生态环境学报 ›› 2025, Vol. 34 ›› Issue (7): 1079-1089.DOI: 10.16258/j.cnki.1674-5906.2025.07.008

• 研究论文【生态学】 • 上一篇    下一篇

基于GSMSR模型的山东省植被NEP时空格局及影响因素

丁馨1(), 刘健2, 魏俐宏1, 解德威1, 郑昭佩1,*()   

  1. 1.山东师范大学地理与环境学院，山东 济南 250358
    2.北京师范大学地理科学学部灾害风险科学研究院，北京 100875
  • 收稿日期:2025-01-19 出版日期:2025-07-18 发布日期:2025-07-11
  • 通讯作者: *E-mail: zzp999@163.com
  • 作者简介:丁馨（2000年生），女，硕士研究生，研究方向为气候变化与区域响应。E-mail: 2023026623@stu.sdnu.edu.cn
  • 基金资助:
    国家社会科学基金项目(21BGL026)

Spatiotemporal Patterns and Influencing Factors of Vegetation Net Ecosystem Productivity in Shandong Province Based on GSMSR Model

DING Xin1(), LIU Jian2, WEI Lihong1, XIE Dewei1, ZHENG Zhaopei1,*()   

  1. 1. College of Geography and Environment, Shandong Normal University, Ji’nan 250358, P. R. China
    2. Institute of Disaster Risk Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, P. R. China
  • Received:2025-01-19 Online:2025-07-18 Published:2025-07-11

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摘要：

旨在探索山东省及其子区域植被净生态系统生产力（Net Ecosystem Productivity，NEP）的时空变化特征及其驱动因素，评估气温（TEM）、降水（PRE）和饱和蒸汽压差（VPD）等因素对植被NEP的影响。基于2000-2020年间的植被净初级生产力（Net Primary Productivity，NPP），采用土壤呼吸地统计（Geostatistical Model of Soil Respiration，GSMSR）模型、植被NEP估算模型和地理探测器工具，通过趋势分析、相关分析和差异分析等研究方法，开展深入研究。结果表明，1）山东省植被NEP在2000-2020年间整体呈上升趋势，空间分布特征表现为从东北向西南递减，沿海地区高于内陆地区。全省植被NEP的年平均值为87.0 g·m−2·a−1（以C计），且超过1/3的地区由碳源区域转变为碳汇区域。2）山东省大部分地区植被NEP与气温、降水呈正相关，与饱和蒸汽压差呈负相关，不同因子间的交互作用普遍对植被NEP的影响大于单一因子的影响。3）山东省4个子区域（鲁西、鲁东、鲁北、鲁中南）植被NEP的变化趋势存在明显的空间分异，鲁中南地区植被NEP增加最为显著，其次是鲁西和鲁东地区，而鲁北地区的增幅相对较小。该研究为深入理解区域生态系统的碳循环动态以及制定相应的生态保护策略提供了科学依据。

关键词: 植被净生态系统生产力（NEP）, 山东省, 时空变化, 地理探测器, 土壤呼吸模型

Abstract:

We aimed to explore the spatial and temporal characteristics of vegetation Net Ecosystem Productivity (NEP) and its drivers in Shandong Province and its sub-regions and to assess the effects of temperature (TEM), precipitation (PRE), and saturated vapor pressure difference (VPD) on vegetation NEP. Based on the Net Primary Productivity (NPP) of vegetation during the period of 2000‒2020, an in-depth study was carried out by using the Geostatistical Model of Soil Respiration (GSMSR) model, the Vegetation NEP Estimation Model and the Geodetector tool, through the trend analysis, correlation analysis and difference analysis and other research methods, to carry out in-depth research. The results of the study showed that 1) the vegetation NEP in Shandong Province showed an overall upward trend from 2000 to 2020, and the spatial distribution was characterized by a decreasing trend from the northeast to the southwest, with the coastal areas being higher than those in the inland areas. The annual average value of vegetation NEP in the province is 87.0 g·m−2·a−1 (in C), and more than one-third of the area has been transformed from a carbon source area to a carbon sink area. 2) Vegetation NEP in most areas of Shandong Province was positively correlated with air temperature and precipitation and negatively correlated with the difference in the saturated vapor pressure. The interactions between different factors generally had a greater effect on vegetation NEP than a single factor. The interaction between different factors generally has a greater effect on vegetation NEP than that of a single factor. 3) There was obvious spatial differentiation in the trend of vegetation NEP in the four sub-regions of Shandong Province (West Shandong, East Shandong, North Shandong, and Central-South Shandong), with the most significant increase in vegetation NEP in the Central-South region, followed by West Shandong and East Shandong, and a relatively small increase in North Shandong. This study provides a scientific basis for understanding the carbon cycle dynamics of regional ecosystems and for formulating appropriate ecological protection strategies.

Key words: vegetation net ecosystem productivity, Shandong Province, spatiotemporal variation, geographical detector, soil respiration model

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