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Snowmelt</title> <meta name="description" content="Search results for: Snowmelt"> <meta name="keywords" content="Snowmelt"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler ml-auto" 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id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="Snowmelt"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 6</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Snowmelt</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6</span> Study Employed a Computer Model and Satellite Remote Sensing to Evaluate the Temporal and Spatial Distribution of Snow in the Western Hindu Kush Region of Afghanistan</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Noori%20Shafiqullah">Noori Shafiqullah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Millions of people reside downstream of river basins that heavily rely on snowmelt originating from the Hindu Kush (HK) region. Snowmelt plays a critical role as a primary water source in these areas. This study aimed to evaluate snowfall and snowmelt characteristics in the HK region across altitudes ranging from 2019m to 4533m. To achieve this, the study employed a combination of remote sensing techniques and the Snow Model (SM) to analyze the spatial and temporal distribution of Snow Water Equivalent (SWE). By integrating the simulated Snow-cover Area (SCA) with data from the Moderate Resolution Imaging Spectroradiometer (MODIS), the study optimized the Precipitation Gradient (PG), snowfall assessment, and the degree-day factor (DDF) for snowmelt distribution. Ground observed data from various elevations were used to calculate a temperature lapse rate of -7.0 (°C km-1). Consequently, the DDF value was determined as 3 (mm °C-1 d-1) for altitudes below 3000m and 3 to 4 (mm °C-1 d-1) for higher altitudes above 3000m. Moreover, the distribution of precipitation varies with elevation, with the PG being 0.001 (m-1) at lower elevations below 4000m and 0 (m-1) at higher elevations above 4000m. This study successfully utilized the SM to assess SCA and SWE by incorporating the two optimized parameters. The analysis of simulated SCA and MODIS data yielded coefficient determinations of R2, resulting in values of 0.95 and 0.97 for the years 2014-2015, 2015-2016, and 2016-2017, respectively. These results demonstrate that the SM is a valuable tool for managing water resources in mountainous watersheds such as the HK, where data scarcity poses a challenge." <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=improved%20MODIS" title="improved MODIS">improved MODIS</a>, <a href="https://publications.waset.org/abstracts/search?q=experiment" title=" experiment"> experiment</a>, <a href="https://publications.waset.org/abstracts/search?q=snow%20water%20equivalent" title=" snow water equivalent"> snow water equivalent</a>, <a href="https://publications.waset.org/abstracts/search?q=snowmelt" title=" snowmelt"> snowmelt</a> </p> <a href="https://publications.waset.org/abstracts/169379/study-employed-a-computer-model-and-satellite-remote-sensing-to-evaluate-the-temporal-and-spatial-distribution-of-snow-in-the-western-hindu-kush-region-of-afghanistan" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/169379.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">69</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5</span> Interannual Variations in Snowfall and Continuous Snow Cover Duration in Pelso, Central Finland, Linked to Teleconnection Patterns, 1944-2010</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Irannezhad">M. Irannezhad</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20H.%20N.%20Gashti"> E. H. N. Gashti</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Mohammadighavam"> S. Mohammadighavam</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Zarrini"> M. Zarrini</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Kl%C3%B8ve"> B. Kløve</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Climate warming would increase rainfall by shifting precipitation falling form from snow to rain, and would accelerate snow cover disappearing by increasing snowpack. Using temperature and precipitation data in the temperature-index snowmelt model, we evaluated variability of snowfall and continuous snow cover duration(CSCD) during 1944-2010 over Pelso, central Finland. MannKendall non-parametric test determined that annual precipitation increased by 2.69 (mm/year, p<0.05) during the study period, but no clear trend in annual temperature. Both annual rainfall and snowfall increased by 1.67 and 0.78 (mm/year, p<0.05), respectively. CSCD was generally about 205 days from 14 October to 6 May. No clear trend was found in CSCD over Pelso. Spearman’s rank correlation showed most significant relationships of annual snowfall with the East Atlantic (EA) pattern, and CSCD with the East Atlantic/West Russia (EA/WR) pattern. Increased precipitation with no warming temperature caused the rainfall and snowfall to increase, while no effects on CSCD. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=variations" title="variations">variations</a>, <a href="https://publications.waset.org/abstracts/search?q=snowfall" title=" snowfall"> snowfall</a>, <a href="https://publications.waset.org/abstracts/search?q=snow%20cover%20duration" title=" snow cover duration"> snow cover duration</a>, <a href="https://publications.waset.org/abstracts/search?q=temperature-index%20snowmelt%20model" title=" temperature-index snowmelt model"> temperature-index snowmelt model</a>, <a href="https://publications.waset.org/abstracts/search?q=teleconnection%20patterns" title=" teleconnection patterns"> teleconnection patterns</a> </p> <a href="https://publications.waset.org/abstracts/16705/interannual-variations-in-snowfall-and-continuous-snow-cover-duration-in-pelso-central-finland-linked-to-teleconnection-patterns-1944-2010" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16705.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">223</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4</span> Hydrological Modeling and Climate Change Impact Assessment Using HBV Model, A Case Study of Karnali River Basin of Nepal</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sagar%20Shiwakoti">Sagar Shiwakoti</a>, <a href="https://publications.waset.org/abstracts/search?q=Narendra%20Man%20Shakya"> Narendra Man Shakya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The lumped conceptual hydrological model HBV is applied to the Karnali River Basin to estimate runoff at several gauging stations and to analyze the changes in catchment hydrology and future flood magnitude due to climate change. The performance of the model is analyzed to assess its suitability to simulate streamflow in snow fed mountainous catchments. Due to the structural complexity, the model shows difficulties in modeling low and high flows accurately at the same time. It is observed that the low flows were generally underestimated and the peaks were correctly estimated except for some sharp peaks due to isolated precipitation events. In this study, attempt has been made to evaluate the importance of snow melt discharge in the runoff regime of the basin. Quantification of contribution of snowmelt to annual, summer and winter runoff has been done. The contribution is highest at the beginning of the hot months as the accumulated snow begins to melt. Examination of this contribution under conditions of increased temperatures indicate that global warming leading to increase in average basin temperature will significantly lead to higher contributions to runoff from snowmelt. Forcing the model with the output of HadCM3 GCM and the A1B scenario downscaled to the station level show significant changes to catchment hydrology in the 2040s. It is observed that the increase in runoff is most extreme in June - July. A shift in the hydrological regime is also observed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hydrological%20modeling" title="hydrological modeling">hydrological modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=HBV%20light" title=" HBV light"> HBV light</a>, <a href="https://publications.waset.org/abstracts/search?q=rainfall%20runoff%20modeling" title=" rainfall runoff modeling"> rainfall runoff modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=snow%20melt" title=" snow melt"> snow melt</a>, <a href="https://publications.waset.org/abstracts/search?q=climate%20change" title=" climate change"> climate change</a> </p> <a href="https://publications.waset.org/abstracts/33253/hydrological-modeling-and-climate-change-impact-assessment-using-hbv-model-a-case-study-of-karnali-river-basin-of-nepal" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33253.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">540</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3</span> Hydrological Response of the Glacierised Catchment: Himalayan Perspective</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sonu%20Khanal">Sonu Khanal</a>, <a href="https://publications.waset.org/abstracts/search?q=Mandira%20Shrestha"> Mandira Shrestha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Snow and Glaciers are the largest dependable reserved sources of water for the river system originating from the Himalayas so an accurate estimate of the volume of water contained in the snowpack and the rate of release of water from snow and glaciers are, therefore, needed for efficient management of the water resources. This research assess the fusion of energy exchanges between the snowpack, air above and soil below according to mass and energy balance which makes it apposite than the models using simple temperature index for the snow and glacier melt computation. UEBGrid a Distributed energy based model is used to calculate the melt which is then routed by Geo-SFM. The model robustness is maintained by incorporating the albedo generated from the Landsat-7 ETM images on a seasonal basis for the year 2002-2003 and substrate map derived from TM. The Substrate file includes predominantly the 4 major thematic layers viz Snow, clean ice, Glaciers and Barren land. This approach makes use of CPC RFE-2 and MERRA gridded data sets as the source of precipitation and climatic variables. The subsequent model run for the year between 2002-2008 shows a total annual melt of 17.15 meter is generate from the Marshyangdi Basin of which 71% is contributed by the glaciers , 18% by the rain and rest being from the snow melt. The albedo file is decisive in governing the melt dynamics as 30% increase in the generated surface albedo results in the 10% decrease in the simulated discharge. The melt routed with the land cover and soil variables using Geo-SFM shows Nash-Sutcliffe Efficiency of 0.60 with observed discharge for the study period. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Glacier" title="Glacier">Glacier</a>, <a href="https://publications.waset.org/abstracts/search?q=Glacier%20melt" title=" Glacier melt"> Glacier melt</a>, <a href="https://publications.waset.org/abstracts/search?q=Snowmelt" title=" Snowmelt"> Snowmelt</a>, <a href="https://publications.waset.org/abstracts/search?q=Energy%20balance" title=" Energy balance"> Energy balance</a> </p> <a href="https://publications.waset.org/abstracts/33015/hydrological-response-of-the-glacierised-catchment-himalayan-perspective" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33015.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">455</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2</span> Application of Hydrological Model in Support of Streamflow Allocation in Arid Watersheds in Northwestern China</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chansheng%20He">Chansheng He</a>, <a href="https://publications.waset.org/abstracts/search?q=Lanhui%20Zhang"> Lanhui Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Baoqing%20Zhang"> Baoqing Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Spatial heterogeneity of landscape significantly affects watershed hydrological processes, particularly in high elevation and cold mountainous watersheds such as the inland river (terminal lake) basins in Northwest China, where the upper reach mountainous areas are the main source of streamflow for the downstream agricultural oases and desert ecosystems. Thus, it is essential to take into account spatial variations of hydrological processes in streamflow allocation at the watershed scale. This paper adapts the Distributed Large Basin Runoff Model (DLBRM) to the Heihe River Watershed, the second largest inland river with a drainage area of about 128,000 km2 in Northwest China, for understanding the transfer and partitioning mechanism among the glacier and snowmelt, surface runoff, evapotranspiration, and groundwater recharge among the upper, middle, and lower reaches in the study area. Results indicate that the upper reach Qilian Mountain area is the main source of streamflow for the middle reach agricultural oasis and downstream desert areas. Large withdrawals for agricultural irrigation in the middle reach had significantly depleted river flow for the lower reach desert ecosystems. Innovative conservation and enforcement programs need to be undertaken to ensure the successful implementation of water allocation plan of delivering 0.95 x 109 m3 of water downstream annually by the State Council in the Heihe River Watershed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DLBRM" title="DLBRM">DLBRM</a>, <a href="https://publications.waset.org/abstracts/search?q=Northwestern%20China" title=" Northwestern China"> Northwestern China</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20variation" title=" spatial variation"> spatial variation</a>, <a href="https://publications.waset.org/abstracts/search?q=water%20allocation" title=" water allocation"> water allocation</a> </p> <a href="https://publications.waset.org/abstracts/40864/application-of-hydrological-model-in-support-of-streamflow-allocation-in-arid-watersheds-in-northwestern-china" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40864.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">302</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1</span> Sources of Precipitation and Hydrograph Components of the Sutri Dhaka Glacier, Western Himalaya</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ajit%20Singh">Ajit Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Waliur%20Rahaman"> Waliur Rahaman</a>, <a href="https://publications.waset.org/abstracts/search?q=Parmanand%20Sharma"> Parmanand Sharma</a>, <a href="https://publications.waset.org/abstracts/search?q=Laluraj%20C.%20M."> Laluraj C. M.</a>, <a href="https://publications.waset.org/abstracts/search?q=Lavkush%20Patel"> Lavkush Patel</a>, <a href="https://publications.waset.org/abstracts/search?q=Bhanu%20Pratap"> Bhanu Pratap</a>, <a href="https://publications.waset.org/abstracts/search?q=Vinay%20Kumar%20Gaddam"> Vinay Kumar Gaddam</a>, <a href="https://publications.waset.org/abstracts/search?q=Meloth%20Thamban"> Meloth Thamban</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Himalayan glaciers are the potential source of perennial water supply to Asia’s major river systems like the Ganga, Brahmaputra and the Indus. In order to improve our understanding about the source of precipitation and hydrograph components in the interior Himalayan glaciers, it is important to decipher the sources of moisture and their contribution to the glaciers in this river system. In doing so, we conducted an extensive pilot study in a Sutri Dhaka glacier, western Himalaya during 2014-15. To determine the moisture sources, rain, surface snow, ice, and stream meltwater samples were collected and analyzed for stable oxygen (δ¹⁸O) and hydrogen (δD) isotopes. A two-component hydrograph separation was performed for the glacier stream using these isotopes assuming the contribution of rain, groundwater and spring water contribution is negligible based on field studies and available literature. To validate the results obtained from hydrograph separation using above method, snow and ice melt ablation were measured using a network of bamboo stakes and snow pits. The δ¹⁸O and δD in rain samples range from -5.3% to -20.8% and -31.7% to -148.4% respectively. It is noteworthy to observe that the rain samples showed enriched values in the early season (July-August) and progressively get depleted at the end of the season (September). This could be due to the ‘amount effect’. Similarly, old snow samples have shown enriched isotopic values compared to fresh snow. This could because of the sublimation processes operating over the old surface snow. The δ¹⁸O and δD values in glacier ice samples range from -11.6% to -15.7% and -31.7% to -148.4%, whereas in a Sutri Dhaka meltwater stream, it ranges from -12.7% to -16.2% and -82.9% to -112.7% respectively. The mean deuterium excess (d-excess) value in all collected samples exceeds more than 16% which suggests the predominant moisture source of precipitation is from the Western Disturbances. Our detailed estimates of the hydrograph separation of Sutri Dhaka meltwater using isotope hydrograph separation and glaciological field methods agree within their uncertainty; stream meltwater budget is dominated by glaciers ice melt over snowmelt. The present study provides insights into the sources of moisture, controlling mechanism of the isotopic characteristics of Sutri Dhaka glacier water and helps in understanding the snow and ice melt components in Chandra basin, Western Himalaya. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=D-excess" title="D-excess">D-excess</a>, <a href="https://publications.waset.org/abstracts/search?q=hydrograph%20separation" title=" hydrograph separation"> hydrograph separation</a>, <a href="https://publications.waset.org/abstracts/search?q=Sutri%20Dhaka" title=" Sutri Dhaka"> Sutri Dhaka</a>, <a href="https://publications.waset.org/abstracts/search?q=stable%20water%20isotope" title=" stable water isotope"> stable water isotope</a>, <a href="https://publications.waset.org/abstracts/search?q=western%20Himalaya" title=" western Himalaya"> western Himalaya</a> </p> <a 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