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The resultant impact of climate change and urbanization has caused extensive disruption to natural hydrological processes, thus enhancing the flood risk in susceptible areas. This study evaluated flood processes in the Pallikaranai Marshland–Buckingham Canal corridor using detailed flood inundation modeling and risk assessment methodology. Important geospatial factors and variables, such as rainfall, Digital Elevation Model (DEM), slope, Land Use Land Cover (LULC), river distance, flow length, and Normalized Difference Water Index (NDWI), were weighed and ranked. These weighted parameters were assimilated to estimate the Flood Hazard Index (FHI), which was subsequently applied to create an intricately mapped flood hazard. The analysis and testing of the involved parameters by assessing flood susceptibility has been facilitated with hydrological modeling, Geographic Information System (GIS), as well as with remote sensing procedures. Deep-learning frameworks, particularly convolutional neural networks, have also shown high predictive capability for regional flood susceptibility (Kalantar et al. 2021). The findings suggest that urban growth has resulted in extensive wetland degradation, elevated surface runoff, and more frequent flooding, particularly during intense rainfall. The FHI-based flood hazard map identifies critical areas at risk of flooding, highlighting the explicit role of land cover changes in flood intensity and frequency. This study underscores the urgent need for sustainable urban planning, wetland conservation, and climate-resilient infrastructure to mitigate flood hazards and enhance longterm urban flood resilience in the region. These results help to better understand urban flood hazards and offer a scientific foundation for future flood management.

This study evaluates the impacts of projected sea level rise (SLR) on coastal flooding across major Indian cities: Mumbai, Kolkata, Chennai, Visakhapatnam, Surat, Kochi, Thiruvananthapuram, and Mangaluru. Machine learning models, including Long Short-Term Memory (LSTM), Random Forest (RF), and Gradient Boosting (GB), has been employed to assess flood risks under four Shared Socioeconomic Pathways (SSP 126, 245, 370, and 585) emission scenarios. The research utilized these models because they demonstrate high performance in handling difficult data relationships and both temporal patterns and sophisticated environmental data. SLR projections provided by computers generate forecasts that combine with digital elevation models (DEMs) to determine coastal flooding risks and locate flood-prone areas. Results reveal that Mumbai and Kolkata face the highest flood risks, particularly under high emission scenarios, while Kochi and Mangaluru exhibit moderate exposure. Model performance is validated using residual analysis and Receiver Operating Characteristic (ROC) curves, confirming reliable predictive accuracy. These findings provide essential information for urban planners and policymakers to prioritize climate adaptation strategies in vulnerable coastal cities.

Floods are brutal, catastrophic natural hazards which affect most human beings in terms of economy and life loss, especially in the large river basins worldwide. The Indus River basin is considered as one of the world’s large river basins, comprising several major tributaries, and has experienced severe floods in its history. There is currently no proper early flood warning system for the Indus River which can help administrative authorities cope with such natural hazards. Hence, it is necessary to develop an early flood warning system by integrating a hydrodynamic model, in situ information, and satellite imagery. This study used Hydrologic Engineering Center–River Analysis System (HEC–RAS) to predict river dynamics under extreme flow events and inundation modeling. The calibration and validation of the HEC–RAS v5 model was performed for 2010 and 2015 flood events, respectively. Manning’s roughness coefficient (n) values were extracted using the land use information of the rivers and floodplains. Multiple combinations of n values were used and optimized in the simulation process for the rivers and floodplains. The Landsat 5 Thematic Mapper (TM), Landsat 8 Operational Land Imager (OLI), Moderate Resolution Imaging Spectroradiometer (MODIS) MOD09A1, and MOD09GA products were used in the analysis. The Normalized Difference Water Index (NDWI), Modified NDWI1 (MNDWI1), and MNDWI2, were applied for the delineation of water bodies, and the output of all indices were blended to produce standard flood maps for accurate assessment of the HEC–RAS-based simulated flood extent. The optimized n values for rivers and floodplains were 0.055 and 0.06, respectively, with significant satisfaction of statistical parameters, indicating good agreement between simulated and observed flood extents. The HEC–RAS v5 model integrated with satellite imagery can be further used for early flood warnings in the central part of the Indus River basin.

Flooding stands as one of the world’s most devastating natural hazards, accounting for lost lives, economic damage, and ecosystem degradation. Over the past five decades, flood inundation models have emerged as essential tools for flood forecasting and risk management. Through an analysis of publications from 1970 to 2023, this review provides a foundational understanding of state-of-the-science flood model developments. The evolution of flood models in recent decades has been marked by significant technological advances, including enhancing traditional numerical modeling approaches and deploying them with widespread use of large-scale simulation and satellite remote sensing. The field has matured substantially over the past 50 years, and it seems to have reached an inflection point at which major research is poised to progress. The most ambitious research directions are those that involve coupling flood models with models in diverse fields and involve: (1) atmospheric sciences to construct a two-way coupled flood-land surface-atmosphere model, (2) epidemiology to assess the health impacts of floods, (3) economics to help develop a flood model damage footprint framework to quantify financial harm to those who occupy urban and agricultural land, (4) ecology to evaluate and quantify flood-induced ecological damage, (5) further development of groundwater flooding, glacial lake outburst flooding, sedimentation-induced flooding, plus investigation of the joint impact of multiple compounding flood types, (6) responsible advancement of AI-based flood models, (7) greater assimilation of multiple data sources that include high resolution satellite and drone imagery, crowdsourcing, and video data. Building on the broad foundation of flood-modeling research conducted over many decades, these eight avenues offer promising opportunities to further address the combined challenges of escalating climate, land-use, and demographic changes.

This paper presents a proposed integrated approach for flood hazardous evaluation in arid and semi-arid areas. Wadi Fatimah in Saudi Arabia is utilized for implementation of such an approach. The approach consists of four stages. In the first stage, a statistical analysis of rainfall data is performed to determine the design storms at specified return periods. In the second stage, geological and geomorphologic analyses are followed to estimate the geomorphic parameters. The third stage concerned with land use and land cover analyses linked with hydrological analysis to estimate the hydrographs. The fourth stage is related to the delineation of the inundation area under two scenarios: the presence and absence of the dam. The statistical analysis proved that some rainfall stations do not follow a Gumbel distribution. The presence of the dam reduces the inundation depth by about 10 %. The reduction in the inundation area due the presence of the dam is about 25 %.

Flood hazard assessment is essential for climate-change management planning. River flow frequency and size affect basin flood regime. This study evaluates the flood hazard of delta region of a large Indian river basin, the Mahanadi River basin, for possible future flood scenarios. Based on flood severity and frequency, basin flood regime change is examined. Four different flood scenarios are considered such as reference scenario (1955–2001), present scenario (1981–2011) and two possible future scenarios, by modifying the peak flood series with percentage change approach. Flood hazard was assessed based on the results of hydrodynamic modelling. Based on flood estimations at head of delta region, flood intensity is increasing in delta region of Mahanadi basin. Floods of higher return period (61 years) in the reference scenario happens to be a lower return period flood (11 years) in possible future scenario. In recent years, high floods have maximum water level profiles equal to or higher than 5- and 10-year return period floods of hypothetical future flood scenarios. Flood inundation evaluation under multiple land-uses shows that economically significant land-uses including farmland, built-up land, and aquaculture are more vulnerable to future floods. In the Mahanadi delta, 'high' and 'very high' flood depth coverage increases and 'low' depth diminishes. Inundation area for 'very high' flood depth is increasing, indicating agricultural vulnerability in the delta region.

Almost 5 years after the 26 December 2004 Indian Ocean tragedy, the 10 August 2009 Andaman tsunami demonstrated that accurate forecasting is possible using the tsunami community modeling tool Community Model Interface for Tsunamis (ComMIT). ComMIT is designed for ease of use, and allows dissemination of results to the community while addressing concerns associated with proprietary issues of bathymetry and topography. It uses initial conditions from a precomputed propagation database, has an easy-to-interpret graphical interface, and requires only portable hardware. ComMIT was initially developed for Indian Ocean countries with support from the United Nations Educational, Scientific, and Cultural Organization (UNESCO), the United States Agency for International Development (USAID), and the National Oceanic and Atmospheric Administration (NOAA). To date, more than 60 scientists from 17 countries in the Indian Ocean have been trained and are using it in operational inundation mapping.

In China, flood inundation modeling is often limited by the lack of high quality topographic dataset, especially in small cities where hydrologic informatization is relatively backward. Coarse-resolution digital elevation models (DEMs) often lead to lower accuracy in simulation results due to the loss of small-scale building features in terrain modeling. Increasing the DEM resolution by resampling techniques may be an available solution to building representation problems in data-sparse areas, which has not yet received much attention and needs to be tested in conjunction with urban inundation models. This paper evaluated the impact of DEM resolution and resampling techniques on the building treatment method (BTM) and the output of urban inundation model to determine whether satisfactory inundation simulation can be achieved based on resampled DEMs with fine resolution. Using the 2016 flood event in Wuhan as the study case, a detailed comparison among 19 modeling scenarios differing in DEM resolutions, resampling techniques and BTMs was subsequently conducted. The outcome of the comparison analysis revealed the sensitivity of the simulated inundation depth to the resampled DEM resolution. Furthermore, the scenario using resampled DEMs with high resolution showed better performance in urban inundation modeling because the building features and the flow paths within building gaps were adequately represented by the BTM. The findings of this study may facilitate the application of BTMs for urban inundation modeling to predict (with useful accuracy) the inundation extent and depth in areas where fine topography data are unavailable.

Physics‐based flood hydrodynamic models are widely used for predicting inundation in urban basins with complex building layouts. While the treatment of urban buildings in these models has been extensively discussed, over‐assumptions can introduce inaccuracies, uncertainties, and excessive computational effort, particularly under data‐scarce conditions. This study proposes a simple yet effective method, the Building Coverage Ratio (BCR) scheme, to account for building effects in city‐scale urban inundation modeling. The BCR scheme quantifies water ion to generate surface runoffs in densely built‐up areas by dynamically adjusting drainage and infiltration volumes based on the proportion of building footprint in each grid cell. This approach improves the accuracy of urban flood predictions when high‐resolution data is unavailable. Validated against a historical rainstorm event in Zhuhai, China, the BCR scheme demonstrated its capability to efficiently and accurately reproduce spatiotemporal inundation patterns. The method significantly improved street‐level flooding simulations, which are often underestimated in traditional approaches that neglect building effects. Results show that simulation accuracy increases from 33% without treatment to over 85% when the BCR scheme was applied to 30 m‐resolution Digital Elevation Model (DEM). As the method relies entirely on open‐source datasets, it offers a practical and transferable solution for urban flood prediction in data‐scarce regions.

Flood hazard is assessed for the data scarce lower Bharathapuzha basin in Kerala, India, using a hydrologic-hydraulic approach. L-moment-based regional flood frequency analysis along with a non-dimensional analysis of hydrographs is used to generate scenarios for flood hazard assessment. A fully hydrodynamic 1D river flow model is calibrated for the rivers of lower Bharathapuzha basin for the year 1992 and validated for the year 1994. The widely available SRTM DEM is used to extract river cross-sections and the limited available discharge and water level data are used to carefully calibrate the 1D river flow model. Subsequently, a coupled 1D-2D flood inundation model is used to simulate the flood inundation extent for the year 2002. In the absence of LIDAR data, a cartographic DEM derived from readily available topo maps is used as an input in the coupled 1D-2D model. Further, in the absence of microwave Synthetic Aperture Radar (SAR) data, the flood inundation extent is validated using the readily available optical IRS-1D WiFS sensor data which is mainly intended for vegetation and drought monitoring. A suitable methodology is used to delineate flood inundation extent from the partial cloud-covered WiFS image. The regional flood frequency estimates and the calibrated and validated flood inundation model are then used to assess the flood hazard.