Work

A new InVEST–genetic algorithm coupled water yield model: Application to water conservation function assessment in the Zhangjiakou-Chengde area

Year: 2025

Type: article

Abstract: • New coupled InVEST-GA model enhances WCF assessment accuracy. • The distinctive spatiotemporal patterns of WCD in ZC from 2001 to 2020. • The WCD increased overall and showed a slight increase trend in more than half area. • Precipitation strongly boosted WCF in ZC, especially in farmland. • Biophysical parameters caused an exponential WCD drop, with grassland most sensitive. Water conservation function (WCF) of the ecosystem is critical for maintaining ecological balance and managing water resource. To address the limitations of empirical parameters in the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) water yield model, this study proposes an InVEST-Genetic Algorithm (InVEST-GA) coupled model that incorporates genetic algorithms for global parameter optimization. This coupled model, combined with high-resolution TerraClimate precipitation data, was used to evaluate the WCF in Zhang-Cheng (Zhangjiakou and Chengde) area. The result indicated that: (1) WCF shows higher values in Baxia (lower plains and hills) and lower in Bashang (high plateau) regions; (2) From 2001 to 2020, water conservation depth (WCD) increased at an average of 0.14 mm/year, with an initial rise followed by a decline; (3) Among ecosystems, croplands exhibited the highest WCD, while grasslands contributed most to water conservation volume; (4) Precipitation positively influenced WCF whereas temperature had a negative temporal effect. Vegetation impacts were complex and varied across ecosystems and years; (5) Sensitivity analysis indicated exponential decline in parameter influence on WCD, with grasslands being the most sensitive. The InVEST-GA framework significantly improves accuracy and offers a robust foundation for eco-hydrological studies and ecosystem service evaluation in the Zhang-Cheng area.

Source: Ecological Indicators

DOI: 10.1016/j.ecolind.2025.114237

Authors: Jingxuan Ma, Xiaomei Jin, Xiulan Yin, Pengfei Liu, Zhenlong Nie

Institutions: Ministry of Natural Resources, Chinese Academy of Geological Sciences, China University of Geosciences (Beijing), China Institute of Geological Environmental Monitoring

Language: English

Topic: Land Use and Ecosystem Services

Subfield: Global and Planetary Change

Field: Environmental Science

Domain: Physical Sciences

SDGs: Clean water and sanitation, Life on land, Climate action

Open Access status: gold

Estimated APC paid: $3,650