The Yarlung Zangbo River (YZR) is the largest river system in the Tibetan Plateau, and its basin is one of the centers of human economic activity in Tibet. Large uncertainties exist in several previous climate change studies in this basin because of limited climate observations. In this paper, we used a meteorological drought index (Standardized Precipitation Evapotranspiration Index, SPEI) and a newly-released gridded climate forcing dataset based on high-quality climate station data to re-evaluate climate change in the YZR Basin during the period of 1961–2014. Results showed that precipitation experienced a statistically insignificant increasing trend at a rate of 6.32 mm/10 years, and its annual mean was 512.40 mm. The basin was sensitive to climate change in terms of the air temperature that significantly increased at the rate of 0.32 °C/10 years. This warming rate was obviously larger than that in many other regions. Analysis of SPEI showed that the basin had no obvious statistical trends in the number of dry/wet episodes, but the severity of dry episode aggravated in terms of duration and magnitude. This study provides a reliable analysis of climate change in the YZR Basin, and suggests this large Tibetan river basin is sensitive to climate change.

Understanding streamflow generation at the catchment scale requires quantifying how different components of the system are linked, and how they respond to meteorological forcing. Here we present a proof‐of‐concept study characterizing and quantifying dynamic linkages between precipitation, groundwater recharge, and streamflow using a data‐driven nonlinear deconvolution and demixing approach, Ensemble Rainfall‐Runoff Analysis (ERRA). Streamflow in our mesoscale, intensively farmed test catchment is flashy, but occurs at time lags that are too long to be plausibly attributed to overland flow. Instead, ERRA's estimates of the impulse responses of groundwater recharge to precipitation, and of streamflow to groundwater recharge, imply that this intermittent streamflow is primarily driven by precipitation infiltrating to recharge groundwater, followed by discharge of groundwater to streamflow. ERRA reveals that streamflow increases nonlinearly with increasing precipitation intensity or groundwater recharge, and exhibits almost no response to precipitation or recharge rates of less than 10 mm d ⁻¹ . Groundwater recharge is both nonlinear, increasing more‐than‐proportionally with precipitation intensity, and nonstationary, increasing with antecedent wetness. Simulations with the infiltration model Hydrus‐1D can reproduce the observed water table time series reasonably well (NSE = 0.70). However, ERRA shows that the model's impulse response is inconsistent with the real‐world impulse response estimated from measured precipitation and groundwater recharge, illustrating that conventional goodness‐of‐fit statistics can be weak tests of model realism. Thus, our proof‐of‐concept study demonstrates how impulse responses estimated by ERRA can help clarify linkages between precipitation and streamflow at the catchment scale, quantify nonlinearity and nonstationarity in hydrologic processes, and critically evaluate simulation models.

HIGHLIGHTS. This study proposed the concept of double-Latin hypercube sampling.This study improved Morris method by using the double-Latin sampling method and a

Reliable precipitation is crucial for hydrological studies over Tibetan Plateau (TP) basins with sparsely distributed rainfall gauges. In this study, four widely used precipitation products, including the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of the water resources (APHRODITE), the High Asia Reanalysis (HAR), and the satellite-based precipitation estimates from Global Precipitation Measurement (GPM) and Tropical Rainfall Measurement Mission (TRMM), were comprehensively evaluated by combining statistical analysis and hydrological simulation over the Upper Brahmaputra (UB) River Basin of TP during 2001–2013. In respect to the statistical assessment, the overall performances of GPM and HAR are comparable to each other, and both are superior to the other two datasets. For hydrological assessment, both daily and monthly GPM-based streamflow simulations perform the best not only at the UB outlet with very good results, but they also illustrate satisfactory results at Yangcun and Lhasa hydrological stations within the UB. Runoff simulation using HAR only performs well at the UB outlet, whereas it shows poor results at both Yangcun and Lhasa stations. The simulated results based on APHRODITE and TRMM show poor performances at UB. Generally, the GPM shows an encouraging potential for hydro-meteorological investigation over UB, although with some bias in flood simulation.

To better understand the mechanisms of the hydro-ecological cycle in the changing environments of the Yangtze River Basin (YZRB), it is valuable to investigate vegetation dynamics and their response to climate change. This study explored the spatial patterns of vegetation dynamics and the essential triggers of regional differences by analyzing vegetation variations in the 1982–2015 period at different time scales and the interannual variability of vegetation sensitivity to climate variability. The results showed that the normalized difference vegetation index (NDVI) increased significantly in the last three decades, but vegetation displayed great spatiotemporal variations at different time scales. The vegetation in the central part of the YZRB dominated by forests and shrublands was more sensitive to climate variability than vegetation in the source region of the YZRB, which was dominated by alpine meadows and tundra (AMT). The contribution of climate variables to the vegetation sensitivity index (VSI) had large spatial differences, but solar radiation and temperature were the dominant factors. Furthermore, 57.9% of the YZRB had increasing VSIs, especially in the south-central part. Consistent with the distributions of elevation and vegetation types, vegetation dynamics in the YZRB were divided into five spatial patterns, with the largest increasing NDVI trend in Region III and the largest VSI in Region IV. Moreover, the VSI exhibited fairly consistent dynamics in all subregions, but the contributions of climate variables to the VSI varied greatly among the different regions.