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Water 2016, 8(9), 381; doi:10.3390/w8090381

Evaluation of Maximum a Posteriori Estimation as Data Assimilation Method for Forecasting Infiltration-Inflow Affected Urban Runoff with Radar Rainfall Input

1
Department of Environmental Engineering (DTU Environment), Technical University of Denmark, Miljøvej, Building 113, 2800 Kgs. Lyngby, Denmark
2
Department of Applied Mathematics and Computer Science (DTU Compute), Technical University of Denmark, Building 305, 2800 Kgs. Lyngby, Denmark
3
Krüger A/S, Veolia Water Solutions and Technologies, Gladsaxevej 363, 2860 Søborg, Denmark
*
Author to whom correspondence should be addressed.
Academic Editor: Ataur Rahman
Received: 25 July 2016 / Revised: 20 August 2016 / Accepted: 30 August 2016 / Published: 6 September 2016
(This article belongs to the Special Issue Hydroinformatics and Urban Water Systems)
View Full-Text   |   Download PDF [3951 KB, uploaded 7 September 2016]   |  

Abstract

High quality on-line flow forecasts are useful for real-time operation of urban drainage systems and wastewater treatment plants. This requires computationally efficient models, which are continuously updated with observed data to provide good initial conditions for the forecasts. This paper presents a way of updating conceptual rainfall-runoff models using Maximum a Posteriori estimation to determine the most likely parameter constellation at the current point in time. This is done by combining information from prior parameter distributions and the model goodness of fit over a predefined period of time that precedes the forecast. The method is illustrated for an urban catchment, where flow forecasts of 0–4 h are generated by applying a lumped linear reservoir model with three cascading reservoirs. Radar rainfall observations are used as input to the model. The effects of different prior standard deviations and lengths of the auto-calibration period on the resulting flow forecast performance are evaluated. We were able to demonstrate that, if properly tuned, the method leads to a significant increase in forecasting performance compared to a model without continuous auto-calibration. Delayed responses and erratic behaviour in the parameter variations are, however, observed and the choice of prior distributions and length of auto-calibration period is not straightforward. View Full-Text
Keywords: real-time control; flow forecasting; data assimilation; auto-calibration; Maximum a Posteriori estimation; linear reservoir models; urban drainage systems real-time control; flow forecasting; data assimilation; auto-calibration; Maximum a Posteriori estimation; linear reservoir models; urban drainage systems
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Pedersen, J.W.; Lund, N.S.V.; Borup, M.; Löwe, R.; Poulsen, T.S.; Mikkelsen, P.S.; Grum, M. Evaluation of Maximum a Posteriori Estimation as Data Assimilation Method for Forecasting Infiltration-Inflow Affected Urban Runoff with Radar Rainfall Input. Water 2016, 8, 381.

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