Future changes in hydro-climatic extremes in the Upper Indus, Ganges, and Brahmaputra River basins

Abstract

Future hydrological extremes, such as floods and droughts, may pose serious threats for the livelihoods in the upstream domains of the Indus, Ganges, Brahmaputra. For this reason, the impacts of climate change on future hydrological extremes is investigated in these river basins. We use a fully-distributed cryospheric-hydrological model to simulate current and future hydrological fluxes and force the model with an ensemble of 8 downscaled General Circulation Models (GCMs) that are selected from the RCP4.5 and RCP8.5 scenarios. The model is calibrated on observed daily discharge and geodetic mass balances. The climate forcing and the outputs of the hydrological model are used to evaluate future changes in climatic extremes, and hydrological extremes by focusing on high and low flows. The outcomes show an increase in the magnitude of climatic means and extremes towards the end of the 21st century where climatic extremes tend to increase stronger than climatic means. Future mean discharge and high flow conditions will very likely increase. These increases might mainly be the result of increasing precipitation extremes. To some extent temperature extremes might also contribute to increasing discharge extremes, although this is highly dependent on magnitude of change in temperature extremes. Low flow conditions may occur less frequently, although the uncertainties in low flow projections can be high. The results of this study may contribute to improved understanding on the implications of climate change for the occurrence of future hydrological extremes in the Hindu Kush–Himalayan region.

Six Centuries of Upper Indus Basin Streamflow Variability and Its Climatic Drivers

“…Over the coming few decades, discharge in the UIB is expected to increase based on climate model predictions due to a combination of predicted warming enhancing the rate of glacial melt and increases in winter and summer precipitation (Ali et al, 2015;Brun et al, 2017;Immerzeel et al, 2013;Kraaijenbrink et al, 2017;Lutz et al, 2014;Wijngaard et al, 2017). At the subregional level, however, these changes might not be uniform.…”

Climate change vs. socio-economic development: Understanding the future South-Asian water gap

“…The surface water availability is generally largest during the monsoon season (Figure 3c) varying from less than 100 mm/year in the floodplains of the Indus (LIB) to more than 3500 mm/year in the 5 mountainous upstream domains of the Ganges and Brahmaputra. In these domains, the large surface water availability can mainly be attributed to the combined contributions from ice and snowmelt, and monsoon precipitation that can reach amounts over 3000 mm/year at the southern margins of the UGB and UBB (Wijngaard et al, 2017). During the winter season (see Fig 3a) the surface water availability is generally lowest with rates less than 100 mm/year in most regions of the IGB.…”

“…The model runs at a spatial resolution of 5 x 5 km and reports on a daily time step. SPHY has been used to assess climate change impacts for high mountain hydrology before (Lutz 25 et al, 2014(Lutz 25 et al, , 2016aWijngaard et al, 2017). The used set up was calibrated and validated using IceSat glacier mass balance data (Kääb et al, 2012), MODIS snow cover data (Hall et al, 2002;Hall and Riggs, 2015) and observed discharge in a study on the impacts of climate change on hydrological extremes in the upstream domains of the IGB (Wijngaard et al, 2017).…”

“…SPHY has been used to assess climate change impacts for high mountain hydrology before (Lutz 25 et al, 2014(Lutz 25 et al, , 2016aWijngaard et al, 2017). The used set up was calibrated and validated using IceSat glacier mass balance data (Kääb et al, 2012), MODIS snow cover data (Hall et al, 2002;Hall and Riggs, 2015) and observed discharge in a study on the impacts of climate change on hydrological extremes in the upstream domains of the IGB (Wijngaard et al, 2017). The model simulates daily discharge by calculating the amount of total runoff for each grid cell, and subsequently by routing the total runoff downstream by means of a simplified routing scheme that requires a digital elevation model (DEM) and a recession coefficient.…”

Correction and Informed Regionalization of Precipitation Data in a High Mountainous Region (Upper Indus Basin) and Its Effect on SWAT-Modelled Discharge

“…The available data also needs a lot of preprocessing, as it represents uncorrected raw precipitation readings, and, therefore, needs checking for quality issues and correction for losses or gaps. Similarly, while most of the weather stations have become operational after the mid-nineties, long-term data is a rear commodity and only available at a limited number of locations.Owing to the complex orography of the UIB region and to the co-action of different hydro-climatic regimes (which affect the amounts, spatial patterns and the seasonality of precipitation), neither the sparse observed station data (or the gridded data products based on them) nor the sensors-based data, fully represents the precipitation regime of the region [11][12][13][14]. Several studies have pointed out that precipitation in the HKH (Hindu Kush Himalayan) -region exhibits large changes over short distances and has a considerable vertical gradient [15][16][17][18][19][20].…”