The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.
HYDRUS: Model Use, Calibration, and Validation
Abstract
The HYDRUS numerical models are widely used for simulating water flow and solute transport in variably saturated soils and groundwater. Applications involve a broad range of steady-state or transient water flow, solute transport, and/or heat transfer problems. They include both short-term, one-dimensional laboratory column flow or transport simulations, as well as more complex, long-duration, multi-dimensional field studies. The HYDRUS models can be used for both direct problems when the initial and boundary conditions for all involved processes and corresponding model parameters are known, as well as inverse problems when some of the parameters need to be calibrated or estimated from observed data. The approach to model calibration and validation may vary widely depending upon the complexity of the application. Model calibration and inverse parameter estimation can be carried out using a relatively simple, gradient-based, local optimization approach based on the Marquardt-Levenberg method, which is directly implemented into the HYDRUS codes, or more complex global optimization methods, including genetic algorithms, which need to be run separately from HYDRUS. In this article, we provide a brief overview of the HYDRUS codes, discuss which HYDRUS parameters can be estimated using internally built optimization routines and which type of experimental data can be used for this, and review various calibration approaches that have been used in the literature in combination with the HYDRUS codes.
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Citations
Cites
An Algorithm for Least-Squares Estimation of Nonlinear Parameters
Marquardt, D. W. (1963) · Journal of the Society for Industrial and Applied Mathematics · doi:10.1137/0111030 · Cited by 30,728
Hydraulic properties of porous media
Brooks, R. H., & Corey, A. T. (1963) · Digital Collections of Colorado (Colorado State University) · Cited by 5,509
Bäck, T. (1996) · Oxford University Press eBooks · doi:10.1093/oso/9780195099713.001.0001 · Cited by 5,113
Verification, Validation, and Confirmation of Numerical Models in the Earth Sciences
Oreskes, N., Shrader-Frechette, K., & Belitz, K. (1994) · Science · doi:10.1126/science.263.5147.641 · Cited by 3,010
Schaap, M. G., Leij, F. J., & van Genuchten, M. T. (2001) · Journal of Hydrology · doi:10.1016/s0022-1694(01)00466-8 · Cited by 2,640
Developing joint probability distributions of soil water retention characteristics
Carsel, R. F., & Parrish, R. S. (1988) · Water Resources Research · doi:10.1029/wr024i005p00755 · Cited by 2,418
The RETC code for quantifying the hydraulic functions of unsaturated soils
van Genuchten, M. T., Leij, F. J., & Yates, S. R. (1992) · Cited by 1,530
Self-citationVrugt, J. A., Gupta, H. V., Bouten, W., & Sorooshian, S. (2003) · Water Resources Research · doi:10.1029/2002wr001642 · Cited by 1,196
Hydraulic conductivity estimation for soils with heterogeneous pore structure
Durner, W. (1994) · Water Resources Research · doi:10.1029/93wr02676 · Cited by 1,175
Metadata
- Authors
- imunek, J. Å., van Genuchten, M. T., & ejna, M. Å.
- Venue
- Transactions of the ASABE
- Published
- 2012
- Volume
- 55
- Issue
- 4
- Pages
- 1263-1276
- ISSN
- 2151-0032 (ISSN-L)
- 2151-0040
- OpenAlex
- W2015758186
- Cited by
- 289
- References
- 58
- FWCI
- 18.02
- Type
- journal article
- Language
- en
- Open access
- closed
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- imunek, J. Å., van Genuchten, M. T., & ejna, M. Å. (2012). HYDRUS: Model Use, Calibration, and Validation. Transactions of the ASABE. https://doi.org/10.13031/2013.42239
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