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Abstract. Compound wind and precipitation (CWP) extremes often cause severe impacts on human society and ecosystems, such as damage to crops and infrastructure. Spatially compounding events with multiple regions affected by CWP extremes in the same winter can impact the global economy and reinsurance industry; however, our understanding of these events is limited. While climate variability modes such as El Niño Southern Oscillation (ENSO) can influence the frequency of precipitation and wind extremes, their individual and combined effects on spatial co-occurrences of CWP extremes across the Northern Hemisphere have not been systematically examined. Here, by combining reanalysis data and climate model simulations, we investigate how two oceanic and two atmospheric variability modes – ENSO, the Atlantic Multidecadal Variability (AMV), the North Atlantic Oscillation (NAO), and the Pacific North American (PNA) – amplify the wintertime (December–February) frequency of daily CWP extremes and associated spatial co-occurrences across the Northern Hemisphere. We find many hotspot regions where concurrent variability mode anomalies significantly amplify wintertime CWP extreme event frequencies compared to single variability modes. By examining the relationships between frequencies of wintertime CWP extremes across regions, we identify dependencies enabling extreme spatially compounding events, that is winters with many regions experiencing CWP extremes. While ENSO is the most influential variability mode for such extreme spatially compounding events, the occurrence of these events increases further when multiple modes of variability are in anomalous phases. In particular, combinations of modes increase both the number of regions and the population exposed to daily CWP extremes in the same winter. For example, combined ENSO- and NAO+ nearly doubles the number of affected regions compared to neutral conditions on average. Our analysis highlights the importance of considering the interplay between variability modes to improve risk management and adapt to the impacts of spatially compounding CWP extremes.

With the acceleration of climate change and urbanisation, Chinese cities are facing increasingly severe flood risks. To address this challenge, China began implementing its sponge city policy in 2013, leveraging low-impact development, green infrastructure construction, and integrated water resource management to enhance urban resilience to floods and improve water security. This study utilises the Web of Science database as a reference, retrieving 201 relevant literature sources. From these, 61 studies closely related to China’s sponge city policy and urban flood management were selected. CiteSpace was employed to conduct keyword co-occurrence and temporal evolution analyses, comprehensively outlining the research hotspots and developmental trajectory of this field. The results indicate that research content has gradually shifted from early engineering-based flood control models to multi-objective, interdisciplinary comprehensive management, encompassing flood risk assessment, policy implementation mechanisms, integration of green infrastructure, and economic feasibility analysis. Based on this, this paper constructs an analytical framework incorporating technical, environmental, institutional, and social dimensions to integrate existing research findings, while identifying gaps in cross-scale coordination, smart management, and public participation. The research conclusions can provide valuable references for future policy optimisation and urban sustainable development.

ABSTRACT High‐resolution precipitation products (e.g., km‐scale and hourly) are essential for capturing extreme rainfall dynamics and improving hydrological models for flood forecasting and related applications. However, single‐source estimates—whether from gauges, radar, or satellites—suffer from inherent limitations such as sparse spatial coverage, limited observational extent, and retrieval uncertainties. Multi‐source precipitation combinations can potentially provide a more accurate estimate by using the strengths of each source. This review presents globally relevant methodologies for constructing high‐resolution precipitation datasets, with a focus on sub‐daily temporal and km‐scale spatial resolutions. Particular emphasis is placed on their applicability to the development of a new multi‐source precipitation dataset for Australia (BRAIN: Blended Rain). We first compile and assess precipitation data sources specific to Australia, particularly those readily available to the Australian Bureau of Meteorology. These include ground‐based gauge observations, radar estimates, satellite‐derived products, reanalysis datasets, and numerical weather prediction outputs. While the primary emphasis is on Australian datasets, many sources provide global coverage, enhancing the broader relevance of this work. We then review globally applicable blending techniques, highlighting methods such as weighted averaging, multifractal blending, data assimilation, and machine learning. Key challenges, including latency, quality control, spatial heterogeneity, and validation, are discussed alongside opportunities for advancing multi‐source precipitation blending. Finally, we recommend specific datasets and blending methods for trial and assessment, considering both current Australian data availability and potential future upgrades. The insights provided aim to support the development of robust high‐resolution precipitation products for hydrological applications in Australia and other regions with similar data integration challenges. This article is categorized under: Science of Water > Methods

The growing threat of dam-break events, fueled by aging infrastructure and climate change, necessitates comprehensive risk management and mitigation strategies. Experimental studies on partial dam-break flows are pivotal for understanding the complex dynamics of these events, particularly in assessing flood risk and refining predictive models. This review synthesizes current experimental investigations on three-dimensional (3D) partial dam-break flows, with an emphasis on breach dynamics, wave impacts, and the role of urban structures. It highlights the challenges in capturing high-resolution 3D flow characteristics and the advancements in measurement techniques such as particle tracking velocimetry and ultrasonic distance meters. The paper discusses the integration of experimental data with numerical models to validate and improve predictive capabilities, stressing the need for continuous refinement of experimental setups and computational approaches. Gaps in the current literature, including the under-representation of irregular breach geometries and complex terrain, are identified, and future research directions are proposed to address these shortcomings. This work underscores the importance of hybrid measurement techniques and interdisciplinary collaboration to enhance dam-break modeling accuracy and flood risk mitigation.

ABSTRACT Forest cover within catchments is a widely adopted Nature‐based Solution (NbS) for flood mitigation, offering hydrological benefits such as rainfall interception, enhanced infiltration, and reduced overland flow. Despite its recognized potential, quantitative reviews remain limited, especially at the catchment scale, with effectiveness varying by spatial scale, forest type, and climate. This review synthesizes 50 international case studies involving forest‐based NbS, selected through structured screening based on intervention type, catchment characteristics, and availability of quantitative flood metrics, and presents a detailed bibliometric and content analysis. Forest cover consistently impacts peak flow across catchments of all sizes, with a generalized linear relationship where the effect magnitude is approximately half the forest cover change. For example, a 20% increase in forest cover tends to reduce peak flow by 10% across small, medium, and large catchments. Across a range of catchment sizes, there are only minor differences in the mean peak flow reductions for different event intensities (up to 1% AEP). An asymmetric hydrological response is evident: deforestation consistently increases peak flows, whereas afforestation yields gradual reductions, which are shaped by forest maturity, spatial distribution, and modeling assumptions. Upstream distributed forest placements offer distinct hydrological benefits. These outcomes highlight the importance of conserving mature forests, preventing deforestation, and optimizing forest placement, while acknowledging potential adverse impacts on water availability during dry periods.

Both sex-related factors and gender-related factors affect the immediate and long term mental and physical health impacts of disasters, including those resulting from public health emergencies, climate-related events, and naturally occurring phenomena. These include sex-specific biological, physiological and genetic processes, mechanisms underlying reproduction, disease outcomes, and stress, immune, and trauma responses. Gendered factors such as roles, relations, identity, and institutional policies that have an impact on caregiving, occupation, gender-based violence, and access to healthcare, also influence the impacts of disasters and emergencies. Sex/gender factors interact with a range of social determinants to affect the equitability of impacts. A rapid review was conducted to examine evidence from Australia, Canada, countries from the European Union, New Zealand, the United Kingdom (UK), and the United States of America (USA) on the influence of sex- and gender-related factors in the context of disasters, such as COVID-19, earthquakes, floods, hurricanes, and wildfires. This article describes and categorizes this evidence with attention to real-world impacts of the interactions between sex, gender, and other equity related factors. Broad considerations for improving research and practices to support more sex and gender research in this area and ultimately, to improve emergency and disaster management, are discussed.

Floating treatment wetlands (FTWs) are engineered systems that utilize floating platforms planted with aquatic vegetation to treat polluted water such as stormwater, agricultural runoff, and wastewater. FTWs have emerged as promising and environmentally sustainable solutions for water purification. This review synthesizes the current knowledge on FTW design, plant selection, and performance evaluation. It highlights key factors influencing nutrient and heavy metal removal, including the hydraulic retention time, mat thickness, and types of plant species. Recent findings on the roles of root architecture, microbial interactions, and seasonal variability in treatment efficiency are also discussed. Additionally, the review explores advanced analytical methods for monitoring water quality and assessing plant growth and contaminant uptake. Case studies from both laboratory- and field-scale experiments illustrate how variation in FTW configurations impacts pollutant removal efficiency. The review concludes by identifying critical research gaps, including the need for standardized monitoring protocols, strategies to enhance long-term performance, and the integration of FTWs with complementary treatment technologies to improve effectiveness across diverse aquatic environments.