Modeling spatial snow-cover distribution using snow-melt models and MODIS images

2020 ◽  
Author(s):  
Dhiraj Raj Gyawali ◽  
András Bárdossy
Keyword(s):  
Spatial Distribution ◽  
Snow Cover ◽  
Snow Depth ◽  
Region Of Interest ◽  
High Sensitivity ◽  
Distribution Data ◽  
Depth Information ◽  
Snow Melt ◽  
Snow Hydrology ◽  
Snow Distribution

<p>Reliable representations of spatial distribution of snow and subsequent snow-melt are critical challenges for hydrological estimations, given their crucial relevance in mountainous regimes especially because of the high sensitivity to climate change. Relatively accurate physically based models are data intensive while in-situ measurements of snow-depth are prone to be non-representative due to local influences. Likewise, lack of snow-depth information and to some extent, cloud cover in the mountains limit the usage of Remote-sensing images in snow estimation. Against this backdrop, this work presents a methodology incorporating available remotely-sensed images (MODIS Snow-cover products) and simple distributed snow-melt models to estimate a time-continuous spatial snow extent in snow dominated regimes. </p><p>The methodology employs relatively cloud-free MODIS composite images to calibrate the spatial distribution of snow simulated by different distributed degree-day models. These variants of models are run in a domain of 500m x 500m grids, and incorporate daily precipitation, daily min-, max- and mean temperatures, and daily radiation data interpolated onto the aforementioned grids. Variations in the models include a simple degree model followed by incorporation of different aspects governing snow hydrology such as precipitation induced melt, radiation, topography, and land use.  The modeled snow depths in each grid are reclassified to ‘1’ (snow depths above a threshold) and ‘0’ (no snow), and calibrated against MODIS snow-cover for cloud-free days with snow. Snow-melt parameters are then estimated for the region of interest. The result is a spatial snow-cover distribution time-series. This approach is replicated in different regions viz. Baden-Württemberg and Bavaria in Germany, and in Switzerland. Results suggest good agreement with MODIS data and the parameters show relative stability across the time domain at the same sites and are transferrable to other regions. Calibration using readily available images used in this method offers adequate flexibility, albeit the simplicity, to calibrate snow distribution in mountainous areas across a wide geographical extent with reasonably accurate precipitation and temperature data. The final validated spatial snow-distribution data can be, as a stand-alone input, coupled with distributed hydrological models to reliably estimate streamflow in data-scarce mountainous catchments.</p>

scholarly journals High resolution snow distribution data from complex Arctic terrain: a tool for model validation

2002 ◽  
Vol 2 (3/4) ◽  
pp. 147-155 ◽  
Author(s):  
Ch. Jaedicke ◽  
A. D. Sandvik
Keyword(s):  
High Resolution ◽  
Snow Cover ◽  
Numerical Models ◽  
The Arctic ◽  
Distribution Data ◽  
Blowing Snow ◽  
Data Set ◽  
Meteorological Model ◽  
Drift Model ◽  
Snow Distribution

Abstract. Blowing snow and snow drifts are common features in the Arctic. Due to sparse vegetation, low temperatures and high wind speeds, the snow is constantly moving. This causes severe problems for transportation and infrastructure in the affected areas. To minimise the effect of drifting snow already in the designing phase of new structures, adequate models have to be developed and tested. In this study, snow distribution in Arctic topography is surveyed in two study areas during the spring of 1999 and 2000. Snow depth is measured by ground penetrating radar and manual methods. The study areas encompass four by four kilometres and are partly glaciated. The results of the surveys show a clear pattern of erosion, accumulation areas and the evolution of the snow cover over time. This high resolution data set is valuable for the validation of numerical models. A simple numerical snow drift model was used to simulate the measured snow distribution in one of the areas for the winter of 1998/1999. The model is a two-level drift model coupled to the wind field, generated by a mesoscale meteorological model. The simulations are based on five wind fields from the dominating wind directions. The model produces a satisfying snow distribution but fails to reproduce the details of the observed snow cover. The results clearly demonstrate the importance of quality field data to detect and analyse errors in numerical simulations.

Measurement of snow cover depth using 100 × 100 meters sampling plot and Structure from Motion method in Adventdalen, Svalbard

2016 ◽  
Vol 6 (2) ◽  
pp. 155-168
Author(s):  
Radim Stuchlík ◽  
Jan Russnák ◽  
Tomáš Plojhar ◽  
Zdeněk Stachoň
Keyword(s):  
Spatial Distribution ◽  
Snow Cover ◽  
Structure From Motion ◽  
Snow Depth ◽  
Surface Model ◽  
Snow Surface ◽  
Reference Volume ◽  
Snow Cover Depth ◽  
Sampling Plot ◽  
Motion Method

We tried to verify the concept of Structure from Motion method for measuring the volume of snow cover in a grid of 100×100 m located in Adventdalen, Central Svalbard. As referencing method we utilized 121 depth measurements in one hectare area. Using avalanche probe a snow depth was measured in mentioned 121 nodes of the grid. We detected maximum snow depth of 2.05 m but snowless parts as well. From gathered depths’ data we geostatistically (ordinary kriging) interpolated snow surface model which we used to determine reference volume of snow at research plot (5 569 m3). As a result, we were able to calculate important metrics and analyze topography and spatial distribution of snow cover at the plot. For taking photos for Structure from Motion method, bare pole in hands with a camera mounted was used. We constructed orthomosaic of research plot.

scholarly journals Subgrid parameterization of snow distribution at a Mediterranean site using terrestrial photography

2016 ◽  
Author(s):  
Rafael Pimentel ◽  
Javier Herrero ◽  
María José Polo
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Scale Effects ◽  
Control Area ◽  
Sigmoid Function ◽  
Snow Cover Fraction ◽  
Snow Distribution ◽  
Snow Model ◽  
Snow Conditions ◽  
High Level

Abstract. Subgrid variability introduces non-negligible scale effects on the GIS-based representation of snow. This heterogeneity is even more evident in semiarid regions, where the high variability of the climate produces various accumulation melting cycles throughout the year and a large spatial heterogeneity of the snow cover. This variability in a watershed can often be represented by snow depletion curves (DCs). In this study, terrestrial photography (TP) of a cell-sized area (30 x 30 m) was used to define local snow DCs at a Mediterranean site. Snow cover fraction (SCF) and snow depth (h) values obtained with this technique constituted the two datasets used to define DCs. A flexible sigmoid function was selected to parameterize snow behaviour on this subgrid scale. It was then fitted to meet five different snow patterns in the control area: one for the accumulation phase and four for the melting phase in a cycle within the snow season. Each pattern was successfully associated with the snow conditions and previous evolution. The resulting DCs were able to capture certain physical features of the snow, which were used in a decision-tree and included in the point snow model formulated by Herrero et al. (2009). The final performance of this model was tested against field observations recorded over four hydrological years (2009–2013). The calibration and validation of this DC-snow model was found to have a high level of accuracy with global RMSE values of 84.2 mm for the average snow depth and 0.18 m2 m-2 for the snow cover fraction in the control area. The use of DCs on the cell scale proposed in this research provided a sound basis for the extension of point snow models to larger areas by means of a gridded distributed calculation.

Modelling the snow distribution at two high arctic sites at Svalbard, Norway, and at an alpine site in central Norway

2004 ◽  
Vol 35 (3) ◽  
pp. 191-208 ◽  
Author(s):  
Oddbjørn Bruland ◽  
Glen E. Liston ◽  
Jorien Vonk ◽  
Knut Sand ◽  
Ånund Killingtveit
Keyword(s):  
Snow Cover ◽  
Frequency Distribution ◽  
Land Surface ◽  
Snow Depth ◽  
Rank Order ◽  
Model Performance ◽  
The Arctic ◽  
Major Influence ◽  
Model Parameters ◽  
Snow Distribution

In Arctic regions snow cover has a major influence on the environment both in a hydrological and ecological context. Due to strong winds and open terrain the snow is heavily redistributed and the snow depth is quite variable. This has a significant influence on the snow cover depletion and the duration of the melting season. In many ways these are important parameters in the climate change aspect. They influence the land surface albedo, the possibilities of greenhouse gas exchange and the length of the plant-growing season, the latter also being important for the arctic terrestrial fauna. The aim of this study is to test to what degree a numerical model is able to recreate an observed snow distribution in sites located in Svalbard and Norway. Snow depth frequency distribution, a snow depth rank order test and the location of snowdrifts and erosion areas were used as criteria for the model performance. SnowTran-3D is the model used in this study. In order to allow for occasions during the winter with milder climate and temperatures above freezing, a snow strengthening calculation was included in the model. The model result was compared to extensive observation datasets for each site and the sensitivity of the main model parameters to the model result was tested. For all three sites, the modelled snow depth frequency distribution was highly correlated to the observed distribution and the snowdrifts and erosion areas were located correspondingly by the model to those observed at the sites.

scholarly journals Modeling the Spatial Distribution of Snow Cover in the Dudhkoshi Region of the Nepal Himalayas

2012 ◽  
Vol 13 (1) ◽  
pp. 204-222 ◽  
Author(s):  
Maheswor Shrestha ◽  
Lei Wang ◽  
Toshio Koike ◽  
Yongkang Xue ◽  
Yukiko Hirabayashi
Keyword(s):  
Spatial Distribution ◽  
Snow Cover ◽  
Land Surface ◽  
Snow Depth ◽  
Hydrological Model ◽  
Lapse Rate ◽  
Distributed Hydrological Model ◽  
Snow Cover Area ◽  
Temperature Lapse Rate ◽  
Snow Cover Extent

Abstract In this study, a distributed biosphere hydrological model with three-layer energy-balance snow physics [an improved version of the Water and Energy Budget–based Distributed Hydrological Model (WEB-DHM-S)] is applied to the Dudhkoshi region of the eastern Nepal Himalayas to estimate the spatial distribution of snow cover. Simulations are performed at hourly time steps and 1-km spatial resolution for the 2002/03 snow season during the Coordinated Enhanced Observing Period (CEOP) third Enhanced Observing Period (EOP-3). Point evaluations (snow depth and upward short- and longwave radiation) at Pyramid (a station of the CEOP Himalayan reference site) confirm the vertical-process representations of WEB-DHM-S in this region. The simulated spatial distribution of snow cover is evaluated with the Moderate Resolution Imaging Spectroradiometer (MODIS) 8-day maximum snow-cover extent (MOD10A2), demonstrating the model’s capability to accurately capture the spatiotemporal variations in snow cover across the study area. The qualitative pixel-to-pixel comparisons for the snow-free and snow-covered grids reveal that the simulations agree well with the MODIS data to an accuracy of 90%. Simulated nighttime land surface temperatures (LST) are comparable to the MODIS LST (MOD11A2) with mean absolute error of 2.42°C and mean relative error of 0.77°C during the study period. The effects of uncertainty in air temperature lapse rate, initial snow depth, and snow albedo on the snow-cover area (SCA) and LST simulations are determined through sensitivity runs. In addition, it is found that ignoring the spatial variability of remotely sensed cloud coverage greatly increases bias in the LST and SCA simulations. To the authors’ knowledge, this work is the first to adopt a distributed hydrological model with a physically based multilayer snow module to estimate the spatial distribution of snow cover in the Himalayan region.

scholarly journals LiDAR snow cover studies on glacier surface: significance of snow- and ice dynamical processes

2013 ◽  
Vol 7 (2) ◽  
pp. 1787-1832 ◽  
Author(s):  
K. Helfricht ◽  
M. Kuhn ◽  
M. Keuschnig ◽  
A. Heilig
Keyword(s):  
Spatial Distribution ◽  
Standard Deviation ◽  
Snow Cover ◽  
Snow Depth ◽  
Laser Scanning ◽  
Snow Accumulation ◽  
Surface Elevation ◽  
High Mountain ◽  
Dynamical Processes ◽  
Elevation Changes

Abstract. The storage of water within the seasonal snow cover is a substantial source for runoff in high mountain catchments. Information about the spatial distribution of snow accumulation is necessary for calibration and validation of hydro-meteorological models. Generally only a small number of precipitation measurements deliver precipitation input for modeling in remote mountain areas. The spatial interpolation and extrapolation of measurements of precipitation is still difficult. Multi-temporal application of Light Detecting And Ranging (LiDAR) techniques from aircraft, so-called airborne laser scanning (ALS), enables to derive surface elevations changes even in inaccessible terrain. Within one snow accumulation season these surface elevation changes can be interpreted as snow depths as a first assumption for snow hydrological studies. However, dynamical processes in snow, firn and ice are contributing to surface elevation changes on glaciers. To evaluate the magnitude and significance of these processes on alpine glaciers in the present state, ALS derived surface elevation changes were compared to converted snow depths from 35.4 km of ground penetrating radar (GPR) profiles on four glaciers in the high alpine region of Ötztal Alps. LANDSAT data were used to distinguish between firn and ice areas of the glaciers. In firn areas submerging ice flow and densification of firn and snow are contributing to a mean relative deviation of ALS surface elevation changes from actually observed snow depths of −20.0% with a mean standard deviation of 17.1%. Deviations between ALS surface elevation changes and GPR snow depth are small along the profiles on the glacier tongues. At these areas mean absolute deviation of ALS surface elevation changes and GPR snow depth is 0.004 m with a mean standard deviation of 0.27 m. Emergence flow leads to distinct positive deviations only at the very front of the glacier tongues. Snow depths derived from ALS deviate less from actually measured snow depths than expected errors of in-situ measurements of solid precipitation. Hence, ALS derived snow depths are an important data source for both, spatial distribution and total sum of the snow cover volume stored on the investigated glaciers and in the corresponding high mountain catchments at the end of an accumulation season.

scholarly journals Estimating spatial distribution of daily snow depth with kriging methods: combination of MODIS snow cover area data and ground-based observations

2015 ◽  
Vol 9 (5) ◽  
pp. 4997-5020 ◽  
Author(s):  
C. L. Huang ◽  
H. W. Wang ◽  
J. L. Hou
Keyword(s):  
Spatial Distribution ◽  
Snow Cover ◽  
Snow Depth ◽  
Western China ◽  
Kriging Interpolation ◽  
Snow Cover Area ◽  
Smoothing Effects ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. Accurately measuring the spatial distribution of the snow depth is difficult because stations are sparse, particularly in western China. In this study, we develop a novel scheme that produces a reasonable spatial distribution of the daily snow depth using kriging interpolation methods. These methods combine the effects of elevation with information from Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover area (SCA) products. The scheme uses snow-free pixels in MODIS SCA images with clouds removed to identify virtual stations, or areas with zero snow depth, to compensate for the scarcity and uneven distribution of stations. Four types of kriging methods are tested: ordinary kriging (OK), universal kriging (UK), ordinary co-kriging (OCK), and universal co-kriging (UCK). These methods are applied to daily snow depth observations at 50 meteorological stations in northern Xinjiang Province, China. The results show that the spatial distribution of snow depth can be accurately reconstructed using these kriging methods. The added virtual stations improve the distribution of the snow depth and reduce the smoothing effects of the kriging process. The best performance is achieved by the OK method in cases with shallow snow cover and by the UCK method when snow cover is widespread.

scholarly journals Development and parameter estimation of snowmelt models using spatial snow-cover observations from MODIS

2021 ◽  
Author(s):  
Dhiraj Raj Gyawali ◽  
András Bárdossy
Keyword(s):  
Snow Cover ◽  
Snow Water Equivalent ◽  
Mean Squared Error ◽  
Snow Melt ◽  
Data Intensive ◽  
Degree Day ◽  
Snow Water ◽  
Snow Distribution ◽  
Wide Scale ◽  
The Time Domain

Abstract. Given the importance of snow on different land and atmospheric processes, accurate representation of seasonal snow evolution including distribution and melt volume, is highly imperative to any water resources development trajectories. The limitation of reliable snow-melt estimation in these regions is however, further exacerbated with data scarcity. This study attempts to develop relatively simpler extended degree-day snow-models driven by freely available snow cover images in snow-dominated regions. This approach offers relative simplicity and plausible alternative to data intensive models as well as in-situ measurements and have a wide scale applicability, allowing immediate verification with point measurements. The methodology employs readily available MODIS composite images to calibrate the snow-melt models on snow-distribution in contrast to the traditional snow-water equivalent based calibration. The spatial distribution of snow cover is simulated using different extended degree-day models calibrated against MODIS snow-cover images for cloud-free days or a set of images representing a period within the snow season. The study was carried out in Baden-Württemberg in Germany, and in Switzerland. The simulated snow cover show very good agreement with MODIS snow cover distribution and the calibrated parameters exhibit relative stability across the time domain. The snow-melt from these calibrated models were further used as standalone inputs to a “truncated” HBV without the snow component in Reuss (Switzerland), and Horb and Neckar (Baden-Wuerttemberg) catchments, to assess the performance of the melt outputs in comparison to a calibrated standard HBV model. The results show slight increase in overall NSE performance and a better NSE performance during the winter. Furthermore, 3–15 % decrease in mean squared error was observed for the catchments in comparison to the results from standard HBV. The increased NSE performance, albeit less, can be attributed to the added reliability of snow-distribution coming from the MODIS calibrated outputs. This paper highlights that the calibration using readily available images used in this method allows a flexible regional calibration of snow cover distribution in mountainous areas across a wide geographical extent with reasonably accurate precipitation and temperature data. Likewise, the study concludes that simpler specific alterations to processes contributing to snow-melt can contribute to identifying the snow-distribution and to some extent the flows in snow-dominated regimes.

Performance evaluation of gridded climate data in snow-melt models calibrated by spatial snow-cover observations from MODIS

2021 ◽  
Author(s):  
Dhiraj Raj Gyawali ◽  
András Bárdossy
Keyword(s):  
Temporal Stability ◽  
High Sensitivity ◽  
Delta Method ◽  
Snow Melt ◽  
Climate Data ◽  
Data Intensive ◽  
Mountainous Regions ◽  
Physically Based ◽  
Snow Distribution ◽  
Wide Scale

<p>Considering the snow effect on land and atmospheric processes, accurate representation of seasonal snow evolution including the distribution and melt volume, is highly imperative to strengthen water resources development trajectories in mountainous regions. However, along with the high sensitivity to climate change, the limitation of reliable snow-melt estimation in these regions is further exacerbated with data scarcity. This study thus attempts to develop relatively simpler degree-day snow-models driven by freely available gridded datasets for data scarce snow-fed regions. The methodology uses readily available MODIS imageries to calibrate the snow-melt models on snow-distribution instead of snow-amount. In addition, freely available cloud masks from geostationary satellites are also used to complement the snow-melt models. The major advantage of this approach is the possibility of regional calibration using freely available reasonably accurate climate data, without the need of direct snow depth measurements. These models offer relative simplicity and plausible alternatives to data intensive physically based model as well as in-situ measurements and have a wide scale applicability allowing immediate verification with point measurements.</p><p>Bavaria region in Germany is selected for this study.  E-OBS (European Observations) gridded precipitation and temperature datasets (0.25 degrees) are considered here instead of the ground measured data to replicate “a data scarce scenario” as in most of the mountainous regions around the globe. The coarser meteorological inputs are downscaled applying the delta method using WorldClim monthly climate surfaces to 0.0833 degrees (~1km) grids. MODIS images are also resampled and upscaled to 1km resolution for uniformity. The qualitative pixel-to-pixel comparison suggest a very good agreement with MODIS data and the calibrated parameter sets depict plausible temporal stability.</p><p>The snow-melt volume will be further used in HBV hydrological model as standalone input to simulate the streamflow in one of the snow-fed catchments in Bavaria and to evaluate the performance of this approach in streamflow. The abstract will the updated as soon as the results are available.</p>

scholarly journals The Effect of Snow Distribution on Gamma-Ray Survey of Snow Cover

1985 ◽  
Vol 6 ◽  
pp. 211-214
Author(s):  
Morten Johnsrud
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Gamma Ray ◽  
Snow Water Equivalent ◽  
Ground Surface ◽  
Snow Water ◽  
Snow Distribution ◽  
One Year ◽  
Source Of Error ◽  
Small Elements

Topography and snowdrifts may cause large variations in the snow cover as well as in snow depth from one year to another. A simple model is developed to study the influence of different snow distributions and the importance of this as a source of error. The ground surface “seen” from the detector will appear as a disc that can be divided into a number of small elements where it is possible to place the wanted snow distribution. Calculations of the gamma radiation field with different snow distributions show how small changes in the snow cover and distribution will influence the measurements as a function of the average snow water equivalent.

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