Regression approaches for hydrograph separation: Implications for the use of discontinuous electrical conductivity data

Abstract

Understanding of runoff generation mechanisms affects the ability to manage streamflow quantity and quality issues. Concerning the baseflow in particular, measurements are almost never available and hydrograph separation is generally applied to characterize its relevant patterns. As an alternative to well-known recursive digital filters and mass balance filtering methods, this paper deals with the use of regression approaches, based on electrical conductivity measurements, as a proxy for total dissolved solids, to separate baseflow from total flow. Particular focus is placed on their flexibility and ability to adapt to discontinuous electrical conductivity data measurements. To illustrate this, we analyze a hydrochemical dataset collected from the Ciciriello experimental catchment (Southern Italy). The main findings are as follows: A comparative analysis suggests that the performance of regressive approaches in the case of daily electrical conductivity measurements is better than that of calibrated recursive digital filters. Weekly monitored electrical conductivity data led to performances comparable to the daily scale monitoring, and even monthly observation leads to a nonsignificant reduction in regression hydrograph filter performance; this shows how spot geochemical data monitoring may present valid and operational alternatives for characterization of baseflow in poorly gauged catchments.