parameterizations at the snow-canopy-atmosphere interface, particularly in complex terrain. Recent and ongoing model developments are aimed at accounting for complex forest snow processes, representing slope and ridge-scale precipitation and snow redistribution processes, as well as improving probabilistic snow forecasts and data assimilation procedures based on remote sensing products.
... We hypothesize that this could have partly been driven by the 250 m resolution of synthetic observations and simulations. At this length scale, snow distribution is typically driven 385 by processes like synoptic weather patterns and their interaction (e.g., orographic lapse rates, wind loading/sheltering, terrainshading, etc.) with static topographical features like elevation, slope, and aspect (e.g., Clark et al., 2011;Lehning et al., 2011;McGrath et al., 2018;Minder et al., 2008;Trujillo et al., 2007). However, we acknowledge that snow in forested and open grid cells is subject to different snow processes. ...
Preprint
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Snow is a vital component of the Earth system. Yet, no snow-focused satellite remote sensing platform currently exists. In this study, we investigate how synthetic observations of snow water equivalent (SWE) representative of a synthetic aperture radar remote sensing platform could improve spatiotemporal estimates of snowpack. We use an Observation System Simulation Experiment, specifically investigating how much snow simulated using popular models and forcing data could be improved by assimilating synthetic observations of SWE. We focus this study across a 24°-by-37° domain in the Western United States and Canada, simulating snow at 250 m resolution and hourly timesteps in water-year 2019. We perform two data assimilation experiments, including: 1) a simulation excluding synthetic observations in forests where canopies obstruct remote sensing retrievals, and 2) a simulation inferring snow distribution in forested grid cells using synthetic observations from nearby canopy-free grid cells. Results found that assimilating synthetic SWE observations improved average SWE biases at peak snowpack timing in shrub, grass, crop, bare-ground, and wetland land cover types from 14 %, to within 1 %. However, forested grid cells contained a disproportionate amount of SWE volume. In forests, SWE mean absolute errors at peak snowpack were 111 mm, and average SWE biases were on the order of 150 %. Here, the data assimilation approach that estimated forest SWE using observations from the nearest canopy-free grid cells substantially improved these SWE biases (18 %) and the SWE mean absolute error (27 mm). Simulations employing data assimilation also improved estimates of the temporal evolution of both SWE and runoff, even in spring snowmelt periods when melting snow and high snow liquid water content prevented synthetic SWE retrievals. In fact, in the Upper Colorado River basin, melt-season SWE biases were improved from 63 % to within 1 %, and the Nash Sutcliffe Efficiency of runoff improved from –2.59 to 0.22. These results demonstrate the value of data assimilation and a snow-focused globally relevant remote sensing platform for improving the characterization of SWE and associated water availability.
... This variance is interesting by itself since it might point to additional environmental factors that control snow accumulation 285 (Wirz et al., 2011;Lehning et al., 2011), most likely local micro-topographic and micro-climatic factors. For example, microtopography of the rock surface can influence local wind dynamics and snow redistribution (Winstral et al., 2002). ...
Preprint
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Water takes part in most physical processes that shape the mountainous periglacial landscapes and initiation of mass wasting. 10 An observed increase in rockfall activity in several mountainous regions was previously linked to permafrost degradation in high mountains, and water that infiltrates into rock fractures is one of the likely drivers of these processes. However, there is very little knowledge on the quantity and timing of water availability for infiltration in steep rock slopes. This knowledge gap originates from the complex meteorological, hydrological and thermal processes that control snowmelt, and also the challenging access and data acquisition in the extreme alpine environments. Here we use field measurement and numerical 15 modeling to simulate the energy balance and hydrological fluxes in a steep high elevation permafrost affected rock slope at Aiguille du Midi (3842 m a.s.l), in the Mont-Blanc massif. Our results provide new information about water balance at the surface of steep rock slopes. Model results suggest that only ~25% of the snowfall accumulates in our study site, the remaining ~75% are redistributed by wind and gravity. Snow accumulation depth is inversely correlated with surface slopes between 40° to 70°. Snowmelt occurs between spring and late summer and most of it does not reach the rock surface due to the formation 20 of an impermeable ice layer at the base of the snowpack. The annual effective snowmelt, that is available for infiltration, is highly variable and ranges over a factor of six with values between 0.05-0.28 m in the years 1959-2021. The onset of the effective snowmelt occurs between May and August, and ends before October. It precedes the first rainfall by one month on average. Sublimation is the main process of snowpack mass loss in our study site. Model simulations at varying elevations show that effective snowmelt is the main source of water for infiltration above 3600 m a.s.l.; below, direct rainfall is the 25 dominant source. The change from snowmelt-dominated to rainfall-dominated water availability is nonlinear and characterized by a rapid increase in water availability for infiltration. We suggest that this elevation of water availability transition is highly sensitive to climate change, if snowmelt-dominated permafrost-affected slopes experience an abrupt increase in water input that can initiate rock slope failure.
... Yet, successfully downscaling GCM-RCM outputs to the local scale remains a largely open problem (e.g. Maraun et al. 2010), especially difficult over a mountain topography due to prevailing gradients and wind effects (Lehning et al. 2011). Switching from meteorological www.kva.se/en ...
Article
In mountain territories, snow avalanches are a prevalent threat. Long-term risk management involves defining meaningful compromises between protection and overall sustainability of communities and their environment. Methods able to (i) consider all sources of losses, (ii) account for the high uncertainty levels that affect all components of the risk and (iii) cope for marked non-stationarities should be employed. Yet, on the basis of a literature review and an analysis of relations to Sustainable Development Goals (SDGs), it is established that snow avalanche risk assessment and mitigation remain dominated by approaches that can be summed up as deterministic, hazard oriented, stationary and not holistic enough. A more comprehensive paradigm relying on formal statistical modelling is then proposed and first ideas to put it to work are formulated. Application to different mountain environments and broader risk problems is discussed.
... Previous snow depth analyses at these study sites (McGrath et al., 2018;Winstral and Marks, 2014), and elsewhere (Clark et al., 2011;Grünewald et al., 2010;Lehning et al., 2011;Saydi and Ding, 2020;, indicate that terrain characteristics strongly control snow depth. Terrain parameters previously shown to strongly influence snow depth include: elevation, slope, aspect (i.e., degrees counterclockwise from south) and vegetation type and structure (e.g., Armesto and Martínez, 1978;Ivanov et al., 2008;Luus et al., 2013;Yang et al., 2020). ...
Article
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Seasonal snow melt dominates the hydrologic budget across a large portion of the globe. Snow accumulation and melt vary over a broad range of spatial scales, preventing accurate extrapolation of sparse in situ observations to watershed scales. The lidar onboard the Ice, Cloud, and land Elevation, Satellite (ICESat-2) was designed for precise mapping of ice sheets and sea ice, and here we assess the feasibility of snow depth-mapping using ICESat-2 data in more complex and rugged mountain landscapes. We explore the utility of ATL08 Land and Vegetation Height and ATL06 Land Ice Height differencing from reference elevation datasets in two end member study sites. We analyze ∼3 years of data for Reynolds Creek Experimental Watershed in Idaho's Owyhee Mountains and Wolverine Glacier in southcentral Alaska's Kenai Mountains. Our analysis reveals decimeter-scale uncertainties in derived snow depth and glacier mass balance at the watershed scale. Both accuracy and precision decrease as slope increases: the magnitudes of the median and median of the absolute deviation of elevation errors (MAD) vary from ∼0.2 m for slopes <5° to >1 m for slopes >20°. For glacierized regions, failure to account for intra- and inter-annual evolution of glacier surface elevations can strongly bias ATL06 elevations, resulting in under-estimation of the mass balance gradient with elevation. Based on these results, we conclude that ATL08 and ATL06 observations are best suited for characterization of watershed-scale snow depth and mass balance gradients over relatively shallow slopes with thick snowpacks. In these regions, ICESat-2 elevation residual-derived snow depth and mass balance transects can provide valuable watershed scale constraints on terrain parameter- and model-derived estimates of snow accumulation and melt.
... Characterizing the spatial distribution of SWE is also 52 necessary to constrain and evaluate hydrologic and land-surface models (Clark et al. 2011; 53 Shuai et al. 2022) and to understand how shifting weather patterns and climate extremes 54 affect snowpack and water quality (e.g., Chen et al. 2020). Since SWE is highly 55 heterogeneous across the landscape (e.g., Lehning et al. 2011), it is difficult for field 56 observations to characterize the spatial distribution of SWE. This has motivated the use of 57 remote sensing approaches, which have been monitoring the snowpack for several decades 58 (Peck et al. 1980). ...
Article
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An accurate characterization of the water content of snowpack, or snow water equivalent (SWE), is necessary to quantify water availability and constrain hydrologic and land surface models. Recently, airborne observations (e.g., lidar) have emerged as a promising method to accurately quantify SWE at high resolutions (scales of ∼100 m and finer). However, the frequency of these observations is very low, typically once or twice per season in the Rocky Mountains of Colorado. Here, we present a machine learning framework that is based on random forests to model temporally sparse lidar-derived SWE, enabling estimation of SWE at unmapped time points. We approximated the physical processes governing snow accumulation and melt as well as snow characteristics by obtaining 15 different variables from gridded estimates of precipitation, temperature, surface reflectance, elevation, and canopy. Results showed that, in the Rocky Mountains of Colorado, our framework is capable of modeling SWE with a higher accuracy when compared with estimates generated by the Snow Data Assimilation System (SNODAS). The mean value of the coefficient of determination R ² using our approach was 0.57, and the root-mean-square error (RMSE) was 13 cm, which was a significant improvement over SNODAS (mean R ² = 0.13; RMSE = 20 cm). We explored the relative importance of the input variables and observed that, at the spatial resolution of 800 m, meteorological variables are more important drivers of predictive accuracy than surface variables that characterize the properties of snow on the ground. This research provides a framework to expand the applicability of lidar-derived SWE to unmapped time points. Significance Statement Snowpack is the main source of freshwater for close to 2 billion people globally and needs to be estimated accurately. Mountainous snowpack is highly variable and is challenging to quantify. Recently, lidar technology has been employed to observe snow in great detail, but it is costly and can only be used sparingly. To counter that, we use machine learning to estimate snowpack when lidar data are not available. We approximate the processes that govern snowpack by incorporating meteorological and satellite data. We found that variables associated with precipitation and temperature have more predictive power than variables that characterize snowpack properties. Our work helps to improve snowpack estimation, which is critical for sustainable management of water resources.
... Elevation, topographic roughness and aspect control snow accumulation and melt in mountainous landscapes, resulting in similar snow depletion patterns from year to year (Erickson et al. 2005;Lehning et al 2011;Schirmer et al. 2011). The SSZ was therefore estimated using the median SCA for the onset of the peak flow period. ...
Technical Report
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A method was developed to identify the snow sensitive zone (SSZ), the area contributing snowmelt during the peak flow period, for eight watersheds in the Kettle River basin. Snow covered area (SCA) was mapped at the onset of the peak flow period for 2010-2020. The SSZ was identified as the median SCA for this period. The SSZ maps can inform planning forest harvest activities to reduce the potential for snowmelt synchronization and increased peak flow.
... Secondly, and also as pointed out in previous literature, microtopography (Hopkinson et al., 2012;Cho et al., 2021), as well as the ground roughness (Lehning et al., 2011;Cho et al., 2021), are additional controls on snow distribution that may confound canopy structure and topography effects. The forest floor at our sites indeed featured a considerable degree of roughness, due to rocks, fallen logs, and shrubs. ...
Article
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Mapping snow in forests is important for understanding the snow cover dynamics in these environments in view of hydrological applications and water resources management. Today, Uncrewed Aerial Vehicles (UAVs) are widely used for snow studies due to their rather cheap and flexible operation. UAV-borne Light detection and ranging (LiDAR) systems are a promising technology for sub-canopy snow mapping at high temporal resolutions, concurrently providing information on both the canopy and the below-canopy snow surface. In this pilot study, we used a UAV-LiDAR system to investigate the snow cover dynamics within two steep forested slopes of opposing aspects in the Swiss Alps at both high spatial resolution and unprecedented temporal resolution. Using a Distance to Canopy Edge (DCE) algorithm to characterize local forest structure, snow depth was analyzed in terms of relative position within variable forest cover. The north-exposed site had higher mean snow depths throughout the season compared to the south-exposed site, especially in canopy gaps. Whereas snow depletion rate was consistent throughout the north-exposed site, snow depletion was much faster in the gaps at the south-exposed slope. Correlation coefficients between snow depths and local canopy closure were weaker at the south-exposed (between −0.5 and − 0.7) than at the north-exposed site (between −0.7 and − 0.9), and rapidly deteriorated right after the peak of winter at the south-exposed slope. This indicates shortwave radiation dominates snowmelt processes at this site, which was thought to be spatially uncorrelated to local canopy cover, unlike accumulation and melt processes on the north-exposed slope that generated snow patterns with a high spatial correlation to local canopy cover throughout the entire season. Calculations of incoming sub-canopy shortwave radiation (SWR) for both sites confirmed this assumption. While our findings encourage the use of UAV-borne LiDAR for further investigations of snow cover dynamics in steep forested slopes, we also outline and discuss technical challenges specific to this application. Our insights allow deriving useful recommendations for future studies using UAV-borne LiDAR over a similar environment.
... Surface roughness is an important parameter in relation to snow distribution (Lehning et al., 2011), and it is particularly crucial in preventing weak layers and avalanche formation and release (Schweizer et al., 2003;Viglietti et al., 2010). The supporting 60 force of tree stems and the heterogeneity of the forest snowpack, influenced by crown interception, reduce the release of slab avalanches (Bebi et al., 2009;McClung and Schaerer, 2006;Schneebeli and Bebi, 2004;Teich et al., 2012b). ...
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Surface roughness influences the release of avalanches and the dynamics of rockfall, avalanches and debris flow, but is often not objectively implemented in natural hazard modelling. For two study areas, a treeline ecotone and a windthrow disturbed forest landscape of the European Alps, we tested seven roughness algorithms using a digital surface models (DSM) with different resolutions (0.1, 0.5 and 1 m) and different moving window areas (9 m−2, 25 m−2 and 49 m−2). The vector ruggedness measure roughness algorithm performed best overall in distinguishing between roughness categories relevant for natural hazard modelling (including shrub forest, high forest, windthrow, snow and rocky land-cover). The results with 1 m resolution were found to be suitable to distinguish between the roughness categories of interest, and the performance did not increase with higher resolution. In order to improve the roughness calculation along the hazard flow direction, we tested a directional roughness approach that improved the reliability of the surface roughness computation in channelized paths. We simulated avalanches on a different elevation models to observe a potential influence of a DSM and a digital terrain model (DTM). Accounting for surface roughness based on a DSM instead of a DTM resulted not only in clearly higher roughness values of forest and shrub vegetation, but also in longer simulated avalanche runouts by 16–27 % in the two study areas. We conclude that directional roughness is promising for achieving better assessments of terrain topography in alpine landscapes and that applying an approach using DSM-based surface roughness could improve natural hazard modelling.
... While it can be assumed that temperature and wet-bulb temperature have a linear lapse rate with height, this might not be the case for snow parameters. It can be expected that snow amounts have a positive elevation gradient (e.g., Lehning et al. 2011;Grünewald et al. 2013), however, the technique using a linear regression should be validated. The VAT is applied to different SNOTEL sites the same way as described before. ...
Article
A high-resolution (4 km) regional climate simulation conducted with the Weather Research and Forecast (WRF) model is used to investigate potential impacts of global warming on skiing conditions in the interior western United States (IWUS). Recent past and near-future climate conditions are compared. The past climate period is from November 1981 to October 2011. The future climate applies to a 30-year period centered on 2050. A pseudo global warming approach is used, with the driver re-analysis dataset perturbed by the CMIP5 ensemble mean model guidance. Using the 30-year retrospective simulation, a vertical adjustment technique is used to determine meteorological parameters in the complex terrain where ski areas are located. For snow water equivalent (SWE), Snow Telemetry sites close to ski areas are used to validate the technique and apply a correction to SWE in ski areas. The vulnerability to climate change is assessed for 71 ski areas in the IWUS considering SWE, artificially produced snow, temperature, and rain. 20 of the ski areas will tend to have fewer than 100 days per season with sufficient natural and artificial snow for skiing. These ski areas are located at either low elevations or low latitudes making these areas the most vulnerable to climate change. Throughout the snow season, natural SWE decreases significantly at the low elevations and low latitudes. At higher elevations changes in SWE are predicted to not be significant in the mid-season. In mid-February, SWE decreases by 11.8% at the top elevations of ski areas while it decreases by 25.8% at the base elevations.
... All four products have relatively lower accuracy in the region of elevation above 4000 m. All products are positively correlated with elevation since the probability of precipitation as snowfall increases with increasing elevation [75,76]. The SDC product was found to perform the best in the region of elevation between 2000-2500 m (RMSE = 0.87 cm). ...
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Snow depth distribution in the Qinghai-Tibetan plateau is important for atmospheric circulation and surface water resources. In-situ observations at meteorological stations and remote observation by passive microwave remote sensing technique are two main approaches for monitoring snow depth at regional or global levels. However, the meteorological stations are often scarce and unevenly distributed in mountainous regions because of inaccessibility, so are the in-situ snow depth measurements. Passive microwave remote sensing data can alleviate the unevenness issue, but accuracy and spatial (e.g., 25 km) and temporal resolutions are low; spatial heterogeneity in snow depth is thus hard to capture. On the other hand, optical sensors such as moderate resolution imaging spectroradiometer (MODIS) onboard Terra and Aqua satellites can monitor snow at moderate spatial resolution (1 km) and high temporal resolution (daily) but only snow area extent, not snow depth. Fusing passive microwave snow depth data with optical snow area extent data provides an unprecedented opportunity for generating snow depth data at moderate spatial resolution and high temporal resolution. In this article, a linear multivariate snow depth reconstruction (LMSDR) model was developed by fusing multisource snow depth data, optical snow area extent data, and environmental factors (e.g., spatial distribution, terrain features, and snow cover characteristics), to reconstruct daily snow depth data at moderate resolution (1 km) for 16 consecutive hydrological years, taking Qinghai-Tibetan Plateau (QTP) as a case study. We found that snow cover day (SCD) and environmental factors such as longitude, latitude, slope, surface roughness, and surface fluctuation have a significant impact on the variations of snow depth over the QTP. Relatively high accuracy (root mean square error (RMSE) = 2.26 cm) was observed in the reconstructed snow depth when compared with in-situ data. Compared with the passive microwave remote sensing snow depth product, constructing a nonlinear snow depletion curve product with an empirical formula and fusion snow depth product, the LMSDR model (RMSE = 2.28 cm, R2 = 0.63) demonstrated a significant improvement in accuracy of snow depth reconstruction. The overall spatial accuracy of the reconstructed snow depth was 92%. Compared with in-situ observations, the LMSDR product performed well regarding different snow depth intervals, land use, elevation intervals, slope intervals, and SCD and performed best, especially when the snow depth was less than 3 cm. At the same time, a long-time snow depth series reconstructed based on the LMSDR model reflected interannual variations of snow depth well over the QTP.
... These results show that topography exerts an important influence on snow accumulation and redistribution. Positive relationships between snow depth or SWE and elevation have been reported in previous studies Lehning et al., 2011;Jia et al., 2014;Deng et al., 2017), although Zhong et al. (2018) found that snow depth decreased with increasing elevation across the Eurasian continent. In this study, however, snow depth and SWE did not steadily vary with elevation, and we observed different characteristic curves as functions of elevation and slope. ...
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Snow properties and their changes are crucial to better understanding of hydrological processes, soil thermal regimes, and surface energy balances. Reliable data and information on snow depth and snow water equivalent (SWE) are also crucial for water resource assessments and socio-economic development at local and regional scales. However, these data are extremely limited and unreliable in northern Xinjiang, China. This study thus aims to investigate spatial variations of snow depth, SWE, and snow density based on winter snowfield surveys during 2015 through 2017 in the Altai Mountains, northwestern China. The results indicated that snow depth (25‒114 cm) and SWE (40‒290 mm) were greater in the alpine Kanas-Hemu region, and shallow snow accumulated (9‒42 cm for snow depth, 26‒106 mm for SWE) on the piedmont sloping plain. While there was no remarkable regional difference in the distribution of snow density. Snow property distributions were strongly controlled by topography and vegetation. Elevation and latitude were the most important factors affecting snow depth and SWE, while snow density was strongly affected by longitude across the Altai Mountains in China. The influence of topography on snow property distributions was spatially heterogenous. Mean snow depth increased from 13.7 to 31.2 cm and SWE from 28.5 to 79.9 mm, respectively, with elevation increased from 400 to 1000 m a.s.l. on the piedmont sloping plain. Snow depth decreased to about 15.1 cm and SWE to about 28.5 mm from 1000 to 1800 m a.s.l., then again increased to about 98.1 cm and 271.7 mm on peaks (∼2000 m a.s.l.) in the alpine Kanas-Hemu. Leeward slopes were easier to accumulate snow cover, especially on north-, east-, and southeast-facing slopes. Canopy interception was also the cause of the difference in snow distribution. Snow depth, SWE, and snow density in forests were reduced by 8%‒53%, 2%‒67% and –4% to +48%, respectively, compared with surrounding open areas. Especially when snow depth was less than 40 cm, snow depth and SWE differences in forests were more exaggerated. This study provides a basic data set of spatial distributions and variations of snow depth, SWE and snow density in the Altai Mountains, which can be used as an input parameter in climate or hydrological models. These first-hand observations will help to better understand the relationship between snow, topography and climate in mountainous regions across northern China and other high-mountain Asian regions.
... Thus, the most relevant impacts of wind and humidity processes on the SC energy and mass balance (sublimation and lateral mass transport) are considered by SG-CL. As lateral snow mass transport is one of the main controlling factors for snow distribution in complex terrain, this also addresses the persistence of inter-and intra-annual SC distributions [39,40]. ...
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We used the spatially distributed and physically based snow cover model SNOWGRID-CL to derive daily grids of natural snow conditions and snowmaking potential at a spatial resolution of 1 × 1 km for Austria for the period 1961–2020 validated against homogenized long-term snow observations. Meteorological driving data consists of recently created gridded observation-based datasets of air temperature, precipitation, and evapotranspiration at the same resolution that takes into account the high variability of these variables in complex terrain. Calculated changes reveal a decrease in the mean seasonal (November–April) snow depth (HS), snow cover duration (SCD), and potential snowmaking hours (SP) of 0.15 m, 42 days, and 85 h (26%), respectively, on average over Austria over the period 1961/62–2019/20. Results indicate a clear altitude dependence of the relative reductions (−75% to −5% (HS) and −55% to 0% (SCD)). Detected changes are induced by major shifts of HS in the 1970s and late 1980s. Due to heterogeneous snowmaking infrastructures, the results are not suitable for direct interpretation towards snow reliability of individual Austrian skiing resorts but highly relevant for all activities strongly dependent on natural snow as well as for projections of future snow conditions and climate impact research.
... For example, Erickson et al. (2005) and Anderton et al. (2004) found that the most effective controller of snow redistribution is wind factor among several other terrain parameters including elevation, slope, and radiation. Impacts of topography on snow cover are considered using surface roughness (Barlage et al., 2010;Lehning et al., 2011) and 3-D mountain radiation with shadow effects (Fan et al., 2019) as well. ...
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Snow cover over the Tibetan Plateau (TP) plays an important role in Asian climate. State‐of‐the‐art models, however, show significant simulation biases. In this study, we assess the main uncertainty associated with model physics in snow cover modeling over the TP using ground‐based observations and high‐resolution snow cover satellite products from the Moderate Resolution Imaging Spectroradiometer (MODIS) and FengYun‐3B (FY3B). We first conducted 10‐km simulations using the Noah with multiparameterization (Noah‐MP) land surface model by optimizing physics‐scheme options, which reduces 8.2% absolute bias of annual snow cover fraction (SCF) compared with the default model settings. Then, five SCF parameterizations in Noah‐MP were optimized and assessed, with three of them further reducing the annual SCF biases from around 15% to less than 2%. Thus, optimizing SCF parameterizations appears to be more important than optimizing physics‐scheme options in reducing the uncertainty of snow modeling. As a result of improved SCF, the positive bias of simulated surface albedo decreases significantly compared to the GLASS albedo data, particularly in high‐elevation regions. This substantially enhances the absorbed solar radiation and further reduces the annual mean biases of ground temperature from −3.5 to −0.8°C and snow depth from 4.2 to 0.2 mm. However, the optimized model still overestimates SCF in the western TP and underestimates SCF in the eastern TP. Further analysis using a higher‐resolution (4 km) simulation driven by topographically adjusted air temperature shows slight improvement, suggesting a rather limited contribution of the finer‐scale land surface characteristics to SCF uncertainty.
... This cutoff term-also referred to as scale break length, scale break distance, or simply scale break-is typically assumed to infer a change in the physical processes driving the spatial structure of the snow. Because manual snow measurements are time consuming and potentially risky, the emergence of light detection and ranging (lidar) technology has become a milestone for snow measurement (e.g., Deems et al., 2013) and scaling studies (e.g., Lehning et al., 2011;Tedesche et al., 2017). ...
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Understanding and characterizing the spatial distribution of snow are critical to represent the energy balance and runoff production in mountain environments. In this study, we investigate the interannual and seasonal variability in snow depth scaling behavior at the Izas experimental catchment of the Spanish Pyrenees (2,000 to 2,300 m above sea level). We conduct variogram analyses of 24 snow depth maps derived from terrestrial light detection and ranging scans, acquired during six consecutive snow seasons (2011–2017) that span a range of hydroclimatic conditions. We complement our analyses with bare ground topography data and wind speed and direction measurements. Our results show temporal consistency in the spatial variability of snow depth, with short‐range fractal behavior and scale break lengths that are similar to the optimal search distance (25 m) previously reported for the topographic position index, a terrain‐based predictor of snow depth. Beyond the 25‐m scale break, there is little to no fractal structure. We report a long‐range scale break of the order of 185–300 m for most dates—aligned with the dominant wind direction—and patterns between anisotropies in scale break lengths of shallow snow cover and directional terrain scaling behavior. The temporal consistency of snow depth scaling patterns suggests that, in addition to guiding the spatial configuration of physically based models, fractal analysis could be used to inform the design of independent variables for statistical models used to predict snow depth and its variability.
... Although some studies have documented interannual variability in the spatial patterns of winter balance (e.g., Björnsson et al., 1998;Hodgkins et al., 2005), there is a mounting literature that documents quasi-stationarity of interannual patterns of both snow distribution on ice-free landscapes (e.g., Deems et al., 2008;Sturm and Wagner, 2010;Schirmer et al., 2011) and winter balance on glaciers (e.g., Taurisano et al., 2007;Sold et al., 2016;McGrath et al., 2018). This persistence in spatial patterns is attributed to persistence of synoptic scale weather patterns including the prevailing winds (e.g., Winstral and Marks, 2002), topography (e.g., Erickson et al., 2005) and its associated roughness (e.g., Lehning et al., 2011), and in the case of ice-free landscapes, vegetation (e.g., Sturm and Wagner, 2010). ...
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Efficient collection of snow depth and density data is important in field surveys used to estimate the winter surface mass balance of glaciers. Simultaneously extensive, high resolution, and accurate snow-depth measurements can be difficult to obtain, so optimisation of measurement configuration and spacing is valuable in any survey design. Using in-situ data from the ablation areas of three glaciers in the St. Elias Mountains of Yukon, Canada, we consider six possible survey designs for snow-depth sampling and N = 6–200+ sampling locations per glacier. For each design and number of sampling locations, we use a linear regression on topographic parameters to estimate winter balance at unsampled locations and compare these estimates with known values. Average errors decrease sharply with increasing sample size up to N ≈ 10–15, but reliable error reduction for any given sampling scheme requires significantly higher N. Lower errors are often, but not always, associated with sampling schemes that employ quasi-regular spacing. With both real- and synthetic data, the common centreline survey produces the poorest results overall. The optimal design often requires sampling near the glacier margin, even at low N. The unconventional “hourglass” design performed best of all designs tested when evaluated against known values of winter balance.
... Grünewald et al. (2014) attributed the spatial trends of decreasing snow depths at higher elevation sites to the dominance of steep, rocky exposures above a certain elevation. Lehning et al. (2011) explained the spatial variability of snow only by altitude and land surface roughness. They statistically showed that rougher terrain holds less snow than smooth terrain. ...
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The temporal evolution of seasonal snow cover and its spatial variability in environments such as mountains, prairies or polar regions is strongly influenced by the interactions between the atmospheric boundary layer and the snow cover. Wind-driven coupling processes affect both mass and energy fluxes at the snow surface with consequences on snow hydrology, avalanche hazard, and ecosystem development. This paper proposes a review on these processes and combines the more recent findings obtained from observations and modeling. The spatial variability of snow accumulation across multiple scales can be associated to wind-driven processes ranging from orographic precipitation at large scale to preferential-deposition of snowfall and wind-induced transport of snow on the ground at smaller scales. An overview of models of varying complexity developed to simulate these processes is proposed in this paper. Snow ablation is also affected by wind-driven processes. Over continuous snow covers, turbulent fluxes of latent and sensible heat, as well as blowing snow sublimation, modify the mass, and energy balance of the snowpack and their representation in numerical models is associated with many uncertainties. As soon as the snow cover becomes patchy in spring local heat advection induces the development of stable internal boundary layers changing heat exchange toward the snow. Overall, wind-driven processes play a key role in all the different stages of the evolution of seasonal snow. Improvements in process understanding particularly at the mountain-ridge and the slope scale, and processes representations in models at scales up to the mountain range scale, will be the basis for improved short-term forecast and climate projections in snow-covered regions.
... The mountain snow cover is heterogeneously distributed across a complex landscape (Jost et al., 2007;Lehning et al., 2011) and is notoriously difficult to characterize. With ASO, the approximated and more uniform modeled snow distribution can be replaced with observations from the airborne lidar. ...
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Accurately simulating the spatiotemporal distribution of mountain snow water equivalent improves estimates of available meltwater and benefits the water resource management community. In this paper we present the first integration of lidar-derived distributed snow depth data into a physics-based snow model using direct insertion. Over four winter seasons (2013–2016) the National Aeronautics and Space Administration/Jet Propulsion Laboratory (NASA/JPL) Airborne Snow Observatory (ASO) performed near-weekly lidar surveys throughout the snowmelt season to measure snow depth at high resolution over the Tuolumne River Basin above Hetch Hetchy Reservoir in the Sierra Nevada Mountains of California. The modeling component of the ASO program implements the iSnobal model to estimate snow density for converting measured depths to snow water equivalent and to provide temporally complete snow cover mass and thermal states between flights. Over the four years considered in this study, snow depths from 36 individual lidar flights were directly inserted into the model to provide updates of snow depth and distribution. Considering all updates to the model, the correlation between ASO depths and modeled depths with and without previous updates was on average r² = 0.899 (root-mean-square error = 12.5 cm) and r² = 0.162 (root-mean-square error = 41.5 cm), respectively. The precise definition of the snow depth distribution integrated with the iSnobal model demonstrates how the ASO program represents a new paradigm for the measurement and modeling of mountain snowpacks and reveals the potential benefits for managing water in the region.
... One-sixth of the Earth's population depends directly on the water supply from snow and ice melt in mountain areas [1]. Thus, significant research effort has been applied to the study of snow and ice dynamics in these regions [2][3][4][5], with particular focus on mountain hydrology [6][7][8][9]. The snowpack dynamics and its spatial extent also control many mountain processes, including soil erosion, plant survival [10], and glacier surface mass balance [11][12][13]. ...
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This work presents an extensive evaluation of the Crocus snowpack model over a rugged and highly glacierized mountain catchment (Arve valley, Western Alps, France) from 1989 to 2015. The simulations were compared and evaluated using in-situ point snow depth measurements, in-situ seasonal and annual glacier surface mass balance, snow covered area evolution based on optical satellite imagery at 250 m resolution (MODIS sensor), and the annual equilibrium-line altitude of glaciers, derived from satellite images (Landsat, SPOT, and ASTER). The snowpack simulations were obtained using the Crocus snowpack model driven by the same, originally semi-distributed, meteorological forcing (SAFRAN) reanalysis using the native semi-distributed configuration, but also a fully distributed configuration. The semi-distributed approach addresses land surface simulations for discrete topographic classes characterized by elevation range, aspect, and slope. The distributed approach operates on a 250-m grid, enabling inclusion of terrain shadowing effects, based on the same original meteorological dataset. Despite the fact that the two simulations use the same snowpack model, being potentially subjected to same potential deviation from the parametrization of certain physical processes, the results showed that both approaches accurately reproduced the snowpack distribution over the study period. Slightly (although statistically significantly) better results were obtained by using the distributed approach. The evaluation of the snow cover area with MODIS sensor has shown, on average, a reduction of the Root Mean Squared Error (RMSE) from 15.2% with the semi-distributed approach to 12.6% with the distributed one. Similarly, surface glacier mass balance RMSE decreased from 1.475 m of water equivalent (W.E.) for the semi-distributed simulation to 1.375 m W.E. for the distribution. The improvement, observed with a much higher computational time, does not justify the recommendation of this approach for all applications; however, for simulations that require a precise representation of snowpack distribution, the distributed approach is suggested.
... Expanding the application to three dimensions, the consideration moves from perimeter versus area to area versus volume. The fractal nature of landforms in three dimensions could manifest in the study of features such as mountains, and understanding the fractal nature of mountains and how they have variations at different length scales can lead to new analyses and predictions (e.g., Lehning et al., 2011). In three dimensions, continually increasing surface area versus a steady-state volume calculation can affect various analyses, such as models of radiative cooling which are based in part on exposed surface area relative to enclosed volume. ...
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The primary product of planetary geologic and geomorphologic mapping is a group of lines and polygons that parameterize planetary surfaces and landforms. Many different research fields use those shapes to conduct their own analyses, and some of those analyses require measurement of the shape's perimeter or line length, sometimes relative to a surface area. There is a general lack of discussion in the relevant literature of the fact that perimeters of many planetary landforms are not easily parameterized by a simple aggregation of lines or even curves, but they instead display complexity across a large range of scale lengths; in fewer words, many planetary landforms are fractals. Because of their fractal nature, instead of morphometric properties converging on a single value, those properties will change based on the scale used to measure them. Therefore, derived properties can change—in some cases, by an order of magnitude or more—just when the measuring length scale is altered. This can result in significantly different interpretations of the features. Conversely, instead of a problem, analysis of the fractal properties of some landforms has led to diagnostic criteria that other remote sensing data cannot easily provide. This paper outlines the basic issue of the fractal nature of planetary landforms, gives case studies where the effects become important, and provides the recommendation that geologic mappers consider characterizing the fractal dimension of their mapped units via a relatively simple, straightforward calculation.
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We use snow course and airborne gamma data available over North America to compare the validation of gridded snow water equivalent (SWE) products when evaluated with one reference dataset versus the other. We assess product performance across both non-mountainous and mountainous regions, determining the sensitivity of relative product rankings and absolute performance measures. In non-mountainous areas, product performance is insensitive to the choice of SWE reference dataset (snow course or airborne gamma): the validation statistics (bias, unbiased root mean squared error, and correlation) are consistent with one another. In mountainous areas, the choice of reference dataset has little impact on relative product ranking but a large impact on assessed error magnitudes (bias and unbiased root mean squared error). Further analysis indicates the agreement in non-mountainous regions occurs because the reference SWE estimates themselves agree up to spatial scales of at least 50 km, comparable to the grid spacing of most available SWE products. In mountain areas, there is poor agreement between the reference datasets, even at short distances (< 5 km). We determine that differences in assessed error magnitudes result primarily from the range of SWE magnitudes sampled by each method, although their respective spatiotemporal distribution and elevation differences between the reference measurements and grid centroids also play a role. We use this understanding to produce a combined reference SWE dataset for North America, applicable to future gridded SWE product evaluations and other applications.
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Plain Language Summary Precise simulations of snow depth (SD) are crucial for understanding changes in the Earth's energy balance and helping efforts to combat climate change. In this research, we evaluate how well the latest phase of the Coupled Model Intercomparison Project (CMIP6) can simulate the daily SD across Canada. By comparing these simulations to various SD data sets, we looked at average daily SD, how long snow remains on the ground, and how quickly it accumulates or melts away, focusing on 11 major catchments in Canada. Our study found that the CMIP6 models tend to simulate more SD, by about 57.7% on average, and suggest that snow cover remains around 30.5 days longer than observed durations. Although three specific models excel in matching the annual peak of daily SD, we noticed biases that point to a need for improving how these models represent various land covers. Our findings highlight the importance of making these models more accurate by using more detailed information about land cover properties, which helps better predict SD and understand its role in climate change.
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Snow is a vital component of the earth system, yet no snow-focused satellite remote sensing platform currently exists. In this study, we investigate how synthetic observations of snow water equivalent (SWE) representative of a synthetic aperture radar remote sensing platform could improve spatiotemporal estimates of snowpack. We use a fraternal twin observing system simulation experiment, specifically investigating how much snow simulated using widely used models and forcing data could be improved by assimilating synthetic observations of SWE. We focus this study across a 24∘×37∘ domain in the western USA and Canada, simulating snow at 250 m resolution and hourly time steps in water year 2019. We perform two data assimilation experiments, including (1) a simulation excluding synthetic observations in forests where canopies obstruct remote sensing retrievals and (2) a simulation inferring snow distribution in forested grid cells using synthetic observations from nearby canopy-free grid cells. Results found that, relative to a nature run, or assumed true simulation of snow evolution, assimilating synthetic SWE observations improved average SWE biases at maximum snowpack timing in shrub, grass, crop, bare-ground, and wetland land cover types from 14 %, to within 1 %. However, forested grid cells contained a disproportionate amount of SWE volume. In forests, SWE mean absolute errors at the time of maximum snow volume were 111 mm and average SWE biases were on the order of 150 %. Here the data assimilation approach that estimated forest SWE using observations from the nearest canopy-free grid cells substantially improved these SWE biases (18 %) and the SWE mean absolute error (27 mm). Simulations employing data assimilation also improved estimates of the temporal evolution of both SWE and runoff, even in spring snowmelt periods when melting snow and high snow liquid water content prevented synthetic SWE retrievals. In fact, in the Upper Colorado River region, melt-season SWE biases were improved from 63 % to within 1 %, and the Nash–Sutcliffe efficiency of runoff improved from -2.59 to 0.22. These results demonstrate the value of data assimilation and a snow-focused globally relevant remote sensing platform for improving the characterization of SWE and associated water availability.
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Sublimation is the main ablation component of snow in the upper areas of the semiarid Andes (∼ 26 to ∼ 32∘ S and ∼ 69 to ∼ 71∘ W). This region has elevations up to 6000 m, is characterized by scarce precipitation, high solar radiation receipt, and low air humidity, and has been affected by a severe drought since 2010. In this study, we suggest that most of the snowmelt runoff originates from specific areas with topographic and meteorological features that allow large snow accumulation and limited mass removal. To test this hypothesis, we quantify the spatial distribution of snowmelt runoff and sublimation in a catchment of the semiarid Andes using a process-based snow model that is forced with field data. Model simulations over a 2-year period reproduce point-scale records of snow depth (SD) and snow water equivalent (SWE) and are also in good agreement with an independent SWE reconstruction product as well as satellite snow cover area and indices of winter snow absence and summer snow persistence. We estimate that 50 % of snowmelt runoff is produced by 21 %–29 % of the catchment area, which we define as “snowmelt hotspots”. Snowmelt hotspots are located at mid-to-lower elevations of the catchment on wind-sheltered, low-angle slopes. Our findings show that sublimation is not only the main ablation component: it also plays an important role shaping the spatial variability in total annual snowmelt. Snowmelt hotspots might be connected with other hydrological features of arid and semiarid mountain regions, such as areas of groundwater recharge, rock glaciers, and mountain peatlands. We recommend more detailed snow and hydrological monitoring of these sites, especially in the current and projected scenarios of scarce precipitation.
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Water takes part in most physical processes that shape mountainous periglacial landscapes and initiation of mass-wasting processes. An observed increase in rockfall activity in high mountain regions was previously linked to permafrost degradation, and water that infiltrates into rock fractures is one of the likely drivers of processes related to thawing and destabilization. However, there is very little knowledge of the quantity and timing of water availability for infiltration into steep rock slopes. This knowledge gap originates from the complex meteorological, hydrological, and thermal processes that control snowmelt, as well as challenging access and data acquisition in extreme alpine environments. Here we use field measurements and numerical modeling to simulate the energy balance and hydrological fluxes on a steep high-elevation permafrost-affected rock slope at Aiguille du Midi (3842 m a.s.l, France), in the Mont Blanc massif. Our results provide new information about water balance at the surface of steep rock slopes. Model results suggest that only ∼ 25 % of the snowfall accumulates in our study site; the remaining ∼ 75 % is likely transported downslope by wind and gravity. The snowpack thickness was found to decrease with surface slopes between 40 and 70∘. We found that among all water fluxes, sublimation is the main process of snowpack mass loss at our study site. Snowmelt occurs between spring and late summer, but most of it may not reach the rock surface due to refreezing and the formation of an impermeable ice layer at the base of the snowpack, which was observed at the field site. The annual snowmelt that is available for infiltration (i.e., effective snowmelt) is highly variable in the simulated years 1959–2021, and its onset occurs mostly between May and August and ends before October. By applying the model to a range of altitudes, we show that effective snowmelt is the main source of water for infiltration above 3600 m a.s.l.; below, direct rainfall on the snow-free surface is the dominant source. This change from snowmelt- to rainfall-dominated water input leads to an abrupt, nonlinear increase in water availability at altitudes below 3600 m a.s.l and may point to higher sensitivity of permafrost-affected rock slopes to climate change at these altitudes.
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The ground thermal regime and permafrost development have an important influence on geomorphological processes in periglacial regions and ultimately landscape development. About 10 % of unstable rock slopes in Norway are potentially underlain by widespread permafrost. Permafrost thaw and degradation may play a role in slope destabilisation, and more knowledge about rock wall permafrost in Norway is needed to investigate possible links between the ground thermal regime, geomorphological activity and natural hazards. We assess spatio-temporal permafrost variations in selected rock walls in Norway over the last 120 years. Ground temperature is modelled using the two-dimensional ground heat flux model CryoGrid 2D along nine profiles crossing instrumented rock walls in Norway. The simulation results show the distribution of permafrost is sporadic to continuous along the modelled profiles. Results suggest that ground temperature at 20 m depth in steep rock faces increased by 0.2 ∘C per decade on average since the 1980s, and rates of change increase with elevation within a single rock wall section. Heat flow direction is primarily vertical within mountains in Norway. Nevertheless, narrow ridges may still be sensitive to even small differences in ground surface temperature and may have horizontal heat fluxes. This study further demonstrates how rock wall temperature increase rates and rock wall permafrost distribution are influenced by factors such as surface air temperature uncertainties; surface offsets arising from the incoming shortwave solar radiation; snow conditions on, above and below rock walls; and rock wall geometry and size together with adjacent blockfield-covered plateaus or glaciers.
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Accurate knowledge of snow depth distributions in forested regions is crucial for applications in hydrology and ecology. In such a context, understanding and assessing the effect of vegetation and topographic conditions on snow depth variability is required. In this study, the spatial distribution of snow depth in two agro-forested sites and one coniferous site in eastern Canada was analyzed for topographic and vegetation effects on snow accumulation. Spatially distributed snow depths were derived by unmanned aerial vehicle light detection and ranging (UAV lidar) surveys conducted in 2019 and 2020. Distinct patterns of snow accumulation and erosion in open areas (fields) versus adjacent forested areas were observed in lidar-derived snow depth maps at all sites. Omnidirectional semi-variogram analysis of snow depths showed the existence of a scale break distance of less than 10 m in the forested area at all three sites, whereas open areas showed comparatively larger scale break distances (i.e., 11–14 m). The effect of vegetation and topographic variables on the spatial variability in snow depths at each site was investigated with random forest models. Results show that the underlying topography and the wind redistribution of snow along forest edges govern the snow depth variability at agro-forested sites, while forest structure variability dominates snow depth variability in the coniferous environment. These results highlight the importance of including and better representing these processes in physically based models for accurate estimates of snowpack dynamics.
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Landscape characteristics, seasonal changes in the environment, and daylight conditions influence space use and detection of prey and predators, resulting in spatiotemporal patterns of predation risk for the prey. When predators have different hunting modes, the combined effects of multiple predators are mediated by the physical landscape and can result in overlapping or contrasting patterns of predation risk. Humans have become super‐predators in many anthropogenic landscapes by harvesting game species and competing with large carnivores for prey. Here, we used the locations of wolf (Canis lupus)‐killed and hunter‐killed moose (Alces alces) in south‐central Scandinavia to investigate whether environmental and anthropogenic features influenced where wolves and hunters killed moose. We predicted that the combined effects of wolves and hunters would result in contrasting spatial risk patterns due to differences in hunting modes. We expected these contrasting spatial risk patterns also to differ temporally. During the hunting season, the probability of a wolf kill increased with distance to bogs, whereas it decreased with increasing building density and distance to clearcuts and young forests. After the hunting season, the probability of a wolf kill increased with increasing terrain ruggedness and decreased with increasing building density, distance to main roads, and distance to clearcuts and young forests. The probability of a hunter kill was highest closer to bogs, main and secondary roads, in less rugged terrain and in areas with lower building density. Hunters killed all moose during the day, whereas wolves killed most moose at night during and after the hunting season. Our findings suggest that environmental and anthropogenic features mediate hunting and wolf predation risk. Additionally, we found that hunter‐ and wolf‐killed moose exhibited contrasting spatial associations to landscape features, most likely due to the different hunting modes displayed by hunters and wolves. However, wolf predation and hunting risks also contrasted over time since wolves killed mostly at night and hunters were restricted to hunting during daytime and during the hunting season. This temporal segregation in risk might therefore suggest that moose could minimize risk exposure by taking advantage of spatiotemporally vacant hunting domains.
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Snow is involved in and influences water–energy processes at multiple scales. Studies on land surface snow phenology are an important part of cryosphere science and are a hot spot in the hydrological community. In this study, we improved a statistical downscaling method by introducing a spatial probability distribution function to obtain regional snow depth data with higher spatial resolution. Based on this, the southern Gansu Plateau (SGP), an important water source region in the upper reaches of the Yellow River, was taken as a study area to quantify regional land surface snow phenology variation, together with a discussion of their responses to land surface terrain and local climate, during the period from 2003 to 2018. The results revealed that the improved downscaling method was satisfactory for snow depth data reprocessing according to comparisons with gauge-based data. The downscaled snow depth data were used to conduct spatial analysis and it was found that snow depth was on average larger and maintained longer in areas with higher altitudes, varying and decreasing with a shortened persistence time. Snow was also found more on steeper terrain, although it was indistinguishable among various aspects. The former is mostly located at high altitudes in the SGP, where lower temperatures and higher precipitation provide favorable conditions for snow accumulation. Climatically, factors such as precipitation, solar radiation, and air temperature had significantly singular effectiveness on land surface snow phenology. Precipitation was positively correlated with snow accumulation and maintenance, while solar radiation and air temperature functioned negatively. Comparatively, the quantity of snow was more sensitive to solar radiation, while its persistence was more sensitive to air temperature, especially extremely low temperatures. This study presents an example of data and methods to analyze regional land surface snow phenology dynamics, and the results may provide references for better understanding water formation, distribution, and evolution in alpine water source areas.
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In this study, we classified the variability in snow cover persistence across China by using a novel method; continuous snow cover days and variability of snow cover were used as the evaluation indicators based on a long-term Advanced Very High Resolution Radiometer (AVHRR) snow cover extent (SCE) product. The product has been generated by the snow research team in the Northwest Institute of Eco-Environment and Resources (NIEER), Chinese Academy of Sciences. There were obvious differences in snow cover classification in three snow cover areas (northern Xinjiang, northeast China, and the Tibetan Plateau): northern Xinjiang was dominated by persistent snow cover, most regions of northeast China were covered by persistent and periodic variable snow cover. There was the most abundant snow cover classification in the Tibetan Plateau. The extents of persistent and periodic variable snow cover were gradually shrinking due to rising temperatures and decreasing snowfall during 1981–2019. In contrast, non-periodic variable snow cover areas increased significantly. This method takes into account the stability, continuity, and variability of snow cover, and better captures the characteristics and changes of snow cover across China. Based on our research, we found that snow disasters in ephemeral-type (belong to non-periodic variable snow cover) regions cannot be well prevented because of the unfixed snow cover timing. Therefore, we recommend that monitoring and forecasting of snow cover in these snow cover regions should be strengthened.
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Surface roughness influences the release of avalanches and the dynamics of rockfall, avalanches and debris flow, but it is often not objectively implemented in natural hazard modelling. For two study areas, a treeline ecotone and a windthrow-disturbed forest landscape of the European Alps, we tested seven roughness algorithms using a photogrammetric digital surface model (DSM) with different resolutions (0.1, 0.5 and 1 m) and different moving-window areas (9, 25 and 49 m2). The vector ruggedness measure roughness algorithm performed best overall in distinguishing between roughness categories relevant for natural hazard modelling (including shrub forest, high forest, windthrow, snow and rocky land cover). The results with 1 m resolution were found to be suitable to distinguish between the roughness categories of interest, and the performance did not increase with higher resolution. In order to improve the roughness calculation along the hazard flow direction, we tested a directional roughness approach that improved the reliability of the surface roughness computation in channelised paths. We simulated avalanches on different elevation models (lidar-based) to observe a potential influence of a DSM and a digital terrain model (DTM) using the simulation tool Rapid Mass Movement Simulation (RAMMS). In this way, we accounted for the surface roughness based on a DSM instead of a DTM, which resulted in shorter simulated avalanche runouts by 16 %–27 % in the two study areas. Surface roughness above a treeline, which in comparison to the forest is not represented within the RAMMS, is therefore underestimated. We conclude that using DSM-based surface roughness in combination with DTM-based surface roughness and considering the directional roughness is promising for achieving better assessment of terrain in an alpine landscape, which might improve the natural hazard modelling.
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Airborne LiDAR can support high resolution watershed-scale snow depth mapping that provides the spatial coverage necessary to inform water supply forecasts for mountainous headwaters. This research utilized LIDAR and machine learning to evaluate snow depth drivers and to assess the feasibility of sampling datasets for the spatial imputation of snow depth at the watershed-scale under mid-winter and melt onset conditions. We present a Random Forest based method of extrapolating LiDAR snow depth model values from two flight lines, with insights for future operational use. Models of watershed-scale snow depth developed from spatially constrained flightline training samples correlated with more spatially widespread LiDAR snow depth data but were outperformed by models generated from training data sampled across the entire watershed. Random Forest simulations produced R² values ranging from 0.41 to 0.61 and RMSE values from 0.7 m to 1.0 m (p < 0.05). By evaluating the performance of snow depth drivers within each simulation, we found that aspect and topographic position index were important drivers regardless of seasonality. LiDAR sampling and machine learning imputation provide a viable framework for substantially reducing the cost of LiDAR-based mountain snowpack monitoring over complex source water regions, but further work is needed to optimize flight path sampling configurations.
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Under a warming climate, an improved understanding of the water stored in snowpacks is becoming increasingly important for hydropower planning, flood risk assessment and water resource management. Due to inaccessibility and a lack of ground measurement networks, accurate quantification of snow water storage in mountainous terrains still remains a major challenge. Remote sensing can provide dynamic observations with extensive spatial coverage, and has proved a useful means to characterize snow water equivalent (SWE) at a large scale. However, current SWE products show very low quality in the mountainous areas due to very coarse spatial resolution, complex terrain, large spatial heterogeneity and deep snow. With more high-quality satellite data becoming available from the development of satellite sensors and platforms, it provides more opportunities for better estimation of snow conditions. Meanwhile, machine learning provides an important technique for handling the big data offered from remote sensing. Using the Överuman Catchment in Northern Sweden as a case study, this paper explores the potentials of machine learning for improving the estimation of mountain snow water storage using satellite observations, topographic factors, land cover information and ground SWE measurements from the spatially distributed snow survey. The results show that significantly improved SWE estimation close to the peak of snow accumulation can be achieved in the catchment using the random forest regression. This study demonstrates the potentials of machine learning for better understanding the snow water storage in mountainous areas.
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Understanding the spatial variability of the snowpack is valuable for hydrologists and ecologists seeking to predict hydrological processes in a cold region. Snow distribution is a function of interactions among static variables, such as terrain, vegetation, and soil properties, and dynamic meteorological variables, such as solar radiation, wind speed and direction, and soil moisture. However, identifying the dominant physical drivers responsible for spatial patterns of the snowpack, particularly for ephemeral, shallow snowpacks, has been challenged due to the lack of the high-resolution snowpack and physical variables with high vertical accuracy as well as inherent limitations in traditional approaches. This study uses an Unpiloted Aerial System (UAS) lidar-based snow depth and static variables (1-m spatial resolution) to analyze field-scale spatial structures of snow depth and apply the Maximum Entropy (MaxEnt) model to identify primary controls over open terrain and forests at the University of New Hampshire Thompson Farm Research Observatory, New Hampshire, United States. We found that, among nine topographic and soil variables, plant functional type and terrain roughness contribute up to 80% and 76% of relative importance in the MaxEnt framework to predicting locations of deeper or shallower snowpacks, respectively, across a mixed temperate forested and field landscape. Soil variables, such as organic matter and saturated hydraulic conductivity, were also important controls (up to 70% and 81%) on snow depth spatial variations for both open and forested landscapes suggesting spatial variations in soil variables under snow can control thermal transfer among soil, snowpack, and surface-atmosphere. This work contributes to improving land surface and snow models by informing parameterization of the sub-grid scale snow depths, down-scaling remotely sensed snow products, and understanding field scale snow states.
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The annual consistency of spatial patterns of snow accumulation and melt suggests that the evolution of these patterns, known as depletion curves, is useful for estimating basin water content and runoff prediction. Theoretical snow cover depletion curves are used in models to parameterize fractional snow-covered area (fSCA) based on modeled estimates of snow accumulation and snowmelt. Directly measuring the spatio-temporal snow distribution, characterization of depletion curves, and understanding how they vary across mountainous landscapes was not possible until the recent U.S. National Aeronautics and Space Administration (NASA) Airborne Snow Observatory (ASO). Herein, for the first time, high-resolution spatio-temporal snow depth information from the ASO is used to derive observation-based snow cover depletion curves across physiographic gradients by estimating the slope of the fSCA–snow depth relationship (i.e. depletion slope). The depletion slope reveals important insights into snow processes as it is strongly related to snow depth variability (r2 = 0.58). Regression tree analysis between observed depletion slopes and physiography, particularly vegetation height and terrain roughness, displays clear nonlinear dynamics and explains 31% of the variance in depletion slope. This unique observation-based analysis of snow cover depletion curves has implications for energy and water flux calculations across many earth system models. HIGHLIGHTS Relationships between snow depth and fSCA (i.e. depletion slope) were robust over the 4 years of study.; Significant spatial variability in depletion slope is well correlated with snow depth variability.; Increased vegetation height and decreased terrain roughness were associated with more homogeneous snowpacks and lower depletion slopes.;
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Climate patterns over preceding years affect seasonal water and moisture conditions. The linkage between regional climate and local hydrology is challenging due to scale differences, both spatially and temporally. In this study, variance, correlation, and singular spectrum analyses were conducted to identify multiple hydroclimatic phases during which climate teleconnection patterns were related to hydrology of a small headwater basin in Idaho, USA. Combined field observations and simulations from a physically based hydrological model were used for this purpose. Results showed statistically significant relations between climate teleconnection patterns and hydrological fluxes in the basin, and climate indices explained up to 58% of hydrological variations. Antarctic Oscillation (AAO), North Atlantic Oscillation (NAO), and Pacific North America (PNA) patterns affected mountain hydrology, in that order, by decreasing annual runoff and rain on snow (ROS) runoff by 43% and 26% during a positive phase of NAO and 25% and 9% during a positive phase of PNA. AAO showed a significant association with the rainfall-to-precipitation ratio and explained 49% of its interannual variation. The runoff response was affected by the phase of climate variability indices and the legacy of past atmospheric conditions. Specifically, a switch in the phase of the teleconnection patterns of NAO and PNA caused a transition from wet to dry conditions in the basin. Positive AAO showed no relation with peak SWE and ROS runoff in the same year, but AAO in the preceding year explained 24 and 25% (p < 0.05) of their variations, suggesting that the past atmospheric patterns are equally important as the present conditions in affecting local hydrology. Areas sheltered from the wind and acted as a source for snow transport showed the lowest (40% below normal) ROS runoff generation, which was associated with positive NAO that explained 33% (p < 0.01) of its variation. The findings of this research highlighted the importance of hydroclimatic phases and multiple year variations that must be considered in hydrological forecasts, climate projections, and water resources planning.
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Collecting spatially representative data over large areas is a challenge within snow monitoring frameworks. Identifying consistent trends in snow accumulation properties enables increased sampling efficiency by minimizing field collection time and/or remote sensing costs. Seasonal snowpack depth estimations during mid‐winter and melt onset conditions were derived from airborne Lidar over the West Castle Watershed in the southern Canadian Rockies on three dates. Each dataset was divided into five sets of snow depth driver classes: elevation, aspect, topographic position index, canopy cover and slope. Datasets were quality controlled by eliminating snow depth values above the 99th percentile value, which had a negligible effect on average snow depths. Consistent trends were observed among driver classes with peak snow accumulation occurring within the treeline ecotone, north‐facing aspects, open canopies, topographic depressions and areas with low slope angle. Although mid‐winter class trends for each driver were similar and watershed‐scale snow depth distributions were significantly correlated (0.76, p < .01), depth distributions within the same driver class of the three datasets were not correlated due to recent snowfall events, redistribution and settling processes. Trends in driver classes during late season melt onset were similar to mid‐winter conditions but watershed scale distribution correlation results varied with seasonality (0.68 mid‐winter 2014 and melt onset 2016; 0.65 mid‐winter 2017 and melt onset 2016, p < .1). This is due to the differing stages of accumulation or ablation and the upward migration in the 0°C isotherm during spring, when snow depth can be declining in valley bottoms while still increasing at higher elevations. The observed consistency in depth driver controls can be used to guide future integrated snow monitoring frameworks.
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The small scale distribution of the snowpack in mountain areas is highly heterogeneous, and is mainly controlled by the interactions between the atmosphere and local topography. However, the influence of different terrain features in controlling variations in the snow distribution depends on the characteristics of the study area. As this leads to uncertainties in high spatial resolution snowpack simulations, a deeper understanding of the role of terrain features on the small scale distribution of snow depth is required. This study applied random forest algorithms to investigate the temporal evolution of snow depth in complex alpine terrain using as predictors various topographical variables and in situ snow depth observations at a single location. The high spatial resolution (1 m x 1 m) snow depth distribution database used in training and evaluating the random forests was derived from terrestrial laser scanner (TLS) devices at three study sites, in the French Alps (2 sites) and the Spanish Pyrenees (1 site). The results show the major importance of two topographic variables, the topographic position index and the maximum upwind slope parameter. For these variables the search distances and directions depended on the characteristics of each site and the TLS acquisition date, but are consistent across sites and are tightly related to main wind directions. The weight of the different topographic variables on explaining snow distribution evolves while major snow accumulation events still take place and minor changes are observed after reaching the annual snow accumulation peak. Random forests have demonstrated good performance when predicting snow distribution for the sites included in the training set with R2 values ranging from 0.82 to 0.94 and mean absolute errors always below 0.4 m. Oppositely, this algorithm failed when used to predict snow distribution for sites not included in the training set, with mean absolute errors above 0.8 m
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The degree to which the hydrologic water balance in a snow‐dominated headwater catchment is affected by annual climate variations is difficult to quantify, primarily due to uncertainties in measuring precipitation inputs and evapotranspiration (ET) losses. Over a recent three‐year period, the snowpack in California's Sierra Nevada fluctuated from the lowest in recorded history (2015) to historically large (2017), with a relatively average year in between (2016). This large dynamic range in climatic conditions presents a unique opportunity to investigate correlations between annual water availability and runoff in a snow‐dominated catchment. Here, we estimate ET using a water balance approach where the water inputs to the system are spatially constrained using a combination of remote sensing, physically based modeling, and in‐situ observations. For all three years of this study, the NASA Airborne Snow Observatory (ASO) combined periodic high‐resolution snow depths from airborne lidar with snow density estimates from an energy and mass balance model to produce spatial estimates of snow water equivalent (SWE) over the Tuolumne headwater catchment at 50‐m resolution. Using observed reservoir inflow at the basin outlet and the well‐quantified snowmelt model results that benefit from periodic ASO snow depth updates, we estimate annual ET, runoff efficiency (RE), and the associated uncertainty across these three dissimilar water years. Throughout the study period, estimated annual ET magnitudes remained steady (222 mm in 2015, 151 mm in 2016, and 299 mm in 2017) relative to the large differences in basin input precipitation (547 mm in 2015, 1060 mm in 2016, and 2211 mm in 2017). These values compare well with independent satellite‐derived ET estimates and previously published studies in this basin. Results reveal that ET in the Tuolumne does not scale linearly with the amount of available water to the basin, and that RE primarily depends on total annual snowfall proportion of precipitation. This article is protected by copyright. All rights reserved.
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It is well‐known that snow plays an important role in land surface energy balance; however, modeling the sub‐grid variability of snow is still a challenge in large scale hydrological and land surface models. High‐resolution snow depth data and statistical methods can reveal some characteristics of the sub‐grid variability of snow depth, which can be useful in developing models for representing such sub‐grid variability. In this study, snow depth was measured by airborne Lidar at 0.5 m resolution over two mountainous areas in southwestern Wyoming, Snowy Range and Laramie Range. To characterize sub‐grid snow depth spatial distribution, measured snow depth data of these two areas were meshed into 284 grids of 1 km × 1 km. Also, nine representative grids of 1 km × 1 km were selected for detailed analyses on the geostatistical structure and probability density function (PDF) of snow depth. It was verified that land cover is one of the important factors controlling spatial variability of snow depth at the 1 km scale. PDFs of snow depth tend to be Gaussian distributions in the forest areas. However, they are eventually skewed as non‐Gaussian distribution, largely due to the no‐snow areas effect, mainly caused by snow redistribution and snow melt. Our findings show the characteristics of sub‐grid variability of snow depth and clarify the potential factors that need to be considered in modeling sub‐grid variability of snow depth.
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There is significant uncertainty regarding the spatiotemporal distribution of seasonal snow on glaciers, despite being a fundamental component of glacier mass balance. To address this knowledge gap, we collected repeat, spatially extensive high-frequency ground-penetrating radar (GPR) observations on two glaciers in Alaska during the spring of 5 consecutive years. GPR measurements showed steep snow water equivalent (SWE) elevation gradients at both sites; continental Gulkana Glacier's SWE gradient averaged 115 mm 100 m-1 and maritime Wolverine Glacier's gradient averaged 440 mm 100 m-1 (over > 1000 m). We extrapolated GPR point observations across the glacier surface using terrain parameters derived from digital elevation models as predictor variables in two statistical models (stepwise multivariable linear regression and regression trees). Elevation and proxies for wind redistribution had the greatest explanatory power, and exhibited relatively time-constant coefficients over the study period. Both statistical models yielded comparable estimates of glacier-wide average SWE (1 % average difference at Gulkana, 4 % average difference at Wolverine), although the spatial distributions produced by the models diverged in unsampled regions of the glacier, particularly at Wolverine. In total, six different methods for estimating the glacier-wide winter balance average agreed within ±11 %. We assessed interannual variability in the spatial pattern of snow accumulation predicted by the statistical models using two quantitative metrics. Both glaciers exhibited a high degree of temporal stability, with ∼85 % of the glacier area experiencing less than 25 % normalized absolute variability over this 5-year interval. We found SWE at a sparse network (3 stakes per glacier) of long-term glaciological stake sites to be highly correlated with the GPR-derived glacier-wide average. We estimate that interannual variability in the spatial pattern of winter SWE accumulation is only a small component (4 %–10 % of glacier-wide average) of the total mass balance uncertainty and thus, our findings support the concept that sparse stake networks effectively measure interannual variability in winter balance on glaciers, rather than some temporally varying spatial pattern of snow accumulation.
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The mean precipitation measurements in a Mediterranean Alpine catchment in Sierra Nevada show an inversion of the gradient with the altitude beyond a certain threshold. Is it due to a real pattern or it can be explained by systematic error of solid precipitation measurement in gauges? Can we assess climatic fields in an alpine catchment from gauge measurement? This article describes a research developed to answer both questions in the Alto Genil Basin. As commonly happens in most of the basins, the spatio-temporal information from climate gauges is limited; therefore to reduce uncertainty in estimates of climatic fields, some secondary information should be introduced. Since orographic conditions clearly influence precipitation, the relationship between this climatic variable and elevation is usually included as secondary information into the estimates. However, while there is a clear relationship between temperature and elevation, the relationship between precipitation and elevation is not so simple. In this article the analysis of the data performed allow us to demonstrate that there is a real inversion of the gradient within this Mediterranean Alpine area as other authors previously pointed in some tropical and subtropical zones. The intensity of this phenomenon and the altitude threshold from which it appears can be altered as a consequence of the undercath of the solid precipitation. To estimate precipitation fields, we have used different hypotheses about the intensity of the undercatch taking into account empirical corrections obtained for nearby mountain ranges. An analysis of the sensitivity of the results to the assumed undercatch hypothesis shows that it is not possible to estimate properly precipitation fields (the sensitivity of the results to the adopted hypothesis is high) in these alpine areas if we only have information about the precipitation measurements at the stations.
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Snow depth is one of the key physical parameters for understanding land surface energy balance, soil thermal regime, water cycle, and assessing water resources from local community to regional industrial water supply. Previous studies by using in situ data are mostly site specific; data from satellite remote sensing may cover a large area or global scale, but uncertainties remain large. The primary objective of this study is to investigate spatial variability and temporal change in snow depth across the Eurasian continent. Data used include long-term (1966–2012) ground-based measurements from 1814 stations. Spatially, long-term (1971–2000) mean annual snow depths of >20 cm were recorded in northeastern European Russia, the Yenisei River basin, Kamchatka Peninsula, and Sakhalin. Annual mean and maximum snow depth increased by 0.2 and 0.6 cm decade-1 from 1966 through 2012. Seasonally, monthly mean snow depth decreased in autumn and increased in winter and spring over the study period. Regionally, snow depth significantly increased in areas north of 50∘ N. Compared with air temperature, snowfall had greater influence on snow depth during November through March across the former Soviet Union. This study provides a baseline for snow depth climatology and changes across the Eurasian continent, which would significantly help to better understanding climate system and climate changes on regional, hemispheric, or even global scales.
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Snow in rock faces plays a key role in the alpine environment for permafrost distribution, snow water storage or run off in spring. However, a detailed assessment of snow depths in steep rock walls has never been attempted. To understand snow distribution in rock walls a high-resolution terrestrial laser scanner (TLS), including a digital camera, was used to obtain snow depth (HS) data with a resolution of one metre. The mean HS, the snow covered area and their evolution in the rock face were compared to a neighbouring smoother catchment and a flat field station at similar elevation. Further we analyzed the patterns of HS distribution in the rock face after different periods and investigated the main factors contributing to them. In a first step we could show that with TLS reliable information on surface data of a steep rocky surface can be obtained. In comparison to the flatter sites in the vicinity, mean HS in the rock face was lower during the entire winter, but trends of snow depth changes were similar. We observed repeating accumulation and ablation patterns in the rock face, while maximum snow depth loss always occurred at those places with maximum snow depth gain. Further analysis of the main factors contributing to the snow depth distribution in the rock face revealed terrain-wind-interaction processes to be dominant. Processes related to slope angle seem to play a role, but no linear function of slope angle and snow depth was found. Further analyses should involve measurements in rock faces with other characteristics and higher temporal resolutions to be able to distinguish individual processes better. Additionally the relation of spatial and temporal distribution of snow depth to terrain-wind interactions should be tested.
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Snow in rock faces plays a key role in the alpine environment for permafrost distribution, snow water storage or runoff in spring. However, a detailed assessment of snow depths in steep rock walls has never been attempted. To understand snow distribution in rock faces a high-resolution terrestrial laser scanner (TLS), including a digital camera, was used to obtain interpolated snow depth (HS) data with a grid resolution of one metre. The mean HS, the snow covered area and their evolution in the rock face were compared to a neighbouring smoother catchment and a flat field station at similar elevation. Further we analyzed the patterns of HS distribution in the rock face after different weather periods and investigated the main factors contributing to those distributions. In a first step we could show that with TLS reliable information on surface data of a steep rocky surface can be obtained. In comparison to the flatter sites in the vicinity, mean HS in the rock face was lower during the entire winter, but trends of snow depth changes were similar. We observed repeating accumulation and ablation patterns in the rock face, while maximum snow depth loss always occurred at those places with maximum snow depth gain. Further analysis of the main factors contributing to the snow depth distribution in the rock face revealed terrain-wind-interaction processes to be dominant. Processes related to slope angle seem to play a role, but no simple relationship between slope angle and snow depth was found. Further analyses should involve measurements in rock faces with other characteristics and higher temporal resolutions to be able to distinguish individual processes better. Additionally, the relation of spatial and temporal distribution of snow depth to terrain – wind interactions should be tested.
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The snow surface height was precisely measured, with a laser scanner, before and after the passage of two dry-mixed avalanches in Vallée de la Sionne during the winter of 2005–2006. The measurements were used to calculate the depth of the deposited snow along each entire avalanche path with a height resolution of 100 mm and a horizontal resolution of 500 mm. These data are much more accurate than any previous measurements from large avalanches and show that the deposit depth is strongly negatively correlated with the slope angle. That is, on steep slopes the deposit is shallow, and on gentle slopes the deposit is deep. The time evolution of the snow depth, showing the initial erosion and final deposition as the avalanche passed, was also observed at one position using a frequency-modulated continuous wave radar. Measurements at a nearby position gave flow speed profiles and showed that the avalanche tail consists of a steady state subcritical flow that lasts for about 100 s. Eventually, the tail slowly decelerates as the depth slightly decreases, and then it comes to rest. We show that the dependency between the slope angle and the deposition depth can be explained by both a cohesive friction model and the Pouliquen hstop model.
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We present an analysis of high resolution laser scanning data of snow depths of three different slopes in the Wannengrat catchment (introduced in part 1) using omnidirectional and directional variograms for three specific terrain features; cross-loaded slopes, lee slopes, and windward slopes. A break in scaling behavior was observed in all subareas, which can be seen as the roughness scale of bare earth terrain which is modified by the snow cover. In the wind-protected lee slope a different scaling behavior was observed, compared to the two wind-exposed areas. The wind-exposed areas have a smaller ordinal intercept γ, a smaller short range fractal dimension D, and a larger scale break distance L than the wind-protected lee slope. Snow depth structure inherits characteristics of dominant NW storms, which results, e.g., in a trend toward larger break distances in the course of the accumulation season. This can be interpreted as a result of surface smoothing at increasing scales. Similar scaling characteristics were obtained for two different years at the end of the accumulation season. Since snow depth structure is altered strongly by NW storms, this inter-annual consistency may strongly depend on their frequency in an accumulation period. With the analysis of directional variograms anisotropies of fractal parameters were detected, which were related to dominant wind directions.
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Terrestrial and airborne laser scanning (TLS and ALS) techniques have only recently developed to the point where they allow wide-area measurements of snow distribution in varying terrain. In this paper we present multiple TLS measurements showing the snow depth development for a series of precipitation events. We observe that the pattern of maximum accumulation is similar for the two years presented here (correlation up to r = 0.97). Storms arriving from the northwest show persistent snow depth distributions and contribute most to the final accumulation pattern. Snow depth patterns of maximum accumulation for the two years are more similar than the distribution created by any two pairs of individual storms. Based on the strong link between accumulation patterns and terrain, we investigated the ability of a model based on terrain and wind direction to predict accumulation patterns. This approach of Winstral et al. (2002), which describes wind exposure and shelter, was able to predict the general accumulation pattern over scales of slopes but failed to match observed variance. Furthermore, a high sensitivity to the local wind direction was demonstrated. We suggest that Winstral et al.'s model could form a useful tool for application from hydrology and avalanche risk assessment to glaciology.
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Mandelbrot's fractal geometry has sparked considerable interest in the remote sensing community since the publication of his highly influential book in 1977. Fractal models have been used in several image processing and pattern recognition applications such as texture analysis and classification. Applications of fractal geometry in remote sensing rely heavily on estimation of the fractal dimension. The fractal dimension (D) is a central construct developed in fractal geometry to describe the geometric complexity of natural phenomena as well as other complex forms. This paper provides a survey of several commonly used methods for estimating the fractal dimension and their applications to remote sensing problems. Methodological issues related to the use of these methods are summarized. Results from empirical studies applying fractal techniques are collected and discussed. Factors affecting the estimation of fractal dimension are outlined. Important issues for future research are also identified and discussed.
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The inhomogeneous snow distribution found in alpine terrain is the result of wind and precipitation interacting with the snow surface. During major snowfall events, preferential deposition of snow and transport of previously deposited snow often takes place simultaneously. Both processes, however, are driven by the local wind field, which is influenced by the local topography. In this study, the meteorological model Advanced Regional Prediction System (ARPS) was used to compute mean flow fields of 50-m, 25-m-, 10-m-, and 5-m grid spacing to investigate snow deposition patterns resulting from two snowfall events on a mountain ridge in the Swiss Alps. Only the initial adaptation of the flow field to the topography is calculated with artificial boundary conditions. The flow fields then drive the snow deposition and transport module of Alpine3D, a model of mountain surface processes. The authors compare the simulations with partly new measurements of snow deposition on the Gaudergrat ridge. On the basis of these four grid resolutions, it was possible to investigate the effects of numerical resolution in the calculation of wind fields and in the calculation of the associated snow deposition. The most realistic wind field and deposition patterns were obtained with the highest resolution of 5 m. These high-resolution simulations confirm the earlier hypothesis that preferential deposition is active at the ridge scale and true redistribution-mainly via saltation-forms smaller-scale deposition patterns, such as dunes and cornices.
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Snowpack properties vary dramatically over a wide range of spatial scales, from crystal microstructure to regional snow climates. The driving forces of wind, energy balance, and precipitation interact with topography and vegetation to dominate snow depth variability at horizontal scales from 1 to 1000 m. This study uses land surface elevation, vegetation surface elevation, and snow depth data measured using airborne lidar at three sites in north-central Colorado. Fractal dimensions are estimated from the slope of a log-transformed variogram and demonstrate scale-invariant, fractal behavior in the elevation, vegetation, and snow depth datasets. Snow depth and vegetation topography each show two distinct fractal distributions over different scale ranges (multifractal behavior), with short-range fractal dimensions near 2.5 and long-range fractal dimensions around 2.9 at all locations. These fractal ranges are separated by a scale break at 15-40 m, depending on the site, which indicates a process change at that scale. Terrain has a fractal distribution over nearly the entire range of scales available in the data. Directional differences in the fractal dimensions for each parameter are also present at multiple scales, and are related to the wind direction frequency distributions at each site. The results indicate that different sampling resolutions may yield different results and allow rescaling in specific scale ranges. Resolutions of 10 m and finer are consistently self-similar, as are resolutions greater than 30 m, though the coarser resolutions show nearly random distributions.
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In mountain regions wind is known to cause snow redistribution. While physically based models of snow redistribution have been developed for flat to gently rolling terrain, extension of these findings to steep terrain has been limited by the complexity of wind fields in such areas. In this study, we applied a nonhydrostatic and compressible atmospheric prediction model to steep alpine topography and compared the results to a fully distributed data set of snow depth estimations. The results show reduced horizontal wind velocity as well as an increasing downward vertical wind velocity over areas with the largest winter accumulation, which are mostly glacierized. We show that the wind velocity normal to the local surface, which should be zero in a nondivergent flow field and is a direct measure of increased or decreased local deposition, is a function of small-scale features of local topography. The correlation between wind fields, snow accumulation, and glacierization suggests that accurate modeling of wind fields over glacierized areas in complex terrain is a key factor for understanding the mass balance distribution of glaciers.
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An analysis of snow cover measurement data in a number of physiographic regions and landscapes has shown that fields of snow cover characteristics can be considered as random fields with homogeneous increments and that these fields exhibit statistical self-similarity. A physically based distributed model of snowmelt runoff generation developed for the Upper Kolyma River basin (the catchment area is about 100 000 km2) has been used to estimate the sensitivity of snowmelt dynamics over the basin and flood hydrographs to the parameterization of subgrid effects based on the hypothesis of statistical self-similarity of the maximum snow water equivalent fields. Such parameterization of subgrid effects enables us to improve the description of snowmelt dynamics both within subgrid areas and over the entire river basin. The snowmelt flood hydrographs appear less sensitive to the self-similarity of snow cover over subgrid areas than to the dynamics of snowmelt because of a too large catchment area of the basin under consideration. However, for certain hydrometeorological conditions and for small river basins this effect may lead to significant changes of the calculated hydrographs. Copyright © 2001 John Wiley & Sons, Ltd.
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Our understanding of snow distribution in the mountains is limited as a result of the complex controls leading to extreme spatial variability. More accurate representations of snow distribution are greatly needed for improvements to hydrological forecasts, climate models, and for the future testing and validation of remote-sensing retrieval algorithms. In this study, the relative performances of four spatial interpolation methods were evaluated to estimate snow water equivalent for three 1 km2 study sites in the Colorado Rocky Mountains. Each study site is representative of different topographic and vegetative characteristics. From 1 to 11 April 2001, 550 snow depth measurements and approximately 16 snow density profiles were obtained within each study site. The analytical methods used to estimate snow depth over the 1 km2 areas were (1) inverse distance weighting, (2) ordinary kriging, (3) modified residual kriging and cokriging, and (4) a combined method using binary regression trees and geostatistical methods. The independent variables used were elevation, slope, aspect, net solar radiation, and vegetation. Using cross-validation procedures, each method was assessed for accuracy. The tree-based models provided the most accurate estimates for all study sites, explaining 18-30% of the observed variability in snow depth. Kriging of the regression tree residuals did not substantially improve the models. Cokriging of the residuals resulted in a less accurate model when compared with the tree-based models alone. Binary regression trees may have generated the most accurate estimates out of all methods evaluated: however, substantial portions of the variability in observed snow depth were left unexplained by the models. Though the data may have simply lacked spatial structure, it is recommended that the characteristics of the study sites, sampling strategy, and independent variables be explored further to evaluate the causes for the relatively poor model results.
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1] The inhomogeneous snow distribution found in alpine terrain is the result of wind and precipitation interacting with the (snow) surface over topography. We introduce and explain preferential deposition of precipitation as the deposition process without erosion of previously deposited snow and thus in absence of saltation. A numerical model is developed, describing the relevant processes of saltation, suspension, and preferential deposition. The model uses high-resolution wind fields calculated with a meteorological model, ARPS. The model is used to simulate a 120 h snow storm period over a steep alpine ridge, for which snow distribution measurements are available. The comparison to measurements shows that the model captures the larger-scale snow distribution patterns and predicts the total additional lee slope loading well. However, the spatial resolution of 25 m is still insufficient to capture the smaller-scale deposition features observed. The model suggests that the snow distribution on the ridge scale is primarily caused by preferential deposition and that this result is not sensitive to model parameters such as turbulent diffusivity, drift threshold, or concentration in the saltation layer.
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1] The mountain snow cover is an important source of water but also leads to natural hazards, such as avalanches and floods. We use data collected during winters 1999/2000 to 2007/2008 by 239 automatic and manual measurement stations in Switzerland to highlight spatial characteristics of extreme snowfall. With the help of extreme value theory based on a ''peaks-over-threshold'' approach and a Poisson point process representation, we analyze spatial patterns and correlation characteristics. Our analyses show that a significant number of stations do not follow the Gumbel distribution. In particular, low altitude stations in the Swiss Plateau are heavy tailed because of rare extraordinary snowfall events. Spatial characteristics of extreme snowfall are compared to those of the mean snowfall. Altitudinal dependence and spatial distribution of mean and extreme snowfall are similar. Both mean snowfall and extreme snowfall show an increase of magnitude between 400 and 2200 m a.s.l. and a constant or slightly decreasing magnitude at higher altitudes. Below 1200 m a.s.l., the increase with altitude is stronger because of the rain-snow transition. Another finding is that the spatial correlation pattern of extreme snowfall is similar to that of mean snowfall, both of which are determined by the main climatological regions of Switzerland. An analysis based on those stations with a long record shows that extreme snowfall was 10% lower in the nine winters investigated than in the long-term period, but the main spatial characteristics of the two periods show no change.
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1] The number of days with a snow depth above a certain threshold is the key factor for winter tourism in an Alpine country like Switzerland. An investigation of 34 long-term stations between 200 and 1800 m asl (above sea level) going back for at least the last 60 years (1948 – 2007) shows an unprecedented series of low snow winters in the last 20 years. The signal is uniform despite high regional differences. A shift detection analysis revealed a significant step-like decrease in snow days at the end of the 1980's with no clear trend since then. This abrupt change resulted in a loss of 20% to 60% of the total snow days. The stepwise increase of the mean winter temperature at the end of the 1980's and its close correlation with the snow day anomalies corroborate the sensitivity of the mid-latitude winter to the climate change induced temperature increase.
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The spatio-temporal variability of the mountain snow cover determines the avalanche danger, snow water storage, permafrost distribution and the local distribution of fauna and flora. Using a new type of terrestrial laser scanner, which is particularly suited for measurements of snow covered surfaces, snow depth was monitored in a high alpine catchment during an ablation period. From these measurements snow water equivalents and ablation rates were calculated. This allowed us for the first time to obtain a high resolution (2.5 m cell size) picture of spatial variability of the snow cover and its temporal development. A very high variability of the snow cover with snow depths between 0–9 m at the end of the accumulation season was observed. This variability decreased during the ablation phase, while the dominant snow deposition features remained intact. The average daily ablation rate was between 15 mm/d snow water equivalent at the beginning of the ablation period and 30 mm/d at the end. The spatial variation of ablation rates increased during the ablation season and could not be explained in a simple manner by geographical or meteorological parameters, which suggests significant lateral energy fluxes contributing to observed melt. It is qualitatively shown that the effect of the lateral energy transport must increase as the fraction of snow free surfaces increases during the ablation period.
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Spatial climate data sets of 1971–2000 mean monthly precipitation and minimum and maximum temperature were developed for the conterminous United States. These 30-arcsec (∼800-m) grids are the official spatial climate data sets of the U.S. Department of Agriculture. The PRISM (Parameter-elevation Relationships on Independent Slopes Model) interpolation method was used to develop data sets that reflected, as closely as possible, the current state of knowledge of spatial climate patterns in the United States. PRISM calculates a climate–elevation regression for each digital elevation model (DEM) grid cell, and stations entering the regression are assigned weights based primarily on the physiographic similarity of the station to the grid cell. Factors considered are location, elevation, coastal proximity, topographic facet orientation, vertical atmospheric layer, topographic position, and orographic effectiveness of the terrain. Surface stations used in the analysis numbered nearly 13 000 for precipitation and 10 000 for temperature. Station data were spatially quality controlled, and short-period-of-record averages adjusted to better reflect the 1971–2000 period.
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In this study, LIDAR snow depths, bare ground elevations (topography), and elevations filtered to the top of vegetation (topography + vegetation) in five 1-km2 areas are used to determine whether the spatial distribution of snow depth exhibits scale invariance, and the control that vegetation, topography, and winds exert on such behavior. The one-dimensional and mean two-dimensional power spectra of snow depth exhibit power law behavior in two frequency intervals separated by a scale break located between 7 m and 45 m. The spectral exponents for the low-frequency range vary between 0.1 and 1.2 for the one-dimensional spectra, and between 1.3 and 2.2 for the mean two-dimensional power spectra. The spectral exponents for the high-frequency range vary between 3.3 and 3.6 for the one-dimensional spectra, and between 4.0 and 4.5 for the mean two-dimensional spectra. Such spectral exponents indicate the existence of two distinct scaling regimes, with significantly larger variations occurring in the larger-scale regime. Similar bilinear power law spectra were obtained for the fields of vegetation height, with crossover wavelengths between 7 m and 14 m. Further analysis of the snow depth and vegetation fields, together with wind data, support the conclusion that the break in the scaling behavior of snow depth is controlled by the scaling characteristics of the spatial distribution of vegetation height when snow redistribution by wind is minimal and canopy interception is dominant, and by the interaction of winds with features such as surface concavities and vegetation when snow redistribution by wind is dominant.