首页|基于气候变化情景的汉江流域景观生态风险变化模拟

基于气候变化情景的汉江流域景观生态风险变化模拟

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景观生态风险评估是识别生态系统脆弱地区并进行重点治理的必要手段.现有方法多采用土地利用变化数据进行生态风险分析,在多因素综合评估方面尚有欠缺,尤其是难以将气候变化和社会经济发展相结合预测气候变化情景下景观生态风险演变.针对此问题,耦合传统景观生态风险评估模型与深度学习模型,构建多因素影响下生态景观风险的预测模型,并模拟汉江流域景观生态风险变化.结果表明:①起点期(2000-2015年)情景下,汉江流域较高生态风险等级主要连片集中在丹江口下游地区;②SSP370和SSP585情景下均主要以较高生态风险等级为主,比较连片集中分布在丹江口以下区域;③SSP370和SSP585情景下汉江流域内高生态风险等级面积在2042年显著增加,其中SSP370情景下的高生态风险等级的面积平均每10 a增加14.58%.研究提出的多因素景观生态风险预测方法可为气候变化条件下流域的生态风险评估和相关生态补偿政策的制定提供借鉴.
Simulation of Landscape Ecological Risk Change in Hanjiang River Basin under SSP-RCP Scenarios
Landscape ecological risk assessment plays a vital role in identifying vulnerable ecosystem areas for targe-ted management.While current methods primarily rely on land-use change data for ecological risk analysis,they of-ten lack a comprehensive evaluation of multiple factors,especially the prediction of landscape ecological risk dy-namics under climate change scenarios integrating climate variations and socio-economic trends.To tackle this is-sue,we constructed a predictive model for ecological landscape risk influenced by diverse factors by integrating tra-ditional landscape ecological risk assessment models with deep learning technique,and further applied this model to simulating the change in landscape ecological risks of Hanjiang River Basin.Findings reveal that:1)during the baseline period(2000-2015),higher ecological risk levels predominantly clustered in the downstream of Dan-jiangkou reservoir;2)both SSP370 and SSP585 scenarios exhibited elevated ecological risk levels,particularly concentrated in the downstream of Danjiangkou;3)the high ecological risk area in Hanjiang River basin signifi-cantly expanded under the 2042 scenario for SSP370 and SSP585,with an average increase of 14.58%per decade under the SSP370 scenario.The proposed landscape ecological risk prediction approach in consideration of multiple factors serves as a valuable reference for ecological risk assessment in the basin under changing climatic conditions and the formulation of ecological compensation policies.

climate changelandscape ecological riskdeep learningSSP-RCPHanjiang River Basin

吴启亮、郑航、刘悦忆、陈进

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东莞理工学院生态环境与建筑工程学院,广东东莞 523808

长江水利委员会长江科学院,武汉 430010

气候变化 景观生态风险 深度学习 SSP-RCP 汉江流域

国家自然科学基金-长江水科学研究联合基金国家自然科学基金青年基金国家自然科学基金面上项目

U20402065190903552179009

2024

长江科学院院报
长江科学院

长江科学院院报

CSTPCD北大核心
影响因子:0.618
ISSN:1001-5485
年,卷(期):2024.41(4)
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