The occurrence of heavy rainfall in the south-eastern hilly region of Bangladesh makes this area highly susceptible to recurrent flash flooding. As the region is the commercial capital of Bangladesh, ...these flash floods pose a significant threat to the national economy. Predicting this type of flooding is a complex task which requires a detailed understanding of the river basin characteristics. This study evaluated the susceptibility of the region to flash floods emanating from within the Karnaphuli and Sangu river basins. Twenty-two morphometric parameters were used. The occurrence and impact of flash floods within these basins are mainly associated with the volume of runoff, runoff velocity, and the surface infiltration capacity of the various watersheds. Analysis showed that major parts of the basin were susceptible to flash flooding events of a ‘moderate’-to-‘very high’ level of severity. The degree of susceptibility of ten of the watersheds was rated as ‘high’, and one was ‘very high’. The flash flood susceptibility map drawn from the analysis was used at the sub-district level to identify populated areas at risk. More than 80% of the total area of the 16 sub-districts were determined to have a ‘high’-to-‘very-high’-level flood susceptibility. The analysis noted that around 3.4 million people reside in flash flood-prone areas, therefore indicating the potential for loss of life and property. The study identified significant flash flood potential zones within a region of national importance, and exposure of the population to these events. Detailed analysis and display of flash flood susceptibility data at the sub-district level can enable the relevant organizations to improve watershed management practices and, as a consequence, alleviate future flood risk.
Short‐ to medium‐range flood forecasts are central to predicting and mitigating the impact of flooding across the world. However, producing reliable forecasts and reducing forecast uncertainties ...remains challenging, especially in poorly gauged river basins. The growing availability of synthetic aperture radar (SAR)‐derived flood image databases (e.g., generated from SAR sensors such as Envisat advanced synthetic aperture radar) provides opportunities to improve flood forecast quality. This study contributes to the development of more accurate global and near real‐time remote sensing‐based flood forecasting services to support flood management. We take advantage of recent algorithms for efficient and automatic delineation of flood extent using SAR images and demonstrate that near real‐time sequential assimilation of SAR‐derived flood extents can substantially improve flood forecasts. A case study based on four flood events of the River Severn (United Kingdom) is presented. The forecasting system comprises the SUPERFLEX hydrological model and the Lisflood‐FP hydraulic model. SAR images are assimilated using a particle filter. To quantify observation uncertainty as part of data assimilation, we use an image processing approach that assigns each pixel a probability of being flooded based on its backscatter values. Empirical results show that the sequential assimilation of SAR‐derived flood extent maps leads to a substantial improvement in water level forecasts. Forecast errors are reduced by as much as 50% at the assimilation time step, and improvements persist over subsequent time steps for 24 to 48 hr. The proposed approach holds promise for improving flood forecasts at locations where observed data availability is limited but satellite coverage exists.
Key Points
Probabilistic flood maps are derived from SAR images
Probabilistic flood maps are assimilated into a flood forecasting model cascade
Water level forecast quality improves substantially in the assimilation time steps, and benefits persist for hours to days
Torrential and long-lasting rainfall often causes long-duration floods in flat and lowland areas in data-scarce Nyaungdon Area of Myanmar, imposing large threats to local people and their ...livelihoods. As historical hydrological observations and surveys on the impact of floods are very limited, flood hazard assessment and mapping are still lacked in this region, making it hard to design and implement effective flood protection measures. This study mainly focuses on evaluating the predicative capability of a 2D coupled hydrology-inundation model, namely the Rainfall-Runoff-Inundation (RRI) model, using ground observations and satellite remote sensing, and applying the RRI model to produce a flood hazard map for hazard assessment in Nyaungdon Area. Topography, land cover, and precipitation are used to drive the RRI model to simulate the spatial extent of flooding. Satellite images from Moderate Resolution Imaging Spectroradiometer (MODIS) and the Phased Array type L-band Synthetic Aperture Radar-2 onboard Advanced Land Observing Satellite-2 (ALOS-2 ALOS-2/PALSAR-2) are used to validate the modeled potential inundation areas. Model validation through comparisons with the streamflow observations and satellite inundation images shows that the RRI model can realistically capture the flow processes (R2 ≥ 0.87; NSE ≥ 0.60) and associated inundated areas (success index ≥ 0.66) of the historical extreme events. The resultant flood hazard map clearly highlights the areas with high levels of risks and provides a valuable tool for the design and implementation of future flood control and mitigation measures.
Flood risk, particularly in Small Island Developing States, is increasing. Although spaceborne Digital Elevation Models (DEMs) have provided a capacity to model flooding at the global scale, their ...relatively coarse resolution (~90 m) has led to a limited ability to provide fine‐scale flood assessments in smaller catchments such as those in Small Island Developing States. Following the release of the TanDEM‐X DEM at ~12‐m resolution, the aim of this research is to determine whether TanDEM‐X can improve flood estimates in comparison to Shuttle Radar Topography Mission (SRTM) and Multi‐Error‐Removed Improved‐Terrain (MERIT) DEMs. Suitable methods to process TanDEM‐X to a Digital Terrain Model (DTM) are identified through testing of seven DTMs produced through combinations of different vegetation removal approaches. Methods include Progressive Morphological Filtering and Image Classification of two TanDEM‐X auxiliary data sets—a Height Error Map and Amplitude map. The LISFLOOD‐FP hydrodynamic model output flood extent and water surface elevation for the TanDEM‐X DTMs, SRTM, and MERIT are compared against the LiDAR model for a catchment in Fiji. The main findings show that the unprocessed TanDEM‐X has improved predictive capacity over SRTM, but not MERIT. The TanDEM‐X processing method combining Image Classification of the Amplitude map and Progressive Morphological Filtering produces the DTM with the highest flood model skill in comparison to all tested DEMs. This DTM reports a 12–14 percentage point higher flood model skill score than MERIT and a lower water surface elevation root‐mean‐square error of 0.11–0.21 m, indicating the suitability of TanDEM‐X for flood modeling.
Plain Language Summary
Flood risk is increasing almost everywhere, making it vital to identify at‐risk areas. Highly accurate elevation data are essential for flood risk estimation, which in high‐income countries is usually provided by LiDAR. However, countries such as Small Island Developing States are often reliant on spaceborne elevation data sets due to the high cost of LiDAR, despite experiencing some of the greatest levels of flood risk. These spaceborne data sets have greater errors than LiDAR and often measure vegetation canopy height instead of ground height, reducing the accuracy of flood estimates used by policy makers to assess risk. This paper aims to identify whether newly released spaceborne data set TanDEM‐X could improve flood estimates in these areas by comparing flood simulations from a hydrodynamic model using TanDEM‐X data with simulations based on other spaceborne data sets and LiDAR for the Ba catchment in Fiji. The results showed that TanDEM‐X performs closest to the LiDAR model but only after vegetation removal processing. Further studies should be conducted in other locations, but these results indicate a possible method for improving inundation estimates in data‐sparse areas. This should provide useful information for flood modeling and disaster management communities—essential given predictions of more extreme rainfall and greater exposure on floodplains.
Key Points
Methods to process TanDEM‐X data for use in flood inundation models are evaluated and presented for the first time
The TanDEM‐X Digital Terrain Model presented in this study improves flood estimates compared to MERIT and SRTM Digital Elevation Models
Two vegetation removal approaches for TanDEM‐X are assessed
Flood events are expected to increase in their frequency and severity, which results in higher flood risk without additional adaptation measures. The information gained from flood risk models is ...essential in effective disaster risk management. However, vulnerability estimations are often a large driver of uncertainty, and flood damage is rarely estimated due to a lack of empirical damage data from flood events. This study uses a unique data set with experienced damages and the implementation of flood damage mitigation (FDM) measures on the household level, collected after the flood event in the Netherlands in 2021. Flood damage models that control for several hazard, exposure, and vulnerability indicators are estimated and allow for additional input in flood risk models. Previous estimates of the effectiveness of FDM measures are prone to a selection bias, as households that do, and do not implement FDM measures systematically differ in their risk profiles. By using an instrumental variable‐estimation, this study overcomes this selection bias and finds significant reductions in flood damage due to FDM measures. These reductions can be incorporated in multivariate flood vulnerability estimations, which indicate that FDM measures significantly reduce flood damage. Providing information on flood hazard, as well as implementing early warning systems, is crucial for ensuring effective flood risk management.
Plain Language Summary
Due to climate change, we can expect more frequent and severe floods in the future. This study investigates how households are affected by flooding and explores ways to reduce potential flood damage. By understanding the impact of floods on households and identifying effective measures, we can better manage flood risks and reduce damages caused by these events. We collected survey data after the 2021 Summer Floods in the Netherlands to understand the factors that contribute to flood damage. Timely warnings before flooding play a crucial role in preparing for such events. They allow households to take emergency actions like placing sandbags or moving belongings to higher floors. These actions can reduce flood damage to buildings by almost 30% of their total value and protect nearly 40% of the value of household contents. In addition to emergency measures, households can take proactive steps to prepare for future floods. This includes using waterproof materials and elevating electrical appliances, such as power sockets or kitchen appliances when constructing new homes or making renovations.
Key Points
Detailed survey data allows for the update and calibration of rarely estimated empirical vulnerability curves for buildings
Flood damage mitigation (FDM) measures have the potential to reduce flood damage to both residential buildings and household contents by half
Updated input for flood risk models in the form of multivariate damage functions that can be adjusted for FDM measures
Changes in climate intensity and frequency, including extreme events, heavy and intense rainfall, have the greatest impact on water resource management and flood risk management. Significant changes ...in air temperature, precipitation, and humidity are expected in future due to climate change. The influence of climate change on flood hazards is subject to considerable uncertainty that comes from the climate model discrepancies, climate bias correction methods, flood frequency distribution, and hydrological model parameters. These factors play a crucial role in flood risk planning and extreme event management. With the advent of the Coupled Model Inter-comparison Project Phase 6, flood managers and water resource planners are interested to know how changes in catchment flood risk are expected to alter relative to previous assessments. We examine catchment-based projected changes in flood quantiles and extreme high flow events for Awash catchments. Conceptual hydrological models (HBV, SMART, NAM and HYMOD), three downscaling techniques (EQM, DQM, and SQF), and an ensemble of hydrological parameter sets were used to examine changes in peak flood magnitude and frequency under climate change in the mid and end of the century. The result shows that projected annual extreme precipitation and flood quantiles could increase substantially in the next several decades in the selected catchments. The associated uncertainty in future flood hazards was quantified using aggregated variance decomposition and confirms that climate change is the dominant factor in Akaki (C2) and Awash Hombole (C5) catchments, whereas in Awash Bello (C4) and Kela (C3) catchments bias correction types is dominate, and Awash Kuntura (C1) both climate models and bias correction methods are essential factors. For the peak flow quantiles, climate models and hydrologic models are two main sources of uncertainty (31% and 18%, respectively). In contrast, the role of hydrological parameters to the aggregated uncertainty of changes in peak flow hazard variable is relatively small (5%), whereas the flood frequency contribution is much higher than the hydrologic model parameters. These results provide useful knowledge for policy-relevant flood indices, water resources and flood risk control and for studies related to uncertainty associated with peak flood magnitude and frequency.
Residential assets, comprising buildings and household contents, are a major source of direct flood losses. Existing damage models are mostly deterministic and limited to particular countries or ...flood types. Here, we compile building-level losses from Germany, Italy and the Netherlands covering a wide range of fluvial and pluvial flood events. Utilizing a Bayesian network (BN) for continuous variables, we find that relative losses (i.e. loss relative to exposure) to building structure and its contents could be estimated with five variables: water depth, flow velocity, event return period, building usable floor space area and regional disposable income per capita. The model’s ability to predict flood losses is validated for the 11 flood events contained in the sample. Predictions for the German and Italian fluvial floods were better than for pluvial floods or the 1993 Meuse river flood. Further, a case study of a 2010 coastal flood in France is used to test the BN model’s performance for a type of flood not included in the survey dataset. Overall, the BN model achieved better results than any of 10 alternative damage models for reproducing average losses for the 2010 flood. An additional case study of a 2013 fluvial flood has also shown good performance of the model. The study shows that data from many flood events can be combined to derive most important factors driving flood losses across regions and time, and that resulting damage models could be applied in an open data framework.
Regional information on stream discharge is needed in order to improve flood estimates based on the limited data availability. Regional flood estimation is fundamental for designing hydraulic ...structures and managing flood plains and water resource projects. It is essential for estimating flood risks during recurrent periods due to suitable distributions. Regional flood frequency analysis is crucial for evaluating design flows in ungauged basins, and can complement existing time series in gauged sites and transfer them to ungauged catchments. Hence, this study aims to perform a regional flood frequency analysis of the Genale–Dawa River Basin of Ethiopia using the index flood and L-moments approach for sustainable water resource management. Three homogeneous hydrological regions were defined and delineated based on homogeneity tests from data of 16 stream-gauged sites, named Region-A, Region-B, and Region-C. The discordancy index of regional data for L-moment statistics was identified using MATLAB. All regions showed promising results of L-moment statistics with discordance measures (discordance index less than 3) and homogeneity tests (combined coefficient of variation (CC) less than 0.3). L-moment ratio diagrams were used to select best fit probability distributions for areas. Generalized extreme value, log-Pearson type III, and generalized Pareto distributions were identified as suitable distributions for Region-A, Region-B, and Region-C, respectively, for accurately modeling flood flow in the basin. Regional flood frequency curves were constructed, and peak flood was predicted for different return periods. Statistical analysis of the gauged sites revealed an acceptable method of regionalization of the basin. This study confirms that the robustness of the regional L-moments algorithm depends on particular criteria used to measure the performance of estimators. The identified regions should be tested with other physical catchment features to enhance flood quantile estimates at gauged and ungauged sites. Henceforth, this study’s findings can be further extended into flood hazard, risk, and inundation mapping of identified regions of the study area. Furthermore, this study’s approach can be used as a reference for similar investigations of other river basins.
Flash floods are a rapid hydrological response that occurs within a short time with rapidly rising water levels and could lead to massive structural, social and economic damages. Therefore, ...generating flood inundation maps becomes necessary to distinguish areas exposed to floods. Hydrodynamic models are commonly used to generate inundation maps; however, they require high computational power and time, depending on the complexity of the model. For that, researchers developed effective, fast and simplified models. Among the simplified models, the Geomorphic Flood Index (GFI) is one of the most useful classifiers to generate inundation maps. Three main objectives are addressed in this study: (1) extend the GFI classifier to predict flood extent maps for uncalibrated rainfall depths, which will enhance early warning models for better risk assessments of extreme events; (2) enhance the accuracy of the simulated inundation maps using different calibration methods; and (3) investigate the performance of the GFI in various terrains with different resolutions. Three case studies in arid regions in Saudi Arabia were examined with different topographies, using terrains of high resolutions of 1 m and resampled low resolutions, as well as various rainfall depths corresponding to 5–100-yr return periods. The HEC-RAS 2D model was used to generate reference flood inundation maps. The obtained flood extent maps show high similarity compared to the reference maps with accuracy above 80%. Strong relationships between rainfall depths and the threshold GFI parameter were developed which allow producing inundation maps for any rainfall event.
Many urban areas experience both fluvial and pluvial floods, because locations next to rivers are preferred settlement areas and the predominantly sealed urban surface prevents infiltration and ...facilitates surface inundation. The latter problem is enhanced in cities with insufficient or non-existent sewer systems. While there are a number of approaches to analyse either a fluvial or pluvial flood hazard, studies of a combined fluvial and pluvial flood hazard are hardly available. Thus this study aims to analyse a fluvial and a pluvial flood hazard individually, but also to develop a method for the analysis of a combined pluvial and fluvial flood hazard. This combined fluvial–pluvial flood hazard analysis is performed taking Can Tho city, the largest city in the Vietnamese part of the Mekong Delta, as an example. In this tropical environment the annual monsoon triggered floods of the Mekong River, which can coincide with heavy local convective precipitation events, causing both fluvial and pluvial flooding at the same time. The fluvial flood hazard was estimated with a copula-based bivariate extreme value statistic for the gauge Kratie at the upper boundary of the Mekong Delta and a large-scale hydrodynamic model of the Mekong Delta. This provided the boundaries for 2-dimensional hydrodynamic inundation simulation for Can Tho city. The pluvial hazard was estimated by a peak-over-threshold frequency estimation based on local rain gauge data and a stochastic rainstorm generator. Inundation for all flood scenarios was simulated by a 2-dimensional hydrodynamic model implemented on a Graphics Processing Unit (GPU) for time-efficient flood propagation modelling. The combined fluvial–pluvial flood scenarios were derived by adding rainstorms to the fluvial flood events during the highest fluvial water levels. The probabilities of occurrence of the combined events were determined assuming independence of the two flood types and taking the seasonality and probability of coincidence into account. All hazards – fluvial, pluvial and combined – were accompanied by an uncertainty estimation taking into account the natural variability of the flood events. This resulted in probabilistic flood hazard maps showing the maximum inundation depths for a selected set of probabilities of occurrence, with maps showing the expectation (median) and the uncertainty by percentile maps. The results are critically discussed and their usage in flood risk management are outlined.
PDF
