Assimilation of MODIS snow cover and real time snow depth point data in a snow dynamic model

Author(s):  
G. Boni ◽  
F. Castelli ◽  
S. Gabellani ◽  
G. Machiavello ◽  
R. Rudari
2016 ◽  
Vol 96 (1) ◽  
pp. 46-55
Author(s):  
Valentina Nikolova ◽  
Aleksandar Penkov

The aim of the present research is to show the advantages of information technology in investigating the snow cover. The snow data is usually taken from the measurement in meteorological stations which are often sparsely and insufficient. The problem in the analysis of the snow cover is how to present point data spatially and what is the most appropriate model. The area of the present research is the western part of Rhodopes mountain (Southern Bulgaria). The relief is variable from low to high mountainous and the climate is influenced by the high altitude and Mediterranean air advections. The spatial analysis of the distribution of snow depth is done in ArcGIS by application of Spatial Statistics Tools and Geostatistical Analyst. We considered altitude, aspect and slope as explanatory variables that could be used for determination of the territorial distribution of the snow depth. These factors are determined on the base of digital elevation model and the relationship between variables is evaluated by application of regression analysis, ordinary less squares (OLS) analysis and geographically weighted regression (GWR). The high values of R2 (above 0.7) show the representativeness of the model. A map of spatial distribution of snow depth is created by Map algebra in GIS environment, applying the regression equation of the relation snow depth - altitude. Inverse distance weighted and ordinary kriging interpolation are also carried out. The research shows that spatial presentation of point snow data and its interpretation should be done taking into account the relief and the exposition of the territory.


2021 ◽  
Author(s):  
Zacharie Barrou Dumont ◽  
Simon Gascoin ◽  
Olivier Hagolle ◽  
Michaël Ablain ◽  
Rémi Jugier ◽  
...  

Abstract. The High Resolution Snow & Ice Monitoring Service was launched in 2020 to provide near real time, pan-European snow and ice information at 20 m resolution from Sentinel-2 observations. Here we present an evaluation of the snow detection using a database of snow depth observations from 1764 stations across Europe over the hydrological year 2016–2017. We find a good agreement between both datasets with an accuracy of 94 % (proportion of correct classifications) and kappa of 0.80. More accurate (+6 % kappa) retrievals are obtained by excluding low quality pixels at the cost of a reduced coverage (−13 % data).


2021 ◽  
Vol 15 (10) ◽  
pp. 4975-4980
Author(s):  
Zacharie Barrou Dumont ◽  
Simon Gascoin ◽  
Olivier Hagolle ◽  
Michaël Ablain ◽  
Rémi Jugier ◽  
...  

Abstract. The High Resolution Snow & Ice Monitoring Service was launched in 2020 to provide near-real-time, pan-European snow and ice information at 20 m resolution from Sentinel-2 observations. Here we present an evaluation of the snow detection using a database of snow depth observations from 1764 stations across Europe over the hydrological year 2016–2017. We find a good agreement between both datasets with an accuracy (proportion of correct classifications) of 94 % and kappa of 0.81. More accurate (+6 % kappa) retrievals are obtained by excluding low-quality pixels at the cost of a reduced coverage (−13 % data).


Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 307
Author(s):  
Chi Zhang ◽  
Naixia Mou ◽  
Jiqiang Niu ◽  
Lingxian Zhang ◽  
Feng Liu

Changes in snow cover over the Tibetan Plateau (TP) have a significant impact on agriculture, hydrology, and ecological environment of surrounding areas. This study investigates the spatio-temporal pattern of snow depth (SD) and snow cover days (SCD), as well as the impact of temperature and precipitation on snow cover over TP from 1979 to 2018 by using the ERA5 reanalysis dataset, and uses the Mann–Kendall test for significance. The results indicate that (1) the average annual SD and SCD in the southern and western edge areas of TP are relatively high, reaching 10 cm and 120 d or more, respectively. (2) In the past 40 years, SD (s = 0.04 cm decade−1, p = 0.81) and SCD (s = −2.3 d decade−1, p = 0.10) over TP did not change significantly. (3) The positive feedback effect of precipitation is the main factor affecting SD, while the negative feedback effect of temperature is the main factor affecting SCD. This study improves the understanding of snow cover change and is conducive to the further study of climate change on TP.


2012 ◽  
Vol 127 ◽  
pp. 271-287 ◽  
Author(s):  
G. Thirel ◽  
C. Notarnicola ◽  
M. Kalas ◽  
M. Zebisch ◽  
T. Schellenberger ◽  
...  

2017 ◽  
Vol 11 (4) ◽  
pp. 1647-1664 ◽  
Author(s):  
Emmy E. Stigter ◽  
Niko Wanders ◽  
Tuomo M. Saloranta ◽  
Joseph M. Shea ◽  
Marc F. P. Bierkens ◽  
...  

Abstract. Snow is an important component of water storage in the Himalayas. Previous snowmelt studies in the Himalayas have predominantly relied on remotely sensed snow cover. However, snow cover data provide no direct information on the actual amount of water stored in a snowpack, i.e., the snow water equivalent (SWE). Therefore, in this study remotely sensed snow cover was combined with in situ observations and a modified version of the seNorge snow model to estimate (climate sensitivity of) SWE and snowmelt runoff in the Langtang catchment in Nepal. Snow cover data from Landsat 8 and the MOD10A2 snow cover product were validated with in situ snow cover observations provided by surface temperature and snow depth measurements resulting in classification accuracies of 85.7 and 83.1 % respectively. Optimal model parameter values were obtained through data assimilation of MOD10A2 snow maps and snow depth measurements using an ensemble Kalman filter (EnKF). Independent validations of simulated snow depth and snow cover with observations show improvement after data assimilation compared to simulations without data assimilation. The approach of modeling snow depth in a Kalman filter framework allows for data-constrained estimation of snow depth rather than snow cover alone, and this has great potential for future studies in complex terrain, especially in the Himalayas. Climate sensitivity tests with the optimized snow model revealed that snowmelt runoff increases in winter and the early melt season (December to May) and decreases during the late melt season (June to September) as a result of the earlier onset of snowmelt due to increasing temperature. At high elevation a decrease in SWE due to higher air temperature is (partly) compensated by an increase in precipitation, which emphasizes the need for accurate predictions on the changes in the spatial distribution of precipitation along with changes in temperature.


2017 ◽  
Vol 11 (1) ◽  
pp. 585-607 ◽  
Author(s):  
Anna Haberkorn ◽  
Nander Wever ◽  
Martin Hoelzle ◽  
Marcia Phillips ◽  
Robert Kenner ◽  
...  

Abstract. In this study we modelled the influence of the spatially and temporally heterogeneous snow cover on the surface energy balance and thus on rock temperatures in two rugged, steep rock walls on the Gemsstock ridge in the central Swiss Alps. The heterogeneous snow depth distribution in the rock walls was introduced to the distributed, process-based energy balance model Alpine3D with a precipitation scaling method based on snow depth data measured by terrestrial laser scanning. The influence of the snow cover on rock temperatures was investigated by comparing a snow-covered model scenario (precipitation input provided by precipitation scaling) with a snow-free (zero precipitation input) one. Model uncertainties are discussed and evaluated at both the point and spatial scales against 22 near-surface rock temperature measurements and high-resolution snow depth data from winter terrestrial laser scans.In the rough rock walls, the heterogeneously distributed snow cover was moderately well reproduced by Alpine3D with mean absolute errors ranging between 0.31 and 0.81 m. However, snow cover duration was reproduced well and, consequently, near-surface rock temperatures were modelled convincingly. Uncertainties in rock temperature modelling were found to be around 1.6 °C. Errors in snow cover modelling and hence in rock temperature simulations are explained by inadequate snow settlement due to linear precipitation scaling, missing lateral heat fluxes in the rock, and by errors caused by interpolation of shortwave radiation, wind and air temperature into the rock walls.Mean annual near-surface rock temperature increases were both measured and modelled in the steep rock walls as a consequence of a thick, long-lasting snow cover. Rock temperatures were 1.3–2.5 °C higher in the shaded and sunny rock walls, while comparing snow-covered to snow-free simulations. This helps to assess the potential error made in ground temperature modelling when neglecting snow in steep bedrock.


2019 ◽  
Vol 1 ◽  
pp. 1-1
Author(s):  
Rex G. Cammack ◽  
Paul Hunt

<p><strong>Abstract.</strong> In many modern sports, athlete tracking for athlete performance analysis is a common practice. Most of the time this athlete tracking is done during training sessions. At some World Tour cycling races the broadcasting company and race organizers use athlete tracking data during race events for various graphical for fans of the sport. This research attempt to use the race real time broadcast of data to produce a web mapping application that will show detailed cycling race tactics and other mapping forms in near real time. This research focuses on data flow and processing for dynamic mapping of complex point data patterns.</p>


2006 ◽  
Vol 3 (4) ◽  
pp. 1569-1601 ◽  
Author(s):  
J. Parajka ◽  
G. Blöschl

Abstract. This study evaluates the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover product over the territory of Austria. The aims are (a) to analyse the spatial and temporal variability of the MODIS snow product classes, (b) to examine the accuracy of the MODIS snow product against in situ snow depth data, and (c) to identify the main factors that may influence the MODIS classification accuracy. We use daily MODIS grid maps (version 4) and daily snow depth measurements at 754 climate stations in the period from February 2000 to December 2005. The results indicate that, on average, clouds obscured 63% of Austria, which may significantly restrict the applicability of the MODIS snow cover images to hydrological modelling. On cloud-free days, however, the classification accuracy is very good with an average of 95%. There is no consistent relationship between the classification errors and dominant land cover type and local topographical variability but there are clear seasonal patterns to the errors. In December and January the errors are around 15% while in summer they are less than 1%. This seasonal pattern is related to the overall percentage of snow cover in Austria, although in spring, when there is a well developed snow pack, errors tend to be smaller than they are in early winter for the same overall percent snow cover. Overestimation and underestimation errors balance during most of the year which indicates little bias. In November and December, however, there appears to exist a tendency for overestimation. Part of the errors may be related to the temporal shift between the in situ snow depth measurements (07:00 a.m.) and the MODIS acquisition time (early afternoon).


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