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Earlier leaf senescence dates are constrained by soil moisture

机译:较早的叶片衰老日期受土壤湿度的限制

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Abstract The unprecedented warming that has occurred in recent decades has led to later autumn leaf senescence dates (LSD) throughout the Northern Hemisphere. Yet, great uncertainties still exist regarding the strength of these delaying trends, especially in terms of how soil moisture affects them. Here we show that changes in soil moisture in 1982–2015 had a substantial impact on autumn LSD in one‐fifth of the vegetated areas in the Northern Hemisphere (>30° N), and how it contributed more to LSD variability than either temperature, precipitation or radiation. We developed a new model based on soil‐moisture‐constrained cooling degree days (CDDSM) to characterize the effects of soil moisture on LSD and compared its performance with the CDD, Delpierre and spring‐influenced autumn models. We show that the CDDSM model with inputs of temperature and soil moisture outperformed the three other models for LSD modelling and had an overall higher correlation coefficient (R), a lower root mean square error and lower Akaike information criterion (AIC) between observations and model predictions. These improvements were particularly evident in arid and semi‐arid regions. We studied future LSD using the CDDSM model under two scenarios (SSP126 and SSP585) and found that predicted LSD was 4.1 ± 1.4 days and 5.8 ± 2.8 days earlier under SSP126 and SSP585, respectively, than other models for the end of this century. Our study therefore reveals the importance of soil moisture in regulating autumn LSD and, in particular, highlights how coupling this effect with LSD models can improve simulations of the response of vegetation phenology to future climate change.
机译:摘要 近几十年来,北半球出现了前所未有的变暖,导致整个北半球的秋叶衰老日期(LSD)推迟。然而,关于这些延迟趋势的强度仍然存在很大的不确定性,特别是在土壤湿度如何影响它们方面。在这里,我们展示了1982-2015年土壤湿度的变化对北半球(>30°N)五分之一植被区的秋季LSD产生了重大影响,以及它如何比温度,降水或辐射对LSD变异性的贡献更大。我们开发了一种基于土壤水分约束降温天数(CDDSM)的新模型来表征土壤水分对LSD的影响,并将其性能与CDD、Delpierre和受春季影响的秋季模型进行比较。结果表明,输入温度和土壤湿度的CDDSM模型在LSD建模方面优于其他三个模型,并且观测和模型预测之间的总体相关系数(R)更高,均方根误差更低,赤池信息准则(AIC)更低。这些改善在干旱和半干旱地区尤为明显。我们在两种情景(SSP126 和 SSP585)下使用 CDDSM 模型研究了未来的 LSD,发现在 SSP126 和 SSP585 下,预测 LSD 分别比本世纪末的其他模型早 4.1 ± 1.4 天和 5.8 ± 2.8 天。因此,我们的研究揭示了土壤湿度在调节秋季LSD中的重要性,特别是强调了将这种效应与LSD模型相结合如何改善植被物候对未来气候变化响应的模拟。

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