The amplitude of the annual temperature cycle (ATC) is a crucial component of Earth’s climate and profoundly influences its phenology and ecosystem dynamics. However, most previous studies on ATC amplitude have been confined to the post-industrial instrumental period. Although a few studies have reconstructed ATC amplitudes over the past few centuries using proxy data, these efforts have been limited to regional scales, leaving the global profile of ATC amplitude from the pre- to post-industrial periods poorly understood. Here, leveraging rigorous evaluation and screening of monthly mean air temperature data derived from eleven CMIP5/CMIP6 models spanning the last millennium, combined with grid-based weighted averaging, we produced reliable ATC amplitude series for global and hemispheric land areas since 850 CE. Our analysis reveals a significant reduction in ATC amplitude since the 1860s across global and Northern Hemispheric lands, whereas the Southern Hemisphere has been relatively stable. The unprecedented decline in ATC amplitude since the late 19th century stands in stark contrast to the modest increases observed during the Medieval Climate Anomaly (ca. 1000-1300 CE) and the Little Ice Age (ca. 1400-1850 CE). These findings, particularly the distinct shift in ATC amplitude between the pre- and post-industrial periods, provide an early global fingerprint of anthropogenic forcing on climate change.

Rapid regional population shifts and spatial polarization have heightened pressure on cultivated land—a critical resource demanding urgent attention amid ongoing urban-rural transition. This study selects Jiangsu province, a national leader in both economic and agricultural development, as a case area to construct a multidimensional framework for assessing the recessive morphological characteristics of multifunctional cultivated land use. We examine temporal dynamics, spatial heterogeneity, and propose an integrated zoning strategy based on empirical analysis. The results reveal that: (1) The recessive morphology index shows a consistent upward trend, with structural breaks in 2007 and 2013, and a spatial shift from “higher in the east and lower in the west” to “higher in the south and lower in the north.” (2) Coordination among sub-dimensions of the index has steadily improved. (3) The index is expected to continue rising in the next decade, though at a slower pace. (4) To promote coordinated multidimensional land-use development, we recommend a policy framework that reinforces existing strengths, addresses weaknesses, and adapts zoning schemes to current spatial conditions. This research offers new insights into multifunctional cultivated land systems and underscores their role in enhancing human well-being, securing food supply, and supporting sustainable urban-rural integration.

Rapid urbanization in China has led to spatial antagonism between urban development and farmland protection and ecological security maintenance. Multi-objective spatial collaborative optimization is a powerful method for achieving sustainable regional development. Previous studies on multi-objective spatial optimization do not involve spatial corrections to simulation results based on the natural endowment of space resources. This study proposes an Ecological Security-Food Security-Urban Sustainable Development (ES-FS- USD) spatial optimization framework. This framework combines the non-dominated sorting genetic algorithm II (NSGA-II) and patch-generating land use simulation (PLUS) model with an ecological protection importance evaluation, comprehensive agricultural productivity evaluation, and urban sustainable development potential assessment and optimizes the territorial space in the Yangtze River Delta (YRD) region in 2035. The proposed sustainable development (SD) scenario can effectively reduce the destruction of landscape patterns of various land-use types while considering both ecological and economic benefits. The simulation results were further revised by evaluating the land-use suitability of the YRD region. According to the revised spatial pattern for the YRD in 2035, the farmland area accounts for 43.59% of the total YRD, which is 5.35% less than that in 2010. Forest, grassland, and water area account for 40.46% of the total YRD—an increase of 1.42% compared with the case in 2010. Construction land accounts for 14.72% of the total YRD—an increase of 2.77% compared with the case in 2010. The ES-FS-USD spatial optimization framework ensures that spatial optimization outcomes are aligned with the natural endowments of land resources, thereby promoting the sustainable use of land resources, improving the ability of spatial management, and providing valuable insights for decision makers.

Human activities have significantly impacted the land surface temperature (LST), endangering human health; however, the relationship between these two factors has not been adequately quantified. This study comprehensively constructs a Human Activity Intensity (HAI) index and employs the Maximal Information Coefficient, four-quadrant model, and XGBoost- SHAP model to investigate the spatiotemporal relationship and influencing factors of HAI-LST in the Yellow River Basin (YRB) from 2000 to 2020. The results indicated that from 2000 to 2020, as HAI and LST increased, the static HAI-LST relationship in the YRB showed a positive correlation that continued to strengthen. This dynamic relationship exhibited conflicting development, with the proportion of coordinated to conflicting regions shifting from 1:4 to 1:2, indicating a reduction in conflict intensity. Notably, only the degree of conflict in the source area decreased significantly, whereas it intensified in the upper and lower reaches. The key factors influencing the HAI-LST relationship include fractional vegetation cover, slope, precipitation, and evapotranspiration, along with region-specific factors such as PM_2.5, biodiversity, and elevation. Based on these findings, region-specific ecological management strategies have been proposed to mitigate conflict-prone areas and alleviate thermal stress, thereby providing important guidance for promoting harmonious development between humans and nature.

Ecosystems along the eastern margin of the Qinghai-Tibet Plateau (EQTP) are highly fragile and extremely sensitive to climate change and human disturbances. To quantitatively assess climate-induced ecosystem responses, this study proposes a Climate-Induced Productivity Index (CIPI) based on the Super Slack-Based Measure (Super-SBM) model using remote sensing data from 2001 to 2020. The results reveal persistently low CIPI values (0.47-0.53) across major ecosystem types, indicating widespread vulnerability to climatic variability. Among these ecosystems, forests exhibit the highest CIPI (0.55), followed by shrublands (0.54), croplands (0.53), grasslands (0.51), and barelands (0.43). The Theil index analysis further demonstrates significant intra-group disparities, suggesting that extreme climatic events amplify CIPI heterogeneity. Moreover, the dominant environmental drivers differ among ecosystem types: the Palmer Drought Severity Index (PDSI) primarily constrains grassland productivity, solar radiation (SRAD) strongly influences shrub and cropland systems, whereas subsurface factors exert greater control in forested regions. This study provides a quantitative framework for evaluating climate-ecosystem interactions and offers a scientific basis for long-term ecological monitoring and security planning across the EQTP.

Precipitation events, which follow a life cycle of initiation, development, and decay, represent the fundamental form of precipitation. Comprehensive and accurate detection of these events is crucial for effective water resource management and flood control. However, current investigations on their spatio-temporal patterns remain limited, largely because of the lack of systematic detection indices that are specifically designed for precipitation events, which constrains event-scale research. In this study, we defined a set of precipitation event detection indices (PEDI) that consists of five conventional and fourteen extreme indices to characterize precipitation events from the perspectives of intensity, duration, and frequency. Applications of the PEDI revealed the spatial patterns of hourly precipitation events in China and its first- and second-order river basins from 2008 to 2017. Both conventional and extreme precipitation events displayed spatial distribution patterns that gradually decreased in intensity, duration, and frequency from southeast to northwest China. Compared with those in northwest China, the average values of most PEDIs in southeast China were usually 2-10 times greater for first-order river basins and 3-15 times greater for second-order basins. The PEDI could serve as a reference method for investigating precipitation events at global, regional, and basin scales.

Accurate prediction of flood events is important for flood control and risk management. Machine learning techniques contributed greatly to advances in flood predictions, and existing studies mainly focused on predicting flood resource variables using single or hybrid machine learning techniques. However, class-based flood predictions have rarely been investigated, which can aid in quickly diagnosing comprehensive flood characteristics and proposing targeted management strategies. This study proposed a prediction approach of flood regime metrics and event classes coupling machine learning algorithms with clustering-deduced membership degrees. Five algorithms were adopted for this exploration. Results showed that the class membership degrees accurately determined event classes with class hit rates up to 100%, compared with the four classes clustered from nine regime metrics. The nonlinear algorithms (Multiple Linear Regression, Random Forest, and least squares-Support Vector Machine) outperformed the linear techniques (Multiple Linear Regression and Stepwise Regression) in predicting flood regime metrics. The proposed approach well predicted flood event classes with average class hit rates of 66.0%-85.4% and 47.2%-76.0% in calibration and validation periods, respectively, particularly for the slow and late flood events. The predictive capability of the proposed prediction approach for flood regime metrics and classes was considerably stronger than that of hydrological modeling approach.

Aeolian deposits across the Yarlung Zangbo River Basin on the southern Tibetan Plateau record the landscape and atmospheric evolution of Earth’s Third Pole. The complex mountain-basin system exhibits nonlinear responses to climate forcing, complicating the interpretation of its high-altitude environmental dynamics. Investigating the magnetic enhancement mechanism of aeolian deposits offers an opportunity to decipher climate signals. Our analysis of three aeolian sections from the basin indicates that magnetic minerals are predominantly low-coercivity ferrimagnetic minerals, and grain sizes fine from upper to lower reaches due to climate shifts from arid to humid. Magnetic enhancement in the upper reaches primarily originates from dust input, while dust input and pedogenesis contribute variably over time in the middle and lower reaches. Similar complex patterns occur in the Ili basin, a mountain-basin system in northwestern China. They differ from the Chinese Loess Plateau, where long-distance-transported dust is well-mixed and the pedogenic enhancement model is applied, and desert peripheries where short-distance dust is transported and the dust input model is applied. We summarize the magnetic enhancement mechanisms in various settings and offer a new framework for applying magnetic techniques in paleoclimate reconstruction within global mountain-basin systems, which highlights the need for caution in interpreting their magnetic susceptibility records.

Landslides pose a formidable natural hazard across the Qinghai-Tibet Plateau (QTP), endangering both ecosystems and human life. Identifying the driving factors behind landslides and accurately assessing susceptibility are key to mitigating disaster risk. This study integrated multi-source historical landslide data with 15 predictive factors and used several machine learning models—Random Forest (RF), Gradient Boosting Regression Trees (GBRT), Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost)—to generate susceptibility maps. The Shapley additive explanation (SHAP) method was applied to quantify factor importance and explore their nonlinear effects. The results showed that: (1) CatBoost was the best-performing model (CA=0.938, AUC=0.980) in assessing landslide susceptibility, with altitude emerging as the most significant factor, followed by distance to roads and earthquake sites, precipitation, and slope; (2) the SHAP method revealed critical nonlinear thresholds, demonstrating that historical landslides were concentrated at mid-altitudes (1400-4000 m) and decreased markedly above 4000 m, with a parallel reduction in probability beyond 700 m from roads; and (3) landslide-prone areas, comprising 13% of the QTP, were concentrated in the southeastern and northeastern parts of the plateau. By integrating machine learning and SHAP analysis, this study revealed landslide hazard-prone areas and their driving factors, providing insights to support disaster management strategies and sustainable regional planning.

Effective conservation relies on robust assessments; however, the lack of waterbird data in the Yellow River Basin (YRB) has led to an underestimation of key habitat significance. This study addressed this gap by evaluating YRB wetland conservation importance using waterbirds as indicators and applying Ramsar, Important Bird Areas (IBA), and East Asian-Australasian Flyway (EAAF) criteria. We integrated coordinated surveys with citizen science data, creating a framework that tackles data deficiencies along the under-monitored Central Asian Flyway (CAF). Our analysis identified 75 priority wetlands, supporting 15 threatened species and 49 exceeding global/flyway 1% thresholds, highlighting the basin’s biodiversity. We observed strong seasonal habitat use, with high-altitude wetlands vital for breeding and migration, and the Yellow River Delta providing year-round refuge. This research also provided data to refine Baer’s Pochard population estimates. Alarmingly, one-third of the identified priority areas, primarily rivers and lakes, remain unprotected. To address this, we recommend systematic surveys, enhanced protected areas, OECMs, and targeted wetland restoration. This study underscores the YRB’s role in regional conservation and provides essential data for adaptive management, particularly emphasizing the CAF’s importance.

The Selenge River Basin (SRB) in Mongolia has faced ecosystem degradation because of climate change and overloading. The dynamics of the pastoral system and the extent of overload under future scenarios have not been documented. This study aims to answer the following questions: Will the typical soums in the SRB become more overgrazed in the future? What optimal strategy should be implemented? Multisource data were integrated and utilized to model the pastoral system of typical soums using a system dynamics approach. Future scenarios under three SSP-RCPs were projected using the model. The conclusions are as follows: (1) From upstream to downstream, rational scenarios for pastoral system transferred from SSP1-RCP2.6 to SSP2-RCP4.5, which reflect improved productivity at the expense of ecosystem stability. (2) Compared with that during the historical period of 2000-2020, the projected carrying capacity of the soums decreases by 15.2%-37.3%, whereas the number of livestock continues to increase. Consequently, the stocking rate is expected to increase from 0.32-1.16 during 2000-2020 to 1.26-2.02 during 2021-2050, indicating that rangeland will become more overloaded. (3) A livestock reduction strategy based on future livestock stock and grassland carrying capacity scenarios was proposed to maintain a dynamic forage-livestock equilibrium. It is suggested that reducing livestock is a practical option for harmonizing grassland conservation with livestock husbandry development.

This study investigates climate- and human-induced hydrological changes in the Zavkhan River-Khyargas Lake Basin, a highly sensitive arid and semi-arid region of Central Asia. Using Mann-Kendall, innovative trend analysis, and Sen’s slope estimation methods, historical climate trends (1980-2100) were analyzed, while land cover changes represented human impacts. Future projections were simulated using the MIROC model with Shared Socioeconomic Pathways (SSPs) and the Tank model. Results show that during the past 40 years, air temperature significantly increased (Z=3.93***), while precipitation (Z=-1.54*) and river flow (Z=-1.73*) both declined. The Khyargas Lake water level dropped markedly (Z= -5.57***). Land cover analysis reveals expanded cropland and impervious areas due to human activity. Under the SSP1.26 scenario, which assumes minimal climate change, air temperature is projected to rise by 2.0℃, precipitation by 21.8 mm, and river discharge by 1.61 m³/s between 2000 and 2100. These findings indicate that both global warming and intensified land use have substantially altered hydrological and climatic processes in the basin, highlighting the vulnerability of western Mongolia’s water resources to combined climatic and anthropogenic influence.