Qi et al. (2026) Spatiotemporal patterns, driving mechanisms, and threshold responses of watershed ecosystem services from a supply-demand flow perspective

Identification

• Journal: Journal of Hydrology Regional Studies
• Year: 2026
• Date: 2026-04-04
• Authors: Ming Qi, Mingcan Sun, Qinping Liu, Hongzhen Tian, Yanchao Sun, Shujiao Yang
• DOI: 10.1016/j.ejrh.2026.103385

Research Groups

• College of Economics and Management, Tiangong University, Tianjin 300387, China
• College of Land Management, Nanjing Agricultural University, Nanjing 210095, China

Short Summary

This study investigates the spatiotemporal patterns, driving mechanisms, and threshold responses of carbon sequestration, water yield, and habitat quality in the Dongting Lake Basin from a supply-demand flow perspective. It reveals differentiated ecosystem service dynamics, process-dependent flow patterns, and nonlinear threshold responses to human disturbance and environmental factors, enabling threshold-driven spatial zoning for targeted watershed management.

Objective

• To examine the spatiotemporal patterns, driving mechanisms, and threshold responses of watershed ecosystem services (carbon sequestration, water yield, habitat quality) from a supply-demand flow perspective.
• To quantify the supply, demand, and ecosystem service supply-demand ratio (ESDR) of carbon sequestration (CS), water yield (WY), and habitat quality (HQ) from 2000 to 2020.
• To map ecosystem service flow pathways and intensities by integrating wind fields, river networks, and ecological connectivity.
• To identify the relative importance, interaction effects, and temporal dynamics of multidimensional drivers influencing ESDR.
• To use partial dependence plots (PDPs) to identify sensitive, recovery, and optimal intervals for ecosystem service regulation.
• To integrate threshold-based driver responses to delineate differentiated spatial management zones and support process-based ecosystem service governance at the basin scale.

Study Configuration

• Spatial Scale: Dongting Lake Basin, central Hunan Province, China (110°40′–114°40′E, 27°30′–30°00′N), with elevations ranging from 17 to 2443 m. All spatial layers were resampled to a 1-kilometer resolution.
• Temporal Scale: 2000 to 2020 (data points for 2000, 2010, and 2020).

Methodology and Data

• Models used:
  • InVEST model (for quantifying CS, WY, and HQ supply)
  • Extreme Gradient Boosting model (XGBoost)
  • Shapley Additive Explanations (SHAP) (for factor importance and interaction analysis)
  • Partial Dependence Plots (PDPs) (for nonlinear threshold identification)
  • Prevailing wind fields (for CS flow simulation)
  • River network connectivity (for WY flow simulation)
  • Circuit theory (for HQ flow simulation)
• Data sources:
  • Satellite/Remote Sensing: China Land Cover Dataset (CLCD), ASTER Global Digital Elevation Model (DEM), MOD11A1.006 Terra Land Surface Temperature and Emissivity Daily Global 1 km, MODIS006MOD13Q1 (NDVI), National Aeronautics and Space Administration (NPP).
  • Gridded/Reanalysis: 1-km monthly precipitation dataset for China, 1-km monthly mean temperature dataset for China, 1-km monthly potential evapotranspiration dataset for China.
  • Geospatial/Derived: China soil map based harmonized world soil database (HWSD), Resource and Environmental Science Data Platform (GDP, Nighttime lights index), Open Spatial Demographic Data and Research (POP), March of the Human Footprint (Human impact index), HydroSHEDS (River basin boundary), OpenStreetMap (Road data).
  • Socio-economic: Hunan Statistical Yearbook, Hunan Water Resources Bulletin.

Main Results

• Ecosystem Service Supply-Demand Ratios (2000–2020):
  • Carbon Sequestration (CS): Supply remained stable (19,479.769 tonnes to 19,379.072 tonnes), while demand nearly tripled (405.033 tonnes to 1165.373 tonnes), leading to an increase in CESDR from 0.019 to 0.075, indicating an expansion of surplus conditions.
  • Water Yield (WY): Supply increased modestly (1172.840 cubic meters to 1272.407 cubic meters), but persistent demand pressure caused WESDR to decline from -0.004 to -0.007, indicating a worsening supply-demand imbalance.
  • Habitat Quality (HQ): Supply decreased slightly (0.760 to 0.752), accompanied by increased demand (0.266 to 0.272), resulting in a decline in HESDR from 0.494 to 0.480.
  • All three ES exhibit a "high in the west, low in the east" supply pattern, with demand concentrated in eastern urban agglomerations, creating spatial mismatches.
• Ecosystem Service Flows (2000–2020):
  • CS flow: Total volume increased approximately threefold (from 1.08 × 10⁵ tonnes to 3.24 × 10⁵ tonnes), predominantly following southwest-northeast prevailing wind directions.
  • WY flow: Total volume increased (from 4.56 × 10⁵ cubic meters to 4.96 × 10⁵ cubic meters) and was structured strictly along upstream-downstream river networks, highlighting hydrological connectivity as a primary organizer.
  • HQ flow: Networks became denser (corridors increased from 39 to 101) but less effective due to a decline in supply patches (43 to 28) and an expansion of demand patches (14 to 60), indicating increasing fragmentation.
• Driving Mechanisms and Interactions:
  • Temporal reorganization: Dominant control of CESDR shifted from landscape configuration to human disturbance. WESDR and HESDR remained consistently dominated by human-related drivers.
  • Interaction effects: Strongest interactions were observed between human disturbance and terrain or hydroclimatic factors, suggesting that mismatch amplification is context-dependent rather than purely additive.
• Nonlinear Threshold Responses:
  • CESDR: Improves rapidly when the Contagion Index (X1) is between 10.614 and 30.166, then stabilizes up to 55.303. A climatic suitability window for potential evapotranspiration (Y8) is identified between 406.546 and 429.131 mm.
  • WESDR: Remains relatively stable only when X1 is below 6.145, deteriorating sharply once this fragmentation threshold is exceeded. It also declines rapidly when the Human Footprint Index (Z5) exceeds its recovery platform.
  • HESDR: Improves with X1 under low-to-moderate aggregation levels (0–5.586), continuously improves when elevation (Y1) exceeds 888.36 m, and has an optimal Y8 range of 372.67–463.01 mm, but declines rapidly beyond Z5 disturbance thresholds.
• Spatial Zoning: The basin was delineated into three functional zones based on threshold responses: Nature Conservation Zone, Transition Zone, and Human Activity Zone, each with distinct management orientations.

Contributions

• Provides a novel, integrated analytical framework that links ecosystem service supply-demand assessment, process-based flow modeling, and nonlinear driver analysis with threshold identification and spatial zoning.
• Moves beyond static supply-demand assessments by explicitly modeling ecosystem service flows through natural transmission pathways (wind fields, river networks, ecological connectivity).
• Utilizes an interpretable machine learning approach (XGBoost-SHAP) to identify dominant drivers, their interaction effects, and temporal dynamics on ecosystem service supply-demand ratios.
• Systematically identifies and quantifies sensitive, recovery, and optimal threshold intervals for ecosystem service regulation, offering a quantitative basis for management and policy design.
• Proposes a threshold-driven spatial zoning scheme for differentiated ecosystem service management, enhancing the operability of conservation and development strategies in complex watershed systems.
• Offers a transferable analytical logic and methodological sequence applicable to other watersheds facing similar ecological-development trade-offs, requiring local recalibration of scale, data, drivers, and thresholds.

Funding

• Tianjin Philosophy and Social Science Planning Project (Grant No. TJGL21–025).

Citation

@article{Qi2026Spatiotemporal,
  author = {Qi, Ming and Sun, Mingcan and Liu, Qinping and Tian, Hongzhen and Sun, Yanchao and Yang, Shujiao},
  title = {Spatiotemporal patterns, driving mechanisms, and threshold responses of watershed ecosystem services from a supply-demand flow perspective},
  journal = {Journal of Hydrology Regional Studies},
  year = {2026},
  doi = {10.1016/j.ejrh.2026.103385},
  url = {https://doi.org/10.1016/j.ejrh.2026.103385}
}