Nonstationary frequency analysis and uncertainty quantification for extreme low lake levels in a large river-lake-catchment system

Yuxue Jia; Qi Zhang; Chenyang Xue; Hongwu Tang

doi:10.1016/j.scitotenv.2023.166329

Nonstationary frequency analysis and uncertainty quantification for extreme low lake levels in a large river-lake-catchment system

Sci Total Environ. 2023 Dec 10:903:166329. doi: 10.1016/j.scitotenv.2023.166329. Epub 2023 Aug 24.

Authors

Yuxue Jia¹, Qi Zhang², Chenyang Xue¹, Hongwu Tang³

Affiliations

¹ Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; University of Chinese Academy of Sciences, 19 Yuquan Road, Beijing 100049, China.
² Yangtze Institute for Conservation and Development, Hohai University, Nanjing 210024, China. Electronic address: qizhang@hhu.edu.cn.
³ Yangtze Institute for Conservation and Development, Hohai University, Nanjing 210024, China; College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210024, China.

PMID: 37633398
DOI: 10.1016/j.scitotenv.2023.166329

Abstract

Extreme hydrological events have become increasingly frequent on a global scale. The middle Yangtze River also faces a substantial challenge in dealing with extreme flooding and drought. However, the long-term characteristics of the extreme hydrological regime have not yet been adequately recognized. Moreover, there is uncertainty in the extreme value estimation, and this uncertainty needs to be distinguished and quantified. In this study, we investigated the nonstationary frequency characteristics of extreme low lake levels (ELLLs), taking the Poyang Lake as an example. Daily lake levels from 1960 to 2022 were utilized to estimate the return level using the generalized Pareto distribution (GPD). The uncertainty from three sources, i.e., the parameter estimator, threshold selection, and covariate, was quantified via variance decomposition. The results indicate that (1) the parameter estimator is the predominant source of uncertainty, with a contribution rate of approximately 87 %. The total uncertainty of the covariate, threshold, and interaction term is only 13 %. (2) Two indexes, namely the annual minimum water level (WL_min) and the days with peak over the 90 % threshold per year (DPOT₉₀), decreased (0.01-0.03 m/year) and increased (0.17-1.39 days/year), respectively, indicating a progressively severe drought trend for Poyang Lake. (3) The return level with return period of 5 to 100 years significantly decreased after the early 21st century. A large spatial heterogeneity was identified for the variation in the return level, and the change rate of the return level with a 100-year return period ranged from 5 % to 40 % for the whole lake. (4) The ELLLs had a stronger correlation with the catchment discharge than with the Yangtze River discharge and the large-scale atmospheric circulation indices. This study provides a methodology with reduced uncertainty for nonstationary frequency analysis (NFA) of ELLLs exemplified in large river-lake systems.

Keywords: Extreme low lake levels; Generalized Pareto distribution; Nonstationary frequency analysis; Poyang Lake; Uncertainty.