CONTEXT: As part of sustainable crop intensification, multiple cropping has emerged as a promising solution for enhancing agricultural productivity without expanding cropland. Although existing studies have explored conditions required for multiple cropping adoption, a comprehensive, global assessment of the potential for transition from single to multiple cropping remains lacking. OBJECTIVE: This study aims to i) identify the most influential determinants affecting global cropping systems from biophysical, agricultural input-related, and socio-economic perspectives; ii) quantify their associations with single versus multiple cropping at 30 arc-min resolution; and iii) assess the potential for adopting multiple cropping on cropland currently under single cropping for maize, wheat, rice, and soybean. METHODS: We employed eXtreme Gradient Boosting (XGBoost) to quantify relationships between cropping systems and global variables, including climate, water, environment, agriculture, and socio-economics with consistent temporal coverage (1998-2002). To delineate potential transition zones, we applied K-means clustering to these variable groups across four crops, comparing the similarities and differences in growing conditions in single and multiple cropping systems. RESULTS AND CONCLUSIONS: Climate variations and agricultural inputs are the most important sets of variables shaping multiple cropping potential. Single cropping systems on 80 million hectares (8 % of global single-cropped land) could transition to multiple cropping across the four crops. Transition potential is, on average, 35 % higher in irrigated systems than in rainfed systems, and the area suitable for transition is 1.7 times larger in irrigated systems. These areas are concentrated in North America, Southeast Asia, and Southern Europe. SIGNIFICANCE: Our findings highlight both promising targets for sustainable intensification and critical data gaps under current climatic conditions, thereby helping to prioritize regions for subsequent, site-specific analysis and targeted interventions toward sustainable food systems under a changing climate.

Endowed with opportunities from both land and ocean, coastal areas attract expanding human populations and economic activities. At the same time, they face growing societal and environmental pressures from both the above river catchments and the bordering sea due to climate change, ecosystem degradation, and expansion of built-up areas. Despite the accumulation of human population, economic activities, and environmental impacts, we lack social-ecological systems analysis on water-related risks to world’s coastal human population. To address this research gap, we analyze the spatial extent of six globally important water stressors to people within the world’s coastal zone (100 km from the coastal line) and classify this zone globally into 12 groups by distance from the coastline and elevation from the mean sea level. Adopting the approaches of the UN Sendai Framework and IPCC, we produce risk maps from the stressor maps by multiplying them with population exposure and vulnerability. For most risks, geographical hotspots are the Chinese coast, Bay of Bengal, Gujarat, and the Island of Java. The analysis reveals fundamental differences between water stressors and related risks, often mixed in scholarly literature. Both manifest specific geographic patterns and latitudinal profiles. Our study highlights the importance of high-resolution spatial analysis of vulnerability, exposure, and risks posed by water related stressors in the world’s coastal zone, in a manner prompted by key policy bodies to promote policy design and shared responsibility for managing stress-prone areas.

Climate risks are increasing globally due to climate change, driven by intensifying climate hazards and changes in socioeconomic conditions that drive exposure and vulnerability. Climate Risk Assessments (CRAs) constitute a tool to understand such risks based on the analysis of geospatial datasets. However, CRA data are often scattered across different platforms, thereby inhibiting their Findability, Accessibility, Interoperability, and Reusability (FAIR). To make CRA data FAIR, we develop Climate Risk STAC, a living metadata catalog of open-access geospatial datasets that is hosted in a collaborative environment for continuous development. Climate Risk STAC (version 1.0) currently includes 214 metadata entries from nine different hazards, five types of exposed elements, and seven vulnerability categories. All data entries can be explored in a user-friendly browser which eases data selection. We encourage contributions of new datasets to maintain a growing, community-led catalog that reflects state-of-the-art CRA concepts and data.

Socio-economic conditions influence the implementation of proposed solutions for transforming food systems. Here we systematically screen over 1,700 articles and select 349 for detailed review, investigating the role of socio-economic drivers in sustainable food systems transformations across different world contexts. We identify seven sustainable food systems transformations, including sustainable land resources and soil health, precision agricultural practices, diet change and novel food transition, good nutrition and health, food loss and waste reduction, healthy freshwater and marine ecosystems, and climate change mitigation and biodiversity conservation. We propose socio-economic pathways comprising specific socio-economic drivers needed for achieving them and provide actor-specific recommendations to support sustainable food systems transformations.

January 1 is an arbitrary point to start a year for hydrological analysis. Water years offer a solution by shifting the start to a hydrologically meaningful point, but local and global definitions diverge considerably. Here we show that local water years reflect the seasonal cycles of snow accumulation, midlatitude storm tracks, and the intertropical convergence zone, producing a coherent global pattern that no existing global definition reproduces. We introduce the first observation-based global definition of the water year, derived from upstream snow water equivalent and discharge, that recovers this pattern and is applicable at a subcatchment scale. Applying it reveals that the point when a year starts systematically alters estimates of interannual variability, the spatial extent of significant changes, precipitation–flux correlations, and water-balance closure across the global land surface. The start of the water year is thus a first-order yet rarely scrutinized decision in Earth-system and water-cycle analysis.

Cities seek to generate economic prosperity while reducing their dependence on fossil fuel combustion, yet tracking such progress at the city level remains challenging because of the limited and inconsistent emissions and economic data. Here we introduce an objective, globally consistent framework to measure decoupling between fossil fuel use and economic growth, either through reduced fuel use or shifts toward cleaner/more efficient combustion, proxied by tropospheric nitrogen dioxide columns combined with second-level administrative gross domestic product per capita based on purchasing power parity data. Analysing 5,435 cities globally over 2019-2024, we identify significant trends for 2,475 cities and classify them into 4 decoupling states. We find that 80% of these cities, mainly located in China, Europe and North America, enjoy relative decoupling, whereas 16%, mainly located in India and the Middle East, experience fossil fuel-dependent growth. Beyond these patterns, the described scalable satellite-based methodology can be revisited regularly to monitor city-level green growth and support urban policy effectiveness.

Enhancing ecosystem services without compromising crop productivity is a central challenge for sustainable agriculture, yet biophysical drivers of yield variability across farming approaches remain poorly resolved. Here, we provide the first comparative global assessment of how climate, soil, and topographic conditions shape yield responses to agroforestry, cover cropping, no-tillage, and organic farming by synthesizing global meta-analytic datasets and linking field comparisons to a common framework of biophysical moderators. Across all sustainable farming approaches, there was no significant overall yield difference relative to conventional management (1.1

Regenerative farming practices (RFP)-no-tillage (NT), cover crops (CC), agroforestry (AF), and organic farming (OF)-are increasingly promoted to enhance soil health and support sustainable food production. Yet, their global suitability and yield effects remain unclear. We assessed where these practices could increase crop yields across global croplands using a Random Forest model trained on field-scale data from multiple meta-analyses linked with global climate, soil, and environmental datasets at 5-arc-minute resolution. Yield increases varied by practice, covering 5-45% of cropland area. CC showed the greatest potential for yield improvement (45%), followed by AF (41%), NT (37%), and OF (5%). Areas suitable for multiple RFPs often involved combinations such as CC-AF and OF-CC-AF. Our findings highlight spatial variability in RFP impacts and provide insights to guide policies and interventions that promote soil health and enhance food production globally.

The focus on proactive wastewater asset management emphasises the importance of asset lifespan prediction. Although machine learning models show promise in predicting the current condition of the pipes, they fall short in forecasting their lifespan due to the time of failure being unknown (censored); either because the event has not yet occurred or because it is only observed after it has already taken place. This research presents an approach for sewer pipes' lifespan prediction that focuses on preprocessing steps to train various machine learning algorithms on censored pipe inspection datasets without modifying the algorithms. By segmenting the prediction time horizon into shorter periods and using inverse probability of censoring to weight the training data, we achieved high accuracy. The best model achieved an F1-score of 0.83 for failure occurrence and an RMSE of 0.97 years for failure timing, which is an acceptable timing error for sewer asset management decisions. Analysis showed that event definition affects model performance and interpretation of predictions, and that it is easier to predict the utility's decision-making than the pipes' deterioration. This approach performs well with limited data, creating the opportunity for practical use of machine learning even for small utilities.

Cities seek to generate economic prosperity while simultaneously reducing their greenhouse gas emissions. Measuring progress toward these goals has been difficult as city-level results on value creation and emissions are not readily available. We used meteorology-adjusted satellite-derived tropospheric NO₂ pollution trends as a proxy for changes in fossil fuel combustion across 5,435 cities over 2019-2024 and combined this with subnational GDP per capita to map the effectiveness of decoupling efforts globally. Among cities with significant trends (n = 2,444), 59.6% achieved decoupling, 23.8% reduced emissions but also lowered GDP, 11.4% experienced fossil-fuel-dependent economic growth, and 5.2% became poorer and dirtier. Chinese cities dominate the “Green Growth” category, and while European and North American cities are enjoying reduced emissions, GDP evolution is mixed. India and the Middle East had the most cities experiencing fossil-fuel-dependent growth. The approach described can be revisited annually to track city-level green growth.

Climate change, land cover change, water use, and flow regulation are driving river streamflow changes globally, and it is crucial to understand the varying contributions of these drivers to prevent and mitigate harmful impacts caused by streamflow alteration. However, previous, scenario-based approaches on this are notably uncertain and may miss interdependencies between different drivers. Here, to overcome these shortcomings, we use a large sample of observed streamflow data globally to quantify flow regime changes and align those against trends in precipitation, evapotranspiration, water use, and damming. With this study, we achieve unprecedented coverage and detail in analysing how varying streamflow regime changes may be linked to different drivers.We queried the Global Streamflow Indices and Metadata (GSIM) database to yield 5,220 catchments across all continents (surface area greater than than 1,000 km2 and more than ten years of record available). Each catchment was assigned a flow regime change (FRC) class based on linear trends in four streamflow metrics: mean, standard deviation, high flows (95th percentile) and low flows (5th percentile). Within FRC classes, we further separated between catchments in which precipitation shows a decreasing or an increasing trend. Finally, within groups formed by FRCs and precipitation trends, we analysed linear trends in total evapotranspiration and water use, and increases in damming (by degree of regulation; DOR).We find that shift down (mean, low, and high flows decreasing) and shrink (standard deviation and high flows decreasing, low flows increasing) are more common FRCs than shift up (mean, low, and high flows increasing) and expand (standard deviation and high flows increasing, low flows decreasing). Most commonly, precipitation trends are parallel to the FRC – decreasing in the shift down and shrink FRCs and increasing in the shift up and expand FRCs. This is more likely in FRCs describing a shift than in FRCs indicating a change in variability, which suggests that drivers beyond precipitation are more likely to exist in catchments that belong to the shrink and expand FRC classes. Water use trends are comparatively strong between shift down, shrink and expand FRCs but nearly nonexistent in the shift up FRC. The general direction of evapotranspiration trends agrees with precipitation trend direction in all four FRCs. When the FRC class and precipitation trend contradict (e.g. shift down FRC & increasing precipitation trend), we find that changes in water use and damming are often strong. Damming mostly affects streamflow by decreasing and homogenising flow because strongly increasing DOR is also associated with the shrink FRC but changes in DOR are minor within the shift up and expand FRCs Our global large-sample statistical insights agree with process-based understanding on how different human drivers affect streamflow, which provides a promising outlook on identifying the dominant drivers of streamflow change at large scales.

The recent third planetary boundary (PB) assessment replaced the original PB for ‘freshwater use’ with a new PB for ‘freshwater change’. The new PB is defined by the percentage of global land area experiencing streamflow (blue water component of the PB) and root-zone soil moisture (green water) deviations from pre-industrial baseline conditions. Here, we first present the spatiotemporally explicit results of the comprehensive analysis underlying the new PB, and then discuss possible applications of the approach and the challenges related to providing meaningful guidance for water management and policy across scales.We find a clear transgression of both the blue and green water components of the freshwater change PB already during the first half of the 20th century. Our spatiotemporally explicit analysis reveals a general pattern of drying across a significant portion of the tropics and subtropics, contrasting with wetting in temperate and subpolar regions as well as numerous highland areas. This overall pattern is likely attributed to alterations in precipitation patterns associated with global warming. Significant increases in streamflow and soil moisture deviations are also found in regions facing the highest direct human pressures, such as irrigation, flow regulation, and land use change. In many cases, both streamflow and soil moisture deviations have increased – underlining the influence of human impacts on the freshwater cycle as a whole.While our analysis highlights regions undergoing the most substantial freshwater changes and their potential drivers, using the results to guide water policy and management remains challenging. Key knowledge gaps include our limited understanding of the (quantitative) driver–freshwater change–Earth system response relationships, and the mismatches between spatiotemporal scales of 1) human drivers of freshwater change, 2) the Earth system impacts of freshwater change, and 3) water management and governance institutions. We conclude our presentation by proposing a research agenda to bridge these gaps, with a goal to provide policy-relevant information on freshwater change that would enable a stronger adoption of an Earth system perspective in water management and governance.

Many regenerative agriculture practices (RAP) such as not tillage (NT), cover crop (CC), perennials and agroforestry (AF), organic farming (OF) have potential to limit negative environmental outcomes while enhancing soil health and sustaining diverse ecosystem services. However, the magnitude by which yield responses of different RAP are influenced by inherent soil properties, climate and topographical factors are not fully understood. To elucidate such interaction, field scale experiments related to these RAP were first collected across the globe by combining multiple meta-analyses and the yield response was extracted and then linked with global gridded soil, climate and topographical datasets. The findings showed that the RAPs were associated with an overall mean crop yield increase of 5 % with specific increase in crop yield by 48 %, 21 % and 0.28 % respectively for AF, CC and NT while a decrease of 2 % was recorded for OF. The use of RAPs had the greatest yield benefit in tropical, arid and temperate climates and when farming at mid to high elevation areas as well as in soils with low soil organic carbon. Specifically, increase in crop yield occurred consistently for AF, CC and NT in environments located in semi-arid area with aridity index between 0.20 and 0.50 and at elevation between 250 and 1000 m as well as in soils characterized by low soil organic carbon (< 5 g/kg). In addition, NT was associated with increase in crop yield especially when N input was considered in addition to cover crop and weeding in arid and tropical regions. Under the RAPs considered, cereal crops such as maize, rice and soybean resulted in significant crop yield increase especially for growing degree days within 4 000 to 10 000 degrees C. The findings shed new light on the ways in which soil characteristics, climate and topography in relation to RAPs interact to affect crop yield and such results can assist in the development of useful, fact-based recommendations for applying these practices to improve crop yields.

Climate change significantly impacts human mobility, yet understanding the temporal dynamics and cumulative effects of multiple climate events remains understudied. This research investigates the impact of climate extremes--droughts, floods, crop failures, and tropical cyclones--on district-level net migration from 2000 to 2019. Using global climate impact simulations from the Inter-Sectoral Impact Model Intercomparison Project and recent sub-national migration data, we observed initial increases in positive net migration following floods, but this trend declines after one year in regions typically experiencing net migration losses. Droughts show delayed migration responses in similar areas, possibly due to liquidity constraints. Tropical cyclones initially increase net migration, particularly in destination areas, but this is followed by a decline after two years and stabilization thereafter. Importantly, consecutive climate events lead to a combined reduction in positive migration, reflecting the total impacts. These findings highlight the need for migration policies that address both immediate and long-term responses to climate extremes.

Income inequality is one of the most important measures to indicate socioeconomic welfare and quality of life, and has implications for the environment. Yet, especially at the subnational level, comprehensive global data on income distribution are widely missing. Such data are essential for assessing patterns of inequality within countries and their development over time. Here we created seamless global subnational Gini coefficient and gross national income purchasing power parity per capita datasets for the period 1990–2023 and used these to assess the status and trends of income inequality and income, as well as their interplay. We show that while gross national income has increased for most people globally (94%), inequality has also increased for around 46–59% (depending on the national dataset used) of the global population, while it has decreased for 31–36% and has not shown a significant trend for 10–18%. We illustrate heterogeneities in inequality trends between and within countries, analyse plausible confounding factors related to inequality, and highlight the broad utility of the datasets through a case study that investigates correlations with terrestrial ecological diversity. Our dataset and analyses provide valuable insights for relevant stakeholders to direct future research and make informed decisions at the global, national and subnational levels, addressing societal, economic and environmental challenges caused by inequality.

We present a comprehensive gridded GDP per capita dataset downscaled to the admin 2 level (43,501 units) covering 1990–2022. It updates existing outdated datasets, which use reported subnational data only up to 2010. Our dataset, which is based on reported subnational GDP per capita data from 89 countries and 2,708 administrative units, employs various novel methods for extrapolation and downscaling. Downscaling with machine learning algorithms showed high performance (R2 = 0.79 for cross-validation, R2 = 0.80 for the test dataset) and accuracy against reported datasets (Pearson R = 0.88). The dataset includes reported and downscaled annual data (1990–2022) for three administrative levels: 0 (national; reported data for 237 administrative units), 1 (provincial; reported data for 2,708 administrative units for 89 countries), and 2 (municipality; downscaled data for 43,501 administrative units). The dataset has a higher spatial resolution and wider temporal range than the existing data do and will thus contribute to global or regional spatial analyses such as socioenvironmental modelling and economic resilience evaluation. The data are available at https://doi.org/10.5281/zenodo.10976733 .

Despite the global population exceeding eight billion, food production per capita is globally higher than ever. However, this achievement has led to significant environmental impacts, with agriculture being the largest sector contributing to the transgression of many planetary boundaries, including biogeochemical flows, biosphere integrity, land system changes, and freshwater changes. To move towards more sustainable food futures, several opportunities exist, such as reducing food loss, upcycling by-products into livestock and aquaculture feeds, implementing double cropping systems, and developing cultivated meat alternatives.Here first a global overview of the multiple pressures agricultural practices apply on Earth systems is provided, with a focus on their impact on water and land resources, biodiversity, and nutrient pollution. The main emphasis is, however, to synthesize the potential of various interventions that could mitigate these pressures and promote sustainability. These insights are crucial for understanding the intricate links between land use, water systems, and food production,and for identifying effective and equitable future resource management strategies.

Global data have served an integral role in characterizing large-scale groundwater systems, identifying their sustainability challenges, and informing on socioeconomic and ecological dimensions of groundwater. These insights have revealed groundwater as a dynamic component of the water cycle and social–ecological systems, leading to an expansion in groundwater science that increasingly focuses on groundwater’s interactions with ecological, socioeconomic, and Earth systems. This shift presents many opportunities that are conditional on broader, more interdisciplinary system conceptualizations, models, and methods that require the integration of a greater diversity of data in contrast to conventional hydrogeological investigations. Here, we catalogue 144 global open access datasets and dataset collections relevant to groundwater science that span elements of the hydrosphere, biosphere, atmosphere, lithosphere, food systems, governance, management, and other socioeconomic system dimensions. The assembled catalogue offers a reference of available data for use in interdisciplinary assessments, and we summarize these data across their primary system, spatial resolution, temporal range, data type, generation method, level of groundwater representation, and institutional location of lead authorship. The catalogue includes 15 groundwater datasets, 23 datasets derived in relation to groundwater, and 106 datasets associated with groundwater. We find the majority of datasets are temporally static and that temporally dynamic data peak in availability during the 2000–2010 decade. Only a small fraction of temporally dynamic data is derived with any direct representation of groundwater, highlighting the need for greater incorporation of groundwater in Earth system models and data collection initiatives across socioeconomic, governance, and environmental science research communities. A small number of countries, led by the USA, Germany, the Netherlands, and Canada, generate most global groundwater data, reflecting a global North bias in the institutional leadership of these data generation activities. We raise three priority themes for future global groundwater data initiatives, which include: data improvements through prioritizing observed and temporally dynamic data; elevating regional and local scale data and perspectives to address challenges relating to equity and bias; and advancing data sharing initiatives founded on reciprocal benefits between global initiatives and data providers.

Climate change alters the climatic suitability of croplands, likely shifting the spatial distribution and diversity of global food crop production. Analyses of future potential food crop diversity have been limited to a small number of crops. Here we project geographical shifts in the climatic niches of 30 major food crops under 1.5-4 °C global warming and assess their impact on current crop production and potential food crop diversity across global croplands. We found that in low-latitude regions, 10-31% of current production would shift outside the climatic niche even under 2 °C global warming, increasing to 20-48% under 3 °C warming. Concurrently, potential food crop diversity would decline on 52% (+2 °C) and 56% (+3 °C) of global cropland. However, potential diversity would increase in mid to high latitudes, offering opportunities for climate change adaptation. These results highlight substantial latitudinal differences in the adaptation potential and vulnerability of the global food system under global warming.

Industrial food production systems depend on inputs such as fertilisers, pesticides, and commercial animal feeds that are highly traded commodities in global markets. Disturbances in international trade can threaten the local food production if the imports of the key agricultural inputs were drastically reduced. However, despite the importance of the topic, a comprehensive analysis focusing on the import dependency of multiple agricultural inputs at the global level and thus revealing the vulnerability of regions and individual countries does not exist. Here, we analyse the temporal trends of agricultural input trade globally at the national scale from 1991 to 2020 by applying statistics of the use and trade of synthetic fertilisers (N, P, and K), pesticides and livestock and aquaculture feeds (grouped into oilseed feeds and other feed crops). The results show that the import dependency of agricultural inputs has increased over the past 30 years, but there is high variation between countries. Countries with high import dependency combined with high use of these inputs, such as many industrial agricultural producers in South America, Asia as well as Europe, show high vulnerability to trade shocks. Also, our findings highlight that potential agricultural intensification in Sub-Saharan African countries—currently with low use of the inputs per cropland area but high import dependency—can lead to higher dependency on imported agricultural inputs. Therefore, understanding of the past trends and current risks associated with the dependency on imported agricultural inputs should be highlighted to mitigate the risks and build more resilient and sustainable food systems.