Technical note: A novel technique to improve the hydrological estimates at ungauged basins by swapping workspaces

Abstract. The dissimilarity-based methods to perform prediction of flow regimes in ungauged basins have become quite popular in the recent times. Generally, these methods use geomorphological and climatic characteristics of the basins to translate their hydrological properties. However, the methods have been criticized for using selective basin characteristics for the prediction of hydrological data of the basins in the entire study area. Incase these selected descriptors are not strongly related to the hydrological properties of the considered basin; as opposed to the general perception, a considerable magnitude of localized error may be introduced in the final results. To address these drawbacks, we propose a novel technique which assists in identifying a better individual regional model for the prediction of hydrological data at each ungauged basin. The new procedure treats each flow regime as a complete hydrological object. Whereas, the variability in regime shape is determined by using dissimilarity values arranged in a distance matrix executed by considering normalized values of three types of dissimilarities viz; point-to-point dissimilarity, vertical dissimilarity and lateral dissimilarity. On the basis of defined statistical routines, the flow distance matrix is linked with the distance matrices of basin characteristics, acquired by simple comparison of descriptors values, to select most suitable descriptors from the pool of 74 descriptors to form regionalized models. The dissimilarity-based regionalization model thus obtained is primarily coupled with nearest neighbor algorithm to constitute a model space for the initial predictions of the monthly flow regimes. Afterwards, based on the orientation of nearest neighbors of ungauged basin in descriptor space __ the prediction is improved by swapping the model space with the other available models provided the set criteria are fulfilled. The proposed study is conducted in northwestern Italy and the proposed method is tested on the dataset of 124 basins. The basins where the set criteria of model swapping are complied with; the results obtained are statistically better than the initial estimates.