基于随机森林模型的黄土高原草地净初级生产力时空格局及未来演变趋势模拟

doi:10.13866/j.azr.2023.01.13

Abstract

Abstract:

The accurate estimation of grassland net primary productivity (NPP) is crucial to understanding the carbon cycle of grassland ecosystems and their adaption to climate change. Based on 1788 grassland biomass data and 19 environmental factors (climate, vegetation, soil, and topographic factors), we simulated the spatiotemporal dynamics of grassland NPP in the Loess Plateau from 2002 to 2020 using the random forest (RF) model. The future trends of grassland NPP under four future climate scenarios of shared socioeconomic pathways were estimated. Results showed that (1) the RF model had a good accuracy, which indicated that RF can be used to estimate grassland NPP in the Loess Plateau; (2) grassland NPP in the Loess Plateau exhibited a “high in southeastern and low in northwestern” pattern, with a mean value of 276.55 g C·m^-2·a^-1. The highest grassland NPP was observed in Guanzhong Plain of Shaanxi; (3) the grassland NPP in the Loess Plateau showed an overall increasing trend during 2002-2020. Regions experiencing an increase in grassland NPP accounted for 55.01% of the total land area, which is mainly located in Guanzhong Plain, western Gansu, and northern Shanxi; (4) under the wetter and warmer climate, grassland NPP in the Loess Plateau will continually increase by the end of this century. Grassland NPP will increase the most under the SSP585 scenario and the least under the SSP126 scenario. RF can be used to simulate the temporal and spatial trends of grassland NPP in the Loess Plateau. The results provide data support for the protection and sustainable development of grassland ecosystem in the Loess Plateau.

Key words: grassland ecosystems, net primary productivity (NPP), random forest, Shared Socioeconomic Pathway (SSPs), Loess Plateau

LIU Huanhuan, CHEN Yin, LIU Yue, GANG Chengcheng. Simulation of spatial pattern and future trends of grassland net primary productivity in the Loess Plateau based on random forest model[J].Arid Zone Research, 2023, 40(1): 123-131.

Figures/Tables 8

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类别	环境因子	解释	分辨率	数据来源
S	CLAY_S	0~30 cm黏粒含量	250 m	https://data.isric.org/
S	CLAY_T	30~100 cm黏粒含量	250 m
S	SILT_S	0~30 cm粉粒含量	250 m
S	SILT_T	30~100 cm粉粒含量	250 m
S	SAND_S	0~30 cm 砂粒含量	250 m
S	SAND_T	30~100 cm砂粒含量	250 m
S	SOC_S	0~30 cm土壤有机碳含量	250 m
S	SOC_T	30~100 cm土壤有机碳含量	250 m
T	DEM	数字高程模型	30 m	https://srtm.csi.cgiar.org/
T	Slope	坡度	30 m
A	TEM	年均温（2002—2020）	1000 m	http://loess.geodata.cn
A	TEM_4-10	4—10月均温（2002—2020）	1000 m
A	TMN	年最低温度（2002—2020）	1000 m
A	PRE	年降水量（2002—2020）	1000 m
A	PRE_4-10	4—10月降水量（2002—2020）	1000 m
A	ET	蒸散量（2002—2020）	500 m	https://modis.gsfc.nasa.gov
B	SIF	日光诱导叶绿素荧光（2002—2020）	0.05°	https://globalecology.unh.edu
B	FPAR	植被有效光合辐射吸收比例（2002—2020）	500 m	https://modis.gsfc.nasa.gov
B	NDVI	归一化植被指数（2002—2020）	1000 m	https://modis.gsfc.nasa.gov

Simulation of spatial pattern and future trends of grassland net primary productivity in the Loess Plateau based on random forest model

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