MONTPEL:多成分彭曼-蒙蒂斯能量平衡模型

IF 5.6 1区 农林科学 Q1 AGRONOMY
Rami Albasha , Loïc Manceau , Heidi Webber , Michaël Chelle , Bruce Kimball , Pierre Martre
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引用次数: 0

摘要

在作物模型中,机理建模正逐渐取代经验主义,将重点放在叶片层面的生理过程上。这种转变要求模拟冠层下亚日尺度的作物表面温度,但许多作物模型仍依赖经验公式来估算冠层温度,通常以日为单位。我们开发了一个多成分彭曼-蒙蒂斯模型(MONTPEL),该模型可通过灵活的冠层表示("大叶 "与 "分层"、"结块 "与 "阳光-阴影")模拟作物能量平衡,并考虑大气稳定条件。我们利用在不同的气候和水胁迫条件下进行的四次小麦(Triticum aestivum L.)试验的测量结果,分析了模型的行为、灵敏度和准确性。测量数据包括每小时的能量平衡项(总净辐射、土壤热通量、显能通量和潜能通量)、冠层表面或冠层内不同深度叶片的每小时温度,以及不同日期太阳正午前后的日照和阴影叶片温度。MONTPEL再现了测量的能量平衡项,均方根误差(RMSE)在21到87 Wm-2之间,判定系数(R²)超过0.65。该模型模拟冠层温度的精度(RMSE ≤ 2.2 °C,R² ≥ 0.92)与测量尺度保持一致。根据大气稳定性调整空气动力阻力可将模拟冠层温度误差降至最低,特别是在半干旱条件下。作物潜能通量和温度对最大气孔导度(gs,max)参数最为敏感。然而,在所有模拟实验中使用单一的 gs,max 值可获得令人满意的结果,这表明 gs,max 对时间和地点间变化的敏感性较弱。与 "结块 "冠层表示结果相比,区分日照和遮荫冠层部分系统性地导致了较低的潜能通量。分析发现了多组分方法的局限性,特别是当土壤表面温度发生变化时,各叶片层的温度变化不一致。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MONTPEL: A multi-component Penman-Monteith energy balance model

Mechanistic modelling is gradually replacing empiricism in crop models, focusing on leaf-level physiological processes. This shift necessitates simulating crop surface temperature at infra-canopy sub-daily scales but many crop models still rely on empirical formulations for canopy temperature estimation, typically on a daily basis. We developed MONTPEL, a multi-component Penman-Monteith model that allows simulating the crop energy balance with flexible canopy representations (“BigLeaf” vs. “Layered”, “Lumped” vs. “Sunlit-Shaded”) and accounts for atmospheric stability conditions. We analyzed the model behavior, sensitivity and accuracy, using measurements from four wheat (Triticum aestivum L.) experiments conducted under varying pedoclimatic and water stress conditions. Measurements included hourly energy balance terms (total net radiation, soil heat flux, sensible and latent energy fluxes), hourly temperature of the canopy surface or of leaves at different depths inside the canopy, and sunlit and shaded leaf temperatures around solar noon at different dates. MONTPEL reproduced the measured energy balance terms with a root mean square error (RMSE) between 21 and 87 Wm-2 and a coefficient of determination (R²) exceeding 0.65. The model's accuracy in simulating canopy temperature, with RMSE ≤ 2.2 °C and R² ≥ 0.92, remained consistent regardless of measurement scale. Adjusting the aerodynamic resistance for atmospheric stability minimized simulated canopy temperature errors, notably in semi-arid conditions. Crop latent energy flux and temperature were most sensitive to the maximal stomatal conductance (gs,max) parameter. However, using a single gs,max value across the simulated experiments yielded satisfactory results, suggesting a weak sensitivity to the temporal and site-to-site variability of gs,max. Distinguishing sunlit from shaded canopy fractions systematically resulted in lower latent energy fluxes compared to “Lumped” canopy representation results. Analysis identified limitations in the multi-component approach, particularly an unrealistic uniform temperature shift across leaf layers when soil surface temperature changes.

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来源期刊
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.
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