Change detection in hydrological records—a review of the methodology / Revue méthodologique de la détection de changements dans les chroniques hydrologiques

Abstract

Abstract Abstract General guidance is offered as to the methodology of change detection in time series of hydrological data, embracing stages such as preparing a suitable data set, exploratory analysis, application of adequate statistical tests and interpretation of results. Although the paper cannot go into full details of the many existing tests, it gives an easy-to-follow overview, offering practical hints and describing caveats and misconceptions. It serves as a refresher, raising attention to essential things that have often been ignored. A particular recommendation of the paper is that greater use of distribution-free testing methods, particularly resampling methods, should be made. These methods are recommended because they are particularly suited to hydrological data, which are often strongly skewed (non-normal), seasonal and serially correlated. Resampling techniques are flexible, robust and powerful, and require only minimal assumptions to be made about the data. Abstract Abstract General guidance is offered as to the methodology of change detection in time series of hydrological data, embracing stages such as preparing a suitable data set, exploratory analysis, application of adequate statistical tests and interpretation of results. Although the paper cannot go into full details of the many existing tests, it gives an easy-to-follow overview, offering practical hints and describing caveats and misconceptions. It serves as a refresher, raising attention to essential things that have often been ignored. A particular recommendation of the paper is that greater use of distribution-free testing methods, particularly resampling methods, should be made. These methods are recommended because they are particularly suited to hydrological data, which are often strongly skewed (non-normal), seasonal and serially correlated. Resampling techniques are flexible, robust and powerful, and require only minimal assumptions to be made about the data.