Jörg Rieckermann

Towards a better understanding of sewer exfiltration

This paper gives a full review of the importance of sewer leakage, which has received increased attention throughout the last decades. Despite the intensive interdisciplinary research that has been invested, its magnitude is still unclear and a comprehensive solution for the assessment of sewer exfiltration does not seem to be at hand. However, given that mechanisms of exfiltration and the factors influencing its extent are similar all over the world, it seems possible to develop a generic leakage approach. Several methods for modelling sewer leakage are reviewed and the available measuring techniques are critically evaluated. Based on this evaluation, we suggest a unifying framework to facilitate focused model building. Specifically, we identify open research questions and propose to (i) standardise measurement results to enable better understanding, (ii) perform more long-term experiments under realistic field conditions, and (iii) assess the uncertainty of measurement and model results so that findings are not over-interpreted.

Network condition simulator for benchmarking sewer deterioration models.

An accurate description of aging and deterioration of urban drainage systems is necessary for optimal investment and rehabilitation planning. Due to a general lack of suitable datasets, network condition models are rarely validated, and if so with varying levels of success. We therefore propose a novel network condition simulator (NetCoS) that produces a synthetic population of sewer sections with a given condition-class distribution. NetCoS can be used to benchmark deterioration models and guide utilities in the selection of appropriate models and data management strategies. The underlying probabilistic model considers three main processes: a) deterioration, b) replacement policy, and c) expansions of the sewer network. The deterioration model features a semi-Markov chain that uses transition probabilities based on user-defined survival functions. The replacement policy is approximated with a condition-class dependent probability of replacing a sewer pipe. The model then simulates the course of the sewer sections from the installation of the first line to the present, adding new pipes based on the defined replacement and expansion program. We demonstrate the usefulness of NetCoS in two examples where we quantify the influence of incomplete data and inspection frequency on the parameter estimation of a cohort survival model and a Markov deterioration model. Our results show that typical available sewer inventory data with discarded historical data overestimate the average life expectancy by up to 200 years. Although NetCoS cannot prove the validity of a particular deterioration model, it is useful to reveal its possible limitations and shortcomings and quantifies the effects of missing or uncertain data. Future developments should include additional processes, for example to investigate the long-term effect of pipe rehabilitation measures, such as inliners.

Model bias and complexity - Understanding the effects of structural deficits and input errors on runoff predictions

Oversimplified models and erroneous inputs play a significant role in impairing environmental predictions. To assess the contribution of these errors to model uncertainties is still challenging. Our objective is to understand the effect of model complexity on systematic modeling errors. Our method consists of formulating alternative models with increasing detail and flexibility and describing their systematic deviations by an autoregressive bias process. We test the approach in an urban catchment with five drainage models. Our results show that a single bias description produces reliable predictions for all models. The bias decreases with increasing model complexity and then stabilizes. The bias decline can be associated with reduced structural deficits, while the remaining bias is probably dominated by input errors. Combining a bias description with a multimodel comparison is an effective way to assess the influence of structural and rainfall errors on flow forecasts. We investigate how a random bias process behaves as a function of model complexity.We analyze 5 model structures to simulate a stormwater system.The reduction of systematic deviations is associated with decreasing structural deficits.In this study the remaining bias is likely to be dominated by input errors.The method provides sound probabilistic predictions in a relatively efficient way.

Assessment of total uncertainty in cocaine and benzoylecgonine wastewater load measurements.

To check the effectiveness of campaigns preventing drug abuse or indicating local effects of efforts against drug trafficking, it is beneficial to know consumed amounts of substances in a high spatial and temporal resolution. The analysis of drugs of abuse in wastewater (WW) has the potential to provide this information. In this study, the reliability of WW drug consumption estimates is assessed and a novel method presented to calculate the total uncertainty in observed WW cocaine (COC) and benzoylecgonine (BE) loads. Specifically, uncertainties resulting from discharge measurements, chemical analysis and the applied sampling scheme were addressed and three approaches presented. These consist of (i) a generic model-based procedure to investigate the influence of the sampling scheme on the uncertainty of observed or expected drug loads, (ii) a comparative analysis of two analytical methods (high performance liquid chromatography-tandem mass spectrometry and gas chromatography-mass spectrometry), including an extended cross-validation by influent profiling over several days, and (iii) monitoring COC and BE concentrations in WW of the largest Swiss sewage treatment plants. In addition, the COC and BE loads observed in the sewage treatment plant of the city of Berne were used to back-calculate the COC consumption. The estimated mean daily consumed amount was 107 ± 21 g of pure COC, corresponding to 321 g of street-grade COC.

Commercial microwave links instead of rain gauges: fiction or reality?

Commercial microwave links (MWLs) were suggested about a decade ago as a new source for quantitative precipitation estimates (QPEs). Meanwhile, the theory is well understood and rainfall monitoring with MWLs is on its way to being a mature technology, with several well-documented case studies, which investigate QPEs from multiple MWLs on the mesoscale. However, the potential of MWLs to observe microscale rainfall variability, which is important for urban hydrology, has not been investigated yet. In this paper, we assess the potential of MWLs to capture the spatio-temporal rainfall dynamics over small catchments of a few square kilometres. Specifically, we investigate the influence of different MWL topologies on areal rainfall estimation, which is important for experimental design or to a priori check the feasibility of using MWLs. In a dedicated case study in Prague, Czech Republic, we collected a unique dataset of 14 MWL signals with a temporal resolution of a few seconds and compared the QPEs from the MWLs to reference rainfall from multiple rain gauges. Our results show that, although QPEs from most MWLs are probably positively biased, they capture spatio-temporal rainfall variability on the microscale very well. Thus, they have great potential to improve runoff predictions. This is especially beneficial for heavy rainfall, which is usually decisive for urban drainage design.

Water quality-based assessment of urban drainage impacts in Europe - where do we stand today?

Traditionally, design and optimisation of urban drainage systems was mainly driven by cost efficiency, surface flood prevention, and later by emission reduction. More recent procedures explicitly include ecological conditions of the receiving water in the definition of acceptable pollutant discharges via sewer system and treatment plant outlets. An ambient Water Quality based impact Assessment (WQA) principle therefore requires an integrative system optimisation. However, a broad range of mostly national WQA protocols exist across Europe varying in structure and complexity, assessment concept, spatial and temporal scope and handling of uncertainty. This variety inherently implies a considerable risk of subjectivity in the impact assessment with highly variable outcomes. The present review identifies differences and similarities of WQA protocols in use and discusses their strengths and weaknesses through: (i) a systematic comparison of WQA protocols by selected attributes, (ii) a review of real-life cases reported in the literature and expert interviews, and (iii) an illustration of our main findings by applying selected WQA in an instructive example. The review discusses differences in structure and concept, which are mainly identified for simplistic WQA protocols. The application of selected protocols to an example case shows a wide variety of numerical results and conclusive decisions. It is found that existing protocols target different questions within the decision making process, which users should be more aware of. Generally, to make assessments more reliable, further fundamental research is required to fully understand the relationship between stressors and stream ecosystem responses which will make assessments more reliable. Technically, tools suggested in WQA protocols show severe deficiencies and an uncertainty assessment should be mandatory.

Assessing the performance of sewer rehabilitation on the reduction of infiltration and inflow.

Inflow and Infiltration (I/I) into sewer systems is generally unwanted, because, among other things, it decreases the performance of wastewater treatment plants and increases combined sewage overflows. As sewer rehabilitation to reduce I/I is very expensive, water managers not only need methods to accurately measure I/I, but also they need sound approaches to assess the actual performance of implemented rehabilitation measures. However, such performance assessment is rarely performed. On the one hand, it is challenging to adequately take into account the variability of influential factors, such as hydro-meteorological conditions. On the other hand, it is currently not clear how experimental data can indeed support robust evidence for reduced I/I. In this paper, we therefore statistically assess the performance of rehabilitation measures to reduce I/I. This is possible by using observations in a suitable reference catchment as a control group and assessing the significance of the observed effect by regression analysis, which is well established in other disciplines. We successfully demonstrate the usefulness of the approach in a case study, where rehabilitation reduced groundwater infiltration by 23.9%. A reduction of stormwater inflow of 35.7%, however, was not statistically significant. Investigations into the experimental design of monitoring campaigns confirmed that the variability of the data as well as the number of observations collected before the rehabilitation impact the detection limit of the effect. This implies that it is difficult to improve the data quality after the rehabilitation has been implemented. Therefore, future practical applications should consider a careful experimental design. Further developments could employ more sophisticated monitoring methods, such as stable environmental isotopes, to directly observe the individual infiltration components. In addition, water managers should develop strategies to effectively communicate statistically not significant I/I reduction ratios to decision makers.

Fast mechanism-based emulator of a slow urban hydrodynamic drainage simulator

Gaussian process (GP) emulation is a data-driven method that substitutes a slow simulator with a stochastic approximation. It is then typically orders of magnitude faster than the simulator at the costs of introducing interpolation errors. Our approach, the mechanism-based GP emulator, uses knowledge of the simulator mechanisms in addition to the information gained from previous simulator runs, so called design data. In this study, we investigate how the degree of incorporating mechanisms into the design of the GP emulator influences emulation accuracy. Similarly to the previous results, we get a significant gain in accuracy already when using the simplest approximation of the mechanisms by a single linear reservoir. However, in this case, we again considerably improve emulation accuracy when using the next two approximations. This allows us to decreases the required number of design data to achieve a similar accuracy as a non-mechanistic emulator. We substitute a hydrological model with a faster mechanism-based GP emulator.We compare the gains in emulation accuracy by considering various mechanisms.Already the simplest mechanisms lead to an improvement of emulation accuracy.The emulator leads to a good accuracy already for very small design data sets.The suggested emulator can shorten, e.g., computation time for model calibration.

Assessing the potential of using telecommunication microwave links in urban drainage modelling

The ability to predict the runoff response of an urban catchment to rainfall is crucial for managing drainage systems effectively and controlling discharges from urban areas. In this paper we assess the potential of commercial microwave links (MWL) to capture the spatio-temporal rainfall dynamics and thus improve urban rainfall-runoff modelling. Specifically, we perform numerical experiments with virtual rainfall fields and compare the results of MWL rainfall reconstructions to those of rain gauge (RG) observations. In a case study, we are able to show that MWL networks in urban areas are sufficiently dense to provide good information on spatio-temporal rainfall variability and can thus considerably improve pipe flow prediction, even in small subcatchments. In addition, the better spatial coverage also improves the control of discharges from urban areas. This is especially beneficial for heavy rainfall, which usually has a high spatial variability that cannot be accurately captured by RG point measurements.

Comparison of two stochastic techniques for reliable urban runoff prediction by modeling systematic errors

In urban rainfall-runoff, commonly applied statistical techniques for uncertainty quantification mostly ignore systematic output errors originating from simplified models and erroneous inputs. Consequently, the resulting predictive uncertainty is often unreliable. Our objective is to present two approaches which use stochastic processes to describe systematic deviations and to discuss their advantages and drawbacks for urban drainage modeling. The two methodologies are an external bias description (EBD) and an internal noise description (IND, also known as stochastic gray-box modeling). They emerge from different fields and have not yet been compared in environmental modeling. To compare the two approaches, we develop a unifying terminology, evaluate them theoretically, and apply them to conceptual rainfall-runoff modeling in the same drainage system. Our results show that both approaches can provide probabilistic predictions of wastewater discharge in a similarly reliable way, both for periods ranging from a few hours up to more than 1 week ahead of time. The EBD produces more accurate predictions on long horizons but relies on computationally heavy MCMC routines for parameter inferences. These properties make it more suitable for off-line applications. The IND can help in diagnosing the causes of output errors and is computationally inexpensive. It produces best results on short forecast horizons that are typical for online applications.