Flood Water Depth Research Articles

The Gondola: a boat to respond to the history, and the morpho-dynamics of the Venice Lagoon

Origin and evolution of the gondola have been reconstructed from historical and documentary written and pictorial sources (figures in books, engravings and paintings), in relation to the physical environment of the Lagoon, its morpho-dynamics with shallow and deep waters, marshes and canals. Particular care has been applied to the physical and hydrodynamic aspects in order to interpret the data and understand how the ancient Venetians could have had an advanced knowledge of the tidal range from sea stairs, tidal phase from the algae colour and direction, bottom depth from surface ripples, and exchanges between the Lagoon and the sea from suspended debris. The various elements of the boat are considered in terms of use and physical advantage, e.g. rowing style, cabin, hull and decorations. The boat evolution has been followed and documented over the centuries. This study clarifies the original shape of the gondola in the 11th century, from the analysis of the contemporary manuscripts and Byzantine mosaics in Ravenna and Venice, and a tablet of the early 14th century. In the 16th century, the gondola changed shape and size. The reason of this restyling was unknown, but has been found in the contemporary edicts, diaries and chronicles and other historical sources. The transformation was kept as a military secret, as well the production of a fleet of small boats provided of rostrums to sail on the rivers Po and Adige and fight against Ferrara and its Allies of the League of Cambrai. A comparison with the Viking- Norman vessels of Bayeux, Palermo and some codices show several strong analogies with the military gondolas of the 16th century. An overview of the written and pictorial sources has allowed to classify the various gondola types and recognize several technical details. Finally, the size and depth have been considered to interpret the ancient chronicles that mentioned that in the occasion of the most extreme storm surges, the gondolas could ride on San Marco Square, i.e. the flooding water depth necessary to float and to navigate over street paving.

EMPTY CONTAINERS, OVERFLOWING RIVERS: STATE FUNDED FENCING AND IMPACTS IN EAGLE PASS, TEXAS

Abstract This study uses hydraulic modeling to examine the impacts of two new fence sections at Eagle Pass, Texas: a container fence and a state-funded fence section south, and downstream, of the already modeled federal border fence. We used the model Nays2DFlood to compare fence and non-fence conditions at various recurrence intervals to determine how the fence is affecting flood extents, water depth, and water velocity. Water depth is deeper in the channel and the floodplain and shallower directly at the fence line when compared to non-fence conditions. Water velocity is faster within the channel and the floodplain and slower at the fence line during fence conditions. These impacts have the potential to adjust sediment regimes at this location and downstream of this area, altering water quality and channel morphology. Demographic analysis also show that particularly susceptible populations, including a majority Latino, low income individuals, those under 5, and those 65–74, are present in large numbers at these fence sections and are therefore vulnerable to flooding. Supplemental material is available for this article online.

Flood Depth Estimation in Agricultural Lands From L and C-Band Synthetic Aperture Radar Images and Digital Elevation Model

Flood depth is one of the important attributes in damage assessment especially in areas where agro-based activities takes place. Assessing impacted region turns out to be exceptionally difficult in the case of low-lying catchments with arable lands, which inundates due to extreme precipitation and flood modelling becomes impossible. Under these circumstances, SAR derived flood maps are valuable in removing different constraints related to flood modelling with high degree of profundity. Depth estimation from non-contact based methods requires, flood boundary as a primary input. However, detecting flood depth in emerging vegetation poses a complex challenge in terms of boundary estimation due to composite signatures that are manifested on SAR data. In this paper, a new approach is proposed to extract flood boundaries by fusing SAR data of two different frequencies, which are sensitive to water level changes. In the first step, wavelet fusion is applied to combine L- and C- band SAR data followed by Otsu’s segmentation method to extract varying levels of flood boundaries. In the second step, SRTM Digital Elevation Model (DEM) with 30m horizontal resolution is used on each boundary for statistical analysis based on which water surface levels are extracted. In the final step, depth levels are calculated from the water surface elevation and DEM. Floodwater Depth Estimation tool (FwDET) derived depth measurements are used to calibrate the statistical thresholds for derived flood depths. The study carried out on 2016 Assam flood event shows maximum flood depth of 1.56 meters in the selected study area and the results are verified with evidence-based ground truth collections which showed RMSE error of 0.25 meters from the measured values.

A Comprehensive Assessment of Floodwater Depth Estimation Models in Semiarid Regions

AbstractSimple models continue to be important for continental‐scale floodwater depth mapping due to the prohibitively expensive cost of calibrating and applying hydrodynamic models. This paper investigates the accuracy of three simple models for floodwater depth estimation from remote sensing derived water extent and/or Digital Elevation Models (DEMs) in semiarid regions. The three models are Height Above Nearest Drainage (HAND; Nobre et al., 2011, https://doi.org/10.1016/j.jhydrol.2011.03.051), Teng Vaze Dutta (TVD; Teng et al., 2013, http://hdl.handle.net/102.100.100/97033?index=1), and Floodwater Depth Estimation Tool (FwDET; Cohen, Brakenridge, et al., 2018, https://doi.org/10.1111/1752-1688.12609). The model accuracy and nature of errors are established using industry's best practice hydrodynamic models as benchmarks in three regions in eastern Australia. The overall results show that FwDET tends to underestimate (by 0.32 m at 50th percentile) while HAND and TVD overestimate floodwater depth for almost all floods (by 0.97 and 0.98 m, respectively). We quantify how switching DEM from 5 m LiDAR to national or global data sets DEM‐H (Gallant et al., 2011, https://ecat.ga.gov.au/geonetwork/srv/eng/catalog.search#/metadata/72759), MERIT (Yamazaki et al., 2019, https://doi.org/10.1029/2019WR024873), or FABDEM (Hawker et al., 2022, https://doi.org/10.1088/1748-9326/ac4d4f) can affect different models differently; and we evaluate model performance against reach geomorphology and magnitude of flood events. The findings emphasize the importance of choosing a model that is fit for the intended application. By describing the applicability, advantages, and limitations of these models, this paper assists practitioners to choose the most appropriate model based on characteristics of their study area, type of problems they try to solve, and data availability.

High Resolution 3D Mapping of Hurricane Flooding from Moderate-Resolution Operational Satellites

Floods are often associated with hurricanes making landfall. When tropical cyclones/hurricanes make landfall, they are usually accompanied by heavy rainfall and storm surges that inundate coastal areas. The worst natural disaster in the United States, in terms of loss of life and property damage, was caused by hurricane storm surges and their associated coastal flooding. To monitor coastal flooding in the areas affected by hurricanes, we used data from sensors aboard the operational Polar-orbiting and Geostationary Operational Environmental Satellites. This study aims to apply a downscaling model to recent severe coastal flooding events caused by hurricanes. To demonstrate how high-resolution 3D flood mapping can be made from moderate-resolution operational satellite observations, the downscaling model was applied to the catastrophic coastal flooding in Florida due to Hurricane Ian and in New Orleans due to Hurricanes Ida and Laura. The floodwater fraction data derived from the SNPP/NOAA-20 VIIRS (Visible Infrared Imaging Radiometer Suite) observations at the original 375 m resolution were input into the downscaling model to obtain 3D flooding information at 30 m resolution, including flooding extent, water surface level and water depth. Compared to a 2D flood extent map at the VIIRS’ original 375 m resolution, the downscaled 30 m floodwater depth maps, even when shown as 2D images, can provide more details about floodwater distribution, while 3D visualizations can demonstrate floodwater depth more clearly in relative to the terrain and provide a more direct perception of the inundation situations caused by hurricanes. The use of 3D visualization can help users clearly see floodwaters occurring over various types of terrain conditions, thus identifying a hazardous flood from non-hazardous flood types. Furthermore, 3D maps displaying floodwater depth may provide additional information for rescue efforts and damage assessments. The downscaling model can help enhance the capabilities of moderate-to-coarse resolution sensors, such as those used in operational weather satellites, flood detection and monitoring.

Sensitivity of Remote Sensing Floodwater Depth Calculation to Boundary Filtering and Digital Elevation Model Selections

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.

Human-centered flood mapping and intelligent routing through augmenting flood gauge data with crowdsourced street photos

The number and intensity of flood events have been on the rise in many regions of the world. In some parts of the U.S., for example, almost all residential properties, transportation networks, and major infrastructure (e.g., hospitals, airports, power stations) are at risk of failure caused by floods. The vulnerability to flooding, particularly in coastal areas and among marginalized populations is expected to increase as the climate continues to change, thus necessitating more effective flood management practices that consider various data modalities and innovative approaches to monitor and communicate flood risk. Research points to the importance of reliable information about the movement of floodwater as a critical decision-making parameter in flood evacuation and emergency response. Existing flood mapping systems, however, rely on sparsely installed flood gauges that lack sufficient spatial granularity for precise characterization of flood risk in populated urban areas. In this paper, we introduce a floodwater depth estimation methodology that augments flood gauge data with user-contributed photos of flooded streets to reliably estimate the depth of floodwater and provide ad-hoc, risk-informed route optimization. The performance of the developed technique is evaluated in Houston, Texas, that experienced urban floods during the 2017 Hurricane Harvey. A subset of 20 user-contributed flood photos in combination with gauge readings taken at the same time is used to create a flood inundation map of the experiment area. Results show that augmenting flood gauge data with crowdsourced photos of flooded streets leads to shorter travel time and distance while avoiding flood-inundated areas.

A downscaling model for derivation of 3-D flood products from VIIRS imagery and SRTM/DEM

As one of the costliest and most frequent natural disasters, flood is a major threat to human lives and property. Flood forecasting, simulation, and monitoring plays an important role in disaster relief and mitigation. With the support from the JPSS (Joint Polar Satellite System) Proving Ground and Risk Reduction (PGRR) Program, the VIIRS (Visible Infrared Imaging Radiometer Suite) global 375-m flood products in near real-time, daily composition, and 5-day composition have been developed and derived flood extent represented in water fractions from Suomi-NPP and NOAA-20. The products have shown good quality for the spatial distribution of floodwater. However, the moderate spatial resolution is a limiting factor when obtaining inundation detail. Moreover, no vertical information on the floodwater is included in the products. Based on water’s self-leveling nature, it is feasible to construct the vertical structure of floodwater using the VIIRS 375-m water fractions and high-resolution Digital Elevation Model (DEM) such as the 30-m Shuttle Radar Topography Mission (SRTM)/DEM through a downscaling process (Li et al., 2013). With impact factors including topography, land cover, tree cover, river network and watershed, the downscaling process has been integrated into a model called the Downscaling Model. By using the 30-m SRTM/DEM, the model can downscale the VIIRS 375-m floodwater fraction products into 30-m flood extent and water depth products. These products include flood information in both horizontal and vertical directions and thus are called 3-D flood products in this study. This paper presents a comprehensive introduction to the model with the results evaluated against high-resolution satellite images from Landsat-8 Operational Land Imager (OLI), Sentinel-2, and GeoEye, aerial photos, and river gauge observations. Evaluation results indicate reliable quality and promising performance of the model and the downscaled flood products. A routine system has been set up at the Cooperative Institute for Satellite Studies (CIMSS) at University of Wisconsin-Madison to generate experimental VIIRS downscaled flood extent and water depth products over the Continental USA (CONUS). The routinely generated VIIRS 30-m floodwater depth maps are available at: https://floods.ssec.wisc.edu/?products=VIIRS-3Dflood.

Flood hazard and susceptibility assessment in a semi-arid environment: A case study of Seyad basin, south of Morocco

Flood, is a constant phenomenon, especially in semi-arid and flood plain regions are one of the most destructive natural hazards endangering a people's life, property, and physical and economic well-being. This paper focuses on hydraulic modelling using the HEC-RAS model in combination with the Watershed Modeling System (WMS) tools and compares the results to the Flood Hazard Index (FHI) method using GIS in the Seyad Basin situated in the southwestern region of Morocco with an area of 1512.85 km2. The goal of the study is to evaluate flood hazards in the Seyad Basin in which the cities of Taghjijt, Aday, Amtoudi, and Tagriante are located. The HEC-RAS approach combines the hydraulic surface water flow model and the digital terrain model data. This combination allows the mapping of the flood zones by using the WMS tools. This approach predicts flood occurrence probability for different times and determines the intensity of the flood (depth and velocity of floodwater) using the existing hydrological data.On the other hand, the Flood Hazard Index method (FHI) presents a multi-criteria index to assess areas prone to flood hazards, using six physical parameters: permeability, slope, distance from rivers, land use, drainage density, and flow accumulation. A weight is calculated from the analytic hierarchy process method and applied to each parameter. HEC-RAS method allows the mapping of a flood with a flood water surface profile that shows the flood depth for Annual Exceedance Probability (AEP). At the same time, FHI permits establishing flood hazard levels without showing the water depth. In both approaches, six simulations were performed with the return periods of 10, 20, 50, 100, 200, and 500 years. The simulation revealed that the most susceptible areas to flooding are the areas along the Wadi Seyad.

Flood vulnerability assessment at the local scale using remote sensing and GIS techniques: a case study in Da Nang City, Vietnam

Abstract This paper has developed a cost-efficient framework for flood vulnerability assessment at a local scale using a multi-parametric approach integrated with the Open Source Geographical Information System (GIS) and Open Remote Sensing data. The study focuses on generating a set of criteria considering three dimensions of flood vulnerability: exposure, sensitivity, and adaptive capacity (AC) on an index-based approach. These indicators were decided based on a robust analysis considering the physical and socio-economic conditions of the study area. The flood exposure was generated from the geomorphological and hydrological parameters integrated with the flood water depth, the distance to river channels, and the Modified Normalized Difference Water Index. The flood sensitivity was determined by the aggregation of local income, land use, poverty index, population density, and other parameters reflecting the socio-economic condition. The AC has been evaluated based on the Normalized Difference Vegetation Index, the density of the community service facilities, and other factors related to the coping capacity to flood. Finally, the flood vulnerability at the local scale was determined based on the integration of its contributing factors using the Analytical Hierarchical Process-based aggregated model. Results indicated that a total of 20 parameters impacted the flood vulnerability of the research area. The findings also confirmed that among the indicators of flood vulnerability of Da Nang City, the flood depth, land-use condition, and drainage system are the key factors affecting the vulnerability level. The empirical assessment showed that the study area is significantly affected by flood vulnerability with more than 60% of the area having the vulnerability level from moderate to very high. In addition, this paper points out that the vulnerability research should be localized and is not always based on the administrative units. This practice can make the decision-making process and adaptation plan more appropriate locally. Especially, this study attempted to evaluate the accuracy of the flood vulnerability map for the first time by using field survey data and the statistical report on flood damage that most of the previous studies have not conducted yet. This framework provides a valuable toolkit for flood management in data-scarce regions all over the world.

Update of a biogeochemical model with process-based algorithms to predict ammonia volatilization from fertilized cultivated uplands and rice paddy fields

Abstract. Accurate simulation of ammonia (NH3) volatilization from fertilized croplands is crucial to enhancing fertilizer-use efficiency and alleviating environmental pollution. In this study, a process-oriented model, CNMM–DNDC (Catchment Nutrient Management Model–DeNitrification–DeComposition), was evaluated and modified using NH3 volatilization observations from 44 and 19 fertilizer application events in cultivated uplands and paddy rice fields in China, respectively. The major modifications for simulating NH3 volatilization from cultivated uplands were primarily derived from a peer-reviewed and published study. NH3 volatilization from cultivated uplands was jointly regulated by wind speed, soil depth, clay fraction, soil temperature, soil moisture, vegetation canopy, and rainfall-induced canopy wetting. Moreover, three principle modifications were made to simulate NH3 volatilization from paddy rice fields. First, the simulation of the floodwater layer and its pH were added. Second, the effect of algal growth on the diurnal fluctuation in floodwater pH was introduced. Finally, the Jayaweera–Mikkelsen model was introduced to simulate NH3 volatilization. The results indicated that the original CNMM–DNDC not only performed poorly in simulating NH3 volatilization from cultivated uplands but also failed to simulate NH3 volatilization from paddy rice fields. The modified model showed remarkable performances in simulating the cumulative NH3 volatilization of the calibrated and validated cases, with drastically significant zero-intercept linear regression of slopes of 0.94 (R2 = 0.76, n = 40) and 0.98 (R2 = 0.71, n = 23), respectively. The simulated NH3 volatilization from cultivated uplands was primarily regulated by the dose and type of the nitrogen fertilizer and the irrigation implementation, while the simulated NH3 volatilization from rice paddy fields was sensitive to soil pH; the dose and depth of nitrogen fertilizer application; and flooding management strategies, such as floodwater pH and depth. The modified model is acceptable to compile regional or national NH3 emission inventories and develop strategies to alleviate environmental pollution.

3D Inundation Mapping: A Comparison Between Deep Learning Image Classification and Geomorphic Flood Index Approaches

Inundation mapping is a critical task for damage assessment, emergency management, and prioritizing relief efforts during a flooding event. Remote sensing has been an effective tool for interpreting and analyzing water bodies and detecting floods over the past decades. In recent years, deep learning algorithms such as convolutional neural networks (CNNs) have demonstrated promising performance for remote sensing image classification for many applications, including inundation mapping. Unlike conventional algorithms, deep learning can learn features automatically from large datasets. This research aims to compare and investigate the performance of two state-of-the-art methods for 3D inundation mapping: a deep learning-based image analysis and a Geomorphic Flood Index (GFI). The first method, deep learning image analysis involves three steps: 1) image classification to delineate flood boundaries, 2) integrate the flood boundaries and topography data to create a three-dimensional (3D) water surface, and 3) compare the 3D water surface with pre-flood topography to estimate floodwater depth. The second method, i.e., GFI, involves three phases: 1) calculate a river basin morphological information, such as river height (hr) and elevation difference (H), 2) calibrate and measure GFI to delineate flood boundaries, and 3) calculate the coefficient parameter (α), and correct the value of hr to estimate inundation depth. The methods were implemented to generate 3D inundation maps over Princeville, North Carolina, United States during hurricane Matthew in 2016. The deep learning method demonstrated better performance with a root mean square error (RMSE) of 0.26 m for water depth. It also achieved about 98% in delineating the flood boundaries using UAV imagery. This approach is efficient in extracting and creating a 3D flood extent map at a different scale to support emergency response and recovery activities during a flood event.

Extreme rainfall-induced urban flood monitoring and damage assessment in Wuhan (China) and Kumamoto (Japan) cities using Google Earth Engine.

Floods in urban areas result in a detrimental impact on the natural environment and human health and pose major risks to assets and communication systems. In cities with high population density, the magnitude of flood damage largely depends upon flood inundation as well as floodwater depths. The present study compared recent flood inundation extent, damages caused, and possible floodwater depth in two highly developed metropolises of China and Japan, i.e., Wuhan and Kumamoto cities, for the year 2020. Sentinel-1 satellite data-driven change detection algorithm in Google Earth Engine (GEE) was applied to identify potentially flooded regions. Major land use land cover classes such as urban areas and croplands affected by the flood were mapped in conjunction with the exposed population. ALOS PALSAR digital elevation model (DEM) was used to study the inundation depth. The study revealed that 322 km2 of the area has been inundated by floodwater in Wuhan city with 230 km2 and 140 km2 areas under damaged croplands and urban regions. Around 817,095 people were exposed to this natural catastrophe in Wuhan. The city Kumamoto has witnessed an inundation area of about 505 km2 with damaged cropland of 350 km2 and an urban area of 83 km2.

Linking Urban Floods to Citizen Science and Low Impact Development in Poorly Gauged Basins under Climate Changes for Dynamic Resilience Evaluation

Cities must develop actions that reduce flood risk in the face of extreme rainfall events. In this study, the dynamic resilience of the Gregorio catchment (São Carlos, Brazil) was assessed. The catchment lacks environmental monitoring and suffers from recurrent floods. The resilience curves were made considering the water depth in the drainage system as the performance index, obtained by simulations with SWMM and HEC-RAS. The calibration of the flood extension was performed using citizen science data. The contribution to increasing the dynamic resilience by implementing decentralized low impact development (LID) practices was also evaluated. For this purpose, bioretention cells were added to the SWMM simulations. The resilience curves were then calculated for the current and future climate scenario, with and without LID, for return periods of 5, 10, 50, and 100 years and duration of 30, 60, and 120 min. Intensity–duration–frequency curves (IDFs) updated by the regional climate model MIROC5 for 2050 and 2100 were used. The results showed a significant improvement in the system’s resilience for light storms and the current period due to LID practice interventions. Efficiencies were reduced for moderate and heavy storms with no significant drops in floodwater depth and resilience regardless of the scenario.

A Deep Learning Method for Floodwater Depth Prediction on Roadways from Side-View Real and Synthetic Images of Vehicles

A Deep Learning Method for Floodwater Depth Prediction on Roadways from Side-View Real and Synthetic Images of Vehicles

Flood Depth Estimation during Hurricane Harvey Using Sentinel-1 and UAVSAR Data

In August 2017, Hurricane Harvey was one of the most destructive storms to make landfall in the Houston area, causing loss of life and property. Temporal and spatial changes in the depth of floodwater and the extent of inundation form an essential part of flood studies. This work estimates the flood extent and depth from LiDAR DEM (light detection and ranging digital elevation model) using data from the Synthetic Aperture Radar (SAR)–Unmanned Aerial Vehicle Synthetic Aperture Radar (UAVSAR) and satellite sensor—Sentinel-1. The flood extent showed a decrease between 29–30 August and 5 September 2017. The flood depths estimated using the DEM were compared with the USGS gauge data and showed a correlation (R2) greater than 0.88. The use of Sentinel-1 and UAVSAR resulted in a daily temporal repeat, which helped to document the changes in the flood area and the water depth. These observations are significant for efficient disaster management and to assist relief organizations by providing spatially precise information for the affected areas.

Flash Flood Water Depth Estimation Using SAR Images, Digital Elevation Models, and Machine Learning Algorithms

In this article, the local spatial correlation of multiple remote sensing datasets, such as those from Sentinel-1, Sentinel-2, and digital surface models (DSMs), are linked to machine learning (ML) regression algorithms for flash floodwater depth retrieval. Edge detection filters are applied to remote sensing images to extract features that are used as independent features by ML algorithms to estimate flood depths. Data of dependent variables were obtained from the Hydrologic Engineering Center’s River Analysis System (HEC-RAS 2D) simulation model, as applied to the New Cairo, Egypt, post-flash flood event from 24–26 April 2018. Gradient boosting regression (GBR), random forest regression (RFR), linear regression (LR), extreme gradient boosting regression (XGBR), multilayer perceptron neural network regression (MLPR), k-nearest neighbors regression (KNR), and support vector regression (SVR) were used to estimate floodwater depths; their outputs were compared and evaluated for accuracy using the root-mean-square error (RMSE). The RMSE accuracy for all ML algorithms was 0.18–0.22 m for depths less than 1 m (96% of all test data), indicating that ML models are relatively portable and capable of computing floodwater depths using remote sensing data as an input.

Prediction of floodwater impacts on vehicle blockages at bridges using artificial neural network

During extreme flood events, various debris like floating vehicles can block the bridges in urban rivers and floodplains. Blockage of vehicles can influence the floodwater hydrodynamics and potentially on the flood risk implications. Such obstructions often raise upstream water levels with back water effects, causing more water to be redirected into nearby metropolitan areas. This study attempts at evaluating artificial neural network (ANN) model in predicting the variations in floodwater depths and velocities along the channel centreline based on the changes in flowrate and distances from the inlet. The floodwater depth and velocity variations were obtained for three different types of bridges at specified sites along the channel centreline with three incoming discharges. A multilayer feedforward neural network (FFNN) model was used to investigate the effects of discharge (Q) and distance, on depth variation rate (D) or velocity (V). Additionally, a comparison study was done between 2 input 1 output and 2 input 2 output i.e. single output (depth variation rate (D) or velocity (V) versus multi-output depth variation rate (D) and velocity (V) for all the three models of bridges that are blocked by vehicles. The study has applied 12 training algorithms (TA) in the ANN modelling to optimize the TA that is most suitable for the dataset of three different bridges. The optimization is based on the performance criterion namely regression (R), mean squared error (MSE), mean absolute error (MAE), mean absolute percentage (MAPE), accuracy and coefficient of determinant (R2). Bayesian regularization backpropagation (BR) training algorithm gives a highest accuracy when compared in all three bridges. The scenario 2 input 2 output gave greatest accuracy results compared to 2 input 1 output. The findings showed a reliable estimation of significant impacts on the flow propagations and the hydrodynamic processes along rivers and floodplains. This study can help the decision makers in effective river and floodplain management practices.

Salt Marsh Accretion With and Without Deep Soil Subsidence as a Proxy for Sea-Level Rise

The relation between salt marsh accretion and flooding regime was quantified by statistical analysis of a unique dataset of accretion measurements using sedimentation-erosion bars, on three barrier islands in the Dutch Wadden Sea over a period of c. 15 years. On one of the islands, natural gas extraction caused deep soil subsidence, which resulted in gradually increasing flooding frequency, duration, and depth, and can thus be seen as a proxy for sea-level rise. Special attention was paid to effects of small-scale variation e.g., in distance to tidal creeks or marsh edges, elevation of the marsh surface, and presence of livestock. Overall mean accretion rate was 0.44 ± 0.0005 cm year−1, which significantly exceeded the local rate of sea-level rise of 0.25 ± 0.009 cm year−1. A multiple regression approach was used to detect the combined effect of flooding regime and the local environment. The most important flooding-related factors that enhance accretion are mean water depth during flooding and overall mean water depth, but local accretion strongly decreases with increasing distance to the nearest creek or to the salt marsh edge. Mean water depth during flooding can be seen as an indicator for storm intensity, while overall mean water depth is a better indicator for storm frequency. The regression parameters were used to run a simple model simulating the effect of various sea-level scenarios on accretion and show that, even under extreme scenarios of sea-level rise, these salt marshes can probably persist for the next 100 years, although the higher parts may experience more frequent inundation.

Flood inundation modeling using HEC-RAS: the case of downstream Gumara river, Lake Tana sub basin, Ethiopia

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 1 m 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 52.064 km2 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 1787 m with an inundated area of 58.806, 58.843, and 58.827 km2 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.