Spatial Distribution Of Snow Depth Research Articles

Topographic and vegetation controls of the spatial distribution of snow depth in agro-forested environments by UAV lidar

Abstract. 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.

Methodology for Calculating the Spatial Distribution of Snow Depth in Complex Terrain with Insufficient Meteorological Information: A Case Study for the Anyuy Range

Methodology for Calculating the Spatial Distribution of Snow Depth in Complex Terrain with Insufficient Meteorological Information: A Case Study for the Anyuy Range

Snow depth mapping from stereo satellite imagery in mountainous terrain: evaluation using airborne laser-scanning data

Abstract. Accurate knowledge of snow depth distributions in mountain catchments is critical for applications in hydrology and ecology. Recently, a method was proposed to map snow depth at meter-scale resolution from very-high-resolution stereo satellite imagery (e.g., Pléiades) with an accuracy close to 0.5 m. However, the validation was limited to probe measurements and unmanned aircraft vehicle (UAV) photogrammetry, which sampled a limited fraction of the topographic and snow depth variability. We improve upon this evaluation using accurate maps of the snow depth derived from Airborne Snow Observatory laser-scanning measurements in the Tuolumne river basin, USA. We find a good agreement between both datasets over a snow-covered area of 138 km2 on a 3 m grid, with a positive bias for a Pléiades snow depth of 0.08 m, a root mean square error of 0.80 m and a normalized median absolute deviation (NMAD) of 0.69 m. Satellite data capture the relationship between snow depth and elevation at the catchment scale and also small-scale features like snow drifts and avalanche deposits at a typical scale of tens of meters. The random error at the pixel level is lower in snow-free areas than in snow-covered areas, but it is reduced by a factor of 2 (NMAD of approximately 0.40 m for snow depth) when averaged to a 36 m grid. We conclude that satellite photogrammetry stands out as a convenient method to estimate the spatial distribution of snow depth in high mountain catchments.

SPATIOTEMPORAL ANALYSIS OF SNOW DEPTH ON FIRST-YEAR ICE BASED ON FY3B/MWRI IN THE ARCTIC

Abstract. As an important factor in the stability of the climate system in the northern hemisphere, the Arctic has recently attracted considerable attention. In the Arctic, most sea ice is covered by snow year-round, except in the snow-melting season. Given its high albedo and low thermal conductivity, snow cover on sea ice is considered a key component of amplified warming in the Arctic. However, in Arctic regions, the only products available are for snow depths on first-year ice. Therefore, this paper studies the temporal and spatial changes of snow depth on first-year ice in the Arctic using the snow depth on sea ice product determined from the Microwave Radiation Imager onboard the Feng Yun-3B satellite. We averaged the daily snow depth on first-year ice data to give monthly and annual values over the period 2011–2018, using flags for multiyear ice and melting points. Taking the 2012 data as an example, the analysis results show that the spatial distribution of snow depth in the monthly and annual data is similar over the whole first-year ice area. The snow depth basically decreases with latitude, and the distribution features exhibit little variation by month and year. The weekly mean snow depth on first-year ice begins to increase from October/November due to snowfall, and reaches a maximum value in late April/early May of the next year. There is no obvious law governing the inter-annual variation of snow depth in the Arctic from 2011–2018.

The Utility of Optical Satellite Winter Snow Depths for Initializing a Glacio‐Hydrological Model of a High‐Elevation, Andean Catchment

AbstractInformation about end‐of‐winter spatial distribution of snow depth is important for seasonal forecasts of spring/summer streamflow in high‐mountain regions. Nevertheless, such information typically relies upon extrapolation from a sparse network of observations at low elevations. Here, we test the potential of high‐resolution snow depth data derived from optical stereophotogrammetry of Pléiades satellites for improving the representation of snow depth initial conditions (SDICs) in a glacio‐hydrological model and assess potential improvements in the skill of snowmelt and streamflow simulations in a high‐elevation Andean catchment. We calibrate model parameters controlling glacier mass balance and snow cover evolution using ground‐based and satellite observations, and consider the relative importance of accurate estimates of SDICs compared to model parameters and forcings. We find that Pléiades SDICs improve the simulation of snow‐covered area, glacier mass balance, and monthly streamflow compared to alternative SDICs based upon extrapolation of meteorological variables or statistical methods to estimate SDICs based upon topography. Model simulations are found to be sensitive to SDICs in the early spring (up to 48% variability in modeled streamflow compared to the best estimate model), and to temperature gradients in all months that control albedo and melt rates over a large elevation range (>2,400 m). As such, appropriately characterizing the distribution of total snow volume with elevation is important for reproducing total streamflow and the proportions of snowmelt. Therefore, optical stereo‐photogrammetry offers an advantage for obtaining SDICs that aid both the timing and magnitude of streamflow simulations, process representation (e.g., snow cover evolution) and has the potential for large spatial domains.

A quality control approach to better characterize the spatial distribution of snow depth over New Brunswick, Canada

AbstractThe aim of this work is to develop, from a high‐resolution climate analysis, a quality control standard methodology applied to manual snow cover (HS) series managed by the Snow Survey Database in New Brunswick (Canada). The database collected snow depth data biweekly starting at the end of January until the end of April. A 30‐year (1981–2010) analysis of 60 weather stations belonging to two independent meteorological networks was performed. A quality control of the climatic series was performed to evaluate the homogeneity. Three snow depth climatic areas were defined by means of two geostatistical methods (Kriging and Cluster analyses) applied on monthly snow depth, precipitation and temperature data series. Then, for each cluster, the climatological thresholds that characterize a snow fall event during the cold months were detected. Subsequently, a quality control on the daily snow depth series recorded during the January to April period was performed. For each daily series, outlier values were identified by checking both the sudden day‐to‐day changes and extreme thresholds (95th percentile). The quality control was then carried out to the manual series and the observed doubtful events were compared with the snow depth values recorded in the nearby stations. The results show that for the daily snow depth series, the highest number of suspect events was recorded during the months of March and April, and the analysis also shows that there are rain‐on‐snow events. As for the manual records, questionable snow depth errors randomly distributed in the series were highlighted. Finally, in order to improve the spatial distribution of stations located in the Canadian territory, the results give evidence that, thanks to the high‐resolution climatic analysis, the proposed approach provides all the benchmarks required to conduct a quality control of snow depth series in absence of other auxiliary variables.

Spatial distribution of snow depth based on geographically weighted regression kriging in the Bayanbulak Basin of the Tianshan Mountains, China

Snow depth is a general input variable in many models of agriculture, hydrology, climate and ecology. This study makes use of observational data of snow depth and explanatory variables to compare the accuracy and effect of geographically weighted regression kriging (GWRK) and regression kriging (RK) in a spatial interpolation of regional snow depth. The auxiliary variables are analyzed using correlation coefficients and the variance inflation factor (VIF). Three variables, Height, topographic ruggedness index (TRI), and land surface temperature (LST) , are used as explanatory variables to establish a regression model for snow depth. The estimated spatial distribution of snow depth in the Bayanbulak Basin of the Tianshan Mountains in China with a spatial resolution of 1 km is obtained. The results indicate that 1) the result of GWRK's accuracy is slightly higher than that of RK (R2 = 0.55 vs. R2 = 0.50, RMSE (root mean square error) = 0.102 m vs. RMSE = 0.077 m); 2) for the subareas, GWRK and RK exhibit similar estimation results of snow depth. Areas in the Bayanbulak Basin with a snow depth greater than 0.15 m are mainly distributed in an elevation range of 2632.00–3269.00 m and the snow in this area comprises 45.00–46.00% of the total amount of snow in this basin. However, the GWRK resulted in more detailed information on snow depth distribution than the RK. The final conclusion is that GWRK is better suited for estimating regional snow depth distribution.

Backward snow depth reconstruction at high spatial resolution based on time‐lapse photography

AbstractWe report a methodology for reconstructing the daily snow depth distribution at high spatial resolution in a small Pyrenean catchment using time‐lapse photographs and snow depletion rates derived from an on‐site measuring meteorological station. The results were compared with the observed snow depth distribution, determined on a number of separate occasions using a terrestrial laser scanner (TLS). The time‐lapse photographs were projected onto a digital elevation model of the study site, and converted into snow presence/absence information. The melt‐out date (MOD; first occurrence of melt out after peak snow accumulation) was obtained from the projected photograph series. Commencing the backward reconstruction for each grid cell at the MOD, the method uses simulated snow depth depletion rates using a temperature index approach, which are extrapolated to the grid cells of the domain to arrive at the snow distribution of the previous day. Two variants of the reconstruction techniques were applied (1) using a spatially constant degree day factor (DDF) for calculating the daily expected snow depth depletion rate, and (2) allowing a spatially distributed DDF calculated from two consecutive TLS acquisitions compared to the snow depth depletion rate observed at the meteorological station. Validation revealed that both methods performed well (average R2 = 0.68; standard RMSE = 0.58), with better results obtained from the spatially distributed approach. Nevertheless, the spatially corrected DDF reconstruction, which requires TLS data, suggests that the constant DDF approach is an efficient, and for most applications sufficiently accurate and easily reproducible method. The results highlight the usefulness of time‐lapse photography for not only determining the snow covered area, but also for estimating the spatial distribution of snow depth. Copyright © 2016 John Wiley & Sons, Ltd.

Comparison of artificial neural network and decision tree models in estimating spatial distribution of snow depth in a semi-arid region of Iran

There is no doubt that snow cover plays an important role in the hydrological cycle of mountainous basins. Therefore, it is essential to measure snow parameters such as snow depth and snow water equivalent in these areas. The aim of this study is to estimate the snow depth from terrain parameters in the Sakhvid Basin, Iran using artificial neural networks (ANNs) and M5 algorithm of decision tree. For this purpose, snow depths were measured in 206 sites based on systematic network. Furthermore, 30 terrain parameters were extracted from a digital elevation model (DEM) of the basin. The results indicated that the decision tree model is the most suitable method to estimate snow depth in the study area with a Nash–Sutcliffe Efficiency (Ens) of 0.80, followed by ANNs with an Ens of 0.73. Moreover, the most significant parameters in the M5 decision tree algorithm are: channel network base level, stream power, wetness index and height.

Snow accumulation of a high alpine catchment derived from LiDAR measurements

Abstract. The spatial distribution of snow accumulation substantially affects the seasonal course of water storage and runoff generation in high mountain catchments. Whereas the areal extent of snow cover can be recorded by satellite data, spatial distribution of snow depth and hence snow water equivalent (SWE) is difficult to measure on catchment scale. In this study we present the application of airborne LiDAR (Light Detecting And Ranging) data to extract snow depths and accumulation distribution in an alpine catchment. Airborne LiDAR measurements were performed in a glacierized catchment in the Ötztal Alps at the beginning and the end of three accumulation seasons. The resulting digital elevation models (DEMs) were used to calculate surface elevation changes throughout the winter season. These surface elevation changes were primarily referred to as snow depths and are discussed concerning measured precipitation and the spatial characteristics of the accumulation distribution in glacierized and unglacierized areas. To determine the redistribution of catchment precipitation, snow depths were converted into SWE using a simple regression model. Snow accumulation gradients and snow redistribution were evaluated for 100 m elevation bands. Mean surface elevation changes of the whole catchment ranges from 1.97 m to 2.65 m within the analyzed accumulation seasons. By analyzing the distribution of the snow depths, elevation dependent patterns were obtained as a function of the topography in terms of aspect and slope. The high resolution DEMs show clearly the higher variation of snow depths in rough unglacierized areas compared to snow depths on smooth glacier surfaces. Mean snow depths in glacierized areas are higher than in unglacierized areas. Maximum mean snow depths of 100 m elevation bands are found between 2900 m and 3000 m a.s.l. in unglacierized areas and between 2800 m and 2900 m a.s.l. in glacierized areas, respectively. Calculated accumulation gradients range from 8% to 13% per 100 m elevation band in the observed catchment. Elevation distribution of accumulation calculated by applying these seasonal gradients in comparison to elevation distribution of SWE obtained from airborne laser scanning (ALS) data show the total redistribution of snow from higher to lower elevation bands. Revealing both, information about the spatial distribution of snow depths and hence the volume of the snow pack, ALS data are an important source for extensive snow accumulation measurements in high alpine catchments. These information about the spatial characteristics of snow distribution are crucial for calibrating hydrological models in order to realistically compute temporal runoff generation by snow melt.

Subgrid variability of snow water equivalent at operational snow stations in the western USA

AbstractThe spatial distribution of snow water equivalent (SWE) is a key variable in many regional‐scale land surface models. Currently, the assimilation of point‐scale snow sensor data into these models is commonly performed without consideration of the spatial representativeness of the point data with respect to the model grid‐scale SWE. To improve the understanding of the relationship between point‐scale snow measurements and surrounding areas, we characterized the spatial distribution of snow depth and SWE within 1‐, 4‐ and 16‐km2 grids surrounding 15 snow stations (snowpack telemetry and California snow sensors) in California, Colorado, Wyoming, Idaho and Oregon during the 2008 and 2009 snow seasons. More than 30 000 field observations of snowpack properties were used with binary regression tree models to relate SWE at the sensor site to the surrounding area SWE to evaluate the sensor representativeness of larger‐scale conditions. Unlike previous research, we did not find consistent high biases in snow sensor depth values as biases over all sites ranged from 74% overestimates to 77% underestimates. Of the 53 assessments, 27 surveys indicated snow station biases of less than 10% of the surrounding mean observed snow depth. Depth biases were largely dictated by the physiographic relationship between the snow sensor locations and the mean characteristics of the surrounding grid, in particular, elevation, solar radiation index and vegetation density. These scaling relationships may improve snow sensor data assimilation; an example application is illustrated for the National Operational Hydrologic Remote Sensing Center National Snow Analysis SWE product. The snow sensor bias information indicated that the assimilation of point data into the National Operational Hydrologic Remote Sensing Center model was often unnecessary and reduced model accuracy. Copyright © 2012 John Wiley & Sons, Ltd.

Embedded‐sensor network design for snow cover measurements around snow pillow and snow course sites in the Sierra Nevada of California

The design of sensor networks for measuring the mean and spatial distribution of snow depth at the scale of 1–16 km2 was evaluated by deploying an embedded‐sensor network consisting of ultrasonic snow depth sensors to capture the variable physiographic features around an operational snow course in Yosemite National Park in the Sierra Nevada of California. Manual snow surveys were also carried out during accumulation and ablation periods. Four years of continuous data from the embedded‐sensor network showed that snow depths during both accumulation and ablation periods can vary as much as 50% based on variability in topography and vegetation across a 0.4 ha study area. Spatial snow surveys showed that such a sensor network can be deployed so as to capture both the variability and mean for accumulation and ablation periods across a 1 km2 area surrounding the sensor network, with a broader network required to extend this to 4 and 16 km2 areas. In forested areas, higher canopy densities, greater than 60% closure, were associated with the lowest snow depths. Analysis of historical snow course records from 14 sites in Yosemite, including the 10 spatial measurements made during each monthly snow course survey, showed snow depths across the 300 m snow course transects to be relatively uniform, with 68% of all monthly values having standard deviations no more than 10% of the mean. Although existing snow courses do little to help define the spatial patterns of snow distribution at the 1–16 km2 scales, it is feasible to extend the representativeness of current operational networks by deploying low‐cost embedded‐sensor networks nearby. Such networks should be strategically located to also capture elevational differences in snow accumulation and melt, as well as local‐scale variability in canopy cover and aspect.

Comparison of artificial neural network and combined models in estimating spatial distribution of snow depth and snow water equivalent in Samsami basin of Iran

Snow water equivalent (SWE) is a key parameter in hydrological cycle, and information on regional SWE is required for various hydrological and meteorological applications, as well as for hydropower production and flood forecasting. This study compares the snow depth and SWE estimated by multivariate linear regression (MLR), discriminant function analysis, ordinary kriging, ordinary kriging-multivariate linear regression, ordinary kriging-discriminant function analysis, artificial neural network (ANN) and neural network-genetic algorithm (NNGA) models. The analysis was performed in the 5.2 km2 area of Samsami basin, located in the southwest of Iran. Statistical criteria were used to measure the models’ performances. The results indicated that NNGA, ANN and MLR methods were able to predict SWE at the desirable level of accuracy. However, the NNGA model with the highest coefficient of determination (R 2 = 0.70, P value < 0.05) and minimum root mean square error (RMSE = 0.202 cm) provided the best results among the other models. The lower SWE values were registered in the east of study area and higher SWE values appeared in the west of study area where altitude was higher.

Application of Satellite Microwave Images in Estimating Snow Water Equivalent1

Abstract: Flood forecast and water resource management requires reliable estimates of snow pack properties [snow depth and snow water equivalent (SWE)]. This study focuses on application of satellite microwave images to estimate the spatial distribution of snow depth and SWE over the Great Lakes area. To estimate SWE, we have proposed the algorithm which uses microwave brightness temperatures (Tb) measured by the Special Sensor Microwave Imager (SSM/I) radiometer along with information on the Normalized Difference Vegetation Index (NDVI).The algorithm was developed and tested over 19 test sites characterized by different seasonal average snow depth and land cover type. Three spectral signatures derived from SSM/I data, namely T19V‐T37V (GTV), T19H‐T37H (GTH), and T22V‐T85V (SSI), were examined for correlation with the snow depth and SWE. To avoid melting snow conditions, we have used observations taken only during the period from December 1‐February 28. It was found that GTH, and GTV exhibit similar correlation with the snow depth/SWE and are most should be used over deep snowpack. In the same time, SSI is more sensitive to snow depth variations over a shallow snow pack. To account for the effect of dense forests on the scattering signal of snow we established the slope of the regression line between GTV and the snow depth as a function of NDVI. The accuracy of the new technique was evaluated through its comparison with ground‐based measurements and with results of SWE analysis prepared by the National Operational Hydrological Remote Sensing Center (NOHRSC) of the National Weather Service. The proposed algorithm was found to be superior to previously developed global microwave SWE retrieval techniques.

Topographic, meteorologic, and canopy controls on the scaling characteristics of the spatial distribution of snow depth fields

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.

Persistence of topographic controls on the spatial distribution of snow in rugged mountain terrain, Colorado, United States

We model the spatial distribution of snow depth across a wind‐dominated alpine basin using a geostatistical approach with a complex variable mean. Snow depth surveys were conducted at maximum accumulation from 1997 through 2003 in the 2.3 km2 Green Lakes Valley watershed in Colorado. We model snow depth as a random function that can be decomposed into a deterministic trend and a stochastic residual. Three snow depth trends were considered, differing in how they model the effect of terrain parameters on snow depth. The terrain parameters considered were elevation, slope, potential radiation, an index of wind sheltering, and an index of wind drifting. When nonlinear interactions between the terrain parameters were included and a multiyear data set was analyzed, all five terrain parameters were found to be statistically significant in predicting snow depth, yet only potential radiation and the index of wind sheltering were found to be statistically significant for all individual years. Of the five terrain parameters considered, the index of wind sheltering was found to have the greatest effect on predicted snow depth. The methodology presented in this paper allows for the characterization of the spatial correlation of model residuals for a variable mean model, incorporates the spatial correlation into the optimization of the deterministic trend, and produces smooth estimate maps that may extrapolate above and below measured values.

A generalized additive model for the spatial distribution of snowpack in the Spanish Pyrenees

A generalized additive model (GAM) was used to model the spatial distribution of snow depth in the central Spanish Pyrenees. Statistically significant non-linear relationships were found between distinct location and topographical variables and the average depth of the April snowpack at 76 snow poles from 1985 to 2000. The joint effect of the predictor variables explained more than 73% of the variance of the dependent variable. The performance of the model was assessed by applying a number of quantitative approaches to the residuals from a cross-validation test. The relatively low estimated errors and the possibility of understanding the processes that control snow accumulation, through the response curves of each independent variable, indicate that GAMs may be a useful tool for interpolating local snow depth or other climate parameters. Copyright © 2005 John Wiley & Sons, Ltd.

The features of sea-ice cover, snow distribution and its densification in the central arctic ocean

Based on the observation of sea-ice cover and measurement of snow depths and stratigraphy during China’s First North Pole Scientific Expedition, three types of surface topography of sea ice and correspondingly three categories of snow distribution in the central Arctic are classified. It is considered that the classification will help to determine the sites for snow depth measurement, stratigraphy observation and snowpits sampling. The snow cover is slowly accumulated during the long Arctic winter, approximately from September to early May next year, while its ablation shows abrupt from south to north. By the end of August, the snow cover is almost totally removed. The spatial distribution of snow depth is characterized by a northwardly decreasing trend, which is proposed to result from the remote vapour sources, i.e., the major vapour over the Arctic regions is transported from mid-latitudes. The stratigraphy of the snowpits are characterized by the extensively existed depth hoar at the deeper part of the pits, which is probably a signal of the beginning of the long Arctic winter. The present of infiltration-congelation ice adhering to sea ice surface at the end of the ablation season indicates that the annual accumulation is approximately equals to the annual ablation near north pole.

SMALL-SCALE SPATIAL STRUCTURE OF SHALLOW SNOWCOVERS

The results of a field study of the small-scale spatial structure of the depth of shallow seasonal snowcovers in prairie and arctic environments are presented. It is shown that the spatial distribution of snow depth is fractal at small scales, becoming random at scales beyond some limiting length. This is due to the autocorrelation of depth at small sampling distances. The transition of fractal to random behaviour is indexed by a 'cutoff length', which is defined by the intersection of the 'fractal' slope and horizontal tangent of a logarithmic plot of the standard deviation of depth versus sampling distance. The magnitude of the cutoff length is related to the degree of macroscopic variability of the underlying topography. An increase in length due to the effects of macroscopic topographic variability on snowcover accumulation is confirmed by de-trending field measurements. The de-trended data shown a cutoff length for wheat stubble and fallow surfaces of approximately 30 m, which is consistent with the distance determined from measurements on 'flat' fields. The implications of the transition of snow depth from fractal to random structure on the scales of snow sampling and modelling are presented. The cutoff length may provide a statistic for stratifying shallow snowcovers, by linking snowcover properties to the underlying topography.