Quantifying uncertainty in flood predictions due to river bathymetry estimation

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.

Fusing stacked autoencoder and long short-term memory for regional multistep-ahead flood inundation forecasts

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.

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.

Despite the recent developments in continental‐scale streamflow and flood inundation modeling frameworks, effects of time‐specific and spatially explicit storage‐release dynamics of numerous dams and reservoirs remain underexplored. This paper fills this knowledge gap by directly inserting operational daily flow release data at 175 dam locations into a streamflow simulation of ∼1.2 million river reaches in the Mississippi River Basin (MRB), and therefore quantifying the effect of these regulations on streamflow and flood inundation extents. Using a streamflow routing model called the Routing Application for Parallel computatIon of Discharge (RAPID) and flood inundation mapping model called AutoRoute, two simulation scenarios were constructed respectively including and excluding the daily flow releases from those dams and reservoirs for a 10‐year period (2005–2014). Flood inundation maps were simulated for peak flow conditions at a ∼10‐m hyper spatial resolution. Kling‐Gupta efficiency (KGE) values show that streamflow model performance considerably improved when reservoirs were included in the modeled system, varying from 2% in the eastern region to 380% in the drier western region. Despite small variation of streamflow model improvement with reservoir release inclusion in the eastern region of the basin, the flow model was able to better capture observed peak flows. For a 1% change in streamflow, we observe a 0.8% change in estimated flood inundation. Comparisons to three observed flood events in the MRB demonstrate that the flood inundation estimates improve when percent change in streamflow is relatively high. Overall, inclusion of reservoir release resulted in substantial improvement in continental‐scale streamflow and flood inundation mapping.

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

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.

This study aimed at mapping flood inundation areas in the Tana River Delta. The 30 m DEM and digitized streams and road networks data were collected through remote sensing techniques followed by ground truthing. The data used also included Tana River discharge data at Garsen gauging station. Probability distributions were fitted on the annual maximum discharges. The 4 parameter generalized Gamma probability distribution was the best-fitting distribution according to goodness-of-fit criteria and the Q-Q plots and was used to generate the flood quantiles for different return periods. The generated flood quantiles were; 286 m3 /s, 369 m3 /s, 400 m3 /s, 405 m3 /s, 439 m3 /s, and 449 m3 /s for 2-year, 5-year, 10-year, 20-year 50-year, and 100-year return periods respectively. GIS Flood Tool in ArcGIS used flood quantiles to produce flood extents and inundation depths. The largest part of the Tana River delta was predicted to be flooded with inundation depths of upto 1.8 m causing inundation of settlements and leading to the death of people and livestock, as well as the destruction of properties and infrastructure including roads and buildings, farms, crops, among others. The study established that 31% of the schools located along the Tana River in the Garsen sub-catchment are exposed to inundation depths above 0.5 m. Different flood management options were identified for Tana River Delta and were subjected to Multicriteria Analysis for a decision to be arrived at in choosing the best. Advanced technology in recession agriculture, early warning systems, and forecasting were ranked as the optimal options for managing floods in Tana River Delta. The outcomes of this study contribute to preparedness for floods and guide development decisions within the mapped areas.

Recurring annual floods creates havoc to the life and property of millions of people around the world. Along with damaging the natural habitat of the living beings, floods terminally affects the growth and development of crops. Accurate flood inundation forecasting plays a crucial role in analysing the risk associated with crop damage due to flooding. Keeping this in view, this study attempts to simulate the flood inundation extent and depth with a daily lead-time of up to 3 days. The Mahanadi River delta in eastern India, which is one of the highly flood prone river deltas in the world is considered as the study area. The rainfall forecasts for the river basin are first bias-corrected using a newly developed bias-correction technique employing copula functions and self-organizing maps. Forcing the hydro-meteorological inputs to a conceptual hydrological model, the discharge forecasts up to 3 days lead-time are obtained. For further improvement, the errors in the discharge forecasts are updated using the state-of-the-art deep learning model, Long-Short Term Memory. Finally, the forecasted inundation depth and extent are simulated by forcing the hydrodynamic 1D-2D MIKE FLOOD model with the improved daily discharge forecasts as the upstream inflow boundary conditions. The hydrological model-simulated discharges after performing error updation are found to be reasonably accurate with a Nash-Sutcliffe Efficiency of >0.90. More than 50% of the observed flood inundated area are found to coincide with the model simulated inundations.

Floods can cause huge damage to society, the economy, and the environment. As a result, it is vital to determine the extent and type of land cover in flooded areas quickly and accurately in order to facilitate disaster relief and mitigation efforts. Synthetic aperture radar (SAR) is an all-weather, 24 h data source used to extract information about flood inundations, and its primary aim is to extract water body information for flood monitoring. In this study, we have studied the backscattering characteristics of water and non-water, combined the threshold segmentation method with Markov random fields (MRF), and embedded simulated annealing (SA) in the process of image noise reduction, resulting in the development of a water extraction method KI-MRF-SA with high accuracy in classification and high automation. Furthermore, object-scale adaptive convolutional neural networks (OSA-CNN) are introduced for the classification of optical images before the flood in order to provide reference data for flood inundation analysis. The method proposed in this study consists of the following three steps: (1) The Kittler and Illingworth (KI) thresholding algorithm is used for the segmentation of SAR images in order to determine the initial flood inundation extent; (2) MRF and SA algorithms are employed as a means to optimize the initial flood inundation extent, and the results are combined across multiple polarizations by using an intersection operation to determine the final flood inundation extent; and (3) As part of the flood mapping process, land cover types before the flood are classified using OSA-CNN and combined with flood inundation extents. According to the experimental results, it is evident that the proposed KI-MRF-SA method is capable of distinguishing water from non-water with significantly higher accuracy (3–5% improvement in the overall accuracy) than conventional thresholding methods. Combined with the classification method of OSA-CNN proposed in our earlier research, the overall classification accuracy of flood-affected areas could reach 92.7%.

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...

The most frequent natural hazards and the most costly in terms of property damage in study area is Flood. The purpose of this work is use Geographic Information Systems (GIS) Hydrological tools to model and map flood event in Ifo Local Government Area of Ogun state, Nigeria using Shuttle Radar Topographic Mission ( SRTM) data and land use maps as input data layers. In this study, flood inundation depth is calculated with GIS in order to understand the temporal, spatial scale and transition. Flood inundation map and 3D views of the hydrological modelling results are provided for a better understanding of the severity of flooding in the location. It is recommended that the processed data from this study be used as input for future research and the development of flood warning systems for the area. It will also be a base study for future study when rainfall data will be available.

Mapping floods from remote sensing data and quantifying the effects of surface obstruction by clouds and vegetation

Understanding urbanization and its impact on flooding and flood risk is crucial to better manage flood risk in the future. This study analyzed land use/land cover changes and how urbanization would impact flooding and flood risk in Kathmandu Valley of Nepal, and assessed flood risk by integrating flood hazards based on hydrologic–hydraulic modeling with the Analytic Hierarchy Process-based Multi-Criteria Decision Analysis (AHP-MCDA) approach. Land cover maps for past years were generated using Landsat satellite images, and land use/land cover maps for future years were projected based on machine learning techniques. Flood simulations were conducted using a rainfall runoff inundation model with land cover maps for different flood scales to analyze the impact of urbanization and land cover changes on flood runoff, flood inundation extent, and flood inundation volume. Then, we comprehensively assessed flood risk by integrating hazard conditions simulated under different land cover conditions using a hydrologic–hydraulic model and the AHP-MCDA approach. The results showed that the flood inundation extent and the peak inundation volume for a 200-year flood may increase in the future by 10.66% and 15.04%, respectively, as a result of urbanization. The results also highlighted that urbanization may lead to an expansion of high-risk and very-high-risk areas in the future by 3.2% and 9.4%, respectively, indicating an increase in the valley’s flood vulnerability and greater severity of flood hazards.