Biological processes underpin the persistence of dryland productivity following extreme wet years

Global warming has induced more years of above-average rainfall, significantly affecting the interannual variability of the terrestrial global carbon cycle. An extreme wet year can cause changes to vegetation structure and function that persist beyond itself, referred to as “legacy effects”. The physical and biological mechanisms underlying these effects are poorly understood, introducing uncertainty into climate–carbon models to accurately represent post–wet year vegetation dynamics. Here we used multi-source satellite-derived vegetation productivity metrics, as well as eddy covariance (EC) measurements, to investigate the legacy effects of extreme wet years on the productivity of Australia’s drylands. We found that the impact of the 2010–2011 extreme wet year extended beyond generating a record-breaking carbon uptake, which exceeded the 40-year mean by more than 1.5 standard deviations. It also resulted in a widespread positive legacy effect in the following year. Specifically, up to ..., , # Data from: Biological processes underpin the persistence of dryland productivity following extreme wet years

Dataset DOI: [10.5061/dryad.51c59zwnb](https://doi.org/10.5061/dryad.51c59zwnb)

## Description of the data and file structure

This repository provides the codes used in the analysis for the manuscript:

***Biological Processes Underpin the Persistence of Dryland Productivity Following Extreme Wet Years***.

* **Figure 1 and Figure 2** were produced using **MATLAB** scripts.
* **Figure 3** was produced using **R** scripts.

All codes are provided for reproducibility and transparency of the analysis.

* The MATLAB scripts include procedures for detrending, normalization, regression, and legacy effect quantification.
* The R scripts include procedures for recursive feature elimination (RFE), random forest modeling, and SHAP value analysis.

The codes are generalized:

* Input data are expected in raster format for gridded spatial data, including vegetation indices and meteorolo...,

全球变暖导致多年降雨量高于平均水平，显著影响了全球陆地碳循环的年际变异性。极端湿润年份可引发植被结构与功能的改变，且这种改变会持续至当年之后，这类效应被称为**遗留效应（legacy effects）**。目前学界对这类效应背后的物理与生物机制尚缺乏充分认知，这给气候-碳循环模型准确模拟湿润年份后植被动态带来了不确定性。本研究利用多源卫星反演的植被生产力指标，以及涡度协方差（eddy covariance, EC）观测数据，探究了极端湿润年份对澳大利亚旱地生产力的遗留效应。研究发现，2010-2011年极端湿润年份的影响不止于创造了破纪录的碳吸收量——该碳吸收量较40年平均值超出1.5个标准差以上——还在次年引发了大范围的正向遗留效应。具体而言，至多……

# 数据来源：《极端湿润年份后旱地生产力持续的生物过程机制》
数据集DOI: [10.5061/dryad.51c59zwnb](https://doi.org/10.5061/dryad.51c59zwnb)

## 数据与文件结构说明
本数据集仓库提供了论文《极端湿润年份后旱地生产力持续的生物过程机制》分析所用的代码。
* **图1与图2**通过**MATLAB**脚本生成。
* **图3**通过**R**脚本生成。
所有代码均已公开，以保障分析的可重复性与透明度。
* MATLAB脚本包含去趋势、归一化、回归以及遗留效应量化的分析流程。
* R脚本包含递归特征消除（recursive feature elimination, RFE）、随机森林建模以及SHAP值分析的分析流程。
本代码已做通用化适配：
* 输入数据需为栅格格式的网格化空间数据，涵盖植被指数与气象学相关数据……