干旱区研究

干旱区研究 >

2023 , Vol. 40 >Issue 1: 123 - 131

DOI: https://doi.org/10.13866/j.azr.2023.01.13

生态与环境

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

  • 刘欢欢 ,
  • 陈印 ,
  • 刘悦 ,
  • 刚成诚
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  • 1.西北农林科技大学草业与草原学院，陕西 杨凌 712100
    2.西北农林科技大学水土保持研究所，陕西 杨凌 712100
    3.中国科学院水利部水土保持研究所，陕西 杨凌 712100
刘欢欢（1999-），男，硕士研究生，研究方向为草地生态遥感. E-mail: liuhh75@nwafu.edu.cn

收稿日期: 2022-05-26

  修回日期: 2022-08-13

  网络出版日期: 2023-02-24

基金资助

陕西省自然科学基金(2021JQ-171);青海省防灾减灾重点实验室开放基金(QFZ-2021-Z06);国家自然科学基金项目(31602004);国家科技基础条件平台建设项目(2005DKA32300)

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

  • Huanhuan LIU ,
  • Yin CHEN ,
  • Yue LIU ,
  • Chengcheng GANG
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  • 1. College of Grassland Agriculture, Northwest A & F University, Yangling 712100, Shaanxi, China
    2. Institute of Soil and Water Conservation, Northwest A & F University, Yangling 712100, Shaanxi, China
    3. Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling 712100, Shaanxi, China

Received date: 2022-05-26

  Revised date: 2022-08-13

  Online published: 2023-02-24

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摘要

准确估算草地净初级生产力（Net Primary Productivity，NPP）是了解草地生态系统碳循环过程和科学评估草地对气候变化响应与适应机制的关键。以黄土高原草地生态系统为研究对象，基于1788个草地生物量数据及19个环境因子数据（包含气候、植被、土壤和地形因子），利用随机森林模型模拟了2002—2020年黄土高原草地NPP的时空动态，并估算了共享社会经济路径（Shared Socioeconomic Pathway，SSPs）4个未来气候情景下，黄土高原草地NPP的未来演变趋势。结果表明：（1） 随机森林模型的模拟精度较好，可用于黄土高原草地NPP估算；（2） 黄土高原草地NPP在空间上整体呈现“东南高西北低”的空间分布特征，年均值为276.55 g C·m-2，其中陕西关中地区草地NPP最高；（3） 2002—2020年，黄土高原草地NPP总体呈现增加趋势，其中55.01%的区域草地NPP增加，主要集中在陕西关中地区、甘肃西部地区以及山西北部地区；（4） 在气候暖湿化背景下，到本世纪末，黄土高原草地NPP均呈增加趋势，其中SSP585情景下草地NPP增加最多，SSP126情景下增加最少。利用随机森林模型能够较好模拟黄土高原草地NPP时空格局及未来演变趋势，为黄土高原草地生态系统保护及可持续发展提供数据支持。

关键词： 草地生态系统; 净初级生产力; 随机森林; 共享社会经济路径; 黄土高原

本文引用格式

刘欢欢 , 陈印 , 刘悦 , 刚成诚 . 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[J]. 干旱区研究, 2023 , 40(1) : 123 -131 . DOI: 10.13866/j.azr.2023.01.13

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

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