Search Results (518)

This study advances research on river extreme events by applying the peaks-over-threshold (POT) approach to Lithuanian rivers. Extreme flow regimes were analysed for three rivers representing distinct hydrological regions and one large river. Results from the annual maximum series and three POT samples (POT1, POT2, and POT3) demonstrated the added value of the POT approach, as it enabled substantially more information on flood magnitude, frequency, and seasonality to be extracted from a single daily discharge time series. Trend analysis and seasonal flood frequency assessment revealed pronounced differences among rivers in regions with contrasting runoff-generation processes. Overall, the POT approach provided a more comprehensive characterisation of extreme flow behaviour, particularly for rivers susceptible to frequent flash flooding. Full article

The study analyzes flood susceptibility in the Banat Hydrographic Area (Romania) using an integrated GIS framework based on MCDA–AHP multicriteria analysis and the multitemporal evaluation of static and dynamic factors for two scenarios (2005 and 2023). The results highlight differences between the two scenarios, mainly driven by variations in precipitation: although the moderate class remains dominant (~56% of the area), the share of high and very high susceptibility classes is lower in 2023 (~6%) compared to 2005 (~17%), accompanied by an expansion of the low susceptibility class (~26% to ~37%). Validation using flood extent data from April 2005 shows that approximately 99% of the affected area falls within the moderate, high, and very high susceptibility classes (χ² = 9475, p < 0.001). The multitemporal analysis indicates high stability (75% of the territory), while 25.35% exhibits transitions toward lower susceptibility classes. Dynamic factors show differentiated roles: precipitation exerts a dominant regional control (95.44% of the area), while LULC changes contribute locally. The differences between scenarios should be interpreted as a model response to climatic variability rather than as structural changes in intrinsic susceptibility. The approach provides a reproducible framework for susceptibility assessment and supports spatial planning and risk management. Full article

Mountain flood susceptibility in complex mountainous basins is strongly influenced by terrain–climate interactions; however, the linkage between spatial susceptibility patterns and hydrological processes remains poorly understood. This study proposes a process-oriented framework that explicitly links flood susceptibility patterns with hydrological processes, moving beyond conventional approaches that rely on independent model integration. The Baishuijiang River Basin, located in Wenxian County, southern Gansu Province, China, is selected as a representative mountainous watershed for this analysis. The specific conclusions are as follows: (1) Flood susceptibility was mapped using a Particle Swarm Optimization (PSO)-enhanced Maximum Entropy (MaxEnt) model based on multi-source environmental variables, including climatic, terrain, soil, land cover, and vegetation factors. The model achieved high predictive accuracy (Area Under the Receiver Operating Characteristic Curve (AUC) = 0.912), identifying precipitation of the driest month (bio14), elevation, and land use as dominant controlling factors. Medium-to-high-susceptibility areas account for approximately 22% of the basin and are mainly distributed along river valleys and flow convergence areas. These patterns are strongly associated with reduced infiltration capacity under dry antecedent conditions and enhanced flow concentration in steep terrain, and they exhibit clear nonlinear responses and threshold effects. (2) Hydrological simulations using Hydrologic Engineering Center–Hydrologic Modeling System (HEC-HMS) show good agreement with observed runoff (Nash–Sutcliffe Efficiency (NSE) = 0.74−0.85). Sensitivity analysis indicates that runoff dynamics are primarily controlled by the Curve Number (CN), recession constant, and ratio to peak, corresponding to infiltration capacity, recession processes, and peak discharge amplification. The spatial consistency between high-susceptibility areas and areas of strong runoff response demonstrates that susceptibility patterns can be physically explained through hydrological processes, providing a process-based interpretation rather than a purely statistical prediction. (3) Future projections indicate that medium–high-susceptibility areas remain generally stable but show a gradual expansion (+5.2% ± 0.8%) and increasing concentration along river corridors under climate change scenarios. This reflects intensified precipitation variability and enhanced runoff concentration processes, suggesting a climate-driven amplification of flood risk in hydrologically connected areas. Overall, this study goes beyond conventional susceptibility assessment by establishing a physically interpretable framework that provides a consistent linkage between environmental controls, susceptibility patterns, and hydrological responses. The proposed approach is transferable to similar mountainous basins with strong terrain–climate interactions, although uncertainties related to data limitations and single-basin application remain and require further investigation. Full article

Flood damage assessment remains challenging, as conventional flood risk management mainly relies on hydraulic hazard maps that do not explicitly reproduce observed damage patterns. Recent advances in remote sensing and machine learning (ML) enable the integration of environmental and socio-economic data with historical impact information to improve flood damage modeling. This study proposes an explainable machine learning framework for flood damage susceptibility mapping, using observed institutional damage records from the 2011 and 2013 flood events combined with 17 geospatial flood risk factors (FRFs) representing hazard, exposure, and vulnerability. This approach enables the capture of non-linear relationships between flood damage and FRFs. For comparison purposes, the same framework was also applied using hydraulically modeled flood extents corresponding to return periods of 30, 200, and 500 years. The framework was tested along the Basilicata Ionian coast in southern Italy, a Mediterranean region characterized by complex geomorphology, intense rainfall events, and recurrent flood impacts. An eXtreme Gradient Boosting (XGBoost) model was trained using 17 FRFs related to hazard, exposure, and vulnerability at a spatial resolution of 20 m. The model achieved high performance with an accuracy of 0.988, an F1-score for the minority class of 0.860, and an ROC-AUC (test) of 0.996. High to very high flood damage probability was predicted in approximately 4.1% of the study area, mainly in low-lying floodplains near river corridors and infrastructure. SHAP-based explainability analysis revealed that damage susceptibility was predominantly driven by hazard and exposure factors: Drainage density (17.10%), Railway distance (16.33%), and Elevation (15.42%), extreme precipitation (Max rainfall, 10.66%) and Street distance (7.51%), with socio-economic vulnerability contributing less than 4%. The observed damage target exhibited clear threshold-like patterns (e.g., sharp risk increases below ~25/35 m elevation or within ~150/200 m of road infrastructure), contrasting with the smoother, continuous gradients produced by hydraulic scenarios. This analysis identified the most influential predictors and their response ranges. The proposed framework complements hydraulic hazard mapping by explicitly modeling observed flood damage, supporting flood risk assessment in flood-prone coastal regions. Full article

The Burullus–Baltim coastal zone of Egypt’s Nile Delta represents a critical geoheritage sand-dune system functioning as the primary natural defense line against inundation of the central Nile Delta. This ecosystem is increasingly threatened by intensive black sand mining, raising concerns regarding long-term coastal sustainability. Black sand extraction disrupts dune integrity by reducing sediment density and heavy mineral content, thereby lowering resistance to wind forcing and accelerating aeolian transport. This study assesses historical dune migration and extraction-driven changes in aeolian dynamics using high-resolution satellite imagery, ERA5 wind reanalysis (1975–2024), and integrated analytical–numerical modeling, with implications for sustainable coastal management. A dominant northwesterly wind regime drives eastward and southward dune migration of 3.22 m/yr and 1.7 m/yr, respectively (2010–2025). Black sand mining since 2022 has measurably reduced heavy mineral content and bulk density, altering grain-size distribution and making dunes significantly more susceptible to wind entrainment. Coupled Bagnold and AeoLiS modeling predicts an 8.21% rise in mass transport rates and a corresponding acceleration in dune migration following extraction. These findings demonstrate that black sand mining amplifies aeolian transport and increases sand encroachment risks to nearby settlements, infrastructure, and agricultural lands. The results highlight the trade-offs between resource extraction and coastal dune ecosystem services, particularly flood protection and land stability, emphasizing the need for regulated mining, bioengineered dune stabilization, and predictive modeling to enhance the Nile Delta’s long-term resilience. Full article

To accurately evaluate flood susceptibility in Shenzhen and support long-term flood control planning, this study develops a GIS-based multi-model machine learning framework. Nine factors—including elevation, slope, and distance to rivers—were selected, with multicollinearity ruled out via Pearson correlation and VIF tests. A balanced sample set comprising 741 historical waterlogging points (2020–2024) and equal non-waterlogging sites was constructed. In addition to comparing five base models (Decision Tree, SVM, Logistic Regression, Naïve Bayes, LDA), the study introduces a voting ensemble for model integration and applies SHAP for both global and local interpretability. Key findings include: (1) improved predictive accuracy and robustness via ensemble learning (AUC = 0.8131), outperforming individual models; (2) flood susceptibility mapping reveals a distinct spatial pattern—higher risk in western coastal areas and lower risk in eastern mountainous zones—with 68.3% of historical waterlogging points located in high-susceptibility zones. The model is trained on waterlogging records from 2020 to 2024, which may not fully capture longer-term climatic or urban dynamics. This work directly supports sustainable urban development by providing a replicable framework for flood risk mitigation that reduces long-term economic and social vulnerabilities. Full article

Urban pluvial flooding presents growing challenges for disaster risk management, yet most susceptibility studies rely on watershed-based frameworks that inadequately capture the localized dynamics of urban systems. This study proposes a city-scale flood susceptibility modeling framework for Cincinnati, Ohio. Cincinnati was chosen because it is a city with a documented history of severe urban flooding, including a once-in-a-century storm in 2016. Multi-source historical flood data were compiled from NOAA storm event records and crowdsourced reports to enhance spatial coverage. Four machine learning algorithms (Random Forest, Support Vector Machine, XGBoost, and Logistic Regression) were implemented to identify the most effective approach for urban pluvial flood prediction. Random Forest (RF) and Support Vector Machine (SVM) achieved the highest accuracy (0.84) and demonstrated strong discriminatory power. RF was selected as the optimal model because it had a higher AUC (90%) and the lowest RMSE (0.35). To assess generalizability, the RF model was validated on updated land use data and flood records from a 2020 storm event. It demonstrated robust performance (accuracy = 0.89, RMSE = 0.36, precision = 0.75, recall = 1, and AUC = 0.95), despite urban development changes. This study’s novelty lies in combining multi-source flood records with a grid-based machine learning framework and rigorously validating model robustness under evolving urban conditions. The results advance urban pluvial flood susceptibility modeling and offer actionable guidance for evidence-based flood risk management worldwide. Full article

Every year, floods disrupt the lives of hundreds of millions of people worldwide. Their impacts are further intensified by climate change, rapid urbanization, and land-use changes, making it crucial to identify areas most susceptible to flooding. While machine learning (ML) models have proven effective in identifying flood susceptibility, their validity and the integration of human risk remain underexplored in geomorphologically complex and highly flood-prone regions. This study developed an ensemble ML framework for flood susceptibility mapping in the Kosi Megafan, located in Nepal and India. We compared its performance with established ML models and a one-dimensional convolutional neural network (1D-CNN), validated results using Dartmouth Flood Observatory (DFO) and Sentinel-1 SAR (Synthetic Aperture Radar) data, and assessed the population exposed to high-risk zones. A total of 13 (8 retained) flood conditioning factors (FCFs) were derived from remote sensing datasets, and a flood inventory was created to train multiple ML models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Support Vector Machine (SVM), 1D-CNN, and a Stacked Ensemble model. Among these, the stacked ensemble model achieved the highest performance (AUC = 0.76, accuracy = 0.70, precision = 0.69, recall = 0.72, F1-score = 0.70). The resulting susceptibility map identified high-risk zones mainly in the southern and southwestern Megafan, showing strong spatial agreement with the Sentinel-1-derived flood inventory and the DFO flood data (1992–2022). This study highlights the effectiveness of combining SAR-derived flood evidence with ensemble ML approaches for accurate and scalable flood susceptibility mapping in data-scarce, hazard-prone basins. Ultimately, the research supports efforts to build resilience and mitigate the long-term impact of flooding in the region. Full article

Digital elevation models (DEMs) are foundational for critical tasks such as flood inundation simulation, disaster risk assessment, and ecosystem monitoring in coastal zones, yet their vertical accuracy is significantly compromised by complex terrain and surface characteristics. This study quantitatively decomposes the vertical errors of three 30 m global DEMs (COP30, NASADEM, and AW3D30) across the subtropical coastal region of Southeast China using ICESat-2 ATL08 data as a reference. By integrating an eXtreme Gradient Boosting (XGBoost) model with SHapley Additive exPlanations (SHAP), we successfully decoupled systematic biases from random noise. The results show that NASADEM achieved the lowest RMSE (7.775 m), followed by COP30 and AW3D30. While the Terrain Ruggedness Index (TRI) and categorically encoded Land Cover were identified as the universally dominant error drivers across all datasets, explainable analysis revealed distinct secondary mechanisms: X-band COP30 is notably susceptible to canopy height, exhibiting significant positive bias in forests exceeding 15 m; C-band NASADEM shows a systematic bias related to topographic position, typically overestimating ridges and underestimating valleys; and optical AW3D30 is significantly affected by stereo-matching errors. Furthermore, the analysis quantified a systematic error component of ~40%. These findings provide a data-driven basis for DEM selection and highlight that accuracy improvements should prioritize vegetation removal for radar DEMs and enhanced stereo-matching for optical models. Full article

Flood susceptibility mapping in high-altitude ungauged basins faces a structural dichotomy: physically based models often suffer from systematic biases due to uncertain satellite precipitation, whereas data-driven models are prone to overfitting and lack physical consistency in data-scarce regions. To resolve this, this study proposes a Physically constrained Particle Swarm Optimization–Random Forest (P-PDRF) framework, validated in the Lhasa River Basin. The core innovation lies in coupling a hydrological model with statistical learning by utilizing the maximum daily runoff depth as a “Relative Hydraulic Intensity Index.” This approach leverages the topological correctness of physical simulations to circumvent absolute forcing errors. Furthermore, a Physiographically Constrained Negative Sampling (PCNS) strategy and a PSO-optimized “Shallow Tree” configuration are introduced to enforce structural regularization against stochastic noise. Empirical results demonstrate that P-PDRF achieves superior generalization (AUC = 0.942), significantly outperforming standard Random Forest, Support Vector Machine, and Analytic Hierarchy Process models. Ablation studies confirm that the dynamic index outweighs the static Topographic Wetness Index in feature importance, effectively correcting topographic artifacts where static models misclassify arid depressions as high-risk zones. This study offers a scalable Physics-Informed Machine Learning solution for the global “Prediction in Ungauged Basins” initiative. Full article

Urban disaster management frequently relies on isolated single-hazard assessments and static census data. This conventional approach systematically obscures the highly dynamic, time-varying nature of population exposure to co-located environmental hazards. This study develops an observation-based, time-adaptive multi-hazard exposure prioritization framework to quantify these spatiotemporal variations. We integrate seismic amplification susceptibility, derived from shear-wave velocity estimates, and empirical pluvial flooding footprints with hourly dynamic living population data at a 250 m grid resolution in Seoul, South Korea. Results indicate that multi-hazard integration refines spatial prioritization, with 11% of high-priority areas diverging from single-hazard models, primarily driven by highly amplifiable alluvial deposits. Furthermore, dynamic living population data revealed clear diurnal exposure shifts. Business districts exhibited a daytime-to-nighttime exposure ratio of 3.35, whereas residential areas showed an inverse ratio of 0.69, demonstrating that identical physical conditions generate markedly different exposure patterns depending on the daily urban rhythm. Based on these temporal dynamics, we classified high-priority zones into Persistent (79.4%), Day-peak (10.3%), and Night-peak (10.3%) transition types. These findings suggest that urban exposure must be managed as a time-varying attribute rather than a static feature. The proposed classification supports targeted mitigation: structural improvements for Persistent areas, dynamic crowd management for Day-peak zones, and localized alerts for Night-peak zones. Driven by globally accessible mobile data, this framework provides a transferable foundation for exposure-informed urban resilience planning across diverse metropolitan environments. Full article

Flood events represent a major natural threat, and identifying the key factors contributing to flood occurrence has gained considerable attention in 2010 and 2022 in the Swat River, Pakistan. In this study, Google Earth Engine was utilized to extract flood-related indices for the Mohmand Dam catchment, Pakistan. Different types of datasets were used to calculate fourteen influencing parameters. These indices were processed and normalized in ArcMap 10.8 and Python to enhance their visual and analytical representation. Two multi-criteria analyses with AHP, FAHP, and five machine learning models, including logistic regression, K-nearest neighbors, random forest, support vector machine, and multi-layer perception, were applied to determine the relative importance of each parameter and produce a flood susceptibility map. The results indicate that rainfall, LULC, and soil texture are the most influential factors, each contributing 11.11% to flood susceptibility. The random forest approach demonstrated stronger predictive performance than the AHP and FAHP techniques. The flood susceptibility map reveals that approximately 31.67% (4320.40 km²) of the study area falls under high flood risk. This methodology provides valuable support for planners, policymakers, hydrologists, and disaster management authorities in developing effective flood mitigation, watershed management, and resilience strategies. Full article

Given the increasing frequency and intensity of floods, which are mostly caused by continuous climate change and growing human pressures on the environment, accurately identifying areas that are susceptible to flooding is a crucial priority for risk reduction and long-term land use planning. Thus, this research examines multilevel flood susceptibility mapping across North Macedonia, using 328 past flood occurrences, 14 conditioning variables derived from a digital elevation model, simplified lithology, and calculated direct runoff. The methodology integrates fuzzy set theory (Fuzzy), analytic hierarchy process (AHP), weighted linear combination (WLC), and random forest (RF) approaches. The two-stage process employs distinct sets of conditioning factors in sequential flood susceptibility mapping: first, generating Fuzzy/AHP/WLC predictions and pseudo-absence data, and second, producing five RF predictions by varying pseudo-absences and binary cutoffs. Validation results indicate that the very high susceptibility class (0.8–1.0) of the Fuzzy/AHP/WLC model predicted 46.6% of flood pixels within 31.6% of the total area. In comparison, the very high susceptibility class of the RF models predicted 88.5%, 78.3%, 60.6%, 48.5%, and 28.3% of flood pixels within 54.7%, 42.2%, 30.5%, 27.0%, and 25.1% of the total area, respectively. The RF models achieved area under the curve (AUC) values exceeding 0.850, with a maximum of 0.966. Additionally, areas of high and low uncertainty were highlighted using a standard deviation map created from the RF models, highlighting agreement/disagreement and potential locations for methodological improvement and focused sampling. The findings also highlight the potential of the multilevel technique for mapping flood susceptibility and call for more research into its potential for future studies and practical uses. Full article

Riverine and pluvial flooding triggered by extreme monsoon rainfall is intensifying under climate change, yet flood-risk products in many coastal municipalities remain too coarse for parcel-scale prevention and climate-adaptive planning. This study presents a 1 m LiDAR–GIS flood susceptibility framework validated against consecutive record-breaking floods in Dangjin City, South Korea (July 2024: 214.6 mm; July 2025: 377.4 mm). Five terrain parameters—elevation, slope, topographic wetness index, flow accumulation, and distance to stream—were integrated into a weighted Flood Susceptibility Index ($\mathrm{FSI}=0.20\cdot \widehat{E}+0.30\cdot \widehat{S}+0.25\cdot \widehat{T}+0.15\cdot \widehat{F}+0.10\cdot \widehat{D}$) and classified into four risk strata using K-means clustering (k = 4), identifying a high-risk zone of 0.3119 km² (5.00% of the 6.18 km² analysis domain). A Monte Carlo sensitivity analysis (n = 5000; ±0.10 weight perturbation) confirmed classification robustness (CV = 5.21%, mean Pearson r = 0.992). Static bathtub inundation scenarios (Δh = 0.5–2.0 m above the 5th-percentile baseline elevation of 13.29 m AMSL) produced footprint expansion from 0.370 to 0.572 km², capturing all nine observed flood inventory points at the 2.0 m threshold, with flow-connectivity analysis confirming that 91.7–93.1% of predicted inundation is hydraulically connected to the D8-derived stream network. Spatial validation yielded a combined IoU of 6.51%, with a progressive increase from 3.33% (2024) to 6.50% (2025) confirming that the FSI correctly tracks flood-extent expansion with increasing rainfall intensity. Relying exclusively on topographic data and standard GIS algorithms, the framework supports scientifically grounded flood risk governance in data-limited municipalities, directly aligned with SDG 11, SDG 13, and Sendai Framework Target B. Full article

Extreme precipitation constitutes one of the most devastating climatic resulting from global climate change. Jilin Province, a significant commodities grain base in China by a temperate monsoon climate, is particularly susceptible to flood disasters caused by extreme precipitation, usually occurring from late July to early August. The 2010 flood impacted moreover 5.12 million individuals and resulted in direct economic damages amounting to 45.1 billion CNY. However, research on the spatiotemporal characteristics and future trends of extreme precipitation in Jilin Province is still quite inadequate. This study examined the spatiotemporal distribution and future forecasts of extreme precipitation utilizing daily meteorological data from 31 stations (1960–2019) and three CMIP6 models (CanESM5, MPI-ESM1-2-HR, FGOALS-g3) under SSP2-4.5 and SSP5-8.5 scenarios. Eleven extreme precipitation indices, as specified by the WMO, were analyzed utilizing linear regression, the Mann–Kendall test, wavelet analysis, and inverse distance weighting interpolation. The findings indicated that from 1960 to 2019, extreme precipitation demonstrated traits of “increased frequency and total amount, decreased intensity”, with a significant decline in CDD (−2.184 d·(10a)⁻¹, p < 0.001), a notable increase in PRCPTOT (1.493 mm·(10a)⁻¹, p < 0.05), and a significant reduction in SD II (−0.016 mm·d⁻¹·(10a)⁻¹, p < 0.01). The majority of indicators had a predominant cycle of 30 to 50 years. A significant northwest-to-southeast gradient characterized most indicators, with PRCPTOT varying from 327.5 mm in Baicheng to 824.3 mm in Tonghua. Future projections (2025–2100) suggested scenario-dependent intensification. Under SSP5-8.5, all three models forecast substantial increases in precipitation amount indices (PRCPTOT: 2.071–2.457 mm·(10a)⁻¹) and SD II (0.010–0.013 mm·d⁻¹·(10a)⁻¹), reversing the past downward trend in intensity. The anticipated alterations exhibited a northwest-to-southeast gradient, with PRCPTOT increases above 230 mm in the central and southeastern regions. These findings offer a scientific basis for flood management and climate change adaptation in Jilin Province and analogous areas. Full article