Model bias and complexity – Understanding the effects of structural deficits and input errors on runoff predictions. (February 2015)

• Record Type: Journal Article
• Authors: Del Giudice, D.; Reichert, P.; Bareš, V.; Albert, C.; Rieckermann, J.
• Is Part Of: Environmental modelling & software. Volume 64(2015:Feb.)
• Page Start: 205
• Page End: 214
• Publication Date: 2015-02
• Subjects: Model structural deficits -- Rainfall errors -- Stochastic uncertainty analysis -- Bayesian bias description -- Hydrodynamic simulations -- Model comparison; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2014.11.006
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Oversimplified models and erroneous inputs play a significant role in impairing environmental predictions. To assess the contribution of these errors to model uncertainties is still challenging. Our objective is to understand the effect of model complexity on systematic modeling errors. Our method consists of formulating alternative models with increasing detail and flexibility and describing their systematic deviations by an autoregressive bias process. We test the approach in an urban catchment with five drainage models. Our results show that a single bias description produces reliable predictions for all models. The bias decreases with increasing model complexity and then stabilizes. The bias decline can be associated with reduced structural deficits, while the remaining bias is probably dominated by input errors. Combining a bias description with a multimodel comparison is an effective way to assess the influence of structural and rainfall errors on flow forecasts. Highlights: We investigate how a random bias process behaves as a function of model complexity. We analyze 5 model structures to simulate a stormwater system. The reduction of systematic deviations is associated with decreasing structural deficits. In this study the remaining bias is likely to be dominated by input errors. The method provides sound probabilistic predictions in a relatively efficient way.