Quantitative Assessment of the Effects of Climate Change and Human Activities on Grassland NPP in Altay Prefecture

doi:10.5814/j.issn.1674-764x.2021.06.003

Abstract

Abstract:

Grassland degradation in Altay Prefecture is of considerable concern as it is a threat that hinders the sustainable development of the local economy and the stable operation of the livestock industry. Quantitative assessment of the relative contributions of climate change and human activities, which are considered as the dominant triggers of grassland degradation, to grassland variation is crucial for understanding the grassland degradation mechanism and mitigating the degraded grassland in Altay Prefecture. In this paper, the Carnegie-Ames-Stanford Approach model and the Thornthwaite memorial model were adopted to simulate the actual net primary productivity (NPP_A) and potential net primary productivity (NPP_P) in the Altay Prefecture from 2000 to 2019. Meanwhile, the difference between potential NPP and actual NPP was employed to reflect the effects of human activities (NPP_H) on the grassland. On this basis, we validated the viability of the simulated NPP using the Pearson correlation coefficient, investigated the spatiotemporal variability of grassland productivity, and established comprehensive scenarios to quantitatively assess the relative roles of climate change and human activities on grassland in Altay prefecture. The results indicate three main points. (1) The simulated NPP_A was highly consistent with the MOD17A3 dataset in spatial distribution. (2) Regions with an increased NPP_A accounted for 70.53% of the total grassland, whereas 29.47% of the total grassland area experienced a decrease. At the temporal scale, the NPP_A presented a slightly increasing trend (0.83 g C m^?2 yr^?1) over the study period, while the trends of NPP_P and NPP_H were reduced (?1.31 and ?2.15 g C m^?2 yr^?1). (3) Compared with climate change, human activities played a key role in the process of grassland restoration, as 66.98% of restored grassland resulted from it. In contrast, inter-annual climate change is the primary cause of grassland degradation, as it influenced 55.70% of degraded grassland. These results could shed light on the mechanisms of grassland variation caused by climate change and human activities, and they can be applied to further develop efficient measures to combat desertification in Altay Prefecture.

Key words: grassland degradation, net primary productivity, climate change, human activities, Altay Prefecture

TIAN Jie, XIONG Junnan, ZHANG Yichi, CHENG Weiming, HE Yuchuan, YE Chongchong, HE Wen. Quantitative Assessment of the Effects of Climate Change and Human Activities on Grassland NPP in Altay Prefecture[J]. Journal of Resources and Ecology, 2021, 12(6): 743-756.

Figures/Tables 13

Fig. 1 Location of the study area: (a) The base map is 500 m DEM; (b) Grassland type map used in the study.

Table 1 Significance test of the NPPA change trend and classification levels

P and Slope values	Classification level
Slope<0, P<0.01	Extremely Significant Decrease (ESD)
Slope<0, 0.01≤P<0.05	Significant Decrease (SD)
Slope<0, 0.05≤P	Not Significant Decrease (NSD)
Slope>0, P<0.01	Extremely Significant Increase (ESI)
Slope>0, 0.01≤P<0.05	Significant Increase (SI)
Slope>0, 0.05≤P	Not Significant Increase (NSI)

Table 2 Methods for assessing the relative contributions of climate change and human activities to grassland restoration or degradation

Hypothesis	Scenario	S_P	S_H	Relative relation	Relative roles of climate change and human activities
S_A>0 (Restoration)	Scenario 1	>0	>0	\|S_P\|>\|S_H\|	Climate is the dominant trigger responsible for grassland restoration (CDR)
	Scenario 2	<0	<0	\|S_P\|<\|S_H\|	Human activities are the dominant factor that controls the grassland restoration (HDR)
	Scenario 3	>0	<0		Climate change and human activities act together to promote grassland restoration (BDR)
S_A<0 (Degradation)	Scenario 4	>0	>0	\|S_P\|<\|S_H\|	Grassland degradation is mainly caused by human activities such as overgrazing, over-reclamation (HDD)
	Scenario 5	<0	<0	\|S_P\|>\|S_H\|	Climate plays a dominant role in grassland degradation (CDD)
	Scenario 6	<0	>0		Climate change and human activities are both responsible for grassland (BDD)

Fig. 2 Comparison between the mean NPP of the CASA model and the mean NPP of the MOD17A3 products from 2000 to 2019: (a) Correlation analysis of the two data sources; (b) The mean NPP of the CASA model; (c) The mean NPP of the MOD17A3 products.

Table 3 Classified results of evolving tendency of NPPA from 2000 to 2019 in Altay Prefecture

H and Slope value	Classification level
Slope<0, H<0.1 and Slope>0, 0.9≤H	Strong Favorable Direction (SFD)
Slope<0, 0.1≤H<0.3 and Slope>0, 0.7≤ H<0.9	Weak Favorable Direction (WFD)
Slope<0, 0.9≤ H and Slope>0, H<0.1	Strong Unfavorable Direction (SUD)
Slope<0, 0.7≤ H<0.9 and Slope>0, 0.1≤H<0.3	Weak Unfavorable Direction (WUD)
Slope<0, 0.5≤H<0.7 and Slope>0, 0.3≤H<0.5	Uncertain Direction (UD)
Slope<0, 0.3≤H<0.5 and Slope>0, 0.5<H<0.7	Continuously Unchanged (CU)

Fig. 3 Dynamic analysis of grassland net primary productivity in Altay Prefecture: (a) Significance test of NPPA change trend from 2000 to 2019; (b) Spatial distribution of the direction of the evolving tendency. Note: The abbreviations in figure see in Table 1 and Table 3.

Fig. 4 The trends of NPPP and NPPH from 2000 to 2019

Fig. 5 Relative contributions of climate and human factors to grassland changes in Altay Prefecture from 2000 to 2019 Note: (a) Restoration effect. CDR, HDR, and BDR denote grassland restoration that is dominated by climate change, human activities, and the combination of the two factors, respectively; (b) Degradation effect. HDD, CDD, and BDD denote grassland degradation that is dominated by human activities, climate change, and the combination of the two factors, respectively.

Fig. 6 The relative contributions of climate change and human activities for either restoration or degradation in the different types of grassland

Table 4 Comparisons of the values simulated in this study with those of other studies

Study area	Study period	Mean annual NPP (g C m^‒2 yr^‒1)	Reference
Altay Prefecture	2000-2020	64.25 (Temperate desert)	This study
Xinjiang	2001-2014	65.73 (Temperate desert)	Zhang et al., 2020
Xinjiang	2000-2010	57.68 (Temperate steppe)	Yang et al., 2014
Xinjiang	2000-2014	54.66 (Temperate desert)	Ren et al., 2017

Fig. 7 Spatial distribution of correlation coefficients between NPPA and influencing factors Note: (a) Annual total precipitation; (b) Annual mean temperature.

Fig. 8 Correlations between meteorological factors (annual total precipitation and annual mean temperature) and NPPA in different grassland types

Fig. 9 The trends of the non-agricultural population and livestock over the study period from 2000 to 2019

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