2016
DOI: 10.3126/jhm.v9i1.15584
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Estimation of Discharge From Upper Kabul River Basin, Afghanistan Using the Snowmelt Runoff Model

Abstract: In this study, we estimated discharge from Upper Kabul River basin in the Hindu Kush Mountain (Paghman range) in Afghanistan. The Upper Kabul River basin covers an area of 1633.8km2 with a maximum elevation of 4522 m and minimum elevation of 1877 m. The Kabul River is one of the main rivers in Afghanistan and sustains a significant flow of water in summer months due to the melting of snow. In this study, daily discharge from Upper Kabul River basin, west of Kabul basin, for 2009 and 2011 is estimated by using … Show more

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Cited by 14 publications
(10 citation statements)
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“…A test carried out in the watershed of the Kuban River basin in Russia showed that the SRM can simulate both diurnal water flow and snow water equivalents in hilly places with high accuracy [33]. Streamflow estimates for short to medium term periods (e.g., 1-15 days) have been generated effectively using the SRM in three snowmelt-dominated basins in Idaho, United States [34], and estimation of discharge using Snowmelt Runoff Modeling in the Upper Kabul River, Afghanistan region [35], Salang River basin, Afghanistan [33], Taleghan watershed, Iran [36], Nuranang river catchment Arunachal Pradesh, India [37], Langtang River basin, Nepal [38], Manasi River basin, China [39], the Karakoram and Himalayan Regions of Pakistan [40], Yu-rungkash watershed, China [41], and Horo-Dehno watershed, Iran [42]. Nourani et al [43] studied the snowmelt runoff model (SRM)to estimate the effect of snow on the surface flow of the Aji-Chay basin, northwest Iran, using two calibration techniques to enhance the calibration, applying multi-station calibration (MSC) and single-station calibration (SSC) strategies in order to investigate their effects on modeling accuracy; they found a 15% improvement in model performance with the MSC strategy.…”
Section: Introductionmentioning
confidence: 99%
“…A test carried out in the watershed of the Kuban River basin in Russia showed that the SRM can simulate both diurnal water flow and snow water equivalents in hilly places with high accuracy [33]. Streamflow estimates for short to medium term periods (e.g., 1-15 days) have been generated effectively using the SRM in three snowmelt-dominated basins in Idaho, United States [34], and estimation of discharge using Snowmelt Runoff Modeling in the Upper Kabul River, Afghanistan region [35], Salang River basin, Afghanistan [33], Taleghan watershed, Iran [36], Nuranang river catchment Arunachal Pradesh, India [37], Langtang River basin, Nepal [38], Manasi River basin, China [39], the Karakoram and Himalayan Regions of Pakistan [40], Yu-rungkash watershed, China [41], and Horo-Dehno watershed, Iran [42]. Nourani et al [43] studied the snowmelt runoff model (SRM)to estimate the effect of snow on the surface flow of the Aji-Chay basin, northwest Iran, using two calibration techniques to enhance the calibration, applying multi-station calibration (MSC) and single-station calibration (SSC) strategies in order to investigate their effects on modeling accuracy; they found a 15% improvement in model performance with the MSC strategy.…”
Section: Introductionmentioning
confidence: 99%
“…This model was used successfully in more than one hundred mountainous basins [35], including those of HKH region [28,[36][37][38][39]. SRM was tested by World Metrological Organization [32] and it was used to investigate the impacts of climatic changes on seasonal river flows in North America, the Swiss Alps, the Himalaya, and in the Upper Indus Basin [40,41].…”
Section: Introductionmentioning
confidence: 99%
“…Afghanistan is a country dominated by a dry climate, with most of the area categorized by effects of global climate changes on hydrological systems, particularly on mountain snow and glacier melting, can adapt the timing and quantity of water stream in mountain watersheds. So, correct stream current simulation and prediction is of great importance to water resources management and forecasting [24] .…”
Section: Introductionmentioning
confidence: 99%