Estimating the dynamics and driving factors of gross primary productivity over the Chinese Loess Plateau by the modified vegetation photosynthesis model

IF 5.8 2区环境科学与生态学 Q1 ECOLOGY

Ecological Informatics Pub Date : 2024-09-27 DOI:10.1016/j.ecoinf.2024.102838

Enjun Gong , Jing Zhang , Zhihui Wang , Jun Wang

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引用次数: 0

Abstract

Gross primary productivity (GPP) is a key parameter in research on the global carbon cycle and changes. Understanding the spatiotemporal dynamics and influencing factors of GPP on the Loess Plateau (LP) helps identify the health status of ecosystems, thereby enabling the implementation of effective conservation and restoration measures. In this study, we used a modified vegetation photosynthesis model (VPM) to simulate a long-term series of GPP in the LP from 2001 to 2022, and the impacts of different land-use patterns and meteorological factors on GPP were investigated using a transition matrix, linear regression, and partial correlation analyses. The findings suggested that the modified simulation yielded a more reliable performance (coefficient of determination (R²) = 0.89, root mean square error (RMSE) = 143.47 gC·m⁻²·yr⁻¹) and was suitable for further research endeavors. (1) The GPP on the LP significantly increased by 232.65 TgC from 2001 to 2022. The southeastern region contributed more than the northwestern region, and the GPP exhibited higher multi-year averages and growth rates below 1000 m elevation. (2) Forests in the southeastern region of the LP, characterized by a heightened growth rate, will influence future spatial variations in GPP increases across the LP. Despite the decline in grassland and cultivated land areas, substantial land coverage has significantly contributed to the overall GPP growth. Urbanization encroaching on cultivated land has emerged as a key contributor to the decline in GPP in low-altitude regions. (3) Air temperature was the main physical driving force for GPP change in the LP. Additionally, the GPP in forested regions exhibited a negative correlation with rainfall, whereas the GPP in areas undergoing the return of cropland to forest–grassland and cropland reclamation correlated negatively with solar radiation. (4) The attribution analysis indicated that the surge in vegetation GPP on the LP was collectively driven by human activities and meteorological changes, with human activities dominating these changes by 61.41 %. This study deepens the understanding of terrestrial ecology in semi-humid regions and provides scientific insights for implementing ecological governance strategies in the LP.

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利用改进的植被光合作用模型估算中国黄土高原总初级生产力的动态和驱动因素

总初级生产力（GPP）是研究全球碳循环及其变化的一个关键参数。了解黄土高原 GPP 的时空动态和影响因素有助于确定生态系统的健康状况，从而实施有效的保护和恢复措施。本研究利用改进的植被光合作用模型（VPM）模拟了黄土高原 2001 年至 2022 年的 GPP 长期序列，并利用过渡矩阵、线性回归和偏相关分析研究了不同土地利用模式和气象因子对 GPP 的影响。研究结果表明，修改后的模拟结果性能更可靠（判定系数（R2）= 0.89，均方根误差（RMSE）= 143.47 gC-m-2-yr-1），适合进一步开展研究工作。(1) 从 2001 年到 2022 年，LP 上的 GPP 显著增加了 232.65 TgC。东南部地区的贡献率高于西北部地区，海拔 1000 米以下地区的 GPP 多年平均值和增长率更高。(2) 低纬度地区东南部的森林具有生长速度快的特点，将影响整个低纬度地区未来 GPP 增长的空间变化。尽管草地和耕地面积有所减少，但大量的土地覆盖仍极大地促进了整个 GPP 的增长。城市化侵蚀耕地成为低海拔地区 GPP 下降的主要原因。(3) 气温是低海拔地区 GPP 变化的主要物理驱动力。此外，森林地区的 GPP 与降雨量呈负相关，而退耕还林还草和耕地开垦地区的 GPP 与太阳辐射呈负相关。(4）归因分析表明，LP 上植被 GPP 的激增是由人类活动和气象变化共同驱动的，其中人类活动占 61.41%。这项研究加深了人们对半湿润地区陆地生态的认识，为在 LP 地区实施生态治理战略提供了科学依据。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Ecological Informatics 环境科学-生态学

CiteScore

8.30

自引率

11.80%

发文量

346

审稿时长

46 days

期刊介绍： The journal Ecological Informatics is devoted to the publication of high quality, peer-reviewed articles on all aspects of computational ecology, data science and biogeography. The scope of the journal takes into account the data-intensive nature of ecology, the growing capacity of information technology to access, harness and leverage complex data as well as the critical need for informing sustainable management in view of global environmental and climate change. The nature of the journal is interdisciplinary at the crossover between ecology and informatics. It focuses on novel concepts and techniques for image- and genome-based monitoring and interpretation, sensor- and multimedia-based data acquisition, internet-based data archiving and sharing, data assimilation, modelling and prediction of ecological data.