Conceptual Flood Inundation Modelling: Computationally Efficient Methods for Large Data-scarce River Basins

A rapid urban flood inundation and damage assessment model

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.

The flood hazard mapping in a river basin is crucial for flooding risk management, mitigation strategies, and flood forecasting and warning systems, among other benefits. One approach for this mapping is based on the HAND (Height Above Nearest Drainage) terrain descriptor, directly derived from the Digital Elevation Model (DEM), in which each pixel represents the elevation difference of this point in relation to the river drainage network to which it is connected. Considering the Mamanguape river basin (3,522.7 km²; state of Paraíba, Brazil) as the study location, the present research applied this method and verified it as for five aspects: consideration of a spatially variable minimum drainage area for denoting the river drainage initiation; the impact of considering a depressionless DEM; evaluation of hydrostatic condition; effect of incorporating an existing river vector network; and comparative analysis of basin morphology regarding longitudinal river profiles. According to the results, adopting a uniform minimum drainage area for the river network initiation is a simplification that should be avoided, using a spatially variable approach, which influences the amount and spatial distribution of flooded areas. Additionally, considering the depressionless DEM leads to higher values of HAND and to a smaller flooded area (difference ranging between 3% and 99%), when compared with the use of DEM with depression, despite 3.1% of the pixels representing depressions. The use of the depressionless DEM is recommended, whereas the DEM pre-processing by incorporating a vector network (stream burning) generates dubious results regarding the relation between HAND and the morphological pattern presented in the DEM. Moreover, the estimation of flooded areas based on HAND does not guarantee the hydrostatic condition, but this disagreement comprises a negligible area for practical purposes.

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.

Flood is one of the most frequent natural disasters in Indonesia and worldwide. Therefore, this study aimed to compare and evaluate flood-prone mapping model using Height Above Nearest Drainage (HAND) and Topographic Wetness Index (TWI) model in data-scarce areas. HAND and TWI models were used to estimate flood-prone level, with field survey and image interpretation as primary methodologies. The data used was Digital Elevation Model (DEM) imagery with a resolution of 10 meters, incorporating elevation, slope, and hydrological parameters namely flow accumulation, direction, and distance. The mapping flood-prone areas were categorized as very prone, prone, moderate, not prone, and very not prone. The results showed that there were differences between HAND and TWI models in terms of area and percentage. The differences in flood inundation characteristics produced by HAND model were mainly due to variations in elevation and proximity to drainage channels. In contrast, TWI model focused on topography, soil moisture, and runoff potential. The differences between the two models also emphasized the importance of terrain characteristics in model predictions. The comparable predictive ability of HAND and TWI models presents an alterReceived: 2024-08-15 Revised: 2 024-09-12Accepted: 2025-03-22 Published: 2025-05-26

Flood risk mapping and analysis have gained much attention globally due to the increasing rate of flood disasters in most coastal and riverine areas. As a key element of flood risk study, mapping and delineation of flood hazard areas are very important for risk management and disaster reduction. We analysed potential flood hazards zones in Ogunpa river basin using integrated hydraulic modelling, the height above nearest drainage (HAND) model and multi criteria decision analysis (MCDA). An event-based flood simulation using FLO-2D model was done for the 28 h of the flooding event, while HAND model was employed in modelling the terrain to delineate inundation extent in the basin. In addition, seven flood causative factors were integrated using the Analytical Hierarchical Process (AHP) to delineate potential hazard zones in the study area. Flood inundation model result from FLO-2D revealed inundation depths between 0 and 5.24 m, an indication of intense flood hazards capable of causing lots of damages. MCDA results were classified into least, low, moderate, high and extreme flood hazard zones, while the classified HAND model include none, very low, low, moderate and high flood hazard zones. The MCDA results revealed that the downstream portions of the basin are mostly susceptible to moderate flood hazard compared to low vulnerability observed majorly in the upriver parts of the catchment. The comparison between the estimated flood depths ranges of 0.3–5.2 m by the FLO-2D model and the HAND model value range of 0–5 m (high flood hazard zone) revealed a strong similarity. In all, areas closed to the river course are highly vulnerable to flooding. The output of this study can serve as policy and decision-making tools for future flood hazard analysis, prevention and reduction plans at the research location.

PRIMo: Parallel raster inundation model

This opinion piece summarises recent progress in the development of global flood models (GFMs) in support of flood impact modelling and identifies potential areas for model improvement over the next 5–10 years. In many parts of the world, flood hazard data are absent or lack the accuracy and precision required for most practical applications, including climate change impact assessment. With the hydrological cycle expected to intensify due to climate change, better modelling of flood hazard is needed as a prerequisite to understanding how flood risk might change in the future with climate. The past decade has seen substantial advances in the modelling of flood inundation in data scarce areas along with the emergence of global flood models that could form the foundation of global impact assessments. In summarising these advances, four key themes emerge linked to topography, extreme flow estimation, river network parameterisation and numerical modelling of inundation. Progress in each of these themes will be needed to deliver the next generation of global flood hazard data.

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

<p>Event magnitude, societal vulnerability, and exposure define hazard impact. In New Zealand, flooding is the most common and damaging hazard at the decadal scale. Residents within the Marokopa catchment (west coast of the Waikato region) identify flood and erosion as significant local hazards. Flooding is influenced by a diverse range in factors, from environmental factors in the catchment, such as hydrology and climate, to socio-political policies and community awareness. Each of these factors is themselves influenced by climate change, and therefore requires study at the local and national scales. A mixed-methods approach was used to analyse flood and erosion through application of the Land Use and Capability Indicator (LUCI). Qualitative analysis along with rainfall-runoff, inundation, and holistic ecosystem service (ES) modelling are used to evaluate both flood and erosion extent, but also influencing factors. This research used a unique, mixed-methods approach to research a traditionally quantitative topic, improve on the understanding of karstic rainfall-runoff modelling and support LUCI development through application in a geomorphologically distinct location. Local knowledge facilitated both temporal and spatial outlining of flood and erosion extent at macro and catchment-scales. Bespoke rainfall-runoff modelling of the Marokopa upper catchment defined localised rainfall, seasonality and the karstic system as significant influences on runoff, with poor to excellent model-fit. Preliminary inundation findings outlined tidal, upper catchment bank-overflow, and overland flow as significant mechanisms of flooding. Finally, flood and erosion mitigation ecosystem services were modelled, with synergistic comparisons also analysed. Priority areas for future land management and hazard mitigation investment include the Marokopa floodplains ~5 km inland from the coast. Novel integration of physical and social observations outlines current flood risk extent and evaluates factors which contribute to flooding, providing a thorough knowledge base for future flood modelling within the Marokopa catchment.</p>

General strike (also known as hartal) is used as a mode of protest by organizations and political parties in India. It is generally thought that hartals negatively impact the healthcare delivery in a society. We used the Right to Information Act to obtain data from government health centers in Kerala state in India for four hartal days (H-day) and two control days (A-day and B-day) for each H-day, from sixteen health centers including 6 Community Health Center (CHC), 6 Secondary Health Center (SHC), and 4 Tertiary Health Center (THC). Data on emergency room visits was available for six HCs. 15 HCs had a statistically significant decrease in the number of outpatient visits on H-day. There was no difference in the number of outpatient visits between the two control days (A and B) in 15 HCs, suggesting the lack of a posthartal surge in visits. Median decrease in outpatient visits in CHCs, SHCs, and THCs was 50.4%, 59.5%, and 47.4%, respectively. Hartal did not impact the number of emergency room visits in 6 out of 7 health centers assessed. Our study identified a significant harmful impact on government healthcare delivery due to hartals in Kerala. These findings have major public health implications.

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.

A key aspect of large river basins partially neglected in large‐scale hydrological models is river hydrodynamics. Large‐scale hydrologic models normally simulate river hydrodynamics using simplified models that do not represent aspects such as backwater effects and flood inundation, key factors for some of the largest rivers of the world, such as the Amazon. In a previous paper, we have described a large‐scale hydrodynamic approach resultant from an improvement of the MGB‐IPH hydrological model. It uses full Saint Venant equations, a simple storage model for flood inundation and GIS‐based algorithms to extract model parameters from digital elevation models. In the present paper, we evaluate this model in the Solimões River basin. Discharge results were validated using 18 stream gauges showing that the model is accurate. It represents the large delay and attenuation of flood waves in the Solimões basin, while simplified models, represented here by Muskingum Cunge, provide hydrographs are wrongly noisy and in advance. Validation against 35 stream gauges shows that the model is able to simulate observed water levels with accuracy, representing their amplitude of variation and timing. The model performs better in large rivers, and errors concentrate in small rivers possibly due to uncertainty in river geometry. The validation of flood extent results using remote sensing estimates also shows that the model accuracy is comparable to other flood inundation modelling studies. Results show that (i) river‐floodplain water exchange and storage, and (ii) backwater effects play an important role for the Amazon River basin hydrodynamics. Copyright © 2011 John Wiley & Sons, Ltd.

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.