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Flood inundation modelling: A review of methods, recent advances and uncertainty analysis
Abstract
This paper reviews state-of-the-art empirical, hydrodynamic and simple conceptual models for determining flood inundation. It explores their advantages and limitations, highlights the most recent advances and discusses future directions. It addresses how uncertainty is analysed in this field with the various approaches and identifies opportunities for handling it better. The aim is to inform scientists new to the field, and help emergency response agencies, water resources managers, insurance companies and other decision makers keep up-to-date with the latest developments. Guidance is provided for selecting the most suitable method/model for solving practical flood related problems, taking into account the specific outputs required for the modelling purpose, the data available and computational demands. Multi-model, multi-discipline approaches are recommended in order to further advance this research field.
... In this case, the resistance parameter is selected based on a uniform distribution (Pappenberger et al. 2005). The range of resistance parameters varies in a certain space (Teng et al. 2017) depending on the morphology: i) cascade and plane bed: 0.03-0.5 and ii) step-pool: 0.03-0.7 (Cedillo et al. 2021b). • The models are run with the chosen parameter sets. ...
... • The likelihood of obtaining accurate predictions with a given roughness factor is determined. For this purpose, a likelihood function must be arbitrarily selected (Blasone et al. 2008;Jung and Merwade 2012;Teng et al. 2017). Water depth readings taken at staff gauges are used for comparison. ...
... • The parameter sets are divided into behavioral and nonbehavioral sets. Thus, a cutoff threshold is arbitrarily chosen (Blasone et al. 2008;Jung and Merwade 2012;Teng et al. 2017). • The likelihood in the behavioral model is rescaled to obtain the cumulative density function of the residuals at each staff gauge (Aronica et al. 1998). ...
Generalized Likelihood Uncertainty Estimation (GLUE) is a widely used methodology for propagating uncertainty through models. However, GLUE has been criticized because of the random selection of two components: i) the likelihood function, which is used to determine the probability that a given set of parameters reflects the observed data, and ii) the cutoff threshold, which is used to divide models into behavioral and nonbehavioral groups. In this research, a GLUE procedure is implemented based on three mountain river morphologies (cascade, step-pool, and plane bed) with different flow characteristics (high, moderate and low flow) located in the Quinuas River basin. Geometry, flow, bed material, and field roughness data are available for the studied reaches. The simple Fuzzy-rule provides different results than metric-based likelihood functions, so a modification of the simple fuzzy-rule is suggested. The metric-based-likelihood functions influence likelihood curve shape and uncertainty values for a certain threshold when the system under study do not meet the model simplifications. The cutoff threshold is proven necessary for reducing uncertainty; however, this value cannot be too stringently set because there are many cases in which observations fall outside the 5% and 95% confidence intervals, producing outliers. A reasonable cutoff threshold seems to be 12%, which is the uncertainty in the water depth estimated with the continuity equation.
... Australia's climate vulnerability status has elicited an upsurge in the prevalence of more recurrent and austere extreme rainfall intensity storm events, imposing an incessant residual urban flooding risk [1]. Urban flooding has become a devastating environmental issue worldwide, primarily owing to the strong intensity of these events [1], as well as the minuscule response period availability [2], and the innumerous repercussions upon urban ecosystems [3] [4]. In contrast, the urban flood prediction process is contemplated to be decidedly complex [4], pertaining to the intricate topography and inherent non-linearity associated with influential hydrological processes of urban catchment [5], with varying spatial and temporal resolutions [5] [6], in conjunction with limited data availability [7] [8] [9]. ...
... Recently, numerous hydrological, empirical and hydrodynamic models have been employed for the urban flood forecasting process, primarily involving understanding the urban catchment response state to intense storm events [2] [10] [11]. However, various limitations allied with these models including general assumption of linear relationships between input-output variables [11] [12], the necessity of detailed datasets [13], internal inconsistencies [14] [15] and high computation costs [14] [15], momentously impact the performance, prediction accuracy and reliability of these results [13] [14][15] [16]. ...
... On an overall scale, comparing the performance of both the models, for the training and testing datasets, the ANN model has clearly outperformed the RORB model in terms of performance, results reliability and prediction accuracy as exhibited by the results (as seen in Table 1 above and Figs. [2][3][4], and further depicted by the stronger, higher positive correlation, higher goodness of fit achievement, excellent predictive skill and the co-existence of minimal differences between the model predictions and the observed records. ...
Australia’s surging climate vulnerability status has amplified the likelihood for prevalence of more recurrent and austere intense rainfall events, triggering an incessant residual flooding risk within the urban landscape. The complex topography and inherent non-linearity of influential hydrological processes of urban catchments limits the prediction accuracy of conventional hydrological models, leading to over/(under)estimation of expected flood flow. Recently, Artificial Neural Networks (ANNs) have shown commendable progress in offsetting most limitations allied with conventional models - especially in simulating highly complex, non-linear relationships without needing to understand underlying physical processes. Thus, this research proposes an ANN-based enhanced accuracy flow estimation model for accurate simulation of expected flood flow in urban catchments. Gardiners Creek catchment, located in east Melbourne was selected as the study area, where the model is calibrated with historical storm event data and performance is assessed against Victorian Water Industry standard hydrological model, RORB results. The study results depict that the ANN model outperforms the Runoff Routing Burroughs (RORB) model, in terms of superior performance, excellent predictive skill and lower minimal error, highlighting that the ANN model has good understanding and is capable of accurately modelling the current catchment response state to storm events, and can be considered for implementation in improving the accuracy of current flood flow estimation practices, and also for tackling emerging challenges in flood investigation process due to climate change impacts.
... In the context of geophysical flow hazards, understanding the spatial and temporal evolution of the flow play crucial roles in the hazard analysis process. With advancements in computing power, there has been an increasing utilization of numerical models and visualization techniques to analyze geophysical flow hazards (Teng et al., 2017;Luo et al., 2022;Trujillo--Vela et al., 2022). Numerical modelling of geophysical flows often generates extensive datasets, organized in tables or arrays, containing numerous numerical values representing various dependent variables. ...
... Mesh-based Eulerian methods are the conventional approaches in CFD simulations. The majority of the numerical models used in geophysical flow disaster risk management are mesh-based Eulerian models (Teng et al., 2017;Trujillo-Vela et al., 2022). ...
... The framework comprises a numerical modeling tool (orange) with a built-in or external visualization tool, the post-processing scripts (gray), and the VR visualization (green) as shown in Fig. 2. The first step in implementing the framework is to choose the appropriate numerical simulation model for generating the geophysical flow data. We opted for mesh-based numerical models which are based on the Eulerian frame of reference due to their widespread use (Teng et al., 2017;Trujillo-Vela et al., 2022). For most of such numerical models, the key input parameters include the terrain that serves as the computational domain, parameters governing initial and boundary conditions, time step for storing output data, and additional parameters that vary based on the specific numerical and rheological models utilized. ...
This paper presents a comprehensive Virtual Reality (VR) based framework for visualizing numerical simulations of geophysical flows in a realistic and immersive manner. The framework allows integrating output data from various mesh-based Eulerian numerical models into a VR environment, enabling users to interact with and explore the terrain and geophysical flows through the VR experience. Three case studies, including a snow avalanche, quick clay landslide, and flash flood in Norway, demonstrate its versatility. The VR environment offers intuitive menus and user interactions, allowing users to read flow depth and velocity values, facilitating a direct link between numerical data and their visual representation. This framework can reshape geophysical flow hazard identification and disaster management by integrating physics-based numerical modeling results into VR Environments, thus enhancing knowledge dissemination among experts, the general public, non-expert stakeholders, and policymakers. The paper also highlights challenges and opportunities identified during the integration, guiding future developments.
... scale being the preferred method in populated areas (Fleischmann, Paiva, & Collischonn, 2019). Hydrodynamic models are specific in their use of mathematical equation solving to represent water movement relying upon laws of physic in the river and in the floodplain (Teng et al., 2017). ...
... According to Shustikova et al. (2019), the spatial resolution at which hydrodynamic models operate is 30 m for regional scales and less than 10 m for local scales. High-resolution LiDAR (Light Detection And Ranging) digital elevation models (DEMs) are now routinely used in hydrodynamic models to characterise the floodplain (Teng et al., 2017). Hydrodynamic modelling also requires information regarding river geometry (bathymetry). ...
Topo-bathymetric LiDAR (TBL) can provide a continuous digital elevation model (DEM) for terrestrial and submerged portions of rivers. This very high horizontal spatial resolution and high vertical accuracy data can be promising for flood plain mapping using hydrodynamic models. Despite the increasing number of papers regarding the use of TBL in fluvial environments, its usefulness for flood mapping remains to be demonstrated. This review of real-world experiments focusses on three research questions related to the relevance of TBL in hydrodynamic modelling for flood mapping at local and regional scales: (i) Is the accuracy of TBL sufficient? (ii) What environmental and technical conditions can optimise the quality of acquisition? (iii) Is it possible to predict which rivers would be good candidates for TBL acquisition? With a root mean square error (RMSE) of 0.16 m, results from real-world experiments confirm that TBL provides the required vertical accuracy for hydrodynamic modelling. Our review highlighted that environmental conditions, such as turbidity, overhanging vegetation or riverbed morphology, may prove to be limiting factors in the signal's capacity to reach the riverbed. A few avenues have been identified for considering whether TBL acquisition would be appropriate for a specific river. Thresholds should be determined using geometric or morphological criteria, such as rivers with steep slopes, steep riverbanks, and rivers too narrow or with complex morphologies, to avoid compromising the quality or the extent of the coverage. Based on this review, it appears that TBL acquisition conditions for hydrodynamic modelling for flood mapping should optimise the signal's ability to reach the riverbed. However, further research is needed to determine the percentage of coverage required for the use of TBL as a source of bathymetry in a hydrodynamic model, and whether specific river sections must be covered to ensure model performance for flood mapping. K E Y W O R D S flood mapping, hydrodynamic modelling, LiDAR, riverbed elevation, topo-bathymetry, uncertainties
... Some of the impacts of climate change include increased temperatures, sea level rise, and intense severe weather events like droughts and floods [1,2]. Floods are one of the most frequent, destructive, costly, and widespread natural disasters worldwide [3][4][5][6]. Floods are known to affect societies, economies, and ecosystems and, at certain times and places, can have devastating impacts [7,8]. They cause casualties and property damage [3] on every inhabited continent and cause severe losses that negatively affect regional socio-economic development, industry, agriculture, and infrastructure [5], as well as cultural heritage [9][10][11]. ...
... Floods are known to affect societies, economies, and ecosystems and, at certain times and places, can have devastating impacts [7,8]. They cause casualties and property damage [3] on every inhabited continent and cause severe losses that negatively affect regional socio-economic development, industry, agriculture, and infrastructure [5], as well as cultural heritage [9][10][11]. The reason for flood generation is mainly due to heavy or prolonged rainfall and can have significant impacts on the water load of rivers, streams, and canals. ...
Sentinel-2 data are crucial in mapping flooded areas as they provide high spatial and spectral resolution but under cloud-free weather conditions. In the present study, we aimed to devise a method for mapping a flooded area using multispectral Sentinel-2 data from optical sensors and Geographical Information Systems (GISs). As a case study, we selected a site located in Northern Italy that was heavily affected by flooding events on 3 October 2020, when the Sesia River in the Piedmont region was hit by severe weather disturbance, heavy rainfall, and strong winds. The method developed for mapping the flooded area was a thresholding technique through spectral water indices. More specifically, the Normalized Difference Water Index (NDWI) and the Modified Normalized Difference Water Index (MNDWI) were chosen as they are among the most widely used methods with applications across various environments, including urban, agricultural, and natural landscapes. The corresponding flooded area product from the Copernicus Emergency Management Service (EMS) was used to evaluate the flooded area predicted by our method. The results showed that both indices captured the flooded area with a satisfactory level of detail. The NDWI demonstrated a slightly higher accuracy, where it also appeared to be more sensitive to the separation of water from soil and areas with vegetation cover. The study findings may be useful in disaster management linked to flooded-area mapping and area rehabilitation mapping following a flood event, and they can also valuably assist decision and policy making towards a more sustainable environment.
... Accurate estimation of flood risk in coastal areas is of paramount importance, particularly in the face of increasing frequency and severity of extreme weather events catalyzed by climate change and sea-level rise (IPCC, 2022). While flood hazard assessment has evolved from simple empirical methods to more complex probabilistic methods, the challenges of data quality and model complexity remain (Teng et al., 2017;Moftakhari et al., 2019;Santos et al., 2021;Abbaszadeh et al., 2022;Jafarzadegan et al., 2023). The need for highquality data of adequate record length and the intricacy of integrating all relevant factors into the models are ever-present challenges (Teng et al., 2017;Moftakhari et al., 2019;Bensi et al., 2020;Santos et al., 2021;Abbaszadeh et al., 2022;Jafarzadegan et al., 2023). ...
... While flood hazard assessment has evolved from simple empirical methods to more complex probabilistic methods, the challenges of data quality and model complexity remain (Teng et al., 2017;Moftakhari et al., 2019;Santos et al., 2021;Abbaszadeh et al., 2022;Jafarzadegan et al., 2023). The need for highquality data of adequate record length and the intricacy of integrating all relevant factors into the models are ever-present challenges (Teng et al., 2017;Moftakhari et al., 2019;Bensi et al., 2020;Santos et al., 2021;Abbaszadeh et al., 2022;Jafarzadegan et al., 2023). ...
Compound flooding ( CF ) events, driven by coincident/concurrent and mutually reinforcing factors such as heavy rainfall, storm surges, and river discharge, pose severe threats to coastal communities around the Globe. Moreover, the exacerbating influence of climate change and sea-level rise further amplifies these risks. This study delves into the complex and multifaceted issue of compound coastal flooding in two freshwater-influenced systems on the Gulf Coast of the United States – Southeast Texas and South Alabama. We first conduct a robust statistical analysis to evaluate the significance of non-stationarity, multi-dimensionality, and non-linearity of interactions among various drivers of CF . Second, to assess the extent to which current flood resilience policies and guidelines account for these characteristics of CF events, we perform a critical review of existing policy documents. The results of the statistical analysis reveal significant compounding and shifts in the statistics of flood drivers that emphasize the pressing need for a multi-mechanism, non-stationary approach to flood hazard assessment. We also found an evident lack of appropriate language/recommendation in policy documents of solid tools that systematically take non-stationarity, multi-dimensionality, and non-linearity of CF into account. By identifying the gaps between current policy measures and the detected complexities of CF , we seek to provide insights that can inform more effective flood resilience policies and design guidelines. Through this robust analysis, we aspire to bridge the divide between research and policy.
... For larger basins, gaging records are commonly used when they are available, with the threats due to non-stationarity considered. Modeling advances have been incorporated into advanced integrated software packages that are available through open sources and commercial outlets [52]. In the US, models are available from the US Geological Survey [53], Agricultural Research Service [54], US Army Corps of Engineers [55], and Natural Resources Conservation Service [56]. ...
... The USACE has benchmarked its flagship floodplain model [66] and FEMA has issued general guidance about modeling [67]. It is inevitable that models will give different results, which was predicted in studies of hydrologic and hydraulic sources of uncertainty and continues to be the case [52]. ...
Losses from flood disasters are increasing globally due to climate-driven forces and human factors such as migration and land use changes. The risks of such floods involve multiple factors and stakeholders, and frameworks for integrated approaches have attracted a global community of experts. The paper reviews the knowledge base for integrated flood risk management frameworks, including more than twenty bibliometric reviews of their elements. The knowledge base illustrates how integrated strategies for the reduction of flood risk are required at different scales and involve responses ranging from climate and weather studies to the construction of infrastructure, as well as collective action for community resilience. The Integrated Flood Management framework of the Associated Programme on Flood Management of the World Meteorological Organization was developed more than twenty years ago and is explained in some detail, including how it fits within the Integrated Water Resources Management concept that is managed by the Global Water Partnership. The paper reviews the alignment of the two approaches and how they can be used in tandem to reduce flood losses. Success of both integrated management approaches depends on governance and institutional capacity as well as technological advances. The knowledge base for flood risk management indicates how technologies are advancing, while more attention must be paid to social and environmental concerns, as well as government measures to increase participation, awareness, and preparedness. Ultimately, integrated flood management will involve solutions tailored for individual situations, and implementation may be slow, such that perseverance and political commitment will be needed.
... While high-fidelity models offer precision, they come with substantial computational demands. Strategies such as non-physics-based (simplified) methods 29 and model emulation, as demonstrated by Ivanov et al. 24 and Fraehr et al. 30 , seek to strike a balance between computational efficiency and prediction accuracy. Sustaining prediction accuracy requires accounting for a wide range of flooding scenarios and inundation behaviours 30 . ...
... However, these approaches may encounter challenges when adapting to diverse flood scenarios or diverse landscape contexts 31 . Simplified methods, for instance, are particularly suitable for applications where dynamic effects play a minimal role, and the focus is primarily on the final or maximum flood extent and water levels 29 . Moreover, surrogate models may struggle when faced with inputs outside their training scope or complex, non-linear interactions among flood drivers 32 . ...
Despite considerable advances in flood forecasting during recent decades, state-of-the-art, operational flood early warning systems (FEWS) need to be equipped with near-real-time inundation and impact forecasts and their associated uncertainties. High-resolution, impact-based flood forecasts provide insightful information for better-informed decisions and tailored emergency actions. Valuable information can now be provided to local authorities for risk-based decision-making by utilising high-resolution lead-time maps and potential impacts to buildings and infrastructures. Here, we demonstrate a comprehensive floodplain inundation hindcast of the 2021 European Summer Flood illustrating these possibilities for better disaster preparedness, offering a 17-hour lead time for informed and advisable actions.
... Various flood hazard, vulnerability, and risk prediction methods have been categorized into physically based or simulation model-based approaches and empirical approaches (Wu et al. 2015;Lyu et al. 2019). Physically based approaches typically employ calibrated computer-based hydraulic simulation models to simulate synthetic design flood events and generate flood hazard maps (Teng et al. 2017;Nkwunonwo et al. 2020). Damage associated with different flood stages is assessed using depth-damage curves, and risk is represented by the monetary damage computed for various exceedance probabilities (Wright 1994;Tsakiris 2014). ...
Flood risk mapping is instrumental in guiding land-use decisions, development planning, disaster management, and mitigation strategies. However, the accuracy of such maps relies heavily on the availability of comprehensive data. When such data are lacking, empirical approaches are employed to estimate flood risk. Several recent studies have developed flood risk maps using multicriteria decision-making (MCDM), such as the analytical hierarchy process (AHP). However, flood risk mapping methods using MCDM techniques are zero-dimensional models, and they cannot be associated with a flood of a particular exceedance probability. Notably, flood inundation models can predict floods and help map flood parameters for floods with different return periods. Accordingly, this study proposes a new framework for mapping flood risk for floods with different return periods by integrating inundation maps obtained from a flood simulation model with an MCDM framework in a geographic information system (GIS) environment. The proposed method integrates remote sensing data, hydraulic modeling, and AHP combined with a sensitivity analysis to develop a flood risk map. The applicability of the proposed framework is demonstrated by employing it to create flood risk maps for flood events with different return periods in the East Fork White River (EFWR) in Columbus, Indiana, USA. The results reveal a significant correspondence between high-risk zones identified in the flood risk maps and areas with high values on an available flood damage map of the study area, confirming the efficacy of the proposed framework. This study highlights the potential of the methodology as a valuable tool for generating flood risk maps in areas where comprehensive flood risk assessment data are limited. Additionally, the flexibility of the GIS-based approach allows for the adaptation and application of the methodology to different geographic locations and flood scenarios. Thus, the proposed framework offers a robust and practical approach to flood risk mapping with potential applications in disaster management and land-use planning strategies.
... Recently, the increasing availability of useroriented computational codes has encouraged the use of two-dimensional (2-D) SWE models, as discussed by Pilotti, et al. [174]. Teng, et al. [175] reviewed several popular software/models capable of modeling flood inundation, while Néelz and Pender [176] compared the performance of some common 2-D software. ...
This dissertation aims to investigate the factors behind flash flooding in Erbil's central district, located in the Kurdistan Region of Iraq, and develop a methodology for assessing flood hazards in the city, despite limited data accessibility. In this thesis, each factor was investigated, including analyzing extreme precipitation events in the last two decades, including their spatial and temporal distribution of rainfall, intensity, and exceedance probability, and examines the impact of changes in Land Use and Land Cover (LULC) on the hydrological response of the Erbil basin. The hydrodynamic model's input data were generated using GIS-based modeling interface. HEC-RAS 2-D software package's suitability was ensured by evaluating two building representation techniques and two mathematical models (Diffusion-Wave Equations (DWE) and Shallow-Water Equations (SWE)) using the Toce River urban flood experimental model. The study utilized a two-dimensional hydrodynamic model (HEC-RAS 2-D) to assess the susceptibility, vulnerability, and socioeconomic impact of flooding in the study area. Using the model, flood hazard maps were created to show the extent of potential flooding in the study area during various rainfall events and return periods. Ultimately, the study concludes that without essential engineering measures, there is an increased probability of flooding in the center of Erbil.
... However, because it reviews all types of flooding, the mitigation methods related to urban flooding are not specific. Teng et al. (2017) and Fenton (2019) provide processes for flood and risk assessment methods, but provide minimal detail on urban flooding. There are also many studies on urban flood simulation models (hydrological models, hydraulic models, simplified models), but it is still difficult to determine spatial priorities and implement solutions with a future perspective (Qi et al., 2022). ...
Estimating potential changes in future flood patterns based on anticipated changes in hydrological characteristics within the basin is crucial for mitigating flood damage and managing flood risk. In this study, nonparametric probability models are used to estimate future rainfall patterns in Seoul under the GCM‐based climate change scenarios (CCS), and the estimated future daily rainfall data was temporally downscaled to hourly units using the KNNR‐GA technique. Changes in flood hazard and runoff characteristics of the target area based on the estimated future rainfall data are quantitatively assessed. The results highlight that under CCS, flood runoff may increase further into the future, resulting in more significant changes in flood patterns and accelerating the increase in flood hazard. The delta change factor of flood risk indicators increased relatively significantly in more severe CCS. This study also proposed a process to estimate future flood runoff and mitigation effects according to CCS by reflecting various flood mitigation measures in the urban drainage system model. These findings can offer valuable insights for setting the direction of current and future mitigation measures.
... The assessment of flood-hazard variability within a floodvulnerable urban zone is essential for management and description of danger to property, infrastructure, and people. Flood hazard is commonly assessed based on the outcome of physically based models that simulate the water movement across the floodplain (Teng et al. 2017). The primary parameters used to measure flood hazard include flood depth (Beadenkopf 2013) and flow velocity (Kreibich et al. 2009), either considered individually or combined to establish an appropriate flood hazard indicator. ...
Digital Elevation Models (DEMs) play a crucial role in flood management. This study aims to assess the effect of various global DEMs (GDEMs), including ALOS-12.5 m, ALOS-30 m, SRTM-30 m, SRTM-90 m, and NASADEM-30 m, on flood risk modeling in a densely urban area. The 1D-2D MIKE FLOOD hydraulic model was employed for the flood modeling. The process involved using a high-resolution DEM (Pleiades-1A 1 m) as the reference map (RM1), along with other GDEMs, to simulate a 50-year return period flood. The performance of GDEMs was then assessed in terms of flood inundation extent, flood hazard, and flood damage estimation, assessing their accuracy against the RM1. The study also explored the trade-offs between accuracy and efficiency by examining the effects of substituting the high-resolution map with a 5-m resolution map (Res_5 m) created through resampling. Results revealed that GDEMs tend to overestimate flood extent and underestimate depth, leading to inaccurate flood risk assessments. Among the GDEMs, NASADEM-30 and SRTM-30 outperformed others in simulating flood inundation extent but resulted in a more uniform flood depth distribution; approximately 70% of the flood extent was categorized with depths of less than 0.3 m, nearly double that of the RM1. This discrepancy led to an underestimation and overestimation of higher (H3-H6) and lower (H1) hazard levels by approximately 50%, respectively. Furthermore, GDEMs significantly overestimated flood damages, with NASADEM-30 showing a 161% overestimation compared to the RM1. Ultimately, the Res_5 m was a viable alternative for urban flood simulations as it led only to a modest 6% decrease in the flood zone area.
... The assessment of flood-hazard variability within a floodvulnerable urban zone is essential for management and description of danger to property, infrastructure, and people. Flood hazard is commonly assessed based on the outcome of physically based models that simulate the water movement across the floodplain (Teng et al. 2017). The primary parameters used to measure flood hazard include flood depth (Beadenkopf 2013) and flow velocity (Kreibich et al. 2009), either considered individually or combined to establish an appropriate flood hazard indicator. ...
Digital Elevation Models (DEMs) play a crucial role in flood management. This study aims to assess the effect of various global DEMs (GDEMs), including ALOS-12.5 m, ALOS-30 m, SRTM-30 m, SRTM-90 m, and NASADEM-30 m, on flood risk modeling in a densely urban area. The 1D-2D MIKE FLOOD hydraulic model was employed for the flood modeling. The process involved using a high-resolution DEM (Pleiades-1A 1 m) as the reference map (RM1), along with other GDEMs, to simulate a 50-year return period flood. The performance of GDEMs was then assessed in terms of flood inundation extent, flood hazard, and flood damage estimation, assessing their accuracy against the RM1. The study also explored the trade-offs between accuracy and efficiency by examining the effects of substituting the high-resolution map with a 5-m resolution map (Res_5 m) created through resampling. Results revealed that GDEMs tend to overestimate flood extent and underestimate depth, leading to inaccurate flood risk assessments. Among the GDEMs, NASADEM-30 and SRTM-30 outperformed others in simulating flood inundation extent but resulted in a more uniform flood depth distribution; approximately 70% of the flood extent was categorized with depths of less than 0.3 m, nearly double that of the RM1. This discrepancy led to an underestimation and overestimation of higher (H3-H6) and lower (H1) hazard levels by approximately 50%, respectively. Furthermore, GDEMs significantly overestimated flood damages, with NASADEM-30 showing a 161% overestimation compared to the RM1. Ultimately, the Res_5 m was a viable alternative for urban flood simulations as it led only to a modest 6% decrease in the flood zone area.
... Hawker et al. (2018) cited that it is the high accuracy of DEM that enhances flood estimates. Shuttle Radar Topography Mission (SRTM) DEM is the most widely used topographic product in hydrodynamic modelling due to its global coverage (Teng et al. 2017). Most of the open-source global remote sensing DEMs are surface models that include heights of forest, vegetation and built-up structures. ...
Flood hazard assessment of cities gained significance globally due to rise in frequency of flood events and rapid urbanisation. Uncertainties in flood inundation models largely depend on the quality of input datasets, among which topography plays a vital role. This study demonstrates the effectiveness of global terrain models in simulating accurate flooding and generating hazard maps while considering the influence of land-use dynamics focussing on data-scarce regions. Open-source forest and building removed digital elevation model (FABDEM) and a terrain model TDX-12 DTM derived from TanDEM-X 12 DSM using a simple-morphological-filtering technique are considered for comparing their performance in simulating a flood event occurred in Surat city during the year 2006. Spatially varying short-term urban growth scenario for the year 2035 is developed by utilizing historical land-use maps of the study area and urban growth indicators. These are combined using multi-criteria-decision-making techniques and Cellular-Automata-Markov-Chain model. The FABDEM based hydrodynamic model performed better (Root-Mean-Squared-Error RMSE of 1.59 m) than TDX-12 DTM based model (RMSE: 1.88 m). Intercomparison of hazard maps of the FABDEM and TDX-12 DTM with ground-surveyed TopoDEM based-model showed an overall accuracy of 71.8% and 72.8%; for the future scenario 71.8% and 75.5% respectively. In a span of 29 years, a notable increase in hazard magnitude of 7.5% is solely attributed to change in land dynamics. In this study, though the FABDEM based-model showed better RMSE than TDX-12 DTM, the model is relatively less successful in capturing high-hazard regions. The DEMs processed for removal of non-ground objects yield accurate models than globally trained models.
Graphical Abstract
... Faktor lain yang menyebabkan banjir adalah dampak dari aktivitas manusia. Hal ini termasuk perubahan penggunaan lahan, pembuangan sampah yang tidak terkendali, perkembangan kawasan kumuh di sekitar sungai atau sistem drainase, serta kurangnya perencanaan yang baik dalam mengatasi banjir [3,4], curah hujan yang sangat tinggi melebihi kapasitas saluran di wilayah tersebut serta, saluran pembuangan yang tidak mampu menampung debit air yang ada, dan penumpukan endapan sedimen yang mengurangi kapasitas tampungan juga menjadi penyebab terjadinya banjir dan genangan [5,6]. Genangan air memiliki dampak yang merugikan, diantaranya mengakibatkan kerusakan Infrastruktur, kerugian ekonomi, serta mengganggu pasokan air bersih [7]. ...
Abstrak: Kecamatan Sindang yang terletak di Kabupaten Indramayu, secara berulang mengalami genangan saat hujan terjadi, terutama di sepanjang saluran drainase di Perumahan Graha Artha Residence. Faktor utama pemicunya adalah perubahan lingkungan yang awalnya pada tahun 2015 merupakan lahan rawa yang berfungsi sebagai resapan air, mengakibatkan sistem drainase yang ada saat ini tidak dapat mengatasi secara efisien aliran air hujan yang tinggi dan sering mengakibatkan genangan, Penelitian ini bertujuan untuk menganalisis saluran drainase di perumahan Graha Artha dengan menggunakan perangkat lunak Storm Water Management Model (SWMM). Data yang diperlukan untuk analisis diperoleh dari survei lapangan dan pemodelan dengan mempertimbangkan periode ulang Q2, Q5, dan Q10 tahun. Hasil analisis menunjukkan tinggi genangan rata-rata selama periode ulang Q2 tahun adalah 205,509 mm dengan lama genangan 1,81 jam. Pada periode ulang Q5 tahun, tinggi genangan rata-rata mencapai 218,900 mm dengan lama genangan 1,82 jam, sementara pada periode ulang Q10 tahun, tinggi genangan rata-rata mencapai 230,490 mm dengan lama genangan 1,91 jam. Selanjutnya, kapasitas rata-rata saluran eksisting di perumahan Graha Artha sebesar 0,0015 m3 ternyata tidak mampu menampung debit intensitas pada periode ulang Q2 tahun, Q5 tahun, maupun Q10 tahun. Sebagai solusi, dilakukan evaluasi dengan menambah kedalaman saluran dari 0,3 meter menjadi 1 meter. Hasil evaluasi menunjukkan bahwa dengan penambahan kedalaman tersebut, volume rata-rata saluran meningkat dari 0,0035 m3 menjadi 0,0130 m3, mengindikasikan peningkatan kemampuan saluran dalam menampung debit air. Hasil penelitian ini memberikan pemahaman yang lebih mendalam tentang kondisi drainase di perumahan Graha Artha dan memberikan rekomendasi untuk peningkatan kapasitas saluran guna mengurangi risiko genangan air di masa yang akan datang. Kata kunci: genangan; sistem drainase; kapasitas saluran; kala ulang; (SWMM) 1. Pendahuluan Banjir adalah peristiwa ketika air tidak mampu ditampung oleh saluran pembuangan atau mengalami hambatan aliran, sehingga mengakibatkan luapan air yang menyebabkan genangan di daerah sekitarnya, yang biasanya disebut sebagai dataran banjir. Banjir secara teoritis diakibatkan oleh volume air di suatu badan air seperti sungai atau danau yang meluap dan kemudian melimpas dari bendungan sehingga air keluar dari sungai dan merendam daratan, beberapa faktor seperti ukuran dan sifat sungai di dalam dan sekitar sungai. Tinggi muka air di hilir juga menentukan terjadi atau tidaknya banjir di suatu wilayah tersebut [1] Dampak yang sangat signifikan akibat banjir bagi
... (Kadam & Sen, 2012;Patro et al., 2009;Pramanik et al., 2010;Timbadiya et al., 2015) Flood inundation models can be categorized into several types based on their complexity, purpose, and available data. These models can be hydraulic models, hydrological models, empirical models, hydrodynamic models, lumped and distributed models, etc. (Afshari et al., 2018;Nkwunonwo et al., 2020;Teng et al., 2017;Vashist & Singh, 2020). Each type of flood inundation model has advantages and disadvantages, and the best model for the analysis will rely on a variety of factors, including the data available, computational resources, modeling goals, and the desired level of accuracy and detail. ...
... InfoWorks RS suggests a range of abilities to model and simulate the conditions of rivers, including flood dynamics, water levels, speeds, sediment erosion, and water quality. It uses progressive computational procedures and hydraulic models to precisely represent the complex hydraulic processes happening in river systems (Teng et al. 2017). Adnan et al. (2016) conducted a study to evaluate flood patterns and create a comprehensive flood map for the Segamat River in Malaysia. ...
... InfoWorks RS suggests a range of abilities to model and simulate the conditions of rivers, including flood dynamics, water levels, speeds, sediment erosion, and water quality. It uses progressive computational procedures and hydraulic models to precisely represent the complex hydraulic processes happening in river systems (Teng et al. 2017). Adnan et al. (2016) conducted a study to evaluate flood patterns and create a comprehensive flood map for the Segamat River in Malaysia. ...
Abstract
Stormwater management modeling tools have been utilized to enhance stormwater operating
systems, assess modeling system efficiency, and evaluate the impacts of urban growth
on stormwater runoff and water quality. This review explores the potential of stormwater
management strategies and Artificial Intelligence modeling tools in enhancing water quality.
The study focuses on evaluating stormwater modeling tools for planning and improving
stormwater systems, assessing modeling efficiency, and understanding the impacts of new
development on stormwater runoff and water quality. Various stormwater modeling tools
are compared to aid in water management in urban and rural settings, which is crucial due
to increasing storm intensity from climate change. The review debates the advantages and
limitations of different modeling tools, particularly in modeling stormwater quantity and
quality under different scenarios. It also examines tools used for predicting and analysing
stormwater runoff during storm events in diverse locations. The assessment of modeling
tools is centred on their support for Green Infrastructure (GI) practices, considering factors
like modeling accuracy, data availability, and requirements. The study highlights the
importance of these tools in managing water in urban areas and safeguarding water sources
during stormwater events. Notably, the accuracy and efficacy of stormwater modeling tools
are influenced by input data quality, calibration methods, and standardization metrics, with
the widely used Stormwater Management Model (SWMM) being a common modeling tool.
... Currently, the simulation of earthquakes with supercomputers has been an active research field, and there is significant effort being invested by researchers in developing open-access datasets to facilitate further data-driven research (Kovner, 2022). There has also been significant research for fire and flood modelling using both physics-based and machine learning approaches Teng et al., 2017). However, there is relatively less research specifically focused on immersive visualisation for extreme events, especially for intelligent visualisation that can adapt dynamically to different environments in simulated scenarios. ...
Realistic immersive visualisation can provide a valuable method for studying extreme events and enhancing our understanding of their complexity, underlying dynamics and human impacts. However, existing approaches are often limited by their lack of scalability and incapacity to adapt to diverse scenarios. In this chapter, we present a review of existing methodologies in intelligent visualisation of extreme events, focusing on physical modelling, learning-based simulation and graphic visualisation. We then suggest that various methodologies based on deep learning and, particularly, generative artificial intelligence (AI) can be incorporated into this domain to produce more effective outcomes. Using generative AI, extreme events can be simulated, combining past data with support for users to manipulate a range of environmental factors. This approach enables realistic simulation of diverse hypothetical scenarios. In parallel, generative AI methods can be developed for graphic visualisation components to enhance the efficiency of the system. The integration of generative AI with extreme event modelling presents an exciting opportunity for the research community to rapidly develop a deeper understanding of extreme events, as well as the corresponding preparedness, response and management strategies.
... Traditionally, flood visualisation has focused on mapping of flood extent, velocity and depth derived from hydrodynamic models. An excellent review on modelling and visualising flood inundation is provided by Teng et al. (2017). Flood mapping entails tracking the evolution of a flood wave in space and time as it follows the path of gravity and disperses over the downstream terrain. ...
This chapter canvasses the latest developments in the modelling and communication of environmental extremes, with a focus on floods. Three scenarios are explored. The first refers to real-time prediction, including the current modelling basis that is adopted, and the visualisation/communication strategies in place. The second refers to an environmental extreme event that is conditional to a failure scenario, as is the case when an existing infrastructure (i.e. levee or spillway in an extreme flood) fails. The third, more complex scenario is the occurrence of a compound or joint extreme, possibly in the future, where extreme storms will intensify. A compound extreme here could represent a flood event that follows from an incident of rare storm conditions on a fire-damaged landscape. While the modelling challenges are significant, visualisation is even more challenging, as the scenario occurs under a hypothetical future. Demonstrating how coupled models can support the anticipation of extreme event scenarios, the chapter considers implications for risk assessment and communication that can support future preparedness and resilience. Surveying knowledge gaps that still need to be bridged, the authors formulate a list of key requirements in the fields of data availability, processing and representation.
... Therefore, we adopt the method of model simulation to compensate for data deficiencies by simulating the interaction between rainfall intensity and water accumulation depth. The simulation methodology for rainstorm-induced waterlogging models is well established and widely employed in the academic community, providing strong support for this article [45][46][47]. Building upon previous research, this paper further expands the analytical approach for simulation results by applying cluster analysis to reveal the relationship between rainfall intensity and water accumulation depth. Additionally, flood resilience assessment is introduced as an important component of the warning model construction. ...
Against the backdrop of increasingly severe global climate change, the risk of rainstorm-induced waterlogging has become the primary threat to the safety of historic and cultural districts worldwide. This paper focuses on the historic and cultural districts of Beijing, China, and explores techniques and methods for identifying extreme rainstorm warnings in cultural heritage areas. Refined warning and forecasting have become important non-engineering measures to enhance these districts’ waterlogging prevention control and emergency management capabilities. This paper constructs a rainstorm-induced waterlogging risk warning model tailored for Beijing’s historical and cultural districts. This model system encompasses three sets of models: a building waterlogging early-warning model, a road waterlogging early-warning model, and a public evacuation early-warning model. During the construction of the model, the core concepts and determination methods of “1 h rainfall intensity water logging index” and “the waterlogging risk index in historical and cultural districts” were proposed. The construction and application of the three models take into full account the correlation between rainfall intensity and rainwater accumulation, while incorporating the characteristics of flood resilience in buildings, roads, and the society in districts. This allows for a precise grading of warning levels, leading to the formulation of corresponding warning response measures. Empirical tests have shown that the construction method proposed in this paper is reliable. The innovative results not only provide a new perspective and method for the early-warning of rainstorm-induced waterlogging, but also offer scientific support for emergency planning and response in historical and cultural districts.
... In hydrological methods, simulation is made based on the characteristics of rainfall, infiltration, and runoff in the river basin area (Chen et al. 2015). Hydrodynamic models require high-resolution topographic data and are computationally intensive, which makes it unaffordable to prepare flood risk maps, especially in developing countries (Teng et al. 2017;Samela et al. 2018;Karim et al. 2023). ...
Preparing a map of flood hazard is susceptibility an important step in flood risk management. Therefore, it is necessary to use methods that reduce errors and increase the accuracy of identifying flood hazard areas. This study was conducted to prepare a map of the flood hazard index (FHI) and evaluate subjective and objective multicriteria decision analysis (MCDA) weighting methods. Talar basin, which is located in the north of Iran, has been investigated as a case study for this research. Seven factors influencing flood, including elevation, slope, flow accumulation, distance from the river, rainfall intensity, land cover, and geology, were considered to create a flood hazard map. The weighting of these factors has been performed by the Analytical Hierarchy Process (AHP), sensitivity analysis of AHP (AHPS), Shannon Entropy (SE), and Entropy-AHP. The maps created with the data of past floods were validated with the Accuracy index and Kappa index methods. The results showed that the FHI-SE method was more accurate than others, with an accuracy value of 0.979. FHI-SEA, FHIS, and FHI methods were placed in the next priorities, respectively. Based on the SE method, the factors of distance from the river, elevation, and slope have respectively obtained the highest weight value in creating the flood hazard index map. Distance from river variable was classified separately for mountain and plain regions to reduce the overestimation of flood hazard areas in mountainous areas. The objective weighting method has provided higher accuracy than the subjective weighting method, such as AHP.
... A flood inundation model is effective for planning, designing, and analyzing urban drainage systems and flood events in cities (Bulti and Abebe 2020;Fan et al. 2017;Nkwunonwo et al. 2020;Teng et al. 2017). Conventional modeling methods, such as one-dimensional (1D) sewer models or the combination of a 1D sewer model and a 1D surface network model (1D-1D), fall short in delivering precise results when floodwaters surpass curbs in urban regions (Mark et al. 2004). ...
Pluvial flooding is a critical issue in cities worldwide, particularly in lowland areas with old and deteriorating drainage systems. The primary driver of pluvial flooding is extreme rainfall; other drivers include urbanization, inadequate drainage systems, improper solid-waste management, and the tidal backwater effect. However, the interplay between these drivers makes predicting pluvial floods difficult and complex. Previous studies in developing countries seldom used water-level data or simulation modeling to identify the causes of pluvial flooding. In this study, rainfall data and water-level variations in an open channel drain and a receiving river controlled by sluice gates were collected and evaluated in detail to investigate pluvial flooding events. To predict these events, we generated a hydrodynamic model using InfoWorks ICM and verified its results using water logger data and official field reports. Analysis shows that drainage-system failures due to solid blockage and receiving water-level variation contribute more to pluvial flood occurrence than heavy rainfall. Lastly, we discuss measures to mitigate pluvial flooding in Yangon, Myanmar. The proposed monitoring and modeling approach can suitably predict pluvial flooding occurrence and provide useful quantitative data for flood risk management.
... Estas zonas se pueden delimitar a través de simulaciones hidrodinámicas en las que intervienen insumos hidrológicos y topográficos. Cada uno de estos insumos tiene asociada una incertidumbre natural que se propaga a través de los modelos y repercute en los resultados de simulaciones (Teng et al., 2017). La cuantificación y análisis de la propagación de la incertidumbre cobra importancia en los mapas de amenaza por inundaciones, en los que se basa el ordenamiento territorial. ...
Contexto: La variabilidad natural (incertidumbre) espacial y la temporal de los insumos que intervienen en la modelación hidrodinámica, para la evaluación de la amenaza y la delimitación de las zonas potencialmente inundables, se propagan en las variables de salida de los modelos. En la práctica, suele considerarse la variabilidad natural temporal en términos de periodo de retorno, pero poco se ha analizado la propagación de la incertidumbre espacial del coeficiente de rugosidad. Metodología: En este trabajo se presenta el análisis de la propagación de la incertidumbre a través del método primer orden segundo momento del coeficiente de rugosidad de Manning, en las variables de salida de profundidad, velocidad e intensidad de flujo del modelo numérico de aguas someras, implementado en HEC RAS 2D. Se analizó una inundación súbita simulada en el municipio de Mocoa (Putumayo) y la correspondiente probabilidad de alcanzar un nivel de daño para diferentes periodos de retorno, a propósito de los eventos ocurridos en marzo de 2017. Resultados: Se obtiene un procedimiento aplicable a la evaluación de la amenaza de inundación, en el que se integra la variabilidad natural temporal asociada a las hidrógrafas con diferentes periodos de retorno, y la variabilidad natural espacial relacionada con el coeficiente de rugosidad de Manning. Conclusiones: La propagación de la incertidumbre establece una relación directa entre el aumento del caudal (periodos de retorno) y el aumento de la incertidumbre en la evaluación del indicador de amenaza por inundación.
... These models produce flood depths in gridded-cell form through simulated dike failures by "stress testing" under conditions such as overflow, geotechnical instability (i.e., structure, erosion, slope), and other mechanisms. A more comprehensive review of well-known H&H and flood modeling tools, such as ADCIRC, Delft3D, Mike and more, can be seen in [65,66]. While this modeling is highly technical and occurs over large scales, uncertainties can exist at smaller scales within urban outputs due to the low-resolution spatial and topographic inputs [67,68]. ...
Floods are consistently ranked as the most financially devastating natural disasters worldwide. Recent flood events in the Netherlands, Caribbean, and US have drawn attention to flood risks resulting from pluvial and fluvial sources. Despite shared experiences with flooding, these regions employ distinct approaches and flood management strategies due to differences in governance and scale—offering a three-site case study comparison. A key, yet often lacking, factor for flood risk and damage assessments at the parcel level is building elevation compared to flood elevation. First-floor elevations (FFEs) are a critical element in the vulnerability of a building flooding. US-based flood insurance policies require FFEs; however, data availability limitations exist. Drone-based FFEs were measured in all locations to assess the flood vulnerabilities of structures. Flood vulnerability profiles revealed 64% of buildings were vulnerable to a form of inundation, with 40% belonging to “moderate” or “major” inundation, and inundation elevation means (IEMs) of −0.55 m, 0.19 m, and 0.71 m within the US, Netherlands, and Puerto Rico sites, respectively. Spatial statistics revealed FFEs were more responsible for flood vulnerabilities in the US site while topography was more responsible in the Netherlands and Puerto Rico sites. Additional findings in the Puerto Rico site reveal FFEs and next highest floor elevations (NHFEs) vulnerable to future sea level rise (SLR) flood elevations. The findings within the Netherlands provide support for developing novel multi-layered flood risk reduction strategies that include building elevation. We discuss future work recommendations and how the different sites could benefit significantly from strengthening FFE requirements.
... Urban flooding can have devastating effects on people and their communities (Galloway et al. 2018). High population densities and commercial activity in certain areas typically lead to infrastructure and property damage, along with financial losses (Salimi and Al-Ghamdi 2019;Wan Mohtar et al. 2020), lost wages for people who are unable to get to work, time lost in traffic jams and rerouting results in blocked roads, inundation of homes in low-lying regions, disruption to communication systems, the closure of schools and the consequent effects on students, and unexpected power outages (Anni et al. 2020;Fattah et al. 2018;Teng et al. 2017;Weber 2019). ...
Urban areas are becoming more susceptible to severe storms, flash floods, and drainage system failures due to climate change, population growth, and urbanization. Flood modeling is a useful method for managing storm water drainage networks, predicting behavior, and evaluating effective solutions to structural and operational problems. This research describes the application of the Stormwater Management Model (SWMM) to evaluate the performance and effectiveness of the rainwater network, identify flood-prone locations, and determine the extent of floods in the center of Kerbala Governorate, Iraq. Saif Saad neighborhood was chosen as a case study. The model's validity was confirmed using the occurrence of actual rainfall by the coefficient of determination (R² = 0.8952), normalized mean square error (NMSE = 0.0964), and Nash–Sutcliffe efficiency (NSE = 0.7152), and the model's performance was reasonably good. Simulation results indicated that the system works well under near-term rainfall events, except for some sites that require maintenance and the diversion of surplus water to nearby green spaces. Over time, in periods of medium and far future until the year 2100, the system showed an increase in manhole floods, exceeding 0.1 m³/s. The percentage of flooding in manholes was more than 13% in the worst case, and continued floods for longer periods could potentially negatively affect the current drainage infrastructure. The study provides technical support for decision-makers to address these issues. By providing a comprehensive view of flood-prone areas and sites, as well as the flood percentage for each under different climate change scenarios, with the help of the Geographic Information System (GIS) software to represent future rain events. It suggests increasing the depth of manholes most vulnerable (especially R18, R98, and R101A manholes) to flooding and correcting slopes to achieve sustainability and a good service rate for the storm drainage system.
... Hydrodynamic models are the dominant methods for replicating fluid motion and quantifying accurate risk [16]. Despite progress in model accuracy and computational efficiency, physical models for flood risk assessment are still not suitable for very large areas at high resolutions [17,18]. Moreover, strategies based on physical infrastructure resistance cannot appropriately handle uncertainty and unexcepted change while providing substantial protection [19]. ...
Global warming is exacerbating flood hazards, making the robustness of flood risk management a critical issue. Without considering future scenarios, flood risk analysis built only on historical knowledge may not adequately address the coming challenges posed by climate change. A comprehensive risk analysis framework based on both historical inundations and future projections to tackle uncertainty is still lacking. In this view, a scenario-based, data-driven risk analysis framework that for the first time integrates recent historical floods and future risk trends is here presented, consisting of flood inundation-prone and high-risk zones. Considering the Poyang Lake Eco-Economic Zone (PLEEZ) in China as the study area, we reproduced historical inundation scenarios of major flood events by using Sentinel-1 imagery from 2015 to 2021, and used them to build the risk baseline model. The results show that 11.7% of the PLEEZ is currently exposed to the high-risk zone. In the SSP2-RCP4.5 scenario, the risk would gradually decrease after peaking around 2040 (with a 19.3% increase in high-risk areas), while under the traditional fossil fuel-dominated development pathway (SSP5-RCP8.5), the risk peak would occur with a higher intensity about a decade earlier. The attribution analysis results reveal that the intensification of heavy rainfall is the dominant driver of future risk increase and that the exploitation of unused land such as wetlands induces a significant increase in risk. Finally, a hierarchical panel of recommended management measures was developed. We hope that our risk analysis framework inspires newfound risk awareness and provides the basis for more effective flood risk management in river basins.
... Prediction of flooding processes induced by rainfall is another essential component in a flood forecasting system, which may involve the use of a wide variety of hydraulic or hydrodynamic models (Teng et al., 2017). Accurate flood inundation predictions are traditionally provided by running two-dimensional hydrodynamic models that solve shallow water equations (SWEs) (Wang et al., 2022b). ...
Large-scale hydrodynamic models generally rely on fixed-resolution spatial grids and model parameters as well as incurring a high computational cost. This limits their ability to accurately forecast flood crests and issue time-critical hazard warnings. In this work, we build a fast, stable, accurate, resolution-invariant, and geometry-adaptative flood modeling and forecasting framework that can perform at large scales, namely FloodCast. The framework comprises two main modules: multi-satellite observation and hydrodynamic modeling. In the multi-satellite
observation module, a real-time unsupervised change detection method and a rainfall processing and analysis tool are proposed to harness the full potential of multi-satellite observations in large-scale flood prediction. In the hydrodynamic modeling module, a geometry-adaptive physics-informed neural solver (GeoPINS) is introduced, benefiting from the absence of a requirement for training data in physics-informed neural networks (PINNs) and featuring a fast, accurate, and resolution-invariant architecture with Fourier neural operators. To adapt to complex river geometries, we reformulate PINNs in a geometry-adaptive space. GeoPINS demonstrates impressive performance on popular partial differential equations across regular and irregular domains. Building upon GeoPINS, we propose a sequence-to-sequence GeoPINS model to handle long-term temporal series and extensive spatial domains in large-scale flood modeling. This model employs sequence-to-sequence learning and hard-encoding of boundary conditions. Next, we establish a benchmark dataset in the 2022 Pakistan flood using a widely accepted finite difference numerical solution to assess various flood prediction methods. Finally, we validate the model in three dimensions - flood inundation range, depth, and transferability of spatiotemporal downscaling - utilizing SAR-based flood data, traditional hydrodynamic benchmarks, and concurrent optical remote sensing images. Traditional hydrodynamics and sequence-to-sequence GeoPINS exhibit exceptional agreement during high water levels, while comparative assessments with SAR-based flood depth data show that sequence-tosequence GeoPINS outperforms traditional hydrodynamics, with smaller prediction errors. The experimental results for the 2022 Pakistan flood demonstrate that the proposed method enables high-precision, large-scale flood modeling with an average MAPE of 14.93% and an average MAE of 0.0610m for 14-day water depth predictions while facilitating real-time flood hazard forecasting using reliable precipitation data. FloodCast is publicly available at https://github.com/HydroPML/FloodCast.
Flood risk assessment is primarily performed by a single flood driver at a specific location. A significant flaw in this approach is the oversight of the nonlinear interactions between various flood drivers (e.g., river flooding, tides, storm surges, and fluvial regimes), potentially resulting in compound flooding. This oversight can lead to underestimating the socioeconomic consequences of compound floods, which often surpass the risks posed by individual drivers acting alone. This study employs a deep learning model, mainly Long Short-Term Memory to predict water levels in tidal rivers under the influence of various flood drivers. The model is used to predict water levels at specific locations, using upstream river discharge, downstream water levels, and initial water levels as input variables. To account for coincidence/concurrence of drivers, we use Copula functions as a probabilistic approach to model the correlation between peak river discharge and coastal water levels as input features for the DL Model. The application of the proposed method is illustrated by applying it to a case study in the Buffalo Bayou area near Houston, TX. The results show that, for a 50-year flood, considering prior water level conditions represented by the 10th and 90th percentile baseflow scenarios, the projected flood inundation area can vary significantly, ranging from 30% to 70% for the same return period. The proposed methodology advances flood hazard assessment in coastal regions by capturing the complex interplay of different flood drivers and offering a robust yet practical flood inundation mapping approach.
Flooding is one of the extreme hydrological phenomena. It is a recurring natural disaster that causes loss of life and property in many parts of the world, particularly during the monsoon season. It is important to address such issues for local government and policymakers to manage the flood properly. One such flood management activity is to develop a flood-prone area that depicts the spatial and temporal extent of flood accurately. The integration of the Hydrologic Engineering Centre - River Analysis System (HEC-RAS) model and geospatial tools have emerged as a crucial approach for identifying and mapping flood-prone areas. The successful application of the HEC-RAS model generally depends on the topographical data, which represents the channel and floodplain geometry. In floodplain geometry, discrete cross-sections play a vital role to develop the floodplain map, particularly in the flat topographical region. To extract these data it needs a high-quality digital elevation model (DEM), such as light detection and ranging (LiDAR). However, due to a lack of high-resolution topographical data, flood hazard mapping in developing countries is rare. In common practice, the centerline of the river is considered the flow path for the channel. The orientation of the cross-sections is perpendicular to this line and extends to reach the limits of the floodplain. But, it is difficult to define the limits and it may depend on the magnitude of the flood. Hence, in this study, the HEC RAS model coupled with ArcGIS has been applied to the Ganga River, which traverses through the Bihar state of India to study the effect of cross-sectional width to define the floodplain. The Bihar state is facing substantial hardships from annual flooding events with approximately 16.5% of India’s flood-prone area. The extreme flood values for 5, 10, and 25 years of return period have been determined and the influence of the three different cross-sectional widths to mapping the floodplain has been investigated. This novel perspective adds dimension to the understanding of flood dynamics and its implications for flood risk assessment. In this analysis, it has been observed that, with an increase in the width of the cross-section, the floodplain area also getting increased. In this topographical region, keeping a fixed flow path will underestimate the flood-prone area. The width of the flow path depends on the topography of the region and the river flow. The outcomes of this analysis provide valuable insights into the flood-inundated areas for the specified return periods, enabling the identification and prioritization of flood-prone zones.
Drainage modeling that accurately captures urban storm inundation serves as the foundation for flood warning and drainage scheduling. In this paper, we proposed a novel coupling ideology that, by integrating 2D-1D and 1D-2D unidirectional processes, overcomes the drawback of the conventional unidirectional coupling approach that fails to properly represent the rainfall surface catchment dynamics, and provides more coherent hydrological implications compared to the bidirectional coupling concept. This paper first referred to a laboratory experimental case from the literature, applied and analyzed the coupling scheme proposed in this paper and the bidirectional coupling scheme that has been widely studied in recent years, compared the two coupling solutions in terms of the resulting accuracy and applicability, and discussed their respective strengths and weaknesses to validate the reliability of the proposed method. The verified proposed coupling scheme was then applied to the modeling of a real drainage system in a region of Nanjing, China, and the results proved that the coupling mechanism proposed in this study is of practical application value.
Flash floods are an increasing hazard to human infrastructure and life. Effective disaster management and mitigation require accurate and fast predictions for decision-making. The Flood Inundation Parallel Computation (FIP) software presented in this paper shows how to fully exploit the tremendous computational capabilities of graphics processing units (GPUs) to accelerate shallow water solvers beyond the current state-of-the-art. The time efficient explicit shallow water scheme for structured grids RMG, is introduced and implemented in FIP. The optimized GPU implementation of FIP achieves balanced memory- and instruction throughputs of up to 80%. Validation and performance tests using laboratory and historical flood cases are presented that demonstrate the accuracy and effectiveness of FIP in predicting flood inundation. Our implementation achieves a fourfold speedup in comparison to state-of-the-art approaches. It enables faster-than real-time simulations of areas of over 600 km2 at 1 m resolution on consumer-grade GPUs.
In the Casablanca-Mohammedia corridor (Morocco), flooding episodes have happened frequently over the past 20 years, damaging coastal settlements through overtopping and overflowing processes. In this context, a realistic assessment of the flood risk on this coastline is required. For this, the marine water level variations were computed by combining the involved variables (astronomical tide, storm surge, wave run-up, and sea level rise) during energetic events. They were compared with the seafront altitude to delineate the maximum spatial extent of flooded areas for the current and future (2100) time horizons. These variables were obtained through numerical and empirical modeling using topobathymetry, tide gauge, wind, and reanalysis data for wave and atmospheric pressure. Statistical methods were used to determine trends and distributions, including linear regression and the GEV
model. Our approach was validated by comparing the estimated results of the total water level with the observations made in situ during previous events. Results show that flooding occurs mainly at high tides. The run-up is the largest contributor to total water level during energetic events (45–60% in structure defense areas against ~ 35% in natural areas). Currently, the floodable area for all of Casablanca Mohammedia’s coastline (109 km2) is estimated to be ~ 23.5 km2, of which ~ 13.9 km2 is urban. This area would grow by 10.87% and 20.9% by 2100, respectively. The most vulnerable zones are Mohammedia, Ain Sbâa, and Merzeg quarters, as well as Tamaris beaches. The touristic quarters of Ain Diab and the promenades on either side of the Hassan II Mosque are also vulnerable and can be dangerous for pedestrians. This study is crowned by the proposal of numerous necessary
protection and adaptation measures, considering the specificities of the sections characterizing this coastline.
Floods are major hazard in Mzuzu City, Malawi. This study applied geospatial and hydrological modeling techniques to map flood incidences and hazard in the city. Multi-sensor [Sentinel 1, Sentinel 2, and Moderate Resolution Imaging Spectroradiometer (MODIS)] Normalized Difference Vegetation Index (NDVI) datasets were used to determine the spatio-temporal variation of flood inundation. Ground control points collected using a participatory GIS mapping approach were used to validate the identified flood hazard areas. A Binary Logistic Regression (BLR) model was used to determine and predict the spatial variation of flood hazard as a function of selected environmental factors. The Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS) was used to quantify the peak flow and runoff contribution needed for flood in the city. The runoff and peak flow from the HEC-HMS model were subjected to extreme value frequency analysis using the Gumbel Distribution approach before input into the Hydrologic Engineering Center River Analysis System (RAS) (HEC-RAS). The HEC-RAS model was then applied to map flood inundated areas producing flood extents maps for 100, 50, 20, and 10-year return periods, with rain-gauge and Climate Prediction Center MORPHed precipitation (CMORPH) satellite-based rainfall inputs. Results revealed that selected MODIS and Sentinel datasets were effective in delineating the spatial distribution of flood events. Distance from the river network and urban drainage are the most significant factors ( p < 0.05) influencing flooding. Consequently, a relatively higher flood hazard probability and/susceptibility was noted in the south-eastern and western-most regions of the study area. The HEC-HMS model calibration (validation) showed satisfactory performance metrics of 0.7 (0.6) and similarly, the HEC-RAS model significantly performed satisfactorily as well ( p < 0.05). We conclude that bias corrected satellite rainfall estimates and hydrological modeling tools can be used for flood inundation simulation especially in areas with scarce or poorly designed rain gauges such as Mzuzu City as well as those affected by climate change. These findings have important implications in informing and/updating designs of flood early warning systems and impacts mitigation plans and strategies in developing cities such as Mzuzu.
Flood risk management often requires the use of geomorphological features to identify flood zones, and the use of hydraulic models to predict inundation dynamics and related impacts on the surrounding area. In this study, we used a hydraulic river simulation model to identify potential flood-prone zones on a small scale. It concentrated on a 2.5-kilometer section of the Larbaâ Wadi, which crosses the rural center of Sebt Boukellal. For estimating the peak discharge that occurs in the return periods of 10, 20, 50, and 100 years of the drainage area, we used the Rational method. Standard tables to estimate Manning’s coefficient and direct field measurements to feed the model. Model simulation has shown stability of the steady state, which witnesses the accuracy of the estimated and measured characteristics of the river system. During the calibration phase, we compared the model outputs to the observed floods and made adjustments to align the simulation with the field observations. Indeed, the 50-year flood remarkably matched the extent of the flood that occurred on September 27, 2000. The obtained results have shown that even for a 10year return period, the overflow affects properties within the floodplain. The 100-year flood exceeded the river’s capacity, causing water to spill onto the rural center’s streets and cultivated fields. The water level reached an elevation of 552.14 meters at Sebt Boukellal’s marketplace. These results were consistent with recent floods and confirm previous observations, indicating that the model precisely predicted the river’s behavior. The findings have shown that floods spanned large regions and suggested urgent intervention to protect lives and properties.
Numerous mathematical models were developed for flood hazard mapping, flood inundation modeling and flood risk assessment. This study aims to have a comprehensive literature review of various mathematical models available in literature for flood modeling. For this study a total of 42 research articles are reviewed from year 1995 to 2020. The assessment is carried out on the basis of the model’s dimensionality i.e.; one dimensional (1-D), two-dimensional (2-D), coupled 1-D/2-D or three-dimensional (3-D), and numerical solutions available in literature i.e.; Finite element, Finite difference, Finite volume or some others methods for the models. The study reviewed the literature for flood modeling and prepared a table of various models used for flood modeling. It was concluded that coupled models are more preferable than other models because they had the strength of both 1-D and 2-D models and computationally efficient with less computational time. For open channel modeling models based on finite difference method are preferred over numerical solution techniques.
Flood occurrences have persisted in India since ancient times.Certain regions in India frequently see devastating floods on an annual basis due to localised disparities in climatic patterns and precipitation levels. According to the Central Water Commission (2012), approximately 49.82 million hectares, equivalent to fifteen percent of India's land area, is susceptible to the risk of floods. The concept of “flood forecasting” refers to the systematic determination of the probability, magnitude, temporal occurrence, and duration of flood events within a designated geographical region. There are several methodologies available for flood prediction, including data-driven models, physically-based models, and hybrid models that combine elements of both approaches. When considering the suitable models to employ,it is crucial to consider factors such as the availability of data,the characteristics of the catchment area,and the required level of precision in forecasting. The efficacy of one's written work is in a literature review or a research piece and hinges upon the adeptness with which data is presented in a manner that exhibits the subject matter in a lucid and cohesive fashion. We have endeavoured to collate all the relevant research and discern any deficiencies in our comprehension.
Evaluation of the performance of flood models is a crucial step in the modeling process. Considering the limitations of single statistical metrics, such as uncertainty bounds, Nash Sutcliffe efficiency, Kling Gupta efficiency, and the coefficient of determination, which are widely used in the model evaluation, the inherent properties and sampling uncertainty in these metrics are demonstrated. A comprehensive evaluation is conducted using an ensemble of one‐dimensional Hydrologic Engineering Center's River Analysis System (HEC‐RAS) models, which account for the uncertainty associated with the channel roughness and upstream flow input, of six reaches located in Indiana and Texas of the United States. Specifically, the effects of different prior distributions of the uncertainty sources, multiple high‐flow scenarios, and various types of measurement errors in observations on the evaluation metrics are investigated using bootstrapping. Results show that the model performances based on the uniform and normal priors are comparable. The statistical distributions of all the evaluation metrics in this study are significantly different under different high‐flow scenarios, thus suggesting that the metrics should be treated as “random” variables due to both aleatory and epistemic uncertainties and conditioned on the specific flow periods of interest. Additionally, the white‐noise error in observations has the least impact on the metrics.
Climate change and urbanization are increasing the risk of flood disasters in vulnerable areas. Urban road infrastructure can be affected by floods, and subsequent restoration creates an additional carbon emission burden. These emissions are likely to compromise local decarbonization efforts, but there remains a lack of tools for quantifying the environmental impact of reconstruction projects after disasters. This study aims to develop an assessment framework to reveal the carbon footprint of post-flood road network restoration projects. An interdisciplinary model was developed, integrating knowledge in hydrology, civil engineering, and environmental science. This paper presents scenario simulations with the maximum flood depth (MFD) ranging from 0.1 m to 5.0 m and a case study in Carlisle, UK. Results show that floods with MFD over 0.5 m had significant impacts on pavements. The resilience to floods on low-volume roads was weaker, but the restoration emission intensity of main roads was significantly higher. From the perspective of an urban case, after a 70-hour flood event with an average MFD of 0.9 m, the total carbon footprint of road network restoration was 73.89 tCO 2 e, offsetting about 0.49% of local carbon reduction efforts for the month. Specifically, emissions from restoring low-volume roads accounted for 58.64%. Indirect carbon emissions from material production (approx. 66%) and delivery (approx. 30%) were much higher than direct emissions from onsite tasks (approx. 4%). Measures such as material improvement, delivery optimization, and construction material reuse can help mitigate the emission burden of restoration projects. A major uncertainty in the computation was ignoring the differences induced by the climatic context, but addressing this limitation would require support from pavement damage experts. This study provides quantitative tools for understanding infrastructure-related environmental impacts caused by flood disasters. The assessment framework proposed in this work is also expected to be applied to wider spatial-temporal scenarios to enrich decision-making references.
Rainfall serves as a significant factor contributing to slope stability challenges in mountainous areas, and simulating the process of slope rainwater movement is a crucial approach for analyzing the stability of slopes triggered by rainfall. By combining computer numerical simulation technology with traditional hydraulic and hydrological calculation theories, it is possible to create an efficient and precise rainwater movement model that can simulate and analyze the process of rainwater movement on slopes. Utilizing natural slopes as the focal point of our research, the cellular automaton method was applied to simulate rainfall runoff on slopes, and a Cellular Automata (CA) based model for rainwater movement process was developed. This model modified the Green-Ampt (G-A) infiltration model by adopting an elliptical water content curve and introducing a coefficient that quantifies the ratio of saturated to unsaturated depth. Additionally, we refined the rules governing runoff generation and convergence within the slope and on its surface, enabling a comprehensive simulation of the entire rainwater movement process on the slope. Furthermore, the effectiveness of the model was validated through analytical solutions derived from simplified assumptions, laboratory experiments on infiltration and runoff in the flume, and a case study of a natural slope. The results show that the infiltration calculation results of the rainwater movement model are closer to the experimental values, and their overall values are slightly higher than the measured values, which are basically consistent with the model test results; The runoff calculation results show a phenomenon of initially increasing and gradually approaching the measured values compared to the measured values. When applying the model to an actual slope, it was found that the model comprehensively accounts for the influence of slope seepage, infiltration and runoff process, has better performance compared to G-A modified model. The model can be used to describe the spatial distribution and temporal variation of infiltration and runoff processes.
Stormwater management modeling tools have been utilized to enhance stormwater operating systems, assess modeling system efficiency, and evaluate the impacts of urban growth on stormwater runoff and water quality. This review explores the potential of stormwater management strategies and Artificial Intelligence modeling tools in enhancing water quality. The study focuses on evaluating stormwater modeling tools for planning and improving stormwater systems, assessing modeling efficiency, and understanding the impacts of new development on stormwater runoff and water quality. Various stormwater modeling tools are compared to aid in water management in urban and rural settings, which is crucial due to increasing storm intensity from climate change. The review debates the advantages and limitations of different modeling tools, particularly in modeling stormwater quantity and quality under different scenarios. It also examines tools used for predicting and analysing stormwater runoff during storm events in diverse locations. The assessment of modeling tools is centred on their support for Green Infrastructure (GI) practices, considering factors like modeling accuracy, data availability, and requirements. The study highlights the importance of these tools in managing water in urban areas and safeguarding water sources during stormwater events. Notably, the accuracy and efficacy of stormwater modeling tools are influenced by input data quality, calibration methods, and standardization metrics, with the widely used Stormwater Management Model (SWMM) being a common modeling tool.
Floods cause physical damage and impact the availability of food, water, and crops. Effective disaster management and disaster risk reduction strategies require a quick and accurate mapping of these phenomena. The study area selected is the Krishnaraja Nagar taluk, Mysore districts, Karnataka having an area of 608 Km2. In this study, the analysis of a flood event was conducted using the temporal GRDH SAR pictures in C-band from Sentinel-1. Additionally, the co-polarized Vertical transmit, and Vertical received (VV) Synthetic Aperture Radar (SAR) images were utilized to map the extent of the flooded area. Two methods of change detection are applied to the temporal SAR images: Otsu's Automatic thresholding method using Matlab R2020a, utilizing a pre-flood image dated 02 August 2018 that shares identical image characteristics with the flood images captured on 14 August 2018; and flood mapping based on Normalized Difference Flood Index (NDFI) using Sentinel Application Platform (SNAP) software. By dividing the SAR image's non-water and open-water regions, the threshold approach was used to extract the flooded areas. In order to identify the actual flooded region, permanent water bodies were later removed from the open water. An analysis of the overlay flood maps was conducted to determine the total area inundated. After processing the SAR data and conducting threshold operations, the flooded area estimates from NDFI is 28.10 km2, and by Otsu's method flooded area is 21.92 km2. It is concluded from the study that the SAR information, sideways with GIS, can be used efficiently for floodwater plotting, real-time analysis, and analysing the spread of floodwater in a flood-prone zone.
Uncertainty pervades the representation of systems in the water-environment-agriculture cross-sector. Successful methods to address uncertainties have largely focused on standard mathematical formulations of bio-physical processes in a single sector, such as partial or ordinary differential equations. More attention to integrated models of such systems is warranted. Model components representing the different sectors of an integrated model can have less standard, and different, formulations to one another, as well as different levels of epistemic knowledge and data informativeness. Thus uncertainty is not only pervasive but also crosses boundaries and propagates between system components. Uncertainty assessment (UA) cries out for more eclectic treatment in these circumstances, some of it being more qualitative and empirical. Here we discuss the various sources of uncertainty in such a cross-sectoral setting and ways to assess and manage them. We have outlined a fast growing set of methodologies, particularly in the computational mathematics literature on uncertainty quantification (UQ), that seem highly pertinent for uncertainty assessment. There appears to be considerable scope for advancing UA by integrating relevant UQ techniques into cross-sectoral problem applications. Of course this will entail considerable collaboration between domain specialists who often take first ownership of the problem and computational methods experts.
http://link.springer.com/book/10.1007/978-3-319-23576-9
We address two critical choices in Global Sensitivity Analysis (GSA): the choice of the sample size and of the threshold for the identification of insensitive input factors. Guidance to assist users with those two choices is still insufficient. We aim at filling this gap. Firstly, we define criteria to quantify the convergence of sensitivity indices, of ranking and of screening, based on a bootstrap approach. Secondly, we investigate the screening threshold with a quantitative validation procedure for screening results. We apply the proposed methodologies to three hydrological models with varying complexity utilizing three widely-used GSA methods (RSA, Morris, Sobol’). We demonstrate that convergence of screening and ranking can be reached before sensitivity estimates stabilize. Convergence dynamics appear to be case-dependent, which suggests that “fit-for-all” rules for sample sizes should not be used. Other modellers can easily adopt our criteria and procedures for a wide range of GSA methods and cases. Open access: http://www.sciencedirect.com/science/article/pii/S1364815216300251
This paper presents a spatial Global Sensitivity Analysis (GSA) approach in a 2D shallow water equations based High Resolution (HR) flood model. The aim of a spatial GSA is to produce sensitivity maps which are based on Sobol index estimations. Such an approach allows to rank the effects of uncertain HR topographic data input parameters on flood model output. The influence of the three following parameters has been studied: the measurement error, the level of details of above-ground elements representation and the spatial discretization resolution. To introduce uncertainty, a Probability Density Function and discrete spatial approach have been applied to generate 2,000 DEMs. Based on a 2D urban flood river event modelling, the produced sensitivity maps highlight the major influence of modeller choices compared to HR measurement errors when HR topographic data are used. The spatial variability of the ranking is enhnaced.
The spatially and temporally variable parameters and inputs to complex groundwater models typically result in long runtimes which hinder comprehensive calibration, sensitivity and uncertainty analysis. Surrogate modeling aims to provide a simpler, and hence faster, model which emulates the specified output of a more complex model in function of its inputs and parameters. In this review paper, we summarize surrogate modeling techniques in three categories: data-driven, projection, and hierarchical-based approaches. Data-driven surrogates approximate a groundwater model through an empirical model that captures the input-output mapping of the original model. Projection based models reduce the dimensionality of the parameter space by projecting the governing equations onto a basis of orthonormal vectors. In hierarchical or multi-fidelity methods the surrogate is created by simplifying the representation of the physical system, such as by ignoring certain processes, or reducing the numerical resolution. In discussing the application to groundwater modeling of these methods, we note several imbalances in the existing literature: a large body of work on data-driven approaches seemingly ignores major drawbacks to the methods; only a fraction of the literature focuses on creating surrogates to reproduce outputs of fully distributed groundwater models, despite these being ubiquitous in practice; and a number of the more advanced surrogate modeling methods are yet to be fully applied in a groundwater modeling context. This article is protected by copyright. All rights reserved.
As sea-level rises, the frequency of coastal marine flooding events is changing. For accurate assessments,
several other factors must be considered as well, such as the variability of sea-level rise and storm surge
patterns. Here, a global sensitivity analysis is used to provide quantitative insight into the relative
importance of contributing uncertainties over the coming decades. The method is applied on an urban
low-lying coastal site located in the north-western Mediterranean, where the yearly probability of
damaging flooding could grow drastically after 2050 if sea-level rise follows IPCC projections. Storm
surge propagation processes, then sea-level variability, and, later, global sea-level rise scenarios become
successively important source of uncertainties over the 21st century. This defines research priorities that
depend on the target period of interest. On the long term, scenarios RCP 6.0 and 8.0 challenge local
capacities of adaptation for the considered site.
This paper addresses the issue of performing global sensitivity analysis of model output with dependent inputs. First, we define variance-based sensitivity indices that allow for distinguishing the independent contributions of the inputs to the response variance from their mutual dependent contributions. Then, two sampling strategies are proposed for their non-parametric, numerical estimation. This approach allows us to estimate the sensitivity indices not only for individual inputs but also for groups of inputs. After testing the accuracy of the non-parametric method on some analytical test functions, the approach is employed to assess the importance of dependent inputs on a computer model for the migration of radioactive substances in the geosphere.
Daily, or more frequent, maps of surface water have important applications in environmental and water resource management. In particular, surface water maps derived from remote sensing imagery play a useful role in the derivation of spatial inundation patterns over time. MODIS data provide the most realistic means to achieve this since they are daily, although they are often limited by cloud cover during flooding events, and their spatial resolutions (250-1000 m pixel) are not always suited to small river catchments. This paper tests the suitability of the MODIS sensor for identifying flood events through comparison with streamflow and rainfall measurements at a number of sites during the wet season in Northern Australia. This is done using the MODIS Open Water Likelihood (OWL) algorithm which estimates the water fraction within a pixel. On a temporal scale, cloud cover often inhibits the use of MODIS imagery at the start and lead-up to the peak of a flood event, but there are usually more cloud-free data to monitor the flood's recession. Particularly for smaller flood events, the MODIS view angle, especially when the view angle is towards the sun, has a strong influence on total estimated flood extent. Our results showed that removing pixels containing less than 6% water can eliminate most commission errors when mapping surface water. The exception to this rule was for some spectrally dark pixels occurring along the edge of the MODIS swath where the relative azimuth angle (i.e., angle between the MODIS' and sun's azimuth angle) was low. Using only MODIS OWL pixels with a low view angle, or a range distance of less than 1000 km, also improves the results and minimizes multi-temporal errors in flood identification and extent. Given these limitations, MODIS OWL surface water maps are sensitive to the dynamics of water movement when compared to streamflow data and does appear to be a suitable product for the identification and mapping of inundation extent at large regional/basin scales.
Sensitivity analysis (SA) is generally recognized as a worthwhile step to diagnose and remedy difficulties in identifying model parameters, and indeed in discriminating between model structures. An analysis of papers in three journals indicates that SA is a standard omission in hydrological modeling exercises. We provide some answers to ten reasonably generic questions using the Morris and Sobol SA methods, including to what extent sensitivities are dependent on parameter ranges selected, length of data period, catchment response type, model structures assumed and climatic forcing. Results presented demonstrate the sensitivity of four target functions to parameter variations of four rainfall runoff models of varying complexity (4-13 parameters). Daily rainfall, streamflow and pan evaporation data are used from four 10-year data sets and from five catchments in the Australian Capital Territory (ACT) region. Similar results are obtained using the Morris and Sobol methods. It is shown how modelers can easily identify parameters that are insensitive, and how they might improve identifiability. Using a more complex objective function, however, may not result in all parameters becoming sensitive. Crucially, the results of the SA can be influenced by the parameter ranges selected. The length of data period required to characterize the sensitivities assuredly is a minimum of five years. The results confirm that only the simpler models have well-identified parameters, but parameter sensitivities vary between catchments. Answering these ten questions in other case studies is relatively easy using freely available software with the Hydromad and Sensitivity packages in R.
To timely obtain accurate pixel water surface proportion information through remote sensing is extremely significant to the ecological restoration in inland river basins and for the precise management of water resources. In respect to the insufficient extraction of water surface proportion information present in pixels in most of the current water information models, a simple model Enhanced Water Index (EWI) based on Modified Normalized Difference Water Index (MNDWI) has been introduced. EWI, which is oriented toward the sub-pixel level analysis of water surface proportion mapping of inland river basin, has been put forward based on the analysis of typical spectral signatures such as desert, soil, and vegetation along with MNDWI in accordance with the Landsat TM band features. The analysis is done by using methods of pixel-based EWI value with different water proportions which are analyzed through the introduction of the linear hybrid simulation between the water body and the corresponding background. Lastly, the effect of EWI model has been tested in the medium and lower reaches of Tarim. The correction coefficient for sub-pixel level water surface proportion predicted by the EWI model and the experimental data is $bm{R}^bm{2} ;=; bm{0.72}$ . Results showed that the model was able to effectively extract the information about pixel water surface proportion in inland river basins. This study proves that EWI model has great potential in its application for water proportion mapping applications.
An important issue in taking account of uncertainty in flood inundation mapping is the communication of the meaning of the outputs from an uncertainty analysis. In part this is because uncertainty estimation in this domain is not a simple statistical problem in that it involves knowledge uncertainties as well as statistical (aleatory) uncertainties in most of the important sources of uncertainty (estimated upstream discharges, effective roughness coefficients, flood plain and channel geometries and infrastructure, choice of model, fragility of defences, etc.). Thus, assumptions are required associated with the knowledge or lack of knowledge about these different sources of uncertainty. A framework has been developed in the form of a sequence of condition trees to help define these assumptions. Since stakeholders in the process can potentially be involved in making and recording decisions about those assumptions the framework also serves as a means of communicating the assumptions. Recording the decisions also serves to provide an audit trail for later evaluation of the decisions and hence the resulting analysis. Communication can also be helped in this type of spatial problem by effective visualization techniques and a visualization tool has been developed for both a web-based service using Google Maps™ and a desktop application using the Matlab™ numerical package.
The Australian Water Resource Assessment (AWRA) modelling system has been in development since 2008 to enable the Bureau of Meteorology to meet its legislated role in providing an annual National Water Account and a regular Australian Water Resource Assessment Report. The system uses available observations and an integrated landscape-groundwater-river water balance model to estimate the stores and fluxes of the water balance required for reporting. AWRA constitutes a unique example of implementing a coupled landscape, groundwater and regulated river system model at a continental scale and rolled out at high priority regions (National Water Account (NWA) regions).
The results for AWRA-L (landscape) implementation across 607 gauged catchments show that n both calibration and validation, the model typically provides streamflow predictions that are similar to those from other widely used conceptual hydrological models. The AWRA-R (river) model includes newly developed components for floodplain inundation modelling, accounting for irrigation diversions and groundwater surface water interactions. The results show that the model performs extremely well in majority of the modelling regions and it provides all the water fluxes and stores required for NWA.
The software architecture developed as part of AWRA integrates the individual components in a seamless manner with transfer of fluxes between the components at a daily time step for operational implementation. The system is fully functional on the Bureau’s operating system and used for supporting the production of AWRA and NWA reports. The Bureau has used the AWRA modelling system to undertake water resource assessments across the country and already published one Water Resource Assessment (2010) and two National Water Accounts (2010, 2011). There has been a steady and continuous improvement in the AWRA model performance and the Bureau is currently undertaking the next round of Water Resource Assessments (2012) and a National Water Account (2012) using the current version of the AWRA system. It is anticipated that what-if scenario modelling and forecasting water resource availability will eventually come into scope in the next three years when the retrospective components of the system are fully implemented and operating efficiently and effectively.
This paper presents a new computationally efficient hydraulic model for
simulating the spatially distributed dynamics of water surface
elevation, wave speed, and inundation extent over large data sparse
domains. The numerical scheme is based on an extension of the hydraulic
model LISFLOOD-FP to include a subgrid-scale representation of
channelized flows, which allows river channels with any width below that
of the grid resolution to be simulated. The scheme is shown to be
numerically stable and scalable, before being applied to an 800 km reach
of the river Niger in Mali. The Niger application focused on the
performance of four different model structures: a model without channels
(two-dimensional (2-D) model), a model without a floodplain
(one-dimensional (1-D) model), a model of the main channels and
floodplain (1-D/2-D model), and the subgrid approach developed here.
Inclusion of both the channel network and the floodplain was shown to be
essential, meaning that large scale models of this region, including
routing models for land surface schemes, will require a floodplain
component. Including subgrid-scale channels on the floodplain changed
inundation dynamics over the delta significantly and increased
simulation accuracy in terms of water level, wave propagation speed, and
inundation extent. Furthermore, only the subgrid model showed a
consistent parameterization when calibrated against either gauge or
ICESat water level data, suggesting that connectivity provided by small
channels is a strong control on the hydraulics of the floodplain, or, at
the very least, that low resolution gridded hydraulic models require
additional connectivity to represent the delta flow dynamics.
Modern environmental management and decision-making is based on the use
of increasingly complex numerical models. Such models have the advantage
of allowing representation of complex processes and heterogeneous system
property distributions inasmuch as these are understood at any
particular study site. The latter are often represented stochastically,
this reflecting knowledge of the character of system heterogeneity at
the same time as it reflects a lack of knowledge of its spatial details.
Unfortunately, however, complex models are often difficult to calibrate
because of their long run times and sometimes questionable numerical
stability. Analysis of predictive uncertainty is also a difficult
undertaking when using models such as these. Such analysis must reflect
a lack of knowledge of spatial hydraulic property details. At the same
time, it must be subject to constraints on the spatial variability of
these details born of the necessity for model outputs to replicate
observations of historical system behavior. In contrast, the rapid run
times and general numerical reliability of simple models often
promulgates good calibration and ready implementation of sophisticated
methods of calibration-constrained uncertainty analysis. Unfortunately,
however, many system and process details on which uncertainty may depend
are, by design, omitted from simple models. This can lead to
underestimation of the uncertainty associated with many predictions of
management interest. The present paper proposes a methodology that
attempts to overcome the problems associated with complex models on the
one hand and simple models on the other hand, while allowing access to
the benefits each of them offers. It provides a theoretical analysis of
the simplification process from a subspace point of view, this yielding
insights into the costs of model simplification, and into how some of
these costs may be reduced. It then describes a methodology for paired
model usage through which predictive bias of a simplified model can be
detected and corrected, and postcalibration predictive uncertainty can
be quantified. The methodology is demonstrated using a synthetic example
based on groundwater modeling environments commonly encountered in
northern Europe and North America.
This paper reports on the formation of the Flood Risk Management Research Consortium and the research that is under way within the 'towards whole systems modelling' (WSM) priority area. Funding for the consortium is provided from a wide range of research funders in the UK including the national research councils, government departments and agencies with responsibility for flood risk management. The research portfolio has been formulated to address key research needs identified by the funders. This briefing note explains the relationship between the planned research in the WSM area and the UK Environment Agency's Strategy for Flood Risk Management. Additionally, it provides an introduction to the companion research papers in this issue by Villanueva and Wright, Hunter et al., Lin et al., and Néelz et al. The research reported in these companion papers focuses on flood inundation modelling and is an important subset of the work to be undertaken within WSM.
A practical introduction, the second edition of Fluid Simulation for Computer Graphics shows you how to animate fully three-dimensional incompressible flow. It covers all the aspects of fluid simulation, from the mathematics and algorithms to implementation, while making revisions and updates to reflect changes in the field since the first edition. Highlights of the Second Edition • New chapters on level sets and vortex methods • Emphasizes hybrid particle–voxel methods, now the industry standard approach • Covers the latest algorithms and techniques, including: fluid surface reconstruction from particles; accurate, viscous free surfaces for buckling, coiling, and rotating liquids; and enhanced turbulence for smoke animation • Adds new discussions on meshing, particles, and vortex methods The book changes the order of topics as they appeared in the first edition to make more sense when reading the first time through. It also contains several updates by distilling author Robert Bridson’s experience in the visual effects industry to highlight the most important points in fluid simulation. It gives you an understanding of how the components of fluid simulation work as well as the tools for creating your own animations.
The further development of two-dimensional finite element models of river flood flow is currently constrained by a lack of data for rigorous parameterization and validation. Remote sensing techniques have the potential to overcome a number of these constraints thereby allowing a research design for model development. This is illustrated with reference to a case study of a two-dimensional finite element model applied to the Missouri River, Nebraska and compared with a synchronous Landsat TM image of flood inundation extent. The case study allows research needs for the integration of hydraulic modelling and remote sensing to be defined. © 1997 John Wiley & Sons, Ltd.
This article describes an operational flood forecasting system set up for the city of Dijon, France. This system assimilates real-time flow data at an hourly time step with the stationary Kalman filter to update hydraulic states. It uses a semi-distributed hydrologic model to integrate rainfall measurements and forecasts and provide discharge forecasts at several points on the watershed. It also offers powerful data management tools and an elaborated graphical interface available from any computer connected to the Internet. The hydrologic model was calibrated using a semi-distributed approach. Its simulation and forecasting performances are analyzed. The performances of the system on a recent flood event are also investigated.
To achieve fast flood modelling for large scale problems, a two-dimensional cellular automata based model has been developed in this study. This model uses simple transition rules and a weight-based system instead of solving complex shallow water equations. The cellular automata feature allows the proposed model to be implemented in a parallel computing environment such that the modelling efficiency is improved significantly due to the combination of simplification and parallelisation. The proposed model has been tested on four hypothetical case studies and one real world example, and the outputs compared to those from traditional physically based hydraulic models. Results show that the proposed model is capable of producing good agreement with other hydraulic models, using a fraction of computational time. In the case of the real world example, the model run times are up to 8 times faster than the a widely use commercial hydraulic model. This rapid and accurate attributes of the proposed model have de
This paper argues that there are fundamental problems in the application of physically-based models for practical prediction in hydrology. These problems result from limitations of the model equations relative to a heterogeneous reality; the lack of a theory of subgrid scale integration; practical constraints on solution methodologies; and problems of dimensionality in parameter calibration. It is suggested that most current applications of physically-based models use them as lumped conceptual models at the grid scale. Recent papers on physically-based models have misunderstood and misrepresented these limitations. There are practical hydrological problems requiring physically-based predictions, and there will continue to be a need for physically-based models but ideas about their capabilities must change so that future applications attempt to obtain realistic estimates of the uncertainty associated with their predictions, particularly in the case of evaluating future scenarios of the effects of management strategies.
Models are increasingly being relied upon to inform and support natural resource management. They are incorporating an ever broader range of disciplines and now often confront people without strong quantitative or model-building backgrounds. These trends imply a need for wider awareness of what constitutes good model-development practice, including reporting of models to users and sceptical review of models by users. To this end the paper outlines ten basic steps of good, disciplined model practice. The aim is to develop purposeful, credible models from data and prior knowledge, in consort with end-users, with every stage open to critical review and revision. Best practice entails identifying clearly the clients and objectives of the modelling exercise; documenting the nature (quantity, quality, limitations) of the data used to construct and test the model; providing a strong rationale for the choice of model family and features (encompassing review of alternative approaches); justifying the techniques used to calibrate the model; serious analysis, testing and discussion of model performance; and making a resultant statement of model assumptions, utility, accuracy, limitations, and scope for improvement. In natural resource management applications, these steps will be a learning process, even a partnership, between model developers, clients and other interested parties.
Assessment of risk and uncertainty is crucial for natural hazard risk management, facilitating risk communication and informing strategies to successfully mitigate our society's vulnerability to natural disasters. Written by some of the world's leading experts, this book provides a state-of-the-art overview of risk and uncertainty assessment in natural hazards. It presents the core statistical concepts using clearly defined terminology applicable across all types of natural hazards and addresses the full range of sources of uncertainty, the role of expert judgement and the practice of uncertainty elicitation. The core of the book provides detailed coverage of all the main hazard types and concluding chapters address the wider societal context of risk management. This is an invaluable compendium for academic researchers and professionals working in the fields of natural hazards science, risk assessment and management and environmental science and will be of interest to anyone involved in natural hazards policy.
Advances in remote sensing have enabled hydraulic models to run at fine scale resolutions, producing precise flood inundation predictions. However, running models at finer resolutions increases their computational expense, reducing the feasibility of running the multiple model realisations required to undertake uncertainty analysis. Furthermore, it is possible that precision gained by running fine scale models is smoothed out when treating models probabilistically. The aim of this paper is to determine the level of spatial complexity that is required when making probabilistic flood inundation predictions. The Imera basin, Sicily is used as a case study to assess how changing the spatial resolution of the hydraulic model LISFLOOD-FP impacts on the skill of conditional probabilistic flood inundation maps given model parameter and boundary condition uncertainties. We find that model performance deteriorates at resolutions coarser than 50 m. This is predominantly caused by changes in flow pathways at coarser resolutions which lead to non-stationarity in the optimum model parameters at different spatial resolutions. However, although it is still possible to produce probabilistic flood maps that contain a coherent outline of the flood extent at coarser resolutions, the reliability of these maps deteriorates at resolutions coarser than 100 m. Additionally, although the rejection of non-behavioural models reduces the uncertainty in probabilistic flood maps the reliability of these maps is also reduced. Models with resolutions finer than 50 m offer little gain in performance yet are more than an order of magnitude computationally expensive which can become infeasible when undertaking probabilistic analysis. Furthermore, we show that using deterministic, high resolution flood maps can lead to a spurious precision that would be misleading and not representative of the overall uncertainties that are inherent in making inundation predictions.
Distinctive overbank sediments deposited since European settlement on the floodplain of the Brandywine Creek, Pennsylvania, are used to calibrate and test a diffusion model of overbank deposition. -from Author
In view of increasing application of sensitivity assessment (SA) to environmental simulation models, a relatively short, informal introduction to aims and methods of SA is given. Their variety, motivation and scope are illustrated by outlines of a broad selection of approaches. Methods based on derivatives, algebraic analysis, sparse sampling, variance decomposition, Fourier analysis and binary classification are included.
Rapid and accurate inundation modelling in large floodplains is critical for emergency response and environmental management. This paper describes the development and implementation of a floodplain inundation model that can be used for rapid assessment of inundation in very large floodplains. The model uses high resolution DEM (such as LiDAR DEM) to derive floodplain storages and connectivity between them at different river stages. We tested the performance of the model across several large floodplains in southeast Australia for estimating floodplain inundation extent, volume, and water depth for a few recent flood events. The results are in good agreement with those obtained from high resolution satellite imageries and MIKE 21 two-dimensional hydrodynamic model. The model performed particularly well in the reaches that have confined channels with above 85 % agreement with the flood maps derived from Landsat TM imagery in cell-to-cell comparison. While the model did not performance as well in the flat and complex floodplains, the overall level of agreement of the modelled inundation maps with the satellite flood maps was still satisfactory (60-75 %). The key advantage of this model is demonstrated by its capability to simulate inundation in large floodplains (over 2000 km2) at a very high resolution of 5-m with more than 81 million cells at a reasonably low computational cost. The model is suitable for practical floodplain inundation simulation and scenario modelling under current and future climate conditions.
An ability to quantify the reliability of probabilistic flood inundation predictions is a requirement not only for guiding model development but also for their successful application. Probabilistic flood inundation predictions are usually produced by choosing a method of weighting the model parameter space, but previous study suggests that this choice leads to clear differences in inundation probabilities. This study aims to address the evaluation of the reliability of these probabilistic predictions. However, a lack of an adequate number of observations of flood inundation for a catchment limits the application of conventional methods of evaluating predictive reliability. Consequently, attempts have been made to assess the reliability of probabilistic predictions using multiple observations from a single flood event.Here, a LISFLOOD-FP hydraulic model of an extreme (>1 in 1000 year) flood event in Cockermouth, UK is constructed and calibrated using multiple performance measures from both peak flood wrack mark data and aerial photography captured post-peak. These measures are used in weighting the parameter space to produce multiple probabilistic predictions for the event. Two methods of assessing the reliability of these probabilistic predictions using limited observations are utilised; an existing method assessing the binary pattern of flooding, and a method developed in this paper to assess predictions of water surface elevation. This study finds that the water surface elevation method has both a better diagnostic and discriminatory ability, but this result is likely to be sensitive to the unknown uncertainties in the upstream boundary condition. This article is protected by copyright. All rights reserved.
The aim of this paper is the estimation of uncertainty in an online data assimilation model applied to a sequential, multiple-step-ahead flood forecasting system. The main aim of the forecasting system under consideration is the derivation of real-time forecasts of the water levels with the maximum possible lead-time. This is achieved through a two-level, sequential data assimilation procedure. In order to extend the maximum lead-time, we incorporate the forecasts obtained from the earlier stages of the forecasting system, both rainfall-water level and water level routing processes. The updating of the gain of each of the subsystems introduces nonlinearity into the system performance. The Generalized Likelihood Uncertainty Estimation (GLUE) technique is used to estimate the uncertainty of model predictions in the decomposed online forecasting system.
A coupled 1D–2D hydrodynamic model linking the channel and flood detention basin for flood simulation with complex topography and irregular boundary was presented in this paper. The 1D Saint-Venant equations were used for governing flows in channel, and the four-point implicit Preissmann scheme was used for discretization. To simulate floods in flood detention basin, the 2D shallow equations were adopted as governing equations, and a well-balanced, unstructured finite-volume method was proposed for the numerical resolution. The 2D model is based on a new formulation of the classical shallow water equations in hyperbolic conservation form. The numerical fluxes are computed by HLLC algorithm, and the MUSCL–Hancock predictor–corrector scheme is used to achieve high-accuracy and high-resolution results. A simple and efficient method was proposed to reflect the coupled hydraulic connection between the channel and the flood detention basin. The novelties of the proposed model include (1) a robust method for wetting/drying treatment and (2) an efficient way to link the 1D and 2D models. The 1D, 2D, and coupled models are all tested through some benchmark cases, and numerical results validated the accuracy of the models. Furthermore, the coupled 1D–2D model was used for a real flood simulation in Jiakouwa flood detention basin, China. The flood-risk information including flood arrival time and maximal water depth was mapped using GIS. Those flood-risk maps can be used as an important decision-making basis of flood control and rescue for the flood control departments at all levels.
S ummary
The optimal design problem is tackled in the framework of a new model and new objectives. A regression model is proposed in which the regression function is permitted to take any form over the space of independent variables. The design objective is based on fitting a simplified function for prediction. The approach is Bayesian throughout.
The new designs are more robust than conventional ones. They also avoid the need to limit artificially design points to a predetermined subset of . New solutions are also offered for the problems of smoothing, curve fitting and the selection of regressor variables.
Flooding resulting from collapse of a dam is a highly destructive event. It is important to accurately predict the flow behaviour so that potential mitigation strategies can be investigated for disaster management planning. The meshless SPH method has previously been able to model this class of extreme flow events. In this paper, we extend the method to include modelling of dam wall fragments. Collisions between dam wall fragments, between fragments and terrain and full two-way coupling between fragments and the free surface water flow is included. This gives a method that can specifically investigate the impact of the dam wall failure scenario on the subsequent inundation. The historical St Francis dam failure is used to demonstrate the impact of including the dam fragments. It also provides a means of quantitatively investigating their effect in terms of arrival time and water height at a downstream power station. The scenario with multiple independently timed failures of different parts of the wall (which closely matches the historical failure) gives excellent agreement with the observed data and gives the best match of all failure scenarios. Traditionally such modelling is performed by solving the two dimensional shallow water equations which is not able to capture the three dimensional nature of the flow in earlier stages of dam flooding. We specifically investigate the three dimensional nature of flow structures and formation of multiple downstream hydraulic jumps. These strongly influence water height and therefore control the extent of flooding of tributary valleys.
Two conceptual storage-based approaches have been developed for incorporating floodplain inundation modelling capability in two water resources planning models. Approach one is a simple method suitable for data limited environment, in which, flow in a river reach within a floodplain is partitioned into two components, in-stream and overbank flow, based on the in-stream capacity. A flood volume–area relationship derived from the flood inundation time series, which is generated by analysing daily MODIS imagery, is used to estimate flooded area for the overbank flow. The losses due to evaporation and groundwater seepage from floodplain are calculated using the estimated flooded area. This approach was implemented in the floodplain area of the Murray–Darling Basin in Australia. The simulated flow was compared with gauged data in 216 stations. The model has produced daily time series of floodplain stores and fluxes. The mass-balance analysis shows that the long term mass balance error was negligible for all floodplain reaches.
A smoothed particle hydrodynamics (SPH) numerical model for shallow water equations (SWEs) is presented for simulating flood inundation owing to rapidly varying flow, such as dam breaks, tsunamis, and levee breaches. Important theoretical and numerical developments have recently been made, and the model in this paper incorporates these developments and implements open boundary conditions, resulting in a general, accurate computational tool suitable for practical application. The method is attractive for flood simulation over large domains in which the extent of inundation is unknown because computation is carried out only in wet areas and is dynamically adaptive. The open boundary algorithm is very general, on the basis of a simplified version of the characteristics method, handling both supercritical and subcritical inflow and outflow. This is tested against reference solutions for flows over a hump involving shocks. The model is then applied to two very different flood inundations resulting from the Okushiri tsunami in Japan and from a hypothetical dyke breach at Thamesmead in the United Kingdom. The SPH-SWE model compares well with established commercial and state-of-the-art finite-volume codes. DOI: 10.1061/(ASCE)HY.1943-7900.0000543. (C) 2012 American Society of Civil Engineers.
[1] New survey techniques provide a large amount of high-resolution data, which can be extremely precious for flood inundation modeling. Such data availability raises the issue as to how to exploit their information content to effectively improve flood risk mapping and predictions. In this paper, we will discuss a number of important issues which should be taken into account in works related to flood modeling. These include the large number of uncertainty sources in model structure and available data; the difficult evaluation of model results, due to the scarcity of observed data; computational efficiency; false confidence that can be given by high-resolution outputs, as accuracy is not necessarily increased by higher precision. Finally, we briefly review and discuss a number of existing approaches, such as subgrid parameterization and roughness upscaling methods, which can be used to incorporate high detailed data into flood inundation models, balancing efficiency and reliability.
[1] Global river models are an essential tool for both earth system studies and water resources assessments. As advanced physical processes have been implemented in global river models, increasing computational cost has become problematic for executing ensemble or long-term simulations. To improve computational efficiency, we here propose the use of a local inertial flow equation combined with a vector-based river network map. A local inertial equation, a simplified formulation of the shallow water equations, was introduced to replace a diffusion wave equation. A vector-based river network map which flexibly discretizes river segments was adopted in order to replace the traditional grid-based map which is based on a Cartesian grid coordinate system. The computational efficiency of the proposed flow routing and river network map was tested by executing hydrodynamic simulations with the CaMa-Flood global river model. The simulation results suggest that the computational efficiency can be improved by more than 300% by applying the local inertial equation. It can be improved by a further 60% by implementing the vector-based river network map instead of a grid-based map. It is found that the vector-based map with evenly distributed flow distances between calculation units allows longer time steps compared to the grid-based map because the latter has very short flow distances between calculation units at high latitudes which critically limit time step length. Considering the improvement in simulation speed, the local inertial equation, and a vector-based river network map are preferable in global hydrodynamic simulations with high computational demands such as ensemble or long-term experiments.
The fundamentals of the smoothed particle hydrodynamics (SPH) method and
its applications in astrophysics are reviewed. The discussion covers
equations of motion, viscosity amd thermal conduction, spatially varying
resolution, kernels, magnetic fields, special relativity, and
implementation. Applications of the SPH method are discussed with
reference to gas dynamics, binary stars and stellar collisions,
formation of the moon and impact problems, fragmentation and cloud
collisions, and cosmological and galactic problems. Other applications
discussed include disks and rings, radio jets, motion near black holes,
supernovae, magnetic phenomena, and nearly incompressible flow.
SUMMARYA review of wetting and drying (WD) algorithms used by contemporary numerical models based on the shallow water equations is presented. The numerical models reviewed employ WD algorithms that fall into four general frameworks: (1) Specifying a thin film of fluid over the entire domain; (2) checking if an element or node is wet, dry or potentially one of the two, and subsequently adding or removing elements from the computational domain; (3) linearly extrapolating the fluid depth onto a dry element and its nodes from nearby wet elements and computing the velocities; and (4) allowing the water surface to extend below the topographic ground surface. This review presents the benefits and drawbacks in terms of accuracy, robustness, computational efficiency, and conservation properties. The WD algorithms also tend to be highly tailored to the numerical model they serve and therefore difficult to generalize. Furthermore, the lack of temporally and spatially defined validation data has hampered comparisons of the models in terms of their ability to simulate WD over real domains. A short discussion of this topic is included in the conclusion. Copyright © 2012 John Wiley & Sons, Ltd.
Effective flood risk management depends on methods for estimating flood hazard and an appraisal of the dominant uncertainties in the analysis. Typically, hydraulic models are used to simulate the extent of flooding for an estimate of the flow in a particular reach for a chosen probability of exceedance. However, this definition causes problems at river confluences where flows derive from multiple sources. Here, a model-based approach was adopted to describe the multisite joint distribution of river flows for three rivers that converge on the city of Carlisle (UK). Monte-Carlo methods were used to generate flood events with realistic spatial dependence between tributaries which would occur over a 1000 year period. To account for the uncertainty in the data used to create the event set, block bootstrapping was used to produce a further 100 runs of the event generator over notional 1000 year periods. Each of the 20 000 events created by this process was then simulated using a 10 m resolution two-dimensional hydraulic model of the whole city to demonstrate the feasibility of the approach. Spatial dependence was found to be important because no single event caused the maximum flood extent at all locations and assuming perfect correlation between tributaries overestimated flood hazard. Uncertainty in estimates of inundation probability was significant to the extent that confidence intervals in risk estimates were larger than expected; however, the interaction of flows with the flood defences and valley topography gave a distinct structure to the inundation probabilities and risk. Copyright © 2012 John Wiley & Sons, Ltd.
The 4th IPCC report highlights the increased vulnerability of the coastal areas from floods due to sea-level rise (SLR). The existing coastal flood control structures in Bangladesh are not adequate to adapt these changes and new measures are urgently necessary. It is important to determine the impacts of SLR on flooding to analyse the performance of the existing structures and corresponding impact to plan for suitable adaptation and mitigation measures to reduce the impacts of floods on coastal zone. The study aims to develop a comprehensive understanding of the possible effects of SLR on floods in the coastal zone of Bangladesh. A hydrodynamic model, which is a combination of surface and river parts, was utilized for flood simulation. The tool was applied under a range of future scenarios, and results indicate both spatial variability of risk and changes in flood characteristics between now and under SLR. Estimated impact on population, infrastructure and transportation is also exposed. These types of impact estimation would be of value to flood plain management authorities to minimize the socio-economic impact.
Since the topographical data obtained from LiDAR (Light Detection and Ranging) measurements is superior in resolution and accuracy as compared to conventional geospatial data, over the last decade aerial LiDAR (Light Detection and Ranging) has been widely used for obtaining geospatial information. However, digital terrain models made from LiDAR data retain some degree of uncertainty as a result of the measurement principles and the operational limitations of LiDAR surveying. LiDAR cannot precisely measure topographical elements such as ground undulation covered by vegetation, curbstones, etc. Such instrumental and physical uncertainties may impact an estimated result in an inundation flow simulation. Meanwhile, how much and how these topographical uncertainties affect calculated results is not understood. To evaluate the effect of topographical uncertainty on the calculated inundation flow, three representative terrains were prepared that included errors in elevation. Here, the topographical uncertainty that was introduced was generated using a fractal algorithm in order to represent the spatial structure of the elevation uncertainty. Then, inundation flows over model terrains were calculated with an unstructured finite volume flow model that solved shallow water equations. The sensitivity of the elevation uncertainty on the calculated inundated propagation, especially the local flow velocity, was evaluated. The predictability of inundation flow over complex topography is discussed, as well as its relationship to topographical features.
The performance of flood inundation models is often assessed using satellite observed data; however, these data have inherent uncertainty. In this study we determine the patterns of uncertainty in an ERS-2 SAR image of flooding on the River Dee, UK and, using LISFLOOD-FP, evaluate how this uncertainty can influence the assessment of flood inundation model performance. The flood outline is intersected with high resolution LiDAR topographic data to extract water levels at the flood margin, and to estimate patterns of uncertainty the gauged water levels are used to create a reference water surface slope for comparison with the satellite-derived water levels. We find the residuals between the satellite data points and the reference line to be spatially clustered.
The growing availability of multi-temporal satellite data has increased opportunities for monitoring large rivers from space. A variety of passive and active sensors operating in the visible and microwave range are currently operating, or planned, which can estimate inundation area and delineate flood boundaries. Radar altimeters show great promise for directly measuring stage variation in large rivers. It also appears to be possible to obtain estimates of river discharge from space, using ground measurements and satellite data to construct empirical curves that relate water surface area to discharge. Extrapolation of these curves to ungauged sites may be possible for the special case of braided rivers. Where clouds, trees and floating vegetation do not obscure the water surface, high-resolution visible/infrared sensors provide good delineation of inundated areas. Synthetic aperture radar (SAR) sensors can penetrate clouds and can also detect standing water through emergent aquatic plants and forest canopies. However, multiple frequencies and polarizations are required for optimal discrimination of various inundated vegetation cover types. Existing single- polarization, fixed-frequency SARs are not suÅcient for mapping inundation area in all riverine environments. In the absence of a space-borne multi-parameter SAR, a synergistic approach using single-frequency, fixed-polarization SAR and visible/infrared data will provide the best results over densely vegetated river floodplains. #1997 John Wiley & Sons, Ltd.
Global warming can potentially lead to changes in future rainfall and runoff and can significantly impact the regional hydrology and future availability of water resources. All the large-scale climate impact studies use the future climate projections from global climate models (GCMs) to estimate the impact on future water availability. This paper presents results from a detailed assessment to investigate the capability of 15 GCMs to reproduce the observed historical annual and seasonal mean rainfalls, the observed annual rainfall series and the observed daily rainfall distribution across south-east Australia. The assessment shows that the GCMs can generally reproduce the spatial patterns of mean seasonal and annual rainfalls. However, there can be considerable differences between the mean rainfalls simulated by the GCMs and the observed rainfall. The results clearly show that none of the GCMs can simulate the actual annual rainfall time series or the trend in the annual rainfall. The GCMs can also generally reproduce the observed daily (ranked) rainfall distribution at the GCM scale. The GCMs are ranked against their abilities to reproduce the observed historical mean annual rainfall and daily rainfall distribution, and, based on the combined score, the better GCMs include MPI-ECHAM5, MIUB, CCCMA_T47, INMCM, CSIRO-MK3·0, CNRM, CCCMA_T63 and GFDL 2·0 and those with poorer performances are MRI, IPSL, GISS-AOM, MIROC-M, NCAR-PCM1, IAP and NCAR-CCSM. However, the reduction in the combined score as we move from the best- to the worst-performing GCMs is gradual, and there is no evident cut-off point or threshold to remove GCMs from climate impact studies. There is some agreement between the results here and many similar studies comparing the performance of GCMs in Australia, but the results are not always consistent and do significantly disagree with several of the studies. Copyright © 2010 John Wiley & Sons, Ltd.
Bayesian theory of model calibration provides a coherent framework for distinguishing and encoding multiple sources of uncertainty in probabilistic predictions of flooding. This paper demonstrates the use of a Bayesian approach to computer model calibration, where the calibration data are in the form of spatial observations of flood extent. The Bayesian procedure involves generating posterior distributions of the flood model calibration parameters and observation error, as well as a Gaussian model inadequacy function, which represents the discrepancy between the best model predictions and reality. The approach is first illustrated with a simple didactic example and is then applied to a flood model of a reach of the river Thames in the UK. A predictive spatial distribution of flooding is generated for a flood of given severity.
The generalized likelihood uncertainty estimation (GLUE) methodology is applied to the problem of predicting the spatially distributed, time-varying probabilities of inundation of all points on a floodplain. Advantage is taken of the relative independence of different effective conveyance parameters to minimize the simulations required. Probability estimates are based on conditioning predictions of Monte Carlo realizations of a distributed quasi-two-dimensional flood routing model using maps of maximum inundation and aerial photographs of flooding in the area. The methodology allows posterior distributions of conveyance parameters to be estimated and maps of inundation potential probabilities to be drawn up for flood events of different magnitudes. The results suggest that combining information from different magnitude events should be done with care, as the distributions of effective parameter values may vary with event magnitude. The value of accurate topographic information that is consistent with mapped inundation is also highlighted. The methodology can be used to obtain dynamic probabilities of floodplain inundation in real time forecasting.