Sensitivity of the agro-hydrological model CRITERIA-1D to the Leaf Area Index parameter

Tamara Ricchi, V. Alagna, G. Villani, F. Tomei, A. Toscano, G. Baroni
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Abstract

Water availability is strongly variable in space and time, also due to the climate change. Agriculture is a sector specially affected by the water scarcity problem considering that is one of the main users. Irrigation scheduling simulation models play an important role in this context by estimating plant water requirements and supporting best water management practices. Representative model parameters and input data are however fundamental to achieve good model performances. The objective of this work was to assess the sensitivity of the agro-hydrological model CRITERIA-1D to the leaf area index (LAI) parameter, commonly used to characterize the plant status and to represent its developing stages. The model has been set up using, on the one hand, literature LAIMAX and LAIMIN values and, on the other hand, ground measured values, obtained by means of a ceptometer. Results show significant differences between the irrigation water requirements estimated between the two scenarios. For this reason, the study underlines the need to adopt accurate crop parameters and to integrate real-time crop measurements for the estimation of the irrigation water requirement. Smaller differences are quantified, however, when looking at the deep percolation estimated by the model highlighting the importance of considering multiple outputs for a comprehensive assessment of the model.
农业水文模型criterion - 1d对叶面积指数参数的敏感性
由于气候变化,水的可用性在空间和时间上变化很大。农业是受缺水问题影响特别大的部门,因为它是用水的主要使用者之一。灌溉调度模拟模型通过估算植物需水量和支持最佳水管理实践在这方面发挥重要作用。然而,具有代表性的模型参数和输入数据是实现良好模型性能的基础。本研究的目的是评估农业水文模型CRITERIA-1D对叶面积指数(LAI)参数的敏感性,叶面积指数通常用于表征植物状态和代表其发育阶段。该模型的建立,一方面使用文献LAIMAX和LAIMIN值,另一方面使用传感器获得的地面测量值。结果表明,两种情景下估算的灌溉需水量存在显著差异。因此,该研究强调需要采用准确的作物参数,并结合实时作物测量来估计灌溉需水量。然而,当观察模型估计的深层渗透时,较小的差异被量化,这突出了考虑多种输出对模型进行综合评估的重要性。
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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