Journal of Resources and Ecology >
Quantitative Assessment of the Effects of Climate Change and Human Activities on Grassland NPP in Altay Prefecture
TIAN Jie, E-mail: tj104959@163.com |
Received date: 2021-04-01
Accepted date: 2021-06-15
Online published: 2021-11-26
Supported by
The Science and Technology Project of Xizang Autonomous Region(XZ201901-GA-07)
The Key Research and Development Project of Sichuan Science and Technology Department(2021YFQ0042)
The Science and Technology Bureau of Altay Region in Yili Kazak Autonomous Prefecture(Y99M4600AL)
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 (NPPA) and potential net primary productivity (NPPP) 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 (NPPH) 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 NPPA was highly consistent with the MOD17A3 dataset in spatial distribution. (2) Regions with an increased NPPA accounted for 70.53% of the total grassland, whereas 29.47% of the total grassland area experienced a decrease. At the temporal scale, the NPPA presented a slightly increasing trend (0.83 g C m‒2 yr‒1) over the study period, while the trends of NPPP and NPPH 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.
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 . DOI: 10.5814/j.issn.1674-764x.2021.06.003
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 | SP | SH | Relative relation | Relative roles of climate change and human activities |
---|---|---|---|---|---|
SA>0 (Restoration) | Scenario 1 | >0 | >0 | |SP|>|SH| | Climate is the dominant trigger responsible for grassland restoration (CDR) |
Scenario 2 | <0 | <0 | |SP|<|SH| | 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) | ||
SA<0 (Degradation) | Scenario 4 | >0 | >0 | |SP|<|SH| | Grassland degradation is mainly caused by human activities such as overgrazing, over-reclamation (HDD) |
Scenario 5 | <0 | <0 | |SP|>|SH| | 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. 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 2019Note: (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 factorsNote: (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 |
We are grateful to the editor and anonymous reviewers. We would like to thank Prof. Zhu Wenquan from Beijing Normal University for his help on this work. We also appreciate the dataset were provided by the National Cryosphere Desert Data Center.
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