Decadal variations in the driving factors of increasing water-use efficiency in China's terrestrial ecosystems from 2000 to 2022

IF 5.8 2区环境科学与生态学 Q1 ECOLOGY

Ecological Informatics Pub Date : 2024-11-13 DOI:10.1016/j.ecoinf.2024.102895

Zhongen Niu , Honglin He , Ying Zhao , Bin Wang , Lili Feng , Yan Lv , Mengyu Zhang , Jiayi Fan , Zhihao Li

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引用次数: 0

Abstract

Ecosystem water-use efficiency (WUE) is a crucial indicator for evaluating carbon and water cycles. Although greening and climate change have significantly altered the WUE in Chinese terrestrial ecosystems, the roles of physiological and ecological processes are not fully understood. To address this, WUE is broken down into two key ratios: gross primary productivity to transpiration (GPP/T), which mainly reflects the effect of plant physiological processes, and transpiration to evapotranspiration (T/ET), which primarily indicates the impact of vegetation changes. Both ratios are influenced by climate change. This study employed a newly developed satellite-based ecosystem service process model Carbon and Exchange between Vegetation, Soil, and Atmosphere-ecosystem service (CEVSA-ES) to examine the impact of GPP/T and T/ET on WUE in China's terrestrial ecosystems from 2000 to 2022, alongside an analysis of the environmental variables affecting these ratios. This study revealed a general increase in WUE during the study period with significant interdecadal differences. Between 2000 and 2010, WUE was relatively stable (slope = 0.0023 g C kg⁻¹ H₂O a⁻¹, p > 0.05), primarily because the decrease in GPP/T (p < 0.05) offset the increase in T/ET (p < 0.01). In contrast, from 2010 to 2022, a notable increase in WUE was observed (slope = 0.0145 g C kg⁻¹ H₂O a⁻¹, p < 0.01), driven primarily by an increase in GPP/T (p < 0.01), whereas T/ET remained relatively unchanged (p > 0.05). Factors affecting GPP/T and T/ET showed considerable variability. Precipitation had the main influence on GPP/T, accounting for 70 % of its variation. The initial decade of the 21st century experienced an overall precipitation deficiency, followed by a sustained surplus in the subsequent years, resulting in interdecadal fluctuations in GPP/T. In contrast, T/ET was affected by a combination of factors, including the leaf area index, temperature, and precipitation, contributing 39 %, 29 %, and 32 %, respectively. The present study advances our understanding of the interaction of terrestrial ecosystem with the atmosphere amid global changes, offering crucial insights for forecasting the future dynamics of carbon and water cycles.

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2000 至 2022 年中国陆地生态系统提高用水效率驱动因素的十年变化

生态系统水分利用效率（WUE）是评估碳和水循环的重要指标。尽管绿化和气候变化已显著改变了中国陆地生态系统的水分利用效率，但生理和生态过程的作用尚未完全明了。针对这一问题，WUE 被分解为两个关键比率：主要反映植物生理过程影响的总初级生产力与蒸腾（GPP/T）比率，以及主要反映植被变化影响的蒸腾与蒸发（T/ET）比率。这两个比率都受到气候变化的影响。本研究采用新开发的基于卫星的生态系统服务过程模型 "碳与植被、土壤和大气交换-生态系统服务（CEVSA-ES）"，研究了 2000 年至 2022 年中国陆地生态系统 GPP/T 和 T/ET 对 WUE 的影响，并分析了影响这些比率的环境变量。研究结果表明，在研究期间，WUE 总体呈上升趋势，但年代间差异显著。2000 年至 2010 年期间，WUE 相对稳定（斜率 = 0.0023 g C kg-1 H2O a-1，p > 0.05），这主要是因为 GPP/T 的减少（p < 0.05）抵消了 T/ET 的增加（p < 0.01）。相反，从 2010 年到 2022 年，观察到 WUE 显著增加（斜率 = 0.0145 g C kg-1 H2O a-1，p < 0.01），主要是由于 GPP/T 的增加（p < 0.01），而 T/ET 保持相对不变（p > 0.05）。影响 GPP/T 和 T/ET 的因素变化很大。降水对 GPP/T 的影响最大，占其变化的 70%。21 世纪最初十年降水量总体不足，随后几年降水量持续过剩，导致 GPP/T 出现年代际波动。相比之下，叶面积指数、温度和降水等综合因素对 T/ET 的影响分别为 39%、29% 和 32%。本研究加深了我们对全球变化中陆地生态系统与大气相互作用的理解，为预测碳循环和水循环的未来动态提供了重要启示。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Ecological Informatics 环境科学-生态学

CiteScore

8.30

自引率

11.80%

发文量

346

审稿时长

46 days

期刊介绍： The journal Ecological Informatics is devoted to the publication of high quality, peer-reviewed articles on all aspects of computational ecology, data science and biogeography. The scope of the journal takes into account the data-intensive nature of ecology, the growing capacity of information technology to access, harness and leverage complex data as well as the critical need for informing sustainable management in view of global environmental and climate change. The nature of the journal is interdisciplinary at the crossover between ecology and informatics. It focuses on novel concepts and techniques for image- and genome-based monitoring and interpretation, sensor- and multimedia-based data acquisition, internet-based data archiving and sharing, data assimilation, modelling and prediction of ecological data.