Estimation of snow cover extent in carpathian mountains using integrated GIS and remote sensing information

Water Resources availability is very important to social and economic well-being of the people and has huge impacts on the socio-economic scenarios of a country. Precipitation and snow cover area assessment is some of the major inputs in hydrologic modelling and also for assessing and managing water resources in a basin. The change in the water availability in a basin has huge socio-economic impacts because of the water usage for food production, industries, and many others. The main aim of this study was to measure the snow cover area and precipitation from 2001 to 2015 in the Kabul basin. Moderate Resolution Image Spectroradiometer (MODIS) and Tropical Rainfall measuring Mission (TRMM) data were used to study snow cover area and precipitation respectively during 2001-2015. 8-day snow cover product for 15 years (January) was used to analyse the snow cover while monthly data of TRMM (3B43) were used to analyse the rainfall from 2001-2015. Different image processing techniques were applied on the data retrieved using GIS and Remote Sensing softwares. Initially, SCA was seen increasing, but during the last 3-4 years, it kept decreasing gradually. Rainfall was initially recorded as low, while later on, it was recorded high and reached the highest during 2010. Keywords: MODIS; Snow Cover; TRMM; Precipitation; Kabul Basin; Remote Sensing Copyright (c) 2020 Geosfera Indonesia Journal and Department of Geography Education, University of Jember This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

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Links between spring snow cover and long-term alpine vegetation change

10.5194/egusphere-egu2020-8737 ◽

2020 ◽

Author(s):

Shengwei Zong ◽

Christian Rixen

Keyword(s):

Remote Sensing ◽

Snow Cover ◽

Plant Species ◽

Climate Warming ◽

Vegetation Change ◽

Alpine Vegetation ◽

Alpine Ecosystems ◽

Snow Cover Extent ◽

The Relationship

Snow is an important environmental factor determining distributions of plant species in alpine ecosystems. During the past decades, climate warming has resulted in significant reduction of snow cover extent globally, which led to remarkable alpine vegetation change. Alpine vegetation change is often caused by the combined effects of increasing air temperature and snow cover change, yet the relationship between snow cover and vegetation change is currently not fully understood. To detect changes in both snow cover and alpine vegetation, a relatively fine spatial scales over long temporal spans is necessary. In this study in alpine tundra of the Changbai Mountains, Northeast China, we (1) quantified spatiotemporal changes of spring snow cover area (SCA) during half a century by using multi-source remote sensing datasets; (2) detected long-term vegetation greening and browning trends at pixel level using Landsat archives of 30 m resolution, and (3) analyzed the relationship between spring SCA change and vegetation change. Results showed that spring SCA has decreased significantly during the last 50 years in line with climate warming. Changes in vegetation greening and browning trend were related to distributional range dynamics of a dominant indigenous evergreen shrub Rhododendron aureum, which extended at the leading edge and retracted at the trailing edge. Changes in R. aureum distribution were probably related to spring snow cover changes. Areas with decreasing R. aureum cover were often located in snow patches where probably herbs and grasses encroached from low elevations and adjacent communities. Our study highlights that spring SCA derived from multi-source remote sensing imagery can be used as a proxy to explore relationship between snow cover and vegetation change in alpine ecosystems. Alpine indigenous plant species may migrate upward following the reduction of snow-dominated environments in the context of climate warming and could be threatened by encroaching plants within snow bed habitats.

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Evaluation of Remotely Sensed Snow Water Equivalent and Snow Cover Extent over the Contiguous United States

Journal of Hydrometeorology ◽

10.1175/jhm-d-18-0007.1 ◽

2018 ◽

Vol 19 (11) ◽

pp. 1777-1791 ◽

Cited By ~ 12

Author(s):

Nicholas Dawson ◽

Patrick Broxton ◽

Xubin Zeng

Keyword(s):

United States ◽

Remote Sensing ◽

Snow Cover ◽

Snow Depth ◽

Snow Water Equivalent ◽

Remote Sensing Data ◽

Sensing Data ◽

Snow Cover Extent ◽

Snow Water ◽

The Mean

Abstract Global snow water equivalent (SWE) products derived at least in part from satellite remote sensing are widely used in weather, climate, and hydrometeorological studies. Here we evaluate three such products using our recently developed daily 4-km SWE dataset available from October 1981 to September 2017 over the conterminous United States. This SWE dataset is based on gridded precipitation and temperature data and thousands of in situ measurements of SWE and snow depth. It has a 0.98 correlation and 30% relative mean absolute deviation with Airborne Snow Observatory data and effectively bridges the gap between small-scale lidar surveys and large-scale remotely sensed data. We find that SWE products using remote sensing data have large differences (e.g., the mean absolute difference from our SWE data ranges from 45.8% to 59.3% of the mean SWE in our data), especially in forested areas (where this percentage increases up to 73.5%). Furthermore, they consistently underestimate average maximum SWE values and produce worse SWE (including spurious jumps) during snowmelt. Three additional higher-resolution satellite snow cover extent (SCE) products are used to compare the SCE values derived from these SWE products. There is an overall close agreement between these satellite SCE products and SCE generated from our SWE data, providing confidence in our consistent SWE, snow depth, and SCE products based on gridded climate and station data. This agreement is also stronger than that between satellite SCE and those derived from the three satellite SWE products, further confirming the deficiencies of the SWE products that utilize remote sensing data.

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The Swiss snow climatology as seen by reanalyses and further gridded datasets

10.5194/ems2021-282 ◽

2021 ◽

Author(s):

Monika Goeldi ◽

Stefanie Gubler ◽

Christian Steger ◽

Simon C. Scherrer ◽

Sven Kotlarski

Keyword(s):

Remote Sensing ◽

Snow Cover ◽

Snow Water Equivalent ◽

Spatiotemporal Variability ◽

Data Sets ◽

Data Set ◽

Climate Services ◽

Snow Cover Extent ◽

Snow Water

Snow cover is a key component of alpine environments and knowledge of its spatiotemporal variability, including long-term trends, is vital for a range of dependent systems like winter tourism, hydropower production, etc. Snow cover retreat during the past decades is considered as an important and illustrative indicator of ongoing climate change. As such, the monitoring of surface snow cover and the projection of its future changes play a key role for climate services in alpine regions.In Switzerland, a spatially and temporally consistent snow cover climatology that can serve as a reference for both climate monitoring and for future snow cover projections is currently missing. To assess the value and the potential of currently available long term spatial snow data we compare a range of different gridded snow water equivalent (SWE) datasets for the area of Switzerland, including three reanalysis-based products (COSMO-REA6, ERA5, ERA5-Land). The gridded data sets have a horizontal resolution between 1 and 30 km. The performance of the data sets is assessed by comparing them against three reference data sets with different characteristics (station data, a high-resolution 1km snow model that assimilates snow observations, and an optical remote sensing data set). Four different snow indicators are considered (mean SWE, number of snow days, date of maximum SWE, and snow cover extent) in nine different regions of Switzerland and six elevation classes.The results reveal high temporal correlations between the individual datasets and, in general, a good performance regarding both countrywide and regional estimates of mean SWE. In individual regions, however, larger biases appear. All data sets qualitatively agree on a decreasing trend of mean SWE during the previous decades particularly at low elevations, but substantial differences can exist. Furthermore, all data sets overestimate the snow cover fraction as provided by the remote sensing reference. In general, reanalysis products capture the general characteristics of the Swiss snow climatology but indicate some distinctive deviations &#8211; e.g. like a systematic under- respectively overestimation of the mean snow water equivalent.

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Remote Sensing of Snow Cover Using Spaceborne SAR: A Review

Remote Sensing ◽

10.3390/rs11121456 ◽

2019 ◽

Vol 11 (12) ◽

pp. 1456 ◽

Cited By ~ 14

Author(s):

Ya-Lun S. Tsai ◽

Andreas Dietz ◽

Natascha Oppelt ◽

Claudia Kuenzer

Keyword(s):

Remote Sensing ◽

Snow Cover ◽

Meteorological Data ◽

Weather Condition ◽

Natural Phenomenon ◽

Snow Cover Extent ◽

Wet Snow ◽

Snow Conditions ◽

Interferometric Sar ◽

Processing Techniques

The importance of snow cover extent (SCE) has been proven to strongly link with various natural phenomenon and human activities; consequently, monitoring snow cover is one the most critical topics in studying and understanding the cryosphere. As snow cover can vary significantly within short time spans and often extends over vast areas, spaceborne remote sensing constitutes an efficient observation technique to track it continuously. However, as optical imagery is limited by cloud cover and polar darkness, synthetic aperture radar (SAR) attracted more attention for its ability to sense day-and-night under any cloud and weather condition. In addition to widely applied backscattering-based method, thanks to the advancements of spaceborne SAR sensors and image processing techniques, many new approaches based on interferometric SAR (InSAR) and polarimetric SAR (PolSAR) have been developed since the launch of ERS-1 in 1991 to monitor snow cover under both dry and wet snow conditions. Critical auxiliary data including DEM, land cover information, and local meteorological data have also been explored to aid the snow cover analysis. This review presents an overview of existing studies and discusses the advantages, constraints, and trajectories of the current developments.

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Snow-cover reconstruction methodology for mountainous regions based on historic in situ observations and recent remote sensing data

The Cryosphere ◽

10.5194/tc-9-451-2015 ◽

2015 ◽

Vol 9 (2) ◽

pp. 451-463 ◽

Cited By ~ 16

Author(s):

A. Gafurov ◽

S. Vorogushyn ◽

D. Farinotti ◽

D. Duethmann ◽

A. Merkushkin ◽

...

Keyword(s):

Remote Sensing ◽

Snow Cover ◽

Remote Sensing Data ◽

Snow Cover Area ◽

Sensing Data ◽

Mountainous Regions ◽

Snow Cover Extent ◽

Spatially Distributed ◽

Data Gap ◽

Availability Assessment

Abstract. Spatially distributed snow-cover extent can be derived from remote sensing data with good accuracy. However, such data are available for recent decades only, after satellite missions with proper snow detection capabilities were launched. Yet, longer time series of snow-cover area are usually required, e.g., for hydrological model calibration or water availability assessment in the past. We present a methodology to reconstruct historical snow coverage using recently available remote sensing data and long-term point observations of snow depth from existing meteorological stations. The methodology is mainly based on correlations between station records and spatial snow-cover patterns. Additionally, topography and temporal persistence of snow patterns are taken into account. The methodology was applied to the Zerafshan River basin in Central Asia – a very data-sparse region. Reconstructed snow cover was cross validated against independent remote sensing data and shows an accuracy of about 85%. The methodology can be used in mountainous regions to overcome the data gap for earlier decades when the availability of remote sensing snow-cover data was strongly limited.

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Snow cover reconstruction methodology based on historic in situ observations and recent remote sensing data

The Cryosphere Discussions ◽

10.5194/tcd-8-4645-2014 ◽

2014 ◽

Vol 8 (5) ◽

pp. 4645-4680

Author(s):

A. Gafurov ◽

S. Vorogushyn ◽

A. Merkushkin ◽

D. Duethmann ◽

D. Farinotti ◽

...

Keyword(s):

Remote Sensing ◽

Snow Cover ◽

Remote Sensing Data ◽

Snow Cover Area ◽

Sensing Data ◽

Snow Cover Extent ◽

Spatially Distributed ◽

Temporal Persistence ◽

Data Gap ◽

Availability Assessment

Abstract. Spatially distributed snow cover extent can be derived from remote sensing data with good accuracy. However, such data are available for recent decades only, after satellite missions with proper snow detection capabilities were launched. Yet, longer time series of snow cover area (SCA) are usually required e.g. for hydrological model calibration or water availability assessment in the past. We present a methodology to reconstruct historical snow coverage using recently available remote sensing data and long-term point observations of snow depth from existing meteorological stations. The methodology is mainly based on correlations between station records and spatial snow cover patterns. Additionally, topography and temporal persistence of snow patterns are taken into account. The methodology was applied to the Zerafshan River basin in Central Asia – a very data-sparse region. Reconstructed snow cover was cross-validated against independent remote sensing data and shows an accuracy of about 85%. The methodology can be used to overcome the data gap for earlier decades when the availability of remote sensing snow cover data was strongly limited.

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Snow Cover and Glacier Change Study in Nepalese Himalaya Using Remote Sensing and Geographic Information System

Journal of Hydrology and Meteorology ◽

10.3126/jhm.v6i1.5481 ◽

1970 ◽

Vol 6 (1) ◽

pp. 26-36 ◽

Cited By ~ 12

Author(s):

Arun Bhakta Shrestha ◽

Sharad Prasad Joshi

Keyword(s):

Remote Sensing ◽

Information System ◽

Geographic Information System ◽

Snow Cover ◽

Geographic Information ◽

Late Winter ◽

National Scale ◽

Modis Data ◽

Snow Cover Extent ◽

The Himalaya

Snow cover and glaciers in the Himalaya play a major role in the generation of stream flow in south Asia. Various studies have suggested that the glaciers in the Himalaya are in general condition of retreat. The snowline is also found to be retreating. While there are relatively more studies on glaciers fluctuation in the Himalaya, studies on snow cover is relatively sparse. In this study, snow cover and glacier fluctuation in the Nepalese Himalaya were studied using remote sensing techniques and geographic information system. The study was carried out in two spatial scales: catchments scale and national scale. In catchments scale two catchments: Langtang and Khumbu were studied. Intermittent medium resolution satellite imageries (Landsat) were used to study the fluctuation in snow cover and glacier area in the two catchments. In the national scale study coarse resolution (MODIS) imageries were used to derive seasonal variations in snow cover. An indication of decreasing trend in snow cover is shown by this study, although this result needs verification with more data. The snowline elevation is in general higher in Khumbu compared to Langtang. In both catchments, snowline elevation are higher in east, south-east, south and south-west aspects. The areas of snow cover in those aspects are also greater. The study provides the first multi-year temporal variation in snow cover extent in Nepal. According to the analysis of MODIS data, the snow cover extent over the country is highest during late winter and spring, while it is lowest during summer monsoon season. The snow cover area shows dynamic nature and the variability during late winter and spring is quite large. The snow covered territory of Nepal was divided into four subsections: east, central, west and far-west, and snow line elevations for these subsections were derived from MODIS data. Generally, the snowline elevation is lower in the west than in the east, although the central region shows relatively lower snowline elevation, not following the general zonal trend. Key Words: Snow cover; glacier fluctuation; satellite imageries; trend; snowline DOI: http://dx.doi.org/10.3126/jhm.v6i1.5481 Journal of Hydrology and Meteorology, Vol. 6, No. 1 26-36

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