In this paper, the Huangtupo Riverside Slump 1#, a reservoir landslide with double sliding zones in the Three Gorges Reservoir Area of China, is selected as the prototype for a scaled physical model test subjected to water level fluctuation and rainfall. The spatial-temporal characteristics of the multi-physical monitoring data are thus obtained, including the pore water pressure, earth pressure, surface deformation, and deep deformation. Subsequently, the failure mechanism and evolution process of the landslide model are discussed. The results indicate that the rise and fall of reservoir water correspondingly increase and decrease the pore water pressure and earth pressure at the front edge of the model, while having almost no effect on the trailing edge. The rainfall increases the pore water pressure and soil pressure of the entire model, and the increase is proportional to its duration and intensity but limited by the height of overlying soil. Both the surface deformation and deep deformation increase with the fall of reservoir water and rainfall. Except for the weakening effect of the soil caused by the first rise of reservoir water, which results in a certain surface deformation and deep deformation, the surface deformation has almost no response with the subsequent rises, while the deep deformation decreases with the rises. The Riverside Slump 1-1# exhibits the characteristics of retrogressive failure with whole evolution phases, while the Riverside Slump 1-2# exhibits a composite evolution, in which its middle front belongs to the retrogressive failure within the initial deformation, and the trailing edge belongs to the progressive failure within the accelerated deformation.

Understanding the spatiotemporal dynamics of microbial communities is essential for predicting their ecological roles and interactions with host plants. In a recent study, Wei and colleagues (Microbiol Spectr 13:e02097-24, 2024) investigated fungal diversity across multiple plant and soil compartments in rubber trees over two seasons and two geographically distinct regions in China. Their findings revealed that alpha diversity was primarily influenced by seasonal changes and physicochemical factors, while beta diversity exhibited a strong geographical pattern, shaped by leaf phosphorus and soil available potassium. These results highlight the role of environmental drivers in shaping within-community diversity, while other factors contribute to the differences between fungal communities across the soil-plant continuum. By distinguishing the effects of temporal and spatial factors, this study provides detailed insights into plant-associated microbiomes and emphasizes the need for further research on the functional implications of microbial diversity in the context of changing environmental and agricultural conditions.

Climate change impacts water supply dynamics in the Upper Rio Grande (URG) watersheds of the US Southwest, where declining snowpack and altered snowmelt patterns have been observed. While temperature and precipitation effects on streamflow often receive the primary focus, other hydroclimate variables may provide more specific insight into runoff processes, especially at regional scales and in mountainous terrain where snowpack is a dominant water storage. The study addresses the gap by examining the mechanisms of generating streamflow through multi-modal inferences, coupling the Bayesian Information Criterion (BIC) and Bayesian Model Averaging (BMA) techniques. We identified significant streamflow predictors, exploring their relative influences over time and space across the URG watersheds. Additionally, the study compared the BIC-BMA-based regression model with Random Forest Regression (RFR), an ensemble Machine Learning (RFML) model, and validated them against unseen data. The study analyzed seasonal and long-term changes in streamflow generation mechanisms and identified emergent variables that influence streamflow. Moreover, monthly time series simulations assessed the overall prediction accuracy of the models. We evaluated the significance of the predictor variables in the proposed model and used the Gini feature importance within RFML to understand better the factors driving the influences. Results revealed that the hydroclimate drivers of streamflow exhibited temporal and spatial variability with significant lag effects. The findings also highlighted the diminishing influence of snow parameters (i. e., snow cover, snow depth, snow albedo) on streamflow while increasing soil moisture influence, particularly in downstream areas moving towards upstream or elevated watersheds. The evolving dynamics of snowmelt-runoff hydrology in this mountainous environment suggest a potential shift in streamflow generation pathways. The study contributes to the broader effort to elucidate the complex interplay between hydroclimate variables and streamflow dynamics, aiding in informed water resource management decisions.

Moisture-driven landslides (MDL) are typically associated with elevated soil moisture content and sub-surface pore-water pressure due to temporal clustering of moderate to extreme precipitation over steep terrain leading to mass wasting phenomena such as rock, soil, and debris flows downward along the slope. With their quick response times and short recovery periods, these cascading hazard events are widespread in tectonically active regions, such as the Himalayas, damaging the natural and built environment systems. Due to climate and land use changes, the number of MDLs, including the mountainous Himalayas, is increasing globally. This study first uses Ripley's L-function to compare the spatial clustering of MDLs between two non-overlapping time windows 2007-2015 versus 2016-2022, assuming spatial point process information follows Poisson distribution across the Uttarakhand state (latitude: 28 degrees 42' N - 31 degrees 28' N; longitude: 77 degrees 35'E - 81 degrees 05' E), one of the most landslide-prone areas in the western Himalayas. Then, we investigate the potential physical controls of landslides by considering ranges of conditioning drivers, such as extreme rainfall indices, catchment and soil attributes. While we find evidence of marked spatial clustering of MDLs up to 80 km radial distance, which is more pronounced during the first half (2007-2015) of the time window compared to the latter half (2016-2022), we show that topographic factors contribute significantly to such events with a median contribution of 55% (range 33-60%), followed by the soil properties, and meteorological indices with median contributions lies in the tune of 20-22%. Among topographic factors, slope, form factor, stream power index, and drainage density significantly trigger MDLs. Whereas, soil factors such as cation exchange capacity and soil organic carbon content were identified as the significant factors to mediate landslides. Among meteorological drivers, the number of days with rainfall over 20 mm shows the highest confidence in triggering landslides, followed by the accumulated rainfall of more than 99th percentiles emerging as key conditioning drivers for MDLs. Understanding the spatial dynamics of landslides and their potential drivers enables stakeholders to develop early warning systems, adaptation, and planning, enhancing climate resilience in landslide-prone areas.

Increases in the frequency and intensity of droughts and heat waves are threatening forests around the world. Climate-driven tree dieback and mortality is associated with devastating ecological and societal consequences, including the loss of carbon sequestration, habitat provisioning, and water filtration services. A spatially finegrained understanding of the site characteristics making forests more susceptible to drought is still lacking. Furthermore, the complexity of drought effects on forests, which can be cumulative and delayed, demands investigation of the most appropriate meteorological indicators. To address this research gap, we investigated the drivers of drought-induced forest damage in a particularly drought-affected region of Central Europe using SHapley Additive exPlanations (SHAP) values, an explainable artificial intelligence (XAI) method which allows for the relevance of predictors to be quantified spatially. To develop a reproducible approach that facilitates transferability to other regions, open-source data was used to characterize the meteorological, vegetation, topographical, and soil drivers of tree vulnerability, representing 41 predictors in total. The forest drought response was characterized as a binary variable (damaged or unchanged) at a 30-m resolution based on the Normalized Difference Moisture Index (NDMI) anomaly (%) between a baseline period (2013-2017) and recent years (2018-2022). We revealed critical tipping points beyond which the forest ecosystem shifted towards a damaged state: <81 % tree cover density, <4% of broadleaf trees, and < 24 m canopy height. Our study provides an enhanced understanding of trees' response to drought, which can support forest managers aiming to make forests more climate-resilient, and serves as a prototype for interpretable early-warning systems.

In recent years, frequent flood disasters have posed significant threats to human life and property. From 28 July to 1 August 2023, a basin-wide extreme flood occurred in the Haihe River Basin (23.7 flood). The Gravity Recovery and Climate Experiment satellite can effectively detect the spatiotemporal characteristics of terrestrial water storage anomalies (TWSA) and has been widely used in flood disaster monitoring. However, flood events usually occur on a submonthly scale. This study first utilizes near-real-time precipitation data to illustrate the evolution of the 23.7 extreme flood. We then reconstruct daily TWSA to improve the issues of coarse temporal resolution and data latency and further calculate wetness index (WI) to explore its flood warning. In addition, we analyze soil moisture storage anomalies to provide a comprehensive understanding of flood mechanisms. The study also compares the 2023 floods to a severe flood event in 2021. Results indicate that reconstructed daily TWSA increases by 143.43 mm in 6 days during the 23.7 flood, highlighting the high sensitivity of our approach to extreme events. Moreover, compared to daily runoff data, the WI consistently exceeds warning thresholds 2-3 days in advance, demonstrating the flood warning capability. The flood event 2021 is characterized by long duration and large precipitation extremes, whereas the 2023 flood affects a wider area. This study provides a reference for using daily TWSA to monitor short-term flood events and evaluate the flood warning potential of WI, aiming to enhance near-real-time flood monitoring and support flood prevention and damage mitigation efforts.

The rapid expansion of cropland in Cambodia, the world's seventh-largest rice exporter, has created an imbalance in land use structure. However, there is a lack of quantitative investigation of the loss of ecological land as a result of the expansion of cropland and its drivers. In this research, spatial autocorrelation, landscape pattern index and transfer matrix methods were used based on land use data from 2000 to 2023. Then, the eXtreme Gradient Boosting-SHapley Additive exPlanations (XGBoost-SHAP) and Geographic Detector were used to explore the drivers of cropland expansion. The findings indicate that the expanse of agricultural land in Cambodia has significantly increased by 13.47%. The proportion of cropland to the land area (37.87%) is close to that of forest (40.19%). Cultivated land is dominated by rice fields, supplemented by drylands. Spatial clustering is obvious in both drylands and rice fields. Drylands are mainly concentrated in the eastern and western mountainous areas and the northern border, while rice fields are concentrated in the central plains. Cultivated land encroached on a total of 30,579.27km2 of ecological land, of which 62.88% was dry land and 37.12% was rice fields. Forests and shrubs are the main source of expansion of cropland. In addition, soil type (0.18), elevation (0.17) and GDP (0.17), population (0.52) and their interactions strongly drove the expansion of dryland and rice fields. Cambodia should conduct scientific research to assess the demand for cropland by population growth and economic progress. It should realize the orderly growth of cultivated land, reduce the damage to ecological land, and promote the coordinated development of society, environment and economy.

Winter baseflow (WB) can stabilize freshwater inputs and has important impacts on nutrient migration and the water cycle of a specific region and the oceans. This study systematically analyzed the WB variations in fourteen major Eurasian rivers and found they all had commonly increasing trends (except the Yellow River), with the mean increase ratio of 53.0% (+/- 34.8%, confidence interval 95%) over the past 100 years (the longest time series is 1879-2015). Relative to Northern Eurasia (60 degrees N-70 degrees N) and Southern Eurasia (30 degrees N-40 degrees N), the river WB in middle Eurasia (40 degrees N-60 degrees N) had the largest increase rate (0.60%/year). The increases of the WB in Northern Eurasia and Southern Eurasia have speeded up since the 1990s; on the contrary, they have slowed down or even turned to a decreasing trend after the 1990s in the middle Eurasian rivers. Using multiple linear regression analysis, the quantitative relationship between WB and winter surface air temperature (max, mean and min), snowfall, soil temperature, antecedent precipitation, as well as the river-ice dynamic were determined. We found that the winter air temperature, especially the minimum air temperature was one major factor accounting for WB variation in Eurasia over the past century. When the winter air temperature rises, this leads a reduction in the thickness and volume of river ice, and thus decreases water storage in river ice and leads to an increase in the WB. About 19.6% (6.7%-41.5%) of the winter WB increase in rivers of Siberia was caused by the decreased river ice during the past 100 years. Although groundwater recharge was the dominant reason for WB change, the role of river ice should not be ignored in hydrological study of cold regions.

Water temperature extremes can pose serious threats to the aquatic ecosystems of mountain rivers. These rivers are influenced by snow and glaciermelt, which change with climate. As a result, the frequency and severity of water temperature extremes may change. While previous studies have documented changes in non-extreme water temperature, it is yet unclear how extreme water temperatures change in a warming climate and how their hydro-meteorological drivers differ from those of non-extremes. This study aims to assess temporal changes and spatial variability in water temperature extremes and enhance our understanding of the driving processes across European mountain rivers in the current climate, at both a regional and continental scale. First, we describe the characteristics of extreme events and explore their relationships with catchment characteristics. Second, we assess trends in water temperature extremes and compare them with trends in mean water temperature. Third, we use random forest models to identify the main driving processes of water temperature extremes. Last, we conduct a co-occurrence analysis to examine the relationship between water temperature extremes and hydro-climatic extremes. Our results show that mean water temperature has increased by +0.38 +/- 0.14 ${+}0.38\pm 0.14$degrees C per decade, leading to more extreme events at high elevations in spring and summer. While non-extreme water temperatures are mainly driven by air temperature, water temperature extremes are also importantly influenced by soil moisture, baseflow, and meltwater. Our study highlights the complexity of water temperature dynamics in mountain rivers at the regional and continental scale, especially during water temperature extremes.

Investigation of mercury (Hg) from atmospheric precipitation is important for evaluating its ecological impacts and developing mitigation strategies. Western China, which includes the Tibetan Plateau and the Xinjiang Uyghur Autonomous Region, is one of the most remote region in the world and is understudied in regards to Hg precipitation. Here we report seesaw-like patterns in spatial variations of precipitation Hg in Western China, based on Hg speciation measurements at nine stations over this remote region. The Hg fraction analyzed included total Hg (HgT), particulate-bound Hg (HgP) and methylmercury (MeHg). Spatially, HgT concentrations and percentage of HgP in precipitation were markedly greater in the westerlies domain than those in the monsoon domain, but the higher wet HgT flux, MeHg concentration and percentage of MeHg in precipitation mainly occurred in the monsoon domain. Similar spatial patterns of wet Hg deposition were also obtained from GEOSChem modeling. We show that the disparity of anthropogenic and natural drivers between the two domains are mainly responsible for this seesaw-like spatial patterns of precipitation Hg in Western China. Our study may provide a baseline for assessment of environmental Hg pollution in Western China, and subsequently assist in protecting this remote alpine ecosystem.