Assessment of water resources from mountainous catchments is crucial for the development of upstream rural areas and downstream urban communities. However, lack of data in these mountainous catchments prevents full understanding of the response of hydrology or water resources to climate change. Meanwhile, hydrological modeling is challenging due to parameter uncertainty. In this work, one tributary of the Yarlung Zangbo River Basin (the upper stream of the Brahmaputra River) was used as a case study for hydrological modeling. Tropical Rainfall Measuring Mission (TRMM 3B42V7) data were utilized as a substitute for gauge-based rainfall data, and the capability of simulating precipitation, snow, and groundwater contributions to total runoff by the Soil and Water Assessment Tool (SWAT) was investigated. The uncertainty in runoff proportions from precipitation, snowmelt, and groundwater was quantified by a batch-processing module. Hydrological signatures were finally used to help identify if the hydrological model simulated total runoff and corresponding proportions properly. The results showed that: (1) TRMM data were very useful for hydrological simulation in high and cold mountainous catchments; (2) precipitation was the primary contributor nearly all year round, reaching 56.5% of the total runoff on average; (3) groundwater occupied the biggest proportion during dry seasons, whereas snowmelt made a substantial contribution only in late spring and summer; and (4) hydrological signatures were useful for helping to evaluate the performance of the hydrological model.

Evapotranspiration (ET) is an important element in the water and energy cycle. Potential evapotranspiration (PET) is an important measurement of ET. Its accuracy has significant influence on agricultural water management, irrigation planning, and hydrological modelling. However, whether current PET models are applicable under climate change or not, is still a question. In this study, five frequently used PET models were chosen, including one combination model (the FAO Penman-Monteith model, FAO-PM), two temperature-based models (the Blaney-Criddle and the Hargreaves models) and two radiation-based models (the Makkink and the Priestley-Taylor models), to estimate their appropriateness in the historical and future periods under climate change impact on the Yarlung Zangbo river basin, China. Bias correction methods were not only applied to the temperature output of Global Climate Models (GCMs), but also for radiation, humidity, and wind speed. It was demonstrated that the results from the Blaney-Criddle and Makkink models provided better agreement with the PET obtained by the FAO-PM model in the historical period. In the future period, monthly PET estimated by all five models show positive trends. The changes of PET under RCP8.5 are much higher than under RCP2.6. The radiation-based models show better appropriateness than the temperature-based models in the future, as the root mean square error (RMSE) value of the former models is almost half of the latter models. The radiation-based models are recommended for use to estimate PET under climate change in the Yarlung Zangbo river basin.