Impact of detention dams on the probability distribution of floods

Abstract

Abstract. Detention dams are one of the most effective practices for flood mitigation. Therefore, the impact of these structures on the basin hydrological response is critical for flood management and the design of flood control structures. With the aim of providing a mathematical framework to interpret the effect of flow control systems on river basin dynamics, the functional relationship between inflows and outflows is investigated and derived in a closed form. This allowed the definition of a theoretically derived probability distribution of the peak outflows from in-line detention basins. The model has been derived assuming a rectangular hydrograph shape with a fixed duration and a random flood peak. In the present study, the undisturbed flood peaks are assumed to be Gumbel distributed, but the proposed mathematical formulation can be extended to any other flood-peak probability distribution. A sensitivity analysis of parameters highlighted the influence of detention basin capacity and rainfall event duration on flood mitigation on the probability distribution of the peak outflows. The mathematical framework has been tested using for comparison a Monte Carlo simulation where most of the simplified assumptions used to describe the dam behaviours are removed. This allowed demonstrating that the proposed formulation is reliable for small river basins characterized by an impulsive response. The new approach for the quantification of flood peaks in river basins characterized by the presence of artificial detention basins can be used to improve existing flood mitigation practices and support the design of flood control systems and flood risk analyses.

Hydraulic Efficiency of Green-Blue Flood Control Scenarios for Vegetated Rivers: 1D and 2D Unsteady Simulations

“…The geo-hydrological and ecohydraulic challenges linked to current and future climate processes [1][2][3][4][5][6] highlight the growing need to protect water resources quantitatively and qualitatively in an ever more decisive way, especially in sensitive areas within both natural and urban territories [7,8]. In this context, the hydraulic conveyance of vegetated open channels intersecting anthropogenic settlements is dramatically affected by the temporal evolution of riverine vegetation properties [9][10][11], mainly associated with riverine plants' growth, foliage, and density overall [12,13]. In fact, in the case of vegetated flows, the morphometric and bio-physical changes over time in riverine vegetation canopy features represent a source of hydraulic roughness, in addition to that due to the only riverbanks and bed, to be meticulously considered in the field-scale analysis of global flow resistance [14,15].…”

Freeboard Life-Cycle Benefit-Cost Analysis of a Rental Single-family Residence for Landlord, Tenant, and Insurer

“…The building AAL (𝐴𝐴𝐿 ) is estimated using the method presented in Gnan (2021) and Gnan et al (2022a). Flood depths derived from Monte Carlo simulations (e.g., Brodie, 2013;Hennequin et al, 2018;Kind, 2014;Kind et al, 2020;Qi et al, 2013;Rahim et al, 2021Rahim et al, , 2022aRahman et al, 2002;Taghinezhad et al, 2020;Yu et al, 2013) with the fitted Gumbel extreme value distribution (e.g., Al Assi et al, 2022;Bhat et al, 2019;Gnan et al, 2022b;Kim & Lee, 2021;Manfreda et al, 2021;Mostafiz et al, 2021a;2022b;Rahim et al, 2022b;Singh et al, 2018) are translated to building loss percentages using the U.S. Army Corps of Engineers (USACE; 2000) depth-damage function (DDF) designed for the home's attributes (e.g., onestory or two-or-more stories, with or without basement). The loss percentages are then multiplied by the structure replacement cost (i.e., building value, 𝐵𝑉), and the average of the resulting losses of all Monte Carlo-simulated flooding events is the AAL.…”

Apportionment of Average Annual Flood Loss between Homeowner and Insurer

“…This paper presents a method to derive apportionment factors that are useful in assigning average annual residential building ood loss to the homeowner or the insurer. Flood loss events are modeled at the individual building level using a Monte Carlo simulation [17,18,19,20,21,22,23,24], in which the ood hazard is characterized by the Gumbel extreme value distribution function [25,26,27,28,29]. A depth-damage function (DDF) from United States Army Corps of Engineers [30] is used to estimate the building loss of each ood event, which is apportioned to the homeowner or the insurer.…”