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黄土高原植被-土壤-水文相互作用估算中植被和土壤水分数据同化协调植物水分胁迫响应

IF 5.6 1区农林科学 Q1 AGRONOMY

Agricultural and Forest Meteorology Pub Date : 2025-04-30 DOI:10.1016/j.agrformet.2025.110581

Zunyun Shu , Baoqing Zhang , Liuyang Yu , Xining Zhao

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引用次数: 0

摘要

在地球的生态系统中，植被、土壤和水文过程相互作用。叶面积指数（LAI）与蒸散发（ET）的耦合是控制水分、碳和能量循环的关键过程。然而，由于植物水分胁迫（β）引起的不确定性，目前的陆地表面模型（LSMs）不能充分再现LAI (LAI，ET)，降低了模型对植被变化的生态水文响应的可信度。在此基础上，利用多参数化选项（Noah- mp）跨3个β函数的LSM评价了Noah中cor（LAI，ET）的表现，并探讨了同化LAI和土壤水分（SM）对中国黄土高原地区cor（LAI，ET）的增强作用。我们发现，在不同的β函数下，LAI的实质性变化会轻微影响相应的ET建模，与基于观测的估计值相比，模型cor（LAI，ET）显示出大约10% - 35%的偏差。同化LAI通常可以减少β，并且在三个β函数中平均减少21%的cor（LAI，ET）偏差。这主要是因为从LAI观测中获得的收益反映了真实的植被生长和水分吸收状态，调和了由于β减少而产生的负面影响。然而，SM同化导致三个β函数的cor（LAI，ET）偏差减少12%，增加3%和增加9%，这可能归因于β诱导的碳循环不确定性和插值SM卫星观测误差的增加。多变量同化综合了LAI和SM观测值，最大程度地降低了cor（LAI，ET）偏差，并显著增加了植被蒸腾。我们的研究结果强调数据同化是一种有效的方法来改善这些相互作用的表现。

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Reconciling plant water stress response using vegetation and soil moisture data assimilation for vegetation-soil-hydrology interaction estimation over the Chinese Loess Plateau

Vegetation, soil, and hydrological processes interact with each other in the Earth's ecosystem. The coupling of leaf area index (LAI) and evapotranspiration (ET),

c o r (L A I, E T)

, is a critical process for controlling water, carbon, and energy cycles during these interactions. However, current land surface models (LSMs) inadequately reproduce

c o r (L A I, E T)

owing to plant water stress (

β

)-induced uncertainty, degrading the credibility of modeled ecohydrological responses to vegetation change. Here, we evaluate the performance of

c o r (L A I, E T)

in the Noah with multiparameterization options (Noah-MP) LSM across three

β

functions, and investigate the role of assimilating LAI and soil moisture (SM) in enhancing

c o r (L A I, E T)

over the Chinese Loess Plateau. We find that substantial variations in LAI under different β functions slightly affect corresponding ET modeling, and the modeled

c o r (L A I, E T)

exhibits an approximate 10 %‒35 % bias compared with observation-based estimates. Assimilating LAI generally provides a reduced

β

and 21 % mean reduction in

c o r (L A I, E T)

bias across the three

β

functions. This is mainly because the benefits gained from LAI observations reflect realistic vegetation growth and water uptake states, reconciling the negative effects owing to reduced

β

. However, SM assimilation yields a 12 % reduction, 3 % increase, and 9 % increase in

c o r (L A I, E T)

bias across the three

β

functions, likely attributable to

β

-induced carbon cycle uncertainties and the increased errors in interpolated SM satellite observations. Multivariate assimilation integrates LAI and SM observations and provides the largest reduction in

c o r (L A I, E T)

bias, and considerable increases in vegetation transpiration. Our findings highlight data assimilation as a powerful method to improve the representation of these interactions.

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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.