Publication

Back to overview

Comparison of two stochastic techniques for reliable urban runoff prediction by modeling systematic errors

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Publication date 2015
Author Del Giudice Dario, Löwe Roland, Madsen Henrik, Mikkelsen Peter Steen, Rieckermann Jörg,
Project Using Commercial Microwave Links and Computer Model Emulation to Reduce Uncertainties in Urban Drainage Simulations (COMCORDE)
Show all

Original article (peer-reviewed)

Journal Water Resources Research
Volume (Issue) 51(7)
Page(s) 5004 - 5022
Title of proceedings Water Resources Research
DOI 10.1002/2014WR016678

Abstract

In urban rainfall-runoff, commonly applied statistical techniques for uncertainty quantification mostly ignore systematic output errors originating from simplified models and erroneous inputs. Consequently, the resulting predictive uncertainty is often unreliable. Our objective is to present two approaches which use stochastic processes to describe systematic deviations and to discuss their advantages and drawbacks for urban drainage modeling. The two methodologies are an external bias description (EBD) and an internal noise description (IND, also known as stochastic gray-box modeling). They emerge from different fields and have not yet been compared in environmental modeling. To compare the two approaches, we develop a unifying terminology, evaluate them theoretically, and apply them to conceptual rainfall-runoff modeling in the same drainage system. Our results show that both approaches can provide probabilistic predictions of wastewater discharge in a similarly reliable way, both for periods ranging from a few hours up to more than 1 week ahead of time. The EBD produces more accurate predictions on long horizons but relies on computationally heavy MCMC routines for parameter inferences. These properties make it more suitable for off-line applications. The IND can help in diagnosing the causes of output errors and is computationally inexpensive. It produces best results on short forecast horizons that are typical for online applications.
-