Physically-constrained evapotranspiration models with machine learning parameterization outperform pure machine learning: Critical role of domain knowledge. [PDF]
Kim Y +3 more
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Maximizing multi-source data integration and minimizing the parameters for greenhouse tomato crop water requirement prediction. [PDF]
Lv X +6 more
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Comparison of the performances of six empirical mass transfer-based reference evapotranspiration estimation models in semi-arid conditions. [PDF]
Usta S.
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CMIP6-based global estimates of future aridity index and potential evapotranspiration for 2021-2060. [PDF]
Zomer RJ, Xu J, Spano D, Trabucco A.
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Research on methods for estimating reference crop evapotranspiration under incomplete meteorological indicators. [PDF]
Sun X +9 more
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Assessment of Deep Water-Saving Practice Effects on Crop Coefficients and Water Consumption Processes in Cultivated Land-Wasteland-Lake Systems of the Hetao Irrigation District. [PDF]
Li J +7 more
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Performance evaluation of different empirical models for reference evapotranspiration estimation over Udhagamandalm, The Nilgiris, India. [PDF]
Raja P +12 more
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Parameterization of Four Models to Estimate Crop Evapotranspiration in a Solar Greenhouse. [PDF]
Gao S, Li Y, Gong X, Li Y.
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Monitoring changes in the landscape water balance: validation of satellite- and model-based evapotranspiration data in Lusatia, Germany. [PDF]
Kröcher J +9 more
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