Hydrological models often require input data on soil-water retention (SWR), but obtaining such data is laborious
and costly so that SWR in many places remains unknown. To fill the gap, a prediction of SWR using a pedotransfer
function (PTF) is one of the alternatives. This study aims to select the most suitable existing PTFs in order to predict
SWR for the case of the upper Bengawan Solo (UBS) catchment on Java, Indonesia. Ten point PTFs and two continuous
PTFs, which were developed from tropical soils elsewhere, have been applied directly and recalibrated based on a small
soil sample set in UBS. Scatter plots and statistical indices of mean error (ME), root mean square error (RMSE), model
efficiency (EF) and Pearson’s correlation (r) showed that recalibration using the Shuffled Complex Evolution-University
of Arizona (SCE-UA) algorithm can help to improve the prediction of PTFs significantly compared to direct application
of PTFs. This study is the first showing that improving SWR-PTFs by recalibration for a new catchment based on around
50 soil samples provides an effective parsimonious alternative to developing a SWR-PTF from specifically collected soil
datasets, which typically needs around 100 soil samples or more.