A catchment classification scheme using local variance reduction method

SummaryClassification has been considered a fundamental step towards improved catchment hydrology science. This paper proposes a catchment classification scheme where the classification procedure is based on similarity interpreted as distances between catchments. If many sets of model parameters lead to similar model performance for two catchments, they are considered as similar catchments. Two procedures, namely multidimensional scaling (MDS) and local variance reduction (LVR), are undertaken to construct a configuration of n catchments’ characteristics in a Euclidean space using information about similar performance between the catchments. MDS is used to determine the appropriate dimension of the Euclidean space and LVR is used to obtain the transformation matrix and the coordinates in the transformed Euclidean space. This scheme avoids the idea of parametric regression-based regionalization approaches where a regression function is pre-defined between model parameters and catchment characteristics. The proposed scheme is tested with a research version of the HBV-IWS model on a total number of 27 catchments selected from the Rhine River Basin. The scheme can be extended to regional calibration of rainfall runoff models as well as regional drought or flood studies once similarity within catchments has been established.

► We propose a catchment classification scheme based on similarity of model performance.
► The multidimensional scaling is used to determine the dimension of the Euclidean space.
► Local variance reduction reproduces similarity in the transformed space.
► This scheme avoids the idea of parametric regression-based regionalization approaches.
► It contributes to the field of prediction in ungauged basins.