BBAuthors: Valdes-Abellan, J.; Advisor: -; Participants: Pachepsky, Y.; Martinez, G.; Pla, C. (2019)
Data assimilation (DA) is a promising alternative to infer soil hydraulic parameters from soil water dynamics data. Frequency of measurements and updates are important controls of DA efficiency; however, no strict guidance exists on determining the optimal frequency. In this study, DA was performed with the ensemble Kalman filter (EnKF) with a state augmentation approach to update both model states and parameters. We analyzed updates every 1, 2, 3, 5, 7, 9, 11, and 14 d. Two soil types (sandy loam and loam) and four climates (hot semiarid [Bwh], cold semiarid [Bsk], humid continental [Dfa], and humid subtropical [Cfa]) were considered. Results demonstrate that DA with high update freq... 
