PRIMo: Parallel raster inundation model

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 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.

<p>India is one of the world's most flood-prone countries, with 113 million people exposed to floods. Large-scale hydrological models integrated with complicated Navier–Stokes based hydraulic, and inundation models traditionally address flood preparedness, control, and mitigation. In addition to being highly data-intensive at the fine spatial and temporal resolution, this approach has a considerable computational cost that limits real-time applications. We employ the parameter-free Dynamic Budyko (DB) hydrological model to map observed precipitation with gridded runoff to overcome data scarcity. We propose a time-efficient Slope-corrected, Calibration-free, Iterative Flood Routing and Inundation Model (SCI-FRIM) framework that can be used with any hydrological model to generate a probability map of inundation. To model the catastrophic flood extents that the state of Kerala in India experienced during August 2018, we use gridded 0.25 deg × 0.25 deg IMD precipitation data. We use a parameter-free iterative approach to update flood velocity by assuming that river velocity does not fluctuate geographically across a particular river network at a given time instant. We pre-compute the iterative velocity and model the relationship between flood velocity-discharge and discharge-inundation height for each reach by combining the globally available SRTM/ASTER DEMs with empirically obtained river-reach geometry data (JPL). We compute the reach slope from the absolute vertical error-prone DEM by segmenting the river network into a series of independent channels and extracting the relationship between the channel pixel's elevation and the pixel's distance to the pour point. We use the Height Above Nearest Drainage (HAND) to map the probabilistic spatial extent corresponding to an ensemble of derived reach inundation heights. We then compare the proposed model with observed flood data points provided by the Kerala State Disaster Management Authority (KSDMA). The model captures up to 52% of 370,000 flood data points in a single run for the peak flood day within 15 minutes on a desktop computer. With reliable estimates of empirical bankfull discharge, the proposed model can achieve higher accuracy in lesser time.</p>

The combined effects of climate change and urbanization have led to an increase in urban flooding due to extreme precipitation events, which show significant spatial variation within cities. Therefore, the development of a fine community-scale flooding model is crucial for urban flood prevention and control. This study presents the Urban Flood Inundation Model (UFIM), a refined model that integrates high-resolution topographic data, dynamic real-time 1D–2D coupling, and backflow effects to simulate flood inundation at community scale. The key findings are as follows: (1) Due to its built-in one-to-two-dimensional (1D–2D) connection modeling approach, the model can consider backflow effects, rendering water accumulation simulation more realistic and enhancing simulation accuracy. (2) UFIM achieved high accuracy with a precision of 0.81, recall of 0.77, Probability of Detection (POD) of 94%, and Nash-Sutcliffe Efficiency (NSE) between 0.785 and 0.800 under various actual precipitation events. (3) The model effectively simulated urban flood inundation for different return periods (1-, 5-, 10-, 20-, and 50-years), and different community types (including residential, functional and comprehensive communities). These results highlight the UFIM's potential to support refined flood risk assessment across diverse community types, inform adaptive urban drainage design, and enhance decision-making for resilient planning and emergency response.

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

A physically based distributed hydrologic model is developed in this study for flood inundation simulation combining newly developed overland and channel network flow simulation models with evapotranspiration, unsaturated zone and saturated zone models. The overland flow and river flow models are validated individually with test data, and then coupled with other models. The model can take fine resolution spatial data as input preserving spatial heterogeneity of physical characteristics of a river basin. River embankments play an important role in flood prevention. The model can incorporate river embankment data in flood inundation simulation. The model is applied in a river catchment in Japan to simulate a flood event in 1996. Outputs from the model show good agreements with observed flood hydrographs and surveyed flood inundation. Copyright © 2000 John Wiley & Sons, Ltd.

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.

Calibrating a High-Performance Hydrodynamic Model for Broad-Scale Flood Simulation: Application to Thames Estuary, London, UK

<p>Despite massive flood protection efforts, undertaken in China for millennia, disastrous floods continue to plague the country. We discuss flood hazard and flood risk in China, starting from observed changes and variability and then dealing with flood risk management and projections. We also refer to recognized unsolved problems in hydrology.</p><p><strong>Observed changes and variability</strong></p><p>First, we review results of change detection in observed records of variables related to water abundance, such as intense precipitation, high river flow, and flood damage in China. There is a strong inter-annual and inter-decadal variability, part of which may be due to climate variability, i.e. oscillation in the ocean-atmosphere system (ENSO - <em>El Ni</em><em>ño</em>-Southern Oscillation and PDO – Pacific Decadal Oscillation). Particular large flood events (e.g. in 1998 and 2016) coincide with high values of ENSO indices.</p><p><strong>Flood risk management</strong></p><p>In its efforts to reduce flood risk, China has undertaken both structural and non-structural measures. An emergency manager's nightmare is when several serious emergencies (e.g., covid pandemic and flooding) happen at once, as in the unprecedented covid year 2020, when also record-breaking precipitation occurred in China. Yet, emergency management made it possible to constrain the flood loss.</p><p><strong>Flood risk projections</strong></p><p>In the warming climate, flood risk is likely to increase over much of China. Projections of flood loss changes for global warming scenarios from 1.5° to 4.0°C above the preindustrial temperature are presented. Projections are based on runoff simulations by a hydrological model driven by multiple downscaled general circulation models, the national GDP projected at shared socio­economic pathways, and the “intensity–loss rate” function. Flood losses in China are projected to soar in the future, particularly in lowland regions subject to rapid economic growth. There is a considerable difference between flood losses corresponding to two global climate policy target thresholds, 1.5° and 2.0°C, amounting to over US$60 billion.</p><p>Available flood projections for China are not consistent. Since the spread is large, projections have to be interpreted with caution, because of the impact on decisions related to climate change adaptation and flood risk reduction.</p>

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.

Typically, the analysis and design of fluvial flood defence schemes is based on a single N year extreme flow event using a single survey of the river channel and flood plains. Adopting this approach assumes that the channel capacity is identical for all subsequent N year events. If one assumes that the typical design life for a flood defence scheme is of the order of 50 years, then such an approach is flawed because river channel morphology, and hence flood conveyance, may change considerably over this time scale. Therefore, to provide a more robust estimate of future flood inundation, a sensitivity analysis of these changes should be undertaken. This paper proposes a modeling methodology that combines a stochastic model, for estimating streamflow throughout the design period, and a 1D sediment transport model (HEC-RAS), to enable this sensitivity to be included in flood inundation modeling and defence scheme design. The methodology is demonstrated through conceptual implementation to evaluate the...

<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>

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.