Method for Incorporating Morphological Sensitivity into Flood Inundation Modeling

The potential flood inundation extent can be estimated with flood inundation models, which can differ in the level of physical and numerical modelling complexity included in the solution procedure. In recent years, several studies have highlighted the benefits of shock-capturing flood inundation models, particularly when modelling a high Froude number or supercritical flows, or in areas prone to the occurrence of rapidly varying flood events, such as flash floods. Nonetheless, decision makers are often reluctant to implement more complex modelling tools into practical flood inundation modelling studies, unless evidence is provided to establish when such refined modelling tools should be used. The main objective of this study was to determine a general threshold value of the bottom slope that could be used by decision makers as an orientation guide to ascertain when to use a specific type of flood inundation model. The results obtained suggest that in torrential river basins or catchments (i.e., river basins and catchments with a bed slope generally greater than 1%), the flood inundation modelling should be conducted by using a flood inundation model that include shock-capturing algorithms in the model solution procedure.

The Geomorphic Flood Index (GFI) model can show flood inundation in a watershed based on flood events. Flood inundation can occur because of the backwater effect in the river channel. The different water levels from the river and tide that occur downstream may temporarily disturb the water depth and flow velocity. Because of this disturbance, a backwater effect might occur. The case study is located at the Plumbon River in Semarang City, which has the potential for a backwater effect in the river downstream. Backwater is a hydraulic phenomenon that occurs in rivers and can be simulated using a hydrodynamic model, as it can show the relationship between river discharge and tides. The conventional GFI method does not consider hydraulic effects, such as backwater effects, as part of the calculation. Therefore, modifications will be inserted into the calculation of the hydraulic effect on the modelling of flood inundation. The main purpose of this study is to build a modified GFI method that includes backwater analysis in its modelling to predict the backwater effect that occurs downstream of rivers and its impact on inundation. This study will help rapidly predict flood inundation.

Calculation and visualization of flood inundation based on a topographic triangle network

Innovation and best practice designs can be encouraged through an effective design process. The award-winning Harbertonford Flood Defence Scheme was described as ‘the future of flood defence schemes’ by Sir John Harman, Chairman of the Environment Agency, and delivered a combination of in-village river channel improvement and upstream flood attenuation to alleviate regular flooding and the misery it caused. This paper focuses on how the design was developed and implemented to demonstrate the benefits that can be delivered through good teamwork and effective management of the civil engineering process.

Territory protection against flood water inundation and creation of polder systems are carried out with the help of protection dikes. One of the main requirements to the composition of polder systems in flood plains is a location of border dikes beyond meander belt in order to avoid their erosion when meander development occurs. Meander belt width can be determined on the basis of the analysis of multi-year land surveying pertaining top river-bed building and in the case when such data is not available this parameter is calculated in accordance with the Snishchenko formula. While banking-up a river bed a flooded area is decreasing and, consequently, water level in inter-dike space and rate of flood water are significantly increasing. For this reason it is necessary to locate dikes at a such distance from a river bed which will not cause rather high increase in water level and flow velocity in the inter-dike space. Methodology for hydraulic calculation of river regulation has been developed in order to substantiate design parameters for levee systems, creation of favourable hydraulic regime in these systems and provision of sustainability for dikes. Its main elements are calculations of pass-through capacity of the leveed channel and rise of water level in inter-dike space, and distance between dikes and their crest level. Peculiar feature of the proposed calculated formulae is an interaction consideration of channel and inundated flows. Their mass-exchanging process results in slowing-down of the channel flow and acceleration of the inundated flow. This occurrence is taken into account and coefficients of kinematic efficiency are introduced to the elements of water flow rate in the river channel and flood plain, respectively. The adduced dependencies for determination of a dike crest level (consequently their height) take into consideration a rise of water level in inter-dike space for two types of polder systems: non-inundable (winter) dikes with maximum spring flood rate and inundable (summer) dikes with summer-autumn flood rates. The proposed calculated formulae can be recommended for application at design organizations.

Flooding is a destructive natural hazard that is exacerbating due to climate change, causing significant socio-economic losses. Flood inundation models are critical tools in the mitigation and management of such hazards. Conventional standalone process-based models can be limited in real-time applications due to their computational intensity and the trade-off between accuracy, resolution and speed. Data-driven (machine learning) models offer an alternative route as surrogate models, promising computational efficiency, however, are noted to have limitations of high data-dependency and a black-box nature. Hybridization, implemented in machine learning models to enhance strengths and limit tradeoffs has been shown to increase physics awareness, real-time applicability, and adaptability. This paper conducts a systematic review of state-of-the-art research on hybrid flood models, and presents a definition of hybridization. A classification of hybridization techniques into enhancing inputs, structure and processing is proposed. Sequentially, common metrics used to evaluate flood models are classified into accuracy and speed-based metrics, and the usefulness of hybridization is discussed. An extensive benchmarking framework is proposed for the comparison of models before application, based on quantitative classification of the model and standardized datasets and tests. Finally, a way forward is proposed in the context of physics informed machine learning for flood inundation modeling.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11069-026-08078-w.

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.

In this paper, land cover was predicted from Landsat TM imagery and used to generate spatially-distributed friction coefficients. Flood inundation was then predicted using the raster-based model LISFLOOD-FP, based on friction and three different elevation models. Sensitivity of LISFLOOD-FP to spatially-distributed friction was assessed. It was found that effect of friction on the flood wave is small when compared to the underlying elevation, but is greatest during the recession phase of the hydrograph. Hydraulic models of channel and overland flow allow river discharge to be related to flood inundation extent, by simu- lating flooding based on a scenario discharge. An important boundary condition for such models is surface friction. In particular, floodplain land cover is related to friction that affects the movement of the flood wave. However, the selection of appropriate friction coefficients for hydraulic models is difficult and it is recognised that floodplain friction coefficients have considerable uncertainty and sensitivity associated with them (1). Specific problems are (i) a lack of data sources, (ii) the often coarse spatial resolution of data and (iii) the use of stationary models (e.g., use of average friction over the whole catchment). Remote sensing can provide spatially distributed estimates of land cover, allowing a more informative and accurate representation of floodplain friction in flood inundation mod- els. However, hard classification may not provide sufficiently detailed land cover data at the sub-pixel scale. For example, where Landsat Thematic Mapper (TM) (spatial resolution of 30 m) imagery is used and the flood is only several hundred metres across, hard classification may be inappropriate. There- fore, in this paper, soft classification was used to estimate friction coefficients on a continuous scale for each cell on the floodplain. The objective was then to assess the sensitivity of a flood inundation model to uncertainty in these coefficients.

Abstract. River bathymetry is important for accurate flood inundation modelling but is often unavailable due to the time-intensive and expensive nature of its acquisition. This leads to several proposed and implemented approaches for its estimation. However, the errors in estimations inherent in these methods and how they affect the accuracy of the flood inundation modelling outputs, has not been extensively researched. Hence, to contribute, we investigate the sensitivity of flood predictions to the errors in river slope, width, and bank-full flow used in two formulas – the Uniform Flow and the Conceptual Multivariate Regression – for estimating river bathymetry. In this study, we employed a Monte Carlo framework to introduce random errors into these parameters drawn from a normal distribution with zero mean and a standard deviation set to 10 % of their best estimates. Using this process, we generated 50 simulated river bathymetries for each parameter along with an additional 50 where the errors were applied to all parameters simultaneously. The riverbeds generated from these bathymetries were combined with topographic LiDAR data to create model grids. Each grid was used in the hydrodynamic model LISFLOOD-FP to simulate the 2005 flood event in the Waikanae River area of New Zealand. We assessed the resulting flood inundation predictions for their variability and sensitivity. The results indicate that between two methods, the errors in the parameters in the Uniform Flow formula are associated with greater uncertainty in flood inundation depths and extents compared to the Conceptual Multivariate Regression. Among the parameters, the width errors correspond to the highest uncertainty, while the slope errors correspond to the lowest.

Recent environmental developments have stimulated an interest in conservation and restoration of the historical Willamette River flood plain, both to protect against flooding and to provide wildlife habitat. In order to best utilize scarce resources, we characterized historical and modern river channel and flood‐plain conditions to evaluate changes and help prioritize restoration sites. Using cartographic and photographic data sources, we developed a Geographic Information System (GIS) to map active channels, side channels, islands and tributaries for four separate dates, as well as riparian and flood‐plain vegetation characteristics for pre‐European settlement and modern time periods. Coverages based on flood records and other boundaries were used to partition the flood plain into spatial subsets for analysis. The GIS allowed comparisons between historical and present conditions for a variety of environmental factors. Much of the pre‐settlement channel complexity has been removed. Total channel length in 1995 was 26% less than in 1850, with almost 58% of the river's side channels disconnected from the system. In addition, we found a 72% loss of flood‐plain forest from 1850 to 1995, since it was converted to agricultural and urban land uses. Selected river and flood‐plain variables were made available for a spatial model to prioritize potential locations for flood‐plain restoration.

Flood modeling and fluvial dynamics: A scoping review on the role of sediment transport

Long-term flood-hazard modeling for coastal areas using InSAR measurements and a hydrodynamic model: The case study of Lingang New City, Shanghai

A rapid urban flood inundation and damage assessment model

PRIMo: Parallel raster inundation model

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.