推进卫星模型与再分析农业气象数据的结合,以改善《欧洲水框架指令》下的灌溉管理

IF 5.9 1区 农林科学 Q1 AGRONOMY Agricultural Water Management Pub Date : 2024-07-16 DOI:10.1016/j.agwat.2024.108955
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引用次数: 0

摘要

不久之后,由于人口增长和气候变化等多种因素,缺水问题将进一步恶化。为解决这一问题,《欧洲水框架指令》(WFD)要求提高农业系统的用水效率。在此背景下,该研究旨在提供一种新颖的方法论,其基础是使用基于卫星的分类算法(即人工神经网络和光学梯形模型)、农业水文建模(即基于卫星的 ArcDualKcK)和水文分析、结合不同来源的农业气象数据(即地面数据和 ERA5 陆地数据),绘制灌溉作物图并确定灌区一级的灌溉需水量(IWR)。该研究于 2019-20 年期间在一个名为 "Quota 102,50"(意大利西西里岛东部)的灌区进行,由当地开垦联合体管理。使用 ANN 和 OPTRAM 可以对灌溉作物进行准确检测,2019-20 年期间的总体准确率分别为 82 % 和 88 %。使用 ArcDualKc 模型和标准 FAO-56 方法得出的灌溉水量与农民提供的水量相比普遍偏低。在使用ERA5陆地数据实施ArcDualKc模型时,性能最佳,2019-20年期间的判定系数、残差标准误差和斜率平均值分别为0.99、975.31立方米和0.78。地区尺度上的输出结果与开垦联合体申报的数据相比,在灌溉面积和综合水力资源方面都出现了高估,2019-20 年期间的绝对误差分别约为 1539 公顷和 1431 公顷,约为 9 106 立方米和 12 106 立方米。最后,该研究提供了一个有用的方法框架,以支持水资源管理部门更好地规划和监测现行《水框架指令》下的灌溉用水。
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Advancing in satellite-based models coupled with reanalysis agrometeorological data for improving the irrigation management under the European Water Framework Directive

Soon, water scarcity is expected to worsen due to several factors including the population growth and the climate change. To address this, the European Water Framework Directive (WFD) mandates an increase in the water use efficiency of agrosystems. In this context, the aim of the study was to provide a novel methodological approach, based on the use of satellite-based classification algorithms (i.e., artificial neural networks, ANN, and the Optical Trapezoid Model, OPTRAM), agro-hydrological modelling (i.e., satellite-based ArcDualKc model versus traditional FAO-56 approach) combined with different sources of agrometeorological data (i.e., ground-based versus ERA5 Land data), for mapping the irrigated crops and determining their irrigation water requirements (IWR) at the irrigation district level. The study was carried out, during the period 2019–20, in an irrigation district, named “Quota 102,50” (Eastern Sicily, Italy) and managed by the local reclamation consortium. The use of ANN and of OPTRAM allowed to obtain an accurate detection of the irrigated crops, with overall accuracy of 82 % and 88 %, respectively during 2019–20. The IWR retrieved with the ArcDualKc model and the standard FAO-56 approach were generally underestimated in comparison to the volumes supplied by the farmers. The best performance resulted when the ArcDualKc model was implemented with ERA5 Land data, with average values of coefficient of determination, residual standard error and slope of 0.99, 975.31 m3 and 0.78, respectively, during 2019–20. The outputs at the district scale compared to the data declared by the reclamation consortium resulted in overestimations in terms of both irrigated areas and IWR, with absolute errors of about 1539 ha and 1431 ha, and of about 9 106 m3 and 12 106 m3, respectively, during 2019–20. Finally, the study provided a useful methodological framework for supporting the water management authorities to better planning and monitoring the irrigation water uses under the current WFD.

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来源期刊
Agricultural Water Management
Agricultural Water Management 农林科学-农艺学
CiteScore
12.10
自引率
14.90%
发文量
648
审稿时长
4.9 months
期刊介绍: Agricultural Water Management publishes papers of international significance relating to the science, economics, and policy of agricultural water management. In all cases, manuscripts must address implications and provide insight regarding agricultural water management.
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