Switching the pooling similarity distances: Mahalanobis for Euclidean

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Streamflow prediction in ungauged catchments using copula‐based dissimilarity measures (2010, methodology): “Most commonly, the m-dimensional Euclidean distance in attribute space is used, although, other metrics like the Mahalanobis distance or the Minkowski norm (L1 norm or Manhattan distance) can be found in the literature [Cunderlik and Burn, 2006; Rao and Srinivas, 2006].”