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Floods and droughts often worsen socioeconomic inequalities, as marginalized groups face disproportionate impacts and have fewer resources to recover. Inequalities, in turn, limit their ability to recover, and amplify the impacts of future hydrological extremes, creating vicious cycles of worsening disasters. Yet such floods and drought events can also serve as opportunities for transformative change, addressing underlying vulnerabilities and reducing inequalities. Progress in sociohydrology is essential for better understanding these two-way interactions, requiring case studies and models of human–water systems that account for uneven risk distribution, along with global comparative analyses using emerging datasets. We propose an interdisciplinary research agenda that combines sociohydrological modelling, critical social science perspectives on power and inequality, and the innovative, but carefully scrutinized, use of artificial intelligence (AI) tools, such as text mining. This integrated approach can reveal context-specific dynamics, and help break vicious cycles of disasters and inequalities.

This chapter contributes to the understanding of coupled human-water systems by providing approaches to describing and categorizing the sociohydrological phenomena from the causative perspective, allowing comparison and integration among real-world case studies, thus helping to reduce water resources management dimensionality. It will do this through invoking the principles and practices of systems thinking, and in particular, through organizing the range of sociohydrologic phenomena into a small number of what are known as system archetypes. The use of archetypes serves the dual role of both diagnosing the causes and mechanisms of the generation of emergent phenomena, but also prescribing and testing system-level collective governance solutions to observed phenomena. 

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.

This paper conceptualises droughts as socioecological phenomena coproduced by the recursive engagement of human and non-human transformations. Through an interdisciplinary approach that integrates political ecology, material geographies and hydroclimatology, this work simultaneously apprehends the role of politics and power in reshaping drought, along with the agency of biophysical processes – soil, vegetation, hydrology and microclimate – that co-produce droughts and their spatiotemporal patterning. The drought-stricken Ladismith in Western Cape, South Africa, is the instrumental case study and point of departure of our empirical analysis. To advance a materiality of drought that seriously accounts for the coevolution of biophysical and political transformations, we alter the spatiotemporal and empirical foci of drought analyses thereby retracing Ladismith’s socioecological history since colonial times. In turn, such extended framework exposes the agency of soil, vegetation, hydrology and microclimate and their metabolic exchanges with processes of colonisation, apartheid, capitalist and neoliberal transformations of South African economy. We argue that the narrow pursuit of profits and capital accumulation of the few has produced a fundamental disruption between nature and society which contributed to transform Ladismith’s drought into a socioecological crisis. Whilst advancing debates on materiality, we note two fundamental contributions to the study of drought. First, our approach makes hydrological accounts of droughts less politically naive and socially blind. Second, it develops a political ecology of droughts and socioecological crises more attuned to the materiality of drought. We contend that apprehending the materiality of drought and the active role of its non-human processes can further understandings of the workings of power and the production of socioecological injustices.

The world’s natural floodplains are being lost and degraded by human alterations. The lack of standardized data for quantifying these floodplain alterations, as well as the absence of convergence among data, analysis, and approaches, has led to significant uncertainty. We discover four primary barriers contributing to this problem: (1) discrepancies in defining floodplain alterations, (2) inconsistent floodplain boundaries, (3) insufficient use of data (e.g., over-reliance on population density), and (4) mismatches in the spatial resolution and scale of analyses (e.g., aggregated across basins or countries using temporally fragmented, short-duration data). As the first step to overcome these barriers, we recommend benchmarking global floodplains using high-resolution land-use data and indices of human disturbance and by integrating existing dams and levees within floodplain maps, establishing a global reference floodplain status from which alterations and critical change points can be continuously tracked. We advocate for a new socio-ecohydrological model of floodplain management—one that redefines human alterations by considering both structural changes and functional losses within a balanced framework of benefits and trade-offs.

Over the past 20 years, the Hydrological Ensemble Prediction Experiment (HEPEX) international community of practice has advanced the science and practice of hydrological ensemble prediction and its application in impact- and risk-based decision-making, fostering innovations through cutting-edge techniques and data that enhance water-related sectors. Here, we present insights from those 20 years on the key priorities for (co)creating broadly applicable hydrological forecasting systems that add value across spatial scales and time horizons. We highlight the advancement of hydrological forecasting chains through rigorous data management that incorporates diverse, high-quality data sources, data assimilation techniques, and the application of artificial intelligence (AI) to improve predictive accuracy. HEPEX has played a critical role in enhancing the reliability of water resources and water-related risk management globally by standardizing ensemble forecasting. This effort complements HEPEX's broader initiative to strengthen research to operations, making innovative forecasting solutions both practical and accessible. Additionally, efforts have been made toward supporting the United Nations Early Warnings for All initiative through developing robust and reliable early warning systems by means of global training, education and capacity development, and the sharing of technology. Finally, we note that the integration of advanced science, user-centric methods, and global collaboration can provide a solid framework for improving the prediction and management of hydrological extremes, aligning forecasting systems with the dynamic needs of water resource and risk management in a changing climate. To effectively meet future demands, it is crucial to accelerate the integration of innovative science within operational frameworks, fostering adaptable and resilient hydrological forecasting systems globally. SIGNIFICANCE STATEMENT: We present transformative advancements in hydrological forecast- ing that integrate diverse, high-quality data sources , advanced modeling techniques, including artificial intelligence (AI)/machine learning (ML) to enhance predictive accuracy across different time scales and spatial dimensions. Through Hydrological Ensemble Prediction Experiment (HEPEX)'s contributions in standardizing ensemble predictions, we have significantly improved forecast reli- ability and support across various water-related sectors. The efforts of the HEPEX community of practice also underpin robust early warning systems through extensive global capacity develop- ment and technology sharing, in alignment with the United Nations Early Warnings for All initia- tive. By fostering strategic collaborations, we bridge the research-to-operations gap, promoting forecasting solutions that are both practical and accessible. HEPEX body of work enhances global disaster resilience, making substantial contributions toward sophisticated, actionable hydrological forecasting and management worldwide.

Since its development in the early 2010s, sociohydrology has deepened our understanding of the long-term coevolution of humans and water by integrating insights from both the natural and social sciences, while also fostering an interdisciplinary community. Its modus operandi to date has been to focus on emergent phenomena, manifesting as unintended consequences, in a variety of contexts. The compound disaster that struck Japan's Noto Peninsula in 2024, and similar experiences in other parts of the world, underscore the urgent need for systemic approaches that are co-developed by academia and practitioners and focus on context-specific solutions. This perspective piece thus calls for expanding and mainstreaming sociohydrology toward transdisciplinary praxis-transforming it into a dynamic and solution-oriented field that is more inclusive at all levels. Sociohydrology must become a driving force for innovation-promoting sustainable solutions that engage and empower local actors through transformative-transdisciplinary actions-involving real people in real places. Only through such transformative praxis can we co-create equitable, sustainable, and context-sensitive responses to the world's most pressing water challenges.

Humans face various hazards, making it challenging to apply a precautionary approach to all of them. Instead, individuals prioritize risk reduction based on perceived threats and lived experiences, often considering multiple hazards simultaneously. This study explores how public perceptions of multiple hazards are interconnected and change over time in Italy and Sweden, using data from three representative surveys (n = 12,476) conducted in August 2020, November 2020, and August 2021. We assess risk perception across three dimensions (likelihood, impact, and authority knowledge) for nine hazards, including epidemics, climate change, and natural disasters. Findings reveal that recently encountered hazards, such as COVID-19, become more closely connected to other hazards. At the same time, connections among hazards such as wildfires, droughts, and floods remain stable over time. These results indicate a dual component of public risk perception of multiple hazards, offering valuable insights for shaping public health policies and climate adaptation strategies.

Identifying drought indices that effectively predict future drought impacts remains a critical challenge in seasonal forecasting, as these indices provide the necessary actionable information that enables stakeholders to better anticipate and respond to drought-related challenges. This study evaluates how drought indices balance forecast skill and relevance for estimating impacts across Europe. Using European Centre for Medium-Range Weather Forecasts SEAS5 seasonal predictions and ERA5 reanalysis as benchmarks, we assessed the predictability skill of drought indices over various accumulation periods and their relevance in estimating drought impacts across Europe, with the aim of enhancing impact-based forecasting. To evaluate these relationships, we built upon the findings from a study that utilized drought impact data from the European Drought Impact Report Inventory and employed random forest models to evaluate the significance of various drought indices in predicting sector-specific impacts. Our findings reveal higher predictability in Northern and Southern Europe, particularly during winter and summer, with some regions showing extended predictability up to six months, depending on the season. Focusing on case studies in the UK and Germany, our results highlight regions and seasons where accurate impact predictions are possible. In both countries, high impact predictability was found up to six months ahead, with sectors such as Agriculture, Water Supply, and Tourism in the UK, and Agriculture and Water Transportation in Germany, depending on the region and season. This analysis represents a significant step forward in identifying the most suitable drought indices for predicting impacts across Europe. Our approach not only introduces a new method for evaluating the relationship between drought indices and impacts, but also addresses the challenge of selecting indices for estimating impacts. This framework advances the development of operational impact-based drought forecasting systems for Europe.

To better understand the increasing human impact on the water cycle and the feedbacks between hydrology and society, the International Association of Hydrological Sciences (IAHS) organized the scientific decade “Panta Rhei – Everything Flows: Change in hydrology and society” (2013–2022). A key finding is the need to use integrated approaches to assess the co-evolution of human–water systems in order to avoid unintended consequences of human interventions over long periods of time. Additionally, substantial progress has been made in leveraging new data sources on human behaviour, e.g. through text mining of social media posts. Much has been learned about detecting hydrological changes and attributing them to their drivers, e.g. quantifying climate effects on floods. To achieve further progress, we recommend broadening the understanding, the discipline and training activities, while at the same time pursuing synthesis by focusing on key themes, developing innovative approaches and finding sustainable solutions to the world’s water problems.