Projected Increase in Hydropower Production in India under Climate Change

Ali, Syed Azhar; Aadhar, Saran; Shah, Harsh L.; Mishra, Vimal

doi:10.1038/s41598-018-30489-4

Cited by 72 publications

(52 citation statements)

References 59 publications

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“…We process the raw neutron count rate as follows: to eliminate spurious changes in the count rate, neutron counts are excluded if the hourly count differs more than 20 % from a 6 h moving average. As presented in previous literature (e.g., Zreda et al, 2012;Hawdon et al, 2014;Sigouin and Si, 2016;Andreasen et al, 2017), we correct the neutron count rate (N raw,i ) for time step i for variations in solar activity (F s,i ) and more importantly for changes in in situ air pressure (F p,i ) with…”

Section: Calculating Swe From Neutron Countsmentioning

confidence: 99%

“…The evolution and amount of seasonal snow accumulation in high mountain regions is a key parameter in many climaterelated research fields such as glaciology or hydrology and climate change impacts, risks and adaptation. Changes in snow accumulation in mountain areas caused by climate change are expected to have major impacts on water supply for adjacent lowlands (Barnett et al, 2005;Viviroli et al, 2007Viviroli et al, , 2011, hydropower production (Ali et al, 2018) or winter tourism (Marty et al, 2014;Sturm et al, 2017). In addition, information of the amount of water stored within the annual snowpack (snow water equivalent, SWE) in high mountain regions is crucial for avalanche warning (Castebrunet et al, 2014), flood prevention (Jörg-Hess et al, 2015) or mass balance calculations of glaciers (Sold et al, 2013;Pulwicki et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Continuous and autonomous snow water equivalent measurements by a cosmic ray sensor on an alpine glacier

et al. 2019

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Abstract. Snow water equivalent (SWE) measurements of seasonal snowpack are crucial in many research fields. Yet accurate measurements at a high temporal resolution are difficult to obtain in high mountain regions. With a cosmic ray sensor (CRS), SWE can be inferred from neutron counts. We present the analyses of temporally continuous SWE measurements by a CRS on an alpine glacier in Switzerland (Glacier de la Plaine Morte) over two winter seasons (2016/17 and 2017/18), which differed markedly in the amount and timing of snow accumulation. By combining SWE with snow depth measurements, we calculate the daily mean density of the snowpack. Compared to manual field observations from snow pits, the autonomous measurements overestimate SWE by +2 % ± 13 %. Snow depth and the bulk snow density deviate from the manual measurements by ±6 % and ±9 %, respectively. The CRS measured with high reliability over two winter seasons and is thus considered a promising method to observe SWE at remote alpine sites. We use the daily observations to classify winter season days into those dominated by accumulation (solid precipitation, snow drift), ablation (snow drift, snowmelt) or snow densification. For each of these process-dominated days the prevailing meteorological conditions are distinct. The continuous SWE measurements were also used to define a scaling factor for precipitation amounts from nearby meteorological stations. With this analysis, we show that a best-possible constant scaling factor results in cumulative precipitation amounts that differ by a mean absolute error of less than 80 mm w.e. from snow accumulation at this site.

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Section: Calculating Swe From Neutron Countsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Continuous and autonomous snow water equivalent measurements by a cosmic ray sensor on an alpine glacier

et al. 2019

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“…The VIC model provides only the fluxes for each grid at a daily time step based on the forcing parameters; therefore, to extract the accumulated effect at the particular location, a separate routing scheme developed by Lohmann et al [54,55] was employed. The VIC model has been used by various studies for hydrological assessment [36,[56][57][58][59][60][61]. The routing scheme explicitly routes the surface and subsurface flow within a grid using a unit hydrograph that contributes to a channel network where the node of the channel network represents each grid cell of the VIC model and linearized Saint-Venant equation [62,63].…”

Section: Reservoir Flow Estimation Based On Hydrological Modelingmentioning

confidence: 99%

Deriving the Reservoir Conditions for Better Water Resource Management Using Satellite-Based Earth Observations in the Lower Mekong River Basin

Ali

Sridhar

2019

Remote Sensing

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The Mekong River basin supported a large population and ecosystem with abundant water and nutrient supply. However, the impoundments in the river can substantially alter the flow downstream and its timing. Using limited observations, this study demonstrated an approach to derive dam characteristics, including storage and flow rate, from remote-sensing-based data. Global Reservoir and Lake Monitor (GRLM), River-Lake Hydrology (RLH), and ICESat-GLAS, which generated altimetry from Jason series and inundation areas from Landsat 8, were used to estimate the reservoir surface area and change in storage over time. The inflow simulated by the variable infiltration capacity (VIC) model from 2008 to 2016 and the reservoir storage change were used in the mass balance equation to calculate outflows for three dams in the basin. Estimated reservoir total storage closely resembled the observed data, with a Nash-Sutcliffe efficiency and coefficient of determination more than 0.90 and 0.95, respectively. An average decrease of 55% in outflows was estimated during the wet season and an increase of up to 94% in the dry season for the Lam Pao. The estimated decrease in outflows during the wet season was 70% and 60% for Sirindhorn and Ubol Ratana, respectively, along with a 36% increase in the dry season for Sirindhorn. Basin-wide demand for evapotranspiration, about 935 mm, implicitly matched with the annual water diversion from 1000 to 2300 million m3. From the storage–discharge rating curves, minimum storage was also evident in the monsoon season (June–July), and it reached the highest in November. This study demonstrated the utility of remote sensing products to assess the impacts of dams on flows in the Mekong River basin.

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“…Due to scarcity of rainfall in the arid regions, there is a rapid increase in the uncontrolled urban sprawl along the wadis passes and their floodplain areas; areas which will be under a significant risk due to flash floods [1][2][3][9][10][11]. The flash flood impacts have increased in many areas all over the world especially with recent climate changes [12][13][14][15][16]. Various causative factors are contributors to flash floods (e.g., hydrological and meteorological characteristics, soil and land-use types, geological structures, geomorphology, and vegetation) [8,17,18].…”

Section: Introductionmentioning

confidence: 99%

A Flood Risk Management Program of Wadi Baysh Dam on the Downstream Area: An Integration of Hydrologic and Hydraulic Models, Jizan Region, KSA

et al. 2020

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For public safety, especially for people who dwell in the valley that is located downstream of a dam site, as well as the protection of economic and environmental resources, risk management programs are urgently required all over the world. Despite the high safety standards of dams because of improved engineering and excellent construction in recent times, a zero-risk guarantee is not possible, and accidents can happen, triggered by natural hazards, human actions, or just because the dam is aging. In addition to that is the impact of potential climate change, which may not have been taken into account in the original design. A flood risk management program, which is essential for protecting downstream dam areas, is required. Part of this program is to prepare an inundation map to simulate the impact of dam failure on the downstream areas. The Baysh dam has crucial importance both to protect the downstream areas against flooding, to provide drinking water to cities in the surrounding areas, and to use the excess water for irrigation of the agricultural areas located downstream of the dam. Recently, the Kingdom of Saudi Arabia (KSA) was affected by extraordinary rainstorm events causing many problems in many different areas. One of these events happened along the basin of the Baysh dam, which raised the alarm to the decision makers and to the public to take suitable action before dam failure occurs. The current study deals with a flood risk analysis of Wadi Baysh using an integration of hydrologic and hydraulic models. A detailed field investigation of the dam site and the downstream areas down to the Red Sea coast has been undertaken. Three scenarios were applied to check the dam and the reservoir functionality; the first scenario at 100- and 200-year return period rainfall events, the second scenario according to the Probable Maximum Precipitation (PMP), and the third scenario if the dam fails. Our findings indicated that the Baysh dam and reservoir at 100- and 200-year rainfall events are adequate, however, at the PMP the water will spill out from the spillway at ~8900 m3/s causing flooding to the downstream areas; thus, a well-designed channel along the downstream wadi portion up to the Red Sea coast is required. However, at dam failure, the inundation model indicated that a vast area of the section downstream of the dam will be utterly devastated, causing a significant loss of lives and destruction of urban areas and agricultural lands. Eventually, an effective warning system and flood hazard management system are imperative.

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Projected Increase in Hydropower Production in India under Climate Change

Cited by 72 publications

References 59 publications

Continuous and autonomous snow water equivalent measurements by a cosmic ray sensor on an alpine glacier

Continuous and autonomous snow water equivalent measurements by a cosmic ray sensor on an alpine glacier

Deriving the Reservoir Conditions for Better Water Resource Management Using Satellite-Based Earth Observations in the Lower Mekong River Basin

A Flood Risk Management Program of Wadi Baysh Dam on the Downstream Area: An Integration of Hydrologic and Hydraulic Models, Jizan Region, KSA

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