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Elevation strongly affects quantity and distribution patterns of precipitation and snow. Positive elevation gradients were identified by many studies, usually based on data from sparse precipitation stations or snow depth measurements. We present a systematic evaluation of the elevation–snow depth relationship. We analyse areal snow depth data obtained by remote sensing for seven mountain sites near to the time of the maximum seasonal snow accumulation. Snow depths were averaged to 100 m elevation bands and then related to their respective elevation level. The assessment was performed at three scales: (i) the complete data sets (10 km scale), (ii) sub-catchments (km scale) and (iii) slope transects (100 m scale). We show that most elevation–snow depth curves at all scales are characterised through a single shape. Mean snow depths increase with elevation up to a certain level where they have a distinct peak followed by a decrease at the highest elevations. We explain this typical shape with a generally positive elevation gradient of snow fall that is modified by the interaction of snow cover and topography. These processes are preferential deposition of precipitation and redistribution of snow by wind, sloughing and avalanching. Furthermore, we show that the elevation level of the peak of mean snow depth correlates with the dominant elevation level of rocks (if present).
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... Aside from differences in lidar SWE and V estimates due to melt and sublimation mass losses, it is a priori expected that alpine zone SWE proportions of overall watershed yield will be lowered relative to atmospheric precipitation-based estimates within the same zone. This is because after precipitation has fallen, gravity (Deems et al. 2015) and wind redistribution processes (Grünewald, Bühler, and Lehning 2014) will systematically transport fallen snow to lower elevations. ...
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Modelling precipitation inputs in mountainous terrain is challenging for water resource managers given sparse monitoring sites and complex physical hydroclimatic processes. Government of Alberta weather station uncorrected and bias‐corrected precipitation datasets were used to examine elevational precipitation gradients (EPGs) and seasonality of EPGs for six South‐Saskatchewan River headwater sites (alpine, sub‐alpine, valley). January EPG from valley to alpine sites (730 m elevation difference) using uncorrected precipitation was 19 mm/100 m. Corrected EPG was approximately three times greater (61 mm/100 m). The valley received more precipitation than the alpine (inverse EPG) in late spring and summer. A seasonal signal was present whereby all sites demonstrated 50%–70% lower summertime precipitation relative to winter months, with the greatest seasonal variance at the alpine site. Winter watershed‐level spatialized precipitation volume was compared to modelled snow water equivalent (SWE) associated with two late‐winter airborne lidar surveys. Uncorrected volumes (2020: 64.0 × 10 ⁶ m ³ , 2021: 63.2 × 10 ⁶ m ³ ) were slightly higher than modelled mean SWE (2020: 51.6 × 10 ⁶ m ³ , 2021: 44.2 × 10 ⁶ m ³ ) whereas bias‐corrected (2020: 120.5 × 10 ⁶ m ³ , 2021: 119.7 × 10 ⁶ m ³ ) almost doubled the estimate. Corrected precipitation is assumed closer to the true value. Cumulative sublimation, evaporation and snowmelt losses result in ground‐level snowpack yield that deviates from total atmospheric precipitation in an increasingly negative manner. The 2020/2021 simulations suggest wintertime atmospheric precipitation exceeds late‐winter snowpack accumulation by up to 57% and 63%, respectively. A loss of 16 × 10 ⁶ m ³ (7%) watershed SWE from the alpine zone was partially attributed to redistribution downslope to the treeline‐ecotone. Physical snowpack losses from sublimation and melt, or modelling uncertainty due to precipitation correction and alpine snow‐density uncertainties can also contribute to observed discrepancies between in situ SWE and cumulative precipitation. Ignoring bias‐correction in headwater precipitation estimates can greatly impact headwater precipitation volume estimates and ignoring EPG seasonality is likely to result in under‐estimated winter and over‐estimated summer yields.
... This is set to r 0 = 0.5, meaning that the midpoint of the function occurs at r = R/ 2. With this, the load is represented by an initial-approximately exponentialincrease of h beginning from r = R, slowing to a more linear increase around r = R/2 and finally increasing more slowly toward h max at r = 0 (Figure 8a). This choice reflects the observation that mountain regions often show an initial strongly positive elevation gradient of snow thickness which then levels out at a certain altitude, even decreasing at the highest elevations (Grünewald & Lehning, 2011;Grünewald et al., 2014;Kirchner et al., 2014). ...
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... In alpine environments, snow depth tends to increase with elevation up to a point correlating with prominent rock coverage and then decreases beyond. The decrease in depth at high elevation is likely due to redistribution and preferential deposition factors, such as wind transport, avalanching, and sloughing from steeper to shallower slopes (Grünewald et al., 2014). Additionally, orographic precipitation dynamics can result in varying elevation precipitation patterns (Roe and Baker, 2006). ...
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The majority of the water supply for many Western US states is derived from seasonal snowmelt in mountainous regions. This study aims to address gaps in basin-scale snowpack modeling by using a multi-step, Gaussian-based machine learning model to generate rapid, high-resolution, snow depth estimates at minimal cost by combining citizen-science snowdepth observations with static LiDAR terrain features collected at a single snow-free date. We focus on reducing personnel danger by modifying the algorithm to minimize the exposure of field sample collectors to avalanche-prone terrain. Using snow observations taken solely within a subbasin (∼9-km2) of a larger basin (∼70-km2), a basin-scale snow depth estimate is modeled for a given date throughout the snow season. Results show that a small number of observations (i.e., 10) within a subbasin can realize snow depth across the greater basin with high accuracy, with a root mean squared error (RMSE) of 0.37 m, and Kling-Gupta efficiency (KGE) of 0.59 when compared to the true snow depth distribution. We test the universality of the algorithm by modeling multiple subbasins of differing spatial characteristics and find similar results. The algorithm shows consistent performance across subbasins with varying spatial characteristics and maintains accuracy even when highrisk avalanche areas are excluded. This method exhibits a potential for citizen-scientist data to safely provide seamless modeled snow depth across different spatial ranges in snow-covered basins.
... Previous studies have often identified a positive correlation between elevation and SWE that tapers off at high elevations above the treeline (e.g. Durand et al., 2009;Grünewald et al., 2014;Kirchner et al., 2014;Lehning et al., 2011;Rohrer et al., 1994), which is above the elevation of most of our reference data. Therefore, if reference measurements are consistently collected at higher (lower) elevations relative to a product grid cell centroid, we might expect them to have more (less) SWE compared to the grid cell average. ...
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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.
... Another challenge with ICESat-2 data arises from the spatio-temporal gaps between ground tracks, which are challenging to fill in mountainous regions, where SD (and SWE) can vary greatly over short distances (e.g. Grünewald et al., 2010;Grünewald et al., 2014;Kerr et al., 2013;Mott et al., 2018). ...
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Our understanding of the impact of climate change on water availability and natural hazards in high-mountain regions is limited due to the spatial and temporal scarcity of ground observations of precipitation and snow. Freely available, satellite-based information about the snowpack is currently mainly limited to indirect measurements of snow-covered area or very coarse-scale snow water equivalent (SWE), but only for flat areas in lowlands without vegetation cover. Novel space-based laser altimeters, such as ICESat-2, have the potential to provide high-resolution snow depth data in worldwide mountain regions where no ground observations exist. However, these space-based laser altimeters come with spatial gaps between ground tracks, obtained without repetition at a give location. To overcome these drawbacks, here, we present a combined probabilistic data assimilation and deep learning approach to reconstruct spatio-temporal SWE from observations of snow depth along ground tracks, imitating ICESat-2 tracks in view of a potential future global application. Our approach is based on assimilating SWE and snow cover information in a degree-day model with an iterative ensemble smoother (IES) which allows temporally reconstructing SWE along hypothetical ground tracks separated by 3 km. As input, the degree-day model uses daily precipitation and downscaled air temperature from the ERA5 reanalysis. A feedforward neural network (FNN) is then used for spatial propagation of the daily mean and standard deviation of the updated SWE ensemble members obtained from the IES. The combined IES-FNN approach provides uncertainty-aware spatio-temporally continuous estimates of SWE. We tested our approach in the alpine Dischma valley (Switzerland) using high-resolution snow depth maps obtained from photogrammetric techniques mounted on airplanes and unmanned aerial system observations. Our results show that the IES-FNN model provides reliable estimates at a resolution of approximately 100 m. Even assimilating only one SWE observation during the year (combined with satellite-based melt-out date estimates) produces satisfying results when evaluating the IES-FNN SWE reconstructions on independent dates and smaller ( 4 km2) areas: mean absolute error of 86 mm (78 mm) at Schürlialp (Latschüelfurgga) for average SWE of 180 mm (254 mm), and average spatial linear correlation with the reference SWE of 0.51 (0.48). However, the assimilated SWE observation must not be too early in the accumulation season or too late in the melt season when the snowpack is starting or ending to accumulate or melt, respectively. Smaller distances between ground tracks (1500 m and 500 m) show improved performance of the IES-FNN approach in space, with no significant improvement in terms of temporal reconstruction. Applying the IES-FNN approach to e.g., real ICESat-2 data, remains challenging due to the higher uncertainties associated with these data. However, the approach we propose remains potentially very helpful in addressing the problem of scarcity of ground observations of precipitation and snow in high-mountain regions.
... Therefore, the TOA reflectance and angle information are essential in this process. Numerous studies have shown that the distribution, type, and albedo variations of snow are highly dependent on elevation (Grünewald et al., 2014;Huang et al., 2017;Jain et al., 2009;Trujillo et al., 2012). Additionally, for high-reflectance surfaces like snow, it is necessary to consider topographic influences (Shi and Xiao, 2022). ...
... It is evident in the accompanying line histogram in Fig. 9b that the majority of SWE stations are concentrated at lower elevations. While snow depth and SWE generally increase with elevation, the maximum snow depth in mountainous areas typically occurs near the tree line, with some variability across different sites due to variations in canopy cover (Cartwright et al., 2020;Grünewald et al., 2014). This suggests that SWE measurements at middle to high elevations best capture the peak SWE in these basins. ...
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Seasonal streamflow forecasts provide key information for decision-making in fields such as water supply management, hydropower generation, and irrigation scheduling. The predictability of streamflow on seasonal timescales relies heavily on initial hydrological conditions, such as the presence of snow and the availability of soil moisture. In high-latitude and high-altitude headwater basins in North America, snowmelt serves as the primary source of runoff generation. This study presents and evaluates a data-driven workflow for probabilistic seasonal streamflow forecasting in snow-fed river basins across North America (Canada and the USA). The workflow employs snow water equivalent (SWE) measurements as predictors and streamflow observations as predictands. Gap-filling of SWE datasets is accomplished using quantile mapping from neighboring SWE and precipitation stations, and principal component analysis is used to identify independent predictor components. These components are then utilized in a regression model to generate ensemble hindcasts of streamflow volumes for 75 nival basins with limited regulation from 1979 to 2021, encompassing diverse geographies and climates. Using a hindcast evaluation approach that is user-oriented provides key insights for snow-monitoring experts, forecasters, decision-makers, and workflow developers. The analysis presented here unveils a wide spectrum of predictability and offers a glimpse into potential future changes in predictability. Late-season snowpack emerges as a key factor in predicting spring and summer volumes, while high precipitation during the target period presents challenges to forecast skill and streamflow predictability. Notably, we can predict lower-than-normal and higher-than-normal streamflows during spring to early summer with lead times of up to 5 months in some basins. Our workflow is available on GitHub as a collection of Jupyter Notebooks, facilitating broader applications in cold regions and contributing to the ongoing advancement of methodologies.
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The snow-water equivalent (SWE) of the seasonal snow cover is an important component of the water cycle in the Swiss Alps. It is used for predicting seasonal discharge, for short-range discharge forecasts and also for assessing water quality aspects. The SWE has been measured every two weeks at about 50 stations located between 860 and 2,540 m a.s.l. for more than 30 years. In addition there are special investigation areas with stations located between 600 m and 2,900 m a.s.l. where SWE is measured once per winter. The main characteristics of temporal and spatial SWE distributions are analyzed. The variations of SWE values depend in ranking order on elevation, on the year-to-year variations, on the region and on the exposition. The standardized SWE-values depend mostly on the year-to-year variations and on the region.
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