Yu et al. (2025) A dual-method, multi-scale causal framework reveals seasonal shifts in hydrological causality of a headwater catchment

Identification

• Journal: Journal of Hydrology
• Year: 2025
• Date: 2025-12-30
• Authors: Juan Yu, Yaling Zhang, Hanxu Liang, Hu Liu, Jintao Liu, Carlos R. Mello, Chongli Di, Li Guo
• DOI: 10.1016/j.jhydrol.2025.134891

Research Groups

• State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610000, China
• State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China
• Key Laboratory of Ecohydrology of Inland River Basin, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, China
• State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
• Water Resources Department, Universiade Federal de Lavras, CP 3037, Lavras, MG 37200-900, Brazil
• Institute of Surface-Earth System, Tianjin University, Tianjin 300072, China

Short Summary

This study develops a robust, dual-method, multi-scale causal inference framework to investigate hydrological interactions in a headwater catchment, revealing scale-dependent causal pathways and significant seasonal shifts in runoff generation mechanisms.

Objective

• To establish a robust causal inference framework that integrates multi-timescale analysis with a dual-method, consensus-based strategy to elucidate causal relationships among water cycle components and their seasonal shifts in a headwater catchment.

Study Configuration

• Spatial Scale: Headwater catchment (Shale Hills).
• Temporal Scale: Hourly data from November 2009 to October 2010, decomposed into daily, weekly, decadal (10-day), and monthly dynamics.

Methodology and Data

• Models used: Convergent Cross Mapping (CCM) and PCMCI algorithm for causal inference; Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) for time series decomposition.
• Data sources: Hourly observational data for precipitation (P), runoff (Q), soil moisture (SM), groundwater level (GW), evapotranspiration (ET), and snow depth (SD) from the Shale Hills headwater catchment.

Main Results

• Causal interactions are scale-dependent, with complexity diminishing as the temporal scale increases.
• The primary causal pathway is P → SM → (GW) → Q, exhibiting strong causal strengths (>0.55) across all scales.
• Soil moisture (SM) acts as a critical hub, dynamically connecting system components.
• A significant seasonal shift in runoff generation mechanisms was observed:
  • During the snow-free period, both SM and GW exert strong causal influence on Q (>0.6).
  • Under snow-dominated conditions, the SM → Q link is significantly stronger (0.74) compared to GW → Q.

Contributions

• Establishes a novel and transferable causal inference framework that integrates multi-timescale analysis with a dual-method, consensus-based strategy (CCM and PCMCI) for enhanced robustness.
• Provides deeper insights into scale-dependent hydrological interactions and mechanistic shifts in complex catchments.
• Elucidates the critical role of soil moisture and seasonal variations in runoff generation mechanisms within headwater catchments.

Funding

• Not specified in the provided text.

Citation

@article{Yu2025dualmethod,
  author = {Yu, Juan and Zhang, Yaling and Liang, Hanxu and Liu, Hu and Liu, Jintao and Mello, Carlos R. and Di, Chongli and Guo, Li},
  title = {A dual-method, multi-scale causal framework reveals seasonal shifts in hydrological causality of a headwater catchment},
  journal = {Journal of Hydrology},
  year = {2025},
  doi = {10.1016/j.jhydrol.2025.134891},
  url = {https://doi.org/10.1016/j.jhydrol.2025.134891}
}