Pang et al. (2026) Enhanced identification of watershed hydrological extremes using an improved drought index incorporating hydrological non-stationarity

Identification

• Journal: Journal of Hydrology
• Year: 2026
• Date: 2026-06-23
• Authors: Xiaoteng Pang, Jianwei Liu, Qin Zhang, Haihua Jing, Xinghan Xu, Longhai Shen, Jianxu Han
• DOI: 10.1016/j.jhydrol.2026.135910

Research Groups

• School of Hydraulic Engineering, Dalian University of Technology (PR China)
• Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (PR China)

Short Summary

This study develops a non-stationary standardized runoff index (NSRI) framework to characterize watershed conditions under changing environmental conditions. The proposed framework outperforms the traditional standardized runoff index (SRI) in identifying hydrological extremes.

Objective

• To develop an improved drought index that incorporates temporal, seasonal, and meteorological drivers of non-stationarity in hydrological time series.

Study Configuration

• Spatial Scale: Watershed scale (Taoer River Basin)
• Temporal Scale: Long-term (annual to decadal) and short-term (seasonal)

Methodology and Data

• Models used: Generalized additive model for location, scale, and shape (GAMLSS)
• Data sources: Hydrological time series from the Taoer River Basin

Main Results

• The NSRI framework outperforms SRI in identifying hydrological extremes with higher probability of detection (POD), Heidke skill score (HSS), and AUC.
• Response-variable transformation plays a dominant role in improving model performance.

Contributions

• This study provides an improved drought index that captures both the risk and driving mechanisms of hydrological extremes under non-stationary conditions, offering a robust basis for flood and drought risk management.

Funding

• This research was supported by the National Natural Science Foundation of China (Grant No. 52179035) and the Chinese Academy of Sciences (CAS) Strategic Priority Research Program (Grant No. XDA20060404).

Citation

@article{Pang2026Enhanced,
  author = {Pang, Xiaoteng and Liu, Jianwei and Zhang, Qin and Jing, Haihua and Xu, Xinghan and Shen, Longhai and Han, Jianxu},
  title = {Enhanced identification of watershed hydrological extremes using an improved drought index incorporating hydrological non-stationarity},
  journal = {Journal of Hydrology},
  year = {2026},
  doi = {10.1016/j.jhydrol.2026.135910},
  url = {https://doi.org/10.1016/j.jhydrol.2026.135910}
}