Next Article in Journal
Efficiency Assessment of Existing Pumping/Hydraulic Network Systems to Mitigate Flooding in Low-Lying Coastal Regions under Different Scenarios of Sea Level Rise: The Mazzocchio Area Study Case
Next Article in Special Issue
Automated Floodway Determination Using Particle Swarm Optimization
Previous Article in Journal
Efficient Low-Cost Anaerobic Treatment of Wastewater Using Biochar and Woodchip Filters
Open AccessArticle

Nonstationary Flood Frequency Analysis Using Univariate and Bivariate Time-Varying Models Based on GAMLSS

1
State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300072, China
2
Tianjin Zhongshui Science and Technology Consulting Co., Ltd., Tianjin 300170, China
3
Pearl River Comprehensive Technology Center (Information Center) of Pearl River Water Resources Commission of the Ministry of Water Resources, Guangzhou 510611, China
*
Author to whom correspondence should be addressed.
Water 2018, 10(7), 819; https://doi.org/10.3390/w10070819
Received: 16 May 2018 / Revised: 9 June 2018 / Accepted: 14 June 2018 / Published: 21 June 2018
(This article belongs to the Special Issue Flood Risk and Resilience)
With the changing environment, a number of researches have revealed that the assumption of stationarity of flood sequences is questionable. In this paper, we established univariate and bivariate models to investigate nonstationary flood frequency with distribution parameters changing over time. Flood peak Q and one-day flood volume W1 of the Wangkuai Reservoir catchment were used as basic data. In the univariate model, the log-normal distribution performed best and tended to describe the nonstationarity in both flood peak and volume sequences reasonably well. In the bivariate model, the optimal log-normal distributions were taken as marginal distributions, and copula functions were addressed to construct the dependence structure of Q and W1. The results showed that the Gumbel-Hougaard copula offered the best joint distribution. The most likely events had an undulating behavior similar to the univariate models, and the combination values of flood peak and volume under the same OR-joint and AND-joint exceedance probability both displayed a decreasing trend. Before 1970, the most likely combination values considering the variation of distribution parameters over time were larger than fixed parameters (stationary), while it became the opposite after 1980. The results highlight the necessity of nonstationary flood frequency analysis. View Full-Text
Keywords: nonstationarity; univariate model; GAMLSS; bivariate model; copulas nonstationarity; univariate model; GAMLSS; bivariate model; copulas
Show Figures

Figure 1

MDPI and ACS Style

Zhang, T.; Wang, Y.; Wang, B.; Tan, S.; Feng, P. Nonstationary Flood Frequency Analysis Using Univariate and Bivariate Time-Varying Models Based on GAMLSS. Water 2018, 10, 819.

AMA Style

Zhang T, Wang Y, Wang B, Tan S, Feng P. Nonstationary Flood Frequency Analysis Using Univariate and Bivariate Time-Varying Models Based on GAMLSS. Water. 2018; 10(7):819.

Chicago/Turabian Style

Zhang, Ting; Wang, Yixuan; Wang, Bing; Tan, Senming; Feng, Ping. 2018. "Nonstationary Flood Frequency Analysis Using Univariate and Bivariate Time-Varying Models Based on GAMLSS" Water 10, no. 7: 819.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop