Advancing in satellite-based models coupled with reanalysis agrometeorological data for improving the irrigation management under the European Water Framework Directive

Giuseppe Longo-Minnolo, D’Emilio, Alessandro, Daniela Vanella and Simona Consoli

Agricultural Water Management, 2024, vol. 301, issue C

Abstract: 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.

Keywords: Artificial neural networks; OPTRAM; ArcDualKc; ERA5 Land; Irrigated areas; Irrigation water requirements (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:agiwat:v:301:y:2024:i:c:s0378377424002907

DOI: 10.1016/j.agwat.2024.108955

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