five

Data sources.

收藏
NIAID Data Ecosystem2026-05-02 收录
下载链接:
https://figshare.com/articles/dataset/Data_sources_/29408567
下载链接
链接失效反馈
官方服务:
资源简介:
Exploring Land use and climate change-based multi-scenario simulation of ecosystem service trade-offs/synergies is of great importance to regional ecological security and sustainable development. Taking the Central Yunnan Urban Agglomeration (CYUA) as a case study, six different scenarios of LULC-RCP were established to quantitatively assess four key ecosystem services(ESs) of water yield (WY), carbon stock (CS), soil conservation (SR) and habitat quality (HQ) with multiple objective programming and patch-generating land use simulation(MOP-PLUS) and integrated valuation of ecosystem services and tradeoffs (InVEST) models. The ESs were revealed regarding spatio-temporal trade-offs/synergies using Spearman correlation and geographically weighted regression (GWR). It was found that: (1)the ESs in CYUA is characterized with high spatial heterogeneity in 2030; specifically, the distribution of WY and SR was low in the northwestern region and high in the southeastern region, while the distribution of HQ and CS was high in the western region and the periphery, and low in the eastern and central regions; (2) the trade-offs between WY-HQ, and WY-CS, and the synergies between WY-SR, HQ-SR, HQ-CS, HQ-CS, and HQ-SR; (3) under the six different scenarios, the spatial distribution of trade-offs/synergies between the four ESs was consistent: the SR-HQ, SR-CS, and WY-CS showed an overall weak synergistic relationship; the HQ-CS showed an overall weak trade-offs; the HQ-WY, CS-WY showed an overall weak synergistic relationship in the northern and southern areas and an overall weak trade-off relationship in the center. The findings of this study may provide a theoretical foundation for ecosystem management in CYUA and offer technical support for the evaluation of national land space.
创建时间:
2025-06-25
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作