The potential of satellite precipitation products (SPPs) in monitoring and mitigating hydrometeorological disasters caused by extreme rainfall events has been extensively demonstrated. However, there is a lack of comprehensive assessment regarding the performance of SPPs over the Qinghai-Tibet Plateau (QTP). Therefore, this research aimed to evaluate the effectiveness of five SPPs, including CMORPH, IMERG-Final, PERSIANN-CDR, TRMM-3B42V7, and TRMM-3B42RT, in identifying variations in the occurrence and distribution of intense precipitation occurrences across the QTP during the period from 2001 to 2015. To evaluate the effectiveness of the SPPs, a reference dataset was generated by utilizing rainfall measurements collected from 104 rainfall stations distributed across the QTP. Ten standard extreme precipitation indices (SEPIs) were the main focus of the evaluation, which encompassed parameters such as precipitation duration, amount, frequency, and intensity. The findings revealed the following: (1) Geographically, the SPPs exhibited better retrieval capability in the eastern and southern areas over the QTP, while displaying lower detection accuracy in high-altitude and arid areas. Among the five SPPs, IMERG-Final outperformed the others, demonstrating the smallest inversion error and the highest correlation. (2) In terms of capturing annual and seasonal time series, IMERG-Final performs better than other products, followed by TRMM-3B42V7. All products performed better during summer and autumn compared to spring and winter. (3) The statistical analysis revealed that IMERG-Final demonstrates exceptional performance, especially concerning indices related to precipitation amount and precipitation intensity. Moreover, it demonstrates a slight advantage in detecting the daily rainfall occurrences and occurrences of intense precipitation. On the whole, IMERG-Final’s ability to accurately detect extreme precipitation events on annual, seasonal, and daily scales is superior to other products for the QTP. It was also noted that all products overestimate precipitation events to some extent, with TRMM-3B42RT being the most overestimated.

Clarifying the spatio-temporal patterns of ecosystem services trade-off/synergy relationships (ESTSs) and their attribution in the Yellow River Basin is crucial to constructing ecological civilization in China. This study first analyzed the spatio-temporal change of ecosystem services (ESs) including the water yield, soil conservation, carbon sequestration, and habitat quality in the Yellow River Basin during 2000–2020 based on the InVEST and RUSLE models. Then, the spatial autocorrelation methods were used to quantify the spatio-temporal differentiation of ESTSs, and the Geo-detector method was employed to identify the contributions of driving factors associated with the natural, social-economic, and regional policy aspects of the ESTSs. Finally, the random forest and analysis of variance methods were used to validate the reasonability of major driving factors obtained by the Geo-detector. The main findings include: (1) In 2000–2020, water yield, soil conservation, and habitat quality increased, and carbon sequestration decreased. The ESs had a spatial pattern of high in the east and low in the west. (2) Overall, there were synergistic relationships between the four Ess. In the spatial distribution of ESTSs, the expansion of the synergy zone and trade-off zone occupied the majority. The synergy zones tended to be concentrated in the northwest and southeast of the study area. In contrast, the trade-off zones were more scattered than the synergy zone, mainly focused on the east-central and southwestern parts of the Yellow River Basin. (3) Geo-detector and random forest both showed that natural factors had a strong explanatory power on ESTSs, in which NDVI is a key driver. Both the results of Geo-detector and the analysis of variance showed that the interactions between natural factors exerted the most significant influence on ESTSs, followed by the interaction between natural factors and socio-economic factors.

Water conservation is an essential parameter for maintaining the ecological balance. The Three-River Source Region (TRSR) cannot be an exception, since it is one of the most influential water conservation reserves in the Qinghai–Tibet Plateau in China. Therefore, the realization of its scientific significance can determine its future regional sustainable development and the optimal allocation of water resources. The study of the past is critical to predict the future temporal and spatial changes in the water conservation of the TRSR. The first task of this study was to obtain the optimal runoff simulations in the TRSR from 1981 to 2014 by calibrating the adjustable the parameters of the Soil and Water Assessment Tool (SWAT) model. Then, the water conservation of the TRSR from 1981 to 2014 was quantified. Finally, the future water conservation of the TRSR was also predicted using the optimal SWAT model. The predication took into consideration the three terms including the near-term (2015–2044), mid-term (2045–2074), and long-term (2075–2099) in three different climate scenarios of SSP1-1.9 (SSP119), SSP2-4.5 (SSP245), and SSP5-8.5 (SSP585). The main findings are as follows: (1) both the coefficient of determination (R²) and Nash–Sutcliffe efficiency coefficient (NSE) for runoff simulation on the three sub-basins reached above 0.78 during the calibration and validation periods, which indicates the reasonableness of the SWAT model in the TRSR. (2) From 1981 to 2014, the water conservation capacity of the TRSR showed an increasing trend (0.5135 mm/year), and its changes had significant positive correlations with precipitation and temperature. The Yellow River Source (YR) and the Yangtze River Source (YZ) had the strongest and weakest water conservation capacities, respectively. (3) From 2015 to 2099, the water conservation in the TRSR in the SSP119, SSP245, and SSP585 scenarios decreased first and then increased, increased first and then decreased, and increased steadily, respectively.

Fire plays a critical role in both the formation and degradation of ecosystems; however, there are still significant uncertainties in the estimation of burned areas (BAs). This study systematically evaluated the performance of ten global climate models (GCMs) in simulating global and regional BA during a historical period (1997–2014) using the Global Fire Emissions Database version 4.1s (GFED4s) satellite fire product. Then, six of the best models were combined using Bayesian Model Averaging (BMA) to predict future BA under three Shared Socioeconomic Pathways (SSPs). The results show that the NorESM2-LM model excelled in simulating both global annual and monthly BA among the GCMs. GFDL-ESM4 and UKESM1-0-LL of the GCMs had the highest Pearson’s correlation coefficient (PCC), but they also exhibited the most significant overestimation of monthly BA variations. The BA fraction (BAF) for GCMs was over 90% for small fires (50%). After evaluation and screening, six models (CESM2-WACCM-FV2, NorESM2-LM, CMCC-ESM2, CMCC-CM2-SR5, GFDL-ESM4, and UKESM1-0-LL) were selected for weighting in an optimal ensemble simulation using BMA. Based on the optimal ensemble, future projections indicated a continuous upward trend across all three SSPs from 2015 to 2100, except for a slight decrease in SSP126 between 2071 and 2100. It was found that as the emission scenarios intensify, the area experiencing a significant increase in BA will expand considerably in the future, with a generally high level of reliability in these projections across most regions. This study is crucial for understanding the impact of climate change on wildfires and for informing fire management policies in fire-prone areas in the future.

Fluctuations in precipitation usually affect the ecological environment and human socioeconomics through events such as floods and droughts, resulting in substantial economic losses. The high-resolution models in the Coupled Model Intercomparison Project Phase 6 (CMIP6) are vital for simulating precipitation patterns in China; however, significant uncertainties still exist. This study utilized the quantile mapping (QM) method to correct biases in nine high-resolution Earth System Models (ESMs) and then comprehensively evaluated their precipitation simulation capabilities over the Chinese mainland from 1985 to 2014. Based on the selected models, the Bayesian Model Averaging (BMA) method was used to integrate them to obtain the spatial–temporal variation in precipitation over the Chinese mainland. The results showed that the simulation performance of nine models for precipitation from 1985 to 2014 was significantly improved after the bias correction. Six out of the nine high-resolution ESMs were selected to generate the BMA ensemble model. For the BMA model, the precipitation trend and the locations of significant points were more closely aligned with the observational data in the summer than in other seasons. It overestimated precipitation in the spring and winter, while it underestimated it in the summer and autumn. Additionally, both the BMA model and the worst multi-model ensemble (WMME) model exhibited a negative mean bias in the summer, while they displayed a positive mean bias in the winter. And the BMA model outperformed the WMME model in terms of mean bias and bias range in both the summer and winter. Moreover, high-resolution models delivered precipitation simulations that more closely aligned with observational data compared to low-resolution models.

Recently, Integrated Multi-satellite Retrievals for the Global Precipitation Measurement (IMERG) mission and European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) precipitation datasets have been widely used in remote sensing and atmospheric studies, respectively, because of their high accuracy. A dataset of 268 site-gauge precipitation measurements over the Yellow River Basin in China was used in this study to comprehensively evaluate the performance of three high-resolution precipitation products, each with a spatial resolution of 0.1°, consisting of two satellite-derived datasets, IMERG and multisource weighted-ensemble precipitation (MSWEP), and one ERA5-derived dataset, ERA5-Land. The results revealed that the spatial distribution of IMERG annual precipitation closely resembled that of the observed rainfall and generally exhibited a downward trend from southeast to northwest. Among the three products, IMERG had the best performance at the annual scale, whereas ERA5-Land had the worst performance due to significant overestimation. Specifically, IMERG demonstrated the highest correlation coefficient (CC) above 0.8 and the lowest BIAS and root mean square error (RMSE), with values in most regions of 24.79 mm/a and less than 100 mm/a, respectively, whereas ERA5-Land presented the highest RMSE exceeding 500 mm/a, BIAS of 1265.7 mm/a, and the lowest CC below 0.2 in most regions. At the season scale, IMERG also exhibited the best performance across all four seasons, with a maximum of 17.99 mm/a in summer and a minimum of 0.55 mm/a in winter. Following IMERG, the MSWEP data closely aligned with the observations over the entire area in summer, southern China in spring and winter, and middle China in autumn. In addition, IMERG presented the highest Kling–Gupta efficiency coefficient (KGE) of 0.823 at the annual scale and the highest KGE (>0.77) across all four seasons among the three products compared with ERA5-Land and MSWEP, which had KEG values of −2.718 and −0.403, respectively. Notably, ERA5-Land exhibited a significant positive deviation from the observations at both the annual and seasonal scales, whereas the other products presented relatively smaller biases.