Parsimonious rainfall-runoff model construction supported by time series processing and validation of hydrological extremes - Part 1: Step-wise model-structure identification and calibration approach

•Rainfall-runoff submodels identified in a case-specific, step-wise, data-based way.•Model-structure building supported by sequential time series processing steps.•Model performance evaluation includes extreme high and low flow statistics.•Model performance evaluation accounts for dependency and heteroscedasticity of model residuals.

SummaryCommon problems faced by rainfall-runoff modellers are data limitation, model overparameterization and related problems of parameter identifiability. Depending on the application, possible solutions to overcome these problems include the use of parsimonious conceptual models, avoid the use of a fixed pre-defined model conceptualization, but apply a “top-down” or “downward” method to allow the model structure to be adjusted or inferred from available data and field evidence.This paper presents a top-down procedure that starts from a generalized model structure framework that is adjusted in a case-specific parsimonious way. The model-structure building is done in a transparent, step-wise way, where separate parts of the model structure are identified and calibrated based on multiple and non-commensurable information derived from river flow series by means of a number of sequential time series processing tasks. These include separation of the high frequency (e.g., hourly, daily) river flow series into subflows, split of the series in approx. independent quick and slow flow hydrograph periods, and the extraction of independent peak and low flows. The model building and calibration account for the statistical assumptions and requirements on independency and homoscedasticity of the model residuals. Next to identification of the subflow recessions and related routing submodels, equations describing quick and slow runoff sub-responses and soil water storage are derived from the time series data. The method includes testing of the model performance for peak and low flow extremes.