植被覆盖度和物候变化对典型黑沙蒿灌丛生态系统总初级生产力的影响
收稿日期: 2020-11-23
录用日期: 2021-05-25
网络出版日期: 2021-06-28
基金资助
国家自然科学基金(32071843);国家自然科学基金(31670708);国家自然科学基金(32071842);国家自然科学基金(31670710);中央高校基本科研业务费专项资金(2015ZCQ-SB-02)
Effects of land cover and phenology changes on the gross primary productivity in an Artemisia ordosica shrubland
Received date: 2020-11-23
Accepted date: 2021-05-25
Online published: 2021-06-28
Supported by
National Natural Science Foundation of China(32071843);National Natural Science Foundation of China(31670708);National Natural Science Foundation of China(32071842);National Natural Science Foundation of China(31670710);Fundamental Research Funds for the Central Universities(2015ZCQ-SB-02)
气候变化和人类活动是植被生产力年际尺度变化的重要驱动因素, 明晰二者对植被生产力的共同影响对于生态系统可持续管理至关重要。气候变化可能导致植被物候变化, 进而影响植被生产力。目前尚不清楚毛乌素沙地典型植被物候如何响应气候变化, 并因此影响生态系统总初级生产力(GPP)。此外, 植被恢复(覆盖度增加)和物候变化对GPP的共同影响有待明确。该研究选取典型黑沙蒿(Artemisia ordosica)灌丛生态系统, 结合MODIS遥感数据与涡度相关数据, 利用植被光合模型(VPM), 模拟并分析了2005-2018年间植被覆盖度和物候变化对GPP的影响。结果表明: (1) VPM模型能够较好地模拟涡度相关法观测的GPP动态(GPPFlux), 而MODIS遥感产品(MOD17A2H)则显著低估GPPFlux; (2)研究期内年均归一化差异植被指数(NDVI)、最大NDVI (NDVImax)和年总GPP均显著增加, 表明植被恢复促进了植被生产力增加; (3)基于NDVI和GPP日序列估算的生长季开始日期显著提前(2.1 d·a-1), 生长季结束日期显著推迟(1.5 d·a-1), 二者共同促使生长季长度延长(3.6 d·a-1); (4)物候期延长促进了GPP增加, 生长季长度每延长1天, 全年GPP显著增加6.44 g C·m-2·a-1; (5)植被覆盖度增加和生长季延长分别可以解释79%和57%的GPP增加; (6)尽管植被覆盖度和物候变化均促进GPP增加, 但前者是其增加的主要驱动因素。鉴于植被覆盖度增加和生长季延长也可能导致生态系统呼吸和蒸散发增加, 未来研究仍需探究生态系统碳汇能力、水分利用效率和水分承载力对气候变化和人类活动的响应。此外, 该研究主要探讨GPP在年际尺度的变化趋势及影响因素, 未来需要研究GPP的年际变异规律及驱动因素, 尤其是对降水年际变异和极端干旱事件的响应。
原媛, 母艳梅, 邓钰洁, 李鑫豪, 姜晓燕, 高圣杰, 查天山, 贾昕 . 植被覆盖度和物候变化对典型黑沙蒿灌丛生态系统总初级生产力的影响[J]. 植物生态学报, 2022 , 46(2) : 162 -175 . DOI: 10.17521/cjpe.2020.0387
Aims We aimed to examine how changes in vegetation cover and phenology affect the trend of gross primary productivity (GPP) in an Artemisia ordosica shrubland in the Mau Us Desert during the first two decades of the 21th century.
Methods We used the vegetation photosynthesis model (VPM) in combination with remote sensing data (MODIS) to simulate GPP dynamics during 2005-2018. Eddy covariance (EC) measurements of GPP (GPPFlux) were used to parameterize and validate the VPM model. The “derivation and threshold” approach was used to determine the start (SOS) and the end of the growing season (EOS), as well as the length of the growing season (LOS) for each year. Ordinary least squares (OLS) regression was used to examine the variations in temperature, normalized differences vegetation index (NDVI), and GPP over time. OLS, multiple, and partial correlation analyses were used to test the relationships among temperature, NDVI, phenology, and GPP.
Important findings (1) Modeled GPP well captured the dynamics of GPPFlux, whereas the MODIS product (MOD17A2H) significantly underestimated GPPFlux. (2) NDVI, annual maximum NDVI(NDVImax), and annual GPP all showed an increasing trend during 2005-2018, indicating the role of vegetation recovery in promoting GPP. (3) SOS showed an advancing trend (2.1 d·a-1) and EOS showed a delaying trend (1.5 d·a-1), and therefore both SOS and EOS contributed to the increasing trend of LOS (3.6 d·a-1). (4) Annual GPP was higher with greater LOS (6.44 g C·m-2·a-1 for 1 day increase in LOS). (5) Increases in vegetation cover and growing season length explained 79% and 57% of the variations in GPP, respectively. (6) Increases in vegetation cover played a more important role than growing season extension in promoting GPP.
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