College of Earth and Environment Sciences, Lanzhou University, Lanzhou 730000, China; ICube Laboratory (UMR 7357), CNRS, University of Strasbourg, 300 bd Sébastien Brant, CS 10413, F-67412 Illkirch, France.
ICube Laboratory (UMR 7357), CNRS, University of Strasbourg, 300 bd Sébastien Brant, CS 10413, F-67412 Illkirch, France; Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
Sci Total Environ. 2021 Feb 10;755(Pt 1):142506. doi: 10.1016/j.scitotenv.2020.142506. Epub 2020 Sep 26.
Lagged precipitation effect explains a large proportion of annual aboveground net primary productivity in some dryland ecosystems. Using satellite-derived plant productivity and precipitation datasets in the Northern Hemisphere drylands during 2000-2018, we identify 1111 pixels mainly located in the Tibetan Plateau, the western US, and Kazakhstan where productivities are significantly correlated with previous-year precipitation (hereafter, the lagged type). Differences in climatic and edaphic factors between the lagged and unlagged (pixels where productivities are not correlated with previous-year precipitation) types are evaluated. Permutational multivariate analysis of variance shows that the two types differ significantly regarding six climatic and edaphic factors. Compared to unlagged type, water availability, soil organic carbon, total nitrogen, field capacity, silt content and radiation are more sensitive to changes in precipitation in lagged type. Water availability is the most important factor for distinguishing the two types, followed by soil organic carbon, total nitrogen, field capacity, soil texture, and radiation. Our study suggests that the altered sensitivities of several climatic and edaphic factors to precipitation collectively affect the lagged effect of precipitation on productivity in drylands.
滞后降水效应解释了一些旱地生态系统中很大一部分地上净初级生产力的年际变化。本研究利用 2000-2018 年北半球旱地卫星衍生的植物生产力和降水数据集，在青藏高原、美国西部和哈萨克斯坦共鉴定出 1111 个像素点，这些地区的生产力与前一年的降水显著相关（以下简称滞后型）。评估了滞后型和非滞后型（生产力与前一年降水不相关的像素点）之间气候和土壤因素的差异。随机排列多元方差分析表明，这两种类型在六个气候和土壤因素方面存在显著差异。与非滞后型相比，滞后型对降水变化更敏感的因素包括水分可用性、土壤有机碳、总氮、田间持水量、粉粒含量和辐射。水分可用性是区分这两种类型的最重要因素，其次是土壤有机碳、总氮、田间持水量、土壤质地和辐射。本研究表明，几个气候和土壤因素对降水变化的敏感性变化共同影响了旱地降水对生产力的滞后效应。
