Modis Snowline Elevation Changes During Snowmelt Runoff Events in Europe

Parajka, J.; Bezak, Nejc; Burkhart, John F.; Hauksson, Bjarki; Holko, Ladislav; Hundecha, Yeshewatesfa; Jeníček, Michal; Krajčí, Pavel; Mangini, Walter; Molnár, Péter; Riboust, Philippe; Rizzi, Jonathan; Şensoy, Aynur; Thirel, Guillaume; Viglione, Alberto

doi:10.2478/johh-2018-0011

Cited by 16 publications

(11 citation statements)

References 29 publications

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“…Comparisons of MODIS-derived snowlines from high-resolution DEMs and field-based studies have shown good agreement [5,38]. Additionally, the RSLE method by [14] has been widely used to investigate snow dynamics [37,[39][40][41][42]. This method estimates the elevation at which the number of snow-free pixels above the elevation line and the number of snowy pixels below the elevation line are at their minimum above and below the elevation line.…”

Section: Snowline Altitude Estimationmentioning

confidence: 95%

“…The snowline altitude was derived from the MODIS snow product and elevation data using the regional snowline elevation (RSLE) method developed by [14]. The snowline has been widely derived using the MODIS snow product in different parts of the world [35][36][37]. Comparisons of MODIS-derived snowlines from high-resolution DEMs and field-based studies have shown good agreement [5,38].…”

Section: Snowline Altitude Estimationmentioning

confidence: 99%

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Contemporary Snow Changes in the Karakoram Region Attributed to Improved MODIS Data between 2003 and 2018

Thapa

Sher

2020

Water

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Snowmelt significantly contributes to meltwater in most parts of High Mountain Asia. The Karakoram region is one of these densely glacierized and snow-covered regions. Several studies have reported that glaciers in the Karakoram region remained stable or experience slight mass loss. This trend has called for further investigation to understand changes in other components of the cryosphere. This study estimates the comparative snow cover area (SCA) and snowline altitude (SLA) changes between 2003 and 2018 in the Karakoram region and its subbasins, including Hunza, Shigar, and Shyok. We used three different 8-day composite snow products of the Moderate Resolution Imaging Spectroradiometer (MODIS) in this study including (1) Original Aqua (MYD10A2), (2) Original Terra (MOD10A2), and (3) Improved Terra-Aqua (MOYDGL06*) snow products from 2003 to 2018. We used Mann–Kendall and Sen Slope methods to assess trends in the SCA and SLA. Our results show that the original snow products are significantly biased when investigating seasonal and annual trends. However, discarding a cloud cover of >20% in the original products improves the results and makes them more comparable to our improved snow product. The original products (without cloud removal) overestimate the SCA during summer and underestimate the SCA during winter and year-round throughout the Karakoram region. The bias in the mean annual SCA between improved and Aqua and Terra cloud threshold products for the Karakoram region is found to be −1.67% and 1.1%, respectively. The improved (MOYDGL06*) product reveals a statistically insignificant decreasing trend of the SCA on the annual scale between 2003 and 2018 in the Karakoram region and all three subbasins. The annual trends decreased at −0.13%, −0.1%, −0.08%, and −0.05% in the Karakoram, Hunza, Shigar, and Shyok, respectively. The monthly trends were slightly positive overall in December. The annual maximum SLA shows a statistically significant upward trend of 13 m above sea level (m a.s.l.) per year for the entire Karakoram region. This finding suggests a significant uncertainty in water resource planning based on the original snow data, and this study recommends the use of the improved snow product for a better understanding.

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Section: Snowline Altitude Estimationmentioning

confidence: 95%

Section: Snowline Altitude Estimationmentioning

confidence: 99%

Contemporary Snow Changes in the Karakoram Region Attributed to Improved MODIS Data between 2003 and 2018

Thapa

Sher

2020

Water

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“…To date, different EO techniques (e.g., optical, radar, passive microwave, altimetry) have provided a timely, promising, and efficient approach to retrieve snow dynamics, including snowmelt processes [10]. Previous studies characterized snowmelt processes with several spatial parameters: SCA [11,12], snow line elevation (SLE) [13][14][15], or with temporal parameters: snowmelt onset (SMO) [3,16], snow cover duration (SCD) [17,18], and snow persistence (SP) [19,20]. Besides, to further capture the spatiotemporal dynamics of SCA throughout an ablation season, snow depletion curves (SDCs) are often utilized [21,22].…”

Section: Introductionmentioning

confidence: 99%

Deriving Regional Snow Line Dynamics during the Ablation Seasons 1984–2018 in European Mountains

Dietz

Kuenzer

2019

Remote Sensing

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Snowmelt in the mid-latitude European mountains is undergoing significant spatiotemporal changes. Regional snow line elevation (RSLE) is an appropriate indicator for assessing snow cover variations in mountain areas. To derive regional snow line dynamics during the ablation seasons 1984–2018, the present study unprecedentedly introduced a readily applicable framework. The framework constitutes four steps: atmospheric and topographic correction, snow classification, RSLE retrieval, and regional snow line retreat curve (RSLRC) derivation. The developed framework has been successfully applied to 8641 satellite images acquired by Landsat, ASTER, and Sentinel-2. The results of the intra-annual regional snow line variations show that: (1) regional snow lines in the Alpine catchments preserve the longest; (2) RSLEs are lower in the northern Pyrenees than in the southern part; (3) regional snow lines persist the shortest in the Carpathian catchments; and (4) during the end of the ablation season 2018, intermediate snowfall events in the catchments Adda, Tagliamento, and Uzh are observed. In terms of the long-term inter-annual variations, significantly accelerating snow line recession is detected in the northern Pyrenean catchment Ariege. In the Alpine catchment Alpenrhein and Drac, RSLRCs are shifting towards lower accumulated air-temperature (AT) significantly, with the magnitude of −3.77 °C·a−1 (Alpenrhein) and −3.99 °C·a−1 (Drac).

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“…Space-born remote sensing data have the potential to provide estimates of certain snow properties [25]. In the visible (VIS) and near infrared (NIR) spectral range space-borne remote sensors (e.g., MODIS, AVHRR, Sentinel-2) can determine the snow cover extent (SCE) and snow cover fraction (SCF) at a high spatial resolution and long time-series of these data exist (e.g., [26][27][28][29][30][31][32][33]). The observation of snow cover area is of particular value in headwaters of mountainous regions [34][35][36] and one can expect to obtain volume information thanks to recent advances in photogrammetry and in the availability of stereo image [37].…”

Section: Introductionmentioning

confidence: 99%

Review of Snow Data Assimilation Methods for Hydrological, Land Surface, Meteorological and Climate Models: Results from a COST HarmoSnow Survey

et al. 2018

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The European Cooperation in Science and Technology (COST) Action ES1404 “HarmoSnow”, entitled, “A European network for a harmonized monitoring of snow for the benefit of climate change scenarios, hydrology and numerical weather prediction” (2014-2018) aims to coordinate efforts in Europe to harmonize approaches to validation, and methodologies of snow measurement practices, instrumentation, algorithms and data assimilation (DA) techniques. One of the key objectives of the action was “Advance the application of snow DA in numerical weather prediction (NWP) and hydrological models and show its benefit for weather and hydrological forecasting as well as other applications.” This paper reviews approaches used for assimilation of snow measurements such as remotely sensed and in situ observations into hydrological, land surface, meteorological and climate models based on a COST HarmoSnow survey exploring the common practices on the use of snow observation data in different modeling environments. The aim is to assess the current situation and understand the diversity of usage of snow observations in DA, forcing, monitoring, validation, or verification within NWP, hydrology, snow and climate models. Based on the responses from the community to the questionnaire and on literature review the status and requirements for the future evolution of conventional snow observations from national networks and satellite products, for data assimilation and model validation are derived and suggestions are formulated towards standardized and improved usage of snow observation data in snow DA. Results of the conducted survey showed that there is a fit between the snow macro-physical variables required for snow DA and those provided by the measurement networks, instruments, and techniques. Data availability and resources to integrate the data in the model environment are identified as the current barriers and limitations for the use of new or upcoming snow data sources. Broadening resources to integrate enhanced snow data would promote the future plans to make use of them in all model environments.

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Modis Snowline Elevation Changes During Snowmelt Runoff Events in Europe

Cited by 16 publications

References 29 publications

Contemporary Snow Changes in the Karakoram Region Attributed to Improved MODIS Data between 2003 and 2018

Contemporary Snow Changes in the Karakoram Region Attributed to Improved MODIS Data between 2003 and 2018

Deriving Regional Snow Line Dynamics during the Ablation Seasons 1984–2018 in European Mountains

Review of Snow Data Assimilation Methods for Hydrological, Land Surface, Meteorological and Climate Models: Results from a COST HarmoSnow Survey

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