Li et al. (2026) Dynamic analysis of drought propagation in the context of climate change and watershed characterization: a quantitative study based on GAMLSS and Copula models

Identification

• Journal: Natural hazards and earth system sciences
• Year: 2026
• Date: 2026-01-07
• Authors: Min Li, Zilong Feng, Mingfeng Zhang, Lijie Shi, Yuhang Yao
• DOI: 10.5194/nhess-26-1-2026

Research Groups

• College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou, China
• Key Laboratory of Flood & Drought Disaster Defense, the Ministry of Water Resources, Nanjing, China
• JiLin Province Water Resource and Hydropower Consultative Company of P.R CHINA, Changchun, China
• Guangxi Hydraulic Research Institute, Nanning, China

Short Summary

This study quantitatively analyzed the dynamic propagation of meteorological to hydrological drought in the Luanhe River Basin under climate change, using GAMLSS and Copula models to assess the influence of climatic factors and watershed characteristics. It found that non-stationary drought indices better capture propagation dynamics, showing increased hydrological drought probability and thresholds, especially in spring and winter, driven by large-scale climate indices and meteorological elements, further modulated by watershed characteristics.

Objective

• To quantitatively analyze the dynamic propagation of meteorological drought to hydrological drought in the Luanhe River Basin under climate change, assessing the influence of large-scale climatic indices, meteorological elements, and watershed characteristics on seasonal drought propagation probabilities and thresholds using non-stationary drought indices (NSPI, NSRI) derived from GAMLSS and Copula models.

Study Configuration

• Spatial Scale: Luanhe River Basin, China (approximately 44,750 km²), analyzed at a grid resolution of 0.25° latitude × 0.25° longitude (58 grid points) and divided into 11 sub-regions (upstream, midstream, downstream).
• Temporal Scale: Data period from 1960–2014 (for climate indices and GLDAS data) and 1981–2015 (for LAI data), with analysis focused on seasonal drought characteristics using a 3-month time scale.

Methodology and Data

• Models used:
  • Generalized Additive Model for Location, Scale, and Shape (GAMLSS) for constructing non-stationary drought indices (NSPI, NSRI).
  • Copula function (specifically Clayton Copula) for calculating joint drought distributions, conditional probabilities, and drought propagation thresholds.
  • Standardized Precipitation Index (SPI) and Standardized Runoff Index (SRI) for stationary drought characterization.
  • Mann–Kendall (M-K) trend analysis for meteorological variable trends.
• Data sources:
  • Large-scale climatic indices (Nino3.4, Atlantic Multidecadal Oscillation (AMO), Southern Oscillation Index (SOI), Pacific Decadal Oscillation (PDO), Arctic Oscillation (AO), North Atlantic Oscillation (NAO), North Pacific (NP)): National Oceanic and Atmospheric Administration (NOAA).
  • Average monthly precipitation, temperature, wind speed, specific humidity, evapotranspiration, and runoff datasets: Global Land Data Assimilation System (GLDAS Noah Land Surface Model L4 monthly 0.25° × 0.25° V2.0).
  • Leaf area index (LAI) (0.25° spatial resolution): Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) LAI3g version 2.
  • Slope data (90 m resolution): Geospatial Data Cloud site, Computer Network Information Center, Chinese Academy of Sciences.

Main Results

• Non-stationary drought indices (NSPI, NSRI) incorporating meteorological factors demonstrated better performance than standardized drought indices (SPI, SRI) in simulating precipitation and runoff series.
• AMO-1 and AMO-24 were identified as significant climate indices affecting precipitation, while temperature, specific humidity, and wind speed were key meteorological factors influencing runoff in the Luanhe River Basin.
• Under non-stationary conditions, the probabilities of meteorological drought propagating to hydrological drought were generally higher than under stationary conditions, particularly for severe and extreme hydrological droughts.
• Spring and winter showed a higher susceptibility to hydrological drought and more severe hydrological drought propagation compared to summer and autumn, especially in the upstream and midstream regions.
• Drought propagation thresholds (the SPI critical threshold for triggering hydrological drought) increased in most regions under non-stationary conditions, implying that hydrological droughts are more likely to be triggered.
• In winter, drought propagation thresholds in the midstream and upstream regions significantly increased by 0.1–0.2 under non-stationary conditions compared to stationary conditions.
• Increased temperature was identified as a key factor contributing to the occurrence of hydrological drought, particularly in winter.
• Watershed characteristics, including slope, evapotranspiration, and leaf area index, significantly influenced the spatial variability of drought propagation thresholds by altering runoff generation processes.
• Evapotranspiration and shallow soil moisture content (10–40 cm and 40–100 cm underground) showed a high correlation (absolute correlation coefficient > 0.5) with drought propagation characteristics.

Contributions

• This study is among the few to analyze the effects of meteorological factors and watershed characteristics on drought propagation using non-stationary drought indices (NSPI, NSRI) in the Luanhe River Basin.
• It quantitatively assessed the influence of large-scale climatic indices and regional meteorological elements on seasonal drought propagation probabilities and thresholds.
• Demonstrated the superior ability of GAMLSS-constructed non-stationary drought indices to capture the dynamic characteristics of drought propagation under changing environmental conditions compared to stationary indices.
• Identified specific climatic and watershed characteristics that modulate drought propagation in the Luanhe River Basin, offering valuable insights for drought early warning and water resource management strategies.

Funding

• State Key Laboratory of Hydraulic Engineering Intelligent Construction and Operation (No. HESS-2206)
• Open Fund of Key Laboratory of Flood & Drought Disaster Defense, the Ministry of Water Resources (KYFB202307260034)

Citation

@article{Li2026Dynamic,
  author = {Li, Min and Feng, Zilong and Zhang, Mingfeng and Shi, Lijie and Yao, Yuhang},
  title = {Dynamic analysis of drought propagation in the context of climate change and watershed characterization: a quantitative study based on GAMLSS and Copula models},
  journal = {Natural hazards and earth system sciences},
  year = {2026},
  doi = {10.5194/nhess-26-1-2026},
  url = {https://doi.org/10.5194/nhess-26-1-2026}
}