Search Results (451)

The rising global temperature and changing climate patterns have increased the frequency and intensity of extreme heat events, droughts, and heavy precipitation, significantly affecting agriculture, water resources, and ecosystems. Solar radiation management (SRM) has been proposed as a geoengineering strategy to mitigate these effects by reducing incoming solar radiation. This study evaluated future trends and variability in rainfall and temperature extremes in the Philippines under GeoMIP (G6Solar and G6Sulfur) and ScenarioMIP (SSP2-4.5 and SSP5-8.5) projections. Using five General Circulation Models (GCMs) and a suite of 10 climate indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI), changes in extreme precipitation and temperature across different climate zones in the Philippines were assessed. Climate projections for the future (2041–2070) scenario were analyzed using bias correction, downscaling, and spatial interpolation techniques. Trend analysis was evaluated using the Mann–Kendall test and Sen’s slope estimator, while variability was assessed through statistical methods. The results show widespread warming and increased extreme precipitation, but these trends vary significantly across regions. Non-uniform responses emerge across scenarios, with some northern regions experiencing decreases in specific precipitation indices despite the broader warming trend under SRM and non-SRM conditions. These findings provide critical insights into the potential impacts of SRM on future climate extremes in the Philippines and guidance on climate policy recommendations for decision-makers and stakeholders. Full article

Reliable intensity–duration–frequency (IDF) curves are essential for the design of stormwater drainage systems and flood mitigation infrastructure; however, conventional IDF relationships assume stationarity and may underestimate future rainfall extremes under climate change. This study developed climate-informed IDF curves for Makkah, Saudi Arabia, using hourly observed rainfall records (1985–2025) and projections from five CMIP6 Global Climate Models (EC-Earth3-CC, CNRM-CM6-1, GFDL-ESM4, MPI-ESM1-2-LR, and UKESM1-0-LL) under the SSP245 and SSP585 scenarios. Spatial downscaling was first carried out using bilinear interpolation, after which the resulting data were corrected for systematic bias using the Delta Change method. Daily precipitation projections were subsequently disaggregated to an hourly timescale using an enhanced KNN-MOF approach. Annual maximum precipitation series were then derived for durations of 1, 2, 3, 6, 12, and 24 h and fitted to a range of candidate probability distributions. The goodness of fit was evaluated using the log-likelihood, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). Across the five CMIP6 models, two emission scenarios, and six rainfall durations, the Log-Pearson Type III distribution consistently yielded the most satisfactory fit. Historical analysis estimated 100-year rainfall depths ranging from 7.84 mm (1 h) to 38.29 mm (24 h), while future projections indicated substantially higher design rainfall intensities under several climate models. For example, under the SSP585 scenario, the 100-year 1 h rainfall intensity reached 29.73 mm h⁻¹ for EC-Earth3-CC, whereas MPI-ESM1-2-LR projected a 102% increase in the 6 h 100-year intensity relative to SSP245. Sherman equations were successfully fitted to develop continuous IDF relationships, while bootstrap resampling and Bayesian inference quantified projection uncertainty. The multi-model ensemble indicated increasing uncertainty with return period, particularly for the 100-year event, highlighting the importance of incorporating uncertainty into engineering design. The proposed framework provides robust climate-informed IDF curves for supporting resilient urban drainage design, flood-risk assessment, and water resources planning in arid environments. Full article

Climate change is altering global temperature and precipitation patterns, with particularly strong effects expected in Mediterranean regions, where reduced groundwater recharge and increased evapotranspiration may affect groundwater-dependent ecosystems. This study provides a preliminary, indicator-based assessment of the potential sensitivity of the Cabecera del Bornova Groundwater Body (Guadalajara, Spain), which sustains the Somolinos karst wetland, under natural conditions and protected as a Natural Groundwater Reserve and Natural Lacustrine Reserve. Empirical correlations were established between accumulated deviations of historical precipitation and observed piezometric levels in two monitoring piezometers using second-degree polynomial functions. The most informative relationships were obtained for piezometer ZE01, particularly at the daily scale, whereas the second piezometer showed weaker relationships. These functions were applied to regionalized climate projections generated with the FICLIMA methodology from ten CMIP6 models under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 scenarios from the IPCC Sixth Assessment Report. The results indicate a general decreasing tendency in empirical piezometric-level indicators throughout the 21st century, although the magnitude of the response is highly sensitive to the selected rainfall station, temporal resolution, climate model and scenario. Extreme projected declines are interpreted as extrapolation-sensitive outputs rather than deterministic predictions of aquifer drawdown or groundwater-reserve depletion. Direct impacts on lagoon level, spring discharge or wetland extent cannot be quantified with the dataset. The results highlight the need to expand piezometric monitoring, instrument the Manadero del Bornova spring, monitor lagoon water levels and develop physically based recharge and groundwater-flow models. Full article

Extreme rainfall-induced forest aboveground biomass (AGB) loss poses a serious threat to ecosystem stability and carbon stocks amid intensifying climate extremes. However, quantitative assessments of this loss remain scarce, and the spatial patterns of biomass loss and the nonlinear effects of its controlling factors have been insufficiently explored. This study used a typical extreme rainfall event (26–29 July 2023) in the tableland-gully region of the Loess Plateau as a case study to quantify the spatial patterns of forest AGB loss and to disentangle the nonlinear responses of its controlling factors. GF-7 (0.65 m) and GF-2 (1 m) imagery, combined with band differencing and object-based image analysis (OBIA), were used to identify forest AGB loss patches. To enable patch-level loss estimation, the 30 m AGB dataset was statistically downscaled to 1 m. Global Moran’s I and Getis-Ord Gi* statistics were applied to characterize spatial clustering, and an XGBoost model coupled with Shapley Additive Explanations (SHAP) was employed to identify dominant predictors and their nonlinear responses. This event triggered a total of 67,855 forest AGB loss patches (overall accuracy = 0.93; F1 score = 0.93), with a cumulative area of 17.29 km² and a total loss of 119,825.38 Mg. AGB loss exhibited significant spatial clustering, displaying a west-high–east-low gradient consistent with rainfall distribution. Cumulative rainfall was the dominant predictor; the median grain size of the Last Glacial Maximum loess unit (L1-1 MD), elevation, proximity to roads and rivers, fractional vegetation cover (FVC), and slope aspect contributed additional spatial variation through distinct nonlinear relationships. These findings provide a quantitative basis for event-scale assessment of extreme rainfall-induced forest carbon loss and for informing vegetation restoration and carbon conservation strategies under climate extremes. Full article

Dengue virus (DENV) remains a major global public health challenge, driven by the expanding distribution of Aedes vectors, rapid urbanization, and increasing climate variability. This review aimed to synthesize current evidence on dengue transmission dynamics, immunopathogenesis, molecular epidemiology, diagnostic and therapeutic advances, vaccine development, and the influence of climatic factors on disease patterns. A comprehensive search of major electronic databases was conducted to identify relevant literature, followed by a qualitative synthesis of the evidence. The evidence highlights complex transmission cycles involving Aedes aegypti and Aedes albopictus, with transmission strongly influenced by temperature, humidity, rainfall, and extreme climate events. Advances in immunopathogenesis research have improved understanding of mechanisms associated with severe disease, including antibody-dependent enhancement and dysregulated inflammatory responses. Diagnostic innovations, such as reverse transcription polymerase chain reaction (RT-PCR), NS1 antigen detection, and point-of-care technologies, have enhanced case identification, while prevention strategies increasingly incorporate integrated vector management, digital surveillance systems, and emerging vaccines. Integrating epidemiological, molecular, and climatic information may strengthen early warning systems, improve outbreak prediction, and support more effective dengue prevention and control strategies. Full article

Global Navigation Satellite Systems (GNSS) have emerged as a reliable and cost-effective tool for estimating atmospheric precipitable water vapor (PWV), particularly in regions with limited meteorological instrumentation. In this study, the performance of three tropospheric mapping functions—the Global Mapping Function (GMF), Niell Mapping Function (NMF), and Vienna Mapping Function 1 (VMF1)—was evaluated for PWV estimation using GPS observations collected during the 2009–2011 period in southwestern Mexico, a region characterized by high atmospheric variability and frequent extreme weather events. GPS data from three stations (TECO, COL2, and PENA) were processed using the GAMIT/GLOBK 10.71 software, and the resulting PWV estimates were validated against independent radiosonde observations and the European Centre for Medium-Range Weather Forecasts (ECMWF) Fifth-Generation Reanalysis (ERA5) data. The results show that GPS-derived PWV successfully captures the seasonal variability of atmospheric water vapor, with maximum values during the summer rainy season. High correlations were obtained with both radiosonde and ERA5 data, particularly at the TECO station (R = 0.95–0.99), where RMSE values ranged from 3.27 to 5.46 mm and BIAS values from $-2.73$ to $-1.47$ mm. In contrast, larger discrepancies were observed at COL2 and PENA, mainly due to horizontal separation and altitude differences relative to the radiosonde site, highlighting the importance of spatial representativeness during validation. Among the evaluated mapping functions, no single model consistently outperformed the others across all stations, years, and reference datasets. Nevertheless, GMF and NMF generally exhibited more stable and consistent performance, whereas VMF1 showed greater variability under the adopted processing strategy. Additionally, a clear relationship was identified between PWV and precipitation records, indicating that increases in PWV coincided with periods of intense rainfall and suggesting its potential as an indicator of atmospheric conditions favorable for precipitation events. Overall, this study shows that GPS-derived PWV can reproduce the seasonal variability of atmospheric water vapor under the adopted processing strategy and demonstrates the importance of mapping function selection and spatial representativeness for accurate PWV estimation. Full article

Abu Dhabi Sabkha, located along the southern margin of the Arabian Gulf, is among the most scientifically significant arid coastal systems on Earth. Submitted by the United Arab Emirates to the UNESCO World Heritage Tentative List in 2018 under natural heritage criterion (viii), the site is currently the only documented coastal sabkha where all four diagnostic sedimentary layers, lagoon mud, microbial mat, gypsum mud, and anhydrite nodules, co-occur within a single coastal setting, south of Al Dhabaya and Abu Al Abyadh Islands. This review evaluates Abu Dhabi Sabkha as a geological heritage system by synthesizing its formation processes, UNESCO significance, complete sabkha sequence, microbial and evaporite processes, climatic setting, environmental sensitivity, and conservation requirements. The site operates under a hyper-arid climatic regime with a documented warming trend of +0.43 °C per decade over 1981–2025 (NASA POWER MERRA-2), confirming that the evaporative processes sustaining its geological record are intensifying. Microbial mats facilitate dolomite mineralization via Extracellular Polymeric Substances at a scale unmatched elsewhere in the Arabian Gulf, while gypsum mud and anhydrite nodules record advanced supratidal evaporitic development. Comparative analysis with sabkha systems in Qatar, Kuwait, Saudi Arabia, and Oman confirms that no other regional coastal sabkha preserves equivalent stratigraphic completeness within a single, actively forming setting. The site faces mounting pressures from urban expansion, coastal reclamation, off-road vehicle movement, groundwater modification, and the increasing frequency of extreme rainfall events, all of which can permanently damage fragile surface structures and microbial systems. Sustainable conservation requires protection zoning, controlled access, multi-scale satellite and drone monitoring, groundwater tracking, and integration into coastal planning frameworks. Abu Dhabi Sabkha should be recognized not as marginal saline terrain, but as a globally important geoheritage system whose scientific value depends on the active preservation of biological, chemical, and physical processes operating together within an irreplaceable coastal setting. Full article

Precipitation nowcasting plays an important role in mitigating the impacts of extreme weather events on social production and daily life. However, existing methods still face two major limitations. (1) Convolutional neural network-based methods are insufficient in modeling the temporal dependencies of radar echo sequences, which may lead to information loss in the prediction results. (2) Most existing methods enlarge the receptive field by stacking convolutional layers. This strategy makes it difficult to obtain a truly global receptive field and effectively model global dependencies, resulting in limited accuracy in heavy rainfall prediction. In addition, spatiotemporal information at different time steps is not fully integrated, and the multi-scale directional features of rainbands are often ignored. To address these issues, this paper proposes ST-TriMambaUNet, which consists of an encoder, a decoder, and a feature enhancement module. First, a Spatiotemporal Fusion Attention (STFA) was designed, including global spatial attention and temporal attention. It can effectively learn long-range spatial correlations and capture the temporal dependencies of radar echo sequences in a parallel manner. Second, a Multi-Scale Interaction Mamba (MSIM) module was developed with three branches. The first branch leverages Mamba to model global spatiotemporal dependencies with linear complexity. The second branch promotes spatiotemporal information interaction through channel shuffle and further combines Mamba to model global spatiotemporal dependencies. The third branch designs Multi-Scale Directional Convolution (MSDC) to learn the multi-scale directional features of rainbands. Finally, the features from the three branches are dynamically fused through the designed adaptive gated fusion mechanism. This enhances the model’s representation capability for strong-echo core regions and multi-scale precipitation band structures. Experimental results on two public datasets, SEVIR and CIKM, demonstrated that the proposed ST-TriMambaUNet achieved clear advantages in both overall prediction accuracy and heavy rainfall scenarios. In particular, under the high-threshold precipitation scenarios of SEVIR (160, 181, and 219), the CSI was improved by up to 10.19%. In the heavy rainfall scenario of CIKM at 40 dBZ, CSI, POD, and HSS were improved by 5.29%, 8.07%, and 4.65%, respectively. Full article

Flooding is one of the most frequent and damaging natural disasters, accounting for nearly half of global disasters and posing a major challenge in Indonesia, where floods represent approximately 77% of all nationally recorded disaster events. Rapid urbanisation, land-use change, and climate-induced extreme rainfall have intensified flood risks nationwide. However, existing vulnerability assessments remain fragmented and localised, limiting their relevance for national-scale adaptation planning. This study develops a measurable and explainable framework for assessing urban flood vulnerability across Indonesia using cloud-based geospatial data and interpretable machine learning. The approach integrates CEMS-GLOFAS (flood hazard), WorldPop (population exposure), SRTM (topography), and ESA WorldCover (land cover) datasets within Google Earth Engine (GEE). Flood vulnerability is quantified through a modified Flood Vulnerability Index (FVI) combining hazard, exposure, and physical vulnerability components. The Extreme Gradient Boosting (XGBoost) model predicts FVI values, while SHapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDPs) enhance model transparency and identify the influence of key variables such as flood depth, population density, and elevation. The model achieved high predictive accuracy (R² = 0.89; RMSE = 0.04728 FVI units, dimensionless) and revealed substantial spatial heterogeneity across 514 districts, with the highest FVI (0.75–0.85) in Banda Aceh, Mojokerto, Pasuruan, Samarinda, and Merauke. The integration of GEE and explainable AI offers a transparent, scalable framework to support data-driven flood risk mitigation and urban climate resilience in Indonesia. Full article

With global warming and rapid urbanization, short-duration summer rainstorms are becoming more intense and localized, posing growing challenges to urban flood resilience. However, their spatiotemporal characteristics, vertical structures, and environmental drivers remain poorly understood. Here, we combine multi-source remote sensing datasets and China’s new-generation satellite-borne dual-frequency precipitation radar observations to investigate summer rainstorms in Nanjing, China, during 2017–2024. Results reveal pronounced spatiotemporal heterogeneity, with higher rainfall intensities concentrated over urban and adjacent areas. During the study period, rainstorm intensity and duration increased by 7.44% and 38.63%, respectively, while the affected area decreased by 8.18%, indicating a transition toward more localized yet more intense rainfall events. Environmental analyses suggest that large-scale thermodynamic conditions and regional topographic forcing provide a favorable background for convection development, while local urban thermal effects may further modulate rainfall enhancement. Three-dimensional radar detection of an illustrative rainstorm event indicates an inverted-cone vertical structure, suggesting a mixed convective-stratiform precipitation structure involving both warm-rain and ice-phase processes. An Explainable Bayesian-Optimized XGBoost (EBOX) model further identifies near-surface air temperature and specific humidity as the primary environmental factors associated with rainstorm occurrence and development. Overall, this study highlights the value of integrating satellite remote sensing with explainable artificial intelligence to improve understanding of urban extreme rainfall and provide new insights into how climate change, topography, and urbanization jointly shape precipitation extremes in rapidly urbanizing monsoon regions. Full article

Extreme rainfall events intensify flood-related hazards in mountainous national parks and their surrounding communities, where complex terrain and coupled hazard processes create major challenges for spatial risk management. This study focuses on the Tangjiahe district of the Giant Panda National Park and develops an integrated framework for flood-related multi-hazard identification and zoning. The 100-year flood process was simulated using Hydrologic Engineering Center’s River Analysis System (HEC-RAS), runoff retention was assessed using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, and slope stability risk zoning was conducted using the Analytic Hierarchy Process (AHP). Based on multi-source spatial overlay, Integrated Flood-Related Multi-Hazard Risk Zoning was generated. Spatial statistical analyses, including Global Moran’s I, Local Indicators of Spatial Association (LISA), and Getis-Ord Gi*, supported the identification of clustered high-risk areas and hotspot zones. In parallel, Disaster Prevention and Control Zoning was established, classifying the study area into multiple management-oriented zones to support differentiated spatial governance and targeted management. The proposed framework provides a practical approach for integrating multi-hazard processes into spatial planning and disaster risk management in mountainous protected areas. Full article

Under global warming, compound extreme events such as Drought–Flood Abrupt Alternation (DFAA) are becoming increasingly common, yet the structural asymmetry and spatial dynamic evolution between Drought to Flood (D–F) and Flood to Drought (F–D) processes remain under-researched. Using high-resolution daily precipitation data spanning 1961 to 2022 from nine major river basins in China, DFAA events were identified via the Standardized Weighted Average Precipitation (SWAP) index coupled with run theory, and their evolution was analyzed using multidimensional spatiotemporal metrics. Our results reveal a spatial frequency and severity mismatch, where southern basins exhibit high frequency occurrences dominated by slight to moderate events, whereas northern and inland basins experience lower overall frequency but a significantly higher proportion of severe events. Spatial polarity asymmetry is evident, with D–F events dominating nationwide and exceeding 74% in northern and inland basins, while southern humid basins exhibit a more balanced D–F/F–D structure. Temporally, D–F processes involve prolonged moisture accumulation, whereas F–D processes manifest as short-lived post-rainfall moisture deficits. Based on risk trajectories, basins were categorized into four impact patterns: highly oscillatory pattern, intensifying pattern, long-cycle accumulative pattern, and baseline pattern. Ultimately, regional DFAA risks are governed by polarity asymmetry and non-stationary evolution rather than absolute frequency alone, providing a critical scientific basis for basin-specific disaster mitigation strategies under climate change. Full article

Mangrove forests deliver globally significant climate-mitigation and coastal-protection benefits, yet their resilience to climate extremes remains poorly quantified—a key uncertainty for sustainable coastal management. We reassess the unprecedented 2015–2016 mangrove dieback along ~1000 km of the Gulf of Carpentaria, northern Australia, to determine its driver and whether the collapse was structurally abrupt. Combining a mangrove-extent mask, an 11-year radar backscatter series, satellite precipitation, the modeled sea level, the reanalysis temperature and atmospheric dryness, and an El Niño index, we show that an apparent abrupt radar decline during the event was an artifact of non-vegetated tidal-flat and open-water pixels: once analysis was restricted to mangrove pixels, the signal remained stable throughout. Independent spaceborne lidar confirmed that canopy structure concentrates within the mapped mangrove zones, validating the mask. The dieback coincided with a strong sea-level fall, with anomalies reaching about −15 cm, under near-to-above-average rainfall and low atmospheric dryness, indicating that sea-level fall, not rainfall deficit, was the proximate stressor. These findings advance sustainable, mask-applied satellite monitoring of blue-carbon ecosystems and provide an evidence base for climate-adaptive coastal-resilience planning under intensifying climate variability. Full article

Indian metropolitan cities such as Mumbai grapple with rapid urbanisation, extreme urban density, high built-up areas, loss of green cover, and shrinking open spaces, resulting in increased impermeable surfaces, urban heat island effects, and frequent flooding occurrences. Modern stormwater management has increasingly been characterised by integrated grey-green approaches; however, cities in the Global North benefit from established policies, technical expertise, and financial resources that enable the systematic and large-scale integration of Blue-Green Infrastructure (BGI) through district-wide geospatial assessment frameworks, unlike many cities in the Global South. Despite growing interest in nature-based stormwater solutions, there remains a dearth of geospatial empirical research from India examining the placement, distribution, performance, and functionality of BGI integrated with existing stormwater management systems in cities such as Mumbai. Furthermore, hydrological modelling using tools such as the Storm Water Management Model (SWMM) for the design, planning, and implementation of BGI in Indian cities remains largely unexplored. This study explores the role of BGI strategies in improving urban stormwater management within high-density Indian cities under a 25-year return period extreme rainfall scenario. Using an integrated approach that combines QGIS-based spatial analysis with EPA-SWMM hydrologic-hydraulic modelling, the research examines runoff behaviour, identifies flooding hotspots, and evaluates the effectiveness of Low Impact Development (LID)-based BGI measures such as permeable pavements, infiltration trenches, and green roofs applied at the ward level in Mumbai’s F/North and G/North Wards. Detailed land use classification, spatial mapping, and rainfall simulation corresponding specifically to a 25-year return period rainfall event was used to assess pre- and post-intervention conditions. The findings indicate that the applied BGI measures led to a 12.6% reduction in peak runoff (137.6 m³/s to 120.2 m³/s) and a 5.5% decrease in total runoff volume (783,510 m³ to 740,410 m³). More importantly, the peak flooding flow rate decreased by 45% (94.1 m³/s to 51.7 m³/s), demonstrating that BGI measures can efficiently reduce peak flooding flows by extending runoff hydrographs during extreme rainfall events. These findings are specifically applicable to the simulated 25-year return period extreme rainfall scenario and may vary under different rainfall intensities or return periods. Less extreme events could potentially experience even greater relative reductions or prevent flooding altogether, while also easing downstream hydraulic loads. Overall, strategically placed BGI interventions can significantly reduce surface runoff and peak flow, thereby enhancing stormwater resilience within spatially constrained urban environments. This study provides a replicable, data-driven framework for catchment-scale stormwater planning in dense Indian cities under extreme rainfall conditions, offering practical insights into methods, local contextual considerations, and spatial planning strategies for policymakers and urban planners seeking to retrofit and adapt existing infrastructure under increasing hydrologic stress and climate variability. Full article

In the context of global climate change, intensified salinity fluctuations driven by altered precipitation, extreme rainfall events, and typhoons have emerged as a major threat to coastal mollusk aquaculture. In this study, integrated physiological and transcriptomic analyses were performed to investigate the responses of Pacific abalone (DD, Haliotis discus hannai) and its hybrid (DF, H. discus hannai ♀ × H. fulgens ♂) to hypo- and hypersalinity stress. Two salinity breakpoints (BPS1 for hyposalinity, BPS2 for hypersalinity) were identified using heart rate monitoring to indicate the osmotic tolerance thresholds of the abalone. The BPS1 and BPS2 values did not differ significantly between the DD and DF groups. However, a subsequent 30-day culture trial confirmed that exposure to the salinity level corresponding to BPS1 significantly reduced growth and survival of both DD and DF groups. To explore the molecular mechanisms underlying these two salinity breakpoints in abalone, the transcriptomes of hemocytes and gill tissues were profiled under both stress conditions. Both hypo- and hypersalinity stress induced pronounced transcriptomic responses in abalone, accompanied by upregulated differentially expressed genes (DEGs) significantly enriched in pathways like TNF and NF-κB signaling, including genes like piap, diap2, birc7-a, birc2, and birc3. However, abalone exhibited more intense responses to hypersalinity stress, as reflected by a greater number of annotated differentially expressed genes (DEGs) and more complex transcriptional regulation. Overall, this study integrates physiological assessment based on heart rate monitoring, aquaculture trials, and transcriptomic analysis to advance our mechanistic understanding of osmotic stress adaptation in abalone, while laying a scientific foundation for the sustainable development of abalone aquaculture under global climate change. Full article