RIAfitter is a Fortran95 code that fits the five parameters of the RIA parameterization of de Rooij et al. (2021) and de Rooij (2022) of the soil water retention curves on observed values of the volumetric water content for an arbitrary number of matric potentials. The version that is fitted is that of de Rooij (2022), in which the matric potential at oven-dryness (hd) and alpha are among the fitting parameters, and the matric potential at the junction point (hj) is a derived parameter. Each data point needs to have an estimate of the standard deviation of the error of the matric potential, and of the water content. These are used to calculate the weight assigned to each data point. The code minimizes the root mean square error (RMSE) of the fitted vs. the observed values using shuffled complex evolution. It also generates a regular grid of map points with the associated RMSE that covers the parameter space. The code evaluates ten convergence criteria for each fitting parameter. Five of these apply to all parameters simultaneously. See Subroutine ConvergenceCheck for details. To guard against local minima, three runs are performed. If so desired, two of these runs have starting points based on the map of the parameter space. Based on the RMSE, the best fit is determined. If at least two runs return the same RMSE, the run with the largest total number of times a parameter met a convergence criterion is selected. If at least two runs score the the same on both criteria, the run with the smallest rank number is picked (Run 1 over run 2, run 2 over run 3). The correlation matrix of the fitting parameters is calculated based on a random sample of a prescribed number of fits prior to and including the final fit. A table with points on the soil water retention curve (and its components) is written to output. Together with the code, a document that desribes the code and its use is made available. N.B. The theory underlying version 1.1 is given in: de Rooij, G.H. 2022. Technical note: A sigmoidal soil water retention curve without asymptote that is robust when dry–range data are unreliable. Hydrology and Earth System Sciences, in press.

{"references": ["de Rooij, G.H., J. Mai, and R. Madi. 2021. Sigmoidal water retention function with improved behavior in dry and wet soils. Hydrology and Earth System Sciences 25:983\u20131007. Doi:10.5194/hess-25-983-2021", "de Rooij, G.H. 2022. Technical note: A sigmoidal soil water retention curve without asymptote that is robust when dry\u2013range data are unreliable. Hydrology and Earth System Sciences, in press."]}