Constraining coupled hydrological-hydraulic flood model by past storm events and post-event measurements in data-sparse regions

Hdeib, Rouya; Abdallah, Chadi; Colin, François; Brocca, Luca; Moussa, Roger

doi:10.1016/j.jhydrol.2018.08.008

Cited by 22 publications

(7 citation statements)

References 77 publications

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“…The coefficient λ is the ratio of scale invariance between the known duration D and the duration to be assessed, d, and β is the self-similarity index of the studied rainfall process. The qth moments of rainfall intensity are obtained from Equation (10) as follows [46]:…”

Section: Scaling Rainfall Extremesmentioning

confidence: 99%

“…The MRL plot identifies a range of possible threshold values in the interval 10 mm ≤ u ≤ 30 mm. Figure 5 presents the GPD parameter stability plots for the modified scale (scale parameter of the GPD), and shape parameter, ξ, for thresholds in [10,30] mm. Based on stability characteristics of the GPD stability plots, while also considering the uncertainty of the parameters represented using 95% confidence intervals shown as vertical lines for each threshold, a threshold between 14 mm ≤ u ≤ 27 mm is considered to be a good candidate.…”

Section: Extreme Rainfall Assessmentmentioning

confidence: 99%

“…Flood modelling is a powerful tool in understanding the hydrodynamics of historic flood events, and in some cases, the construction of accurate IDF (intensity-durationfrequency)/DDF (depth-duration-frequency) curves can be used to predict future events that will cause damage to the urban fabric [8]. There exist several studies combining flood inundation modelling with hydrological modelling and the unit hydrograph theory, mainly focusing on fluvial flooding in small or larger catchments [9][10][11][12][13][14], using DEMs (Digital Elevation Models) or DTMs (Digital Terrain Models) with a computational grid resolution ranging between 5 m and 100 m. However, it should be noted that the combination of an incorrect representation of urban features in the flood model such as buildings, bridges, infrastructure, etc., and the resampling of the DEM/DTM multiple times might cause large inconsistencies [1,15,16] and overestimation of the flooding hazard in areas with minor inundation issues and vice versa. Unlike studies modelling fluvial flood inundation in urban areas, studies focusing on the exposure assessment of urban areas to pluvial floods are rather limited, and started receiving significant interest quite recently.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Urban Flood Modelling under Extreme Rainfall Conditions for Building-Level Flood Exposure Analysis

Iliadis,

Galiatsatou,

Glenis

et al. 2023

Hydrology

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The expansion of urban areas and the increasing frequency and magnitude of intense rainfall events are anticipated to contribute to the widespread escalation of urban flood risk across the globe. To effectively mitigate future flood risks, it is crucial to combine a comprehensive examination of intense rainfall events in urban areas with the utilization of detailed hydrodynamic models. This study combines extreme value analysis techniques applied to rainfall data ranging from sub-hourly to daily durations with a high-resolution flood modelling analysis at the building level in the centre of Thessaloniki, Greece. A scaling procedure is employed to rainfall return levels assessed by applying the generalised extreme value (GEV) distribution to annual maximum fine-temporal-scale data, and these scaling laws are then applied to more reliable daily rainfall return levels estimated by means of the generalised Pareto distribution (GPD), in order to develop storm profiles with durations of 1 h and 2 h. The advanced flood model, CityCAT, is then used for the simulation of pluvial flooding, providing reliable assessments of building-level exposure to flooding hazards. The results of the analysis conducted provide insights into flood depths and water flowpaths in the city centre of Thessaloniki, identifying major flowpaths along certain main streets resulting in localised flooding, and identifying around 165 and 186 buildings highly exposed to inundation risk in the study area for 50-year storm events with durations of 1 h and 2 h, respectively. For the first time in this study area, a detailed analysis of extreme rainfall events is combined with a high-resolution Digital Terrain Model (DTM), used as an input into the advanced and fully featured CityCAT hydrodynamic model, to assess critical flowpaths and buildings at high flood risk. The results of this study can aid in the planning and design of resilient solutions to combat urban flash floods, as well as contribute to targeted flood damage mitigation and flood risk reduction.

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Section: Scaling Rainfall Extremesmentioning

confidence: 99%

Section: Extreme Rainfall Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Urban Flood Modelling under Extreme Rainfall Conditions for Building-Level Flood Exposure Analysis

Iliadis,

Galiatsatou,

Glenis

et al. 2023

Hydrology

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“…For example, Li et al [9] proposed an external coupling model where the TOPMODEL (TOPography-based hydrological MODEL, developed by Lancaster University, Lancaster, UK) was employed to obtain the flood hydrograph, and Mike Flood was used to simulate the floodplain inundation, but there was less accuracy in the estimation of the low flows in tributaries. An external coupling model connecting SWAT (Soil and Water Assessment Tool) and GEMSS (Generalized Environmental Modeling System for Surface waters, a 3D time-dependent hydrodynamic model) was developed by Dargahi and Setegn [10], which was used to simulate flooding in the Tana Basin, and similar methods were used in studies of other catchments [11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

A Multigrid Dynamic Bidirectional Coupled Surface Flow Routing Model for Flood Simulation

Shen

Jiang

Zhou

et al. 2021

Water

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Surface flow routing is an important component in hydrologic and hydrodynamic research. Based on a literature review and comparing the different coupling models (the hydrologic model and hydrodynamic model), a multigrid dynamic bidirectional coupled surface flow routing model (M-DBCM), consisting of diffusion wave equations (DWEs) and shallow water equations (SWEs), is proposed herein based on grids with different resolutions. DWEs were applied to obtain runoff routing in coarse grid regions to improve the computational efficiency, while the DWEs and SWEs were bidirectionally coupled to detail the flood dynamics in fine grid regions to obtain good accuracy. In fine grid zones, the DWEs and SWEs were connected by an internal moving boundary, which ensured the conservation of mass and momentum through the internal moving boundary. The DWEs and SWEs were solved by using the time explicit scheme, and different time steps were adopted in regions with different grid sizes. The proposed M-DBCM was validated via three cases, and the results showed that the M-DBCM can effectively simulate the process of surface flow routing, which had reliable computational efficiency while maintaining satisfactory simulation accuracy. The rainfall runoff in the Goodwin Creek Watershed was simulated based on the proposed M-DBCM. The results showed that the discharge hydrographs simulated by the M-DBCM were closer to the measured data, and the simulation results were more realistic and reliable, which will be useful in assisting flood mitigation and management.

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“…Other researchers (Choi and Mantilla, 2015;Grimaldi et al, 2013;Montanari et al, 2009) adopted similar coupling methods to investigate flood risks. Many advanced opens-source and commercial modelling packages (SWAT (Liu et al, 2015), HEC-HMS (Hdeib et al, 2018), DHI MIKE (Rayburg and Thoms, 2009), etc. ), can be easily applied to UCM.…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Bidirectional Coupled Hydrologic-Hydrodynamic Model for Flood Prediction

Jiang

Zhou

et al. 2019

Preprint

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Abstract. As one of the main natural disasters, flood disaster poses a great threat to township development and property security. Numerous hydrological models and hydrodynamic models have been developed and implemented for flood simulation, risk prediction and inundation assessment. In this study, a dynamic and bidirectional coupled hydrodynamic-hydrologic-hydrodynamic model (DBCM) is developed to predict and evaluate inundation impact in a catchment in mountain area. Based on characteristic theory, the proposed method is able to dynamically adapt and alternate the simulation domain of hydrologic model, and/or hydrodynamic model according to the local flow condition, and a key feature of the proposed model is the dynamic coupling splitting the hydrologic and hydrodynamic simulation domains. The proposed model shows good prediction accuracy and overcomes the shortage existing in previous unidirectional coupling model (UCM). Existing numerical examples and physical experiments were both used to validate the performance of DBCM. Compared to UCM, results from DBCM show good agreements with analytical and measured data which indicates that the proposed model effectively reproduces flood propagation process and accounts for surface flow interaction between non-inundation region and inundation region. Finally, DBCM is applied to predict the flood in the Longxi river basin, and the simulation results show the capability of DBCM in conducting flood event simulation in interested catchment which can support flood risk early warning and future management.

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Constraining coupled hydrological-hydraulic flood model by past storm events and post-event measurements in data-sparse regions

Cited by 22 publications

References 77 publications

Urban Flood Modelling under Extreme Rainfall Conditions for Building-Level Flood Exposure Analysis

Urban Flood Modelling under Extreme Rainfall Conditions for Building-Level Flood Exposure Analysis

A Multigrid Dynamic Bidirectional Coupled Surface Flow Routing Model for Flood Simulation

A Dynamic Bidirectional Coupled Hydrologic-Hydrodynamic Model for Flood Prediction

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