The shape of water retention curves can be characterized by several models, one of them known as the van Genuchten model:
- is the water retention curve ;
- is suction pressure ( or cm of water);
- saturated water content ;
- residual water content ;
- is related to the inverse of the air entry suction, (, or cm−1); and,
- is a measure of the pore-size distribution, (dimensionless).
Based on this parametrization a prediction model for the shape of the unsaturated hydraulic conductivity - saturation - pressure relationship was developed.
Read more about this topic: Water Retention Curve
Other articles related to "shape parameters, parameter, parameters, shape parameter":
... Therefore the geometric mean of a beta distribution with shape parameters α and β is the exponential of the digamma functions of α and β as follows While for a beta distribution with ... limits apply Following are the limits with one parameter finite (non zero) and the other approaching these limits The accompanying plot shows the difference between the mean and ... zero, one can observe an evident asymmetry of the geometric mean with respect to the shape parameters α and β ...
... in the FRW universe, the can only expand or contract the only dynamical parameter is overall size of the, parameterized by the scale factor ... Its evolution is described by a scale factor as well as by two shape parameters ... Values of the shape parameters describe distortions of the that preserve its volume and also maintain a constant Ricci curvature scalar ...
... density function of the beta distribution, for 0 ≤ x ≤ 1, and shape parameters α > 0 and β > 0, is a power function of the variable x and of its reflection (1 − x) as ... of α and q instead of β for the shape parameters of the beta distribution, reminiscent of the nomenclature traditionally used for the parameters of the Bernoulli distribution, because ... that a random variable X is Beta-distributed with parameters α and β will be denoted by Other notations for Beta-distributed random variables used in the ...
... in terms of its mean μ (0 < μ < 1) and the addition of both shape parameters ν = α + β (ν > 0)( p ... Denoting by αPosterior and βPosterior the shape parameters of the posterior beta distribution resulting from applying Bayes theorem to a binomial likelihood ... This parametrization may be useful in Bayesian parameter estimation ...
... show the log geometric variances and log geometric covariance versus the shape parameters α and β ... geometric variances and log geometric covariance are close to zero for shape parameters α and β greater than 2, and that the log geometric variances rapidly rise in value for shape parameter values α and β less ... are positive for all values of the shape parameters ...
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