Flash floods are often responsible for deaths and damage to infrastructure. The objective of this work is to create a data-driven model to understand how predisposing factors influence the spatial variation of the triggering factor (rainfall intensity) in the case of flash floods in the continental area of Portugal. Flash floods occurrences were extracted from the DISASTER database. We extracted the accumulated precipitation from the Copernicus database by considering two days of duration. The analysed predisposing factors for flooding were extracted considering the whole basin where each occurrence is located. These factors include the basin area, the predominant lithology, drainage density, and the mean or median values of elevation, slope, stream power index (SPI), topographic wetness index (TWI), roughness, and four soil properties. The Random Forest algorithm was used to build the models and obtained mean absolute percentage error (MAPE) around 19%, an acceptable value for the objectives of the work. The median of SPI, mean elevation and the area of the basin are the top three most relevant predisposing factors interpreted by the model for defining the rainfall input for flash flooding in mainland Portugal.

Research on urban flood risk has highlighted the need for more comprehensive flood risk assessments in low-income and vulnerable communities. This study aims to examine the causes, impacts and existing flood risk management measures in the Somali region of Ethiopia. The study used a mixed research methodology, including a cross-sectional survey, to collect original qualitative and quantitative data.. In addition to flood risk and vulnerability assessment, the study evaluated urban flood risk management measures through soil protection service curve number, production distribution network and supply chain risk management methods.The results suggest that flooding in Dolo-Ado is increasing due to heavy rainfall and flooding, as well as inadequate flood control measures and geographical location. Soil Conservation Service Curve Number analysis shows that the arid landscape of Dolo-ado is predominantly shrub and barren with significant differences in land cover types. The low infiltration capacity, high runoff potential and frequent heavy rainfall are the main factors contributing to the area's high soil vulnerability to flash floodsConsequently, qualitative results also confirm that this has resulted in extensive infrastructure damage, displacement, loss of livelihoods, ecosystem disruption and disruption to community life, as well as water and health problems. In addition, flood risks are more severe for vulnerable urban communities, impacting services, the economy and the environment. Therefore, inadequate preventive measures for effective supply chain management are urgent and crucial for resilience. This study implies that urban planning and policies should be changed and prioritize the integration of production distribution networks and flood risk management in the supply chain to effectively mitigate floods. Climate change-responsive and integrated urban planning, improved drainage systems, early warning, emergency planning and community engagement are critical for flood preparedness, adaptation and resilience and require further research and modeling techniques.

Floods are devastating natural disasters causing significant damage worldwide, especially in southern Latin America, where recurrent river floods lead to severe impacts. This study proposes an innovative flood modelling approach using a naive Bayes classifier to simulate flood extents at a regional scale, incorporating spatial and temporal variability. Using 12 features, including topography, soil properties, precipitation and discharge, the model was trained with multiple flood events, avoiding sampling limitations and evaluating optimal pre-processing strategies for continuous data. The predictive capacity resulted in high performance metrics, with temporal validation accuracy (AC) up to 0.98 and a critical success index (CSI) of 0.58, and spatial validation achieved an AC up to 0.97 and CSI of 0.56, outperforming the hydrodynamic model by 65%. A reduced model with significant features improved computational efficiency and achieved a CSI exceeding 0.60. This practical tool supports flood risk management and enhances resilience in vulnerable regions.

Flood hazard has resulted in the loss of thousands of lives and large-scale damage to properties. This study has explored, analyzed, and categorized the flood hazard and risk levels of Arba Minch City in South Ethiopia by integrating geospatial and Analytical Hierarchy Process techniques. Data were acquired from DEM with 12.5 m resolution, Landsat 8 OLI, ortho-rectified, and surveyed data from the Municipality. Slope, Elevation, Rainfall, Aspect, Curvature, Topographic Wetness Index, Topographic Roughness Index, Drainage Density, Distance from River, Soil Types, Land Use Land Cover, and Population Density parameters were used. Standard classification criteria were set based on literature and experts' judgment. Data were rasterized, resampled, and reclassified into five classes through the natural break method and readjustment. The flood hazard map was produced using the weighted overlay technique with hazard levels of low (7.39%), moderate (56.13%), and high (36.48%). Whereas, very low and very high remained nil. The flood risk levels were produced ascendingly as 2.4%, 17.3%, 17%, 44%, and 19.4%, respectively. The validity of the model was confirmed by the ROC-AUC Value of 0.923 being fitted with flood damage sites of Shara, Limat, Airport, Agriculture Research Center, Konso Sefer, Ashewamado, Gurba, and Arba Minch University campuses. Slope, elevation, rainfall, aspect and curvature were the top priority flood hazard parameters. The hazard map, population density, and land use land cover inputs have significant weights for flood risks. Thus, the study findings urge that the stakeholders should take integrated and consistent flood risk reduction and management measures.

In mid-July 2021, a quasi-stationary extratropical cyclone over parts of western Germany and eastern Belgium led to unprecedented sustained widespread precipitation, nearly doubling climatological monthly rainfall amounts in less than 72 h. This resulted in extreme flooding in many of the Eifel-Ardennes low mountain range river catchments with loss of lives, and substantial damage and destruction. Despite many reconstructions of the event, open issues on the underlying physical mechanisms remain. In a numerical laboratory approach based on a 52-member spatially and temporally consistent high-resolution hindcast reconstruction of the event with the integrated hydrological surface-subsurface model ParFlow, this study shows the prognostic capabilities of ParFlow and further explores the physical mechanisms of the event. Within the range of the ensemble, ParFlow simulations can reproduce the timing and the order of magnitude of the flood event without additional calibration or tuning. What stands out is the large and effective buffer capacity of the soil. In the simulations, the upper soil in the highly affected Ahr, Erft, and Kyll river catchments are able to buffer between about one third to half of the precipitation that does not contribute immediately to the streamflow response and leading eventually to widespread, very high soil moisture saturation levels. In case of the Vesdre river catchment, due to its initially higher soil water saturation levels, the buffering capacity is lower; hence more precipitation is transferred into discharge.

Bridges are important social infrastructure, and in particular, the stability of the back-fill behind the abutment determines the safety of the entire bridge. Recent climate change has increased the risk of flooding, and damage caused by back-fill erosion and collapse is increasing. The objective of this study is to elucidate the damage mechanism of the back-fill of bridge abutments during floods and to propose new reinforcement techniques. In the experiments, indoor open channel tests using a scaled model were conducted to verify the effectiveness of the Gabion Faced Reinforced Soil Wall (GRW), which is a reinforcement method integrating gabions and geosynthetics to reduce the collapse of the back-fill due to flooding. The result of the study showed that the GRW was effective in preventing the collapse of the back-fill due to flooding. As a result, the time until complete collapse of the back-fill was three times longer in the case where GRW was installed than in the case where no countermeasures were taken. This suggests that GRW may be effective during flood events. However, boiling due to changes in pore water pressure occurred inside the back-fill, resulting in progressive sediment discharge. In particular, the effect of the gabion installation geometry was observed, confirming that the corner design is important to control scour. This study experimentally verified the effectiveness of the reinforced soil wall and provided knowledge that contributes to improving the durability of abutment back-fill during flooding. In the future, quantitative evaluation will be conducted to establish a more practical design method.

This study analyzes the effects of Hurricane Eta on the Chiriqui Viejo River basin, revealing the significant impact of extreme weather events on the hydrological dynamics of the region. The maximum rainfall recorded on November 4, 2020, reached 223.8 mm, while the flow in Paso Canoa reached 638.03 m3/s, demonstrating the magnitude of the event and the inability of the basin to handle such high volumes of water. Through a detailed analysis, it was observed that soil saturation resulted in direct runoff of up to 70.0 mm that same day, which shows that the infiltration capacity of the soil was quickly exceeded. Despite the damage observed, there are currently no advanced hydrological studies on extreme events in critical basins such as the Chiriqui Viejo River. This lack of research reflects a serious lack of planning and assessment of the risks associated with phenomena of this magnitude. One of the most critical problems found is the lack of specialized hydrology professionals, who are essential to carry out detailed studies and ensure sustainable management of water resources. In a context where climate change increases the frequency and intensity of extreme events, the absence of hydrologists in the region puts the resilience of the basin to future disasters at risk. The basin's hydraulic system demonstrated its inability to handle high flows, underscoring the need to improve flood control and water retention infrastructure. In addition, the lack of effective hydrological planning and coordination in the management of hydraulic infrastructures compromises both the safety of downstream communities and the sustainability of hydroelectric reservoirs, vital for the region.

This article investigates the influence of climatic and geographical characteristics in south-western region of Bangladesh on the temporal dynamics of post-cyclone impacts, with a critical focus on biophysical contexts. By quantitatively assessing the environmental consequences of cyclones Amphan (2020), Yaas (2021), Mocha (2023) and Remal (2024), the study offers a nuanced understanding of flood damage extent and vegetation health, measured through advanced remote sensing and geospatial techniques. Using Sentinel-1 (GRD) and Sentinel-2 (MSI) satellite imageries from 2020 to 2024, the study has examined post-cyclone changes of vegetation health and flood damage extent using available indices such as Normalized Difference Vegetation Index (NDVI) and Soil-Adjusted Vegetation Index (SAVI). The results exhibit substantial spatial disparities occurred due to the cyclone events, with NDVI variations ranging from - 0.124 to 0.546 (Amphan), - 0.033 to 0.498 (Mocha), - 0.086 to 0.458 (Yaas), and - 0.061 to 0.362 (Remal), indicating significant ecological stress. Corresponding SAVI changes ranged from - 0.001 to 0.396 (Amphan), - 0.029 to 0.338 (Mocha), - 0.002 to 0.345 (Yaas), and - 0.0524 to 0.269 (Remal). Negative indices underscore potential vegetation degradation, while positive values indicate resilience or post-cyclone recovery. Furthermore, flood damage analysis indicates to a more severe and unevenly distributed impact than previously recognized, particularly in areas with pre-existing vulnerabilities with the damage extent variations between - 35.918 to - 2.0093 (Amphan), - 35.334 to - 4.4059 (Mocha), - 34.806 to - 0.94921 (Yaas), and - 48.469 to 0.00255 (Remal). The Geographically Weighted Regression (GWR), model demonstrates a robust relationship, with r2 values of 0.894, 0.889, 0.899, and 0.95, indicating that approximately 85% of the ecological changes are driven by fluctuations of vegetation due to flood. The insight from this research provides a foundation of flood damage assessment technique occurred by cyclones in a short span of time to aid immediate policy recommendations to enhance resilience in remote areas of the coastal regions of Bangladesh.

Floods and erosion are natural hazards that present a substantial risks to both human and ecological systems, particularly in coastal regions. Flooding occurs when water inundates typically dry areas, causing widespread damage, while erosion gradually depletes soil and rock through processes driven by water and wind. This study proposes an innovative approach that integrates Deep Neural Decision Forest (DeepNDF), Feedforward Neural Network (FNN), autoencoders, and Bidirectional Recurrent Neural Networks (Bi-RNN) models for flood prediction, enhanced through transfer learning for erosion mapping in coastal environments. Utilizing multi-source datasets from the United States Geological Survey (USGS), Climate Hazards Group InfraRed Precipitation with Station (CHIRPS), the National Algerian Institute of Cartography, and Sentinel-2 imagery, the key conditioning factors using Geographic Information System (GIS) were generated. The conditioning factors included elevation, slope, flow direction, curvature, distance from rivers, distance from roads, hillshade, topographic wetness index (TWI), stream power index (SPI), geology, and land use/land cover (LULC), as well as rainfall. To ensure the modeling reliability, the performance was rigorously evaluated using multiple statistical metrics, including the Area Under the Curve-Receiver Operating Characteristic (AUC-ROC), Precision, Recall, and F1 Score. The DeepNDF model achieved the highest performance for flood prediction with an AUC-ROC of 0.97, Precision of 0.93, Recall of 0.92, and an F1 Score of 0.925, while the transfer learning approach significantly improved erosion prediction, reaching an AUC-ROC of 0.92, Precision of 0.90, Recall of 0.92, and an F1 Score of 0.91. The analysis indicated that flood risks predominantly affected rangeland (18.68%) and bare ground (20.48%), while cropland was found to face the highest erosion risk, affecting approximately 3,471 km2. This research advances predictive modelling in hydrology and environmental science, providing valuable insights for disaster mitigation and resilience planning strategies in coastal areas.

Study region: The study focuses on the Indus River Basin and southern Pakistan, severely affected by flooding in 2022. Study focus: This study assessed how land surface temperature, snow cover, soil moisture, and precipitation contributed to the deluge of 2022. This study mainly investigated MODIS-AIRS land surface temperature, MODIS snow cover (NDSI), SMAP soil moisture, and GPM IMERG precipitation accumulation. Furthermore, different flood visualization and mapping techniques were applied to delineate the flood extent map using Landsat 8-9, Sentinel-2 MSI, and Sentinel-1 SAR data. New hydrological insights for the region: The region experienced some of the most anomalous climatic events in 2022, such as prolonged heatwaves as observed with higher-than-average land surface temperatures and subsequent rapid decline in snow cover extent during the spring, increased soil moisture followed by an abnormal amount of extreme monsoon precipitation in the summer. The upper subbasins experienced more than 8 degrees C in positive temperature anomaly, indicating a warmer climate in spring. Subsequently, the snow cover declined by more than 25 % in the upper subbasins. Further, higher surface soil moisture values (> 0.3 m3/m3) were observed in the basin during the spring due to the rapid snow and ice melt. Furthermore, the basin received more than 200 mm of rainfall compared to the long-term average rainfall of about 98 mm, translating to about 300 % more rainfall than usual in July and August. The analysis helps understand the spatial and temporal variability within the basin and facilitates the understanding of factors and their intricate connections contributing to flooding.