Evapotranspiration (ET) has undergone profound changes as a result of global climate change and anthropogenic activities. The construction of the Three Gorges Reservoir (TGR) has led to changes in its land use/land cover (LUCC) and local climate, which in turn has changed ET processes in the TGR region. In this paper, the CLM4.5 land surface model is used to simulate and analyze the spatiotemporal variability of ET between 1993 and 2013. Four experiments were conducted to quantify the contribution rate of climate change and LUCC to changes in ET processes. The results show that the climate showed a warming and drying trend from 1993 to 2013, and the LUCC indicates decreasing cropland with increasing forest, grassland, water bodies and urban areas. These changes increased the mean annual ET by 13.76 mm after impoundment. Spatially, the vegetation transpiration accounts for the largest proportion in ET. The decreasing relative humidity and increasing wind speeds led to an increase in vegetation transpiration and ground evaporation, respectively, in the center of the TGR region, while the LUCC drove changes in ET in water bodies, urban areas and high-altitude regions in the TGR region.

Global precipitation products (GPPs) are vital in weather forecasting, efficient water management, and monitoring floods and droughts. However, the precision of these datasets varies considerably across different climatic regions and topographic conditions. Therefore, the accuracy assessment of the precipitation dataset is crucial at the local scale before its application. The current study initially compared the performance of recently modified and upgraded precipitation datasets, including Climate Research Unit Time-Series (CRU TS v4.08), fifth-generation ERA5-Land (ERA-5), Integrated Multi-satellite Retrievals for GPM (IMERG) final run (IMERG v07B), and Multi-Source Weighted-Ensemble Precipitation (MSWEP v2.8), against ground observations on the provincial basis across Pakistan from 2003 to 2020. Later, the study area was categorized into four regions based on the elevation to observe the impact of elevation gradients on GPPs’ skills. The monthly and seasonal precipitation estimations of each product were validated against in situ observations using statistical matrices, including the correlation coefficient (CC), root mean square error (RMSE), percent of bias (PBias), and Kling–Gupta efficiency (KGE). The results reveal that IMERG7 consistently outperformed across all the provinces, with the highest CC and lowest RMSE values. Meanwhile, the KGE (0.69) and PBias (−0.65%) elucidated, comparatively, the best performance of MSWEP2.8 in Sindh province. Additionally, all the datasets demonstrated their best agreement with the reference data toward the southern part (0–500 m elevation) of Pakistan, while their performance notably declined in the northern high-elevation glaciated mountain regions (above 3000 m elevation), with considerable overestimations. The superior performance of IMERG7 in all the elevation-based regions was also revealed in the current study. According to the monthly and seasonal scale evaluation, all the precipitation products except ERA-5 showed good precipitation estimation ability on a monthly scale, followed by the winter season, pre-monsoon season, and monsoon season, while during the post-monsoon season, all the datasets showed weak agreement with the observed data. Overall, IMERG7 exhibited comparatively superior performance, followed by MSWEP2.8 at a monthly scale, winter season, and pre-monsoon season, while MSWEP2.8 outperformed during the monsoon season. CRU TS showed a moderate association with the ground observations, whereas ERA-5 performed poorly across all the time scales. In the current scenario, this study recommends IMERG7 and MSWEP2.8 for hydrological and climate studies in this region. Additionally, this study emphasizes the need for further research and experiments to minimize bias in high-elevation regions at different time scales to make GPPs more reliable for future studies.

This study analyzes the duration and division of the flood season in the Fenhe River Basin over the period of 1957–2014 based on daily precipitation data collected from 14 meteorological stations. The Mann–Kendall detection, the multiscale moving t-test, and the Fisher optimal partition methods are used to evaluate the impact of climate change on flood season duration and division. The results show that the duration of the flood season has extended in 1975–2014 compared to that in 1957–1974. Specifically, the onset date of the flood season has advanced 15 days, whereas the retreat date of the flood season remains almost the same. The flood season of the Fenhe River Basin can be divided into three stages, and the variations in the onset and retreat dates of each stage are also examined. Corresponding measures are also proposed to better utilize the flood resources to adapt to the flood season variations.

Variations of precipitation type can exert substantial impacts on hydrological processes, yet few studies have quantified the impacts of precipitation type variations on runoff changes in high−altitude regions. In this study, we attempted to examine the potential impacts of precipitation type variations induced by the warming climate on the runoff changes of the source regions of the Yangtze River and Yellow River basins from 1979 to 2018, where the mean elevation is over 4000 m. A modified precipitation type identification method using the wet-bulb temperature, and a runoff change attribution method based on a modified Budyko framework has been applied. Results showed that fluctuations of precipitation contributed to the majority of the runoff variations in the source regions of the Yangtze River basin, which accounted for 51.64%. However, the changes of characteristic parameter n, which indicates the impacts of the underlying surface, explained 56.22% of the runoff changes in the source regions of the Yellow River. It was shown that the trend of shifting from snowfall to rainfall due to a warming climate could result in runoff decreasing, which contributed to 24.06% and 11.29% of the runoff changes in the two source regions, comparatively.

As important subsystems of the urban environment, water resources and energy are necessary for normal urban functions and play an important supporting role in urbanization. The rapid development of China’s economy is increasingly dependent on these two subsystems. Analyses of the relationship between urbanization and water use or energy consumption have become the focus of attention, but researchers have mainly evaluated the impact on the two subsystems separately without providing an integrated analysis, nor have they revealed the link between water use and energy consumption. We addressed this information gap by using an econometric method to empirically investigate the long-term equilibrium relationships and Granger causal relationships among urbanization, water use, and energy consumption in China, and by conducting a spatiotemporal analysis to identify the trends of water use intensity and energy consumption intensity under the effects of urbanization during 2005–2015. We found long-term equilibrium relationships among urbanization, water use, and energy consumption. Granger causality results reveal the presence of a unidirectional Granger causal relationship running from urbanization to energy consumption and to water use, and bidirectional causality between energy consumption and water use. Moreover, water use intensity and energy consumption intensity decreased significantly under urbanization during the study period.