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Abstract Gridded population and flood hazard data are crucial for flood exposure assessments. However, current assessments incorporate uncertainties related to data selection, yet the mechanisms through which subjective data selection propagate uncertainties in exposure models remain poorly understood. To address this gap, this study conducted a comparative assessment of flood exposure in China using five population datasets and five flood hazard datasets. Furthermore, it explored the absolute and relative impacts of data uncertainties on 100 year return period flood exposure and discussed the underlying causes. Results exhibit substantial variations in flood exposure when different data combinations are employed. Specifically, there is a significant difference of 333 million individuals within the exposure range, with the highest estimate being 2.82 times the lowest one. Overall, the exposure variation was primarily from differences in flood hazards rather than population patterns, but their relative importance differed spatially depending on factors of slope, altitude, and artificial surface coverage. Despite the differences, all 25 data combinations revealed a disproportional larger share of population in floodplains, which was 2.28–3.49 times the share of floodplains. These findings are significant for understanding the uncertainties of flood exposure and can shed lights on informed policies for risk management.

ABSTRACT Urban flood forecasting benefits from high‐resolution inundation maps, but fine‐grid hydrodynamic simulations are computationally costly. We compared three CNN–based super–resolution (SR) models, ResUNet, EDSR, and RCAN, for downscaling physics–based simulations in downtown Portland, Oregon, using paired flood maps at 1 m (HR) and both 4 and 8 m (LR). Performance was assessed using image level metrics (PSNR, SSIM) and flood specific indicators: CSI for flood extent, RMSE for water depth accuracy, and a depth–based severity classification. At 4× upscaling, all SR models outperformed the LR baseline; RCAN performed best (PSNR +57%, SSIM +31%, RMSE −73%, CSI +53%), followed by EDSR (PSNR +50%, SSIM +30%, RMSE −64%, CSI +45%) and ResUNet (RMSE −55%, CSI +40%). Analysis of class–wise recall showed RCAN leading for non–flood (98.06%, +6.59 pp) and severe flood (96.48%, +16.90 pp), while EDSR led for mild flood class (97.95%, +6.49 pp). Errors were most pronounced along wet–dry boundaries and in complex urban geometries, where RCAN and EDSR reduced error magnitude more effectively than ResUNet. Models with larger numbers of parameters required longer training times. Furthermore, the computational cost further increased with more training epochs and especially at 4× upscaling relative to 8×, reflecting differences in model complexity and scaling configuration. Taken together, these findings support SR as a practical complement to physics–based modeling for real time forecasting and planning, while also providing guidance for selecting architectures under varying computational budgets.

Abstract Under the broad consensus on reinforcing flood resilience in underground spaces, the hydraulic properties of metro tunnels have not been thoroughly examined. V-slope configurations are widely adopted as a standard feature in metro tunnel systems. This study aims to enhance the understanding of water propagation mechanisms in such tunnels to optimize the response of metro systems to upcoming floods. Through a combination of scaled physical model experiments and VOF numerical simulations, the research reveals key stages and patterns of water accumulation in V-shaped slope tunnels. The flood propagation process is divided into four stages: downhill flow on a single slope, uphill flow undergoing deceleration and accumulation, emergence of hydraulic jump, and wave reflection and oscillation. By investigating hydraulic jump characteristics and the evolution of submersion under varying conditions, the research highlights the local flow field discontinuity and identifies the incompatibility of existing hydraulic models with metro tunnel flooding prediction. It emphasizes the importance of considering detailed flood front movements and the surge of water depth for early flood warning in metro tunnels. The findings enhance predictive accuracy for inundation timing and dynamic flood progression.

ABSTRACT A comprehensive strategy that incorporates trend analysis, machine learning (ML), and climate model review is needed to improve water resource forecasts and evaluate hydroclimatic variability. The present study effectively combined various forms of categorical and continuous performance metrics for the CMIP6 and the reanalysis datasets in the Upper Godavari Sub‐basin area (UGSB) (India). MERRA2 reanalysis datasets demonstrated the highest accuracy for precipitation forecasting, achieving a POD of 0.82 and CSI of 0.71, while JRA‐55 closely followed with a CSI of 0.69. CMIP6 models exhibited overestimation tendencies, with a mean FAR of 0.34, highlighting their limitations in capturing precipitation extremes. Thereafter, to understand the long‐term variability of the best reanalysis product, trend analysis was also performed using the Mann‐Kendall test, Pettitt's test, Van Neumann ratio (VNR), and Innovative Trend Analysis (ITA). This analysis revealed properly the spatial variability of the precipitation, showing increasing (1.5–2.3 mm/year) and decreasing rates for various stations inside the UGSB. Thereafter, the temporal frequency and the intensity were captured by the Continuous Wavelet Transform (CWT) analysis, which further identified shifts in hydroclimatic variability towards higher frequencies after 2000. Thereafter, the prediction accuracy of prediction datasets of various ML models, which included Random Forest (RF), Multi‐Layer Perceptron (MLP), Long Short‐Term Memory (LSTM), and XGBoost models, were optimised by The Harris Hawks Optimization (HHO) algorithm, and the best optimised model, RF‐HHO, showed reducing RMSE to 4.92 at Ambajogai, 4.81 at Bodhegaon, and 5.21 at Ranjni. The study highlights the importance of combining reanalysis products, trend analysis, and optimised ML models to improve future precipitation predictions and support effective water resource management.

Abstract This study investigates the potential of Stratospheric Aerosol Injection (SAI), a solar climate intervention strategy, to mitigate climate extremes driven by greenhouse gas (GHG) emissions, comparing its effects to those of GHG‐induced warming under the SSP5‐8.5 scenario. Using the UKESM1 climate model and the GeoMIP G6controller scenario, we examine extreme temperature, precipitation, and fire risk indices in a risk‐risk framework. The multi‐latitude G6controller strategy, an improvement on the equatorial injection strategy G6sulfur, reduces global mean temperature from SSP5‐8.5 to SSP2‐4.5, significantly reducing temperature and precipitation extremes. Results show that G6controller effectively reduces temperature extremes relative to SSP5‐8.5, especially in populated areas like Europe and South America, and reduces fire risk in high‐risk areas, such as South America and southern Africa. While both scenarios project broad precipitation increases, G6controller moderates these without introducing new drying relative to SSP5‐8.5, particularly in Southeast Asia. This study highlights G6controller's potential to lessen the magnitude of extreme climate events, offering insights into SAI's regional efficacy and highlighting the trade‐offs between GHG warming with and without solar climate intervention. , Plain Language Summary This study looks at how injecting reflective particles into the middle atmosphere, a process called Stratospheric Aerosol Injection (SAI), might help reduce the severe impacts of climate change, like heatwaves, heavy rainfall, and wildfires. It compares two future scenarios: one where greenhouse gas (GHG) emissions continue to rise and another where SAI is used to cool the planet. The results show that SAI can significantly reduce extreme heat and wildfire risks in many regions while also lessening the intensity of heavy rains. However, the strategy doesn't work equally well everywhere and comes with trade‐offs. This research provides valuable insights for understanding how SAI could help manage climate risks in the future while emphasizing the continued importance of cutting GHG emissions. , Key Points This study compares the impacts on extreme events under climate change with and without a multi‐latitude Stratospheric Aerosol Injection strategy in UKESM1 G6controller reduces extreme heat relative to GHG warming, particularly in populated regions, reducing warming trends by nearly half Fire danger days decrease under G6controller compared to SSP5‐8.5, mitigating wildfire risks by reducing precipitation changes

Extreme precipitation in the Middle Reaches of the Yellow River Basin (MRYRB) has increased significantly and unevenly, heightening the urgency for rapid and accurate monitoring of such extremes. Satellite precipitation data have proved effective in capturing precipitation extremes but have not been validated in the MRYRB. Thus, station-interpolated data were used to validate the reliability of satellite data (GPM IMERG) in characterizing spatiotemporal changes in nine extreme precipitation indices across the entire MRYRB and its ten sub-basins from 2001 to 2022. The results show that all frequency, intensity, and cumulative amount indices exhibit significantly increasing trends. Spatially, extreme precipitation exhibits a clear southeast–northwest gradient. The higher values occur in the southeastern sub-basins. Characterized by high-intensity, short-duration precipitation, the central sub-basins exhibit the lower values of extreme precipitation indices, yet have experienced the most rapid upward trends in those indices. The comparative analysis demonstrates that GPM reliably reproduces indices such as the number of days and amounts with precipitation above a threshold (R10, R20, R95p), maximum precipitation over five days (RX5day), and total precipitation (PRCPTOT) (with regression slopes close to 1, coefficient of determination R2 and Nash-Sutcliffe efficiency (NSE) greater than 0.7, and residual sum of squares ratio (RSR) less than 0.6, with negligible relative bias), particularly in the southern sub-basins. However, it tends to underestimate continuous wet days (CWD) and total precipitation when precipitation is over the 99th percentile (R99p). These findings advance current understanding of GPM applicability at watershed scales and offer actionable insight for water-sediment prediction under the world’s changing climate.

Abstract Changes in snow extremes can have important but as yet underexplored impacts on the 1.2-trillion dollar outdoor recreation industry, impacting essential ecosystem services. These hydroclimate conditions interact with reservation systems used by park managers to manage potential impacts to park resources and visitor experiences. Climate change extremes and associated hazards limit and enable access in different ways: snowpack from extreme wet years can prolong road closures at higher elevations, while extreme snow drought enables early season access. Few studies have attempted to assess and compare the influence of snow conditions and reservation systems among a population of visitors, as we do at Yosemite National Park. Roads were still closed into the 2023 peak season from a record extreme snow deluge, yet entry was unrestricted due to lack of a parkwide day use reservation system. This combination led to higher overall visitation levels but spatially constrained visitor mobility and resulted in more crowded conditions and traffic congestion. We assess the efficacy of the day use reservation system in limiting use by comparing differences from the annual mean and monthly volume and timing of visitors. Results show that annual use levels are influenced more by managed access than by climate extremes, and that results differ by overnight use types and traffic count locations with seasonal access limitations. These results demonstrate a novel aspect of the impacts of changing climate extremes with human use of natural resources and suggest that appropriately managed reservation systems are an essential tool for managing recreation resources in a changing climate.

ABSTRACT Flooding in Ghana's White Volta basin has caused widespread displacement, fatalities, and damage to infrastructure and livelihoods in agriculturally dependent communities. Despite the presence of national agencies such as the Ghana Meteorological Agency (GMet) and Ghana Hydrological Authority (GHA), early warning capabilities remain constrained by limited real‐time data, outdated infrastructure, and weak coordination. As a result, many residents continue to rely on traditional knowledge and informal coping strategies. This study qualitatively assesses the operational state of Flood Early Warning Systems (FEWS) in the White Volta basin, focusing on their effectiveness, limitations, and opportunities for improvement. Using semi‐structured interviews with 18 key stakeholders spanning government agencies, technical experts, and community leaders, we analysed the institutional and technical dynamics of Ghana's FEWS through thematic analysis. Findings reveal that although the myDEWETRA‐VOLTALARM platform offers 5‐day flood forecasts through social media, SMS, and radio, its warnings are often mistrusted or inaccessible to rural populations. Thematic analysis identified four critical gaps: institutional fragmentation, exclusion of local knowledge, inadequate data infrastructure, and last‐mile communication failures. These are complicated by the basin's unique environmental conditions, including transboundary dam releases, intense seasonal rainfall, flat terrain, and poor drainage. We conclude that the current FEWS framework remains insufficient for proactive flood risk governance. Strengthening institutional coordination, integrating community‐based adaptation practices, and investing in localized data and communication infrastructure are essential to improving system legitimacy and resilience. The study contributes to broader discourses on early warning systems in resource‐constrained settings.