Streamflow prediction in ungauged catchments using copula‐based dissimilarity measures

Abstract

There are many procedures in the available literature to perform prediction in ungauged basins. Commonly, the Euclidean metric is used as a proxy of the hydrologic dissimilarity. Here we propose a procedure to find a metric on the basis of dissimilarity measures that are estimated from pairwise empirical copula densities of runoff. A metric is then defined in an transformed space of basin descriptors, whose parameterization is obtained with a variance reducing technique. A hydrologic model was run in an ungauged basin with sets of global parameters obtained from the k nearest neighboring donor basins using various metrics to take into account the uncertainty of its parameterization. Hydrologic model parameters were regionalized with a multiscale parameter regionalization technique whose transfer function parameters were found via calibration. The streamflow in an ungauged basin was found as an ensemble streamflow prediction to account for the uncertainties of the transfer function parameters as well as those to define the metric. This technique was applied in 38 German basins ranging in size from 70 to 4000 km2. For each basin, a number of catchment descriptors and several climatic indices were quantified, e.g., mean slope, aspect, shape factor, mean elevation, and mean monthly temperature in January, among others. Daily streamflow time series correspond to the period from 1961 to 2000. Simulated daily discharge was validated with a Jackknife cross‐validation technique. Nash‐Sutcliffe efficiencies obtained in this way ranged between 0.76 and 0.86. These results suggested that the proposed technique would produce reasonable results in ungauged basins.