Probabilistic interval estimation of design floods under non-stationary conditions by an integrated approach

Abstract

Quantifying the uncertainty of non-stationary flood frequency analysis is very crucial and beneficial for planning and design of water engineering projects, which is fundamentally challenging especially in the presence of high climate variability and reservoir regulation. This study proposed an integrated approach that combined the Generalized Additive Model for Location, Scale and Shape parameters (GAMLSS) method, the Copula function and the Bayesian Uncertainty Processor (BUP) technique to make reliable probabilistic interval estimations of design floods. The reliability and applicability of the proposed approach were assessed by flood datasets collected from two hydrological monitoring stations located in the Hanjiang River of China. The precipitation and the reservoir index were selected as the explanatory variables for modeling the time-varying parameters of marginal and joint distributions using long-term (1954–2018) observed datasets. First, the GAMLSS method was employed to model and fit the time-varying characteristics of parameters in marginal and joint distributions. Second, the Copula function was employed to execute the point estimations of non-stationary design floods. Finally, the BUP technique was employed to perform the interval estimations of design floods based on the point estimations obtained from the Copula function. The results demonstrated that the proposed approach can provide reliable probabilistic interval estimations of design floods meanwhile reducing the uncertainty of non-stationary flood frequency analysis. Consequently, the integrated approach is a promising way to offer an indication on how design values can be estimated in a high-dimensional problem.

Long‐term evolution and non‐stationary behaviours of daily and subdaily precipitation extremes in China

“…Moreover, existing design standards of flood protection engineering are mostly based on stationary assumptions when calculating return levels of the design storm (Hossain et al, 2021). However, studies have reported that extreme precipitation and runoff series show non‐stationary characteristics such as significant trends and change points, which means they no longer follow the stationary assumption (Liu et al, 2022a; Milly et al, 2008; Tramblay et al, 2012; Zhou et al, 2022). If return levels of precipitation extremes in engineering design are still calculated based on stationary assumptions under non‐stationary conditions, two situations will occur: (1) underestimation of design storms, which will result in underestimation of failure risks for hydrological projects; (2) overestimation of design storms, which will result in wasted economic costs due to the overestimation of failure risks for hydrological projects (Rodrigues et al, 2020).…”

Nonstationary Annual Maximum Flood Frequency Analysis Using a Conceptual Hydrologic Model with Time-Varying Parameters

“…Design flood value associated with a specific return period (e.g., 100-year that is most commonly used in relevant studies) is traditionally estimated based on flood frequency analysis through statistical approaches [6]. Recently, with the concept that stationarity is dead [4], various methodologies using a probabilistic model of flood frequency considering nonstationarity have been introduced [7][8][9][10][11][12][13][14][15]. Khaliq et al [16] provide a comprehensive review of these approaches, including the incorporation of trends in distribution parameters, trends in statistical moments, etc.…”