Résumé
Missing data in river flow records represent a loss of information and a serious drawback in water management. In this work, we introduce gaplt, a user-driven case-based reasoning tool for infilling gaps in daily mean river flow records. Given a set of flow time series, gaplt builds a database of artificial gaps for which it computes several flow estimates, to find the best combinations of infilling algorithm and automatically selected donor station(s), according to state-of-the-art performance indicators. We obtained satisfactory results with Nash-Sutcliffe >0.7 for more than half of the similar to 5000 synthetic gaps of various lengths and positions, randomly created along the available records. gaplt was evaluated on 24 daily river discharge time series recorded in Luxembourg over seven years from 01/01/2007 to 31/12/2013. We also discuss the benefits of coupling this approach with user-expertise for an improved infilling of real data gaps. (C) 2016 Elsevier Ltd. All rights reserved.