气候变化领域集成服务门户(CCPortal): Future trends of reference evapotranspiration in Sicily based on CORDEX data and Machine Learning algorithms

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DOI	10.1016/j.agwat.2023.108232
	Future trends of reference evapotranspiration in Sicily based on CORDEX data and Machine Learning algorithms
	Di Nunno, Fabio; Granata, Francesco
发表日期	2023
ISSN	0378-3774
EISSN	1873-2283
卷号	280
英文摘要	In years of increasing impact of climate change effects, a reliable characterization of the spatiotemporal evolutionary dynamics of evapotranspiration can enable a significant improvement in water resource management, especially as regards irrigation activities. Sicily, an insular region of Southern Italy, has exceptionally valuable agricultural production and high irrigation needs. In this study, the ETo reference evapotranspiration in Sicily was first evaluated on the basis of historical and future climate parameters, referring for future values to two climate scenarios characterized by different Representative Concentration Pathways: RCP 4.5 and RCP 8.5. Then, the Hierarchical algorithm was used to divide Sicily into three homogeneous regions, each characterized by specific ETo features. In addition, some Machine Learning (ML) algorithms were used to develop forecasting models based on only historical data. Support Vector Regression (SVR) was used to predict the future values of Tmin and Tmax, while an ensemble model based on Multilayer Perceptron (MLP) and M5P Regression Tree was developed for the ETo forecasting. Predictions made with the ensemble MLP-M5P model were compared with the ETo computed for the RCP 4.5 and RCP 8.5 future climate scenarios. During the forecast period, from 2001 to 2091, evapotranspiration increases were observed for all three clusters. For cluster C1, along the coast, percentage increases of 7.52%, 14.64% and 10.78%, were computed for RCP 4.5, RCP 8.5, and MLP-M5P, respectively, while, for cluster C3, in the inland, percentage increases were higher and equal to 8.12%, 16.71%, and 14.98%, respectively. The ensemble MLP-M5P model led to intermediate trends between RCP 4.5 and RCP 8.5, showing a high correlation with the latter (R2 between 0.93 and 0.98). The developed approach, based on both clustering and forecasting algorithms, provided a comprehensive analysis of the reference evapotranspiration, with the detection of the different homogeneous regions and, at the same time, the evaluation of the evapotranspiration trends, both in coastal and inland areas.
英文关键词	Climate change; Artificial Intelligence algorithms; Clustering; Ensemble models; Mediterranean climate; Irrigation planning
语种	英语
WOS研究方向	Agronomy ; Water Resources
WOS类目	Science Citation Index Expanded (SCI-EXPANDED)
WOS记录号	WOS:000948495700001
来源期刊	AGRICULTURAL WATER MANAGEMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/281167
作者单位	University of Cassino
推荐引用方式 GB/T 7714	Di Nunno, Fabio,Granata, Francesco. Future trends of reference evapotranspiration in Sicily based on CORDEX data and Machine Learning algorithms[J],2023,280.
APA	Di Nunno, Fabio,&Granata, Francesco.(2023).Future trends of reference evapotranspiration in Sicily based on CORDEX data and Machine Learning algorithms.AGRICULTURAL WATER MANAGEMENT,280.
MLA	Di Nunno, Fabio,et al."Future trends of reference evapotranspiration in Sicily based on CORDEX data and Machine Learning algorithms".AGRICULTURAL WATER MANAGEMENT 280(2023).