Investigating Flood Risks of Rainfall and Storm Tides Affected by the Parameter Estimation Coupling Bivariate Statistics and Hydrodynamic Models in the Coastal City

Abstract

: The public health risk caused by urban floods is a global concern. Flood risks are amplified by the interaction of rainfall and storm tides in coastal cities. In this study, we investigate the flood risks of rainfall and storm tides coupling statistical and hydrodynamic models and evaluate the influence of different parameter estimation methods and bivariate return periods (RPs) on flood risks in the coastal city. The statistical model is used to obtain the bivariate design of rainfall and storm tides with the integration of copula function, most-likely weight function and Monte Carlo simulation method. The bivariate designs are adopted as the input boundaries for the hydrodynamic model established by Personal Computer Storm Water Management Model (PCSWMM), and the flood risk is evaluated by the hydrodynamic model. Subsequently, the influence of different parameter estimation approaches (that is, parametric and non-parametric) and bivariate RPs (that is, co-occurrence RP, joint RP, and Kendall RP) on bivariate designs and flood risks are investigated. With Haikou coastal city in China as the case study, the results show that: (1) Gumbel copula is the best function to describe the correlation structure between rainfall and storm tides for the parametric and non-parametric approaches, and the non-parametric approach is a better fit for the observed data; (2) when the Kendall RP is large (more than 100 years), the flood risk is underestimated with an average of 17% by the non-parametric estimation, and the parametric estimation approach is recommended as it is considered the most unfavorable scenario; (3) the types of bivariate RP have the important impact on the flood risk. When there is no specific application need, the Kendall RP can be adopted as the bivariate design standard of flooding facilities since it can describe the dangerous areas more accurately for multivariate scenario. The results can provide references for reasonable flood risk assessment and flooding facility design in coastal cities.

Enhancing Riverbank Protection along the Jamuna River, Bangladesh: Review of Previous Countermeasures and Morphological Assessment through Groyne-based Solutions using Numerical Modeling

“…To establish the input parameters for the proposed alternatives, historical annual peak discharges of the Jamuna River spanning the last 65 years (1956-2021) were scrutinized. Subsequently, three distinct peak discharges were chosen as input data to simulate the selected stretch of the Jamuna River by employing Gumbel's distribution method, [30] as outlined in the following Table 1…”

GIS-based flood susceptibility mapping using bivariate statistical model in Swat River Basin, Eastern Hindukush region, Pakistan

“…Using rainfall data from NASA, we found that southern parts of the SRB were comparatively more susceptible to floods than its northern territories. This is because SRB has semi-arid climatic conditions where the rainfall pattern in lower southern parts is influenced by the summer monsoon, while in upper northern parts, the rainfall pattern is dominated by the winter rainfall, occurring due to the western disturbance of the Mediterranean Sea (Khan et al, 2020;Wang et al, 2020;Hussain et al, 2021;Xu et al, 2022).…”

Investigating the Joint Probability of High Coastal Sea Level and High Precipitation

“…For example, the dependence between observed water levels and precipitation, including impacts of sampling methods and distribution fitting and the resulting flood values, is explored using copulas in [22]. The joint distribution of rainfall and storm surge based on the copula function is investigated in [23]. The effect of internal climate variability on copula-based compound event analysis is studied in a case study in the Netherlands by [24].…”