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The assessment of low-flow in ungauged or poorly-gauged basins where the flow data are unavailable remains a challenge in many parts of the world. This study aims to address the low-flow assessment in the ungauged sub-basins in Thailand by regionalizing low flow indices including the base-flow index (BFI), 95th percentile-flow (Q95) and the annual minimum 7-day moving average flow with a 10-year recurrence interval (7Q10). The framework is demonstrated through the case study of 25 sub-basins of the Upper Ping River basin with available data from 1995-2014. Performance of two widely used regionalization methods, the regression and climate adjustment methods are tested and compared. The accuracy of the methods is assessed by comparing the predicted with the observed low-flow indices calculated in terms of R2, NSE, and RMSE. The results of the regression method indicate that the method performs best for predicting 7Q10 compared to Q95 and BFI. The best R2, RMSE, and NSE values obtained from the regression model for predicting 7Q10 are 0.95, 0.95, and 0.26 respectively. The results of the climate adjustment method show that the method with a comparatively long overlap period is found to improve over the regression method. The longest 15 overlap period tested in this study show that the R2 and RMSE can be improved to almost 1.00 and NSE can be reduced to 0.09. While this study could offer a way forward improving low-flow estimation and water resources management in ungauged basins, further study on non-stationarity of the basin is recommended.
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