A quantitative assessment of the genetic sources of the hydrologic flow regimes in Upper Indus Basin and its significance in a changing climate

Mukhopadhyay, Biswajit; Khan, Asif

doi:10.1016/j.jhydrol.2013.11.059

Cited by 87 publications

(84 citation statements)

References 26 publications

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“…A major part of the flow extracted from the Indus at Tarbela Dam is contributed by snow-and glacier melt of the HKH mountain ranges [1][2][3]. The contribution of the flow from the Upper Indus Basin (UIB) is more than 70%, and it originates from zones of heavy snowfall situated above 4,000 m elevation, as reported in other studies [4][5]. Approximately 50% of the runoff, as a fraction of the total annual discharge, is due to snowmelt from the Western Himalayan catchments [1,5].…”

Section: Introductionmentioning

confidence: 55%

“…The contribution of the flow from the Upper Indus Basin (UIB) is more than 70%, and it originates from zones of heavy snowfall situated above 4,000 m elevation, as reported in other studies [4][5]. Approximately 50% of the runoff, as a fraction of the total annual discharge, is due to snowmelt from the Western Himalayan catchments [1,5]. The maximum precipitation in the UIB occurs during the winter and spring seasons due to the westerlies [6][7].…”

Section: Introductionmentioning

confidence: 69%

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Snowmelt Runoff Modelling under Projected Climate Change Patterns in the Gilgit River Basin of Northern Pakistan

Adnan¹,

Nabi²,

Kang³

et al. 2017

Pol. J. Environ. Stud.

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Pakistan is home to three of the world's largest mountain ranges in the Upper Indus Basin (UIB), where the majority of Pakistan's water resources are located: the Himalayan, Karakorum, and Hindu-Kush. This work estimated the (snow+glacier) and rainfall runoff from one of the major tributaries, the Gilgit River, nestled within the UIB of Pakistan. The snowmelt runoff model (SRM) derived by the cryospheric data from the MODIS (moderate resolution imaging spectroradiometer) was employed to predict the daily discharges of the Gilgit. The SRM was successfully calibrated, and the simulation was undertaken from 2005 to 2010, with a coefficient of model efficiency ranging from 0.84-0.94. The average contributions of (snow+glacier) and rainfall to the stream flows of the Gilgit from 2001-10 were 78.35% and 21.65%, respectively, derived from the SRM. The representative concentration pathways (RCP) 4.5 and 8.5 scenarios of the Intergovernmental Panel on Climate Change (IPCC) AR5 were used to investigate the effects of the changes in temperature on climate of the Gilgit catchment. Under the RCP 4.5 scenario, the air temperature

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Section: Introductionmentioning

confidence: 55%

Section: Introductionmentioning

confidence: 69%

Snowmelt Runoff Modelling under Projected Climate Change Patterns in the Gilgit River Basin of Northern Pakistan

Adnan¹,

Nabi²,

Kang³

et al. 2017

Pol. J. Environ. Stud.

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“…We use the output of ongoing or recently-terminated reanalysis projects over past decades. In this analysis, we use the following six reanalysis products: (1) ERA-Interim [22]; (2) ERA20C [54]; (3) Japanese 55-year reanalysis [55]; (4) NCEP-NCAR reanalysis R1 [29]; (5) NCEP-CFSR [56]; and NASA Modern-Era Retrospective Reanalysis (MERRA) [57] (6). The products and their most important characteristics are summarized in Table 1.…”

Section: Atmospheric Reanalysesmentioning

confidence: 99%

Predictive Uncertainty Estimation on a Precipitation and Temperature Reanalysis Ensemble for Shigar Basin, Central Karakoram

Reggiani

Coccia

Mukhopadhyay

2016

Water

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Abstract:The Upper Indus Basin (UIB) and the Karakoram Range are the subject of ongoing hydro-glaciological studies to investigate possible glacier mass balance shifts due to climatic change. Because of the high altitude and remote location, the Karakoram Range is difficult to access and, therefore, remains scarcely monitored. In situ precipitation and temperature measurements are only available at valley locations. High-altitude observations exist only for very limited periods. Gridded precipitation and temperature data generated from the spatial interpolation of in situ observations are unreliable for this region because of the extreme topography. Besides satellite measurements, which offer spatial coverage, but underestimate precipitation in this area, atmospheric reanalyses remain one of the few alternatives. Here, we apply a proven approach to quantify the uncertainty associated with an ensemble of monthly precipitation and temperature reanalysis data for 1979-2009 in Shigar Basin, Central Karakoram. A Model-Conditional Processor (MCP) of uncertainty is calibrated on precipitation and temperature in situ data measured in the proximity of the study region. An ensemble of independent reanalyses is processed to determine the predictive uncertainty of monthly observations. As to be expected, the informative gain achieved by post-processing temperature reanalyses is considerable, whereas significantly less gain is achieved for precipitation post-processing. The proposed approach indicates a systematic assessment procedure for predictive uncertainty through probabilistic weighting of multiple re-forecasts, which are bias-corrected on ground observations. The approach also supports an educated reconstruction of gap-filling for missing in situ observations.

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“…Glacier meltwater has been found to constitute up to 80% of total annual river discharge in some basins in HMA (Cook et al, 2013;Yu et al, 2013;Lutz et al, 2014;Mukhopadhyay and Khan, 2014). Regional hydrologic studies suggest decreases in snow and ice extent over the coming century will be most detrimental in the Indus and Brahmaputra watersheds because of the significant role glacier runoff plays in these basins (Singh et al, 2006;Kulkarni et al, 2007;Immerzeel et al, 2010Immerzeel et al, , 2013Thayyen and Gergan, 2010;Rupper et al, 2012;Sharif et al, 2012;Khalid et al, 2013;Lutz et al, 2013Lutz et al, , 2014Mukhopadhyay and Khan, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…Regional hydrologic studies suggest decreases in snow and ice extent over the coming century will be most detrimental in the Indus and Brahmaputra watersheds because of the significant role glacier runoff plays in these basins (Singh et al, 2006;Kulkarni et al, 2007;Immerzeel et al, 2010Immerzeel et al, , 2013Thayyen and Gergan, 2010;Rupper et al, 2012;Sharif et al, 2012;Khalid et al, 2013;Lutz et al, 2013Lutz et al, , 2014Mukhopadhyay and Khan, 2014). The Indus basin has one of the world's largest integrated irrigated networks, and more than 215 million people rely on it for agriculture, industrial development, and hydropower generation (Jianchu et al, 2007;Yu et al, 2013;Mukhopadhyay and Khan, 2014).…”

Section: Introductionmentioning

confidence: 99%

Sensitivity of glacier runoff projections to baseline climate data in the Indus River basin

et al. 2015

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Quantifying the contribution of glacier runoff to water resources is particularly important in regions such as High Mountain Asia, where glaciers provide a large percentage of seasonal river discharge and support large populations downstream. In remote areas, direct field measurements of glacier melt rates are difficult to acquire and rarely observed, so hydro-glaciological modeling and remote sensing approaches are needed. Here we present estimates of glacier melt contribution to the Upper Indus watershed over the last 40 years using a suite of seven reanalysis climate datasets that have previously been used in hydrological models for this region, a temperature-index melt model and >29,000 km 2 of ice cover. In particular, we address the uncertainty in estimates of meltwater flux that is introduced by the baseline climate dataset chosen, by comparing the results derived from each. Mean annual glacier melt contribution varies from 8 to 169 km 3 yr −1 , or between 4 and 78% of the total annual runoff in the Indus, depending on temperature dataset applied. Under projected scenarios of an additional 2-4 • C of regional warming by 2100 AD, we find annual meltwater fluxes vary by >200% depending on the baseline climate dataset used and, importantly, span a range of positive and negative trends. Despite significant differences between climate datasets and the resulting spread in meltwater fluxes, the spatial pattern of melt is highly correlated and statistically robust across all datasets. This allows us to conclude with confidence that fewer than 10% of the >20,000 glaciers in the watershed contribute more than 80% of the total glacier runoff to the Indus. These are primarily large, low elevation glaciers in the Karakoram and Hindu Kush. Additional field observations to ground-truth modeled climate data will go far to reduce the uncertainty highlighted here and we suggest that efforts be focused on those glaciers identified to be most significant to water resources.

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A quantitative assessment of the genetic sources of the hydrologic flow regimes in Upper Indus Basin and its significance in a changing climate

Cited by 87 publications

References 26 publications

Snowmelt Runoff Modelling under Projected Climate Change Patterns in the Gilgit River Basin of Northern Pakistan

Snowmelt Runoff Modelling under Projected Climate Change Patterns in the Gilgit River Basin of Northern Pakistan

Predictive Uncertainty Estimation on a Precipitation and Temperature Reanalysis Ensemble for Shigar Basin, Central Karakoram

Sensitivity of glacier runoff projections to baseline climate data in the Indus River basin

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