Guiding the landscape patterns evolution is the key to mitigating river water quality degradation.

Consensus has emerged that landscape pattern evolution significantly impacts the river environment. However, there remains unclear how the landscape pattern evolves possible to achieve a balance between land resource use and water conservation. Thus, simulating future landscape patterns under different scenarios to predict river eutrophication level is critical to propose targeted landscape planning programs and alleviate river water quality degradation. Here, we coupled five water quality parameters (TOC, TN, NO 3 - -N, NH 4 + -N, TP), collected from October 2020 to September 2021, to construct the river eutrophication index (EI) to assess river water quality. Meanwhile, based on redundancy analysis, patch-generating land use simulation model, and stepwise multiple linear regression model comprehensively analyze the Fengyu River watershed landscape patterns evolution and their impact on river eutrophication. Results indicated that current rivers reach eutrophic levels, and EI reaches 40.7. The landscape patterns explain 88.2 % of river eutrophication variation, while the LPI_Con metric is critical and individually explained 21.5 %. Furthermore, eutrophication in the watershed will increase in 2040 under the natural development (ND) scenario, and the EI will reach 44.4. In contrast, farmland protection (FP) scenarios and environmental protection (EP) scenarios contribute to mitigating eutrophication, the EI values are 38.2 and 38.1, respectively. The results provide a potential mechanistic explanation that river eutrophication is a consequence of unreasonable landscape pattern evolution. Guiding the landscape patterns evolution based on critical driver factors from a planning perspective is conducive to mitigating river water quality degradation.

Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

(Copyright © 2023. Published by Elsevier B.V.)