Abstract
Modelling the impact of climate change on streamflow for remote or data sparse regions is a challenge for hydrologists, as large datasets are often needed to adequately characterise the processes that dominate. The Tibetan Plateau, which forms the headwaters of the Brahmaputra and many other major rivers in the Indo-China region, is not closely monitored due to its harsh environment. This lack of monitoring is significant as regards its substantial snow resources, of considerable importance given the influence these have on the supply of water to downstream communities. This research uses a conceptual hydrologic model developed to simulate the impact of the changing climate in such large, snow-covered, data sparse catchments, to adequately understand the likely changes in future water availability in the highly populated Brahmaputra basin and surrounding areas in the Tibetan Plateau. A multivariate nested recursive bias correction (MRNBC) approach is used for correcting systematic biases present in the climate model simulations of temperature and precipitation jointly across multiple timescales preserving the dynamic relationships amongst the variables. The results disclose that monthly snow cover fraction in the near future (2041–2060) and far future (2071–2090) will decrease significantly with respect to the historical period (1981–2000). While the annual streamflow is noted to be increasing for the basin, water supply reliability exhibits a reduction, partly as a result of the increased spill volume arising from greater snowmelt concentrated over the wet period, and partly the increase in evaporation losses due to higher temperatures. While the above results pertain to the Brahmaputra basin, similar changes are expected for the other major rivers that originate in the Tibetan Plateau.
Published Version
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