Enhancing the accuracy and generalizability of reference evapotranspiration forecasting in California using deep global learning

Enhancing the accuracy and generalizability of reference evapotranspiration forecasting in California using deep global learning

Study region: This research focuses on the Central Valley of California, a climatically homogeneous region known for its significant agricultural productivity and reliance on extensive irrigation. Our study utilizes monthly reference evapotranspiration (ETO) time series data from 55 standardized wea...

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Bibliographic Details
Main Authors: Arman Ahmadi, Andre Daccache, Minxue He, Peyman Namadi, Alireza Ghaderi Bafti, Prabhjot Sandhu, Zhaojun Bai, Richard L. Snyder, Tariq Kadir
Format: Article
Language:English
Published: Elsevier 2025-06-01
Series:Journal of Hydrology: Regional Studies
Subjects:
Time series forecasting
Global learning
Deep learning
Reference evapotranspiration
Water management
Online Access:http://www.sciencedirect.com/science/article/pii/S2214581825001648
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