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生态学杂志 ›› 2023, Vol. 42 ›› Issue (10): 2502-2513.doi: 10.13292/j.1000-4890.202310.005

• 研究报告 • 上一篇    下一篇

黄河流域生态系统服务的时空演变及其驱动力

朱春霞1,钟绍卓1*,龙宇2,颜丹1   

  1. 1西南财经大学经济学院, 成都 611130; 2西南财经大学中国西部经济研究院, 成都 611130)

  • 出版日期:2023-10-10 发布日期:2023-10-08

Spatiotemporal variation of ecosystem services and their drivers in the Yellow River Basin, China.

ZHU Chunxia1, ZHONG Shaozhuo1*, LONG Yu2, YAN Dan1   

  1. (1School of Economics, Southwestern University of Finance and Economics, Chengdu 611130, China; 2Western China Economic Research Institute, Southwestern University of Finance and Economics, Chengdu 611130, China).

  • Online:2023-10-10 Published:2023-10-08

摘要: 系统把握黄河流域生态系统服务的时空格局演变与关键驱动力有利于深化流域生态保护和高质量发展。本文基于InVEST模型,刻画了黄河流域1990-2019年产水、净水、碳储、土壤保持和生境支持服务的时空格局,并利用地理探测器模型进行了时空演变的驱动力分析。结果表明:(1) 30年间,产水和土壤保持服务呈同步波动上升变化,而净水、碳储和生境支持服务则基本稳定;空间上,产水和土壤保持服务分别呈现“南高北低”、“脊背与两头低而腹地高”的分布特点,净水、碳储和生境支持服务的空间分布较相似,在河套平原、宁夏平原、汾渭平原以及豫鲁流域较低;(2) 5种生态系统服务均表现出显著的全局自相关性和空间分异性;关于局部异质性,产水、净水和生境支持服务皆以高-高、低-低和不显著3种聚类类型为主,另两种服务的局部集聚不明显;(3) 气候和地理因子是影响产水和土壤保持服务的重要因素,解释力最高的分别是年均降水和坡度;土地利用/覆被类型是净水、碳储和生境支持服务最重要的驱动力。


关键词: 黄河流域, 生态系统服务, 驱动力, InVEST模型, 地理探测器

Abstract: Understanding the spatiotemporal variation of ecosystem services and their drivers is important for the ecological protection and high-quality development in the Yellow River Basin. Based on InVEST model, we analyzed five ecosystem services in the Yellow River Basin from 1990 to 2019, i.e. water yield, water purification, carbon storage, soil conservation and habitat quality. GeoDetector model was used to explore the driving forces. Results showed that: (1) During the study period, water yield and soil conservation fluctuated with an overall rising trend, while water purification, carbon storage, and habitat quality were generally stable. Spatially, water yield was higher in the south and lower in the north, and soil conservation was higher in the hinterland and lower in the north and the two ends. The spatial distributions of water purification, carbon storage, and habitat quality services were relatively similar, which was lower in the Hetao Plain, Ningxia Plain, Fenwei Plain, and Henan-Shandong basin. (2) All the five ecosystem services showed significant global autocorrelation and spatial heterogeneity. In terms of local heterogeneity, water yield, water purification, and habitat quality obviously displayed three cluster types: high-high, low-low, and insignificant clustering, while other two services showed no local clustering. (3) Climatic and geographical factors were the main drivers of water yield and soil conservation changes, among which annual preci-pitation and slope were the most important. Land use and land cover (LULC) was the key factor affecting water purification, carbon storage, and habitat quality.


Key words: Yellow River Basin, ecosystem services, driving force, InVEST model, GeoDetector model.