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Sea-level rise (SLR) is a critical consequence of climate change, posing significant threats to coastal regions worldwide. Accurate and efficient assessment of potential inundation areas is crucial for effective coastal planning and adaptation strategies. This study aimed to explore the utility of free and open-source software for geospatial (FOSS4G) tools for mapping SLR inundation, providing cost-effective solutions that are accessible to researchers and policymakers. We employed a combination of geospatial data, including high-resolution elevation models, tidal data, and projected SLR scenarios. Utilizing widely available FOSS4G tools, like QGIS, GDAL/OGR, and GRASS GIS, we developed an integrated workflow to map inundation extents, using a passive bathtub approach for various SLR scenarios. We demonstrate the approach through a case study in Virginia Key, Florida, however, the methodology can be replicated in any area where the input datasets are available. This paper demonstrates that FOSS4G tools offer a reliable and accessible means to map SLR inundation, empowering stakeholders to assess coastal vulnerabilities and to devise sustainable adaptation measures. The open-source approach facilitates collaboration and reproducibility, fostering a comprehensive understanding of the potential impacts of SLR on coastal ecosystems and communities.

Sea-level rise (SLR) is a critical consequence of climate change, posing significant threats to coastal regions worldwide. Accurate and efficient assessment of potential inundation areas is crucial for effective coastal planning and adaptation strategies. This study aimed to explore the utility of free and open-source software for geospatial (FOSS4G) tools for mapping SLR inundation, providing cost-effective solutions that are accessible to researchers and policymakers. We employed a combination of geospatial data, including high-resolution elevation models, tidal data, and projected SLR scenarios. Utilizing widely available FOSS4G tools, like QGIS, GDAL/OGR, and GRASS GIS, we developed an integrated workflow to map inundation extents, using a passive bathtub approach for various SLR scenarios. We demonstrate the approach through a case study in Virginia Key, Florida, however, the methodology can be replicated in any area where the input datasets are available. This paper demonstrates that FOSS4G tools offer a reliable and accessible means to map SLR inundation, empowering stakeholders to assess coastal vulnerabilities and to devise sustainable adaptation measures. The open-source approach facilitates collaboration and reproducibility, fostering a comprehensive understanding of the potential impacts of SLR on coastal ecosystems and communities. [ABSTRACT FROM AUTHOR]

Floods are catastrophic natural disasters that cause a substantial toll on human lives, infrastructure, and the economy. Structural and non-structural measures are developed for planning flood mitigation strategies. Flood inundation mapping is valuable information for decision-makers and authorities to develop flood mitigation strategies and resource allocation. This study uses the HEC-RAS 2D model for flood inundation mapping in the Krishna River Basin. Digital elevation models (DEMs) of 12.5 and 30 m resolutions were used to model the inundation map. The study also investigated the effect of change in upstream boundary data on the inundated area. The simulated results with 12.5 m resolution DEM are found in good agreement with the validation data and conform to the inundated areas with the available reports. This study proves the 2D capabilities of HEC-RAS and helps the experts with better management practices. HIGHLIGHTS Flood inundation mapping was performed on the Krishna River Basin using the HEC-RAS 2D model.; Digital elevation models (DEMs) of 12.5 m resolution and 30 m resolution were incorporated to perform the inundation mapping.; The study investigated the influence of varying the upstream boundary data on the inundated area.; The simulated results with 12.5 m resolution DEM give a good agreement with the data available for the validation.;

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Water utilities often use extended open surface water reservoirs to produce drinking water. Biotic and abiotic factors influence the water level, leading to alterations in the concentration of the dissolved substances (in cases of flood or drought), entry of new pollutants (in case of flooding) or reduction in the availability and inflow speed of water to the treatment plant (in case of drought). Spaceborne image analysis is considered a significant surrogate for establishing a dense network of sensors to monitor changes. In this study, renowned inundation mapping techniques are examined for their adaptability to the inland water reservoirs' conditions. The results, from the Polyphytos open surface water reservoir in northern Greece, showcase the transferability of the workflows with overall accuracies exceeding—in cases—98%. Hydroperiod maps generated for the area of interest, along with variations in the water surface extent over a four-year period, provide valuable insights into the reservoir's hydrological patterns. Comparison among different inundation mapping techniques for the surface water extent and water level reveal challenges and limitations, which are related to the spatial resolution, the data take frequency and the influence of the landscape synthesis beyond the water reservoir boundaries. [ABSTRACT FROM AUTHOR]

Australian inland riparian wetlands located east of the Great Dividing Range exhibit unique, hydroecological characteristics. These flood-dependent aquatic systems located in water-limited regions are declining rapidly due to the competitive demand for water for human activities, as well as climate change and variability. However, there exist very few reliable data to characterize inundation change conditions and quantify the impacts of the loss and deterioration of wetlands. A long-term time record of wetland inundation maps can provide a crucial baseline to monitor, assess, and assist the management and conservation of wetland ecosystems. This study presents a random forest-based multi-index classification algorithm (RaFMIC) on the Google Earth Engine (GEE) platform to efficiently construct a temporally dense, three-decadal time record of inundation maps of the southeast Australian riparian inland wetlands. The method was tested over the Macquarie Marshes located in the semiarid region of NSW, Australia. The results showed a good accuracy when compared against high-spatial resolution imagery. The total inundated area was consistent with precipitation and streamflow patterns, and the temporal dynamics of vegetation showed good agreement with the inundation maps. The inundation time record was analysed to generate inundation probability maps, which were in a good agreement with frequently flooded areas simulated by a hydrodynamic model and the distribution of flood-dependent vegetation species. The long-term, time-dense inundation maps derived from the RaFMIC method can provide key information to assess the condition and health of wetland ecosystems and have the potential to improve wetland inventory with spatially explicit water regime information. RaFMIC can be adapted over other dryland wetlands, as an effective semiautomated method of mapping long-term inundation dynamics. [ABSTRACT FROM AUTHOR]

Simulation of dam breach scenarios can help in the preparation of emergency action plans for real dam breaks or flash flooding events. The purpose of this study was to identify flood-prone areas in the Al Wala Valley in the governorate of Madaba in Jordan through analysis of the Al Wala Dam. Modelling of dam breaches was conducted under two scenarios: a Clear Day scenario and a Probable Maximum Flood (PMF) scenario. The former scenario does not address the various dam failure modes; rather, it addresses the formation and development of a breach as a result of structural failures like the sliding of dam blocks in the case of a concrete dam or piping failures in the case of embankment dams. The PMF scenarios, however, simulate unsteady flow in pipes and overtopping failure via consideration of runoff hydrography. In the PMF scenario, flood-prone areas can be identified by in-depth analysis of data from previous extreme rainfall events. The related hydrologic and hydraulic data can then be modelled using intensity-duration-frequency curves applied to an hour-by-hour simulation to discover the areas most at risk of flooding in the future. In the present study, data were collected from inlet of flow to Al Wala Valley on 10 January 2013. The collected data, which included rainfall and discharge data, were fed to the HEC-HMS software in order to calibrate the hydrological parameters of the watershed of the Al Wala Dam. Additionally, the HEC-RAS tool was employed to determine the breach outflow hydrography and hydraulic conditions across various critical downstream locations, which were determined by use of dynamic flood wave-routing models. The simulations revealed that, in the case of the Clear Day scenario, downstream inundation would cover an area of 5.262 km2 in the event of a pipe failure. However, in the event of a six-hour storm, a twelve-hour storm, and a twenty-four-hour storm, the flooded area would rise to 6.837 km2, 8.518 km2, and 9.390 km2, respectively. In the event of an overtopping failure, 13.171 km2 would be inundated, according to the Clear Day scenario. On the other hand, in the event of a six-hour storm, a twelve-hour storm, and a twenty four-hour storm, the flooded area would rise to 13.302 km2, 14.249 km2, and 14.594 km2, respectively. [ABSTRACT FROM AUTHOR]

Recent years have seen an increase in floods with severe damage due to the intensity and frequency of rains. One of the periodic hydrological problems affecting Jeddah city, the second-biggest city in Saudi Arabia, is unexpected flash flooding. In dam breaks, water that has been retained is released uncontrollably. This study is related to a flood simulation methodology after a possible break of the New Um Al-Khair Dam, a dam built in 2012 outside residential areas, to replace the Old Um Al-Khair Dam built inside a residential area, which broke in January 2011. In fact, we simulated the impact on flood wave propagation in the study area through the use of GIS techniques coupled with hydrological/hydraulic modeling tools and the development of a flood inundation model. Planning a good emergency response in the future is possible by analyzing a supposed disaster. Based on the likelihood that there will be a flood and the corresponding inundation depth, a flood risk matrix is created as a quantitative tool to estimate flood damage, which is crucial to decision-makers. Negligible, low, moderate, high, and very high-risk categories are assigned according to that flood risk matrix. The results indicated a low to very high risk for 5 years, 50 years and 100 years return periods and a negligible to very high risk for a 200 years return period. To estimate the extent of damage, a quantitative summary of the results has been outlined graphically in order to visualize the scope of the inundation areas. [ABSTRACT FROM AUTHOR]

The Floodwater Depth Estimation Tool (FwDET) calculates water depth from a remote sensing-based inundation extent layer and a Digital Elevation Model (DEM). FwDET's low data requirement and high computational efficiency allow rapid and large-scale calculation of floodwater depth. Local biases in FwDET predictions, often manifested as sharp transitions or stripes in the water depth raster, can be attributed to spatial or resolution mismatches between the inundation map and the DEM. To alleviate these artifacts, we are introducing a boundary cell smoothing and slope filtering procedure in version 2.1 of FwDET (FwDET2.1). We present an optimization analysis that quantifies the effect of differing parameterization on the resulting water depth map. We then present an extensive intercomparison analysis in which 16 DEMs are used as input for FwDET Google Earth Engine (FwDET-GEE) implementation. We compare FwDET2.1 to FwDET2.0 using a simulated flood and a large remote sensing derived flood map (Irrawaddy River in Myanmar). The results show that FwDET2.1 results are sensitive to the smoothing and filtering values for medium and coarse resolution DEMs, but much less sensitive when using a finer resolution DEM (e.g., 10 m NED). A combination of ten smoothing iterations and a slope threshold of 0.5% was found to be optimal for most DEMs. The accuracy of FwDET2.1 improved when using finer resolution DEMs except for the MERIT DEM (90 m), which was found to be superior to all the 30 m global DEMs used. [ABSTRACT FROM AUTHOR]

The major objective of this research is to assess the effects of winter ice cover and simulate the flooding within bridge vicinity of the Grand River, Ohio, the USA for both in the historical period (1959–2014) and future period (2015–2098). The LiDAR data along with field-verified survey data were utilized for the calibration and validation of HEC-RAS. The stage increments up to 6.75 ft were detected at the upstream section of the Bridge due to historical ice jams. Moreover, the increment in the inundation area varied from 24% to 52% for various percentile winter flows. The percentage increase in the inundation area was highest for the 25-percentile flow than the higher percentile flow when the same thickness of ice cover was used in the simulation. The estimated ice thickness using Stefan's equation for various emission scenarios (RCP 2.6, RCP 4.5 and RCP 8.5) for three climate models were found to be decreasing in the future period. As per the standard practice in the United States, authors have used U.S. Customary Unit System (fps unit system) in this paper. [ABSTRACT FROM AUTHOR]

A dam break is a low-probability, high risk catastrophe event that is extremely destructive and has a substantial negative socio-economic impact on downstream and nearby areas. Simulating dam breach and analyzing flood propagation downstream from those events is vital for identifying and minimizing the risks associated downstream of dam location. This study intended to anlayse the effect of overtopping failure of dam for two scenarios (a) base-case scenario (scenario with average value of dam breach parameters from their range) and (b) worst case scenario (the breach with largest geometry, shortest formation time and highest peak outflow magnitude). Further, a hydrodyanmic modelling is perfomed to investigate the sensitivity analysis (local and global) of five dam breach parameters (dam breach elevation, dam breach width, breach formation time, weir coefficient, trigger failure elevation) on breach outflow in a proposed hydropower project located in Nepal. Aeronautical Reconnaissance Coverage Geographic Information System (ArcGIS), Hydraulic Engineering Center River Analysis System (HEC-RAS) and OriginPro 2022b are utilized to analyse the effect of dam breach and parameter sensitivity. Generation of outflow hydrograph shows that worst case scenario has devasting effect downstream with innudation of 1047 of househols and 50.83 kilometers of roads. The breach velocity was recorded as 15.16 m/s and 20.85 m/s for base and worst case respectively. The minimum depth and maximum depth of flooding downstream from dam location was found to be 24.51 m and 73.6 m for base case and 47.43 m and 106.75 m for worst case. Due to backwater effect at Bheri river, peak flow at 14 km downstream from dam reduces significantly to 124852.57 m3/s and 244204.41 m3/s for base and the worst case respectively. From local sensitivty analysis it has been found that, dam breach elevation is more sensitive and triggering failure elevation is less sensitive for peak outflow hydrograph. Whereas dam breach width seems more sensitive and TFE seems least sensitive for peak outflow using Monte Carlo Simulation for gloal sensitivity. [ABSTRACT FROM AUTHOR]

The 2021–2022 flood is one of the most serious flood events in Malaysian history, with approximately 70,000 victims evacuated daily, 54 killed and total losses up to MYR 6.1 billion. From this devastating event, we realized the lack of extreme precipitation and flood inundation information, which is a common problem in tropical regions. Therefore, we developed a Rapid Extreme TRopicAl preCipitation and flood inundation mapping framEwork (RETRACE) by utilizing: (1) a cloud computing platform, the Google Earth Engine (GEE); (2) open-source satellite images from missions such as Global Precipitation Measurement (GPM), Sentinel-1 SAR and Sentinel-2 optical satellites; and (3) flood victim information. The framework was demonstrated with the 2021–2022 Malaysia flood. The preliminary results were satisfactory with an optimal threshold of five for flood inundation mapping using the Sentinel-1 SAR data, as the accuracy of inundated floods was up to 70%. Extreme daily precipitation of up to 230 mm/day was observed and resulted in an inundated area of 77.43 km2 in Peninsular Malaysia. This framework can act as a useful tool for local authorities and scientists to retrace the extreme precipitation and flood information in a relatively short period for flood management and mitigation strategy development. [ABSTRACT FROM AUTHOR]

New methods allow the direct computation of flood inundation maps from lidar data, independently of discharge estimates, hydraulic analysis, or defined cross sections. One method projects the interpolated profile of measured flood levels onto surrounding topography, creating a smooth inundation surface that is entirely based on data and geometrical relationships. A second method computes inundation maps for any simple function that relates the water surface to the elevation of the channel bottom, exploiting their known, sub-parallel character. A final method theoretically combines the elevation of the channel bottom and the upstream catchment area for points along the thalweg, all defined by lidar data. Historical data from stream gauges can be incorporated to generate inundation maps for floods having different return periods. The conceptual simplicity and realism of these maps facilitate data-based planning. [ABSTRACT FROM AUTHOR]

Cyclone global navigation satellite system (CYGNSS) has provided a valuable opportunity for high spatiotemporal monitoring of land surface reflectivity over the past few years. CYGNSS with a constellation of eight microsatellites is able to constantly observe the “scattered” global positioning system signals from the land. In this study, we validate the CYGNSS land reflectivity data in Australia for mapping the spatial extent of the inundated area and for determining temporal changes in surface soil moisture. CYGNSS level 1 data acquired for the period of 2017–2020 is assessed against various measurements, including satellite and ground-based measurements. Empirical mode decomposition is used to better analyze the CYGNSS time series and their relationship with the independent measurements. Furthermore, the mission's ability to capture surface reflectivity changes in response to extreme climatic events is analyzed. The results show that high spatial and temporal resolution CYGNSS data can largely represent the top layer ($\sim$5 cm) soil moisture spatial and temporal variations close to soil moisture active passive. CYGNSS surface reflectivity results are also found to be sensitive to surface water changes and able to depict inundated land surface.

There is a big shortage of drinking water in the western Suez Gulf in Egypt. Fortunately, wind energy is available to cope with the required power for desalination. In previous works the area was subjected to Multi-Criteria Analysis process using a DEM implemented into a GIS environment. For each watershed, a Weighted Standardized Risk Factor (WSRF) was deduced based on the correlation factor for all the main morphological parameters which were obtained from the “HEC-HMS” model results. Former thought of defining the safe and unsafe watersheds based on global scale and dependent only on (WSRF), is nowadays criticized as a watershed with high value for (WSRF) could have safe specific areas for the wind turbines’ establishment. A 2D hydrodynamic model was applied on a Macro Scale to deduce inundation maps then were overlaid with wind speeds maps to define the most appropriate locations for wind turbines.

729Sentinel-1 data are an alternative for monitoring flooded inland surfaces during cloudy periods. Supervised classification approaches with a single-trained model for the entire image demonstrate poor accuracy due to confusing backscatter conditions of the inundated areas in relation with the prevailing land cover features. This study follows instead a pixel-centric approach, which exploits the varying backscatter values of each pixel through a time series of Sentinel-1 images to train local Random Forest classification models per 3×3 pixels, and classifies each pixel in the target Sentinel-1 image, accordingly. Reference training data is retrieved from the timely close Sentinel-2-derived inundation maps. This study aims to identify the furthest mean day difference between the target Sentinel-1 image and available Sentinel-2 high accurate inundation maps (kappa coefficient—k > 0.9) that allows for the estimation of credible inundation maps for the Sentinel-1 target date. Various combinations of Sentinel-2 and Sentinel-1 training datasets are examined. The evaluation for eight target dates confirms that a Sentinel-1 inundation map with a k of 0.75 on average can be generated, when mean day difference is less than 30 days. The increment of the considered Sentinel-2 maps allows for the estimation of Sentinel-1 inundation maps with higher accuracy.

In recent years, as a result of increasingly intensive rainfall events, the associated water erosion and corrosion have led to the increase in breach risk of aging dams in the United States. In this study, a hydrodynamic model was used to the inundation simulation under three hypothetical extreme precipitation-induced homogeneous concrete dam-breach scenarios. All hydraulic variables, including water depth, flow velocity, and flood arriving time over separated nine cross-sections in the Catawba River, were calculated. The hypothetical simulation results illustrate that the impact of Hurricane Florence’s rainfall is far more severe over the downstream of hydraulic facilities than that of the Once-in-a-century storm rainfall event. Although Hurricane Florence’s rainfall observed in Wilmington had not historically happened near the MI Dam site, the river basin has a higher probability to be attacked by such storm rainfall if more extreme weather events would be generated under future warming conditions. Besides, the time for floodwaters to reach cross-section 6 under the Hurricane Gustav scenario is shorter than that under the Once-in-a-century rainfall scenario, making the downstream be inundated in short minutes. Since the probability can be quantitatively evaluated, it is of great worth assessing the risk of dam-break floods in coastal cities where human lives are at a vulnerable stage.

A timely and cost-effective method of creating inundation maps could assist first responders in allocating resources and personnel in the event of a flood or in preparation of a future disaster. The Height Above Nearest Drainage (HAND) model could be implemented into an on-the-fly flood mapping application for a Canada-wide service. The HAND model requires water level (m) data inputs while many sources of hydrological data in Canada only provide discharge (m3/sec) data. Synthetic rating curves (SRCs), created using river geometry/characteristics and the Manning's formula, could be utilized to provide an approximate water level given a discharge input. A challenge with creating SRCs includes representing how multiple different land covers will slow impact flow due to texture and bulky features (i.e., smooth asphalt versus rocky river channel); this relates to the roughness coefficient (n). In our study, two methods of representing multiple n values were experimented with (a weighted method and a minimum-median method) and were compared to using a fixed n method. A custom ArcGIS tool, Canadian Estimator of Ratings Curves using HAND and Discharge (CERC-HAND-D), was developed to create SRCs using all three methods. Control data were sourced from gauge stations across Canada in the form of rating curves. Results indicate that in areas with medium to medium–high river gradients (S > 0.002 m/m) or with river reaches under 5 km, the CERC-HAND-D tool creates more accurate SRCs (NRMSE = 3.7–8.8%, Percent Bias = −7.8%—9.4%), with the minimum-median method being the preferred n method. [ABSTRACT FROM AUTHOR]

Abstract Irrigated rice requires intense water management under typical agronomic practices. Cost effective tools to improve the efficiency and assessment of water use is a key need for industry and resource managers to scale ecosystem services. In this research we advance model‐based decomposition and machine learning to map inundated rice using time‐series polarimetric, L‐band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) observations. Simultaneous ground truth observations recorded water depth inundation during the 2019 crop season using instrumented fields across the study site in Arkansas, USA. A three‐component model‐based decomposition generated metrics representing surface‐, double bounce‐, and volume‐scattering along with a shape factor, randomness factor, and the Radar Vegetation Index (RVI). These physically meaningful metrics characterized crop inundation status independent of growth stage including under dense canopy cover. Machine learning (ML) comparisons employed Random Forest (RF) using the UAVSAR derived parameters to identify cropland inundation status across the region. Outcomes show that RVI, proportion of the double‐bounce within total scattering, and the relative comparison between the double‐bounce and the volume scattering have moderate to strong mechanistic ability to identify rice inundation status with Overall Accuracy (OA) achieving 75%. The use of relative ratios further helped mitigate the impacts of far range incidence angles. The RF approach, which requires training data, achieved a higher OA and Kappa of 88% and 71%, respectively, when leveraging multiple SAR parameters. Thus, the combination of physical characterization and ML provides a powerful approach to retrieving cropland inundation under the canopy. The growth of polarimetric L‐band availability should enhance cropland inundation metrics beyond open water that are required for tracking water quantity at field scale over large areas.

In August 2018, the southern Indian state of Kerala received unusually heavy rainfall leading to largescale flooding and destruction. Reliable flood inundation maps derived from remote sensing techniques help in flood disaster management activities. The freely available Sentinel-1A/B SAR data have the potential for flood inundation mapping due to its all-weather imaging capability. In this study, temporal dual-pol Sentinel-1 SAR data have been utilized. Single-date ALOS-2/PALSAR-2 commercial SAR data were also used to fill the gap between Sentinel-1 acquisitions during the peak flood-period. Two flood-mapping approaches, viz. rule-based classification in case of temporal SAR data and histogram-based thresholding approach in case of single-date imagery, were utilized in the study. Also, flood inundation mapping with different data constraints, i.e. availability of single-date and multi-date imagery has been analysed and discussed. The obtained results were validated with multiple data sources like survey data and secondary data from government agencies. An overall accuracy of 90.6% and a critical success index of 81.6% were achieved with the proposed rule-based classification approach. This study highlights the potential of the combination of Sentinel-1 and ALOS-2/PALSAR-2 data for flood inundation mapping. [ABSTRACT FROM AUTHOR]

Irrigated rice requires intense water management under typical agronomic practices. Cost effective tools to improve the efficiency and assessment of water use is a key need for industry and resource managers to scale ecosystem services. In this research we advance model‐based decomposition and machine learning to map inundated rice using time‐series polarimetric, L‐band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) observations. Simultaneous ground truth observations recorded water depth inundation during the 2019 crop season using instrumented fields across the study site in Arkansas, USA. A three‐component model‐based decomposition generated metrics representing surface‐, double bounce‐, and volume‐scattering along with a shape factor, randomness factor, and the Radar Vegetation Index (RVI). These physically meaningful metrics characterized crop inundation status independent of growth stage including under dense canopy cover. Machine learning (ML) comparisons employed Random Forest (RF) using the UAVSAR derived parameters to identify cropland inundation status across the region. Outcomes show that RVI, proportion of the double‐bounce within total scattering, and the relative comparison between the double‐bounce and the volume scattering have moderate to strong mechanistic ability to identify rice inundation status with Overall Accuracy (OA) achieving 75%. The use of relative ratios further helped mitigate the impacts of far range incidence angles. The RF approach, which requires training data, achieved a higher OA and Kappa of 88% and 71%, respectively, when leveraging multiple SAR parameters. Thus, the combination of physical characterization and ML provides a powerful approach to retrieving cropland inundation under the canopy. The growth of polarimetric L‐band availability should enhance cropland inundation metrics beyond open water that are required for tracking water quantity at field scale over large areas. Key Points: Cropland inundation assessment has largely focused on open waterQuad polarized L‐band SAR can help detect under canopy inundationThe underlying physical mechanisms driving scattering responses and machine learning algorithms had similar outcomes [ABSTRACT FROM AUTHOR]

Floods are extreme events affecting millions of people worldwide and causing loss worth billions. The magnitude and frequency of floods are likely to increase with altered climate, and developing countries tend to suffer the most because of low resilience and adaptive capacity. This research aimed to analyze existing and preferred future flood adaptation strategies in a flood-prone West Rapti River (WRR) Basin of Nepal, using hydrological analysis and flood modelling, and a social survey of 240 households (HHs) and several focus group discussions (FGDs). The specific objectives were to (1) understand the rainfall-flood behaviour of the basin in a simplistic way, (2) carry out flood modelling to generate inundation maps for informing the local people, and (3) identify flood adaptation strategies based on people's perception. Flood inundation maps are generated for four scenarios based on return periods: scenario I (2 years), scenario II (20 years), scenario III (50 years), and scenario IV (100 years). Results show that the southern parts of three rural municipalities (Duduwa, Narainapur, and Rapti Sonari) get inundated almost every year irrespective of the flood magnitude. This information was presented to local communities before administering the HH survey and FGDs so that they could make informed decisions. During the survey, the preference of people's adaptation strategies for the four flood scenarios was explored and prioritized. Our findings suggest that peoples' thoughts and preferences for adaptation strategies changed with exposure to flood magnitudes. For example, "bamboo mesh with sand filled bags"—simplest and least expensive adaptation strategy—was preferred for a less severe flood while a complex and expensive technique "reservoir/flood regulating structures" was preferred for a devastating flood scenario. Thus, this study has highlighted firstly, the importance of inundation maps to understand and inform the local people about floods and their impacts; and secondly, the value of information to the people enabling them to make informed decisions. The novelty of this empirical study lies in a multi-disciplinary assessment framework which integrates scientific information, stakeholder knowledge, and local people's perceptions of flood risks and adaptation strategies for the future. Such an approach of hydro-social analysis has the potential for replication in flood-prone regions globally, with similar bio-physical and socio-economic conditions. [ABSTRACT FROM AUTHOR]

There is a big shortage of drinking water in the western Suez Gulf in Egypt. Fortunately, wind energy is available to cope with the required power for desalination. In previous works the area was subjected to Multi-Criteria Analysis process using a DEM implemented into a GIS environment. For each watershed, a Weighted Standardized Risk Factor (WSRF) was deduced based on the correlation factor for all the main morphological parameters which were obtained from the "HEC-HMS" model results. Former thought of defining the safe and unsafe watersheds based on global scale and dependent only on (WSRF), is nowadays criticized as a watershed with high value for (WSRF) could have safe specific areas for the wind turbines' establishment. A 2D hydrodynamic model was applied on a Macro Scale to deduce inundation maps then were overlaid with wind speeds maps to define the most appropriate locations for wind turbines. [ABSTRACT FROM AUTHOR]

The 2011 Tohoku earthquake damaged many small earthen dams in Japan, causing flood damage and human casualties. Since then, the necessity for developing accurate inundation hazard maps for areas at risk has been highlighted. We constructed a numerical model using two-dimensional shallow-water equations with the finite element method to simulate such flood flows and tested this approach by simulating the 2011 breach of the Aotashin-ike dam and subsequent flow into the downslope Ohya-ike dam. In addition, we investigated the effect of different inflow hydrographs at a collapsed dam's breaching point on simulated flood flow comparing three different peak timings. While the spatial extent of the simulated inundation area was not affected, the timing of flood arrival and the simulated water depth in the drainage channel were moderately affected by the inflow hydrograph; the maximum water level in the drainage channel increased with a peak timing delay. The methodology presented here can determine further efforts to better define inundation risk for Japan's small earthen dams and provides practical procedures to account for downstream topographic features such as spillways and drainage channels. [ABSTRACT FROM AUTHOR]

Disaster recovery poses unique challenges for residents reliant on private wells. Flooding events are drivers of microbial contamination in well water, but the relationship observed between flooding and contamination varies substantially. Here, we investigate the performance of different flood boundaries─the FEMA 100 year flood hazard boundary, height above nearest drainage-derived inundation extents, and satellite-derived extents from the Dartmouth Flood Observatory─in their ability to identify well water contamination following Hurricane Florence. Using these flood boundaries, we estimated about 2600 wells to 108,400 private wells may have been inundated─over 2 orders of magnitude difference based on boundary used. Using state-generated routine and post-Florence testing data, we observed that microbial contamination rates were 7.1-10.5 times higher within the three flood boundaries compared to routine conditions. However, the ability of the flood boundaries to identify contaminated samples varied spatially depending on the type of flooding (e.g., riverine, overbank, coastal). While participation in testing increased after Florence, rates were overall still low. With <1% of wells tested, there is a critical need for enhanced well water testing efforts. This work provides an understanding of the strengths and limitations of inundation mapping techniques, which are critical for guiding postdisaster well water response and recovery.

729Sentinel-1 data are an alternative for monitoring flooded inland surfaces during cloudy periods. Supervised classification approaches with a single-trained model for the entire image demonstrate poor accuracy due to confusing backscatter conditions of the inundated areas in relation with the prevailing land cover features. This study follows instead a pixel-centric approach, which exploits the varying backscatter values of each pixel through a time series of Sentinel-1 images to train local Random Forest classification models per 3×3 pixels, and classifies each pixel in the target Sentinel-1 image, accordingly. Reference training data is retrieved from the timely close Sentinel-2-derived inundation maps. This study aims to identify the furthest mean day difference between the target Sentinel-1 image and available Sentinel-2 high accurate inundation maps (kappa coefficient—k > 0.9) that allows for the estimation of credible inundation maps for the Sentinel-1 target date. Various combinations of Sentinel-2 and Sentinel-1 training datasets are examined. The evaluation for eight target dates confirms that a Sentinel-1 inundation map with a k of 0.75 on average can be generated, when mean day difference is less than 30 days. The increment of the considered Sentinel-2 maps allows for the estimation of Sentinel-1 inundation maps with higher accuracy. [ABSTRACT FROM AUTHOR]

Real‐time flood inundation mapping is vital for emergency response to help protect life and property. Inundation mapping transforms rainfall forecasts into meaningful spatial information that can be utilized before, during, and after disasters. While inundation mapping has traditionally been conducted on a local scale, automated algorithms using topography data can be utilized to efficiently produce flood maps across the continental scale. The Height Above the Nearest Drainage method can be used in conjunction with synthetic rating curves (SRCs) to produce inundation maps, but the performance of these inundation maps needs to be assessed. Here we assess the accuracy of the SRCs and calculate statistics for comparing the SRCs to rating curves obtained from hydrodynamic models calibrated against observed stage heights. We find SRCs are accurate enough for large‐scale approximate inundation mapping while not as accurate when assessing individual reaches or cross sections. We investigate the effect of terrain and channel characteristics and observe reach length and slope predict divergence between the two types of rating curves, and SRCs perform poorly for short reaches with extreme slope values. We propose an approach to recalculate the slope in Manning's equation as the weighted average over a minimum distance and assess accuracy for a range of moving window lengths. Research Impact Statement: The National Water Center will provide on‐demand flood maps for affected areas before, during, and after disaster events. We assess the accuracy of the method used to produce these flood maps. [ABSTRACT FROM AUTHOR]

Maximum flood extent—a key data need for disaster response and mitigation—is rarely quantified due to storm-related cloud cover and the low temporal resolution of optical sensors. While change detection approaches can circumvent these issues through the identification of inundated land and soil from post-flood imagery, their accuracy can suffer in the narrow and complex channels of increasingly developed and heterogeneous floodplains. This study explored the utility of the Operational Land Imager (OLI) and Independent Component Analysis (ICA) for addressing these challenges in the unprecedented 2013 Flood along the Colorado Front Range, USA. Pre- and post-flood images were composited and transformed with an ICA to identify change classes. Flooded pixels were extracted using image segmentation, and the resulting flood layer was refined with cloud and irrigated agricultural masks derived from the ICA. Visual assessment against aerial orthophotography showed close agreement with high water marks and scoured riverbanks, and a pixel-to-pixel validation with WorldView-2 imagery captured near peak flow yielded an overall accuracy of 87% and Kappa of 0.73. Additional tests showed a twofold increase in flood class accuracy over the commonly used modified normalized water index. The approach was able to simultaneously distinguish flood-related water and soil moisture from pre-existing water bodies and other spectrally similar classes within the narrow and braided channels of the study site. This was accomplished without the use of post-processing smoothing operations, enabling the important preservation of nuanced inundation patterns. Although flooding beneath moderate and sparse riparian vegetation canopy was captured, dense vegetation cover and paved regions of the floodplain were main sources of omission error, and commission errors occurred primarily in pixels of mixed land use and along the flood edge. Nevertheless, the unsupervised nature of ICA, in conjunction with the global availability of Landsat imagery, offers a straightforward, robust, and flexible approach to flood mapping that requires no ancillary data for rapid implementation. Finally, the spatial layer of flood extent and a summary of impacts were provided for use in the region’s ongoing hydrologic research and mitigation planning.

Highlights • Interpolation methods are evaluated to perform spatial analysis of sea level change. • Bangladesh, Seychelles, and Cocos Islands have high rates of sea level rise. • Vertical land motion is an important factor affecting sea level changes. • There is a strong relationship between air temperature and sea level rise. • Coastal flooding impacts are examined through flood inundation mapping. Abstract The Indian Ocean which is home to many islands and the low-lying coastal zones have attracted considerable attention due to regional sea level changes. In this study, we examine regional changes in sea level of the Indian Ocean and potential coastal flooding impacts by using tide gauge data. Various interpolation methods are evaluated to predict values at locations where data is unavailable. Based on the cross-validation analysis, the radial basis function is identified as the most optimal interpolation method and is used to analyze the spatial patterns of sea level changes. The analysis reveals that Bangladesh, Seychelles, and Cocos (Keeling) Islands have relatively high rates of sea level rise. These regions would thus be highly vulnerable to coastal flooding induced by the accelerating sea level rise in future decades, posing significant threats to coastal communities and ecosystems. Flooding impacts are examined through inundation mapping in a geographic information system (GIS) environment. In addition, relationships between regional factors (sea surface temperature, air temperature, and vertical land motions) affecting sea level rise are investigated. Our findings indicate that vertical land motion is an important factor affecting sea level changes for the regions of Seychelles and Cocos Islands. There is a strong relationship between air temperature and sea level rise for all studied regions. This study is a first attempt to examine regional changes in sea level of the Indian Ocean and potential coastal flooding impacts by using tide gauge data. The methods used in this study can be applied to other coastal regions around the world. [ABSTRACT FROM AUTHOR]

Abstract: We present a Digital Elevation Model‐based hydrologic analysis methodology for continental flood inundation mapping (CFIM), implemented as a cyberGIS scientific workflow in which a 1/3rd arc‐second (10 m) height above nearest drainage (HAND) raster data for the conterminous United States (CONUS) was computed and employed for subsequent inundation mapping. A cyberGIS framework was developed to enable spatiotemporal integration and scalable computing of the entire inundation mapping process on a hybrid supercomputing architecture. The first 1/3rd arc‐second CONUS HAND raster dataset was computed in 1.5 days on the cyberGIS Resourcing Open Geospatial Education and Research supercomputer. The inundation mapping process developed in our exploratory study couples HAND with National Water Model forecast data to enable near real‐time inundation forecasts for CONUS. The computational performance of HAND and the inundation mapping process were profiled to gain insights into the computational characteristics in high‐performance parallel computing scenarios. The establishment of the CFIM computational framework has broad and significant research implications that may lead to further development and improvement of flood inundation mapping methodologies. [ABSTRACT FROM AUTHOR]

Abstract: The objective of this study was to determine the accuracy of five different digital image processing techniques to map flood inundation extent with Landsat 8–Operational Land Imager satellite imagery. The May 2016 flooding event in the Hempstead region of the Brazos River, Texas is used as a case study for this first comprehensive comparison of classification techniques of its kind. Five flood water classification techniques (i.e., supervised classification, unsupervised classification, delta‐cue change detection, Normalized Difference Water Index [NDWI], modified NDWI [MNDWI]) were implemented to characterize flooded regions. To identify flood water obscured by cloud cover, a digital elevation model (DEM)–based approach was employed. Classified floods were compared using an Advanced Fitness Index to a “reference flood map” created based on manual digitization, as well as other data sources, using the same satellite image. Supervised classification yielded the highest accuracy of 86.4%, while unsupervised, MNDWI, and NDWI closely followed at 79.6%, 77.3%, and 77.1%, respectively. Delta‐cue change detection yielded the lowest accuracy with 70.1%. Thus, supervised classification is recommended for flood water classification and inundation map generation under these settings. The DEM‐based approach used to identify cloud‐obscured flood water pixels was found reliable and easy to apply. It is therefore recommended for regions with relatively flat topography. [ABSTRACT FROM AUTHOR]

River flooding has been causing extensive losses to life and property, which is a serious concern worldwide. To minimize these losses, suitable planning and management practices are required for the floodplain mapping. Flash floods occur almost every year in the deltaic region of Brahmani and Baitarani river basins in India, during the monsoon season. Generally, 1D modelling is considered as a regular practice. But nowadays, model formulations include 1D for the representation of river channels and 2D for representing river floodplains. In the absence of uniform observations, a hybrid model (1D-2D coupled model) has been developed for this deltaic region to identify the extent of inundation and its depth during the flooding, since 1D models alone do not provide detailed information of flooding. Thus, a well-known 2D river hydrodynamic model iRIC was externally coupled with 1D (SWAT and SWMM) models to simulate and visualize flood scenarios and to identify the flood-prone areas. The hydrological model SWAT was calibrated and validated for Brahmani river deltaic basin, with the observed discharge data available. However for Baitarani river basin, observed flow data were missing and only gauge data were available at few monitoring stations. Hence, for Baitarani river basin, the SWMM model was developed and calibrated with the help of Monte Carlo method. Finally, the SWAT- and SWMM-based tributary stream flow outputs were fed together into the iRIC hydrodynamic model as input for flood inundation mapping. The discharge and water gauge data were used for the calibration and validation. The results obtained from the coupled model were found to be in good agreement with the observed data (RMSE value is 0.77 and 0.79 during calibration and validation, respectively), which enabled identification of the flood-prone areas. The developed model may be used as a tool for effective planning and management of natural disasters such as flash floods. [ABSTRACT FROM AUTHOR]

Daily, or more frequent, maps of surface water have important applications in environmental and water resource management. In particular, surface water maps derived from remote sensing imagery play a useful role in the derivation of spatial inundation patterns over time. MODIS data provide the most realistic means to achieve this since they are daily, although they are often limited by cloud cover during flooding events, and their spatial resolutions (250–1000 m pixel) are not always suited to small river catchments. This paper tests the suitability of the MODIS sensor for identifying flood events through comparison with streamflow and rainfall measurements at a number of sites during the wet season in Northern Australia. This is done using the MODIS Open Water Likelihood (OWL) algorithm which estimates the water fraction within a pixel. On a temporal scale, cloud cover often inhibits the use of MODIS imagery at the start and lead-up to the peak of a flood event, but there are usually more cloud-free data to monitor the flood’s recession. Particularly for smaller flood events, the MODIS view angle, especially when the view angle is towards the sun, has a strong influence on total estimated flood extent. Our results showed that removing pixels containing less than 6% water can eliminate most commission errors when mapping surface water. The exception to this rule was for some spectrally dark pixels occurring along the edge of the MODIS swath where the relative azimuth angle (i.e., angle between the MODIS’ and sun’s azimuth angle) was low. Using only MODIS OWL pixels with a low view angle, or a range distance of less than 1000 km, also improves the results and minimizes multi-temporal errors in flood identification and extent. Given these limitations, MODIS OWL surface water maps are sensitive to the dynamics of water movement when compared to streamflow data and does appear to be a suitable product for the identification and mapping of inundation extent at large regional/basin scales.

Flooding periodicity is crucial for biomass production and ecosystem functions in wetland areas. Local monitoring networks may be enriched by spaceborne derived products with a temporal resolution of a few days. Unsupervised computer vision techniques are preferred, since human interference and the use of training data may be kept to a minimum. Recently, a novel automatic local thresholding unsupervised methodology for separating inundated areas from non-inundated ones led to successful results for the Doñana Biosphere Reserve. This study examines the applicability of this approach to Camarque Biosphere Reserve, and proposes alternatives to the original approach to enhance accuracy and applicability for both Camargue and Doñana wetlands in a scientific quest for methods that may serve accurately biomes at both protected areas. In particular, it examines alternative inputs for automatically estimating thresholds while applying various algorithms for estimating the splitting thresholds. Reference maps for Camargue are provided by local authorities, and generated using Sentinel-2 Band 8A (NIR) and Band 12 (SWIR-2). The alternative approaches examined led to high inundation mapping accuracy. In particular, for the Camargue study area and 39 different dates, the alternative approach with the highest overall Kappa coefficient is 0.84, while, for the Doñana Biosphere Reserve and Doñana marshland (a subset of Doñana Reserve) and 7 different dates, is 0.85 and 0.94, respectively. Moreover, there are alternative approaches with high overall Kappa for all areas, i.e., at 0.79 for Camargue, over 0.91 for Doñana marshland, and over 0.82 for Doñana Reserve. Additionally, this study identifies the alternative approaches that perform better when the study area is extensively covered by temporary flooded and emergent vegetation areas (i.e., Camargue Reserve and Doñana marshland) or when it contains a large percentage of dry areas (i.e., Doñana Reserve). The development of credible automatic thresholding techniques that can be applied to different wetlands could lead to a higher degree of automation for map production, while enhancing service utilization by non-trained personnel.

A combined hydrological and hydraulic model is presented for flood prediction in Vietnam. This model is applied to the Huong river basin as a test case study. Observed flood flows and water surface levels of the 2002-2005 flood seasons are used for model calibration, and those of the 2006-2007 flood seasons are used for validation of the model. The physically based distributed hydrologic model WetSpa is used for predicting the generation and propagation of flood flows in the mountainous upper sub-basins, and proves to predict flood flows accurately. The Hydrologic Engineering Center River Analysis System (HEC-RAS) hydraulic model is applied to simulate flood flows and inundation levels in the downstream floodplain, and also proves to predict water levels accurately. The predicted water profiles are used for mapping of inundations in the floodplain. The model may be useful in developing flood forecasting and early warning systems to mitigate losses due to flooding in Vietnam. [ABSTRACT FROM AUTHOR]