Quantifying Uncertainty in Simulation of Sewer Overflow Volume

Abstract

Environmental regulators frequently stipulate the modeling approaches required for water utilities managing sewer networks to demonstrate regulatory compliance. The performance of drainage systems with regard to combined sewer overflow (CSO) discharges is required to be assessed using urban drainage models to prove compliance before large investments can be authorized. However, as far as the authors are aware, the modeling approaches to demonstrate regulatory compliance currently provide no opportunity for considering model uncertainty. This paper therefore addresses a knowledge gap in the role of model uncertainty in environmental compliance studies by describing an objective uncertainty quantification process that enables the water utilities to evaluate and report the uncertainty in their modeling predictions and that is also transparent enough to satisfy regulators. The sewer network was modeled in InfoWorks CS software using a design storm defined by the regulator to test the performance of CSOs. Uncertainty in the model and input parameters was propagated using Monte Carlo simulations with Latin hypercube sampling, and the results were presented to show the trade-off between the infrastructure investment and the risk of spilling.

Interactive comment on “Multivariate autoregressive modelling and conditional simulation for temporal uncertainty propagation in urban water systems” by Jairo Arturo Torres-Matallana et al.

“…Still I would like to see some comparisons to other attempts on quantity (e.g. Sriwastava et al, 2018) and quality (especially measurements taken at CSOs the measured water quality at the WWTP influent is expected to render a low representativity of the conditions at the CSOs -e.g. Brombach et al(2005); Diaz-Fierros T et al 2002)Reply: Thank you for your kind words and suggestion.…”

Quantifying the Impact of Uncertainty within the Longitudinal Dispersion Coefficient on Concentration Dynamics and Regulatory Compliance in Rivers

“…For example, Figure shows that using the Disley et al () equation, the predicted k x could be anywhere between approximately 0 and 10 times the “possible real k x ,” so dividing the predicted k x by each of the randomly drawn Pr values would give 2,000 possible real k x values. A straightforward Monte Carlo simulation was deemed the most suitable approach, due to its conceptual simplicity as well as its ease of explanation to, for example, regulators (Benke et al, ; Helton, ; Sriwastava et al, ). Using an analytical solution of the 1D ADE, given by equation below (Rutherford, ), and the “possible real k x values” (based on the drawn Pr value from step 2), the downstream concentration profile located at 3.8 km was calculated. This was achieved by successively routing the observed upstream concentration profile (at 2.5 km) over each subreach until the concentration profile at the last subreach was obtained (utilizing the geometric and hydraulic data).…”

“…For example, Figure 2 shows that using the Disley et al (2015) equation, the predicted k x could be anywhere between approximately 0 and 10 times the "possible real k x ," so dividing the predicted k x by each of the randomly drawn Pr values would give 2,000 possible real k x values. A straightforward Monte Carlo simulation was deemed the most suitable approach, due to its conceptual simplicity as well as its ease of explanation to, for example, regulators (Benke et al, 2018;Helton, 1993;Sriwastava et al, 2018).…”

“…However, lack of knowledge of water quality processes and the river system of interest can limit the reliability of the model predictions. Modeling uncertainties can thus lead to suboptimal water or infrastructure management decisions (Sriwastava et al, ). Thus, quantifying and communicating the accuracy of water quality predictions is a key component for improving water quality conditions and managing better water resources (Refsgaard et al, ; van Griensven & Meixner, ).…”

A Methodology for the Design of RTC Strategies for Combined Sewer Networks

“…To this end, additional scenarios have been evaluated for the uncontrolled scenario varying parameters not related to RTC within their uncertainty boundaries, as proposed by [30]. The selection of these model parameters and the chosen ranges listed in Table 4 are based on available literature [55].…”