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Journal of Arid Land
Journal of Arid Land  2024, Vol. 16 Issue (1): 110-130    DOI: 10.1007/s40333-024-0052-9     CSTR: 32276.14.s40333-024-0052-9
Research article     
Response of ecosystem carbon storage to land use change from 1985 to 2050 in the Ningxia Section of Yellow River Basin, China
LIN Yanmin1,2, HU Zhirui3, LI Wenhui1,2, CHEN Haonan1,2, WANG Fang1,2,*(), NAN Xiongxiong4, YANG Xuelong5, ZHANG Wenjun5
1College of Geographical Sciences and Planning, Ningxia University, Yinchuan 750021, China
2China-Arab Joint International Research Laboratory for Featured Resources and Environmental Governance in Arid Region, Yinchuan 750021, China
3Ningxia Land Resources Surveying and Monitoring Institute, Yinchuan 750002, China
4State Key Laboratory of Efficient Production of Forest Resources, Yinchuan 750002, China
5Ningxia Lingwu Baijitan National Nature Reserve Administration, Yinchuan 751400, China
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Abstract  

Regional sustainable development necessitates a holistic understanding of spatiotemporal variations in ecosystem carbon storage (ECS), particularly in ecologically sensitive areas with arid and semi-arid climate. In this study, we calculated the ECS in the Ningxia Section of Yellow River Basin, China from 1985 to 2020 using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model based on land use data. We further predicted the spatial distribution of ECS in 2050 under four land use scenarios: natural development scenario (NDS), ecological protection scenario (EPS), cultivated land protection scenario (CPS), and urban development scenario (UDS) using the patch-generating land use simulation (PLUS) model, and quantified the influences of natural and human factors on the spatial differentiation of ECS using the geographical detector (Geodetector). Results showed that the total ECS of the study area initially increased from 1985 until reaching a peak at 402.36×106 t in 2010, followed by a decreasing trend to 2050. The spatial distribution of ECS was characterized by high values in the eastern and southern parts of the study area, and low values in the western and northern parts. Between 1985 and 2020, land use changes occurred mainly through the expansion of cultivated land, woodland, and construction land at the expense of unused land. The total ECS in 2050 under different land use scenarios (ranked as EPS>CPS>NDS>UDS) would be lower than that in 2020. Nighttime light was the largest contributor to the spatial differentiation of ECS, with soil type and annual mean temperature being the major natural driving factors. Findings of this study could provide guidance on the ecological construction and high-quality development in arid and semi-arid areas.

Key words： carbon storage      land use change      nighttime light      Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model      patch-generating land use simulation (PLUS) model      geographical detector (Geodetector)      Yellow River Basin     
Received: 08 August 2023      Published: 31 January 2024
Corresponding Authors: *WANG Fang (E-mail: fangwang0820@nxu.edu.cn)
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LIN Yanmin
HU Zhirui
LI Wenhui
CHEN Haonan
WANG Fang
NAN Xiongxiong
YANG Xuelong
ZHANG Wenjun
Cite this article:

LIN Yanmin, HU Zhirui, LI Wenhui, CHEN Haonan, WANG Fang, NAN Xiongxiong, YANG Xuelong, ZHANG Wenjun. Response of ecosystem carbon storage to land use change from 1985 to 2050 in the Ningxia Section of Yellow River Basin, China. Journal of Arid Land, 2024, 16(1): 110-130.

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http://jal.xjegi.com/10.1007/s40333-024-0052-9     OR     http://jal.xjegi.com/Y2024/V16/I1/110

Fig. 1 Overview of the Ningxia Section of Yellow River Basin (NYRB) based on the digital elevation model (DEM)
Category/Indicator Unit Resolution Time period Source
Land use - 30 m 1985-2020 https://zenodo.org/
Carbon density t/hm2 / 2010s http://www.cern.ac.cn/
Natural factors DEM m 30 m 2009 https://earthexplorer.usgs.gov/
Slope ° 30 m 2009
Aspect - 30 m 2009
Annual mean temperature °C 1 km 1990-2020 http://data.cma.cn/
Annual precipitation mm 1 km 1990-2020
Soil type - 1 km 2010 http://www.resdc.cn/
Vegetation type - 1 km 2000
Socioeconomic factors Gross regional product 1×104 CNY/km2 1 km 1985-2020 http://www.resdc.cn/
Population distribution persons/km2 1 km 1985-2020 http://www.resdc.cn/
Nighttime light - 1 km 1990-2020 https://eogdata.mines.edu/
products/dmsp
Transportation accessibility factor Distance from roads, waterways, and residences km / 2020 https://www.openstreetmap.org/
Table 1 Detailed description of data used in the study
Land use type Carbon density (t/hm2)
Ci_above Ci_below Ci_soil Ci_dead
Cultivated land 3.72 8.33 92.39 0.00
Woodland 7.80 2.34 126.59 0.00
Grassland 1.37 5.07 96.66 0.00
Water body 0.00 0.00 0.00 0.00
Unused land 0.07 0.00 3.14 0.00
Construction land 0.02 0.00 0.00 0.00
Table 2 Carbon density data of different land use types in the NYRB
Interaction type Judgment criteria
Nonlinear attenuation q(X1∩X2)<min[q(X1), q(X2)]
Univariate nonlinear attenuation min[q(X1), q(X2)]<q(X1∩X2)<max[q(X1), q(X2)]
Bivariate enhancement q(X1∩X2)>max[q(X1), q(X2)]
Nonlinear enhancement q(X1∩X2)>q(X1)+q(X2)
Independent q(X1∩X2)=q(X1)+q(X2)
Table 3 Interaction types of the factors driving the spatial differentiation of ecosystem carbon storage (ECS)
Year 1985 1990 1995 2000 2005 2010 2015 2020
ECS (×106 t) 385.37 385.81 394.48 396.86 401.81 402.36 399.53 398.49
Table 4 Total ECS in the NYRB during 1985-2020
Fig. 2 Spatial distribution of ECS (indicated by carbon density) in the NYRB in 1985 (a), 2010 (b), and 2020 (c)
Fig. 3 Spatial distribution of the variations in ECS in the NYRB during 1985-2010 (a) and 2010-2020 (b)
Land use type ECS (×106 t)
1985 1990 1995 2000 2005 2010 2015 2020
Cultivated land 96.35 97.15 109.80 108.87 92.24 99.67 97.47 104.84
Woodland 2.46 2.49 2.70 2.77 2.80 2.82 2.91 3.13
Grassland 285.30 284.93 281.02 284.36 306.15 299.36 298.61 289.98
Unused land 1.26 1.24 0.96 0.86 0.62 0.51 0.54 0.54
Construction land 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Table 5 ECS of different land use types in the NYRB during 1985-2020
Fig. 4 Chord diagrams of mutual conversion of different land use types in the NYRB during 1985-2010 (a) and 2010-2020 (b)
Fig. 5 Spatial distribution of actual (a) and simulated (b) land use distribution in the NYRB in 2020
Land use type Area of land use change (km2)
NDS EPS UDS CPS
2050 2020-2050 2050 2020-2050 2050 2020-2050 2050 2020-2050
Cultivated land 10,800.85 766.14 10,800.85 766.14 10,802.85 766.14 11,927.94 1891.23
Woodland 282.43 54.01 322.42 94.00 282.43 54.01 302.43 74.01
Grassland 26,491.05 -1634.30 27,009.03 -1112.32 25,459.26 -2662.09 25,443.97 -2677.38
Water body 404.89 60.27 444.89 100.27 404.89 60.27 404.89 60.27
Unused land 1654.11 -26.48 1554.12 -126.47 1654.11 -26.48 1554.12 -126.47
Construction land 1859.66 778.36 1359.69 278.38 2889.45 1808.15 1859.64 778.34
Table 6 Area of land use change in the NYRB during 2020-2050 under the four land use scenarios
Land use type ECS (×106 t)
NDS EPS UDS CPS
Cultivated land 112.84 112.84 112.84 124.60
Woodland 3.86 4.41 2.95 3.16
Grassland 273.15 278.51 262.53 262.37
Unused land 0.53 0.50 0.53 0.50
Construction land 0.00 0.00 0.00 0.00
Total carbon storage 390.38 396.26 378.85 390.63
Table 7 ECS of different land use types in the NYRB in 2050 under the four land use scenarios
Fig. 6 Spatial distribution patterns of ECS (indicated by carbon density; a-d) in 2050 and variations in ECS during 2020-2050 (e-h) in the NYRB. (a), ECS under the NDS in 2050; (b), ECS under the EPS in 2050; (c), ECS under the UDS in 2050; (d), ECS under the CPS in 2050; (e), variations in ECS under the NDS during 2020-2050; (f), variations in ECS under the EPS during 2020-2050; (g), variations in ECS under the UDS during 2020-2050; (h), variations in ECS under the CPS during 2020-2050. NDS, natural development scenario; EPS, ecological protection scenario; UDS, urban development scenario; CPS, cultivated land protection scenario.
Fig. 7 Results of factor detector in the geographical detector (Geodetector) analysis showing the influence of each driving factor on the spatial differentiation of ECS in 2020. The q value indicates the influence of each driving factor on the spatial differentiation of ECS.
Fig. 8 Results of interaction detector in the Geodetector analysis showing the combined effect of any two driving factors on the spatial differentiation of ECS in 2020
Land use scenario Neighborhood weight parameter
Cultivated land Woodland Grassland Water body Unused land Construction land
NDS 0.0622 0.2498 0.0850 0.1654 0.0028 0.7429
EPS 0.0438 0.4268 0.0850 0.2806 0.0578 0.2743
UDS 0.0987 0.2498 0.0850 0.1654 0.0028 1.0000
CPS 0.0992 0.3383 0.1980 0.1654 0.0578 0.7429
Table S1 Neighborhood weight parameter of each land use type under the four land use scenarios
Land use scenario Land use type Cultivated land Woodland Grassland Water body Unused land Construction land
NDS Cultivated land 1 0 1 1 1 1
Woodland 0 1 0 0 0 0
Grassland 1 1 1 1 1 1
Water body 1 0 1 1 0 1
Unused land 1 0 1 1 1 1
Construction land 0 0 0 1 0 1
EPS Cultivated land 1 0 1 1 1 1
Woodland 0 1 0 0 0 0
Grassland 1 1 1 1 0 0
Water body 0 0 1 1 0 0
Unused land 1 0 1 1 1 1
Construction land 0 0 0 1 0 1
UDS Cultivated land 1 0 1 1 1 1
Woodland 0 1 0 0 0 0
Grassland 1 1 1 1 1 1
Water body 1 0 1 1 0 1
Unused land 1 0 1 1 1 1
Construction land 0 0 0 0 0 1
CPS Cultivated land 1 0 0 0 0 0
Woodland 0 1 0 0 0 0
Grassland 1 1 1 1 1 1
Water body 1 0 1 1 0 1
Unused land 1 0 1 1 1 1
Construction land 1 0 0 1 0 1
Table S2 Land use transfer cost matrix of the Ningxia Section of Yellow River Basin (NYRB)
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doi: 10.11821/dlxb201903004
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