Quantifying model uncertainty in the simulation of sewer overflow volume

Transcription

1 Quantifying model uncertainty in the simulation of sewer overflow volume Ambuj Sriwastava Marie Curie Early Stage Researcher, University of Sheffield This project has received funding from the European Union s Seventh Framework Programme for research, technological development and demonstration under grant agreement no

2 Motivation Urban drainage models are used to make investment decisions on sewer network infrastructure Any uncertainty in these models can affect the outcome of the decision making process We quantify the uncertainty in CSO spill volume to support decision making on the required storage volume for managing CSO spills to comply with local regulations in Flanders, Belgium

3 Uncertainty Propagation Overview Input ± U Model (± U) Output ± U Input data Model parameters Model structure Solution method Model output Source: Kasia Sawicka, Wageningen University, Netherlands.

4 Uncertainty Propagation Overview Input ± U Model (± U) Output ± U Input data Model parameters Model structure Solution method Model output Observ. data Data ± U Source: Kasia Sawicka, Wageningen University, Netherlands.

5 Objectives Identify the input and model parameters which can contribute most to the uncertainty in the Combined Sewer Overflow spill volume Quantify/Define uncertainty in the selected input and model parameters Propagate the input and model parameter uncertainty through model simulations Quantify the uncertainty in the CSO spill volume

6 Model & Data A small drainage network Herent catchment in Flanders, Belgium modelled in InfoWorks CS version 15.5 Subsystem model has 179 nodes, 175 pipes, 1 CSO and no pumping station The sewer system serves around 2100 inhabitants with a contributing area of about 87 hectares A single composite design storm event f7 to replicate the design guidelines by Flanders Environment Agency (VMM)

7 Sensitivity Analysis Input parameters Unit Minimum Maximum Weir crest m Weir width m Model parameters Unit Minimum Maximum Colebrook-White roughness mm Initial loss value mm Fixed runoff coefficient - Impervious Headloss coefficient Primary discharge coefficient Weir Secondary discharge coefficient - Weir Morris Screening method was selected for Global Sensitivity Analysis (GSA)

8 Sensitivity Analysis Morris screening results: Sensitivity measures: absolute mean 1 (μ*) & standard deviation (σ) of the sensitivity parameter A cutoff threshold of 0.1 has been applied on μ* to select important model and input parameters High value of μ* Input/parameter has high effect on the model output High value of σ suggests non linearity or interaction of the selected input with other model inputs Parameters Absolute mean (μ * ) Standard deviation (σ) Rank Fixed Runoff Coefficient Impervious surfaces Weir Crest Colebrook-White roughness Headloss coefficient Primary weir discharge coefficient Weir Width Initial loss value Secondary weir discharge coefficient Campolongo, F., Cariboni, J., & Saltelli, A. (2007). An effective screening design for sensitivity analysis of large models. Environmental Modelling & Software, 22(10),

9 Quantifying uncertainty in chosen input parameters Fixed Runoff Coefficient Impervious surfaces Assumed to follow a symmetrical normal distribution truncated at the upper limit of 1. Mean is set at 0.8 with a standard deviation of 0.1

10 Quantifying uncertainty in chosen input parameters Weir Crest Random physical measurement Assumed to follow a symmetrical normal distribution Mean is set at 35.35m A range of 10cm is chosen such that 3 x Standard deviation = 10 cm

11 Variation in Colebrook-White Roughness k s Flow survey data consists of at least 5-year flow measurements in Herent, Belgium Measured water depth and flow velocity data were used in calculating roughness Measurements from dry weather periods are used to estimate the probability distribution of CW roughness

12 Quantifying uncertainty in CSO spill volume Method: Monte Carlo simulations were run for 5000 parameter samples Latin hypercube sampling (LHS) was used instead of random sampling to draw samples from the parameter space (R package: lhs ) The three input parameters were assumed to be not correlated to each other

13 Quantifying uncertainty in CSO spill Volume Results:

14 Quantifying uncertainty in CSO spill Volume Results:

15 Conclusion Overall uncertainty in the CSO spill volume was found to be from 0.02 m 3 to m 3 (still an underestimation!) Similar uncertainty analyses can be performed by automated runs of InfoWorks CS simulations and couple of simple codes written in R! Is the requirement to demonstrate no spill volume for a set design storm a sensible way to regulate CSOs?

16 Conclusion More detailed uncertainty analysis shows: Runoff coefficient has a very high dominating effect on the CSO spill volume and the uncertainty in its estimation. Extra care should be given while quantifying the uncertainty in such parameters. Runoff Coefficient and Colebrook-White roughness were found to be not independent or to have a non-linear relationship with CSO spill volume

17 Acknowledgement This study has been conducted during my secondment at Aquafin NV (Belgium) from February April I sincerely, thank Mieke Van Dorpe, Stefan Kroll, and Johan Van Assel for a collaborative work environment and supporting me with the resources I needed for this study.