Articles | Volume 26, issue 20
https://doi.org/10.5194/hess-26-5373-2022
https://doi.org/10.5194/hess-26-5373-2022
Research article
 | 
27 Oct 2022
Research article |  | 27 Oct 2022

FarmCan: a physical, statistical, and machine learning model to forecast crop water deficit for farms

Sara Sadri, James S. Famiglietti, Ming Pan, Hylke E. Beck, Aaron Berg, and Eric F. Wood

Viewed

Total article views: 4,217 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,110 999 108 4,217 42 40
  • HTML: 3,110
  • PDF: 999
  • XML: 108
  • Total: 4,217
  • BibTeX: 42
  • EndNote: 40
Views and downloads (calculated since 31 Mar 2022)
Cumulative views and downloads (calculated since 31 Mar 2022)

Viewed (geographical distribution)

Total article views: 4,217 (including HTML, PDF, and XML) Thereof 4,057 with geography defined and 160 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 19 Jul 2024
Download
Short summary
A farm-scale hydroclimatic machine learning framework to advise farmers was developed. FarmCan uses remote sensing data and farmers' input to forecast crop water deficits. The 8 d composite variables are better than daily ones for forecasting water deficit. Evapotranspiration (ET) and potential ET are more effective than soil moisture at predicting crop water deficit. FarmCan uses a crop-specific schedule to use surface or root zone soil moisture.