Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 539150
Title EmiStatR: a simplified and scalable urban water quality model for simulation of combined sewer overflows
Author(s) Torres-Matallana, Jairo Arturo; Leopold, Ulrich; Klepiszewski, Kai; Heuvelink, Gerard B.M.
Source Water 10 (2018)6. - ISSN 2073-4441
DOI https://doi.org/10.3390/w10060782
Department(s) Soil Geography and Landscape
ISRIC - World Soil Information
PE&RC
Publication type Refereed Article in a scientific journal
Publication year 2018
Keyword(s) Fast surrogate model - Parallel computing - Urban water modelling
Abstract

Many complex urban drainage quality models are computationally expensive. Complexity and computing times may become prohibitive when these models are used in a Monte Carlo (MC) uncertainty analysis of long time series, in particular for practitioners. Computationally scalable and fast "surrogate" models may reduce the overall computation time for practical applications in which often large data sets would be needed otherwise. We developed a simplified semi-distributed urban water quality model, EmiStatR, which brings uncertainty and sensitivity analyses of urban drainage water quality models within reach of practitioners. Its lower demand in input data and its scalability allow for simulating water volume and pollution loads in combined sewer overflows in several catchments fast and efficiently. The scalable code implemented in EmiStatR reduced the computation time significantly (by a factor of around 24 when using 32 cores). EmiStatR can be applied efficiently to test hypotheses by using MC uncertainty studies or long-term simulations.

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