Ecosystem services assessment based on land use simulation: A case study in the Heihe River Basin, China

Land use and land cover (LULC) changes exert large impacts on key ecosystem services (ESs) in arid areas. This study presents a framework to link LULC change with spatial changes in 3 key ESs in arid areas; this framework can contribute to sustainable ecological environment zoning management. In thi...

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Main Authors: Jing Zhao, Zhuang Shao, Chuyu Xia, Kai Fang, Ran Chen, Jun Zhou
Format: Article
Language:English
Published: Elsevier 2022-10-01
Series:Ecological Indicators
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S1470160X22008755
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author Jing Zhao
Zhuang Shao
Chuyu Xia
Kai Fang
Ran Chen
Jun Zhou
author_facet Jing Zhao
Zhuang Shao
Chuyu Xia
Kai Fang
Ran Chen
Jun Zhou
author_sort Jing Zhao
collection DOAJ
description Land use and land cover (LULC) changes exert large impacts on key ecosystem services (ESs) in arid areas. This study presents a framework to link LULC change with spatial changes in 3 key ESs in arid areas; this framework can contribute to sustainable ecological environment zoning management. In this study, taking the Heihe River Basin (HRB) as an example, we first assessed the spatiotemporal changes in 3 ESs, including soil conservation (SC), water yield (WY), and carbon storage (CS), from 1995 to 2020 using the InVEST model. Second, we used the (Future Land Use Simulation) FLUS model to predict future land-use variations in 2030 and 2050 under 4 scenarios (Basic Scenario (BS), Low-impact Scenario (LIS), High-impact Scenario (HIS), and Sustainable Development Scenario (SDS)). Third, we assessed the future spatial distributions of the 3 ESs with the InVEST model based on the output raster of the FLUS model. Spatial autocorrelation analysis was applied to ecological zoning management. The results showed that from 1995 to 2020, WY decreased slightly by 0.03%, and SC decreased by 2.6%, mainly in the midstream and upstream regions of the HRB. CS presented a trend of first decreasing by 1.3% from 1995 to 2010 and then increasing by 0.5% from 2010 to 2020; this trend was distributed in the upper reaches. In 2030 and 2050, under the HIS, CS increased by 14.7% and 22.0%, respectively, while WY increased by 0.20% and 0.47%, respectively. Under the SDS, CS increased by 1.1% and 1.3%, WY decreased by 0.21% and 0.45%, and SC decreased by 3.3% and 10%.
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spelling doaj.art-5b73f3faed8749b09a618b352e7f7e502022-12-22T01:52:17ZengElsevierEcological Indicators1470-160X2022-10-01143109402Ecosystem services assessment based on land use simulation: A case study in the Heihe River Basin, ChinaJing Zhao0Zhuang Shao1Chuyu Xia2Kai Fang3Ran Chen4Jun Zhou5School of Landscape Architecture, Beijing Forestry University, Beijing 100083, ChinaSchool of Landscape Architecture, Beijing Forestry University, Beijing 100083, ChinaFaculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China; Corresponding author.School of Public Affairs, Zhejiang University, 310058, ChinaSchool of Landscape Architecture, Beijing Forestry University, Beijing 100083, ChinaFaculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, ChinaLand use and land cover (LULC) changes exert large impacts on key ecosystem services (ESs) in arid areas. This study presents a framework to link LULC change with spatial changes in 3 key ESs in arid areas; this framework can contribute to sustainable ecological environment zoning management. In this study, taking the Heihe River Basin (HRB) as an example, we first assessed the spatiotemporal changes in 3 ESs, including soil conservation (SC), water yield (WY), and carbon storage (CS), from 1995 to 2020 using the InVEST model. Second, we used the (Future Land Use Simulation) FLUS model to predict future land-use variations in 2030 and 2050 under 4 scenarios (Basic Scenario (BS), Low-impact Scenario (LIS), High-impact Scenario (HIS), and Sustainable Development Scenario (SDS)). Third, we assessed the future spatial distributions of the 3 ESs with the InVEST model based on the output raster of the FLUS model. Spatial autocorrelation analysis was applied to ecological zoning management. The results showed that from 1995 to 2020, WY decreased slightly by 0.03%, and SC decreased by 2.6%, mainly in the midstream and upstream regions of the HRB. CS presented a trend of first decreasing by 1.3% from 1995 to 2010 and then increasing by 0.5% from 2010 to 2020; this trend was distributed in the upper reaches. In 2030 and 2050, under the HIS, CS increased by 14.7% and 22.0%, respectively, while WY increased by 0.20% and 0.47%, respectively. Under the SDS, CS increased by 1.1% and 1.3%, WY decreased by 0.21% and 0.45%, and SC decreased by 3.3% and 10%.http://www.sciencedirect.com/science/article/pii/S1470160X22008755Land use and land coverKey Ecosystem servicesFLUS-InVEST modelThe Heihe River BasinLocal space autocorrelation analysis
spellingShingle Jing Zhao
Zhuang Shao
Chuyu Xia
Kai Fang
Ran Chen
Jun Zhou
Ecosystem services assessment based on land use simulation: A case study in the Heihe River Basin, China
Ecological Indicators
Land use and land cover
Key Ecosystem services
FLUS-InVEST model
The Heihe River Basin
Local space autocorrelation analysis
title Ecosystem services assessment based on land use simulation: A case study in the Heihe River Basin, China
title_full Ecosystem services assessment based on land use simulation: A case study in the Heihe River Basin, China
title_fullStr Ecosystem services assessment based on land use simulation: A case study in the Heihe River Basin, China
title_full_unstemmed Ecosystem services assessment based on land use simulation: A case study in the Heihe River Basin, China
title_short Ecosystem services assessment based on land use simulation: A case study in the Heihe River Basin, China
title_sort ecosystem services assessment based on land use simulation a case study in the heihe river basin china
topic Land use and land cover
Key Ecosystem services
FLUS-InVEST model
The Heihe River Basin
Local space autocorrelation analysis
url http://www.sciencedirect.com/science/article/pii/S1470160X22008755
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