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...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
|
| Series: | Journal of Hydrology: Regional Studies |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214581825001648 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849716180846116864 |
|---|---|
| author | Arman Ahmadi Andre Daccache Minxue He Peyman Namadi Alireza Ghaderi Bafti Prabhjot Sandhu Zhaojun Bai Richard L. Snyder Tariq Kadir |
| author_facet | Arman Ahmadi Andre Daccache Minxue He Peyman Namadi Alireza Ghaderi Bafti Prabhjot Sandhu Zhaojun Bai Richard L. Snyder Tariq Kadir |
| author_sort | Arman Ahmadi |
| collection | DOAJ |
| description | 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 weather stations as part of the California Irrigation Management Information System (CIMIS). Study focus: ETO is a critical component of regional water cycles, indicating atmospheric water demand. This study evaluates the potential of deep learning (DL) models for ETO forecasting, particularly emphasizing the efficacy of a global learning scheme compared to traditional local learning. Global learning involves training forecasting models on pooled data from multiple time series, tested over new instances. We compared the performance of statistical models and advanced DL models, demonstrating significant accuracy enhancements in global learning schemes. We also explored automatic hyperparameter optimization for these models to achieve state-of-the-art forecasting accuracy, yielding RMSE values below 10 mm/month for one-year-ahead forecasts on new, unseen stations. New hydrological insight for the region: Applying global learning methodologies to DL models markedly improved forecasting performance, showcasing an ability to generalize findings to ungauged regions and even newly established weather stations. This suggests a promising avenue for enhancing water resource management efficiency in data-scarce areas. Our findings argue that such data-centric methodological shifts could play a critical role in better managing the irrigation demands of the Central Valley, thereby supporting sustainable water usage and agricultural productivity in the region. |
| format | Article |
| id | doaj-art-0bcdec491782455381cac4d3bb48706c |
| institution | DOAJ |
| issn | 2214-5818 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Hydrology: Regional Studies |
| spelling | doaj-art-0bcdec491782455381cac4d3bb48706c2025-08-20T03:13:07ZengElsevierJournal of Hydrology: Regional Studies2214-58182025-06-015910233910.1016/j.ejrh.2025.102339Enhancing the accuracy and generalizability of reference evapotranspiration forecasting in California using deep global learningArman Ahmadi0Andre Daccache1Minxue He2Peyman Namadi3Alireza Ghaderi Bafti4Prabhjot Sandhu5Zhaojun Bai6Richard L. Snyder7Tariq Kadir8Department of Environmental Science, Policy and Management, University of California, Berkeley, Berkeley, CA, USA; Corresponding author.Department of Biological and Agricultural Engineering, University of California, Davis, Davis, CA, USACalifornia Department of Water Resources, Sacramento, CA, USACalifornia Department of Water Resources, Sacramento, CA, USADepartment of Ocean Engineering, University of Rhode Island, Kingston, RI, USACalifornia Department of Water Resources, Sacramento, CA, USADepartment of Computer Science, University of California, Davis, Davis, CA, USADepartment of Land, Air and Water Resources, University of California, Davis, CA 95616, USACalifornia Department of Water Resources, Sacramento, CA, USAStudy 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 weather stations as part of the California Irrigation Management Information System (CIMIS). Study focus: ETO is a critical component of regional water cycles, indicating atmospheric water demand. This study evaluates the potential of deep learning (DL) models for ETO forecasting, particularly emphasizing the efficacy of a global learning scheme compared to traditional local learning. Global learning involves training forecasting models on pooled data from multiple time series, tested over new instances. We compared the performance of statistical models and advanced DL models, demonstrating significant accuracy enhancements in global learning schemes. We also explored automatic hyperparameter optimization for these models to achieve state-of-the-art forecasting accuracy, yielding RMSE values below 10 mm/month for one-year-ahead forecasts on new, unseen stations. New hydrological insight for the region: Applying global learning methodologies to DL models markedly improved forecasting performance, showcasing an ability to generalize findings to ungauged regions and even newly established weather stations. This suggests a promising avenue for enhancing water resource management efficiency in data-scarce areas. Our findings argue that such data-centric methodological shifts could play a critical role in better managing the irrigation demands of the Central Valley, thereby supporting sustainable water usage and agricultural productivity in the region.http://www.sciencedirect.com/science/article/pii/S2214581825001648Time series forecastingGlobal learningDeep learningReference evapotranspirationWater management |
| spellingShingle | Arman Ahmadi Andre Daccache Minxue He Peyman Namadi Alireza Ghaderi Bafti Prabhjot Sandhu Zhaojun Bai Richard L. Snyder Tariq Kadir Enhancing the accuracy and generalizability of reference evapotranspiration forecasting in California using deep global learning Journal of Hydrology: Regional Studies Time series forecasting Global learning Deep learning Reference evapotranspiration Water management |
| title | Enhancing the accuracy and generalizability of reference evapotranspiration forecasting in California using deep global learning |
| title_full | Enhancing the accuracy and generalizability of reference evapotranspiration forecasting in California using deep global learning |
| title_fullStr | Enhancing the accuracy and generalizability of reference evapotranspiration forecasting in California using deep global learning |
| title_full_unstemmed | Enhancing the accuracy and generalizability of reference evapotranspiration forecasting in California using deep global learning |
| title_short | Enhancing the accuracy and generalizability of reference evapotranspiration forecasting in California using deep global learning |
| title_sort | enhancing the accuracy and generalizability of reference evapotranspiration forecasting in california using deep global learning |
| topic | Time series forecasting Global learning Deep learning Reference evapotranspiration Water management |
| url | http://www.sciencedirect.com/science/article/pii/S2214581825001648 |
| work_keys_str_mv | AT armanahmadi enhancingtheaccuracyandgeneralizabilityofreferenceevapotranspirationforecastingincaliforniausingdeepgloballearning AT andredaccache enhancingtheaccuracyandgeneralizabilityofreferenceevapotranspirationforecastingincaliforniausingdeepgloballearning AT minxuehe enhancingtheaccuracyandgeneralizabilityofreferenceevapotranspirationforecastingincaliforniausingdeepgloballearning AT peymannamadi enhancingtheaccuracyandgeneralizabilityofreferenceevapotranspirationforecastingincaliforniausingdeepgloballearning AT alirezaghaderibafti enhancingtheaccuracyandgeneralizabilityofreferenceevapotranspirationforecastingincaliforniausingdeepgloballearning AT prabhjotsandhu enhancingtheaccuracyandgeneralizabilityofreferenceevapotranspirationforecastingincaliforniausingdeepgloballearning AT zhaojunbai enhancingtheaccuracyandgeneralizabilityofreferenceevapotranspirationforecastingincaliforniausingdeepgloballearning AT richardlsnyder enhancingtheaccuracyandgeneralizabilityofreferenceevapotranspirationforecastingincaliforniausingdeepgloballearning AT tariqkadir enhancingtheaccuracyandgeneralizabilityofreferenceevapotranspirationforecastingincaliforniausingdeepgloballearning |