Population and water withdrawal data sets are currently faced with difficulties in collecting, processing and verifying multi-source time series, and the spatial distribution characteristics of long series are also relatively lacking. Time series is the basic guarantee for the accuracy of data sets, and the production of long series spatial distribution is a realistic requirement to expand the application scope of data sets. Through the time-consuming and laborious basic processing work, this research focuses on the population and water intake time series, and interpolates and extends them to specific land uses to ensure the accuracy of the time series and the demand of spatially distributed data sets. This research provides a set of population density and water intensity products from 1960 to 2020 distributed to the administrative units or the corresponding regions. The data set fills the gaps in the multi-year data set for the accuracy of population density and the intensity of water withdrawal.

The foundation of managing excess nutrients in river is the identification of key physical processes and the control of decisive influencing factors. The existing studies seldom consider the influence of rainfall-runoff relationship and only focus on a few anthropogenic activities and natural attributes factors. To address this issue, a comprehensive set of influencing factors including rainfall-runoff relationship (represented by runoff coefficient), basic physical and chemical parameters of water quality, land use types, landscape patterns, topography, and socioeconomic development was constructed in this study. M–K test and cluster analysis were conducted to identify the temporal mutation and spatial clustering characteristics of NH₃-N and TP in Huangshui River basin, respectively. Partial least squares regression was used to elucidate the linkages between water contaminants and the factors. As shown in the results, the temporal mutations of NH₃-N and TP were obvious in the middle reaches, with 4 out of 7 catchments in the middle reaches have a larger number of mutations of NH₃-N than other catchments. The cluster analysis results of NH₃-N and TP among catchments were similar. This study also indicated that although the Huangshui River basin was located in the upper reaches of the Yellow River, the influences of rainfall-runoff relationship on spatiotemporal changes of NH₃-N and TP in its sub-basins were limited. Only the temporal change of NH₃-N in Jintan catchment in the upstream area was significantly affected by runoff coefficient. The indexes of proportion of water area (PWA), proportion of impervious area (PIA), and proportion of primary industry (PPI) were the top three influencing factors of temporal variation of NH₃-N and TP for most catchments in the middle reaches. The temporal change of NH₃-N in Jintan catchment in the upstream area was obviously affected by runoff coefficient. The spatial variation of NH₃-N and TP were all affected by PWA and proportion of secondary industry significantly. The results of this study can provide theoretical basis and technical support for the control and management of nitrogen and phosphorus pollution in upper reaches of rivers.

The scale and topological relationship of river networks (RN) and water resources zones (WRZ) directly affect the simulation results of global multi-scale hydrological cycle and the accuracy of water resource refined evaluation. However, few existing global hydrological data sets take account of both aspects simultaneously. Here, we constructed a new hydrologic data set with a spatial resolution of 90 m as an upgraded version of the GRNWRZ V1.0. This data set had proper grading and partitioning thresholds and clear coding of topological relationships. Based on maintaining the accuracy of river networks in the GRNWRZ V1.0, we determined the more refined thresholds and created a new coding rule, which made the grading RN and partitioning WRZ more precise and the topological relationship more intuitive. Supported by this data set, the accuracy and efficiency of the large-scale hydrological simulation can be guaranteed. This data set provides fundamental data support for global water resources governance and global hydrological modeling under climate change.

With the increasing impact of climate change on ecosystems, it is crucial to analyze how changes in precipitation and temperature affect global ecosystems. Therefore, this study aims to investigate the spatiotemporal variation characteristics of the Enhanced Vegetation Index (EVI) in the global forest, grassland, shrubland, and tundra (FGST) from 2000 to 2021. We utilized partial correlation analysis and grey relation analysis to assess the responses of different vegetation types to precipitation, temperature, and extreme water and heat indicators. The result shows that, despite a “warmer and drier” trend in FGST (excluding tundra), global climate change has not adversely affected the ongoing vegetation growth. It presents a favorable implication for global carbon dioxide assimilation. Different vegetation types displayed different sensitivities to changes in precipitation and temperature. Shrubland proved to be the most sensitive, followed by grassland, forest, and tundra. As the impacts of global climate change intensify, it becomes crucial to direct our attention toward dynamics of vegetation types demonstrating heightened sensitivity to fluctuations in precipitation and temperature. Our study indicates that, except for forests, extreme precipitation indicators have a stronger impact on EVI than extreme temperature indicators. Forests and tundra have demonstrated heightened susceptibility to the intensity of extreme climatic events, while grasslands and shrublands have been more sensitive to the duration of such events. Understanding these responses can offer valuable insights for developing targeted strategies for adaptation and preservation. Our study enhances comprehension of the feedback relationship between global climate change and vegetation, offering scientific evidence for global climate change evaluation.

The imbalanced, maladjusted, and uncontrolled phenomenon in the regional water resources system is caused by the combined disturbances of climate change and human activities. The socio-economic and ecological systems are restricted, and the combined carrying capacity also changes accordingly. In order to explore the above changes, based on the influence mechanism of climate change, socioeconomic system and ecosystem on water resources system, this paper systematically reviews the relevant research progress in water resources carrying capacity (WRCC), from the basic theory of water resources carrying capacity, the construction of evaluation index system, the classification criteria and evaluation methods, etc. This review provides basic theoretical and technical support for the study of generalized water resources carrying capacity considering the interlinked climate-ecology-society system, which is conducive to the promotion of water resources carrying capacity and the synergistic development of socio-economic and ecological environment under a changing climate.

The lack of high-accuracy, fine-resolution meteorological datasets in China has hindered progress in climate, hydrological, and ecological studies. In this study, we present a 1 km daily dataset spanning 1961–2021 across China, which includes six key variables – average, maximum, and minimum temperature, atmospheric pressure, relative humidity, and sunshine duration – to provide a reliable foundation for advancing related research and applications. The dataset was generated using a novel hierarchical reconstruction framework that leveraged daily observations from 2345 meteorological stations and incorporated topographic attributes. This approach effectively decodes the nonlinear relationships between the meteorological variables and their spatial covariates, ensuring the generation of gridded daily fields that are both high-resolution and spatially continuous. Validation against 146 independent stations confirmed the high accuracy of the dataset. For average, maximum, and minimum temperatures, the errors are minimal (median root mean square errors (RMSEs): 1.16, 1.19, 1.29 °C; median mean errors (MEs): −0.04, −0.10, −0.01 °C), and the consistency with in-situ data is very high (median correlation coefficients (CCs): 0.99, 0.99, 0.99). Atmospheric pressure also shows very small errors (median RMSE: 2.65 hPa; median ME: −0.06 hPa) and strong correlation (median CC: 0.97). Relative humidity exhibits relatively lower accuracy (median RMSE: 6.33 %; median ME: −0.52 %; median CC: 0.90), but it still exceeds standard benchmarks. Sunshine duration maintains high precision (median RMSE: 1.48 h; median ME: 0.05 h; median CC: 0.93), indicating the robustness and reliability of the dataset. Further comparison reveals that in high-altitude and topographically complex regions, the reconstructed product demonstrates higher actual accuracy than suggested by station-to-grid validation, as spatial mismatches between stations and grid cells lead to systematic underestimation. Free access to the dataset is available at https://doi.org/10.11888/Atmos.tpdc.301341 or https://cstr.cn/18406.11.Atmos.tpdc.301341 (last access: 25 November 2025) (Zhao et al., 2024).