Work

Non-stationarity analysis of flood extremes under climate change and human activities in the Yuan River Basin, China

Year: 2024

Type: article

Abstract: Yuan River Basin in China. This study aims to identify temporal variations and non-stationarity in the annual maximum Peak Flow (PF) and Peak Water Level (PWL) series, along with their occurrence dates and intervals series at three stations (Anjiang, Pushi, and Taoyuan). Temporal variations were detected and non-stationary models were developed by incorporating physically-based covariates under climate change and human activities using the Mann-Kendall test, Pettitt test, and the Generalized Additive Models for Location, Scale, and Shape (GAMLSS) framework. (1) The intervals between Pushi and Taoyuan stations for both PF and PWL series have significantly shortened over the past 40 years, while the Taoyuan station showed no significant upward trend over the past 70 years. (2) Covariates associated with precipitation, Normalized Difference Vegetation Index (NDVI), Reservoir Index, and Impervious Area (IA) showed a significantly increased trend within the basin, particularly the annual maximum daily precipitation (P 1 ) at the Pushi station. (3) The non-stationary models performed best when incorporating either the 7-day or 15-day accumulated antecedent precipitation before the flood occurrence date for the flood extremes series, while the inclusion of the IA and mean NDVI three months before the flood occurrence month provided superior fitting for the occurrence dates series. (4) The variability of P 1 , 3-day accumulated precipitation and their overlap with 7-day and 15-day accumulated precipitation were most likely key factors in triggering typical flood extremes. • The flood intervals between Pushi and Taoyuan stations were shorter. • GAMLSS with physically-based covariates applied to the Yuan River Basin. • Flood non-stationarity was affected by 7-day and 15-day antecedent rainfall. • Flood occurrence dates affected by vegetation cover and impervious area.

Source: Journal of Hydrology Regional Studies

DOI: 10.1016/j.ejrh.2024.102131