Study region: The Missouri River Basin, located between the states of Nebraska and Iowa, USA. Study focus: Water level (WL) and discharge (Q) are key hydrological variables, accurate prediction of which in both long-term and short-term (extreme events) scenarios is essential for water resources and flood risk management. We propose a novel hybrid deep learning architecture, CNN-NLSTM-SA, which integrates a Convolutional Neural Network (CNN) branch for extracting local features and a Multi-State LSTM (NLSTM) branch for capturing long-term temporal dependencies. NLSTM, with its child-parent structure, enhances memory propagation and mitigates vanishing and exploding gradients. The outputs of these two branches are fused through a multi-head Self-Attention (SA) mechanism, enabling the model to automatically emphasize the most informative representations. New hydrological insight: The proposed model is evaluated for both long-term and short-term forecasting scales. The long-term scenario leverages extensive historical data to provide a large set of training data, whereas the short-term scenario focuses on extreme events with limited training samples. To mimic real-world operational challenges in poorly gauged or data-scarce basins, the model is also tested under varying station-availability conditions using a Leave-n-Station-Out (LnSO) validation strategy. An ablation study comparing CNN-NLSTM-SA with several single- and dual-branch alternatives (CNN-LSTM-SA, LSTM-SA, CNN-SA, CNN-LSTM) shows the superior performance of the proposed architecture. Overall, CNN-NLSTM-SA demonstrates strong potential for WL and Q prediction in both data-rich and data-limited environments.

Nutrient management represents an enduring effort toward sustainability. However, long-term management planning faces notable challenges, mainly due to substantial investments required under uncertainty of forthcoming climate. To address these challenges, this paper proposes and tests a Multi-climate-scenario (MCS) Multi-epoch Multi-objective Planning (MEMOP) framework (in combination MCS-MEMOP). This framework divides the long-term planning horizon into multiple epochs, allowing nutrient mitigation measures (e.g., fertilization management, filter strip) to be initiated at any epoch, each with its own water quality and investment constraints. To tackle climate uncertainty, it incorporates principles of Robust Decision-Making. MCS-MEMOP generates solution pathways outlining the timeline and progression of management measures, tested here for a case of a small, agriculture-dominated watershed. Considering a single climate scenario, the MEMOP method was compared with Multi-objective Planning (MOP) and Stepwise MOP (SMOP) for a 25-year nutrient management horizon, using the SWAT model to evaluate the test case water quality effects of solution pathways. Results show that MEMOP's multi-epoch approach generates a larger and more diverse set of solutions than MOP and SMOP, offering greater flexibility to select optimal trade-offs among objectives. Additionally, MEMOP solutions exhibit superior cost-effectiveness compared to MOP and SMOP solutions. Applied separately to different climate scenarios, the MEMOP results show that changed climate conditions may significantly alter the Pareto front. In contrast, MCS-MEMOP yields robust solutions that can consistently satisfy 72%∼89% of epoch-specific constraints under new climate conditions in the test case, with a cost increase of 12% that reflects the price of addressing climate uncertainty in this case.

Climate change amplifies many threats to human health. Despite advances in understanding climate change dynamics and impacts, there remains a critical gap in translating scientific knowledge into equitable, and community-driven health interventions. The inaugural One Earth, One Health workshop sought to explore this gap through human-centered design exercises involving interdisciplinary researchers from climate and Earth sciences, engineering, epidemiology, microbiology, and environmental health. Although participants did not co-develop solutions with affected communities, they used stakeholder role-playing to guide ideation and lay groundwork for actionable plans. Through these methods, participants identified community needs and proposed prototype solutions to alleviate health threats exacerbated by global environmental change. Prototypes were organized around infectious diseases, extreme weather, and air quality, as illustrative themes rather than an exhaustive set of risks. Key solutions included strategies for anticipatory systems and early warning (e.g., integrating environmental signals with health data), inclusive communication and infrastructure needs for responding to extreme weather events, and integrated platforms visualizing air quality trends to support tailored, context-aware guidance beyond one-size-fits-all alerts. The workshop highlighted opportunities such as leveraging machine learning, Earth observation, and real-time surveillance to protect communities, but also noted barriers including data quality, technological redundancy, privacy, and governance challenges. Additionally, participants emphasized the need for interdisciplinary teams capable of collaborating across sectors, breaking down silos and addressing gaps in training and education. Overall, the workshop illustrates how process-driven, human-centered approaches can help surface user needs and generate testable prototype concepts, while underscoring the importance of direct community partnership for implementation.

South America hosts one of the world’s largest hydrological systems, yet terrestrial water responses to warming and human influences remain poorly constrained. Here we compare observational and model-based datasets across 95 hydrological catchments, including the Amazon and La Plata basins, to assess trends and extremes in the South American water system during 1980-2010. All datasets consistently show no continental-scale intensification (no increase) of mean precipitation, runoff, or evapotranspiration, challenging expectations of generalized terrestrial water-cycle intensification under warming. Nevertheless, the datasets show shifts in water-flux seasonality and extremes between the first and second halves of the study period. Wet-season high flows increased in the Amazon Basin and dry-season low flows declined in the La Plata Basin, indicating increasing flood and drought risks, respectively. These results highlight a regionally differentiated, complex evolution of the South American hydroclimate.

Record-breaking temperatures and frequent heatwaves have been experienced worldwide in recent years. Heatwaves pose an escalating threat to public health and heat-related impact forecasting is critical to implementing suitable mitigation strategies. Deep learning (DL) models, notably Long Short-Term Memory (LSTM), have been widely applied for heat-related impact forecasting. However, the emergence of state-of-the-art forecasting DL architectures such as Neural Basis Expansion Analysis for Interpretable Time Series Forecasting (N-BEATS) provides a novel solution for long-term heat-related impact forecasting. This study develops, evaluates, and compares multiple time series forecasting models—including advanced DL architectures (N-BEATS, N-HiTS, LSTM), a classical statistical model (ARIMA), and a Naïve seasonal baseline—to predict heat-related morbidity across 21 Swedish counties using data from 2008 to 2023. Both local (individually trained) and global (cross-learning across counties) modeling strategies were explored, incorporating exogenous variables (Heatwave Index and number of people with respiratory disease), and comparing recursive and Multi-Input-Multi-Output (MIMO) forecasting output strategies. Results indicate that the local N-BEATS model achieves superior predictive performance, particularly when both exogenous variables are included. MIMO generally yields a better performance by mitigating error propagation over extended forecasting horizons. Moreover, individually trained N-BEATS models outperform cross-learning global N-BEATS, underscoring the importance of localized adaptation plans. These findings highlight the potential utility of multivariate N-BEATS for more accurate heatwave impact forecasting. This study can complement and support early warning frameworks by integrating the developed impact forecast model with existing hazard models, thereby enabling more proactive public health interventions and improving community resilience to heatwaves.

The rapid rise of machine learning (ML) in hydrology has prompted debate about the discipline's scientific relevance. While ML often outperforms traditional models in streamflow prediction, we argue that this reflects a deeper limitation: persistent fragmentation of hydrological science itself. Narrow focus on isolated components has hindered the development of coherent, scale-relevant understanding of the integrated terrestrial hydrosphere. This is illustrated, for example, by widely divergent estimates of groundwater–streamflow interactions and of water balance-implied ongoing storage changes. We argue that hydrology's future lies not in choosing between ML and physics, but in integrating data-driven and process-based approaches to advance consistent, realistic, and societally relevant understanding of the terrestrial hydrosphere and its multifaceted roles in the Earth System.

Reliable future climate projections and water deficiency assessments require precipitation observations that are both spatially comprehensive and temporally complete, yet many global regions still suffer from observation sparsity. Here we evaluate the distribution of 221,483 internationally exchanged precipitation gauges worldwide, with records across 1900–2022, and further explore where new gauges are most needed under different scenarios. We find that at present only 13.4% of the global land surface meets the World Meteorological Organization requirements for annual precipitation monitoring, indicating widespread scarcity that has serious socioeconomic implications. Europe has the highest continental gauge density (2.4 gauges per 1,000 km²), with Germany leading among countries over 50,000 km² (22.4 gauges per 1,000 km²). Globally, 25% of land surface already requires urgent expansion of gauge networks because of climate variability, including northern South America, northern North America, Central Africa and southern Asia. Considering projected precipitation changes and socioeconomic conditions under a high-emission scenario further identifies high-need regions in India, Greenland, Bolivia and China because of climate sensitivity and socioeconomic vulnerabilities, increasing this share to 32.1% of global land. Our findings highlight important gaps in global precipitation monitoring that require strategic investments in new gauges and underscore the need for open data access.

This study investigates how the seven core resilience principles are integrated into assessments of forest system resilience to natural or human-induced disturbances across engineering, ecological, and social-ecological resilience concepts. Following PRISMA guidelines, a literature search in the Web of Science database using the keywords “resilience”, “forest” and “ecosystem services” yielded 1828 studies, of which 330 met the selection criteria. The most commonly used criterion was diversity, a sub-criterion of “diversity and redundancy”, appearing in 50% of studies. The results indicate that social and governance-related principles, learning and experimentation (7%), participation (11%), and polycentric governance (9%) have not been frequently addressed. Although numerous studies have employed various principles for assessing forest resilience, none have considered all seven principles jointly. This highlights a significant research gap, emphasising the need to quantify these principles in forest systems. Understanding forest-community dynamics is essential for enhancing the long-term resilience and sustainability of both systems.

Understanding the total water flows and pollutant loads to the Baltic Sea is important for effective coastal-marine ecosystem management. Current assessments often overlook the unmonitored flows and submarine groundwater discharge (SGD). This study proposes and outlines a conceptual modelling framework for overcoming this common neglect by integrated quantification of (1) the monitored surface water flows, and the unmonitored (2) surface water flows and (3) SGD from land to the Baltic Sea. The study outlines how unmonitored runoff and SGD can be estimated by various quantification approaches based on commonly available hydro-climatic, hydrogeological, and other characteristic catchment data. It also describes how modules for the different monitored and unmonitored discharge components are linked and should be integrated in modelling to total annual, seasonal, or finer-resolved water flows to the Baltic Sea, and analogously also in other coastal regions around the world. Though quantitative modelling remains ongoing, the conceptualization opens pathways to improve assessments and management of freshwater flows and associated pollutant loads to the Baltic Sea.