Due to their special geographical locations and environments, plateau lakes play a key role in maintaining regional water balance, but lake water storage changes are upsetting this balance. Based on data from lakes on the Tibetan Plateau (TP), this study used the Spatial Processes in Hydrology (SPHY) model to simulate the runoff process in the Siling Co basin from 2000 to 2016 and estimated the changes in water storage of Siling Co and the contribution of each component of runoff into the lake. The results showed that the water storage capacity of Siling Co has increased by 1.2 billion m³/yr, and the lake area continues to expand; declines in precipitation have significantly reduced baseflow (BF), rainfall runoff (RR), and snow runoff (SR), while temperature increases have raised glacier runoff (GR). The simulated average runoff showed that BF, GF, RR, and SR contribute 24%, 22%, 16%, and 38%, respectively, of the flow into Siling Co. Based on hypothetical climate change scenarios and two Shared Socioeconomic Pathways (SSP1-2.6 and SSP3-7.0) from the MRI-ESM2-0 GCMs, this study estimated that a 10% increase in precipitation could lead to a 28% increase in total runoff, while a 1 °C increase in temperature could lead to a 10% decrease in runoff. The average runoff depth of the basin is expected to increase by 30–39 mm, since the temperature and precipitation may increase significantly from 2020 to 2050. The intensification of glacial melting caused by the increase in temperature continues, posing a greater challenge to many water resources management problems caused by the expansion of lakes.

The analysis of drought propagation has garnered mounting attention in the changing global environment. The current studies tend to focus on the propagation characteristics from meteorological to hydrological drought in rivers. Lakes, despite being a key component of watershed ecosystems, have received little attention to their response to meteorological and hydrological droughts. To this end, here, we investigated the characteristics of propagation from meteorological to hydrological drought for a lake in a changing environment. To determine the drought propagation time from meteorological to hydrological drought, we analyzed correlations between the standardized precipitation index (SPI), standardized runoff index (SRI), and standardized water level index (SWI). Lake Baiyangdian in China served as the case study. The results showed that meteorological droughts occur at high frequency but are short in duration, indicating that not every meteorological drought will necessarily lead to a hydrological drought. By contrast, lake hydrological droughts have low frequency and long duration and feature more severe consequences. Comparing drought characteristics before and after a changing environment, we found a reduced frequency of the SPI, SRI, and SWI, yet their duration was prolonged. For the SWI especially, these results were even more pronounced, which suggests the changing environment enabled further intensification of the lake hydrological drought. In addition, more time was needed for a meteorological drought to transition into a lake hydrological drought after a changing environment.

The Tibetan Plateau (TP) is an important water source in Asia, and precipitation and evaporation patterns at different geographical and temporal scales play a significant role in managing water resource distribution. Based on quality control data from 87 meteorological stations, this study analyzed the spatial and temporal evolution patterns of precipitation and pan evaporation (Epan) on the TP in 1966–2016 using the Mann–Kendall test, the moving t-test, wavelet analysis, Sen’s slope method, and correlation analysis. The results revealed that the average mean temperature in the TP area increased by about 2.1 °C during the study period, and precipitation steadily increased at an average rate of 8.2 mm/10a, with summer and autumn precipitation making up about 80% of the year. In contrast, Epan showed an overall decreasing trend at a decline rate of 20.8 mm/10a, with spring and summer Epan values making up about 67% of the year. The time series of the precipitation and Epan within the TP region clearly exhibit nonstationary features. Precipitation is more concentrated in the southeast than in the northwest, while Epan is mostly concentrated in the southwest and northeast of the plateau around the Qaidam Basin. The “evaporation paradox” phenomenon was common in the TP region for about 40 years (1960s–1990s) and gradually faded in the 21st century. In addition, we introduced a standardized precipitation evaporation index (SPEI) to investigate the differences and relationships between precipitation and Epan time series over the past 50 years. The findings indicate that the southern Qinghai was dominated by an arid trend, while the central and southeast TP remained wet. Droughts and floods coexist in the eastern Qinghai and southern Tibet areas with high population concentrations, and the risk of both is rising as the inhomogeneity of precipitation distribution in the TP region will increase in the future. This study can be used as a reference for managing water resources and predicting regional drought and flood risk.

Profile soil moisture (PSM), the soil water content in the whole soil layer, directly controls the major processes related to biological interaction, vegetation growth, and runoff generation. Its spatial heterogeneity, which refers to the uneven distribution and complexity in space, influences refined spatial management and decision-making in ecological, agricultural, and hydrological systems. Satellite instruments and hydrological models are two important sources of spatial information on PSM, but there is still a gap in understanding their potential mechanisms that affect spatial heterogeneity. This study is designed to identify the spatial heterogeneity and the driving factors of two PSM datasets; one is preprocessed from a satellite product (European Space Agency Climate Change Initiative, ESA CCI), and the other is simulated from a distributed hydrological model (the DEM-based distributed rainfall-runoff model, DDRM). Three catchments with different climate conditions were chosen as the study area. By considering the scale dependence of spatial heterogeneity, the profile saturation degree (PSD) datasets from different sources (shown as ESA CCI PSD and DDRM PSD, respectively) during 2017 that are matched in terms of spatial scale and physical properties were acquired first based on the calibration data from 2014–2016, and then the spatial heterogeneity of the PSD from different sources was identified by using spatial statistical analysis and the semi-variogram method, followed by the geographic detector method, to investigate the driving factors. The results indicate that (1) ESA CCI and DDRM PSD are similar for seasonal changes and are overall consistent and locally different in terms of the spatial variations in catchment with different climate conditions; (2) based on spatial statistical analysis, the spatial heterogeneity of PSD reduces after spatial rescaling; at the same spatial scale, DDRM PSD shows higher spatial heterogeneity than ESA CCI PSD, and the low-flow period shows higher spatial heterogeneity than the high-flow period; (3) based on the semi-variogram method, both ESA CCI and DDRM PSD show strong spatial heterogeneity in most cases, in which the proportion of C/(C₀ + C) is higher than 0.75, and the spatial data in the low-flow period mostly show larger spatial heterogeneity, in which the proportion is higher than 0.9; the spatial heterogeneity of PSD is higher in the semi-arid catchment; (4) the first three driving factors of the spatial heterogeneity of both ESA CCI and DDRM PSD are DEM, precipitation, and soil type in most cases, contributing more than 50% to spatial heterogeneity; (5) precipitation contributes most to ESA CCI PSD in the low-flow period, and there is no obvious high contribution of precipitation to DDRM PSD. The research provides insights into the spatial heterogeneity of PSM, which helps develop refined modeling and spatial management strategies for soil moisture in ecological, agricultural, and hydrological fields.

Under global climate change, the temperature and precipitation on the Tibet Plateau have significantly changed, and the melting of glacier and snow has been also affected, subsequently causing changes in runoff. The assessment of runoff and its components on the Tibetan Plateau under climate change is important for water resources management and ecology conservation in alpine regions. Therefore, the Spatial Processes in Hydrology model (SPHY model), a distributed cold-zone hydrological model which contains a glacial ablation module and performs well in alpine region, was used to simulate runoff in the Yangtze River source located in the middle of the Tibetan Plateau during 2000-2020. The input data included the measured runoff from the Zhimenda hydrological station, meteorological data from 16 meteorological stations, digital elevation model (DEM) data, land use data and glacier data. And the coupled model intercomparison project 6 (CMIP6) climate model was used to predict the runoff process for the future 30 years (2020-2050). The results showed that Nash-Sutcliffe efficiency coefficient (NSE), Relative Error (RE) and coefficient of determination (R ² ) in Zhimenda hydrological station for the calibration period, reached 0.900, 0.036 and 0.956, respectively, and NSE, RE and R ² reached 0.828, 0.120 and 0.924, respectively, for the validation period, which shows that the model performed well in most years. In 2000-2020, rainfall runoff contributed most to the total runoff (60.87%), followed by baseflow (22.96%), snowmelt runoff (11.59%) and glacier runoff (4.58%) in the Yangtze River source. The runoff amount of the three source river, Dangqu River, Tuotuo River and Chumar River accounted for about 53% of the total runoff in the Yangtze River Source basin, with rainfall runoff (52.04%) contributing the most and glacier runoff (5.92%) contributing the least. Compared to the contribution of glacial runoff in the Dangqu River (8.87%) and Tuotuo River (6.59%), the proportion of glacial runoff in the Chumar River was very small (0.89%). Under the CMIP6 climate model, the mean runoff depth was predicted to increase approximately 13.5 mm from 2020-2050 compared to 2000-2020.