Günther Leonhardt

Estimating inflow to a combined sewer overflow structure with storage tank in real time: evaluation of different approaches

The performance assessment of storage tanks and combined sewer overflow (CSO) structures in sewer systems requires knowledge of the total inflow from the catchment during rainfall events. Many structures are, however, only equipped with sensors to measure water level and/or outflows. Based on the geometry of the tank, expressed as a level-storage relationship, inflow can be calculated from these data using a simple conceptual storage model. This paper compares a deterministic and a Bayesian approach for estimating the inflow to a CSO structure from measurements of outflows and water level. The Bayesian approach clearly outperforms the deterministic estimation which is very sensitive to measurement errors. Although computationally more demanding, the use of a simple linear storage model allows the online application of the Bayesian approach to repeatedly estimate inflow in short time intervals of a few minutes. The method could thus be used as an online software sensor for inflow to storage structures in sewer systems.

A software-based sensor for combined sewer overflows

A new methodology for online estimation of excess flow from combined sewer overflow (CSO) structures based on simulation models is presented. If sufficient flow and water level data from the sewer system is available, no rainfall data are needed to run the model. An inverse rainfall-runoff model was developed to simulate net rainfall based on flow and water level data. Excess flow at all CSO structures in a catchment can then be simulated with a rainfall-runoff model. The method is applied to a case study and results show that the inverse rainfall-runoff model can be used instead of missing rain gauges. Online operation is ensured by software providing an interface to the SCADA-system of the operator and controlling the model. A water quality model could be included to simulate also pollutant concentrations in the excess flow.

Designing and implementing a multi-core capable integrated urban drainage modelling Toolkit

Development and application of an integrated modelling toolkit for urban water systems. Multi-core capability for high performance applications. Open source and modular design for easy and flexible extension. Integrated urban drainage modelling combines different aspects of the urban water system into a common framework. With increasing pressures of a changing climate, urban growth and economic constraints, the need for wider spread integration is necessary in the interest of a sustainable future. Greater complexity results in greater computational burden but modelling packages will, likewise, need to be flexible enough to allow incorporation of new algorithms. With advancements in modern information technology, a parallel implementation of such a modelling toolkit is mandatory while still leaving its users the flexibility of extensions. The design and implementation of the integrated modelling framework CityDrain3 shows that it is possible to write research code that is high-performance and extensible by many research projects. Three use case scenarios are presented to showcase the application of CityDrain3. The performance advantage of parallelization (up to 40 times compared to its predecessor) and the scalability of the framework are also demonstrated.

The effects of initial soil moisture conditions on swale flow hydrographs

The effects of soil water content (SWC) on the formation of run‐off in grass swales draining into astorm sewer system were studied in two 30‐m test swales with trapezoidal cross sections. Swale1 wa ...

Selection of Calibration Events for Modelling Green Urban Drainage

Urban drainage models are often calibrated using a limited number of rainfall-runoff events, which may be selected in different ways from a longer observation series. This paper compares 13 differe ...

Modelling urban runoff from rain-on-snow events with the US EPA SWMM model for the current and future climate scenarios

AbstractA methodological study of modeling runoff from rain-on-snow events was conducted using the northern Swedish city of Kiruna as a test case, with respect to physiographic, drainage system, an...

A Bayesian method for missing rainfall estimation using a conceptual rainfall–runoff model

ABSTRACT The estimation of missing rainfall data is an important problem for data analysis and modelling studies in hydrology. This paper develops a Bayesian method to address missing rainfall estimation from runoff measurements based on a pre-calibrated conceptual rainfall–runoff model. The Bayesian method assigns posterior probability of rainfall estimates proportional to the likelihood function of measured runoff flows and prior rainfall information, which is presented by uniform distributions in the absence of rainfall data. The likelihood function of measured runoff can be determined via the test of different residual error models in the calibration phase. The application of this method to a French urban catchment indicates that the proposed Bayesian method is able to assess missing rainfall and its uncertainty based only on runoff measurements, which provides an alternative to the reverse model for missing rainfall estimates.