EmiStatR: A simplified and scalable urban water quality model for simulation of combined sewer overflows

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.