Seasonal cycles and trends of water budget components in 18 river basins across Tibetan Plateau: a multiple datasets perspective

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Liu, Wenbin ; Sun, Fubao ; Li, Yanzhong ; Zhang, Guoqing ; Sang, Yan-Fang ; Liu, Jiahong ; Wang, Hong ; Bai, Peng (2016)

The insights of water budgets over Tibetan Plateau (TP) are not fully understood so far due to the lack of quantitative observations of the land surface processes. Here, we investigated the seasonal cycles and trends of water budget components in 18 TP basins through the use of multi-source datasets during the period 1982–2011. A two-step bias correction procedure was applied to calculate the basin-wide evapotranspiration (ET) through the water balance considering the influences of glacier and water storage change. The results indicated that precipitation, which mainly concentrated during June–October (varied among different monsoons impacted basins), is the major contributor to the runoff in TP basins. The basin-wide snow water equivalent (SWE) was relatively higher from mid-autumn to spring for most TP basins. The water cycles intensified under a global warming in most basins except for the upper Yellow and Yalong Rivers, which were significantly influenced by the weakening East Asian monsoon. Corresponded to the climate warming and moistening in the TP and western China, the aridity index (PET/P) in most basins decreased. The general hydrological regimes could be inferred from the perspective of multi-source datasets although there are considerable uncertainties from different datasets, which are comparable to some existing studies using the field observations and complex modeling approaches. The results highlighted the usefulness of integrating the multi-source data (e.g., in situ observations, remote sensing products, reanalysis, land surface model simulations and climate model outputs) for hydrological applications in the data-sparse environments and could be benefit for understanding the water and energy budgets, sustainable management of water resources under a warming climate in the harsh and data-sparse Tibetan Plateau.
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