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

Copyright of Jurnal Teknologi dan Rekayasa Sumber Daya Air is the property of Brawijaya University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The river Vamsadhara runs through the states of Andhra Pradesh (AP) and Odisha along a stretch of coastline that is prone to cyclonic storms. Riverine flow, along with cyclonic gales, is a crucial issue for most of India's coastal districts. As a result, a developed flood forecasting model is required to mitigate the danger of flooding to a certain level. For evaluating maximum water depth and inundation of flood plains induced by storms under existing and future land use circumstances, hydrological and hydraulic models are more prominently studied. The current work employs hydrological and hydrodynamic models to simulate a real flood inundation model for unsteady flow conditions. The technique involves the processing of the digital elevation models (DEM) to generate flood hydrographs that serve as boundary conditions for the reaches. In the present analysis, Manning's roughness coefficient is used as a sensitive parameter. Calibration and validation of extreme flood events that occurred in 2006, 2010, and 2013 using observed water levels yielded good results, with model performances of 0.79, 0.68, and 0.84, respectively. The findings of the generated depth, velocity, and flood extent maps are presented. These maps can be used to plan for flood disasters and long-term watershed management. HIGHLIGHTS To develop a calibrated rainfall-runoff model for a large river basin.; Interactions between hydrologic and hydrodynamic models.; Simulations of two-dimensional unsteady flow hydrodynamic model.; The high-risk zones generated by this model may help to plan flood prevention, mitigation, relief, and restoration efforts by related government agencies.;

This study presents the preparation of floodwater depth, the velocity of the flood, and water surface elevation profile with the application of unsteady flow 2D Hydrodynamic models and GIS. Flood frequency analysis is used for the prediction of peak flow. The water depth up to 1m inundated a large amount of area of 52.5134 km², 52.4756 km² and 52.4403 km² for a 10-, 100-, and 1000 years return period respectively. Much amount of inundated area covered with a velocity value up to 0.15 m/s in all flood events, which is 53.207, 52.656, and 52.064km² for flood events of 10, 100, and 1000-year respectively. The highest water surface elevation in the 10, 100, and 1000-year flood events was observed between 1785 and 1787m with an inundated area of 58.806, 58.843, and 58.827km² respectively. Finally, the result of this study is useful for early warnings, municipal planning purposes, emergency action plans, flood insurance rates, and ecological studies in the research area. [ABSTRACT FROM AUTHOR]

Climate change and intensified human activities are exacerbating the frequency and severity of extreme precipitation events, necessitating more precise and timely flood risk assessments. Traditional models often fail to dynamically and accurately assess flood risks due to their static nature and limited handling of spatiotemporal variations. This study confronts these challenges head-on by developing a novel coupled hydrological-hydrodynamic model integrated with a Block-wise use of the TOPMODEL (BTOP) and the Rainfall-Runoff-Inundation (RRI) model. This integrated approach enables the rapid acquisition of high-precision flood inundation simulation results across large-scale basins, addressing a significant gap in dynamic flood risk assessment and zoning. A critical original achievement of this research lies in developing and implementing a comprehensive vertical-horizontal combined weighting method that incorporates spatiotemporal information for dynamic evaluation indicators, significantly enhancing the accuracy and rationality of flood risk assessments. This innovative method successfully addresses the challenges posed by objective and subjective weighting methods, presenting a balanced and robust framework for flood risk evaluation. The findings from the Min River Basin in China, as a case study, demonstrate the effectiveness of the BTOP-RRI model in capturing the complex variations in runoff and the detailed simulations of flood processes. The model accurately identifies the timing of these peaks, offering insights into the dynamic evolution of flood risks and providing a more precise and timely assessment tool for policymakers and disaster management authorities. The flood risk assessment results demonstrate good consistency with the actual regional conditions. In particular, high-risk areas exhibit distinct characteristics along the river channel, with the distribution area significantly increasing with a sudden surge in runoff. Intense precipitation events expand areas classified as moderate and high risk, gradually shrinking as precipitation levels decrease. This study significantly advances flood risk assessment methodologies by integrating cutting-edge modeling techniques with comprehensive weighting strategies. This is essential for improving the scientific foundation and decision-making processes in regional flood control efforts. • Enhances large-scale watershed flood predictions using integrated hydrological and hydrodynamic modeling. • Introduces a vertical-horizontal subjective-objective combination weighting method for accurate flood risk evaluation. • Offers detailed analysis of flood risk changes over time in Min River Basin. [ABSTRACT FROM AUTHOR]

An accurate flood mapping is a non-structural measure that helps to reduce damages and minimizes losses. The availability of large-scale and good quality topographic input data is of great concern for such studies. The present study involves the integration of Cartosat-1 stereo image pairs (remotely sensed) and ground truth points (surveyed) to develop digital elevation model (DEM) for the Mahanadi delta in India. The integration was performed for two cases: (i) A single Cartosat-1 image at a time and (ii) Aggregation of Cartosat-1 images one after another. Both the cases involve different numbers of ground control points (GCPs) (to be integrated with Cartosat-1 images) to study the quality and suitability of DEMs for flood inundation modeling. The best one among the DEMs prepared for each Cartosat-1 image with different number of GCPs was further used for hydrodynamic modeling. The MIKE 11 model was calibrated for 2009 and validated for 2010 using cross-sections extracted from Cartosat-1 DEMs. Further, bathymetry for the MIKE 21 model was prepared using the best Cartosat-1 DEM. MIKE FLOOD model setup was prepared to simulate flood inundation for the year 2011. The results indicate that DEMs of reasonable quality can be generated by incorporating GCPs and rational polynomial coefficients. The areal extent of simulated flood using MIKE FLOOD is in reasonable agreement with the observed counterpart. [ABSTRACT FROM AUTHOR]

This study investigates the generation of spatially distributed roughness coefficient maps based on image analysis and the extent to which those roughness coefficient values affect the flood inundation modeling using different hydraulic/hydrodynamic modeling approaches ungauged streams. Unmanned Aerial Vehicle (UAV) images were used for the generation of high-resolution Orthophoto mosaic (1.34 cm/px) and Digital Elevation Model (DEM). Among various pixel-based and object-based image analyses (OBIA), a Grey-Level Co-occurrence Matrix (GLCM) was eventually selected to examine several texture parameters. The combination of local entropy values (OBIA method) with Maximum Likelihood Classifier (MLC; pixel-based analysis) was highlighted as a satisfactory approach (65% accuracy) to determine dominant grain classes along a stream with inhomogeneous bed composition. Spatially distributed roughness coefficient maps were generated based on the riverbed image analysis (grain size classification), the size-frequency distributions of river bed materials derived from field works (grid sampling), detailed land use data, and the usage of several empirical formulas that used for the estimation of Manning's n values. One-dimensional (1D), two-dimensional (2D), and coupled (1D/2D) hydraulic modeling approaches were used for flood inundation modeling using specific Manning's n roughness coefficient map scenarios. The validation of the simulated flooded area was accomplished using historical flood extent data, the Critical Success Index (CSI), and CSI penalization. The methodology was applied and demonstrated at the ungauged Xerias stream reach, Greece, and indicated that it might be applied to other Mediterranean streams with similar characteristics and flow conditions. [ABSTRACT FROM AUTHOR]

Reservoirs are fundamental human-built infrastructures that collect, store, and deliver fresh surface water in a timely manner for many purposes. Efficient reservoir operation requires policy makers and operators to understand how reservoir inflows are changing under different hydrological and climatic conditions to enable forecast-informed operations. Over the last decade, the uses of Artificial Intelligence and Data Mining [AI & DM] techniques in assisting reservoir streamflow subseasonal to seasonal forecasts have been increasing. In this study, Random Forest [RF), Artificial Neural Network (ANN), and Support Vector Regression (SVR) are employed and compared with respect to their capabilities for predicting 1 month-ahead reservoir inflows for two headwater reservoirs in USA and China. Both current and lagged hydrological information and 17 known climate phenomenon indices, i.e., PDO and ENSO, etc., are selected as predictors for simulating reservoir inflows. Results show (1) three methods are capable of providing monthly reservoir inflows with satisfactory statistics; (2) the results obtained by Random Forest have the best statistical performances compared with the other two methods; (3) another advantage of Random Forest algorithm is its capability of interpreting raw model inputs; (4) climate phenomenon indices are useful in assisting monthly or seasonal forecasts of reservoir inflow; and (5) different climate conditions are autocorrelated with up to several months, and the climatic information and their lags are cross correlated with local hydrological conditions in our case studies.

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Of many defining characteristics for a flood resilient city and its infrastructure networks, mitigating flooding impacts and recovering quickly to a pre-flood state are to be considered of high importance. With a likely increase in the frequency and intensity of future heavy precipitation and flooding events in Europe, the vulnerability of the electrical distribution network of Maxvorstadt, Munich will also increase. These facts justify the need for quantifying how the electrical distribution network would respond to flooding, and more so, how stakeholders can better prepare for such an event. For a synthetic electrical distribution network of Maxvorstadt, the timing and location of network components failure due to flooding and affected persons without power have been computed for a combination of realistic future flooding events via the Electrical Network Flood Resilience Model developed in this study. It has been learned that most buildings, and therefore their inhabitants, lose power due to the failure of a specific component, Medium Voltage—Low Voltage transformer buses, and that flood risk solutions should focus on protecting network components from inundation to ensure its functionality through flooding events. Solutions like dry proofing such components before severe flooding occurs is recommended for several neighborhoods analyzed in this study.

This study investigates the generation of spatially distributed roughness coefficient maps based on image analysis and the extent to which those roughness coefficient values affect the flood inundation modeling using different hydraulic/hydrodynamic modeling approaches ungauged streams. Unmanned Aerial Vehicle (UAV) images were used for the generation of high-resolution Orthophoto mosaic (1.34 cm/px) and Digital Elevation Model (DEM). Among various pixel-based and object-based image analyses (OBIA), a Grey-Level Co-occurrence Matrix (GLCM) was eventually selected to examine several texture parameters. The combination of local entropy values (OBIA method) with Maximum Likelihood Classifier (MLC; pixel-based analysis) was highlighted as a satisfactory approach (65% accuracy) to determine dominant grain classes along a stream with inhomogeneous bed composition. Spatially distributed roughness coefficient maps were generated based on the riverbed image analysis (grain size classification), the size-frequency distributions of river bed materials derived from field works (grid sampling), detailed land use data, and the usage of several empirical formulas that used for the estimation of Manning’s n values. One-dimensional (1D), two-dimensional (2D), and coupled (1D/2D) hydraulic modeling approaches were used for flood inundation modeling using specific Manning’s n roughness coefficient map scenarios. The validation of the simulated flooded area was accomplished using historical flood extent data, the Critical Success Index (CSI), and CSI penalization. The methodology was applied and demonstrated at the ungauged Xerias stream reach, Greece, and indicated that it might be applied to other Mediterranean streams with similar characteristics and flow conditions.

Annual fluvial and pluvial floods happened in Lower Ping River Basin, Thailand. A flood hazard assessment approach was performed in the present study to evaluate flood impacts. The coupled 1D-2D hydrodynamic (HD) modeling package was applied for different return periods to investigate and classify the flood hazards. The 1D hydrodynamic model was calibrated for flood year 2011 at discharge and water level stations. The 2D hydrodynamic model was calibrated using observed flood map. Flood hazards were categorized based on critical flood depths for flooding scenarios of 2-, 5-, 10-, 25-, 50-, and 100-year return periods. The results showed that 28.8% of the total basin area (2423.8 km2) was flooded during the 100-year return period, and the maximum flood inundation area was observed in Kamphaengphet and Nakhon Sawan provinces. Mostly, the flooded area under each return period was categorized under high hazard, followed by low and medium, and the least area was classified under a very high hazard level. More than 40% of sub-districts' area of Kamphaengphet and Nakhon Sawan provinces were flooded, which is located along the Ping River, while less than 20% sub-districts' area was flooded in Tak Province. The government and local stakeholders need to consider structural and nonstructural measures to reduce flood impacts in three provinces, and priority-based areal flood management approaches should be considered during the implementation of flood mitigation measures. [ABSTRACT FROM AUTHOR]

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

Most flood inundation models do not come with an uncertainty analysis component chiefly because of the complexity associated with model calibration. Additionally, the fact that the models are both data- and compute-intensive, and since uncertainty results from multiple sources, adds another layer of complexity for model use. In the present study, flood inundation modeling was performed in the Godavari River Basin using the Hydrologic Engineering Center—River Analysis System 2D (HEC-RAS 2D) model. The model simulations were generated for six different scenarios that resulted from combinations of different geometric, hydraulic and hydrologic conditions. Thus, the resulted simulations account for multiple sources of uncertainty. The SRTM-30 m and MERIT-90 m Digital elevation Model (DEM), two sets of Manning’s roughness coefficient (Manning’s n) and observed and estimated boundary conditions, were used to reflect geometric, hydraulic and hydrologic uncertainties, respectively. The HEC-RAS 2D model ran in an unsteady state mode for the abovementioned six scenarios for the selected three flood events that were observed in three different years, i.e., 1986, 2005 and 2015. The water surface elevation (H) was compared in all scenarios as well as with the observed values at selected locations. In addition, ‘H’ values were analyzed for two different structures of the computational model. The average correlation coefficient (r) between the observed and simulated H values is greater than 0.85, and the highest r, i.e., 0.95, was observed for the combination of MERIT-90 m DEM and optimized (obtained via trial and error) Manning’s n. The analysis shows uncertainty in the river geometry information, and the results highlight the varying role of geometric, hydraulic and hydrologic conditions in the water surface elevation estimates. In addition to the role of the abovementioned, the study recommends a systematic model calibration and river junction modeling to understand the hydrodynamics upstream and downstream of the junction.

The implementation of various 2D numerical modeling techniques in the flood inundation analysis is vastly explored. However, understanding of the flood inundation uncertainty of this technique still limited. This paper evaluates 2D hydraulic models (HEC-RAS) in term of ability to simulate the surface flow of the large scale physical model. A physical model was developed based on high-resolution DEM (0.5m resolution) data of Sungai Bertam with scale 1/25. A total area of 23 m x 7 m and 20.8 m channel length was constructed. The experimental physical model was run with various inflow value and used to calibrate and analyze the numerical simulation. Although the simulated result showed a good agreement with the physical model experiment, some uncertainty still needs to consider. This uncertainty happens due to the change in channel geometry which leads to the turbulence phenomenon. [ABSTRACT FROM AUTHOR]

Highlights • Detailed flood simulation for a whole city is achieved with parallel computing and upscaling. • Hourly flood simulations are completed in a matter of seconds to minutes. • Subcritical, supercritical and transcritical flows and compound flooding can be simulated. Abstract Simulation of flood inundation at metric resolution is important for making hazard information useful to a wide range of end-users involved in flood risk management, and addressing the alarming increase in flood losses that have been observed over recent decades. However, high data volumes and computational demands make this challenging over large spatial extents comparable to the metropolitan areas of major cities where flood impacts are concentrated, especially for time-sensitive applications such as forecasting and repetitive simulation for uncertainty assessment. Additionally, several factors present difficulties for numerical solvers including combinations of steep and flat topography that promote transcritical flows, the need to resolve flow in relatively narrow features such as drainage channels and roadways in urban areas which channel flood water during extreme events, and the need to depict compound hazards resulting from the interaction of pluvial, fluvial and coastal flooding. A new flood inundation model is presented here to address these challenges. The Parallel Raster Inundation Model (PRIMo) solves the shallow-water equations on an upscaled grid that is far coarser than the underlying raster digital topographic model (DTM), and uses a subgrid modeling approach so that the solution benefits from DTM-scale topographic data. Additionally, an approximate Riemann solver is applied in an innovative way to integrate fluxes between cells, as needed to update the solution by the finite volume method, which makes the method applicable to subcritical, supercritical and transcritical flows. PRIMo is implemented using a two-dimensional domain decomposition approach to Single Process Multiple Data (SPMD) parallel computing, and overlapping communications and computations are implemented to yield ideal parallel scaling for well-balanced test cases. With both a subgrid model and ideal parallel scaling, the model can scale to meet the demands of any application. Several benchmarks are presented to demonstrate predictive skill and the potential for timely, whole-city, metric-resolution flooding simulations. Limitations of the methods and opportunities for improvements are also presented. [ABSTRACT FROM AUTHOR]

One approach for performing uncertainty assessment in flood inundation modeling is to use an ensemble of models with different conceptualizations, parameters, and initial and boundary conditions that capture the factors contributing to uncertainty. However, the high computational expense of many hydraulic models renders their use impractical for ensemble forecasting. To address this challenge, we developed a rating curve library method for flood inundation forecasting. This method involves pre-running a hydraulic model using multiple inflows and extracting rating curves, which prescribe a relation between streamflow and stage at various cross sections along a river reach. For a given streamflow, flood stage at each cross section is interpolated from the pre-computed rating curve library to delineate flood inundation depths and extents at a lower computational cost. In this article, we describe the workflow for our rating curve library method and the Rating Curve based Automatic Flood Forecasting ( RCAFF) software that automates this workflow. We also investigate the feasibility of using this method to transform ensemble streamflow forecasts into local, probabilistic flood inundation delineations for the Onion and Shoal Creeks in Austin, Texas. While our results show water surface elevations from RCAFF are comparable to those from the hydraulic models, the ensemble streamflow forecasts used as inputs to RCAFF are the largest source of uncertainty in predicting observed floods. [ABSTRACT FROM AUTHOR]

Inflow discharge and outflow stage estimates for hydraulic flood models are generally derived from river gauge data. Uncertainties in the measured inflow data and the neglect of rainfall-runoff contributions to the modeled domain downstream of the gauging locations can have a significant impact on these estimated 'whole reach' inflows and consequently on flood predictions. In this study, a method to incorporate rating curve uncertainty and local rainfall-runoff dynamics into the predictions of a reach-scale flood model is proposed. The methodology is applied to the July 2007 floods of the River Severn in UK. Discharge uncertainty bounds are generated applying a nonparametric local weighted regression approach to stage-discharge measurements for two gauging stations. Measured rainfall downstream from these locations is used as input to a series of subcatchment regional hydrological model to quantify additional local inflows along the main channel. A regional simplified-physics hydraulic model is then applied to combine these contributions and generate an ensemble of discharge and water elevation time series at the boundaries of a local-scale high complexity hydraulic model. Finally, the effect of these rainfall dynamics and uncertain boundary conditions are evaluated on the local-scale model. Accurate prediction of the flood peak was obtained with the proposed method, which was only possible by resolving the additional complexity of the extreme rainfall contributions over the modeled area. The findings highlight the importance of estimating boundary condition uncertainty and local rainfall contributions for accurate prediction of river flows and inundation at regional scales. [ABSTRACT FROM AUTHOR]

Where high-resolution topographic data are available, modelers are faced with the decision of whether it is better to spend computational resource on resolving topography at finer resolutions or on running more simulations to account for various uncertain input factors (e.g., model parameters). In this paper we apply global sensitivity analysis to explore how influential the choice of spatial resolution is when compared to uncertainties in the Manning's friction coefficient parameters, the inflow hydrograph, and those stemming from the coarsening of topographic data used to produce Digital Elevation Models (DEMs). We apply the hydraulic model LISFLOOD-FP to produce several temporally and spatially variable model outputs that represent different aspects of flood inundation processes, including flood extent, water depth, and time of inundation. We find that the most influential input factor for flood extent predictions changes during the flood event, starting with the inflow hydrograph during the rising limb before switching to the channel friction parameter during peak flood inundation, and finally to the floodplain friction parameter during the drying phase of the flood event. Spatial resolution and uncertainty introduced by resampling topographic data to coarser resolutions are much more important for water depth predictions, which are also sensitive to different input factors spatially and temporally. Our findings indicate that the sensitivity of LISFLOOD-FP predictions is more complex than previously thought. Consequently, the input factors that modelers should prioritize will differ depending on the model output assessed, and the location and time of when and where this output is most relevant. [ABSTRACT FROM AUTHOR]

Coupled Hydrologic & Hydraulic (H&H) models have been widely applied for flood simulations, yet the modern H&H models suffer from one-way and weak coupling and particularly disregarded run-on infiltration, which could compromise the model accuracy. In this study, we assess the H&H model performance with and without re-infiltration process in extreme flooding events. Results highlight that the re-infiltration process should not be disregarded even in extreme flood simulations. Saturated hydraulic conductivity and antecedent soil moisture are found to be the prime contributors to such differences. For the Hurricane Harvey event, the model performance is verified against stream gauges and high water marks, from which the re-infiltration scheme increases the Nash Sutcliffe Efficiency score by 140% on average and reduces maximum depth differences by 17%. Meanwhile, the recent update of the CREST-iMAP model Version 1.1, which incorporates two-way coupling and re-infiltration scheme, is released for public access. • A coupled hydrologic-hydraulic model takes re-infiltration scheme into account. • The advantages of re-infiltration are quantified and discussed for extreme events. • We advocate the importance of re-infiltration even in extreme events. [ABSTRACT FROM AUTHOR]

Among the various factors that contribute to flood risk, heavy storms, inadequate storm drainage systems, and the concentration of population and assets have usually been considered to be fundamental factors affecting flood damage. Climate change is also a real threat, bringing heavier and more frequent storms. This study presents a methodology for comparatively evaluating the impact of the flood risk factors using a GIS-based flood damage prediction model (FDPM). The FDPM calculates flood inundation depths using the XP-SWMM routine and monetary flood damages using a flood damage estimation model for various storms and catchment conditions. The concept of flood risk in this context is defined as the product of flood damage and the probability of its occurrence. This study produces a flood risk structure in a risk assessment framework. The method is applied to the Kiba drainage area in Tokyo, Japan. The study gives a quantitative evaluation of the changes in flood risk due to risk factors such as increase in asset values, flood control, and climate change using a flood risk impact factor (FRIF). The FRIF is introduced as an index to evaluate the impact of various sources of increased or reduced flood risk to society. [ABSTRACT FROM AUTHOR]

Abstract: Flood control is the key issue when making decisions for dam decommissioning in China, especially in urban areas. The Heiwa Dam is a small aging dam that does not serve its original purpose. The dam owner proposed to decommission it. However, the local water resource managers opposed to this proposal, because they concern the potential changes to flood regime in the city. In order to identify flood inundation changes due to the dam decommissioning, 2D MIKE21 HD with 10-m spatial resolution was used to model the flood inundation for the decommissioning scenario and existing condition. The model was calibrated using the roughness coefficient. Modeling results showed that decommissioning of the Heiwa dam would neither significantly increase flood inundation area nor water depth at 50-yr flood event. Compared with existing-condition, only 1% of the whole inundation area had significant increase in inundation depth with absolute increase from 0.15 to 0.35 m and percentage changes larger than 30%. Modeling results indicated flow constriction due to the limited conveyance capacity of sewer system in the city had greater impact on flooding and flood control than the Heiwa Dam itself. [Copyright &y& Elsevier]

Abstract: Modeling of dam-break flooding in an urban residential area in southern California is presented. Modeling is performed using BreZo, an unstructured grid, Godunov-type, finite volume model that solves the shallow-water equations. The model uses terrain data from a 1.5m Light Detection and Ranging (LiDAR) Digital Terrain Model (DTM) and contour data depicting the reservoir and breach geometry. A spatially distributed Manning coefficient based on a landcover classification derived from digital orthophotos and vector data (e.g., parcel outlines) is also used, and the interception of flow by storm drains is modeled with sink terms in the 2D continuity equation. The model is validated with flood extent and stream flow measurements, and a sensitivity analysis is completed to identify the necessary level of data and model complexity for accuracy purposes. Results show street depressions in the land surface should be resolved by the computational mesh for flood extent and stream flow accuracy. A ca. 5m resolution mesh that spans streets by approximately 3 cells achieves a good balance between accuracy and computational effort. Results also show that heterogeneous resistance is important for stream flow accuracy, and the interception of overland flow by storm sewers is important for flood extent accuracy. The sensitivity of predictions to several additional factors such as the reservoir level, breach geometry and DTM source (LiDAR, National Elevation Data, Shuttle Radar Topography Mission Data) is also reported. [Copyright &y& Elsevier]

This paper presents an innovative deep learning (DL) framework to (a) automatically identify river geometry and flood extent, and (b) predict river flooding depth. To do that, U-Net, an advanced convolutional neural network (CNN), was modified and given the designation of U-Net River. With the modification, the model received an input composite image with two bands of ground elevation and flooding discharge, and the output was water depth. The model was trained and validated based on the outputs from iRIC (a two-dimensional hydraulic model) for a segment of the Green River in the state of Utah. The results showed that the U-Net River could identify the river shape and wetted areas for flooded regions automatically. The maximum difference of predicted river depth obtained from U-Net River and the one obtained from the hydraulic model was 2.7 m. This result suggests a 29% improvement in prediction of the maximum flood depth in the river. • U-Net River , could successfully predict river flooding depth and extent. • The model accurately and automatically detected river geometry and shape. • The model is more objective as it minimized human involvement in flood prediction. • The model decreased the error in predicted river maximum flooding depth by 29%. [ABSTRACT FROM AUTHOR]

This paper introduces an integrated model for flood loss estimation in a river basin. The model is the combination of a physically based distributed hydrologic model and a distributed flood loss estimation model. The hydrologic model considers major processes of the water cycle through physically based governing equations, which are solved to simulate the propagation of water in each of these processes. It is designed to consider the man-made flood control structures, such as river embankments, retarding basins, etc. which affect flooding characteristics. The loss estimation model is formulated based on stage-damage relationships between different flood inundation parameters and landuse features. It calculates the economic loss to different landuse features based on the simulated flood parameters obtained from the hydrologic model for any flood event.A case study illustrates the real world application of the integrated model to a medium size river basin in Japan, which is frequently affected by floods. The simulated river discharge and surface inundation by the flood model show good agreement with the observations. Urban flood loss simulated by the loss estimation model with the simulated flood parameters agrees with the estimated damage using post flood surveyed parameters. [Copyright &y& Elsevier]

Most flood inundation models do not come with an uncertainty analysis component chiefly because of the complexity associated with model calibration. Additionally, the fact that the models are both data- and compute-intensive, and since uncertainty results from multiple sources, adds another layer of complexity for model use. In the present study, flood inundation modeling was performed in the Godavari River Basin using the Hydrologic Engineering Center—River Analysis System 2D (HEC-RAS 2D) model. The model simulations were generated for six different scenarios that resulted from combinations of different geometric, hydraulic and hydrologic conditions. Thus, the resulted simulations account for multiple sources of uncertainty. The SRTM-30 m and MERIT-90 m Digital elevation Model (DEM), two sets of Manning's roughness coefficient (Manning's n) and observed and estimated boundary conditions, were used to reflect geometric, hydraulic and hydrologic uncertainties, respectively. The HEC-RAS 2D model ran in an unsteady state mode for the abovementioned six scenarios for the selected three flood events that were observed in three different years, i.e., 1986, 2005 and 2015. The water surface elevation (H) was compared in all scenarios as well as with the observed values at selected locations. In addition, 'H' values were analyzed for two different structures of the computational model. The average correlation coefficient (r) between the observed and simulated H values is greater than 0.85, and the highest r, i.e., 0.95, was observed for the combination of MERIT-90 m DEM and optimized (obtained via trial and error) Manning's n. The analysis shows uncertainty in the river geometry information, and the results highlight the varying role of geometric, hydraulic and hydrologic conditions in the water surface elevation estimates. In addition to the role of the abovementioned, the study recommends a systematic model calibration and river junction modeling to understand the hydrodynamics upstream and downstream of the junction. [ABSTRACT FROM AUTHOR]

Flood inundation modeling is one of the essential steps in flood hazard mapping. However, the desirable input and calibration data for model building and evaluation are not sufficient or unavailable in many rivers and floodplains of the world. A potential opportunity to fill this gap is offered nowadays by global earth observation data, which can be obtained freely (or at low cost), such as the shuttle radar topography mission (SRTM) and radar altimetry. However, the actual usefulness of these data is still poorly investigated. This study attempts to assess the value of SRTM topography and radar altimetry in supporting flood-level predictions in data-poor areas. To this end, a hydraulic model of a 150-km reach of the Danube River was built by using SRTM topography as input data and radar altimetry of the 2006 flood event as calibration data. The model was then used to simulate the 2007 flood event and evaluated against water levels measured in four stream gauge stations. Model evaluation allows the investigation of the usefulness and limitations of SRTM topography and radar altimetry in supporting hydraulic modeling of floods. [ABSTRACT FROM AUTHOR]