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Abstract Sea turtles face a number of threats from climate change. One pertinent threat is the impact of sea level rise (SLR), which can lead to a loss of nesting habitat and reduced survival of embryos due to inundation and erosion. Here we review the impacts of SLR on sea turtles. We examined 34 studies (66 assessments) on SLR impacts across six species and 40 sites globally, with 62.1% of assessments located in the Americas. Overall, 78.8% of the assessments showed declining trends in historical and projected estimates of nesting habitat area and or nest survival from inundation. Assessments with projected nesting habitat area showed appreciable loss across all site types (coral island, elevated island, barrier island and mainland), regardless of the SLR scenario. The projected percentage of habitat loss and nest flooding was greatest in island sites compared to the mainland sites, highlighting that SLR is likely to be most acute for low-lying islands with no nearby alternative nesting areas. We reviewed the predicted extent of nesting beach loss and examined how natural processes and conservation interventions might mitigate this threat. However, we require more empirical data on the extent of historical nesting habitat loss. There is also little known about a population’s ability to colonise new nesting areas once a site becomes unsuitable. By tracking their routes towards the breeding sites, there is potential to show how resilient sea turtles are to environmental change, as females may be exposed to other suitable nesting sites on their migration route.

ABSTRACT Accurate flood level prediction is crucial for mitigating flood damage caused by typhoons or localized heavy rainfall. However, predicting flood levels is challenging due to changes in river environments and external factors, such as dam or weir operations. To address these challenges, this study proposes a methodology for constructing an optimal combination of input data using basic hydrological information and predicting flood levels in real time through a deep learning model. The study focuses on identifying the best input data combination tailored to each river basin's characteristics, considering both natural runoff rivers and those influenced by dam discharges. The Long Short‐Term Memory (LSTM) model, known for its superior performance in time‐series forecasting, was employed. The results demonstrate high accuracy in flood level prediction, particularly within a 3‐h lead time.

ABSTRACT Sea level rise and storm surges resulting from climate change are expected to increase the frequency and intensity of floods. Aging and/or inefficient drainage systems can significantly exacerbate flood damage. Effective flood defense policies must categorize flood damage and provide a detailed assessment of each contributing factor. In this study, we evaluated the cost of flood damage in Busan—the second most populous city on the coast of South Korea. Flood damage costs were analyzed based on an object‐based approach across 10 scenarios, which accounted for variations in storm surge frequency, climate change impacts, and drainage efficiency. Flood damage costs for four categories, namely buildings, agriculture, human casualties, and vehicles, were examined across different administrative regions. Results reveal that flood damage costs increase with higher storm surge frequencies and climate change effects, while reduced drainage efficiency further amplifies these costs. Given that damage costs are predominantly concentrated in coastal areas, future data‐based flood defense policies should be developed to reflect the specific vulnerabilities and damage patterns of each administrative region.

Abstract Land drainage and flood protection infrastructure such as pump stations, tide/flood gates and levees are used to mitigate flood hazards and maintain productive land. However, such infrastructure fragments aquatic ecosystems, often altering the hydrological, geomorphic and physico‐chemical characteristics of waterways. To better understand the risks to eels in the vicinity of flood relief pump stations, we undertook a passive integrated transponder tag study evaluating the movements of 332 shortfin eels ( Anguilla australis ) across 2.5 years at a pump station with a gravity bypass in New Zealand. We observed clear diel and seasonal patterns in eel movements, with increased eel activity at dusk and during late spring and summer. In addition, we observed a unimodal relationship between daily eel activity and water temperature with peak activity occurring between 14°C and 15°C. The gravity bypass was the primary route used by eels for both downstream (45%) and upstream (20%) movements, while direct passage through the pump was uncommon, occurring in only 28 individuals. Among more than 12,000 recorded encounters with the flood pump and gravity bypass, the most frequent outcomes were avoidance of the bypass and obstruction at the pump (42% and 58% of the encounters across individuals, respectively), whereas direct or delayed entrainment through the flood pump occurred rarely (13.5% and 15.2% of the encounters across individuals, respectively). No eels were entrained in the flood pumps during the autumn downstream migration due to low flows throughout this period that precluded the need for active pump operations. In contrast, we observed eel entrainment during winter and spring, when the pumps were most frequently operating, suggesting that non‐migratory (yellow) eels may have been affected. Practical implications . These results highlight that the risk to eels of pump entrainment and mortality extend beyond the main period of downstream migration that has been the focus of most studies. Pump station design and operational management must allow for bidirectional movements of eels to provide effective functional connectivity.

Abstract Natural disasters can be fatal and necessitate critical thinking in decision-making processes; thus, simulations of multiple scenarios must be evaluated to prepare for such imminent events. An Open World Machine Learning (OWML) framework has been proposed as a focal tool to improve decision-making in natural disaster management. To better understand and formulate a comprehensive foundation, a systematic review of different cases was conducted. In this study, four types of natural disasters-earthquakes, floods, wildfires, and hurricanes-were analyzed using machine learning techniques within the OWML framework. The essence of this approach lies in its ability to accumulate knowledge from various sources and adapt to new, unknown event types. Moreover, by explicitly incorporating both qualitative and quantitative characteristics, the framework enhances predictive accuracy and adaptability. The results demonstrate that OWML models can effectively process large geospatial datasets and respond to looming threats with greater precision. Albeit some limitations, such as data quality and model complexity, the findings suggest that OWML can serve as a foundational tool for governments to reduce expenditure and improve evacuation strategies. The evolutionary nature of the OWML framework allows for continuous learning and adaptation, which is crucial as natural disasters escalate in frequency and intensity due to climate change. In essence, this study provides a significant contribution to natural disaster management by explicitly demonstrating the potential of OWML frameworks in enhancing decision-making processes.

ABSTRACT Wastewater treatment plants (WWTPs) are encountering challenges related to sustainability, efficiency, and economic viability due to stricter discharge standards and the impact of climate change, including heavy rainfall. Enhancing the resilience and performance of WWTPs is therefore crucial. This paper reviews the application of artificial intelligence (AI) models that can improve WWTP resilience in response to extreme rainfall events. It provides an overview of publications on digitalization of WWTPs over the past 33 years, proposes a new classification approach for AI models, and reviews existing AI applications aimed at improving the performance of WWTPs in response to extreme rainfall events. These applications highlight the importance of developing hybrid models that integrate AI with knowledge-driven models to improve accuracy. Furthermore, future AI research in WWTPs should incorporate hydrological forecasting to enable advance estimation of flow rates, and facilitate effective management.

Abstract Climate change presents a significant threat to global health, with both immediate and long-term consequences, especially in low-income countries with limited adaptive capacity. Pregnant women, developing fetuses, and youth are among the most vulnerable populations, yet the specific risks they face remain underexplored. This literature review examines the impact of climate change on maternal mortality, focusing on both direct and indirect pathways. We analyzed peer-reviewed studies that address how climate-related events, such as severe droughts, water scarcity, wildfires, rising sea levels, frequent floods, melting polar ice caps, catastrophic storms, and biodiversity loss contribute to complications during pregnancy and childbirth. The evidence suggests that climate change can indirectly increase maternal health risks and mortality through social, economic, and psychological pathways, including inadequate sanitation, population shifts, disrupted healthcare access, malnutrition, and disease transmission. Our findings reveal a consistent pattern of heightened risk for maternal complications and mortality linked to climate-related events. We recommend conducting more country-specific and comprehensive studies to better understand these relationships and inform effective interventions. Policymakers should prioritize efforts to reduce maternal mortality and improve the health of expectant mothers and their newborns in the context of a changing climate.

Abstract Research on meteorological tsunamis or meteotsunamis—long ocean waves in the tsunami frequency band generated by propagating atmospheric disturbances which resonantly enhance ocean waves—has grown significantly in recent decades. This expansion is due to progress in (a) ocean and atmospheric measurements, including advanced instrumentation with higher precision and smaller sampling time steps, as well as installation of meteotsunami tracking measurement networks, (b) ocean and atmospheric data products, including those related to the upper atmosphere and ionosphere, and (c) supercomputing capabilities and sophisticated atmosphere‐ocean models that successfully simulate both atmospheric planetary processes and mesoscale systems capable of generating meteotsunamis, as well as sea level response to these. Meteotsunamis can induce multi‐meter sea level oscillations in harbors and low‐lying areas, leading to severe flooding, infrastructure damage, injuries, and sometimes fatalities. Traditionally, meteotsunami research focused on individual event analyses using available sea level and lower‐layer atmospheric observations. Recently, efforts have shifted toward global hazard mapping, the development of forecast and early‐warning systems, and toward quantifying projected meteotsunamis intensity and frequency, using climate models. The January 2022 eruption of the Hunga Tonga‐Hunga Ha'apai volcano, which generated acoustic‐gravity waves that circled the globe, has spurred research of planetary meteotsunami waves and their potential to pose coastal hazards worldwide. Additionally, meteotsunamis radiate acoustic‐gravity waves vertically, creating ionospheric oscillations detectable through electron content variations. This review will cover the mentioned developments and conclude with a discussion of research gaps and potential directions for further studies. , Plain Language Summary Sea level extremes are an emerging topic in the era of climate change, as most coastal damage and impacts occur during such events. One of the underrated phenomena contributing to these extremes are meteorological tsunamis or meteotsunamis—long ocean waves in the tsunami frequency band resonantly generated by traveling air pressure/wind disturbances—which are known to substantially impact certain coastlines and locations, causing deaths, injuries, and damage to coastal infrastructure, ships and yachts, buildings, and households, as well as affecting navigational safety. Until about a decade ago, research on meteotsunamis was mostly localized, focusing on specific meteotsunami hotspots. However, in recent years, research has become more global. This was made possible by the development of high‐precision oceanic and atmospheric instruments that collect data at minute or higher resolutions, as well as ultra‐high‐resolution atmosphere‐ocean models capable of reproducing meteotsunamis, supported by high‐performance computing facilities. This review summarizes all aspects of meteotsunamis, from available data, products, and tools that can be used for research, to an in‐depth exploration of the science behind the phenomenon, and its impact on coastal regions. It also discusses the development of forecasting and early‐warning systems. Finally, the review provides recommendations for future research directions on this hazardous phenomenon. , Key Points Minute‐scale sea level oscillations, including meteotsunamis, significantly contribute to extreme sea levels worldwide Generated though atmosphere‐ocean resonances, meteotsunamis can have from local to global impact and be destructive at certain locations Reliable estimates of meteotsunami hazard in present and future climates, along with effective forecasting and warning, remain a challenge

Abstract Flash floods are critical events for emergency management, yet their modeling remains highly challenging, even in smart cities approaches. Physically based hydrological models are often unsuitable at small spatiotemporal scales due to their computational complexity and dependence on detailed local parameters, which are rarely available during flash floods. With the growing availability of hydrological data, machine learning (ML) has emerged as a promising alternative. This work performs a Systematic Literature Review (SLR) to improve our understanding of the research landscape on ML applications for flash flood forecasting, a significant subset of flash flood modeling. From more than 1,200 papers published until January 2024 in Web of Science, SCOPUS/Elsevier, Springer/Nature, and Wiley, 50 were selected following PRISMA guidelines. The inclusion and exclusion criteria removed reviews, retractions, papers focused on post-flood damage assessment (not forecasting), and those with time resolutions of 6 hours or more, retaining only studies with fine-scale temporal data (<6 hours). For each paper, we extracted information on forecasting horizon, study area size, input data, ML techniques, and outcomes (regression or classification). Results show a sharp rise in ML-based flash flood research, with China leading (38%). Nearly all studies rely on rainfall, discharge, and water level data - often in combination. Long short-term memory (LSTM) networks dominate (60%). Unfortunately, only 10% of the selected studies provide access to their datasets. This lack of transparency poses a major barrier to reproducibility, inhibits fair comparative evaluation of models, and ultimately slows methodological progress in flash flood forecasting. Furthermore, our review highlights that no method consistently outperforms others. This variability in performance is likely influenced by factors such as regional hydrological characteristics (e.g., differences between arid and tropical basins), variations in input data quality, and the length of the forecast horizon (e.g., 1- vs. 6-hour prediction). Lastly, we recommend advancing this field through integration with early warning systems, creation of benchmarks, open data practices, and stronger multidisciplinary collaboration.

ABSTRACT The demand for catchment‐based flood management to adapt to climate change is growing, with natural flood management (NFM) receiving increasing attention. NFM has implications for the ‘providers’ of land for measures upstream (the farmers) and the ‘beneficiaries’ of flood reduction downstream (the public). The misalignment of interests from these stakeholder groups may pose a challenge for flood risk managers during the delivery of NFM at the catchment scale. Considering this, a rapid evidence assessment (REA) of 60 peer‐reviewed articles was undertaken. This REA provides an overview of catchment perspectives, compares farmer and public preferences for NFM design, and explores key determinants of scheme acceptance. The public expressed positive perceptions and willingness to pay for NFM, with preferences for measures with large water storage capacity that deliver co‐benefits alongside flood management objectives. For farmers, NFM schemes that contributed to on‐farm conditions, for example, soil stability, were seen as positive, but overall, their willingness to adopt measures was limited. Nevertheless, knowledge of NFM among both groups strongly determined its acceptance. This suggests that resolving misaligned values will require policymakers and practitioners to work with these stakeholders on NFM design and farmer incentives to secure the delivery of future schemes.