Flood Inundation Mapping Research Articles

Sequential data assimilation for real-time probabilistic flood inundation mapping

Abstract. Real-time probabilistic flood inundation mapping is crucial for flood risk warning and decision-making during the emergency period before an upcoming flood event. Considering the high uncertainties involved in the modeling of a nonlinear and complex flood event, providing a deterministic flood inundation map can be erroneous and misleading for reliable and timely decision-making. The conventional flood hazard maps provided for different return periods cannot also represent the actual dynamics of flooding rivers. Therefore, a real-time modeling framework that forecasts the inundation areas before the onset of an upcoming flood is of paramount importance. Sequential data assimilation (DA) techniques are well known for real-time operation of physical models while accounting for existing uncertainties. In this study, we present a DA hydrodynamic modeling framework where multiple gauge observations are integrated into the LISFLOOD-FP model to improve its performance. This study utilizes the ensemble Kalman filter (EnKF) in a multivariate fashion for dual estimation of model state variables and parameters where the correlations among point source observations are taken into account. First, a synthetic experiment is designed to assess the performance of the proposed approach; then the method is used to simulate the Hurricane Harvey flood in 2017. Our results indicate that the multivariate assimilation of point source observations into hydrodynamic models can improve the accuracy and reliability of probabilistic flood inundation mapping by 5 %–7 %, while it also provides the basis for sequential updating and real-time flood inundation mapping.

Full Lifecycle Monitoring on Drought-Converted Catastrophic Flood Using Sentinel-1 SAR: A Case Study of Poyang Lake Region during Summer 2020

During summer 2020, the most catastrophic flood in the 21st century attacked the Poyang Lake region, one of the flood-prone areas in China. To explore the occurrence mechanism and evolution patterns of this drought-converted flood better, a full lifecycle model is developed in this article. Employing Sentinel-1 Synthetic Aperture Radar (SAR) images, with the advantages of high spatial–temporal resolution and all-day and all-weather working capacity, a bimodal threshold was applied to efficiently extract flood inundation mapping. Thus, 61 Sentinel-1 SAR images in 2020 were used to establish inundation sequences for full lifecycle monitoring. This flood presented an abrupt transformation from drought, a long duration, and the slow receding of water, and its area exceeded 3000 km2 from July to early October. In addition, inundation models that reflect the lake area and water level relationship were introduced to assist near-real-time monitoring. Through hydrological and meteorological analysis, compared with results of previous years (from 2010 to 2019), this study found that the water level from July to October in 2020 was at least 17% higher than the mean level at the same period in history and water volume had increased about 44.13 billion m3 during the flooding period. Similarly, the average precipitation from June to September was significantly higher than the same period of previous years. It was the abnormal sustained heavy precipitation and sharp rising of the water level that caused this catastrophic flood. In particular, the Standardized Precipitation Index (SPI) increased from −1.02 in April to 1.31 in July, indicating that the flood was abruptly converted from drought. The inundated areas of several land types during different periods of the full lifecycle were calculated for damage assessment. It was found that cropland was the most heavily impaired with a maximum inundated area of 1375.67 km2, while other land types including forest, grassland, wetland, and impervious surface were relatively less damaged. The study results demonstrate that flood full lifecycle monitoring based on SAR data is helpful to explore the patterns of flood evolution, analyze causes, and assess damage. Simultaneously, focusing on drought-converted floods contributes to the understanding of flood patterns, which provides relevant management departments with decision support for disaster prevention and mitigation.

Direct Integration of Numerous Dams and Reservoirs Outflow in Continental Scale Hydrologic Modeling

AbstractDespite the recent developments in continental‐scale streamflow and flood inundation modeling frameworks, effects of time‐specific and spatially explicit storage‐release dynamics of numerous dams and reservoirs remain underexplored. This paper fills this knowledge gap by directly inserting operational daily flow release data at 175 dam locations into a streamflow simulation of ∼1.2 million river reaches in the Mississippi River Basin (MRB), and therefore quantifying the effect of these regulations on streamflow and flood inundation extents. Using a streamflow routing model called the Routing Application for Parallel computatIon of Discharge (RAPID) and flood inundation mapping model called AutoRoute, two simulation scenarios were constructed respectively including and excluding the daily flow releases from those dams and reservoirs for a 10‐year period (2005–2014). Flood inundation maps were simulated for peak flow conditions at a ∼10‐m hyper spatial resolution. Kling‐Gupta efficiency (KGE) values show that streamflow model performance considerably improved when reservoirs were included in the modeled system, varying from 2% in the eastern region to 380% in the drier western region. Despite small variation of streamflow model improvement with reservoir release inclusion in the eastern region of the basin, the flow model was able to better capture observed peak flows. For a 1% change in streamflow, we observe a 0.8% change in estimated flood inundation. Comparisons to three observed flood events in the MRB demonstrate that the flood inundation estimates improve when percent change in streamflow is relatively high. Overall, inclusion of reservoir release resulted in substantial improvement in continental‐scale streamflow and flood inundation mapping.

2D-HEC-RAS Modeling of Flood Wave Propagation in a Semi-Arid Area Due to Dam Overtopping Failure

Dam overtopping failure and the resulting floods are hazardous events that highly impact the inundated areas and are less predictable. The simulation of the dam breach failure and the flood wave propagation is necessary for assessing flood hazards to provide precautions. In the present study, a two-dimensional HEC-RAS model was used to simulate the flood wave resulting from the hypothetical failure of Al-Udhaim Dam on Al-Udhaim River, Iraq, and the propagation of the resulting dam-break wave along 100 km downstream the dam site for the overtopping scenario. The main objective is to analyze the propagation of the flood wave so that the failure risk on dam downstream areas can be assessed and emergency plans may be provided. The methodology consisted of two sub-models: the first is the dam breach failure model for deriving the breach hydrograph, and the second is the hydrodynamic model for propagating the flood wave downstream of the dam. The breach hydrograph is used as an upstream boundary condition to derive the flood impact in the downstream reach of Al- Udhaim River. The flood inundation maps were visualized in RAS-Mapper in terms of water surface elevation, water depth, flow velocity, and flood arrival time. The maximum recorded values were: 105 m (a.m.s.l.), 18 m, 5.5 m/s, and, respectively. The flow velocity decreased from upstream to downstream of the terrain, which means less risk of erosion in the far reaches downstream of the study area. The inundation maps indicated that the water depth and flow velocity were categorized as Catastrophic limits on the terrain's area. The results offer a way to predict flood extent and showed that the impact of a potential dam break at Al-Udhiam Dam will be serious, therefore, suitable management is needed to overcome this risk. Moreover, the maps produced by this study are useful for developing plans for sustainable flood management. Doi: 10.28991/cej-2021-03091739 Full Text: PDF

Exploring Sentinel-1 and Sentinel-2 diversity for flood inundation mapping using deep learning

Identification of flood water extent from satellite images has historically relied on either synthetic aperture radar (SAR) or multi-spectral (MS) imagery. MS sensors are limited to cloud free conditions, whereas SAR imagery is plagued by noise-like speckle. Prior studies that use combinations of MS and SAR data to overcome individual limitations of these sensors have not fully examined sensitivity of flood mapping performance to different combinations of SAR and MS derived spectral indices or band transformations in color space. This study explores the use of diverse bands of Sentinel 2 (S2) through well-established water indices and Sentinel 1 (S1) derived SAR imagery along with their combinations to assess their capability for generating accurate flood inundation maps. The robustness in performance of S-1 and S-2 band combinations was evaluated using 446 hand labeled flood inundation images spanning across 11 flood events from Sen1Floods11 dataset which are highly diverse in terms of land cover as well as location. A modified K-fold cross validation approach is used to evaluate the performance of 32 combinations of S1 and S2 bands using a fully connected deep convolutional neural network known as U-Net. Our results indicated that usage of elevation information has improved the capability of S1 imagery to produce more accurate flood inundation maps. Compared to a median F1 score of 0.62 when using only S1 bands, the combined use of S1 and elevation information led to an improved median F1 score of 0.73. Water extraction indices based on S2 bands have a statistically significant superior performance in comparison to S1. Among all the band combinations, HSV (Hue, Saturation, Value) transformation of S2 bands provides a median F1 score of 0.9, outperforming the commonly used water spectral indices owing to HSV’s transformation’s superior contrast distinguishing abilities. Additionally, U-Net algorithm was able to learn the relationship between raw S2 based water extraction indices and their corresponding raw S2 bands, but not of HSV owing to relatively complex computation involved in the latter. Results of the paper establishes important benchmarks for the extension of S1 and S2 data-based flood inundation mapping efforts over large spatial extents.

A hybridized model based on neural network and swarm intelligence-grey wolf algorithm for spatial prediction of urban flood-inundation

In regions with lack of hydrological and hydraulic data, a spatial flood modeling and mapping is an opportunity for the urban authorities to predict the spatial distribution and the intensity of the flooding. It helps decision-makers to develop effective flood prevention and management plans. In this study, flood inventory data were prepared based on the historical and field surveys data by Sari municipality and regional water company of Mazandaran, Iran. The collected flood data accompanied with different variables (digital elevation model and slope have been considered as topographic variables, land use/land cover, precipitation, curve number, distance to river, distance to channel and depth to groundwater as environmental variables) were applied to novel hybridized model based on neural network and swarm intelligence-grey wolf algorithm (NN-SGW) to map flood-inundation. Several confusion matrix criteria were used for accuracy evaluation by cutoff-dependent and independent metrics (e.g., efficiency (E), positive predictive value (PPV), negative predictive value (NPV), area under the receiver operating characteristic curve (AUC)). The accuracy of the flood inundation map produced by the NN-SGW model was compared with that of maps produced by four state-of-the-art benchmark models: random forest (RF), logistic model tree (LMT), classification and regression trees (CART), and J48 decision tree (J48DT). The NN-SGW model outperformed all benchmark models in both training (E = 90.5%, PPV = 93.7%, NPV = 87.3%, AUC = 96.3%) and validation (E = 79.4%, PPV = 85.3%, NPV = 73.5%, AUC = 88.2%). As the NN-SGW model produced the most accurate flood-inundation map, it can be employed for robust flood contingency planning. Based on the obtained results from NN-SGW model, distance from channel, distance from river, and depth to groundwater were identified as the most important variables for spatial prediction of urban flood inundation. This work can serve as a basis for future studies seeking to predict flood susceptibility in urban areas using hybridized machine learning (ML) models and can also be applied in other urban areas where flood inundation presents a pressing challenge, and there are some problems regarding required model and availability of input data.

FLOOD INUNDATION AND DAM BREAK ANALYSIS FOR DISASTER RISK MITIGATION (A CASE STUDY OF WAY APU DAM )

This paper presents a numerical modeling to simulate flood inundation and dam break analysis of Way Apu dam for an emergency action plan (EAP), by considering EAP guidelines for ICOLD (International Committee on Large Dam). The Way Apu dam, located in Way Apu River, Maluku, Indonesia is a multi-purpose dam constructed mainly for irrigation, hydropower, water supply, and flood control. Therefore, the dam’s outflow discharge needs to be analyzed, due to the large amount of rainfall in eastern Indonesia. Thus, there is a need to analyze flood inundation mapping and dam break simulation in a bid to identify the flood mitigation risk zone and early warning system, within the downstream area of Way Apu dam. In this study, the flood inundation mapping and dam break simulation were analyzed using HEC-RAS 5.0.7. The analysis results of flood routing generate the outflow peak discharge reduction over spillway were 7.36 % (Ten years return period discharge, Q10) and just 2.14% (Probable maximum flood discharge, QPMF), indicating the simulation results of Hec-Ras 5.0.7 model for conditions with or without dam break did not show significant difference in dam’s downstream area. According to these results, 6 sub-districts are affected by the flood (Grandeng, Waeleman, Wanakarta, Waenetat, Debowae, Mako) with a maximum flood height range between 1-2.5 m (Q10) and 2.9-4.2 m (QPMF). The simulation results of arrival time also showed the estimation of evacuation time for QPMF ranges from 6 - 12 hours.

Flood Inundation Mapping with Limited Observations Based on Physics-Aware Topography Constraint.

Spatial classification with limited observations is important in geographical applications where only a subset of sensors are deployed at certain spots or partial responses are collected in field surveys. For example, in observation-based flood inundation mapping, there is a need to map the full flood extent on geographic terrains based on earth imagery that partially covers a region. Existing research mostly focuses on addressing incomplete or missing data through data cleaning and imputation or modeling missing values as hidden variables in the EM algorithm. These methods, however, assume that missing feature observations are rare and thus are ineffective in problems whereby the vast majority of feature observations are missing. To address this issue, we recently proposed a new approach that incorporates physics-aware structural constraint into the model representation. We design efficient learning and inference algorithms. This paper extends our recent approach by allowing feature values of samples in each class to follow a multi-modal distribution. Evaluations on real-world flood mapping applications show that our approach significantly outperforms baseline methods in classification accuracy, and the multi-modal extension is more robust than our early single-modal version. Computational experiments show that the proposed solution is computationally efficient on large datasets.

Machine-learning and HEC-RAS integrated models for flood inundation mapping in Baro River Basin, Ethiopia

This study presents an integrated machine-learning and HEC-RAS models for flood inundation mapping in Baro River Basin, Ethiopia. ANN and HEC-RAS models were integrated as a predictive hydrological and hydraulic model to generate runoff and the extent of flood, respectively. Daily rainfall and temperature data of 7-years (1999–2005), daily discharge (1999–2005) and 30 m × 30 m gridded Topographical Wetness Index (TWI) were used to train a predictive ANN hydrological model in RStudio. The predictive performance of the developed ANN hydrological model was evaluated in RStudio using Nash–Sutcliffe Efficiency (NSE) values of 0.86 and 0.88 during the training period (1999–2005) and testing period (2006–2008), respectively, with the corresponding observed daily discharge. The validated ANN predictive hydrological model was linked with HEC-RAS to generate the flood extent along the river course. The HEC-RAS model result was calibrated and validated using the water body delineated using Normal Difference Water Index (NDWI) from LANDSAT 8 imagery based on historical flood events of 2005 and 2008. It was found that about 96% of an agreement was made between the flood-prone areas generated in HEC-RAS and the water body delineated using NDWI. Therefore, it is logical to conclude that the integration of a machine-learning approach with the HEC-RAS model has improved the spatiotemporal uncertainties in traditional flood forecasting methods. This integrated model is powerful tool for flood inundation mapping to warn residents of this basin.

Application of ANN and HEC-RAS model for flood inundation mapping in lower Baro Akobo River Basin, Ethiopia

Study regionLower Baro River, Ethiopia. Study focusThis paper presents the novelty of ANN and HEC-RAS model for flood inundation mapping in lower Baro Akobo Basin River, Ethiopia. ANN and HEC-RAS model is applied and successfully improves the accuracy of prediction and flood inundation in the region. This study uses 14 meteorological stations on a daily basis for 1999−2005 and 2006−2008 periods, and Topographical Wetness Index (TWI) to the train and test the model respectively. The runoff time series obtained in ANN model is linked to HEC-RAS and the flood depths were generated. The flood inundation generated in HEC-RAS model result was calibrated and validated in Normal Difference Water Index (NDWI). New hydrological insights for the regionAs the inundation map generated from the runoff values of ANN model reveals, the lower Baro river forms huge inundation depth up to 250 cm. The performance the ANN model was evaluated using Nash-Sutcliffe Efficiency (NSE = 0.86), PBIAS = 8.2 % and R2 = 0.91 and NSE = 0.88, PBIAS = 8.5 % and R2 = 0.93 during the training and testing periods respectively. The generated inundation areas in HEC-RAS and the water bodies delineated in NDWI were covered with 94.6 % and 96 % as overlapping areas during the calibration and validation periods respectively. Therefore, it is concluded that the integration of the ANN approach with the HEC-RAS model has improved the prediction accuracy in traditional flood forecasting methods.

Mapping of Flood Areas Using Landsat with Google Earth Engine Cloud Platform

The Earth Observation (EO) domain can provide valuable information products that can significantly reduce the cost of mapping flood extent and improve the accuracy of mapping and monitoring systems. In this study, Landsat 5, 7, and 8 were utilized to map flood inundation areas. Google Earth Engine (GEE) was used to implement Flood Mapping Algorithm (FMA) and process the Landsat data. FMA relies on developing a “data cube”, which is spatially overlapped pixels of Landsat 5, 7, and 8 imagery captured over a period of time. This data cube is used to identify temporary and permanent water bodies using the Modified Normalized Difference Water Index (MNDWI) and site-specific elevation and land use data. The results were assessed by calculating a confusion matrix for nine flood events spread over the globe. The FMA had a high true positive accuracy ranging from 71–90% and overall accuracy in the range of 74–89%. In short, observations from FMA in GEE can be used as a rapid and robust hindsight tool for mapping flood inundation areas, training AI models, and enhancing existing efforts towards flood mitigation, monitoring, and management.

Mapping flood inundation areas using GIS and HEC-RAS model at Fetam River, Upper Abbay Basin, Ethiopia

Flood occurs due to rapid population growth, land degradation, and climate change, and causes harmful damages to human beings and properties. This can be minimized by giving attention to flood risk measures. One of these measures is inundating flood hazard areas. Therefore, this study was conducted to map flood inundation areas along the Fetam River using GIS and HEC_RAS. Flood inundation mapping is used to define the zones which are more susceptible to flood along the river when the release of a stream surpasses the bank-full stage along the river. Using the past data on river banks and the release of earlier floods besides topographic data, maps were made to illustrate areas predictable to be covered with the flood for different releases. The flooded areas on Fetam River have been represented depending on 5% incidence peak flows for different reoccurrence eras using the HEC-RAS model, GIS for spatial data handling, and HEC-GeoRAS for interfacing among HEC-RAS and GIS. These critical floods were damaging the areas around Fetam River, which is hazardous to social and economic growth due to loss of lives and destruction of properties. Built-up areas and agricultural fields are located along the river banks and are highly susceptible to flooding. The inundated areas were particularly high from Abaya Kalo, Achigi, and Denbun kebeles for all return periods. These flooded areas along the side of the Fetam River are 27.31, 24.85, 20.47, 17.34, and 13.78 km2 for 100, 50, 25, 10, and 5 years return periods, respectively. Generally, this study discovered that flooded areas in the upstream and middle parts of Fetam River are high as related to the downstream parts.

Advanced analysis and integration of remote sensing and in situ data for flood monitoring

The increasingly occurrence of flood events claims our capacity to enhance risk reduction and damage mitigation. The current large availability of satellite data constitutes a fundamental resource for disciplines such as fluvial geomorphology and hydrology that exploit the new technologies and techniques to develop innovative approaches for improving flood phenomenon investigation. The present work, extracted from a PhD thesis, describes the application of advanced analyses and data integrations to detect and to monitor flood events. The new data fusion methodologies are tested in various areas, at different spatial and temporal scales, in various surface conditions. The results (flood extent maps, geomorphic index maps, flood inundation maps, etc.) demonstrate the advantage to use complementary information sources and the progress in addressing scientific research towards the production of operational systems.

AN EFFICIENT ND-POINT DATA STRUCTURE FOR QUERYING FLOOD RISK

Abstract. Governments use flood maps for city planning and disaster management to protect people and assets. Flood risk mapping projects carried out for these purposes generate a huge amount of modelling results. Previously, data submitted are highly condensed products such as typical flood inundation maps and tables for loss analysis. Original modelling results recording critical flood evolution processes are overlooked due to cumbersome management and analysis. This certainly has drawbacks: the ‘static’ maps impart few details about the flood; also, the data fails to address new requirements. This significantly confines the use of flood maps. Recent development of point cloud databases provides an opportunity to manage the whole set of modelling results. The databases can efficiently support all kinds of flood risk queries at finer scales. Using a case study from China, this paper demonstrates how a novel nD-PointCloud structure, HistSFC, improves flood risk querying. The result indicates that compared with conventional database solutions, HistSFC holds superior performance and better scalability. Besides, the specific optimizations made on HistSFC can facilitate the process further. All these indicate a promising solution for the next generation of flood maps.

Modeling nearshore dynamics of extreme storms in complex environments of Connecticut

Flood hazard planning requires the accurate estimation of total water elevation due to predicted tide, surge, and wave runup to design flood protection structures and improve coastal risk planning for severe storms. The beach geomorphology and nearshore hydrodynamic conditions impact the conclusive flood inundation mapping in complex environments. The conventional approaches of flood modeling are limited due to either i) simple static estimates, ii) the application of a coupled circulation and phase-averaged wave models in coarse resolution, iii) failing to calibrate and validate with in-situ data, or iv) not considering sea-level rise projections in mapping the flood extent. We used a fully nonlinear Boussinesq wave model (FUNWAVE-TVD) on the nearshore area with a high-resolution grid to determine total water elevation on the shores. The model adopted the boundary conditions from coupled circulation and wave model of the Long Island Sound (Liu et al., 2020).We applied the model to Branford, Norwalk, and New Haven, CT to represent the complex topo-bathymetric features and the structural interference in shallow water wave dynamics. We compared the model results with the FEMA base flood elevation, the North Atlantic Coast Comprehensive Study (NACCS), and (Liu et al., 2020)’s Long Island Sound FVCOM-SWAVE model. The FUNWAVE-TVD model is found to model wave processes more accurately in shallow water regions compared to the empirical equation application of FEMA and coupled circulation-phase averaged model application of NACCS and FVCOM-SWAVE. We also examined local sea-level rise predictions of storms with 1% and 10% annual exceedance probability by the year 2050 in Connecticut and found that the flood extent of these two storms showed little to no difference. We suggest the planning approaches should consider the increase in the frequency of the storms in the predicted inundation zones due to sea-level rise.

SUITABLE DEM ASSESSMENT STUDY FOR DATA SCARCE CITIES TOWARDS URBAN EXTREME AND NUISANCE FLOOD MAPPING

Abstract. The high-resolution accurate topography data should be used for extreme and nuisance flood inundation modeling and mapping in cities, but not available for many cities, including most developed countries. It is necessary to study and identify an alternate open-source topographic model that satisfies high-resolution topography datasets’ conditions. We analyzed the open-source DEMs visually, elevation histogram statistics, streams and watershed identification, contour statistics, Topographic Wetness Index, and vertical accuracy of other medium-resolution DEMs compared with high-resolution LiDAR data over New York City to determine alternative open-source Digital Elevation Model in the context of urban flood modeling. In high urban sprawl areas, in the context of flood mapping, our findings have shown that the medium resolution DEMs predicted similar to high-resolution DEMs with the same linear errors around RMSE 25–35ft and LE90 30–40ft. Overall, the ALOS AW3D performed better than other open-source DEMs. Even though SRTM predicted well, it inducted smoothness in DEM where more buildings were located. It noted that ALOS PALSAR DEM is not suitable for any urban studies. ASTER DEM has also shown good agreement with LiDAR and observed elevations, but it induced by noise while processing. Finally, it can be suggested that the ALOS AW3D can be used as an alternative source for urban flood modeling which represented footprints of buildings even though it performed average in vertical accuracy.

Flood inundation mapping and hazard assessment of Baitarani River basin using hydrologic and hydraulic model

Flood inundation mapping and hazard assessment of Baitarani River basin using hydrologic and hydraulic model

Rapid and large-scale mapping of flood inundation via integrating spaceborne synthetic aperture radar imagery with unsupervised deep learning

Synthetic aperture radar (SAR) has great potential for timely monitoring of flood information as it penetrates the clouds during flood events. Moreover, the proliferation of SAR satellites with high spatial and temporal resolution provides a tremendous opportunity to understand the flood risk and its quick response. However, traditional algorithms to extract flood inundation using SAR often require manual parameter tuning or data annotation, which presents a challenge for the rapid automated mapping of large and complex flooded scenarios. To address this issue, we proposed a segmentation algorithm for automatic flood mapping in near-real-time over vast areas and for all-weather conditions by integrating Sentinel-1 SAR imagery with an unsupervised machine learning approach named Felz-CNN. The algorithm consists of three phases: (i) super-pixel generation; (ii) convolutional neural network-based featurization; (iii) super-pixel aggregation. We evaluated the Felz-CNN algorithm by mapping flood inundation during the Yangtze River flood in 2020, covering a total study area of 1,140,300 km2. When validated on fine-resolution Planet satellite imagery, the algorithm accurately identified flood extent with producer and user accuracy of 93% and 94%, respectively. The results are indicative of the usefulness of our unsupervised approach for the application of flood mapping. Meanwhile, we overlapped the post-disaster inundation map with a 10-m resolution global land cover map (FROM-GLC10) to assess the damages to different land cover types. Of these types, cropland and residential settlements were most severely affected, with inundation areas of 9,430.36 km2 and 1,397.50 km2, respectively, results that are in agreement with statistics from relevant agencies. Compared with traditional supervised classification algorithms that require time-consuming data annotation, our unsupervised algorithm can be deployed directly to high-performance computing platforms such as Google Earth Engine and PIE-Engine to generate a large-spatial map of flood-affected areas within minutes, without time-consuming data downloading and processing. Importantly, this efficiency enables the fast and effective monitoring of flood conditions to aid in disaster governance and mitigation globally.

Simulation of Dam-Break Flood Wave and Inundation Mapping: A Case study of Attabad Lake

The break study of Landslide or embankment dams is more essential due to uncertainty in their composition and lack of knowledge of their response to other natural events because they are natural and not properly designed for any disaster. The paper aims to improve different methods of hydraulic modeling of dam break. In this present study dam break of Attabad lake is simulated using the computational fluid dynamics technique. The numerical model (FLOW-3D) is developed to solve the Reynolds averaged Navier-Stoke equation fully in 3D to predict peak flow depth at different cross-sections, peak velocity, peak discharge, time to peak depth, and time to peak discharge. The standard RNGturbulence model is employed to simulate turbulence and then flood inundation maps and velocity vectors for flow at villages are drawn. The results show that most of the flood wave modeled through the Hunza river channel, is contained by the flood plain of Hunza River but for some of the villages i.e. Miaun and chalat, which are situated inside the flood plain of Hunza River are at higher risk of inundation but the flood arrival time estimated for these villages is 31 and 44 min respectively which is enough time for the evacuation of the population to safer areas while for some villages like Hassan Abad situated adjacent to Ali Abad is at higher risk of inundation while the estimated flood arrival time for the village is 12 min which are not enough for the evacuation of the population hence will need some extra flood protection structures for flood containment. The estimate of the peak velocities implies higher shear stress in the river plain, risk of heavy erosion, damage to agricultural lands, residencies, and morphological changes are projected. The analysis of the dam break i.e. Peak depths, Peak Velocities, flood arrival time, and flood inundation maps should only be used as a guide in future risk analysis and flood management.

Enhancement of Detecting Permanent Water and Temporary Water in Flood Disasters by Fusing Sentinel-1 and Sentinel-2 Imagery Using Deep Learning Algorithms: Demonstration of Sen1Floods11 Benchmark Datasets

Identifying permanent water and temporary water in flood disasters efficiently has mainly relied on change detection method from multi-temporal remote sensing imageries, but estimating the water type in flood disaster events from only post-flood remote sensing imageries still remains challenging. Research progress in recent years has demonstrated the excellent potential of multi-source data fusion and deep learning algorithms in improving flood detection, while this field has only been studied initially due to the lack of large-scale labelled remote sensing images of flood events. Here, we present new deep learning algorithms and a multi-source data fusion driven flood inundation mapping approach by leveraging a large-scale publicly available Sen1Flood11 dataset consisting of roughly 4831 labelled Sentinel-1 SAR and Sentinel-2 optical imagery gathered from flood events worldwide in recent years. Specifically, we proposed an automatic segmentation method for surface water, permanent water, and temporary water identification, and all tasks share the same convolutional neural network architecture. We utilize focal loss to deal with the class (water/non-water) imbalance problem. Thorough ablation experiments and analysis confirmed the effectiveness of various proposed designs. In comparison experiments, the method proposed in this paper is superior to other classical models. Our model achieves a mean Intersection over Union (mIoU) of 52.99%, Intersection over Union (IoU) of 52.30%, and Overall Accuracy (OA) of 92.81% on the Sen1Flood11 test set. On the Sen1Flood11 Bolivia test set, our model also achieves very high mIoU (47.88%), IoU (76.74%), and OA (95.59%) and shows good generalization ability.