Probabilistic Snow Cover and Ensemble Streamflow Estimations in the Upper Euphrates Basin

Şorman, Ali Arda; Uysal, Gökçen; Şensoy, Aynur

doi:10.2478/johh-2018-0025

Cited by 23 publications

(13 citation statements)

References 52 publications

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“…Snowmelt estimation is thus important in the context of managing the water resources of this region. For this purpose, several studies [56][57][58][59][60] developed different methods for forecasting the daily snow cover and streamflow.…”

Section: Introductionmentioning

confidence: 99%

Multiple Remotely Sensed Lines of Evidence for a Depleting Seasonal Snowpack in the Near East

2019

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The snow-fed river basins of the Near East region are facing an urgent threat in the form of declining water resources. In this study, we analyzed several remote sensing products (optical, passive microwave, and gravimetric) and outputs of a meteorological reanalysis data set to understand the relationship between the terrestrial water storage anomalies and the mountain snowpack. The results from different satellite retrievals show a clear signal of a depletion of both water storage and the seasonal snowpack in four basins in the region. We find a strong reduction in terrestrial water storage over the Gravity Recovery and Climate Experiment (GRACE) observational period, particularly over the higher elevations. Snow-cover duration estimates from Moderate Resolution Imaging Spectroradiometer (MODIS) products point towards negative and significant trends up to one month per decade in the current era. These numbers are a clear indicator of the partial disappearance of the seasonal snow-cover in the region which has been projected to occur by the end of the century. The spatial patterns of changes in the snow-cover duration are positively correlated with both GRACE terrestrial water storage decline and peak snow water equivalent (SWE) depletion from the ERA5 reanalysis. Possible drivers of the snowpack depletion are a significant reduction in the snowfall ratio and an earlier snowmelt. A continued depletion of the montane snowpack in the Near East paints a bleak picture for future water availability in this water-stressed region.

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Section: Introductionmentioning

confidence: 99%

Multiple Remotely Sensed Lines of Evidence for a Depleting Seasonal Snowpack in the Near East

2019

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“…This type of methods relies on the correlations of snow cover in space and time with two basic forms. One is to utilize the spatial and temporal information step by step, usually as a multi-step combination (MSC) method (Parajka and Blöschl, 2008;Da Ronco and De Michele, 2014b;Gurung et al, 2011;Zhou et al, 2013;Şorman et al, 2019). The other is to utilize the spatial and temporal information simultaneously (Li et al, 2017;Xia et al, 2012;Huang et al, 2018;Poggio and Gimona, 2015), which we call one-step utilization (OSU).…”

Section: Spatio-temporal Methodsmentioning

confidence: 99%

“…Because of the high albedo, high thermal emissivity, low thermal conductivity, and water storage ability (Tait et al, 2000;Tekeli and Tekeli, 2012), snow has a significant influence on the energy balance (Robinson et al, 1993;Crawford et al, 2013), the hydrological cycle (Şorman et al, 2007;Kostadinov and Lookingbill, 2015), and climate change (Cohen and Entekhabi, 1999;Brown, 2000). In recent years, more and more attention has been focused on monitoring the spatial and temporal change of snow cover .…”

Section: Introductionmentioning

confidence: 99%

The recent developments in spatio-temporally continuous snow cover product generation

Li¹,

Jing²,

Shen³

et al. 2019

Preprint

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Abstract. The snow cover products of optical remote sensing systems play an important role in research into global climate change, the hydrological cycle, the energy balance, and so on. However, cloud cover results in spatial and temporal discontinuity for long-term snow monitoring. In the last few decades, a large number of cloud removal methods for snow cover products have been proposed. In this paper, our goal is to make a comprehensive summarization of the existing algorithms for generating spatio-temporally continuous snow cover products, and to expose the development trends. The methods of generating spatio-temporally continuous snow cover products are classified into spatial methods, temporal methods, spatio-temporal methods, and multi-source fusion methods. Experiments were conducted to validate the reconstruction effect of the representative methods.

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“…Because of the high albedo, high thermal emissivity, low thermal conductivity, and water storage ability (Tait et al, 2000;Tekeli and Tekeli, 2012), snow has a significant in-fluence on the energy balance (Robinson et al, 1993;Crawford et al, 2013), the hydrological cycle (Şorman et al, 2007;Kostadinov and Lookingbill, 2015), and climate change (Cohen and Entekhabi, 1999;Brown, 2000). In recent years, increasing attention has been focused on monitoring the spatial and temporal change of snow cover (Gao et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

The recent developments in cloud removal approaches of MODIS snow cover product

Jing

Shen

et al. 2019

Hydrol. Earth Syst. Sci.

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Abstract. The snow cover products of optical remote sensing systems play an important role in research into global climate change, the hydrological cycle, and the energy balance. Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products are the most popular datasets used in the community. However, for MODIS, cloud cover results in spatial and temporal discontinuity for long-term snow monitoring. In the last few decades, a large number of cloud removal methods for MODIS snow cover products have been proposed. In this paper, our goal is to make a comprehensive summarization of the existing algorithms for generating cloud-free MODIS snow cover products and to expose the development trends. The methods of generating cloud-free MODIS snow cover products are classified into spatial methods, temporal methods, spatio-temporal methods, and multi-source fusion methods. The spatial methods and temporal methods remove the cloud cover of the snow product based on the spatial patterns and temporal changing correlation of the snowpack, respectively. The spatio-temporal methods utilize the spatial and temporal features of snow jointly. The multi-source fusion methods utilize the complementary information among different sources among optical observations, microwave observations, and station observations.

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Probabilistic Snow Cover and Ensemble Streamflow Estimations in the Upper Euphrates Basin

Cited by 23 publications

References 52 publications

Multiple Remotely Sensed Lines of Evidence for a Depleting Seasonal Snowpack in the Near East

Multiple Remotely Sensed Lines of Evidence for a Depleting Seasonal Snowpack in the Near East

The recent developments in spatio-temporally continuous snow cover product generation

The recent developments in cloud removal approaches of MODIS snow cover product

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