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.