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.
Highlights
Knowledge of hydrological processes is a key point in applied hydrology studies, and the understanding of runoff generation mechanisms is a milestone and, a challenging task in this field of research
Several approaches have been reported in the scientific literature for hydrograph separation purposes, but recursive digital filters and mass balance filtering methods appear to be the most attractive and effective approaches
A large number of recursive digital filters (RDFs) are grounded on the idea that baseflow is a smooth frequency component of the total hydrograph, and, the high-frequency signals are filtered from the low-frequency signals [4,5,6,7]
Summary
Knowledge of hydrological processes is a key point in applied hydrology studies, and the understanding of runoff generation mechanisms is a milestone and, a challenging task in this field of research. While remaining in a framework that considers the use of EC data, which makes the characterization of baseflow patterns more objective, the opportunity for a methodology based on the use of discontinuous EC data that offers a cost-benefit trade-off would be interesting from both a scientific and practical point of view In this context, the current paper reports on the ability of regressive approaches, based on EC data monitoring to identify the baseflow component, with particular emphasis on their flexibility and adaptability to the use of discontinuous EC data and on their ability to represent an approach capable of realistically separating the streamflow component on the basis of a parsimonious dataset. Investigate the ability of regression approaches to identify the baseflow component, through comparison with the application of well-known recursive digital filters and MBF methods; Assess the flexibility and ability of regression approaches in identifying the baseflow pattern for discontinuous EC data monitoring
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