Nonstationary Analyses of the Maximum and Minimum Streamflow in Tamsui River Basin, Taiwan

Abstract

This study aims to detect non-stationarity of the maximum and minimum streamflow regime in Tamsui River basin, northern Taiwan. Seven streamflow gauge stations, with at least 27-year daily records, are used to characterize annual maximum 1- and 2-day flows and annual minimum 1-, 7-, and 30-day flows. The generalized additive models for location, scale, and shape (GAMLSS) are used to dynamically detect evolution of probability distributions of the maximum and minimum flow indices with time. Results of time-covariate models indicate that stationarity is only noted in the 4 maximum flow indices out of 35 indices. This phenomenon indicates that the minimum flow indices are vulnerable to changing environments. A 16-category distributional-change scheme is employed to classify distributional changes of flow indices. A probabilistic distribution with complex variations of mean and variance is prevalent in the Tamsui River basin since approximate one third of flow indices (34.3%) belong to this category. To evaluate impacts of dams on streamflow regime, a dimensionless index called the reservoir index (RI) serves as an alternative covariate to model nonstationary probability distribution. Results of RI-covariate models indicate that 7 out of 15 flow indices are independent of RI and 80% of the best-fitted RI-covariate models are generally worse than the time-covariate models. This fact reveals that the dam is not the only factor in altering the streamflow regime in the Tamsui River, which is a significant alteration, especially the minimum flow indices. The obtained distributional changes of flow indices clearly indicate changes in probability distributions with time. Non-stationarity in the Tamsui River is induced by climate change and complex anthropogenic interferences.

Statistical Analysis and Scenario Modeling of Non-Stationary Runoff Change in the Loess Plateau: A Novel Application of the Generalized Additive Model in Location, Scale and Shape

“…A Generalized Additive Model in Location, Scale, and Shape (GAMLSS) is a semi parametric regression model that analyzes the frequencies of stationary and non-station ary runoff and other features [17][18][19][20][21][22]29,30].…”

“…It supports a variety of random variable frequency distribution types and is extremely useful in constructing linear or nonlinear functional relationships between distribution function position parameters, scale parameters, shape parameters, and explanatory variables [18]. The GAMLSS framework has been widely applied in non-stationary frequency analysis, modeling, and forecasting in hydrology [19][20][21][22]. This GAMLSS feature also allows for cross-driving interactions between runoff and the driving elements, or between the driving elements themselves.…”

Decreasing flood hazard evaluated in Turkey using nonstationary models

“…Therefore, it is increasingly recognized that nonstationary probability distribution models should be assessed, and if they provide an improvement over stationary models, they can be implemented for effective risk management and design (Cheng, AghaKouchak, Gilleland, & Katz, 2014; Katz, 2013; Salas & Obeysekera, 2014). Frequency analyses of hydro‐climatic extremes that account for nonstationarity are attracting attention and a number of efforts have been reported in the literature addressing this issue (Li & Tan, 2015; Shiau & Liu, 2021; Su & Chen, 2019; Tan & Gan, 2015; Villarini et al, 2009; Villarini, James, & Napolitano, 2010; Yan, Xiong, Liu, Hu, & Xu, 2017). In nonstationary frequency analysis, distribution parameters are assumed to vary with the explanatory variables over time (Zhang, Gu, Singh, Xiao, & Chen, 2015).…”