Articles | Volume 18, issue 7
https://doi.org/10.5194/hess-18-2543-2014
© Author(s) 2014. This work is distributed under the Creative Commons Attribution 3.0 License.
Research article
| 
04 Jul 2014
Research article |
| 04 Jul 2014
Kalman filters for assimilating near-surface observations into the Richards equation – Part 3: Retrieving states and parameters from laboratory evaporation experiments
Related authors
Related subject area
Subject: Vadose Zone Hydrology | Techniques and Approaches: Stochastic approaches
Detecting hydrological connectivity using causal inference from time series: synthetic and real karstic case studies
Covariance resampling for particle filter – state and parameter estimation for soil hydrology
Inferring soil salinity in a drip irrigation system from multi-configuration EMI measurements using adaptive Markov chain Monte Carlo
State and parameter estimation of two land surface models using the ensemble Kalman filter and the particle filter
Evaluation of a cosmic-ray neutron sensor network for improved land surface model prediction
Hydrol. Earth Syst. Sci., 26, 2181–2199, 2022
Hydrol. Earth Syst. Sci., 23, 1163–1178, 2019
Hydrol. Earth Syst. Sci., 21, 5375–5383, 2017
Hydrol. Earth Syst. Sci., 21, 4927–4958, 2017
Hydrol. Earth Syst. Sci., 21, 2509–2530, 2017
Cited articles
Camporese, M., Paniconi, C., Putti, M., and Salandin, P.: Ensemble Kalman filter data assimilation for a process based catchment scale model of surface and subsurface flow, Water Resour. Res., 45, W10421, https://doi.org/10.1029/2008WR007031, 2009.
Carsel, R. F. and Parrish, R. S.: Developing joint probability distributions of soil water retention characteristics, Water Resour. Res., 24, 755–769, https://doi.org/10.1029/WR024i005p00755, 1988.
Chirico, G. B., Medina, H., and Romano, N.: Functional evaluation of PTF prediction uncertainty: An application at hillslope scale, Geoderma, 155, 193–202, 2010.
Chirico, G. B., Medina, H., and Romano, N.: Kalman filters for assimilating near-surface observations into the Richards equation – Part 1: Retrieving state profiles with linear and nonlinear numerical schemes, Hydrol. Earth Syst. Sci., 18, 2503–2520, https://doi.org/10.5194/hess-18-2503-2014, 2013.
De Lannoy, G. J. M., Houser, P. R., Pauwels, V. R. N., and Verhoest, N. E. C.: State and bias estimation for soil moisture profiles by an ensemble Kalman filter: Effect of assimilation depth and frequency, Water Resour. Res., 43, W06401, https://doi.org/10.1029/2006WR005100, 2007a.
