Evaluating the accuracy of nine canopy resistance models in estimating winter wheat evapotranspiration using the Penman-Monteith equation.

IF 4.1 2区 生物学 Q1 PLANT SCIENCES Frontiers in Plant Science Pub Date : 2024-11-07 eCollection Date: 2024-01-01 DOI:10.3389/fpls.2024.1470409
Yingnan Wu, Qiaozhen Li, Xiuli Zhong, Xiaoying Liu
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Abstract

Accurate estimation of farmland evapotranspiration (ET) is crucial for agricultural production. The accuracy of the widely used Penman-Monteith (PM) equation for estimating crop ET depends on the quality of input data and their ability to accurately model the canopy resistance (r c). In this study, we evaluated the PM equation in estimating winter wheat ET using nine r c models, with both original and recalibrated parameters, including the Farias (FA), Monteith (MT), Garcίa-Santos (GA), Idso (IS), Jarvis (JA), Katerji-Perrier (KP), Stannard (ST), Todorovic (TD), and Coupled surface resistance (CO) models. We used long-term measurements (2018 to 2023) from the Bowen ratio energy balance method at both daily and seasonal scales. Parameterization was performed using data from the 2020-2021 growing season, while the remaining 4 years were used for verification. The results showed that the FA, KP, and ST models performed better in estimating daily ET with original parameters, achieving a root mean square error (RMSE) of 1.07-1.16 mm d-1 and a mean bias error (MBE) of -0.59-0.02 mm d-1. After parameterization, the performance of acceptable r c models based on RMSE (ranging from 1.07 to 1.22 mm d-1, averaged 1.16 mm d-1) ranked as follows on the daily scale: FA > CO > KP > ST > IS > GA > JA > MT. The r c models were more accurate in simulating ET on a seasonal scale than on the daily scale. Before calibration, the acceptable FA, KP, and MT models overestimated seasonal ET with the MBE ranging from 2.83 to 75.32 mm and RMSE from 29.79 to 82.38 mm. After correction, the suitable r c models based on RMSE values decreased by FA > CO > KP > IS > ST > GA > JA on the seasonal scale, which ranged from 29.79 to 76.35 mm. The performance of the revised r c models improved on both daily and seasonal scales, with RMSE reductions of 29.03% and 68.18%, respectively. Considering both the accuracy and calculation complexity, the FA and KP models were recommended to be used in the PM equation to estimate daily and seasonal ET in semiarid regions. The CO, GA, ST, IS, and JA models can also be used as alternatives, depending on the availability of meteorological parameters.

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利用彭曼-蒙蒂斯方程评估九种冠层阻力模型在估算冬小麦蒸散量方面的准确性。
准确估算农田蒸散(ET)对农业生产至关重要。广泛使用的彭曼-蒙蒂斯(Penman-Monteith,PM)方程估算作物蒸散发的准确性取决于输入数据的质量及其准确模拟冠层阻力(r c)的能力。在本研究中,我们使用九种 r c 模型(包括原始参数和重新校准参数)评估了彭曼-蒙蒂思方程在估算冬小麦蒸散发方面的效果,这些模型包括法里亚斯(Farias,FA)、蒙蒂思(Monteith,MT)、加西亚-桑托斯(Garcίa-Santos,GA)、伊德索(Idso,IS)、贾维斯(Jarvis,JA)、卡特吉-珀里尔(Katerji-Perrier,KP)、斯坦纳德(Stannard,ST)、托多洛维奇(Todorovic,TD)和耦合表面电阻(Coupled surface resistance,CO)模型。我们使用了鲍文比能量平衡法在日尺度和季节尺度上的长期测量结果(2018 年至 2023 年)。利用 2020-2021 年生长季的数据进行了参数化,其余 4 年的数据则用于验证。结果表明,FA、KP 和 ST 模型在使用原始参数估算日蒸散发时表现较好,均方根误差(RMSE)为 1.07-1.16 mm d-1,平均偏差误差(MBE)为 -0.59-0.02 mm d-1。参数化后,根据均方根误差(从 1.07 到 1.22 毫米/天-1,平均为 1.16 毫米/天-1),可接受的 r c 模型的性能在日尺度上排序如下fa > co > kp > st > is > ga > ja > mt。r c 模型模拟季节尺度蒸散发比模拟日尺度蒸散发更准确。校正前,可接受的 FA、KP 和 MT 模型高估了季节性蒸散发,其 MBE 为 2.83 至 75.32 毫米,RMSE 为 29.79 至 82.38 毫米。修正后,基于 RMSE 值的合适 r c 模型在季节尺度上依次为 FA > CO > KP > IS > ST > GA > JA,其 RMSE 值范围为 29.79 至 76.35 毫米。修订后的 r c 模型在日尺度和季节尺度上的性能都有所提高,有效值分别降低了 29.03% 和 68.18%。考虑到精度和计算复杂性,建议在 PM 方程中使用 FA 和 KP 模型估算半干旱地区的日和季节蒸散发。根据气象参数的可用性,CO、GA、ST、IS 和 JA 模型也可作为替代方案。
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来源期刊
Frontiers in Plant Science
Frontiers in Plant Science PLANT SCIENCES-
CiteScore
7.30
自引率
14.30%
发文量
4844
审稿时长
14 weeks
期刊介绍: In an ever changing world, plant science is of the utmost importance for securing the future well-being of humankind. Plants provide oxygen, food, feed, fibers, and building materials. In addition, they are a diverse source of industrial and pharmaceutical chemicals. Plants are centrally important to the health of ecosystems, and their understanding is critical for learning how to manage and maintain a sustainable biosphere. Plant science is extremely interdisciplinary, reaching from agricultural science to paleobotany, and molecular physiology to ecology. It uses the latest developments in computer science, optics, molecular biology and genomics to address challenges in model systems, agricultural crops, and ecosystems. Plant science research inquires into the form, function, development, diversity, reproduction, evolution and uses of both higher and lower plants and their interactions with other organisms throughout the biosphere. Frontiers in Plant Science welcomes outstanding contributions in any field of plant science from basic to applied research, from organismal to molecular studies, from single plant analysis to studies of populations and whole ecosystems, and from molecular to biophysical to computational approaches. Frontiers in Plant Science publishes articles on the most outstanding discoveries across a wide research spectrum of Plant Science. The mission of Frontiers in Plant Science is to bring all relevant Plant Science areas together on a single platform.
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