Zou et al. (2026) Unraveling the Hydrological Dimension of Ecosystem Resilience: Drought-Induced Response of Water Retention and Nonlinear Drivers

Identification

• Journal: Mendeley Data
• Year: 2026
• Date: 2026-02-04
• Authors: Yi Zou, Yungang Li
• DOI: 10.17632/4fv6hbrfvt

Research Groups

• Yunnan University

Short Summary

This paper investigates the hydrological dimension of ecosystem resilience by analyzing drought-induced water retention responses and their nonlinear drivers, presenting a dataset that includes key influencing factors identified using the optimal-parameter geographic detector (OPGD).

Objective

• To unravel the hydrological dimension of ecosystem resilience by studying the drought-induced response of water retention and identifying its nonlinear drivers.

Study Configuration

• Spatial Scale: Not specified in the provided text.
• Temporal Scale: Not specified in the provided text.

Methodology and Data

• Models used: Optimal-parameter geographic detector (OPGD)
• Data sources: Not specified in the provided text for the input data used to generate the described dataset; the study itself produces a dataset containing identified influencing factors and OPGD results.

Main Results

• The study identifies key influencing factors related to the hydrological dimension of ecosystem resilience, specifically concerning drought-induced water retention.
• The results of the optimal-parameter geographic detector (OPGD) analysis are provided within the generated dataset.

Contributions

• Provides a novel dataset containing key influencing factors and OPGD results, which can be used for further research on ecosystem resilience.
• Offers insights into the hydrological factors and nonlinear drivers affecting ecosystem resilience, particularly under drought conditions.

Funding

• Not specified in the provided text.

Citation

@article{Zou2026Unraveling,
  author = {Zou, Yi and Li, Yungang},
  title = {Unraveling the Hydrological Dimension of Ecosystem Resilience: Drought-Induced Response of Water Retention and Nonlinear Drivers},
  journal = {Mendeley Data},
  year = {2026},
  doi = {10.17632/4fv6hbrfvt},
  url = {https://doi.org/10.17632/4fv6hbrfvt}
}