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Journal of Arid Land
Journal of Arid Land  2023, Vol. 15 Issue (7): 871-885    DOI: 10.1007/s40333-023-0019-2     CSTR: 32276.14.s40333-023-0019-2
Research article     
Enhanced soil moisture improves vegetation growth in an arid grassland of Inner Mongolia Autonomous Region, China
ZHANG Hui1,2, Giri R KATTEL1,3, WANG Guojie1, CHUAI Xiaowei4, ZHANG Yuyang1, MIAO Lijuan1,2,*()
1School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China
2State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
3Department of Infrastructure Engineering, the University of Melbourne, Melbourne 3010, Australia
4School of Geography & Ocean Science, Nanjing University, Nanjing 210093, China
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Abstract  

Climate change impacts on grasslands that cover a quarter of the global land area, have become unprecedented during the 21st century. One of the important ecological realms, arid grasslands of northern China, which occupy more than 70% of the region's land area. However, the impact of climate change on vegetation growth in these arid grasslands is not consistent and lacks corresponding quantitative research. In this study, NDVI (normalized difference vegetation index) and climate factors including temperature, precipitation, solar radiation, soil moisture, and meteorological drought were analyzed to explore the determinants of changes in grassland greenness in Inner Mongolia Autonomous Region (northern China) during 1982-2016. The results showed that grasslands in Inner Mongolia witnessed an obvious trend of seasonal greening during the study period. Two prominent climatic factors, precipitation and soil moisture accounted for approximately 33% and 27% of grassland NDVI trends in the region based on multiple linear regression and boosted regression tree methods. This finding highlights the impact of water constraints to vegetation growth in Inner Mongolia's grasslands. The dominant role of precipitation in regulating grassland NDVI trends in Inner Mongolia significantly weakened from 1982 to 1996, and the role of soil moisture strengthened after 1996. Our findings emphasize the enhanced importance of soil moisture in driving vegetation growth in arid grasslands of Inner Mongolia, which should be thoroughly investigated in the future.

Key words： grassland growth      normalized difference vegetation index      climate change      soil moisture      Inner Mongolia     
Received: 09 January 2023      Published: 31 July 2023
Corresponding Authors: *MIAO Lijuan (E-mail: miaolijuan1111@gmail.com)
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ZHANG Hui
Giri R KATTEL
WANG Guojie
CHUAI Xiaowei
ZHANG Yuyang
MIAO Lijuan
Cite this article:

ZHANG Hui, Giri R KATTEL, WANG Guojie, CHUAI Xiaowei, ZHANG Yuyang, MIAO Lijuan. Enhanced soil moisture improves vegetation growth in an arid grassland of Inner Mongolia Autonomous Region, China. Journal of Arid Land, 2023, 15(7): 871-885.

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http://jal.xjegi.com/10.1007/s40333-023-0019-2     OR     http://jal.xjegi.com/Y2023/V15/I7/871

Fig. 1 Changes in grassland NDVIGS (grassland vegetation greenness measured by normalized difference vegetation index (NDVI)) (a), temperature (b), precipitation (c), soil moisture (d), radiation (e), and SPEI-3 (3-month standardized precipitation evapotranspiration index) (f) in Inner Mongolia during the growing season from 1982 to 2016. k and P denote the slope and P-value of each variable, respectively.
Fig. 2 Spatial pattern of grassland NDVIGS (grassland vegetation greenness measured by normalized difference vegetation index (NDVI) (a), temperature (b), SPEI-3 (3-month standardized precipitation evapotranspiration index (c), precipitation (d), radiation (e), and soil moisture (f) in Inner Mongolia during the growing season from 1982 to 2016. Regions with significant trends (P<0.05) were marked in black dots.
Fig. 3 Spatial distributions of primary factor influencing grassland NDVIGS (grassland vegetation greenness measured by normalized difference vegetation index (NDVI)) in Inner Mongolia during the period 1982-2016, and their relative contributions estimated by BRT (boosted regression tree) and MLR (multiple linear regression). (a), primary factor estimated by BRT; (b), primary factor estimated by MLR; (c), relative contributions estimated by BRT; (d), relative contributions estimated by MLR. Primary factor indicated in each grid cell was defined as the driving factor that contributes the most to changes in NDVIGS. Driving factors include Tmp (temperature), Pre (precipitation), Rad (radiation), SM (soil moisture), and SPEI-3 (3-month standardized precipitation evapotranspiration index).
Fig. 4 Spatial patterns of primary factor influencing grassland NDVIGS (grassland vegetation greenness measured by normalized difference vegetation index (NDVI)) estimated by BRT (boosted regression tree) and MLR (multiple linear regression) methods in Inner Mongolia for two sub-periods (i.e., 1982-1996 and 2002-2016). (a and b), primary factors estimated by BRT and MLR from1982 to 1996; (c and d), primary factors estimated by BRT and MLR from 2002 to 2016. The five factors include Tmp (temperature), Pre (precipitation), Rad (radiation), SM (soil moisture), and SPEI-3 (3-month standardized precipitation evapotranspiration index).
Fig. 5 Spatial patterns of influential factors of temperature (a), precipitation (b), soil moisture (c), SPEI-3 (3-month standardized precipitation evapotranspiration index; d), and radiation (e) on grassland NDVIGS (grassland vegetation greenness measured by normalized difference vegetation index (NDVI)) based on standardized coefficient calculated from multiple linear regression. Regions with statistically significant (P<0.05) influence are labelled with black dots.
Fig. 6 Spatial patterns of influence of temperature (a and b), radiation (c and d), precipitation (e and f), SPEI-3 (3-month standardized precipitation evapotranspiration index, g and h), and soil moisture (i and j) on grassland NDVIGS (grassland vegetation greenness measured by normalized difference vegetation index (NDVI)) by standardized coefficient calculated from multiple linear regression during two sub-periods (i.e., 1982-1996 and 2002-2016). Regions with statistically significant (P<0.05) influence are labelled with black dots.
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