A comparison of regionalization methods in monsoon dominated tropical river basins

Abstract

The present study evaluated five regionalization methods: global averaging; regression; spatial proximity; behavioral similarity and artificial neural network (ANN) for Soil and Water Assessment Tool (SWAT), using data from 24 river basins in monsoon dominated tropical river basins of peninsular India. Regionalization was performed for each basin using the remaining 23 basins. The performance of the calibration and thus the regionalization method is limited by the unreliable or erroneous data at the basins. Overall, we found that the regression method outperforms other regionalization methods in terms of predicting the daily as well as peak discharges. It was found that despite showing a better R2 in training, testing and validation, the ANN method performed poorly probably due to a lower number of training data. Therefore, it is suggested that the ANN should be avoided for regionalization in the absence of sufficient training data. Moreover, the regression equations developed in the present study can be utilized to predict SWAT parameters of basins located in the vicinity of the study area. However, the basins located far away from the group of catchments or having diverse characteristics should be avoided for regionalization. HIGHLIGHTS Overall, the regression-based method showed comparatively better performance both in terms of precision and accuracy.; Simpler regression methods are better than complex ANNs when the number of gauged basins are limited.; The performance of the regionalization method is limited by the unreliable or erroneous data at the basin.;

Keywords

• hydrology
• regionalization
• tropical river basins
• ungauged basins