DAMP: A protocol for contextualising goodness-of-fit statistics in sediment-discharge data-driven modelling

SummaryThe decision sequence which guides the selection of a preferred data-driven modelling solution is usually based solely on statistical assessment of fit to a test dataset, and lacks the incorporation of essential contextual knowledge and understanding included in the evaluation of conventional empirical models. This paper demonstrates how hydrologic insight and knowledge of data quality issues can be better incorporated into the sediment-discharge data-driven model assessment procedure: by the plotting of datasets and modelled relationships; and from an understanding and appreciation of the hydrologic context of the catchment being modelled. DAMP: a four-point protocol for evaluating the hydrologic soundness of data-driven single-input single-output sediment rating curve solutions is presented. The approach is adopted and exemplified in an evaluation of seven explicit sediment-discharge models that are used to predict daily suspended sediment concentration values for a small tropical catchment on the island of Puerto Rico. Four neurocomputing counterparts are compared and contrasted against a set of traditional log–log linear sediment rating curve solutions and a simple linear regression model. The statistical assessment procedure provides one indication of the best model, whilst graphical and hydrologic interpretation of the depicted datasets and models challenge this overly-simplistic interpretation. Traditional log–log sediment rating curves, in terms of soundness and robustness, are found to deliver a superior overall product – irrespective of their poorer global goodness-of-fit statistics.

► The numerical assessment of sediment-discharge data-driven models is critiqued.
► The need for an improved understanding of hydrologic context is identified.
► DAMP: a four-point data-driven appraisal modelling protocol is presented.
► Numerous potential solutions are analysed for a small catchment in Puerto Rico.
► Traditional log–log rating curve fitting delivered a superior overall product.