Early warning level identification and evolutionary trend prediction of ecological risk in the upper Chang Jiang (Yangtze R.), China

Abstract

Ecological risk is a dynamic reflection of ecosystem stability and harmonious social development. The role played by risk identification and evolutionary trend prediction as mediators between ecological risk management and prevention is complex. However, current studies have difficulty identifying where, when, and how ecological risk evolves. Here, we constructed a double evaluation index system of ecological risk source hazard and ecological risk receptor loss degree to quantitatively evaluate and simulate ecological risk in the upper Chang Jiang (Yangtze R.) (UYR). Then, we adopted the normal cloud model to identify the ecological risk level at different scales in the UYR. Finally, we leveraged set pair analysis to reveal the future evolution trend of ecological risk in the UYR. The following conclusions were drawn. 1) From 2015 to 2018, the ecological risk in the UYR exhibited significant spatial aggregation characteristics, with a spatial distribution pattern of “high in the west, low in the east”. The risk value increased from [0, 0.28] to [0, 0.32], an increase of 12.49%. 2) The ecological risk level of the UYR in 2015 and 2018 was in a high-alert state, but the risk value showed a downward annual trend. The comprehensive ecological risk value decreased from 0.5295 to 0.5135. 3) The ecological risk of 67% of the cities in the UYR will decrease in the future, and will increase in 33% of the cities. 4) The probability of geological disasters was the most significant ecological risk source in the UYR. Ecosystem service value significantly impacted ecological risk receptors loss degree in the UYR.

Graphical abstract

Keywords

watershed ecological risk / the upper Chang Jiang (Yangtze R.) / early warning

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Data availability

The data sets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

CRediT authorship contribution statement

Dongjie GUAN: Conceptualization, Writing-Original draft preparation. Jiameng CAO: Methodology, Software, Data curation. Danan HUANG: Conceptualization, Supervision. Lilei ZHOU: Visualization, Validation, Formal analysis.

Acknowledgments

This work was partially supported by the National Natural Science Foundation of China (Grant Nos. 42171298 and 42201333), Late Project of National Social Science Foundation in China (No. 20FJYB035), Natural Science Foundation of Chongqing, China (No. CSTB2022NSCQ-BHX0734), Philosophy and Social Science Major Project of Chongqing Municipal Education Commission (Grant No. 24SKZDZX04).

Competing interests

The authors declare that they have no competing interests.

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