Unequal futures: Projecting lake trophic states in China under landscape transformation scenarios

Xinying Shi; Dehua Mao; Ling Luo; Kaishan Song; Hongtao Duan; Sijia Li; Chong Fang; Dong Liu; Hui Tao; Hengxing Xiang; Zongming Wang

doi:10.59717/j.xinn-geo.2026.100247

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Unequal futures: Projecting lake trophic states in China under landscape transformation scenarios

1.
Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
2.
Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
3.
National Earth System Science Data Center, Beijing 100101, China

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Corresponding author: maodehua@iga.ac.cn
Received Date: January 12, 2026

Accepted Date: July 10, 2026

Published Online: July 21, 2026

The Innovation Geoscience https://doi.org/10.59717/j.xinn-geo.2026.100247 | Cite this article

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1. Lakes on Yunnan-Guizhou Plateau are most sensitive to landscape changes.
  
  Model explanations guide threshold-based planning to prevent eutrophication across regions.
  
  Future biofuel cropland growth along scenario RCP2.6 should avoid sacrificing water quality.

ABSTRACT

ABSTRACT

Landscape transformations driven by China's rapid development have contributed to widespread lake eutrophication, but their spatial heterogeneity, underlying mechanisms, and future trajectories remain unclear. We assessed the comprehensive trophic level index (TLI) for 443 Chinese lakes from 2000 to 2020 using integrated datasets of chlorophyll-a (Chl-a), total phosphorus (TP), and Secchi disk depth (SDD) datasets. We modeled TLI responses to landscape changes using an interpretable machine-learning framework. Our findings revealed a national decline in eutrophic lakes (TLI > 50) from 69.1% to 44.2% by 2020, but trends varied regionally. Northwestern lakes improved significantly, with remarkable increases in SDD associated with a 1.2%–2.4% reduction in barren land, whereas southeastern lakes faced persistent pressures from impervious land expansion, with TP rising 6%–10%. Landscape dynamics accounted for 43.9% (Eastern Plain) to 70.1% (Yunnan-Guizhou Plateau) variations in TLI. In the northwest, natural landscape configuration (e.g., woodland largest-patch index) was the strongest predictor. In the southeast, anthropogenic landscape complexity dominated lake trophic dynamics. Land-use projections under Representative Concentration Pathway (RCP) 2.6 indicate potential TLI increases in the Yunnan-Guizhou Plateau, Xinjiang, and Tibetan Plateau due to cropland expansion. RCP8.5 projections produce greater spatial inequality in TLI by 2100. Our findings underscore the necessity for region-tailored land-use strategies to ensure equitable water-quality outcomes under future landscape transformations and to optimize water–food–energy nexus.
- Lake /
- trophic level index (TLI) /
- landscape pattern /
- machine learning /
- future scenarios

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Shi X., Mao D., Luo L., et al. (2026). Unequal futures: Projecting lake trophic states in China under landscape transformation scenarios. The Innovation Geoscience 4:100247. https://doi.org/10.59717/j.xinn-geo.2026.100247

Shi X., Mao D., Luo L., et al. (2026). Unequal futures: Projecting lake trophic states in China under landscape transformation scenarios. The Innovation Geoscience 4:100247. https://doi.org/10.59717/j.xinn-geo.2026.100247

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Unequal futures: Projecting lake trophic states in China under landscape transformation scenarios

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