Under the pronounced warming-wetting trend in Northwest China, understanding vegetation responses to the redistribution of hydrothermal resources is essential for interpreting regional ecohydrological processes. Here, we developed a bivariate Long Short-Term Memory (LSTM) model to simulate leaf area index (LAI) dynamics for four representative vegetation types (cold temperate forest, shrubland, grassland, and cropland), using air temperature and soil moisture as predictors. The model reproduces seasonal vegetation phenology well across vegetation types (R-2 > 0.9), indicating that LSTM effectively captures the cumulative and lagged effects of hydrothermal drivers. However, its performance diverges at the interannual scale. Interannual variability in grasslands in water-limited environments is reasonably represented (R-2 = 0.31), consistent with their sensitivity to short-term hydroclimatic variability under warming-wetting conditions. In contrast, the model fails to reproduce the observed long-term greening trend in forests when driven solely by hydrothermal variables. This contrast suggests distinct underlying mechanisms across ecosystem types. Grassland dynamics are closely linked to high-frequency hydroclimatic variability, whereas forest growth appears to be governed by slower processes and low-frequency drivers, including CO2 fertilization, nitrogen deposition, and ecological inertia. As a result, hydrothermal variables alone are insufficient to explain long-term forest dynamics. Overall, these findings highlight a transition from water-limited to energy- and process-limited controls across vegetation types and underscore the limitations of purely climate-driven models. Integrating biogeochemical processes or process-based constraints into machine learning frameworks may therefore be necessary to improve predictions of long-term vegetation change under climate change.

Intense rainfall is widely recognized as the primary trigger for landslides and is commonly used in early warning systems. Yet, many unexpected failures occur during periods of little or even no rainfall, leading to ineffective warnings and failed evacuations. The mechanisms by which landslides occur without heavy rainfall remain unexplored. Integrating field investigations, machine learning, remote sensing, and numerical simulations, we found that landslide disasters without heavy rainfall are widespread: 75.7% of 1118 catastrophic cases exhibited delayed onset, controlled by antecedent rainfall, the topographic wetness index, and landslide scale. For the first time, we identified three runoff-supply patterns (slope, gully, and creek supply) and two migration stages (surface and subsurface) that together govern strong runoff supply in confluence zones. Our work contributes an innovative perspective on the coupling of early strong runoff and weak geomaterials that triggers delayed failures, with three subsurface runoff stages-interflow, sliding-face flow, and return flow. These results clarify the hydro-geomaterial coupling behind delayed landslides and support improved early warning and prediction to reduce risk.

Rising atmospheric CO2 concentrations, temperature and vapour pressure deficit substantially influence plant photosynthesis and terrestrial carbon uptake, yet how these drivers interact to alter photosynthesis across different climate regimes remains unclear. Here, using globally distributed FLUXNET measurements and satellite-derived machine learning estimates of gross primary production (GPP) for 1982-2022, we reveal an asymmetric shift in vegetation productivity between drylands and humid regions. This shift is led by a substantial slowdown in the rate of increase in dryland GPP since 2001, primarily due to water constraints associated with the rising vapour pressure deficit. By contrast, humid regions exhibit a sustained increase in GPP in response to rising temperatures and atmospheric CO2. Notably, dynamic global vegetation models and Earth system models fail to capture this divergence in both historical simulations and future projections. Given increasing atmospheric aridity and the continued expansion of drylands, we anticipate a broad water constraint on global photosynthetic capacity that may limit the land carbon sink. Consequently, we advocate prioritizing adaptive strategies in drylands and nature-based solutions in humid regions to enhance global climate action.

Inter-basin water transfer is a commonly adopted measure for improving hydrological and ecological conditions within watersheds. However, systematically quantifying the actual improvement effects of water diversion projects on watershed runoff conditions remains a weak link in current hydraulic engineering evaluation. This study conducts complex modeling of the Changjiang-Hanjiang Water Diversion Project (CHWD), the Middle Route of South-to-North Water Diversion Project (MR-SNWD), the Dongjing River diversion, and the cascade reservoirs in the middle and lower reaches of the Hanjiang River with Soil and Water Assessment Tool (SWAT). Based on this modeling framework, the daily runoff process is simulated in the lower Hanjiang River under a scenario with no CHWD, and the impact of the CHWD on the runoff process is investigated in the lower Hanjiang River. The results indicate that: (1) the water supplementation from the CHWD is insufficient to balance the reduction impact on downstream Hanjiang River runoff caused by water withdrawal from the MR-SNWD; (2) the water supplementation from the CHWD is primarily utilized to supply the Dongjing River diversion, with limited effect on increasing runoff in the Hanjiang River section downstream of Dongjing River; (3) the water supplementation from the CHWD contributes to enhancing the stability of the runoff process in the lower Hanjiang River. This study yields a significant insight that gravity-flow water conveyance is suboptimal for inter-basin water transfer in flat terrain regions. These findings offer valuable implications for the planning and design of such projects globally.

Lakes are sensitive indicators of climate change, playing a critical role in hydrological cycles and ecological functions [...]

Based on the hourly precipitation and 10 m wind speed data from the European Centre for Medium-Range Weather Forecasts(ECMWF)Fifth Generation Atmospheric Reanalysis dataset from 1979 to 2023,spa-tiotemporal changes and its corresponding clustering characteristics of compound precipitation and wind speed extremes(PWEs),and the circulation characteristics in different periods in China were studied by using com-pound extreme events definition,trend analysis,spatial statistical analysis,and composite analysis.The results showed that PWEs in China were generally more frequent in the east than in the west.Among the PWEs in each subregion,the highest value was found in East China,where the mean value of the frequency and the days were the most in the range of 4～8 times and 4～8 d,and the corresponding area share reached 78.9%and 71.5%,re-spectively.The overall trend of PWEs from 1979 to 2023 had been decreasing,with the rate of change from 2011 to 2023 being 2.3 times and 3.4 times that of 1979 to 2010.The trend of PWEs from 1979 to 2010 showed an in-creasing trend in the central and eastern region of Eastern China,the central region of Southwest,and the north-ern region of Northwest,and the fastest decrease in Central China.From 2011 to 2023,positive trend values were mainly concentrated in the central region of China,and the Eastern China was the region of the fastest growth with rates of 0.96 times and 1.12 d per decade.In contrast,Southern China exhibited a decrease at rates of 0.81 times·(10a)-1 and-0.77 d·(10a)-1.The hot spot areas were concentrated on the west side of the Hu Line and coast region from 1979 to 2010,and the distribution of hot spot areas from 2011 to 2023 were consistent with the positive distribution of trend change.In addition,PWEs are the result of the combined effects of the high,middle,and low-level atmospheric layers.The enhancement of atmospheric high-level divergence and the weakening of the jet belt promote the upward movement of the atmosphere and the westward extension of the west Pacific subtropical high.The anomalous easterly wind in the middle atmosphere is conducive to the entry of water vapor from the periphery of the Northwest Pacific subtropical high into the inland regionof China,and the anomalous southeast wind in the low-level atmosphere further promotes the transport of water vapor to the inland region of China.The atmospheric circulation characteristics after 2010 also showed the development of PWEs events towards inland region of China.

On May 1, 2024, a small embankment collapse occurred in the early hours of the morning on the Meida Highway in Meizhou City, Guangdong Province, resulting in 48 fatalities. The small-scale collapse caused massive casualties and garnered widespread attention. In detail, there is a significant lack of precipitation at the time of the “5·1” Meida collapse disaster, lagging 10 h behind the peak precipitation. The collapse occurs on a mountainous slope, with a hollow catchment area located above the embankment. Multiple potential streams converge in the area, contributing to the water flow towards the slope. Within the western zone of the Lianhua Mountain fault, the collapse area is crossed by fault lines at approximately 800 m on the upper side and 650 m on the lower side. Bedrock fractures formed by faults act as water conduits. The combination of catchment topography and potential faults enriches the water around the embankment slope, contributing to its instability. The disaster site is situated within granite formations. The refilling soil, composed of weathered granite, exhibits poor hydro-mechanical properties, making the slope particularly susceptible to failure due to the effects of multi-source water infiltration. A key insight from this research is that potentially unstable embankment slopes should be identified by considering the interaction between multi-source water and soil/rock. Greater emphasis should be placed on factors such as fault development and hollow topography above the slope, which influence the effects of multi-source water. These factors should be quantified in future studies to improve the assessment of unstable highway slopes in mountainous regions. The findings and strategies outlined in this study can serve as a valuable reference for assessing both embankment and natural slopes in mountainous areas.

The effects of temperature changes on rice (Oryza sativa L.) yield and its components have been widely documented. However, most existing studies are based on small-scale, short-term field experiments, with few assessing these effects on a large scale or over long periods. Here, the calibrated Crop Environment Resource Synthesis (CERES)-Rice model was used for numerical simulations over six climate regions in the major rice cultivation areas of China for the period of 1989-2018. The simulated results were used to estimate the temperature sensitivity of rice yield with a panel model in each climate region, and the yield sensitivity was then decomposed into the temperature sensitivity of three components: panicle number per unit area (Pan_no), filled grain number per panicle (Grain_no), and grain weight (Grainwt). Results indicated that rice yield exhibited negative temperature sensitivity across all climate regions, driven primarily by the temperature sensitivity of Grain_no in most regions. Additionally, Grainwt did not vary with temperature in all regions. Further analysis suggested that yield, Pan_no, and Grain_no were more sensitive to high temperature degree days (HDD) than to growing degree days (GDD). Under the warmer scenarios, HDD increase played a dominant role in the reduction of Grain_no, while the joint effect of GDD and HDD resulted in an increased Pan_no in most regions. However, the negative effect of temperature on Grain_no outweighed its positive effect on Pan_no, leading to a decline in yield. This study provides insight for understanding the temperature response of rice yield and its components and will be beneficial for developing targeted adaptations to ensure rice sustainable production under global warming.

In water-limited regions, plant–water interactions significantly affect the hydrological cycle and vegetation dynamics, particularly in deep-rooted plantations where deep water uptake mitigates water stress during seasonal and interannual droughts. In this study, we improved the University of Arizona version of the Noah-MP model by incorporating actual soil thickness, along with new subsurface and water table schemes, to evaluate the long-term influence of plant–water interactions in Robinia pseudoacacia L. plantations. We found that soil water content was sensitive to both soil stratification and vertical root distribution, with Nash–Sutcliffe efficiency increasing from less than 0.20 to 0.63 in sensitivity experiments. Plant–water interactions resulted in persistent low soil water content within the root zone, whereas the static vegetation experiment overestimated soil moisture because of unrealistic infiltration. Transpiration and water uptake remained in dynamic equilibrium, and vegetation growth was not limited by water availability. Deep water uptake (>2 m) contributed 0.3–20.5% of transpiration during the growing season, with higher contributions observed in drier years. Compared to precipitation, evapotranspiration was more sensitive to soil water storage in the upper 0–2 m of soil. Our results emphasize the critical role of plant–water interactions in regulating water availability for deep-rooted plantations on the Loess Plateau under changing environmental conditions.

On May 1, 2024, a small embankment collapse occurred in the early hours of the morning on the Meida Highway in Meizhou City, Guangdong Province, resulting in 48 fatalities. The small-scale collapse caused massive casualties and garnered widespread attention. In detail, there is a significant lack of precipitation at the time of the "51" Meida collapse disaster, lagging 10 h behind the peak precipitation. The collapse occurs on a mountainous slope, with a hollow catchment area located above the embankment. Multiple potential streams converge in the area, contributing to the water flow towards the slope. Within the western zone of the Lianhua Mountain fault, the collapse area is crossed by fault lines at approximately 800 m on the upper side and 650 m on the lower side. Bedrock fractures formed by faults act as water conduits. The combination of catchment topography and potential faults enriches the water around the embankment slope, contributing to its instability. The disaster site is situated within granite formations. The refilling soil, composed of weathered granite, exhibits poor hydro-mechanical properties, making the slope particularly susceptible to failure due to the effects of multi-source water infiltration. A key insight from this research is that potentially unstable embankment slopes should be identified by considering the interaction between multi-source water and soil/rock. Greater emphasis should be placed on factors such as fault development and hollow topography above the slope, which influence the effects of multi-source water. These factors should be quantified in future studies to improve the assessment of unstable highway slopes in mountainous regions. The findings and strategies outlined in this study can serve as a valuable reference for assessing both embankment and natural slopes in mountainous areas.

As the world’s largest loess deposit region, the Loess Plateau’s vegetation dynamics are crucial for its regional water–heat balance and ecosystem functioning. Leaf Area Index (LAI) serves as a key indicator bridging canopy architecture and plant physiological activities. Existing studies have made significant advancements in simulating LAI, yet accurate LAI simulation remains challenging. To address this challenge and gain deeper insights into the environmental controls of LAI, this study aims to accurately simulate LAI in the Loess Plateau using deep learning models and to elucidate the spatiotemporal influence of soil moisture and temperature on LAI dynamics. For this purpose, we used three deep learning models, namely Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and Interpretable Multivariable (IMV)-LSTM, to simulate LAI in the Loess Plateau, only using soil moisture and temperature as inputs. Results indicated that our approach outperformed traditional models and effectively captured LAI variations across different vegetation types. The attention analysis revealed that soil moisture mainly influenced LAI in the arid northwest and temperature was the predominant effect in the humid southeast. Seasonally, soil moisture was crucial in spring and summer, notably in grasslands and croplands, whereas temperature dominated in autumn and winter. Notably, forests had the longest temperature-sensitive periods. As LAI increased, soil moisture became more influential, and at peak LAI, both factors exerted varying controls on different vegetation types. These findings demonstrated the strength of deep learning for simulating vegetation–climate interactions and provided insights into hydrothermal regulation mechanisms in semiarid regions.

Landslide susceptibility mapping (LSM) is crucial for disaster prevention in large, complex regions characterized by high-dimensional data. This study proposes a Feature-Selecting Long Short-Term Memory (FS-LSTM) framework to enhance LSM accuracy by integrating feature selection techniques with sequence-based modeling. The Mean Decrease Impurity (MDI) and Information Gain Ratio (IGR) were used to rank landslide conditioning factors (LCFs), and these rankings structured FS-LSTM inputs to assess the impact of feature ordering on model performance. Feature-ordering experiments demonstrated that structured rankings significantly improve model accuracy compared to randomized inputs. Our model outperformed traditional machine learning algorithms, such as logistic regression and Support Vector Machine, as well as standard deep learning models like CNN and basic LSTM, achieving a score of 0.988. The MDI and IGR rankings consistently identified soil type, elevation, and average annual cumulated rainfall as the most influential LCFs, improving the interpretability of the results. Applied to the Yangtze River Basin, the FS-LSTM framework effectively identified landslide-prone areas, aligning with known geological patterns. These findings highlight the potential of combining feature selection with sequence-sensitive deep learning to enhance the robustness and interpretability of LSM. Future studies could expand this approach to other regions and incorporate real-time monitoring systems for dynamic disaster management.

Reliable rainfall thresholds are critical for effective early warning and mitigating the risks of rainfall-induced landslides. Traditional statistical models have limitations in multi-variable modeling, while machine learning models face interpretability challenges. Explainable machine learning methods can address these challenges, but they are rarely applied to rainfall threshold modeling. In this study, we compared the performance of an empirical statistical model and machine learning models for predicting rainfall-induced landslides in Italy. Based on the optimal model, we visualized refined rainfall thresholds at three probability levels and employed SHAP (Shapley Additive Explanations) to enhance model explainability by quantifying the contribution of each input variable to the predictions. The results demonstrated that the XGBoost model achieved a good performance (AUC = 0.917 ± 0.026) with well-balanced sensitivity (0.792 ± 0.075) and specificity (0.812 ± 0.033) in landslide susceptibility modeling. Hydrological factors, particularly total rainfall, were identified as the dominant triggering mechanisms, with SHAP analysis confirming their substantially greater contribution compared to environmental factors in rainfall threshold modeling. The developed visualized threshold maps revealed distinct spatial variations in landslide-triggering rainfall thresholds across Italy, characterized by lower thresholds in gentle slope areas with moderate annual precipitation and higher thresholds in steep slope and mid-to-low-elevation regions, while these regional differences decreased under high-probability scenarios. This study offered a modeling approach for regional rainfall threshold assessment by integrating multi-variable modeling with explainable methods, contributing to the development of landslide early warning systems.

Conventional non-partitioned Landslide Susceptibility Mapping (LSM), which neglects geospatial heterogeneity, often has limitations in accurately capturing local risk patterns. To address this challenge, this study investigated the effectiveness of localized modeling in the environmentally diverse state of Oregon, USA, by comparing ecoregion-based local models with the non-partitioned model. We partitioned Oregon into seven distinct units using the U.S. Environmental Protection Agency (EPA) Level III Ecoregions and developed one global and seven local models with the eXtreme Gradient Boosting (XGBoost) algorithm. A comprehensive evaluation framework, including the Area Under the Curve (AUC), Landslide Density (LD), and the Total Deviation Index (TDI), was used to compare the models. The results demonstrated the clear superiority of the partitioned strategy. Moreover, different ecoregions were found to have distinct dominant landslide conditioning factors, revealing strong spatial non-stationarity. Although all models generated high AUC values (>0.93), LD analysis showed that the local models were significantly more efficient at identifying high-risk zones. This advantage was particularly pronounced in critical, landslide-prone western areas; for instance, in the Willamette–Georgia–Puget Lowland, the local model’s LD value in the ‘very high’ susceptibility class was over 3.5 times that of the global model. High TDI values (some >35%) further confirmed fundamental spatial discrepancies between the risk maps obtained by the two strategies. This research substantiated that, in geographically complex terrains, partitioned modeling is an effective approach for more accurate and reliable LSM, providing a scientific basis for developing targeted regional disaster mitigation policies.

Understanding plant-water relations is essential for effective regional water management and promoting ecologically sustainable development on the Loess Plateau, especially in the context of the Grain for Green project initiated in 1999. This study evaluated the variations in vegetation variables (leaf area index, enhanced vegetation index, solar-induced chlorophyll fluorescence and gross primary production) and hydroclimatic variables (precipitation, total water storage, aridity index and standardized precipitation evapotranspiration index) from 2003 to 2020, along with their interactions across the Loess Plateau. Our analysis revealed a general increase in vegetation variables, with the largest increase observed in the forest expansion areas. Precipitation and the aridity index exhibited significant upwards trends, while total water storage showed a significant decline, particularly in the forest expansion areas. Vegetation variables were more sensitive to changes in total water storage across the Loess Plateau. In the northwest region, where large-scale croplands and grasslands expansion occurred, vegetation variables also showed sensitivity to precipitation. Lag effect analysis revealed short time lags (1-3 months) between vegetation and hydroclimatic variables, expect for total water storage (6 months). Overall, human activities and climate factors contributed 58.4% and 41.6% to the increase in leaf area index, and 52.2% and 47.8% to the increase in gross primary production, respectively. In relatively arid environments, precipitation contributed over 50% to the observed vegetation greening. This study underscores the increasingly significant role of human activities in driving vegetation greening on the Loess Plateau, particularly in large-scale afforestation areas.

Long-term exposure to high surface ozone (O-3) concentrations, a complex oxidative atmospheric pollutant, can adversely impact human health. Based on O-3 monitoring data from 261 cities worldwide in 2020, generalized additive model (GAM) and spatial data analysis (SDA) methods were applied in this study to quantitatively evaluate the spatiotemporal distribution of O-3 concentration, exposure risk, and dominant meteorological factors. Results indicated that over 40% of the cities worldwide were exposed to harmful O-3 concentration ranges (4060 mu g/m(3)), with most cities distributed in China and India. Moreover, significant seasonal variations in global O-3 concentrations were observed, presenting as summer (45.6 mu g/m3) > spring (47.3 mu g/m(3)) > autumn (38.0 mu g/m3) > winter (33.6 mu g/m(3)). Exposure analysis revealed that approximately 12.2% of the population in 261 cities were exposed to an environment with high O-3 concentrations (80-160 mu g/m(3)), with about 36.32 million people in major countries. Thus, the persistent increase in high O-3 levels worldwide is a critical factor contributing to threats to human health. Furthermore, GAM results indicated temperature, relative humidity, and wind speed as primary determinants of O-3 variability. The synergy of meteorological factors is critical for understanding O-3 changes. Our findings are important for enforcing robust air quality policies and mitigating public risk.

To reduce the high burden of disease caused by air pollution, the World Health Organization (WHO) released new Air Quality Guidelines (AQG) on September 22, 2021. In this study, the daily fine particulate matter (PM2.5) and surface ozone (O-3) data of 618 cities around the world is collected from 2019 to 2022. Based on the new AQG, the number of attainment days for daily average concentrations of PM2.5 (<= 15 mu g m(-3)) and O-3 (<= 100 mu g m(-3)) is approximately 10% and 90%, respectively. China and India exhibit a decreasing trend in the number of highly polluted days (> 75 mu g m(-3)) for PM. Every year over 68% and 27% of cities in the world are exposed to harmful PM2.5 (> 35 mu g m(-3)) and O3 (> 100 mu g m(-3)) pollution, respectively. Combined with the United Nations Sustainable Development Goals (SDGs), it is found that more than 35% of the world's cities face PM2.5-O-3 compound pollution. Furthermore, the exposure risks in these cities (China, India, etc.) are mainly categorized as "High Risk", "Risk", and "Stabilization". In contrast, economically developed cities are mainly categorized as "High Safety", "Safety", and "Deep Stabilization." These findings indicate that global implementation of the WHO's new AQG will minimize the inequitable exposure risk from air pollution.

Rising surface ozone (O 3 ) levels in China are increasingly emphasizing the potential threats to public health, ecological balance, and economic sustainability. Using a 1 km × 1 km dataset of O 3 concentrations, this research employs subpopulation demographic data combined with a population-weighted quality model. Its aim is to evaluate quantitatively the differences in O 3 exposure among various subpopulations within China, both at a provincial and urban cluster level. Additionally, an exposure disparity indicator was devised to establish unambiguous exposure risks among significant urban agglomerations at varying O 3 concentration levels. The findings reveal that as of 2018, the population-weighted average concentration of O 3 for all subgroups has experienced a significant uptick, surpassing the average O 3 concentration (118 μg/m 3 ). Notably, the middle-aged demographic exhibited the highest O 3 exposure level at 135.7 μg/m 3 , which is significantly elevated compared to other age brackets. Concurrently, there exists a prominent positive correlation between educational attainment and O 3 exposure levels, with the medium-income bracket showing the greatest susceptibility to O 3 exposure risks. From an industrial vantage point, the secondary sector demographic is the most adversely impacted by O 3 exposure. In terms of urban–rural structure, urban groups in all regions had higher levels of exposure to O 3 than rural areas, with North and East China having the most significant levels of exposure. These findings not only emphasize the intricate interplay between public health and environmental justice but further highlight the indispensability of segmented subgroup strategies in environmental health risk assessment. Moreover, this research furnishes invaluable scientific groundwork for crafting targeted public health interventions and sustainable air quality management policies.

Non-technical summary Greenhouse gas emissions and land use change - from deforestation, forest degradation, and agricultural intensification - are contributing to climate change and biodiversity loss. Important land-based strategies such as planting trees or growing bioenergy crops (with carbon capture and storage) are needed to achieve the goals of the Paris Climate Agreement and to enhance biodiversity. The integrated Land Ecosystems Atmospheric Processes Study (iLEAPS) is an international knowledge-exchange and capacity-building network, specializing in ecosystems and their role in controlling the exchange of water, energy and chemical compounds between the land surface and the atmosphere. We outline priority directions for land-atmosphere interaction research and its contribution to the sustainable development agenda.Non-technical summary Greenhouse gas emissions and land use change - from deforestation, forest degradation, and agricultural intensification - are contributing to climate change and biodiversity loss. Important land-based strategies such as planting trees or growing bioenergy crops (with carbon capture and storage) are needed to achieve the goals of the Paris Climate Agreement and to enhance biodiversity. The integrated Land Ecosystems Atmospheric Processes Study (iLEAPS) is an international knowledge-exchange and capacity-building network, specializing in ecosystems and their role in controlling the exchange of water, energy and chemical compounds between the land surface and the atmosphere. We outline priority directions for land-atmosphere interaction research and its contribution to the sustainable development agenda.Technical summary Greenhouse-gas emissions from human activities and land use change (from deforestation, forest degradation, and agricultural intensification) are contributing to climate change and biodiversity loss. Afforestation, reforestation, or growing bioenergy crops (with carbon capture and storage) are important land-based strategies to achieve the goals of the Paris Climate Agreement and to enhance biodiversity. The effectiveness of these actions depends on terrestrial ecosystems and their role in controlling or moderating the exchange of water, heat, and chemical compounds between the land surface and the atmosphere. The integrated Land Ecosystems Atmospheric Processes Study (iLEAPS), a global research network of Future Earth, enables the international community to communicate and remain up to date with developments and concepts about terrestrial ecosystems and their role in global water, energy, and biogeochemical cycles. Covering critically important topics such as fire, forestry, wetlands, methane emissions, urban areas, pollution, and climate change, the iLEAPS Global Research Programme sits center stage for some of the most important environmental questions facing humanity. In this paper, we outline the new challenges and opportunities for land-atmosphere interaction research and its role in supporting the broader sustainable development agenda.Technical summary Greenhouse-gas emissions from human activities and land use change (from deforestation, forest degradation, and agricultural intensification) are contributing to climate change and biodiversity loss. Afforestation, reforestation, or growing bioenergy crops (with carbon capture and storage) are important land-based strategies to achieve the goals of the Paris Climate Agreement and to enhance biodiversity. The effectiveness of these actions depends on terrestrial ecosystems and their role in controlling or moderating the exchange of water, heat, and chemical compounds between the land surface and the atmosphere. The integrated Land Ecosystems Atmospheric Processes Study (iLEAPS), a global research network of Future Earth, enables the international community to communicate and remain up to date with developments and concepts about terrestrial ecosystems and their role in global water, energy, and biogeochemical cycles. Covering critically important topics such as fire, forestry, wetlands, methane emissions, urban areas, pollution, and climate change, the iLEAPS Global Research Programme sits center stage for some of the most important environmental questions facing humanity. In this paper, we outline the new challenges and opportunities for land-atmosphere interaction research and its role in supporting the broader sustainable development agenda.Social Media Summary Future directions for research into land-atmosphere interactions that supports the sustainable development agenda