2015
DOI: 10.1080/02508060.2015.1099192
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Impact of planned water resource development on current and future water demand in the Koshi River basin, Nepal

Abstract: The water resources of the Koshi Basin (87,311 km 2) are largely untapped, and while proposals for their development exist, their impacts on current and future water demand are not quantified. The current study is the first to evaluate the impacts of 11 proposed development projects for hydropower generation and water storage. We find that 29,733 GWh of hydropower could be generated annually and 8382 million m 3 of water could be stored. This could satisfy unmet demand in the current (660 million m 3) basin si… Show more

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Cited by 55 publications
(22 citation statements)
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“…The development of effective strategies to mitigate water stress problems requires good knowledge of water resources originating in the HKKH region, which are commonly investigated using hydrological (Jain et al, 2010;Singh & Jain, 2003) and water resource systems (Chinnasamy et al, 2015;Momblanch et al, 2019;Sharma & De Condappa, 2013) models. In mountainous regions, hydrological models are highly sensitive to errors in precipitation data, which is one of the key inputs used to force them (Andermann et al, 2011;Li et al, 2017;Meng et al, 2014;Remesan & Holman, 2015;Wulf et al, 2016).…”
Section: Introductionmentioning
confidence: 99%
“…The development of effective strategies to mitigate water stress problems requires good knowledge of water resources originating in the HKKH region, which are commonly investigated using hydrological (Jain et al, 2010;Singh & Jain, 2003) and water resource systems (Chinnasamy et al, 2015;Momblanch et al, 2019;Sharma & De Condappa, 2013) models. In mountainous regions, hydrological models are highly sensitive to errors in precipitation data, which is one of the key inputs used to force them (Andermann et al, 2011;Li et al, 2017;Meng et al, 2014;Remesan & Holman, 2015;Wulf et al, 2016).…”
Section: Introductionmentioning
confidence: 99%
“…Stationarity of a variable implies that its probability density function is time invariant, because the natural system that gives rise to this variable fluctuates only within an unchanging envelop of variability [3,4]. Hitherto, most of the hydrological modeling, water resources management, infrastructural design, and operation processes have relied upon the fundamental assumption of stationarity of temperature and precipitation [5][6][7][8]. But the concept of stationarity has been compromised by anthropogenic interventions in the water basins, as well as by the effects of climate change.…”
Section: Introductionmentioning
confidence: 99%
“…In a study in the Yushugou River Basin, China, Wang et al (2015) used δ 18 O to infer the percentage contributions of snowmelt (63%) and groundwater (springs 27%) to flooding of the Yushugou River. Such information on splitting the sources of floods is also critical in the Himalayan region, especially in the Koshi River Basin where annual floods are common (Chinnasamy et al 2015). In another study in Iraq, Mustafa et al (2015) studied the δ 18 O and δ 2 H composition in eight springs to distinguish spring origins, and identified three aquifers in the region: Behkme aquifer, Kometan aquifer and Shiranish aquifer, contributing to spring flow.…”
Section: Isotopes As Tracersmentioning
confidence: 99%