Articles | Volume 12
28 Jun 2007
28 Jun 2007

The role of observation uncertainty in the calibration of hydrologic rainfall-runoff models

T. Ghizzoni, F. Giannoni, G. Roth, and R. Rudari

Abstract. Hydrologic rainfall-runoff models are usually calibrated with reference to a limited number of recorded flood events, for which rainfall and runoff measurements are available. In this framework, model's parameters consistency depends on the number of both events and hydrograph points used for calibration, and on measurements reliability. Recently, to make users aware of application limits, major attention has been devoted to the estimation of uncertainty in hydrologic modelling. Here a simple numerical experiment is proposed, that allows the analysis of uncertainty in hydrologic rainfall-runoff modelling associated to both quantity and quality of available data.

A distributed rainfall-runoff model based on geomorphologic concepts has been used. The experiment involves the analysis of an ensemble of model runs, and its overall set up holds if the model is to be applied in different catchments and climates, or even if a different hydrologic model is used. With reference to a set of 100 synthetic rainfall events characterized by a given rainfall volume, the effect of uncertainty in parameters calibration is studied. An artificial truth – perfect observation – is created by using the model in a known configuration. An external source of uncertainty is introduced by assuming realistic, i.e. uncertain, discharge observations to calibrate the model. The range of parameters' values able to "reproduce" the observation is studied. Finally, the model uncertainty is evaluated and discussed. The experiment gives useful indications about the number of both events and data points needed for a careful and stable calibration of a specific model, applied in a given climate and catchment. Moreover, an insight on the expected and maximum error in flood peak discharge simulations is given: errors ranging up to 40% are to be expected if parameters are calibrated on insufficient data sets.