Development of Improved Stormwater Quality Models

1981 ◽  
Vol 107 (5) ◽  
pp. 957-974
Author(s):  
Thomas K. Jewell ◽  
Donald Dean Adrian
Keyword(s):  
Stormwater Quality ◽  
Quality Models

Stormwater quality models: sensitivity to calibration data

2005 ◽  
Vol 52 (5) ◽  
pp. 61-68 ◽  
Author(s):  
M. Mourad ◽  
J.-L. Bertrand-Krajewski ◽  
G. Chebbo
Keyword(s):  
Suspended Solids ◽  
Calibration Data ◽  
Stormwater Quality ◽  
Local Data ◽  
Sewer System ◽  
Error Sources ◽  
Quality Models ◽  
Sewer Systems ◽  
Complex Models

Stormwater quality modelling is a useful tool in sewer systems management. Available models range from simple to detailed complex ones. The models need local data to be calibrated. In practice, calibration data are rather lacking. Only few measured events are commonly used. In this paper, the effect of the number and the variability of calibration data on models of various levels of complexity are investigated. The study is carried out on “Le Marais” catchment for suspended solids where 40 reliable measured events and good knowledge of the sewer system are available. The method used is based on resampling subsets of measured events among the 40 available ones. Three types of models were calibrated using subsets of events of different sizes and characteristics resampled among the 40 available ones. For each calibration, the model was validated against the remaining events to stand upon the quality of the model. It was found that the models are quite sensitive to calibration data, a problem neglected in practical studies. The use of more complex models does not necessarily improve modelling results since more problems and error sources are to be expected. The findings are specific to “Le Marais” catchment and the models used.

Stormwater quality modelling in combined sewers: calibration and uncertainty analysis

2005 ◽  
Vol 52 (3) ◽  
pp. 63-71 ◽  
Author(s):  
A. Kanso ◽  
G. Chebbo ◽  
B. Tassin
Keyword(s):  
Uncertainty Analysis ◽  
Initial Conditions ◽  
Storm Water ◽  
Stormwater Quality ◽  
Quality Model ◽  
Predictive Capacity ◽  
Large Variability ◽  
Quality Models ◽  
Markov Chain Method ◽  
Urban Stormwater Quality

Estimating the level of uncertainty in urban stormwater quality models is vital for their utilization. This paper presents the results of application of a Monte Carlo Markov Chain method based on the Bayesian theory for the calibration and uncertainty analysis of a storm water quality model commonly used in available software. The tested model uses a hydrologic/hydrodynamic scheme to estimate the accumulation, the erosion and the transport of pollutants on surfaces and in sewers. It was calibrated for four different initial conditions of in-sewer deposits. Calibration results showed large variability in the model's responses in function of the initial conditions. They demonstrated that the model's predictive capacity is very low.

scholarly journals Distribution-Based Calibration of a Stormwater Quality Model

Water ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 1027 ◽  
Author(s):  
Dominik Leutnant ◽  
Dirk Muschalla ◽  
Mathias Uhl
Keyword(s):  
Goodness Of Fit ◽  
Experimental Models ◽  
Model Parameters ◽  
Calibration Data ◽  
Stormwater Quality ◽  
Quality Model ◽  
Test Statistic ◽  
Quality Models ◽  
Parking Lot ◽  
Flat Roof

Stormwater quality models are usually calibrated using observed pollutographs. As current models still rely on simplified model concepts for pollutant accumulation and wash-off, calibration results for continuous pollutant concentrations are highly uncertain. In this paper, we introduce an innovative calibration approach based on total suspended solids (TSS) event load distribution. The approach is applied on stormwater quality models for a flat roof and a parking lot for which reliable distributions are available. Exponential functions are employed for both TSS buildup and wash-off. Model parameters are calibrated by means of an evolutionary algorithm to minimize the distance between a parameterized lognormal distribution function and the cumulated distribution of simulated TSS event loads. Since TSS event load characteristics are probabilistically considered, the approach especially respects the stochasticity of TSS buildup and wash-off and, therefore, improves conventional stormwater quality calibration concepts. The results show that both experimental models were calibrated with high goodness-of-fit (Kolmogorov–Smirnov test statistic: 0.05). However, it is shown that events with high TSS event loads (>0.8 percentile) are generally underestimated. While this leads to a relative deviation of −28% of total TSS loads for the parking lot, the error is compensated for the flat roof (+5%). Calibrated model parameters generally tend to generate wash-off proportional to runoff, which is indicated by mass-volume curves. The approach itself is, in general, applicable and creates a new opportunity to calibrate stormwater quality models especially when calibration data is limited.

Stormwater pollutant loads modelling: epistemological aspects and case studies on the influence of field data sets on calibration and verification

2007 ◽  
Vol 55 (4) ◽  
pp. 1-17 ◽  
Author(s):  
Jean-Luc Bertrand-Krajewski
Keyword(s):  
Case Studies ◽  
Model Calibration ◽  
Current Practice ◽  
Data Sets ◽  
Stormwater Quality ◽  
Quality Models ◽  
Pollutant Loads ◽  
Detention Tank ◽  
Software Packages ◽  
Calibration And Verification

In urban drainage, stormwater quality models have been used by researchers and practitioners for more than 15 years. Most of them were initially developed for research purposes, and have been later on implemented in commercial software packages devoted to operational needs. This paper presents some epistemological problems and difficulties with practical consequences in the application of stormwater quality models, such as simplified representation of reality, scaling-up, over-parameterisation, transition from calibration to verification and prediction, etc. Two case studies (one to estimate pollutant loads at the outlet of a catchment, one to design a detention tank to reach a given pollutant interception efficiency), with simple and detailed stormwater quality models, illustrate some of the above problems. It is hard to find, if not impossible, an “optimum” or “best” unique set of parameters values. Model calibration and verification appear to dramatically depend on the data sets used for their calibration and verification. Compared to current practice, collecting more and reliable data is absolutely necessary.

Stormwater quality models: performance and sensitivity analysis

2010 ◽  
Vol 62 (4) ◽  
pp. 837-843 ◽  
Author(s):  
C. B. S. Dotto ◽  
M. Kleidorfer ◽  
A. Deletic ◽  
T. D. Fletcher ◽  
D. T. McCarthy ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Model Performance ◽  
Urban Environments ◽  
Complex Nature ◽  
Generation Process ◽  
Stormwater Quality ◽  
Quality Models ◽  
Urban Catchments ◽  
Temporal And Spatial ◽  
High Level

The complex nature of pollutant accumulation and washoff, along with high temporal and spatial variations, pose challenges for the development and establishment of accurate and reliable models of the pollution generation process in urban environments. Therefore, the search for reliable stormwater quality models remains an important area of research. Model calibration and sensitivity analysis of such models are essential in order to evaluate model performance; it is very unlikely that non-calibrated models will lead to reasonable results. This paper reports on the testing of three models which aim to represent pollutant generation from urban catchments. Assessment of the models was undertaken using a simplified Monte Carlo Markov Chain (MCMC) method. Results are presented in terms of performance, sensitivity to the parameters and correlation between these parameters. In general, it was suggested that the tested models poorly represent reality and result in a high level of uncertainty. The conclusions provide useful information for the improvement of existing models and insights for the development of new model formulations.

Nutrient Speciation and Refractory Compounds in Water Quality Models

2016 ◽  
Vol 15 (0) ◽  
pp. 9781780408323-9781780408323
Author(s):  
D. L. Clark ◽  
G. Hunt ◽  
M. S. Kasch ◽  
P. J. Lemonds
Keyword(s):  
Water Quality ◽  
Quality Models ◽  
Refractory Compounds ◽  
Water Quality Models
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