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The Peace–Athabasca Delta (PAD) in western Canada is one of the largest inland deltas in the world. Flooding caused by the expansion of lakes beyond normal shorelines occurred during the summer of 2020 and provided a unique opportunity to evaluate the capabilities of remote sensing platforms to map surface water expansion into vegetated landscape with complex surface connectivity. Firstly, multi-source remotely sensed data via satellites were used to create a temporal reconstruction of the event spanning May to September. Optical synthetic aperture radar (SAR) and altimeter data were used to reconstruct surface water area and elevation as seen from space. Lastly, temporal water surface area and level data obtained from the existing satellites and hydrometric stations were used as input data in the CNES Large-Scale SWOT Simulator, which provided an overview of the newly launched SWOT satellite ability to monitor such flood events. The results show a 25% smaller water surface area for optical instruments compared to SAR. Simulations show that SWOT would have greatly increased the spatio-temporal understanding of the flood dynamics with complete PAD coverage three to four times per month. Overall, seasonal vegetation growth was a major obstacle for water surface area retrieval, especially for optical sensors.

Urbanization increases regional impervious surface area, which generally reduces hydrologic response time and therefore increases flood risk. The objective of this work is to investigate the sensitivities of urban flooding to urban land growth through simulation of flood flows under different urbanization conditions and during different flooding stages. A sub-watershed in Toronto, Canada, with urban land conversion was selected as a test site for this study. In order to investigate the effects of urbanization on changes in urban flood risk, land use maps from six different years (1966, 1971, 1976, 1981, 1986, and 2000) and of six simulated land use scenarios (0%, 20%, 40%, 60, 80%, and 100% impervious surface area percentages) were input into coupled hydrologic and hydraulic models. The results show that urbanization creates higher surface runoff and river discharge rates and shortened times to achieve the peak runoff and discharge. Areas influenced by flash flood and floodplain increases due to urbanization are related not only to overall impervious surface area percentage but also to the spatial distribution of impervious surface coverage. With similar average impervious surface area percentage, land use with spatial variation may aggravate flash flood conditions more intensely compared to spatially uniform land use distribution.

Urbanisation is an irreversible trend as a result of social and economic development. Urban areas, with high concentration of population, key infrastructure, and businesses, are extremely vulnerable to flooding and may suffer severe socio-economic losses due to climate change. Urban flood modelling tools are in demand to predict surface water inundation caused by intense rainfall and to manage associated flood risks in urban areas. These tools have been rapidly developing in recent decades. In this study, we present a comprehensive review of the advanced urban flood models and emerging approaches for predicting urban surface water flooding driven by intense rainfall. The study explores the advantages and limitations of existing model types, highlights the most recent advances, and identifies major challenges. Issues of model complexities, scale effects, and computational efficiency are also analysed. The results will inform scientists, engineers, and decision-makers of the latest developments and guide the model selection based on desired objectives.

Pluvial flood has been increasingly understood as a major threat that has presented a significant risk for many cities worldwide. Regarding flood risk management, flood modeling enables to understand, assess and forecast flood conditions and their impact. Likewise, several hydrodynamic models have been developed and their application has been spread. With respect to effective flood modeling, particularly in urbanized floodplains, the choice of an appropriate method, considering contextual requirements, is challenging. This paper gives an overview of prevailing flood modeling approaches in view of their potentials and limitations for modeling pluvial flood in urban settings. The existing methods are categorized into: rapid flood spreading, one-dimensional sewer, overland flow (1D and 2D), sewer-surface coupling approaches (1D–1D and 1D–2D). Each of these techniques is described, by taking aspects influencing the selection of a proper flood modeling method for a particular application into account. This paper would help urban flood managers, and potential users undertake effective flood modeling tasks, balancing between their needs, model complexity and requirements of both input data and time.

Accurate assessment of surface water from satellite and remote sensing data plays an important role in water and flood management and supporting natural ecosystems and human development. Remote sensing imagery has significantly advanced in water extraction methods, particularly in water index, classification, and sub-pixel analysis. Water-index-based approaches offer notable advantages such as speed and convenience among these methods. The unique characteristics of surface water and flooded areas, including their extensive coverage and dynamic nature, make the water index particularly effective for monitoring large regions. However, the complexity of land surfaces in aquatic environments presents challenges that hinder accurate water extraction. These challenges differ across various factors, such as shadows in urban and mountainous areas, small water bodies, muddy water, and water leakage in unshaded regions. The current study introduces a novel Flood/Water Extraction Index (FWEI) for identifying water and flooded areas to address these challenges. The FWEI utilizes the average ratio of visible and near-infrared bands derived from Sentinel-2 images. The proposed index utilizes images with 10-m and average visible bands and more effectively compensates for errors arising from spectral and spatial changes. Therefore, it demonstrates strong performance by more accurately mapping muddy and clear water within small water bodies and narrow rivers. The performance of the offered FWEI index is compared with other indices, including the Normalized Difference Water Indices (NDWI-G, NDWI-F), Modified NDWI (MNDWI-1, MNDWI-2), and the Automatic Water Extraction Index (AWEInsh) without shadow. While other indices excel in specific scenarios, such as built-up or non-built-up areas, and bare lands versus vegetated areas, the FWEI index demonstrates consistently high accuracy and stability in extracting surface water across diverse backgrounds. The FWEI index achieves an average Overall Accuracy (OA) of 94.26% for water extraction and 93.11% for flood extraction. In comparison, the AWEInsh attains an OA of 90.48% and 90.39%, NDWI-F performs at 86.69% and 86.55%, MNDWI-1 at 77.21% and 75.82%, MNDWI-2 at 76.12% and 75.42%, and NDWI-G at 75.26% and 74.78%, respectively. The integration of visible spectral bands with the near-infrared band proves instrumental in enhancing the accuracy of water derivation in complex and expansive environments.

Flooding in urban area is a major natural hazard causing loss of life and damage to property and infrastructure. The major causes of urban floods include increase in precipitation due to climate change effect, drastic change in land use–land cover (LULC) and related hydrological impacts. In this study, the change in LULC between the years 1966 and 2009 is estimated from the toposheets and satellite images for the catchment of Poisar River in Mumbai, India. The delineated catchment area of the Poisar River is 20.19 km 2 . For the study area, there is an increase in built-up area from 16.64 to 44.08% and reduction in open space from 43.09 to 7.38% with reference to total catchment area between the years 1966 and 2009. For the flood assessment, an integrated approach of Hydrological Engineering Centre-Hydrological Modeling System (HEC-HMS), HEC-GeoHMS and HEC-River analysis system (HEC-RAS) with HEC-GeoRAS has been used. These models are integrated with geographic information system (GIS) and remote sensing data to develop a regional model for the estimation of flood plain extent and flood hazard analysis. The impact of LULC change and effects of detention ponds on surface runoff as well as flood plain extent for different return periods have been analyzed, and flood plain maps are developed. From the analysis, it is observed that there is an increase in peak discharge from 2.6 to 20.9% for LULC change between the years 1966 and 2009 for the return periods of 200, 100, 50, 25, 10 and 2 years. For the LULC of year 2009, there is a decrease in peak discharge from 10.7% for 2-year return period to 34.5% for 200-year return period due to provision of detention ponds. There is also an increase in flood plain extent from 14.22 to 42.5% for return periods of 10, 25, 50 and 100 years for LULC change between the year 1966 and year 2009. There is decrease in flood extent from 4.5% for 25-year return period to 7.7% for 100-year return period and decrease in total flood hazard area by 14.9% due to provisions of detention pond for LULC of year 2009. The results indicate that for low return period rainfall events, the hydrological impacts are higher due to geographic characteristics of the region. The provision of detention ponds reduces the peak discharge as well as the extent of the flooded area, flood depth and flood hazard considerably. The flood plain maps and flood hazard maps generated in this study can be used by the Municipal Corporation for flood disaster and mitigation planning. The integration of available software models with GIS and remote sensing proves to be very effective for flood disaster and mitigation management planning and measures.

Flash floods are highly devastating, however there is no effective management for their water in Saudi Arabia, therefore, it is crucial to adopt Rainfall Water Harvesting (RWH) techniques to mitigate the flash floods and manage the available water resources. The goal of this study is to create a potential flood hazard map and a map of suitable locations for RWH in Wadi Nisah, Saudi Arabia to identify potential areas for rainwater harvesting and dam construction for both a flood mitigation and water harvesting. This research was carried out using a spatiotemporal distributed model based on multi-criteria decision analysis by combining Geographic Information System (GIS), Remote Sensing (RS), and Multi-Criteria Decision-Making tools (MCDM). The flood hazard mapping criteria were elevation, drainage density, slope, direct runoff depth at 50 years return period, Topographic witness index, and Curve Number, while the criteria for RWH were Slope, Land cover, Stream order, Lineaments density, and Average of annual max-24 h Rainfall. In multi-criteria decision analysis, 21.55% of the total area for Wadi Nisah was classified as extremely dangerous and dangerous; 65.29% of the total area was classified as moderate; and 13.15% of the total area was classified as safe and very safe in flash flood hazard classes. Only 15% of Wadi Nisah has a very high potentiality for RWH and 27.7%, 57.31% of the basin has a moderate and a low or extremely low potentiality of RWH, respectively. ranged from 3976104.499 m3 to 4328509.123 m3; and the maximum surface area of reservoirs ranged from 1268372.63 m2 to 1505825.676.14 m2.

Floods are among the most severe and impacting natural disasters. Their occurrence rate and intensity have been significantly increasing worldwide in the last years due to climate change and urbanization, bringing unprecedented effects on human lives and activities. Hence, providing a prompt response to flooding events is of crucial relevance for humanitarian, social and economic reasons. Satellite remote sensing using synthetic aperture radar (SAR) offers a great deal of support in facing flood events and mitigating their effects on a global scale. As opposed to multi-spectral sensors, SAR offers important advantages, as it enables Earth’s surface imaging regardless of weather and sunlight illumination conditions. In the last decade, the increasing availability of SAR data, even at no cost, thanks to the efforts of international and national space agencies, has been deeply stimulating research activities in every Earth observation field, including flood mapping and monitoring, where advanced processing paradigms, e.g., fuzzy logic, machine learning, data fusion, have been applied, demonstrating their superiority with respect to traditional classification strategies. However, a fair assessment of the performance and reliability of flood mapping techniques is of key importance for an efficient disasters response and, hence, should be addressed carefully and on a quantitative basis trough synthetic quality metrics and high-quality reference data. To this end, the recent development of open SAR datasets specifically covering flood events with related ground-truth reference data can support thorough and objective validation as well as reproducibility of results. Notwithstanding, SAR-based flood monitoring still suffers from severe limitations, especially in vegetated and urban areas, where complex scattering mechanisms can impair an accurate extraction of water regions. All such aspects, including classification methodologies, SAR datasets, validation strategies, challenges and future perspectives for SAR-based flood mapping are described and discussed.

On 26 August 2020, a devastating flash flood struck Charikar city, Parwan province, Afghanistan, causing building damage and killing hundreds of people. Rapid identification and frequent mapping of the flood-affected area are essential for post-disaster support and rapid response. In this study, we used Google Earth Engine to evaluate the performance of automatic detection of flood-inundated areas by using the spectral index technique based on the relative difference in the Normalized Difference Vegetation Index (rdNDVI) between pre- and post-event Sentinel-2 images. We found that rdNDVI was effective in detecting the land cover change from a flash flood event in a semi-arid region in Afghanistan and in providing a reasonable inundation map. The result of the rdNDVI-based flood detection was compared and assessed by visual interpretation of changes in the satellite images. The overall accuracy obtained from the confusion matrix was 88%, and the kappa coefficient was 0.75, indicating that the methodology is recommendable for rapid assessment and mapping of future flash flood events. We also evaluated the NDVIs’ changes over the course of two years after the event to monitor the recovery process of the affected area. Finally, we performed a digital elevation model-based flow simulation to discuss the applicability of the simulation in identifying hazardous areas for future flood events.

Timely detection of flooding is paramount for saving lives as well as evaluating levels of damage. Floods generally occur under specific weather conditions, such as excessive precipitation, which makes the presence of clouds very likely. For this reason, radar-based sensors are most suitable for near-real-time flood mapping. The public dataset Sen1Floods11 recently released by the Cloud to Street is one example of ongoing beneficial initiatives to employ deep learning for flood detection with synthetic aperture radar. The present study used this dataset to improve flood detection using well-known segmentation architectures, such as SegNet and UNet, as networks. In addition, this study provided a deeper understanding of which set of polarized band combination is more suitable for distinguishing permanent water, as well as flooded areas from the SAR image. The overall performance of the models with various kinds of labels and a combination of bands to detect all surface water areas were also assessed. Finally, the trained models were tested on a completely different location at Kerala, India, during the 2018 flood for verifying their performance in the real-world situation of a flood event outside of the given test set in the dataset. The results prove that trained models can be used as off-the-shelf models to achieve an intersection over union (IoU) as high as 0.88 in comparison with optical images. The omission and commission error were less than 6%. However, the most important result is that the processing time for the whole satellite image was less than 1 min. This will help significantly for providing analysis and near-real-time flood mapping services to first responder organizations during flooding disasters.