Bias-correcting input variables enhances forecasting of reference crop evapotranspiration
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Bias-correcting input variables enhances forecasting of reference crop evapotranspiration ; volume:25 ; number:9 ; year:2021 ; pages:4773-4788 ; extent:16
Hydrology and earth system sciences ; 25, Heft 9 (2021), 4773-4788 (gesamt 16)
- Creator
- DOI
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10.5194/hess-25-4773-2021
- URN
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urn:nbn:de:101:1-2021090905294518450879
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:28 AM CEST
Data provider
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Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Yang, Qichun
- Wang, Quan J.
- Hakala, Kirsti
- Tang, Yating
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Correcting Thornthwaite potential evapotranspiration using a global grid of local coefficients to support temperature-based estimations of reference evapotranspiration and aridity indices
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Bias-correcting individual inputs prior to combined calibration leads to more skillful forecasts of reference crop evapotranspiration
Reconstructing climate trends adds skills to seasonal reference crop evapotranspiration forecasting
Machine learning techniques in estimation of eggplant crop evapotranspiration
Estimation of irrigated crop artificial irrigation evapotranspiration in China
Reference crop evapotranspiration database in Spain (1961–2014)
Input-Output Methods in Forecasting
Correcting Thornthwaite potential evapotranspiration using a global grid of local coefficients to support temperature-based estimations of reference evapotranspiration and aridity indices
Calibration of mass transfer-based models to predict reference crop evapotranspiration
Mulching effects on soil evaporation, crop evapotranspiration and crop coefficients: a review aimed at improved irrigation management
Lettuce evapotranspiration and crop coefficients using eddy covariance and remote sensing observations
Classifying methods to quantify crop nitrogen input requirements
Economic impact of higher crop and input prices on AFPC's representative crop farms
Bias-correcting individual inputs prior to combined calibration leads to more skillful forecasts of reference crop evapotranspiration
Reconstructing climate trends adds skills to seasonal reference crop evapotranspiration forecasting
Machine learning techniques in estimation of eggplant crop evapotranspiration
Estimation of irrigated crop artificial irrigation evapotranspiration in China
Reference crop evapotranspiration database in Spain (1961–2014)
Input-Output Methods in Forecasting
Correcting Thornthwaite potential evapotranspiration using a global grid of local coefficients to support temperature-based estimations of reference evapotranspiration and aridity indices
Calibration of mass transfer-based models to predict reference crop evapotranspiration
Mulching effects on soil evaporation, crop evapotranspiration and crop coefficients: a review aimed at improved irrigation management
Lettuce evapotranspiration and crop coefficients using eddy covariance and remote sensing observations
Classifying methods to quantify crop nitrogen input requirements