<scp>M</scp>onte <scp>C</scp>arlo‐based flood modelling framework for estimating probability weighted flood risk

Kalyanapu, Alfred J.; Judi, David R.; McPherson, Timothy N.; Burian, Steven J.

doi:10.1111/j.1753-318x.2011.01123.x

Cited by 53 publications

(42 citation statements)

References 35 publications

(47 reference statements)

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“…When using a detailed ground surface, flooding from a node is no longer restricted to a limited number of predefined overland flow paths between manholes (Djordjevic et al, 2004). Kalyanapu et al (2012) used a Monte Carlo-based flood modelling framework for estimating the probabilityweighted methods for expanding the applicability of the 2D model. Flooding can also pool in local depressions in the ground mesh; when the depth of water in such pools is high enough, water can spill over from the low areas (Soares-Frazão et al, 2008;Jamieson et al, 2012).…”

Section: D and 2d Flood Modelsmentioning

confidence: 99%

Courtyard-level sewer data-enhanced two-dimensional hydraulic model for urban flood hazard assessment in Kunming, China

Xie

Cai

et al. 2014

Water Policy

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This paper describes a study of urban flooding in downtown Kunming, China, simulating a major flood event that occurred in July 2008 using an improved two-dimensional (2D) hydraulic model enhanced with courtyardlevel sewer data (CLSD). Although municipal authorities are not responsible for 'private' courtyard sewers, available records were specifically added to this model, enhancing its accuracy and usefulness. Geographic information system (GIS) flood maps, a mapping overlay approach and statistical method compared both predicted results and the recorded flood area. A statistical method also provided a measure of the correlation between the extent of the predicted flood areas and recorded flood areas (parameter 'F'). Results of the improved 2D/CLSD model showed a correlation value for F of 51, 32.6% higher than the basic one-dimensional municipal-level sewer data (1D/MLSD) model; 26.2% higher than an interim version of the model that included a 2D ground surface (2D/MLSD). The 2D/CLSD model predicted flooding in 10 of the 12 courtyards with observed flooding. This was a major improvement over the 1D/MLSD model (three out of 12) and the 2D/MLSD model (five out of 12). Thus a CLSD-enhanced 2D hydraulic model potentially improves accuracy in predicting, mapping and understanding flood risk in urban areas.

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Section: D and 2d Flood Modelsmentioning

confidence: 99%

Courtyard-level sewer data-enhanced two-dimensional hydraulic model for urban flood hazard assessment in Kunming, China

Xie

Cai

et al. 2014

Water Policy

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“…For simulating downstream flood hazards in terms of spatially varied flood depths and velocities, a two-dimensional (2D) flood model called Flood2D-GPU is applied (Kalyanapu et al 2011). The Flood2D-GPU is an unsteady numerical flood model solving the nonlinear hyperbolic shallow water equations using a firstorder accurate upwind difference scheme (Patankar 1980;Ferziger and Peri c 2002).…”

Section: Two-dimensional Flood Modelmentioning

confidence: 99%

“…The Flood2D-GPU is an unsteady numerical flood model solving the nonlinear hyperbolic shallow water equations using a firstorder accurate upwind difference scheme (Patankar 1980;Ferziger and Peri c 2002). The model has been used in various applications including river flooding, dam break simulations, population at risk studies, and flood damage estimation (Kalyanapu et al 2011;Kalyanapu et al 2012;Kalyanapu et al 2013, manuscript submitted to J. The future model time step is constrained using the Courant condition.…”

Section: Two-dimensional Flood Modelmentioning

confidence: 99%

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Toward a Methodology to Investigate the Downstream Flood Hazards on the American River due to Changes in Probable Maximum Flood due to Effects of Artificial Reservoir Size and Land-Use/Land-Cover Patterns

et al. 2013

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Recent research in mesoscale hydrology suggests that the size of the reservoirs and the land-use/land-cover (LULC) patterns near them impact the extreme weather [e.g., probable maximum flood (PMF)]. A key question was addressed by W. Yigzaw et al.: How do reservoir size and/or LULC modify extreme flood patterns, specifically PMF via modification of probable maximum precipitation (PMP)? Using the American River watershed (ARW) as a representative example of an impounded watershed with Folsom Dam as the flood control structure, they applied the distributed Variable Infiltration Capacity (VIC) model to simulate the PMF from the atmospheric feedbacks simulated for various LULC scenarios. The current study presents a methodology to extend the impacts of these modified extreme flood patterns on the downstream Sacramento County, California. The research question addressed is, what are the relative effects of downstream flood hazards to population on the American River system under various PMF scenarios for the Folsom Dam? To address this goal, a two-dimensional flood model, the Flood in Two Dimensions-Graphics Processing Unit (Flood2D-GPU), is calibrated using synthetic aperture radar (SAR) and Landsat satellite observations and observed flood stage data. The calibration process emphasized challenges associated with using National Elevation Dataset (NED) digital elevation model (DEM) and topographic light detection and ranging (lidar)-derived DEMs to achieve realistic flood inundation. Following this calibration exercise, the flood model was used to simulate four land-use scenarios (control, predam, reservoir double, and nonirrigation). The flood hazards are quantified as downstream flood hazard zones by estimating flood depths and velocities and its impacts on risk to population using depth-velocity hazard relationships provided by U.S. Bureau of Reclamation (USBR). From the preliminary application of methodology in this study, it is evident from comparing downstream flood hazard that similar trends in PMF comparisons reported by W. Yigzaw et al. were observed. Between the control and nonirrigation, the downstream flood hazard is pronounced by 23.90% for the judgment zone and 22.40% for high hazard zones. Comparing the control and predam scenarios, these differences are amplified, ranging between 0.17% and 21.34%. While there was no change detected in the peak PMF discharges between the control and reservoirdouble scenarios, it still yielded an increase in high hazard areas for the latter. Based on this preliminary bottom-up vulnerability assessment study, it is evident that what was observed in PMF comparisons by W. Yigzaw et al. is confirmed in comparisons between control versus predam and control versus nonirrigation. While there was no change detected in the peak PMF discharges between the control and reservoirdouble scenarios, it still yielded a noticeable change in the total areal extents: specifically, an increase in high hazard areas for the latter. Continued studies in bottom-up vulnerability assessment of flo...

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“…It is thus very advantageous for probabilistic analyses where high computational cost is expected. The model has been successfully applied in flood hazard analysis [31][32][33], flood damage estimation [34] and investigation of the impact of land use/land cover (LULC) change on floods [35].…”

Section: Hydraulic Modelingmentioning

confidence: 99%

Sustainability-Based Flood Hazard Mapping of the Swannanoa River Watershed

2017

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An integrated framework is presented for sustainability-based flood hazard mapping of the Swannanoa River watershed in the state of North Carolina, U.S. The framework uses a hydrologic model for rainfall-runoff transformation, a two-dimensional unsteady hydraulic model flood simulation and a GIS-based multi-criteria decision-making technique for flood hazard mapping. Economic, social, and environmental flood hazards are taken into account. The importance of each hazard is quantified through a survey to the experts. Utilizing the proposed framework, sustainability-based flood hazard mapping is performed for the 100-year design event. As a result, the overall flood hazard is provided in each geographic location. The sensitivity of the overall hazard with respect to the weights of the three hazard components were also investigated. While the conventional flood management approach is to assess the environmental impacts of mitigation measures after a set of feasible options are selected, the presented framework incorporates the environmental impacts into the analysis concurrently with the economic and social influences. Thereby, it provides a more sustainable perspective of flood management and can greatly help the decision makers to make better-informed decisions by clearly understanding the impacts of flooding on economy, society and environment.

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Monte Carlo‐based flood modelling framework for estimating probability weighted flood risk

Cited by 53 publications

References 35 publications

Courtyard-level sewer data-enhanced two-dimensional hydraulic model for urban flood hazard assessment in Kunming, China

Courtyard-level sewer data-enhanced two-dimensional hydraulic model for urban flood hazard assessment in Kunming, China

Toward a Methodology to Investigate the Downstream Flood Hazards on the American River due to Changes in Probable Maximum Flood due to Effects of Artificial Reservoir Size and Land-Use/Land-Cover Patterns

Sustainability-Based Flood Hazard Mapping of the Swannanoa River Watershed

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