Exploring the combined effects of drought and drought-flood abrupt alternation on vegetation using interpretable machine learning model and r-vine copula function

IF 5.6 1区农林科学 Q1 AGRONOMY

Agricultural and Forest Meteorology Pub Date : 2025-05-08 DOI:10.1016/j.agrformet.2025.110568

Lulu Xie , Yi Li , Ziya Zhang , Kadambot H.M. Siddique , Xiaoyan Song

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引用次数: 0

Abstract

Global warming has significantly increased the frequency and intensity of extreme events, such as drought and drought-flood abrupt alternation (DFAA). Vegetation, a crucial component of terrestrial ecosystems, contributes greatly to agricultural production and economic development. Assessing the impacts of droughts and DFAA on vegetation is essential for ecological environment protection, food security, and economic development. This study combines the random forest model with Shapley Additive Explanation (SHAP) values to create an interpretable machine learning model, which is then coupled with the R-Vine Copula function to explore the combined effects of drought (using nonstationary drought indexes) and DFAA (using the SDFAI: short-cycle drought flood abrupt alteration index) on vegetation in the China-Pakistan Economic Corridor (CPEC) from 1981 to 2019. The key findings are as follows: (1) Drought events intensified, the risk of flood-to-drought decreased, the risk of drought-to-flood increased, and net primary productivity (NPP) showed an upward trend; (2) The relative contributions to NPP were NSPEI (21.0 %), SDFAI (31.4 %), and SSMI (47.6 %); (3) A strong upper tail dependence occurred between SSMI and NPP, and a strong lower tail dependence occurred between SDFAI and SSMI. When SSMI acted as an intermediary variable, the indirect correlation between SDFAI and NPP was strong; (4) In flood-to-drought events, the proportions of SHAP<0 and SHAP>0 were 24 % and 76 %, respectively, indicating an antagonistic role of flood-to-drought in promoting vegetation growth in the CPEC. In drought-to-flood events, the corresponding proportions were 73 % and 27 %, respectively, indicating a synergistic effect of drought-to-flood in inhibiting vegetation growth. This study enhances the understanding of the mechanisms by which DFAA impacts vegetation and provides a novel approach for exploring the combined effects of multiple extreme events on vegetation.

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利用可解释机器学习模型和r-vine copula函数探讨干旱和旱涝突变对植被的综合影响

全球变暖显著增加了干旱、旱涝突变等极端事件发生的频率和强度。植被是陆地生态系统的重要组成部分，对农业生产和经济发展具有重要作用。评估干旱和干旱对植被的影响对生态环境保护、粮食安全和经济发展具有重要意义。本研究将随机森林模型与Shapley加性解释（SHAP）值相结合，建立了一个可解释的机器学习模型，然后结合R-Vine Copula函数，探讨了1981 - 2019年干旱（使用非平稳干旱指数）和DFAA（使用SDFAI：短周期旱涝突变指数）对中巴经济走廊（CPEC）植被的联合影响。结果表明：(1)干旱事件加剧，水旱风险降低，旱涝风险增加，净初级生产力（NPP）呈上升趋势；(2) NSPEI（21.0%）、SDFAI（31.4%）和SSMI（47.6%）对NPP的相对贡献最大；(3) SSMI与NPP之间存在较强的上尾依赖性，SDFAI与SSMI之间存在较强的下尾依赖性。当SSMI作为中介变量时，SDFAI与NPP之间的间接相关较强；(4)在水旱事件中，SHAP>；0和SHAP>；0的比例分别为24%和76%，表明水旱对中巴经济走廊植被生长具有拮抗作用。在旱涝事件中，相应的比例分别为73%和27%，表明旱涝在抑制植被生长方面具有协同效应。该研究增强了对DFAA影响植被机制的认识，为探索多种极端事件对植被的综合影响提供了新的途径。

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来源期刊

Agricultural and Forest Meteorology 农林科学-林学

CiteScore

10.30

自引率

9.70%

发文量

415

审稿时长

69 days

期刊介绍： Agricultural and Forest Meteorology is an international journal for the publication of original articles and reviews on the inter-relationship between meteorology, agriculture, forestry, and natural ecosystems. Emphasis is on basic and applied scientific research relevant to practical problems in the field of plant and soil sciences, ecology and biogeochemistry as affected by weather as well as climate variability and change. Theoretical models should be tested against experimental data. Articles must appeal to an international audience. Special issues devoted to single topics are also published. Typical topics include canopy micrometeorology (e.g. canopy radiation transfer, turbulence near the ground, evapotranspiration, energy balance, fluxes of trace gases), micrometeorological instrumentation (e.g., sensors for trace gases, flux measurement instruments, radiation measurement techniques), aerobiology (e.g. the dispersion of pollen, spores, insects and pesticides), biometeorology (e.g. the effect of weather and climate on plant distribution, crop yield, water-use efficiency, and plant phenology), forest-fire/weather interactions, and feedbacks from vegetation to weather and the climate system.