Addressing global challenges and advancing knowledge in the Earth and space sciences requires an equitable, diverse, and inclusive scholarly community where researchers must be freely able to conduct, collaborate on, share, review, and discuss their research on important economic and societal topics such as climate change. The current Executive Orders in the United States focus on censoring research and researchers by banning specific words, removing access to data sets, or by restricting what type of research can be funded or published, therefore compromising the knowledge that researchers are able to produce. As Editors-in-Chief of AGU publications we stand by our mission to support the publication of evidence-based, rigorously vetted research without political pressure. Collectively, our peer-reviewed journals and books provide inclusive publication outlets for the global research community to advance Earth and space sciences and to strengthen the public's trust in scientific evidence.

Heatwaves can greatly impact societies, underscoring the need to extend current heatwave prediction lead times. This study investigates multiple machine learning (ML) model approaches for heatwave occurrence prediction with long lead times of one to five months. Five ML classifiers, built using Google Earth Engine remote sensing datasets, are developed and tested for heatwave prediction for the national scale (case example of Sweden) over time period 1989–2019. The ML modelling is based on 13 final explanatory atmospheric and landscape features. The balanced random forest model exhibits the consistently best performance among the tested ML models, stable across all investigated lead times (from one to five months) with balanced accuracy of around 0.77, even though not overall identifying actual heatwave occurrence (decreased recall for heatwave occurrence from 0.87 to 0.81). Application of SHapley Additive exPlanations technique for model interpretation shows increasing importance of model output with increasing lead time for landscape features such as runoff and soil water. Overall, more frequent heatwave occurrence emerges for places characterized by lower values of geopotential height, evaporation, precipitation, and topographical slope, and higher values of temperature, runoff, and sea level pressure. The study also exemplifies how the developed ML modelling approach could be used to identify and warn for early signs of forthcoming heatwave occurrence, and further step-wise improve the identification and warning toward less uncertainty for shorter lead times. This can facilitate earlier warning in support of better planning of measures to mitigate adverse heatwave impacts, up to several months ahead of their possible occurrence.

Water on land is essential for all societal, ecosystem, and planetary health aspects and conditions, and all life as we know it. Many disciplines consider and model similar terrestrial water phenomena and processes, but comparisons and consistent validations are lacking for the datasets used by various science communities for different world parts, scales, and applications. Here, we present a new global data synthesis that includes and harmonises four comparative datasets for main terrestrial water fluxes and storage changes, and the catchment-wise water balance closure they imply for the 30-year period 1980–2010 in 1561 non-overlapping hydrological catchments around the world. This can be used to identify essential agreements and disagreements of the comparative datasets for spatial variations and temporal changes of runoff, evapotranspiration, water storage, and associated water-balances around the global land area, e.g., for pattern recognition and hypothesis/model testing. The facilitated direct dataset comparison can advance a more coherent, realistic cross-disciplinary understanding of Earth’s water states and changes across regions and scales, from local and up to continental and global.

Status quo water scarcity in the Mediterranean region is expected to be exacerbated as hydro-climatic changes intensify, demanding the development of place-based interventions with careful attention to contextualized economic, environmental, social, and institutional interactions. Key to this will be advancement of contemporary understanding of the combined influences of climate and human activity changes on water availabilityWater availability in the region, in particularly considering that decreases in precipitation may be up to 30%, while increases in mean annual temperature may be 20% above the global average, and given the influence of these factors on the nexus of water sand food security. Freshwater use by and supply to the general population and particular sectors, such as agriculture and tourism, is of particular concern during summer with elevated water demands coupled and reduced replenishment of water supplies. The development of effective and sustainable water management practices to reduce excessive summer-time water demands will be key to the future prosperity, productivity, and longevity of critical sectors including agriculture. Water supply is also threatened by quality deterioration, e.g., by eutrophication of water reservoirs and groundwater contamination by excess nitrogen and salinity. In this chapter, we explore trends in water availabilityWater availability, demand, links with land use, and issues associated with water quality. We further discuss opportunities to improve water availabilityWater availability and food security with coordinated management strategies for: (i) increasing freshwater availabilityWater availability by enhancing storage capacity, rainwater harvesting, and potential inter-basin water transfers; (ii) use of non-freshwater resources, such as desalination, treated wastewater and graywater reuse, and (iii) decreasing water demands. Comprehensive knowledge of the spatial and temporal status of water resources and key water securityWater security challenges can support decision-making and guide water management in the region.

Hydrology plays a crucial role in understanding Earth's intricate water system and addressing water-related problems, including against the backdrop of ongoing climate change. A retrospective review of the evolution of hydrology up to the current state of research is of great importance for understanding this role. While there have been some quantitative reviews of large numbers of hydrological publications, there still remains a lack of overarching hydrological research assessment, particularly with the focus on hydrological basins as fundamental spatial-geographic units of hydrological analysis. Large language models, represented by OpenAI's ChatGPT, have demonstrated powerful textual understanding capabilities, making it possible to extract such overarching and basin information from hydrological publications. Here, we considered publications related to hydrology from Web of Science spanning January 1980 to October 2023, and parsed the information from this extensive body of literature by integrating a large language model and geocoding. These techniques enable quantitative analysis of research characteristics across different spatio-temporal scales, focusing on hotspot topics, collaboration networks, and various basins worldwide. Our study revealed an increase in hydrological research since the 1990 s, with shifts in research priorities from groundwater and nutrients to climate change and ecohydrology. Some basins in North America and Europe have consistently been hotspots for hydrological research. Since the 2010s, there has been a noteworthy increase in interest toward basins in China and South Asia, but attention to many regions with frequent extreme rainfall remains insufficient. Geographical patterns show different preferred research topics for different basins, but climate change has emerged as the most prominent topic across all regions in the last decade. In conclusion, our study provides an effective approach to quantitative analysis of research trends, offering a fresh view on the evolution of hydrology as a research field, its focus on various hydrological basins around the world, and the emergence of overarching and basin-specific hot topics over time.

In this paper we present a framework to aid in the selection of optimal environmental indicators for detecting and mapping extreme events and analyzing trends in heatwaves, meteorological and hydrological droughts, floods, and their compound occurrence. The framework uses temperature, precipitation, river discharge, and derived climate indices to characterize the spatial distribution of hazard intensity, frequency, duration, co-occurrence, and dependence. The relevant climate indices applied are Standardized Precipitation Index, Standardized Precipitation and Evapotranspiration Index (SPEI), Standardized Streamflow Index, heatwave indices based on fixed (HWI $$_\textrm{S}$$ S ) and anomalous temperatures (HWI $$_\textrm{E}$$ E ), and Daily Flood Index (DFI). We selected suitable environmental indicators and corresponding thresholds for each hazard based on estimated extreme event detection performance using receiver operating characteristics (ROC), area under curve (AUC), and accuracy, which is defined as the proportion of correct detections. We assessed compound hazard dependence using a Likelihood Multiplication Factor (LMF). We tested the framework for the case of Sweden, using daily data for the period 1922–2021. The ROC results showed that HWI $$_\textrm{S}$$ S , SPEI12 and DFI are suitable indices for representing heatwaves, droughts, and floods, respectively (AUC > 0.83). Application of these indices revealed increasing heatwave and flood occurrence in large areas of Sweden, but no significant change trend for droughts. Hotspots with LMF > 1, mostly concentrated in Northern Sweden from June to August, indicated that compound drought-heatwave and drought-flood events are positively correlated in those areas, which can exacerbate their impacts. The novel framework presented here adds to existing hydroclimatic hazard research by (1) using local data and historical records of extremes to validate indicator-based hazard hotspots, (2) evaluating compound hazards at regional scale, (3) being transferable and streamlined, (4) attaining satisfactory performance for indicator-based hazard detection as demonstrated by the ROC method, and (5) being generalizable to various hazard types.

The Mediterranean regionMediterranean region is characterized by specific climatic (dry and hot summers and mild winters), geologic (especially karstic formations) and topographic gradients that make it diverse in terms of socio-economic and environmental features, but also unique. It has been considered a biodiversity hotspot but also a climate changeClimate change hotspot, with temperature increases above the global average. Water resources are unevenly distributed, with some areas suffering from water stress. Population growth has been leading to urban expansion, especially in coastal areas, landscape fragmentation and reduction of agricultural land. Both climate changeClimate change and inefficient natural resources management have led to land degradationEnvironmental degradation and desertification, and loss of biodiversity. This chapter presents the main characteristics and some of the most relevant environmental problems of the Mediterranean region that must be understood when paving the way to sustainable development.

Waterborne nutrient loads to downstream ecosystems integrate contributions from both active and legacy sources. Effective mitigation of nutrient pollution and eutrophication around the world requires distinction of these, largely unknown, relative load contributions. Here, the active and legacy contributions to nitrogen and phosphorus loads are distinguished in numerous streams and associated hydrological catchments of Australia, China, Sweden, and USA. The legacy contributions overshadow the active ones in all countries during 2005–2020. China and USA, with higher population densities and related overall human-activity levels, also have substantial active contributions. The median values of legacy concentration contributions of total nitrogen range from 321 (in Sweden) to 1850 μg/L (in USA); whereas the active contributions range from 2.2 (in Australia) to 315 μg/L (in USA). In China, nitrogen data are available only for ammonia, with median concentration contributions of 294 μg/L for legacy and 352 μg/L for active sources. For total phosphorus, the median values of legacy concentration contributions range from 28.8 (in Sweden) to 270 μg/L (in USA), while the active ones range from 0.1 (in Australia) to 67.3 μg/L (in USA). For relatively fast mitigation responses, China and USA need to mitigate their current nutrient emissions, while Australia and Sweden need a shift in mitigation focus to targeting their dominant legacy source contributions. The data-driven method testing in this study supports the used source distinction-attribution approach. This enables consistent source identification and tailoring of targeted measures for effective nutrient load mitigation in various regional contexts.