Abteilung Siedlungswasserwirtschaft

Die kontinuierliche Überwachung von Abwassermenge und -qualität ist entscheidend für den Betrieb und die Optimierung von Abwassersystemen. Aktuelle Messtechniken zur Wasserqualitätsbestimmung, insbesondere im Rohabwasser, sind sehr wartungsintensiv und teuer. Hyperspektralkameras, die in der Fernerkundung und der Industrie eingesetzt werden, könnten eine vielversprechende Alternative zu herkömmlichen spektrometrischen oder ionenselektiven Sensoren darstellen. In einer sechsmonatigen Pilotstudie haben wir Abwasser in einem kleinen Einzugsgebiet mit einer industrietauglichen Hyperspektralkamera (VNIR) und getauchten Spektrometern (UV Vis) überwacht. Beide Systeme wurden anhand von Trübungsmessungen eines Online-Sensors und 467 Stichproben für Ammonium (NH4), Phosphat (PO4) sowie gelöstem organischen Kohlenstoff (DOC) kalibriert. Die Kalibrierung erfolgte mit einem Partial-Least-Squares-Modell. Die Hyperspektralkamera lieferte zufriedenstellende Trübungswerte (RMSE = 14,9 NTU), vergleichbar mit dem Spektrometer (11,7 NTU). Allerdings waren die Ergebnisse für NH4 (7.1 mg/l), PO4 (0.7 mg/l) und DOC (39.3 mg/l) jedoch weniger genau. Interessanterweise lieferte die Spektrometersonde präzisere Resultate für NH4 (4.3 mg/l). Die Studie zeigt das Potenzial der hyperspektralen Technologie für die kontinuierliche Abwasserqualitätsmessung. Vor allem bei der Hardware und den mechanistischen Modellen für den Lichttransport sehen wir Verbesserungspotenzial.

Monitoring water quality in sewers is challenging, particularly because state-of-the-art technologies require contact with the raw wastewater. The presence of fat, oil, grease, and solids makes automated grab sampling difficult and causes sensor fouling. To overcome these limitations, non-contact methods based on light reflectance, such as hyperspectral imaging (HSI), are gaining attention. However, HSI has never been tested for raw wastewater. To assess its accuracy for measuring pollution, we developed a laboratory setup and performed targeted experiments with a combination of raw and diluted wastewater, as well as synthetic turbidity stock solutions. We measured seven pollution variables: chemical oxygen demand, turbidity, dissolved organic compounds, ammonium, total nitrogen, phosphate, and sulphates. We used automated pixel selection and partial least squares regression to retrieve pollution information from the hyperspectral images. Our results, based on 144 samples, suggest that HSI can estimate pollution levels with a precision in the range of state-of-the-art absorbance spectrophotometric methods. Additionally, we found that the combination of pixel and wavelength selection, enabled by the hyperspectral data structure, significantly influences the performance of partial least square modelling. Overall, our findings indicate that HSI is a promising technology for non-contact monitoring of water quality in raw wastewater.

Hydrometers that measure size and velocity distributions of precipitation are needed for research and corrections of rainfall estimates from weather radars and microwave links. Existing optical disdrometers measure droplet size distributions, but underestimate small raindrops and are impractical for widespread always-on IoT deployment. We study the feasibility of measuring droplet size and velocity using a neuromorphic event camera. These dynamic vision sensors asynchronously output a sparse stream of pixel brightness changes. Droplets falling through the plane of focus of a steeply down-looking camera create events generated by the motion of the droplet across the field of view. Droplet size and speed are inferred from the hourglass-shaped stream of events. Using an improved hard disk arm actuator to reliably generate artificial raindrops with a range of small sizes, our experiments show maximum errors of 7% (mean absolute percentage error) for droplet sizes from 0.3 to 2.5mm and speeds from 1.3 to 8.0ms-1. Measurements with the same setup from a commercial PARSIVEL disdrometer show similar results. Both devices slightly overestimate the small droplet volume with a volume overestimation of 25% from the event camera measurements and 50% from the PARSIVEL instrument. Each droplet requires processing of 5000 to 50000 brightness change events, potentially enabling low-power always-on disdrometers that consume power proportional to the rainfall rate. Data and code are available at the paper website https://sites.google.com/view/dvs-disdrometer/home .

Sediments in urban drainage systems (UDS) significantly impact their operation, so effective strategies are required to reduce their negative effects. Monitoring sediment accumulation provides valuable insights into sediment characteristics, sediment transport dynamics, and system performance. However, the effectiveness of monitoring systems is limited due to cost constraints and installation challenges. This study describes the development and application of a new system based on temperature dynamics to measure sediment depths in sewer systems. The methodology involves the analysis of temperature time series under dry weather flow conditions to identify harmonic patterns between wastewater and sediment-bed temperatures. These patterns are increasingly attenuated by increasing sediment depth. This study combines a system called MONitoring Temperatures in SEdiments (MONTSE), which integrates a dual-probe heat-pulse (DPHP) method to characterize sediment thermal properties, and a surrogate model, which includes temperature pattern analysis, to estimate sediment depths. Likewise, laboratory-scale experiments were performed to validate the temperature monitoring system and the surrogate model performance. The maximum absolute errors in measured sediment depths were less than 22 mm, and the uncertainty of the system was estimated at ±7.3 mm. Groundbreaking measurements of thermal properties of UDS sediments were also reported. Reliable information on sediment depths and properties was provided, so the system could significantly optimize sewer system operation and cleaning strategies.

Water quality monitoring in sewer networks remains a technical challenge even though water pollution and control are high priorities since decades. Current water quality monitoring usually analyzes samples in laboratories, allowing only sporadic measurements, or uses immersed sensors in the wastewater, leading to clogging and sensor fouling resulting in expense due to intensive maintenance. Both techniques thus have serious limitations.
Previous research showed that UV-Vis reflectance spectrometry can be used for non-contact monitoring of turbidity (TUR) and Chemical Oxygen Demand (COD), which are two key water quality indicators. Although spectrometer achieve high spectral resolution their limited spatial field of view is problematic for highly inhomogeneous surfaces as is the case wastewater.
In this study, we obtain beyond state-of-art measurement accuracies by combining machine learning techniques with increased spatial field-of-view Multi-Spectral Imaging (MSI) whilst substantially reducing the spectral resolution. We designed and built a dedicated setup with a monochromatic camera and an active illumination of thirteen LEDs covering the spectrum range of 200-700 nm. We acquired and calibrated data on 27 samples with different concentrations of TUR and COD. Machine learning regression models were trained and evaluated with the extracted spectra. We tested the Partial Least Square (PLS), Support Vector Machine (SVM) and Random Forest (RF). PLS regression performed best with excellent correlation coefficients (R²) of the 0.99 for TUR and 0.93 for COD. We obtained similar results with the SVM algorithm (R² = 0.99 and 0.92), whilst RF had lower scores (R² = 0.96 and 0.71).

Sewer sediments are among the main concerns related to urban drainage system management as they represent the largest contribution of suspended solid loads during rainfall events due to their resuspension. This study presents a novel methodology to detect and assess bed deposits in urban drainage systems based on temperature monitoring by using well-known thermodynamics and sediment properties. To illustrate the heat transfer processes in a liquid–sediment system and their relation to accumulation, a lab-scale experimental campaign was performed using sewer sediments and simulating water temperature gradients in sewers. Wastewater temperatures showed a marked daily pattern, while the presence of sediment dampened dynamics. Sediment thickness could therefore be estimated from the time evolution of the temperature differences measured between the bottom of the sediment bed and the water phase. Likewise, experimental data were used to calibrate a 1D heat transfer model, from which several sediment accumulation scenarios were simulated by using real wastewater temperature series. Thus, the influence of sediment properties on accumulation processes was assessed, and the range of potentially measurable sediments within an optimal range of [5–20] cm was identified. As a conclusion, temperature measurements and heat transfer model analysis can be used to approximate and monitor the sediments deposited in urban drainage systems. Future studies will extend the method to spatially-resolved sediment monitoring and active temperature sensing to improve sediment accumulation monitoring capabilities.

Transitioning urban drainage systems to serve water-smart societies requires the involvement of different disciplines and stakeholders. However, stakeholders have different visions and needs from the transitioning process (e.g in terms of financing, policy adaptation and system management) these also vary between regions and countries. Identifying such different needs for stakeholders is necessary to propose practical adaptation strategies. Therefore, evidence of needs as reflected in policy papers and legislation in seven European countries was collected. Knowledgeable individuals in the urban drainage community were asked about their visions. Results show that whilst there is consensus on the challenges, visions on how to transition are diverse, indicating that more interaction between the different stakeholder groups is required to develop consensus. Additionally, organisational and legislative structures often slow down the necessary change processes.

We conclude that the data literacy gap is serious and will even be more difficult to overcome, when skilled workforce is missing due to over aging. Automating repetitive task does not only support high-level operator employees, but helps everyone who cannot program and does not intend to spend working time in front of a computer. This requires moving RICHIE away from a classical computer interface towards a mobile application. We will validate the acceptance and performance in a broad-based test including many operators.

Globally, urbanization, climate change, and ageing assets affect urban water management. One potential solution is recognized in smart urban water systems, which rely on monitoring data from infrastructure elements, e.g., combined sewer overflows (CSOs). Such monitoring data provides evidence on system functioning and performance and allows a dynamic control to exploit all existing infrastructure capacities, for example during heavy rainfalls. As a result, smart urban water systems hold the potential to improve performance outcomes, to reduce environmental impacts on surface waters, and to manage infrastructures more efficiently.
However, despite the availability of digital technologies for monitoring, data transfer, and control, only few examples of smart urban water systems exist. Where monitoring data is already obtained, it is often not optimally handled. Potential reasons cannot be found at the technical level only, but require an understanding of smart urban water systems in their social and socio-technical dimensions. To achieve smart urban water systems, not only technical innovation, but also adaptations of the surrounding social system are needed. [...]

Networked infrastructure systems - including energy, transportation, water, and wastewater systems — provide essential services to society. Globally, these services are undergoing major transformative processes such as digitalization, decentralization, or integrated management. Such processes not only depend on technical changes in infrastructure systems but also include important social and socio-technical dimensions. In this article, we propose a socio-technical network perspective to study the ensemble of social actors and technical elements involved in an infrastructure system, and their complex relations. We conceptualize structurally explicit socio-technical networks of networked infrastructure systems based on methodological considerations from network analysis and draw on concepts from socio-technical system theories and social-ecological network studies. Based on these considerations, we suggest analytical methods to study basic network concepts such as density, reciprocity, and centrality in a socio-technical network. We illustrate socio-technical motifs, i.e., meaningful sub-structures in socio-technical networks of infrastructure management. Drawing on these, we describe how infrastructure systems can be analyzed in terms of digitalization, decentralization, and integrated management from a socio-technical network perspective. Using the example of urban wastewater systems, we illustrate an empirical application of our approach. The results of an empirical case study in Switzerland demonstrate the potential of socio-technical networks to promote a deeper understanding of complex socio-technical relations in networked infrastructure systems. We contend that such a deeper understanding could improve management practices of infrastructure systems and is becoming even more important for enabling future data-driven, decentralized, and more integrated infrastructure management.

Underground infrastructure networks form the backbone of vital supply and disposal systems. However, they are under-monitored in comparison to their value. This is due, in large part, to the lack of energy supply for monitoring and data transmission. In this paper, we investigate a novel, energy harvesting system used to power underground sewer infrastructure monitoring networks. The system collects the required energy from ambient sources, such as temperature differences or residual light in sewer networks. A prototype was developed that could use either a thermoelectric generator (TEG) or a solar cell to capture the energy needed to acquire and transmit ultrasonic water level data via LoRaWAN. Real-world field trials were satisfactory and showed the potential power output, as well as, possibilities to improve the system. Using an extrapolation model, we proved that the developed solution could work reliably throughout the year.

An inadequate correction for wet-antenna attenuation (WAA) often causes a notable bias in quantitative precipitation estimates (QPEs) from commercial microwave links (CMLs) limiting the usability of these rainfall data in hydrological applications. This article analyzes how WAA can be corrected without dedicated rainfall monitoring for a set of 16 CMLs. Using data collected over 53 rainfall events, the performance of six empirical WAA models was studied, both when calibrated to rainfall observations from a permanent municipal rain gauge network and when using model parameters from the literature. The transferability of WAA model parameters among CMLs of various characteristics has also been addressed. The results show that high-quality QPEs with a bias below 5% and root-mean-square error (RMSE) of 1 mm/h in the median could be retrieved, even from subkilometer CMLs where WAA is relatively large compared to raindrop attenuation. Models in which WAA is proportional to rainfall intensity provide better WAA estimates than constant and time-dependent models. It is also shown that the parameters of models deriving WAA explicitly from rainfall intensity are independent of CML frequency and path length and, thus, transferable to other locations with CMLs of similar antenna properties.

Sediment accumulation in urban drainage systems (UDS) is a serious problem, which requires costly maintenance and cleaning programmes. These tasks are often linked to previous experience, but there is no control strategy, nor a complete knowledge of the sediment transport processes. Attempts so far to monitor these processes have been based on techniques that require exhaustive work in their installation or in taking punctual field measurements. Therefore, sediment monitoring in UDS remains challenging. In this work, a new approach based in heat transfer processes is shown. For this purpose, high-resolution temperature data are used to detect sediments from changes in temperature dynamics between the fluid and the sediment bed deposits. A series of batch tests were carried out in adiabatic boxes with different thicknesses of real sewer sediments filled with water. Temperature sensors were installed in the water layer over the sediments and at the bottom of the sediment deposits. An additional sensor was deployed to control room temperature. Heat-cool pulses were programmed in the water layer to simulate water temperature oscillations. As a result, the temperature time series at the bottom of the sediment deposits were attenuated and time-shifted depending on the accumulated thickness above it. These results demonstrate the potential to monitoring sediment accumulation in UDS with passive temperature sensors. Further extensions to distributed temperature monitoring with fibre-optical sensors and active heating are currently being investigated.

We conclude that while distributed sensing of discharge and water level helps understanding the dynamics of the stormwater runoff, it is not always straightforward to make better predictive models of complex urban drainage networks using this data. This analysis - by delineating the bottlenecks towards informative inference - provides via-negativa recommendations for the placement of distributed sensors, when improved model performance is also one of the motivating factors for such data collection campaigns.

Abflussregelungen können oft kostengünstige Massnahmen sein, um den Gewässerschutz bei Regenwetter durch die bessere Auslastung der vorhandenen Infrastruktur zu verbessern. In Zukunft könnte die tatsächliche Leistung von Abflussregelungen mit Betriebsdaten und statistischen Methoden beurteilt werden. Eine verlässliche messdatenbasierte Leistungsanalyse von Regelungssystemen muss Variabilität und Unsicherheiten in den Messdaten berücksichtigen. Der «Potential Outcome»-Ansatz und flexible Regressionsmethoden, wie z. B. Random Forest, scheinen vielversprechende Lösungen zu sein, um den Effekt einer Abflussregelung zu schätzen.

Thermal-hydraulic considerations in urban drainage networks are essential to utilise available heat capacities from waste- and stormwater. However, available models are either too detailed or too coarse; fully coupled thermal-hydrodynamic modelling tools are lacking. To predict efficiently water-energy dynamics across an entire urban drainage network, we suggest the SWMM-HEAT model, which extends the EPA-StormWater Management Model with a heat-balance component. This enables conducting more advanced thermal-hydrodynamic simulation at full network scale than currently possible. We demonstrate the usefulness of the approach by predicting temperature dynamics in two independent real-world cases under dry weather conditions. We furthermore screen the sensitivity of the model parameters to guide the choice of suitable parameters in future studies. Comparison with measurements suggest that the model predicts temperature dynamics adequately, with RSR values ranging between 0.71 and 1.1. The results of our study show that modelled in-sewer wastewater temperatures are particularly sensitive to soil and headspace temperature, and headspace humidity. Simulation runs are generally fast; a five-day period simulation at high temporal resolution of a network with 415 nodes during dry weather was completed in a few minutes. Future work should assess the performance of the model for different applications and perform a more comprehensive sensitivity analysis under more scenarios. To facilitate the efficient estimation of available heat budgets in sewer networks and the integration into urban planning, the SWMM-HEAT code is made publicly available.

Digital technologies can be important to policy-makers and public servants, as these technologies can increase infrastructure performance and reduce environmental impacts. For example, utilizing data from sensors in sewer systems can improve their management, which in turn may result in better surface water quality. Whether such big data from sensors is utilized is, however, not only a technical issue, but also depends on different types of social and institutional conditions. Our article identifies individual, organizational, and institutional barriers at the level of sub-states that hinder the evaluation of data from sewer systems. We employ fuzzy-set Qualitative Comparative Analysis (fsQCA) to compare 23 Swiss sub-states and find that two barriers at different levels can each hinder data evaluation on their own. More specifically, either a lack of vision at the individual level or a lack of resources at the organizational level hinder the evaluation of data. Findings suggest that taking into account different levels is crucial for understanding digital transformation in public organizations.

Commercial microwave links (CMLs) in telecommunication networks can provide relevant information for remote sensing of precipitation and other environmental variables, such as path-averaged drop size distribution, evaporation, or humidity. The CoMMon field experiment (COmmercial Microwave links for urban rainfall MONitoring) mainly focused on the rainfall observations by monitoring a 38 GHz dual-polarized CML of 1.85 km path length at a high temporal resolution (4 s), as well as a co-located array of five disdrometers and three rain gauges over 1 year. The dataset is complemented with observations from five nearby weather stations. Raw and pre-processed data, which can be explored with a custom static HTML viewer, are available at https://doi.org/10.5281/zenodo.4923125 (Špačková et al., 2021). The data quality is generally satisfactory for further analysis, and potentially problematic measurements are flagged to help the analyst identify relevant periods for specific study purposes. Finally, we encourage potential applications and discuss open issues regarding future remote sensing with CMLs.

This paper presents an efficient surrogate modeling strategy for the uncertainty quantification and Bayesian calibration of a hydrological model. In particular, a process-based dynamical urban drainage simulator that predicts the discharge from a catchment area during a precipitation event is considered. The goal of the case study is to perform a global sensitivity analysis and to identify the unknown model parameters as well as the measurement and prediction errors. These objectives can only be achieved by cheapening the incurred computational costs, that is, lowering the number of necessary model runs. With this in mind, a regularity-exploiting metamodeling technique is proposed that enables fast uncertainty quantification. Principal component analysis is used for output dimensionality reduction and sparse polynomial chaos expansions are used for the emulation of the reduced outputs. Sobol’ sensitivity indices are obtained directly from the expansion coefficients by a mere post-processing. Bayesian inference via Markov chain Monte Carlo posterior sampling is drastically accelerated.

Physically-based urban wash-off models are a promising means of studying the transport of finer suspended solids and their associated pollutants during rain events, considering spatial and temporal heterogeneities. This study contributes to the understanding of these models through an in-depth sensitivity analysis to provide the necessary information to simplify the model and deal with parameter identifiability. First, based on twelve tailored high-resolution experiments, the accurate measurement of input variables was used to study the parameters of the Hairsine-Rose sediment transport model through a global sensitivity analysis. Using Standardized Regression Coefficients (SRC) and Extended Fourier Amplitude Sensitivity Test (EFAST) methods, the analysis showed that both the total washed-off mass and the TSS peaks concentration are highly sensitive to the critical mass, which considers the reduction in the detachment of particles when the sediment available decreases and is scattered over the surface. In addition, the rain- and flow-driven detachment parameters were presented as key for smaller and larger sediment particles, respectively. Then, those uncertainties that are associated in field studies with the determination of the model input variables were also considered by conducting a local sensitivity analysis. The initial load of sediment and the mean grain size were seen to be the most important variables, thus underlining the need for very accurate measurements here. Moreover, a precise definition of Harsine-Rose parameters is also necessary to achieve reliable results in order to work on treatment and management techniques to minimize the impact of urban surface contaminants on urban environments.

Im Einzugsgebiet der ARA Basel haben sich in den letzten Jahren grundlegende Veränderungen in Bezug auf die ARA und das Kanalnetz ergeben. Dies eröffnet neue Handlungsoptionen, vor allem in Bezug auf fortgeschrittene Verfahrenstechniken, sowie Automations- und Regelungsstrategien. [...]

In engineering practice, model-based design requires not only a good process-based model, but also a good description of stochastic disturbances and measurement errors to learn credible parameter values from observations. However, typical methods use Gaussian error models, which often cannot describe the complex temporal patterns of residuals. Consequently, this results in overconfidence in the identified parameters and, in turn, optimistic reactor designs. In this work, we assess the strengths and weaknesses of a method to statistically describe these patterns with autocorrelated error models. This method produces increased widths of the credible prediction intervals following the inclusion of the bias term, in turn leading to more conservative design choices. However, we also show that the augmented error model is not a universal tool, as its application cannot guarantee the desired reliability of the resulting wastewater reactor design.

Ubiquitous sensing will create many opportunities and threats for urban water management, which are only poorly understood today. To identify the most relevant trends, we conducted a horizon scan regarding how ubiquitous sensing will shape the future of urban drainage and wastewater management. Our survey of the international urban water community received an active response from both the academics and the professionals from the water industry. The analysis of the responses demonstrates that emerging topics for urban water will often involve experts from different communities, including aquatic ecologists, urban water system engineers and managers, as well as information and communications technology professionals and computer scientists. Activities in topics that are identified as novel will either require (i) cross-disciplinary training, such as importing new developments from the IT sector, or (ii) research in new areas for urban water specialists, for example, to help solve open questions in aquatic ecology. These results are, therefore, a call for interdisciplinary research beyond our own discipline. They also demonstrate that the water management community is not yet prepared for the digital transformation, where we will experience a data demand, i.e. a “pull” of urban water data into external services. The results suggest that a lot remains to be done to harvest the upcoming opportunities. Horizon scanning should be repeated on a routine basis, under the umbrella of an experienced polling organization.

Bei Regenwetter werden viele potenziell toxische Schadstoffe aus Aussenhüllen von Gebäuden ausgewaschen und in die Gewässer eingetragen. Unklar ist allerdings, ob einfache lineare Auswaschfunktionen für Mikroschadstoffe aus Gebäudehüllen für Schmutzfrachtsimulationen ausreichend sind. Um Auswaschungen aus beregneten Gebäudehüllen dynamisch zu berechnen, wurde das "COnstruction Material LEAching Model" (COMLEAM) entwickelt, mit dem Mikroverunreinigungen prozess-basiert beschrieben werden. COMLEAM berücksichtigt allerdings keine Transportprozesse in der Siedlungsentwässerung, so dass es bislang nicht möglich ist, Umwelteinflüsse von Gebäudehüllen abzubilden. In dieser Studie wurde daher COMLEAM an das hydrodynamische Niederschlags-Abfluss Modell SWMM gekoppelt. Insbesondere wurden die Unterschiede von dynamischen und linearen Auswaschmodellen untersucht. Für die Verknüpfung der Modelle wurde eine neue Schnittstelle für COMLEAM programmiert, die sowohl einen Export von SWMM-kompatiblen Schmutzfracht-Zeitreihen, als auch Batch-Simulationen ermöglicht. Die Ergebnisse für eine Fallstudie in Fehraltorf (CH), wo Schmutzfrachten und Konzentrationen des Durchwurzelungsschutzmittels Mecoprop vorhergesagt wurden, zeigen grundsätzlich eine befriedigende Übereinstimmung mit zeitlich hochaufgelösten Daten aus einer Messkampagne. Langzeitprognosen mit dem dynamischen COMLEAM Modell liefern in unserem Fall signifikant unterschiedliche Ergebnisse als lineare Auswaschfunktionen. Allerdings sind diese Unterschiede, in unserem Fallbeispiel, ab einem Gebäudealter von 10 Jahren allerdings vernachlässigbar. Sensitivitätsanalyse deuten weiterhin darauf hin, dass Informationen zum Gebäudebestand, und insbesondere das Alter der Gebäude, sehr sensitiv auf die Simulationsresultate sind.

In this study, we investigate whether commercial microwave links (CML) can bridge the gap between rain gauge and radar. We performed a rainfall-runoff monitoring experiment with rain gauges, CMLs and weather radar in a small urbanized catchment during several months. The results of comparing rainfall data and resulting sewer flows suggest that quantitative precipitation estimates from CMLs require adjustment to local rain gauges to produce satisfactory results in a similar fashion as weather radar. In our study, CML with baseline removal and wet-antenna correction, but without adjustment, lead to substantial bias and underestimate rainfall-runoff ratios by more than 48% (Radar: ca. 40%). In contrast, adjusted CML produced results very similar to those from a dense rain gauge network and underestimated rainfall-runoff by about 11%. Further work is needed to better understand influential factors on the data quality of QPEs from CML. In addition, incentives are needed to motivate both telecommunication companies to provide CML data and weather service companies to use them in their products, i.e. commercializing information from opportunistic sensors.

Mit der fortschreitenden Digitalisierung gilt es heute als Stand der Technik, Messdaten über das Abwassersystem zu erheben, auszuwerten und zu archivieren. Wie der Wert von Messdaten für Betrieb, Planung und Leistungsüberprüfung ausgeschöpft werden kann, ist jedoch unklar. Um einen einheitlichen Einsatz von Messtechnik, z.B. an Regenüberlaufbecken, sowie einen guten Umgang mit erfassten Messdaten in der Schweizer Praxis zu schaffen, sind Anpassungen auf organisatorischer Ebene notwendig.

Commercial microwave links (CMLs), radio connections widely used in telecommunication networks, can provide path-integrated quantitative precipitation estimates (QPEs) which could complement traditional precipitation observations. This paper assesses the ability of individual CMLs to provide relevant QPEs for urban rainfall-runoff simulations and specifically investigates the influence of CML characteristics and position on the predicted runoff. The analysis is based on a 3-year-long experimental data set from a small (1.3 km²) urban catchment located in Prague, Czech Republic. QPEs from real world CMLs are used as inputs for urban rainfall-runoff predictions and subsequent modelling performance is assessed by comparing simulated runoffs with measured stormwater discharges. The results show that model performance is related to both the sensitivity of CML to rainfall and CML position. The bias propagated into the runoff predictions is inversely proportional to CML path length. The effect of CML position is especially pronounced during heavy rainfalls, when QPEs from shorter CMLs, located within or close to catchment boundaries, better reproduce runoff dynamics than QPEs from longer CMLs extending far beyond the catchment boundaries. Interestingly, QPEs averaged from all available CMLs best reproduce the runoff temporal dynamics. Adjusting CML QPEs to three rain gauges located 2–3 km outside of the catchment substantially reduces the bias in CML QPEs. Unfortunately, this compromises the ability of the CML QPEs to reproduce runoff dynamics during heavy rainfalls. More experimental case studies are necessary to provide specific recommendations on CML preprocessing methods tailored to different water management tasks, catchments and CML networks.

Inaccurate description of uncertainty in the error models can cause biases in parameter estimation. When the parameters of the deterministic model and the error model are inferred jointly from the observations, the posterior converges to regions that reflect the processes in both high and low flows. If the nature of errors in low and high flows is different to the extent that the same error description cannot be used for both, biases in inference are introduced. In such cases, the parameter posterior will adjust to the region of the hydrograph with longer proportionate presence in the calibration time series. In this paper we demonstrate that the autoregressive order 1 (AR1) description of errors can lead to sub-optimally performing predictive models if the calibration period has substantial sections of inadequately modelled flows. Inference is performed within the Bayesian framework. We show this for a synthetic example as well as a case study. We also see that the predictive uncertainty bands that we get using the AR1 description can be overconfident and also admit negative values. To mitigate this, we analyze an alternative to additive error models. We use a distribution with a non-negative support, gamma in this study, reflecting the uncertainty in the system response at every time step. The gamma distribution is conditioned on the deterministic model output, which determines its mode and standard deviation. We capture autocorrelation in time using copulas. Given that copulas can capture dependence between different marginals, we use different specifications of the marginal distribution for high and low flows. The results show that 1) biases in parameter estimation can be reduced if a representative error description is attained using the flexibility of a copula-based likelihood. 2) The non-negative support allows to make more realistic uncertainty intervals for low flows. 3) However, the autocorrelation parameter in copulas severely interacts with the model and heteroscedasticity parameters. 4) While the formulation, in principle, should be of added value for parameter inference, in case of less informative priors, the flexibility of this description can produce non-robust inference.

Im Rahmen eines Umwelttechnologieförderungsprojekts des Schweizerischen Bundesamts für Umwelt (BAFU) wurden verschiedene Werkzeuge für die bessere Ausnutzung der vorhandenen Abwasserinfrastruktur entwickelt. Als Kernstück des Projektes wurde ein generischer Abflussregler für die Bewirtschaftung von Entwässerungssystemen entwickelt und durch den Praxispartner eine Entwicklungsumgebung für konkrete Umsetzungen von Abflussregelungen geschaffen. Es stehen dabei verschiedene Zielfunktionen für die Abflussregelung zur Verfügung, darunter auch eine selbst-adaptierende Funktion. Des Weiteren wurden konkrete Software-Werkzeuge entwickelt, welche die praktische Projektierung und Implementation unterstützen. Dazu gehören zum Beispiel ein Screening-Tool zur Potenzialabschätzung und zur automatischen Datenanalyse von Betriebsdaten. Die Ergebnisse deuten auf ein beträchtliches Potential für Abflussregelungen in der Schweiz hin. Eher wissenschaftlich-orientierte Untersuchungen zeigen erstens, dass Schmutzfracht-basierte Regelung anhand von Ersatzparametern wie beispielswiese Temperatur oder Leitfähigkeit möglich sein können. Zweitens scheinen zur Leistungsüberprüfung von Abflussregelungen die Reduktion von Entlastungsvolumen alleine ungeeignet, da sie die Wirkung der Behandlungsanlagen auf den Gewässerschutz vermutlich unterschätzen.

The activation of available in-sewer storage volume has been suggested as a low-cost flood and combined sewer overflow mitigation measure. However, it is currently unknown what the attributes for suitable objective functions to identify the best location for flow control devices are and the impact of those attributes on the results. In this study, we present a novel location model and efficient algorithm to identify the best location(s) to install flow limiters. The model is a screening tool that does not require hydraulic simulations but rather considers steady state instead of simplistic static flow conditions. It also maximises in-sewer storage according to different reward functions that also considers the potential impact of flow control device failure. We demonstrate its usefulness on two real sewer networks, for which an in-sewer storage potential of approximately 2,000 m³ and 500 m³ was estimated with five flow control devices installed.

As in many fields of dynamic modeling, the long runtime of hydrological models hinders Bayesian inference of model parameters from data. By replacing a model with an approximation of its output as a function of input and/or parameters, emulation allows us to complete this task by trading-off accuracy for speed. We combine (i) the use of a mechanistic emulator, (ii) low-discrepancy sampling of the parameter space, and (iii) iterative refinement of the design data set, to perform Bayesian inference with a very small design data set constructed with 128 model runs in a parameter space of up to eight dimensions. In our didactic example we use a model implemented with the hydrological simulator SWMM that allows us to compare our inference results against those derived with the full model. This comparison demonstrates that iterative improvements lead to reasonable results with a very small design data set.

Although CSOs are expensive infrastructures, which are important to reduce pollution from urban areas during wet weather, they are often not operated effectively. Despite of technical difficulties, major problems in CSO monitoring and performance assessment are caused by non-technical bottlenecks. Through a policy analysis we identified organisational and political problems as the biggest obstacles to the effective operation of CSOs. In addition, we investigated effective measures or incentives to overcome organisational and political obstacles. Our results suggest that a surprisingly high 84% of the CSOs operated by Swiss wastewater associations are monitored. In most cases the information is used to improve operation, but not for better planning or for regulatory performance assessment. The most desirable instrument seem to be legislative performance targets, which is preferred by both wastewater associations and cantonal stakeholders. From a political point of view, the cantonal authorities also consider a transfer of competences from the municipalities to the wastewater associations to be effective, i.e. wastewater associations should operate CSOs instead of municipalities. In contrast, Swiss associations prefer financial incentives, such as a one-off subsidy for the installation of CSO monitoring equipment. Before implementing certain instruments, and in particular regulatory instruments, we highly recommend to assess their impact on overall policy processes and individual stakeholders. In the long term, only the implementation of a carefully balanced, uniform strategy can overcome organizational and political barriers and thus can lead to effective success in water protection.

Während für Kläranlagen gesetzlich festgelegte Einleitbedingungen existieren und Betreiber umfangreiche Jahresberichte an Behörden weiterleiten, sind über die Leistungsfähigkeit der Entwässerungsnetze und Sonderbauwerke in der Schweiz, trotz technischer Möglichkeiten durch die fortschreitende Digitalisierung, kaum Informationen vorhanden. Da es keine Eigenkontrollverordnungen gibt, wird der Einfluss von Mischwasserentlastungen aus Regenüberlaufbecken (RÜB) auf die Gewässerqualität bisher hauptsächlich modelliert und nur selten anhand von Messdaten tatsächlich überprüft. In dieser Studie fragen wir deshalb Abwasserverbände und kantonale Fachstellen, welche Informationen in der Schweiz vorhanden sind und wie sie mit Messdaten umgehen. Wir untersuchen zuerst, welche organisatorischen und politischen Hindernisse einem effektiveren Betrieb von RÜB entgegenstehen. Zweitens diskutieren wir wirksame Maßnahmen oder Anreize zur Überwindung dieser Hindernisse. Unsere Ergebnisse deuten darauf hin, dass in der Schweiz bereits 84% der RÜB, die sich im Betrieb von Abwasserverbänden befinden, messtechnisch überwacht werden. Leider werden diese Messdaten hauptsächlich zur Betriebsoptimierung erhoben und weder als Entscheidungsgrundlage verwendet, z.B. in dem sie ausgewertet, geprüft und anschließend archiviert werden, noch zur Erfolgskontrolle an Behörden weitergeleitet. Um langfristig eine vollständige Ausschöpfung des Werts von Messdaten zu erreichen, sollte eine einheitliche Strategie, wie z.B. die Einführung von Zielvorgaben für das Entlastungsverhalten von RÜB, entwickelt werden, damit keine „Daten-Müllberge" entstehen, sondern Messdaten sinnvoll genutzt und so zu einem verbesserten Gewässerschutz bei Regenwetter beitragen können.

Sewer and drainage infrastructure are often not as well catalogued as they should be, considering the immense investment they represent. In this work, we present a fully automatic framework for localizing sewer inlets from image clouds captured from an unmanned aerial vehicle (UAV). The framework exploits the high image overlap of UAV imaging surveys with a multiview approach to improve detection performance. The framework uses a Viola–Jones classifier trained to detect sewer inlets in aerial images with a ground sampling distance of 3–3.5 cm/pixel. The detections are then projected into three-dimensional space where they are clustered and reclassified to discard false positives. The method is evaluated by cross-validating results from an image cloud of 252 UAV images captured over a 0.57-km² study area with 228 sewer inlets. Compared to an equivalent single-view detector, the multiview approach improves both recall and precision, increasing average precision from 0.65 to 0.73. The source code and case study data are publicly available for reuse.

Hydrologic systems are complex - constituting spatially distributed and temporally variable properties and processes. Such systems are generally modelled mathematically after some simplifying assumptions, for example, by aggregating certain variable properties and neglecting many subprocesses. These model structure deficits result in a mismatch between the real and the modelled system response, which entails uncertain predictions. In addition to model structure deficits, the presence of errors in model inputs and in the observations of system response contributes to the uncertainty in the estimation of model parameters. For risk-based decision making and for statistical hypothesis testing using these models, their uncertainties need to be adequately and explicitly stated. Furthermore, attempts need to be made to reduce these uncertainties. [...]

Hydrologische Systeme sind komplex - sie bilden räumlich verteilte und zeitlich veränderliche Eigenschaften und Prozesse ab. Solche Systeme werden mathematisch unter einigen vereinfachenden Annahmen modelliert, beispielsweise durch das Aggregieren bestimmter Variableneigenschaften und das Vernachlässigen einiger Teilprozesse. Diese Defizite in der Modellstruktur führen zu einer Diskrepanz zwischen realem und modelliertem System, was zu Vorhersagen mit grösserer Unsicherheiten führt. Weiterhin trägt das Auftreten von Fehlern in den Input-Parametern sowie in den simulierten Resultaten zu Unsicherheiten bei der Schätzung von Modellparametern bei. Für eine risikobasierte Entscheidungsfindung sowie für statistische Hypothesentests unter Verwendung der Modelle müssen Unsicherheiten angemessen und explizit angegeben und langfristig reduziert werden.

Data are essential in all areas of geophysics. They are used to better understand and manage systems, either directly or via models. Given the complexity and spatiotemporal variability of geophysical systems (e.g., precipitation), a lack of sufficient data is a perennial problem, which is exacerbated by various drivers, such as climate change and urbanization. In recent years, crowdsourcing has become increasingly prominent as a means of supplementing data obtained from more traditional sources, particularly due to its relatively low implementation cost and ability to increase the spatial and/or temporal resolution of data significantly. Given the proliferation of different crowdsourcing methods in geophysics and the promise they have shown, it is timely to assess the state of the art in this field, to identify potential issues and map out a way forward. In this paper, crowdsourcing-based data acquisition methods that have been used in seven domains of geophysics, including weather, precipitation, air pollution, geography, ecology, surface water, and natural hazard management, are discussed based on a review of 162 papers. In addition, a novel framework for categorizing these methods is introduced and applied to the methods used in the seven domains of geophysics considered in this review. This paper also features a review of 93 papers dealing with issues that are common to data acquisition methods in different domains of geophysics, including the management of crowdsourcing projects, data quality, data processing, and data privacy. In each of these areas, the current status is discussed and challenges and future directions are outlined.

Urban drainage systems traditionally lack adequate monitoring and evaluation technologies. Although weather radar and high-resolution remote sensing technologies promise vast advances for rainfall and land-use data collection, it is still challenging to deploy dense hydrological monitoring networks for sewers. Low-power data transmission technology, which is the communication backbone in the Internet-Of-Things (IoT), has the potential to contribute to a game change towards an evidence-based management of urban drainage systems. In this paper, we share our experiences with establishing and operating a wireless sensor network (WSN) at full-scale with approximately a 1 sensor per hectare connected catchment area and a monitoring frequency of 5 minutes or less. Specifically, we discuss i) the monitoring and deployment approach and ii) the adequacy of sensorand LoRaWANTM wireless data transmission technology for monitoring applications in sewer networks. A thorough evaluation of the telemetry performance provides evidence that the proposed technique allows a highly distributed monitoring of drainage capacity, sewer infiltration, performance of retention basins and overflows, with a far less effort compared to conventional methods. Remaining challenges concern the radio network architecture to allow better access to deep underground structures and efficient methods for automated data quality control to handle this type of data in the future.

Obgleich gegenwärtig mittels Regenradar und hochauflösender Fernerkundung immer effizienter Daten zu Niederschlag, Topographie und Landnutzung erhoben werden, ist es nach wie vor schwierig, eine hydrologische Überwachung für Entwässerungssysteme mit hinreichender Informationsdichte und -konsistenz zu realisieren. Ob und inwieweit die Technologie des Niedrigenergiefunks (LPWAN) – als Kommunikations-Backbone des Internet der Dinge (IoT) bekannt – das Potenzial hat, diese Lücke zu schließen, diskutieren wir in diesem Beitrag. Grundlage dafür sind unsere Erfahrungen beim Aufbau und dem bis dato einjährigen Betrieb eines Drahtlossensornetzwerks in einem mittelgroßen Schweizer Siedlungsentwässerungsnetz mit einer Sensordichte von über einem Sensor pro Hektar und einer Überwachungsfrequenz von fünf Minuten. Im Einzelnen erörtern wir i) das zugrundeliegende Monitoringkonzept, ii) die technische Spezifikation der Sensorik und der Datenübertragungstechnologie LoRaWANTM und iii) die bisherigen Erfahrungen zur Systemperformance, illustriert durch ein Anwendungsbeispiel.

System identification, sensitivity analysis, optimization and control, require a large number of model evaluations. Accurate simulators are too slow for these applications. Fast emulators provide a solution to this efficiency demand, sacrificing unneeded accuracy for speed. There are many strategies for developing emulators but selecting one remains subjective. Herein we compare the performance of two kinds of emulators: mechanistic emulators that use knowledge of the simulator's equations, and purely data-driven emulators using matrix factorization. We borrow simulators from urban water management, because more stringent performance criteria on water utilities have made emulation a crucial tool within this field. Results suggest that naive data-driven emulation outperforms mechanistic emulation. We discuss scenarios in which mechanistic emulation seems favorable for extrapolation in time and dealing with sparse and unevenly sampled data. We also point to advances in Machine Learning that have not permeated yet into the environmental science community.

Increasing urbanization makes it more and more important to have accurate stormwater runoff predictions, especially with potentially severe weather and climatic changes on the horizon. Such stormwater predictions in turn require reliable rainfall information. Especially for urban centres, the problem is that the spatial and temporal resolution of rainfall observations should be substantially higher than commonly provided by weather services with their standard rainfall monitoring networks. Commercial microwave links (CMLs) are non-traditional sensors, which have been proposed about a decade ago as a promising solution. CMLs are line-of-sight radio connections widely used by operators of mobile telecommunication networks. They are typically very dense in urban areas and can provide path-integrated rainfall observations at sub-minute resolution. Unfortunately, quantitative precipitation estimates (QPEs) from CMLs are often highly biased due to several epistemic uncertainties, which significantly limit their usability. In this manuscript we therefore suggest a novel method to reduce this bias by adjusting QPEs to existing rain gauges. The method has been specifically designed to produce reliable results even with comparably distant rain gauges or cumulative observations. This eliminates the need to install reference gauges and makes it possible to work with existing information. First, the method is tested on data from a dedicated experiment, where a CML has been specifically set up for rainfall monitoring experiments, as well as operational CMLs from an existing cellular network. Second, we assess the performance for several experimental layouts of "ground truth" from rain gauges (RGs) with different spatial and temporal resolutions. The results suggest that CMLs adjusted by RGs with a temporal aggregation of up to 1 h (i) provide precise high-resolution QPEs (relative error <7%, Nash–Sutcliffe efficiency coefficient >0.75) and (ii) that the combination of both sensor types clearly outperforms each individual monitoring system. Unfortunately, adjusting CML observations to RGs with longer aggregation intervals of up to 24 h has drawbacks. Although it substantially reduces bias, it unfavourably smoothes out rainfall peaks of high intensities, which is undesirable for stormwater management. A similar, but less severe, effect occurs due to spatial averaging when CMLs are adjusted to remote RGs. Nevertheless, even here, adjusted CMLs perform better than RGs alone. Furthermore, we provide first evidence that the joint use of multiple CMLs together with RGs also reduces bias in their QPEs. In summary, we believe that our adjustment method has great potential to improve the space–time resolution of current urban rainfall monitoring networks. Nevertheless, future work should aim to better understand the reason for the observed systematic error in QPEs from CMLs.

Existing urban drainage networks are often operated in a sub-optimal manner. Real-Time Control (RTC) can improve network performance, but its implementation in the real world is not trivial. Here, we suggest two new methods to predict the performance of RTC applied to urban water networks. Specifically, we propose using rainfall information and a robust overflow metric with a classification algorithm to assess RTC performance. Furthermore, we investigate the usage of chess-based rating systems to better predict and assess the efficiency of existing catchments. The results from two case studies suggest that it is possible to achieve fast and robust assessments with one year of data or less.

The design and evaluation of solutions for integrated surface water quality management requires an integrated modelling approach. Integrated models have to be comprehensive enough to cover the aspects relevant for management decisions, allowing for mapping of larger-scale processes such as climate change to the regional and local contexts. Besides this, models have to be sufficiently simple and fast to apply proper methods of uncertainty analysis, covering model structure deficits and error propagation through the chain of sub-models. Here, we present a new integrated catchment model satisfying both conditions. The conceptual "iWaQa" model was developed to support the integrated management of small streams. It can be used to predict traditional water quality parameters, such as nutrients and a wide set of organic micropollutants (plant and material protection products), by considering all major pollutant pathways in urban and agricultural environments. Due to its simplicity, the model allows for a full, propagative analysis of predictive uncertainty, including certain structural and input errors. The usefulness of the model is demonstrated by predicting future surface water quality in a small catchment with mixed land use in the Swiss Plateau. We consider climate change, population growth or decline, socio-economic development, and the implementation of management strategies to tackle urban and agricultural point and non-point sources of pollution. Our results indicate that input and model structure uncertainties are the most influential factors for certain water quality parameters. In these cases model uncertainty is already high for present conditions. Nevertheless, accounting for today's uncertainty makes management fairly robust to the foreseen range of potential changes in the next decades. The assessment of total predictive uncertainty allows for selecting management strategies that show small sensitivity to poorly known boundary conditions. The identification of important sources of uncertainty helps to guide future monitoring efforts and pinpoints key indicators, whose evolution should be closely followed to adapt management. The possible impact of climate change is clearly demonstrated by water quality substantially changing depending on single climate model chains. However, when all climate trajectories are combined, the human land use and management decisions have a larger influence on water quality against a time horizon of 2050 in the study.

Für einen effektiveren Gewässerschutz bei Regenwetter bedarf es nicht nur der Optimierung von Kläranlagen, sondern auch einem Verständnis über die hydraulische Funktionsweise von Sonderbauwerken, wie z.B. Regenüberlaufbecken (RÜB). RÜB, die bei grösseren Regenereignissen verdünntes Mischabwasser in die Gewässer einleiten, werden auf Grundlage von Simulationsmodellen dimensioniert. Ob die berechnete Entlastungshäufigkeit und -dauer auch der tatsächlichen entspricht, wird in der Schweizer Praxis kaum überprüft. Zur Überprüfung können RÜB mit Messgeräten ausgerüstet und wichtige Variablen, wie z.B. der Wasserstand im RÜB erfasst werden. Für Datenfernübertragung und die automatisierte Auswertung sind heute praxistaugliche Technologien verfügbar. Es besteht daher die Vermutung, dass der Einsatz vorhandener Messtechnik an RÜB nicht durch technische Deffizite zu begründen ist, sondern dass das Verständnis für den tatsächlichen Wert und Nutzen von Messdaten bei Betreibern und Behörden fehlt.

Das Ziel dieser Masterarbeit ist es, erstens den heutigen Zustand in der Schweiz in Bezug auf die Ausstattung von RÜB mit Messtechnik und den Umgang mit Messdaten zu erfassen und zu erklären. Davon werden zweitens Instrumente abgeleitet, die dazu beitragen könnten, dass Messdaten von allen Akteuren (Gemeinden, Abwasserverbänden, Kantone, Bund, Ingenieur- und Planungsbüros) genutzt werden. Zur gemeinsamen Untersuchung des technischen Systems und beteiligter Akteure werden methodische Ansätze aus der Policy-Analyse angewendet, wobei der Fokus auf die Abwasserverbände in der Schweiz gelegt wurde. Eine Online-Umfrage wurde durchgeführt und mit deskriptiven und erklärenden statistischen Analysemethoden (wie z.B. Regressionsanalysen) ausgewertet.

Mit der Online-Umfrage werden 73 % der Abwasserverbände in der Schweiz abgedeckt. Überraschend positiv ist, dass insgesamt bereits 84 % der RÜB, die sich im Betrieb von Abwasserverbänden befinden, mit einem Messgerät ausgerüstet sind. Defizite sind jedoch im Umgang mit den Messdaten vorhanden. In 34 % der Abwasserverbände werden Wasserstands-Daten aggregiert, was heute nicht mehr dem Stand der Technik entspricht. Daten werden in 47 % der Abwasserverbände nie oder nur unregelmässig ausgewertet und in 61 % weder an Ingenieurbüros (für Planungszwecke) noch an kantonale Fachstellen (zur Erfolgskontrolle) weitergeleitet. Das liegt vor allem daran, dass Abwasserverbände Messdaten hauptsächlich für die betriebliche Optimierung nutzen. Da Kosteneinsparungen eher im Hintergrund stehen, liegt nahe, dass Subventionen weniger bevorzugt sind. Vielmehr priorisieren Abwasserverbände und Kantone die Einführung von Zielvorgaben.

Die Ergebnisse aus der Online-Umfrage beschränken sich nur auf die Abwasserverbände, Gemeinden werden z.B. nicht untersucht. Da die Situation in der Schweiz sehr heterogen ist, sollten Instrumente regional angepasst werden. Um dies zu untersuchen, wären weitergehende statistische Verfahren, wie z.B. die Clusteranalyse, angebracht.

Die Ergebnisse deuten darauf hin, dass für eine volle Wertausschopfung von Messdaten regulative Instrumente ebenso erforderlich sind wie organisatorische Anpassungen. Dafür sollten Akteure (Bund, Kantone, VSA) zum Beispiel eine Verordnung entwickeln, die Zielvorgaben für RÜB definiert und betriebliche Kompetenzen klar verteilt.

Diese Masterarbeit stellt viel Material bereit, das sich ohne grosse Anpassungen auch für die Untersuchung in anderen Ländern (z.B. Deutschland, Österreich) anbietet.

Seit ca. 10 Jahren folgt die Planung von Anlagen zur Mischwasserbehandlung in der Schweiz den imissionsorientierten Vorgaben des Gewässerschutzgesetzes. Positiv wird beurteilt, dass Handlungsbedarf auf Basis von Gewässeruntersuchungen identifiziert wird und eine breite Evaulation von Massnahmen nicht nur zum besseren Verständnis der Abwassersysteme, sondern oft zu kostengünstigen Lösungen führt. Aufgrund von sich überlappenden Regelwerken und neuem Wissen, u.a. zu Spurenstoffen, wird zurzeit die neue Richtlinie zur „Abwasserbewirtschaftung bei Regenwetter“ erarbeitet, die vor allem auch den Vollzug erleichtern soll. Dazu stellt sie 4 neue Elemente vor: i) die vereinfachte Zulässigkeitsprüfung, ii) Bewertung von Einleitungen in stehende Gewässer, iii) schmutzfrachtbasierte Mindestanforderungen und iv) die Möglichkeit eines Probebetriebs mit Überwachung. Die Erfahrung in der Schweiz zeigt, dass die immissionsbasierte Bewirtschaftung von Abwassersystemen anspruchsvoll ist, aber teilweise zu innovativen Lösungen führt. Der „Preis“ solcher komplexer Planungsinstrumente sind hohe fachliche Anforderungen bei Betreibern, Planern und Regulatoren, um inadequate Massnahmen zu verhindern. Ein guter Wissensstand in der Praxis kann neben Weiterbildungen gegebenenfalls über fachspezifische „Nachbarschaften“ nach dem Modell von Baden-Württemberg erreicht werden. Offene Fragen sind, wie kleine Gewässerschutz-Behörden am besten bei der Bewilligung von Massnahmen unterstützt werden können und wie zukünftige Entwicklungen zum vermehrten Einsatz von Messtechnik den Gewässerschutz bei Regenwetter verbessern können.

In der Siedlungsentwässerung in Deutschland, Österreich und der Schweiz basieren viele Massnahmenplanungen nicht auf Messdaten, sondern auf Annahmen und auf Vorhersagen von Simulationsmodellen. Ausserdem werden Messdaten sehr wenig zur Leistungsbeurteilung und Erfolgskontrolle eingesetzt. In diesem Beitrag vergleichen wir deshalb zum ersten Mal den aktuellen Stand zur Verwendung von Messdaten im deutschsprachigen Raum, den wir in einem Diskurs-Prozess anhand von neun konkreten Fallbeispielen erarbeitet haben. Grundsätzlich zeigt sich eine grosse Variabilität im Umgang mit datenbasierten Konzepten, auch innerhalb der Länder. Sowohl in Österreich als auch in der Schweiz wird die Leistung von Entwässerungsnetzen praktisch nicht anhand von Messdaten überwacht und bewertet. Zur Qualitätssicherung scheint das deutsche Prüfstellen-Modell sehr interessant. Unsere Ergebnisse deuten darauf hin, dass gute Messdaten nicht nur zur sicheren Abwasserableitung und zum Gewässerschutz beitragen, sondern eine sehr breite positive Wirkung auf fast alle Ziele einer nachhaltigen Siedlungswasserwirtschaft haben. Ein datenbasiertes Management benötigt jedoch weitere Entwicklungen im politischen und organisatorischen Bereich sowie in der Ausbildung.

Observations of a hydrologic system response are needed to accurately model system behaviour. Nevertheless, often very few monitoring stations are operated because collecting such reference data adequately and accurately is laborious and costly. It has been recently suggested to use observations not only from dedicated flow meters but also from simpler sensors, such as level or event detectors, which are available more frequently but only provide censored information. Binary observations can be considered as extreme censoring. It is still unclear, however, how to use censored observations most effectively to learn about model parameters. To this end, we suggest a formal likelihood function that incorporates censored observations, while accounting for model structure deficits and uncertainty in input data. Using this likelihood function, the parameter inference is performed within the Bayesian framework. We demonstrate the implementation of our methodology on a case study of an urban catchment, where we estimate the parameters of a hydrodynamic rainfall-runoff model from binary observations of combined sewer overflows. Our results show, first, that censored observations make it possible to learn about model parameters, with an average decrease of 45% in parameter standard deviation from prior to posterior. Second, the inference substantially improves model predictions, providing higher Nash-Sutcliffe efficiency. Third, the gain in information largely depends on the experimental design, i.e. sensor placement. Given the advent of Internet of Things, we foresee that the plethora of censored data promised to be available can be used for parameter estimation within a formal Bayesian framework.

Rainfall input uncertainty is one of the major concerns in hydrological modeling. Unfortunately, during inference, input errors are usually neglected, which can lead to biased parameters and implausible predictions. Rainfall multipliers can reduce this problem but still fail when the observed input (precipitation) has a different temporal pattern from the true one or if the true nonzero input is not detected. In this study, we propose an improved input error model which is able to overcome these challenges and to assess and reduce input uncertainty. We formulate the average precipitation over the watershed as a stochastic input process (SIP) and, together with a model of the hydrosystem, include it in the likelihood function. During statistical inference, we use “noisy” input (rainfall) and output (runoff) data to learn about the “true” rainfall, model parameters, and runoff. We test the methodology with the rainfall-discharge dynamics of a small urban catchment. To assess its advantages, we compare SIP with simpler methods of describing uncertainty within statistical inference: (i) standard least squares (LS), (ii) bias description (BD), and (iii) rainfall multipliers (RM). We also compare two scenarios: accurate versus inaccurate forcing data. Results show that when inferring the input with SIP and using inaccurate forcing data, the whole-catchment precipitation can still be realistically estimated and thus physical parameters can be “protected” from the corrupting impact of input errors. While correcting the output rather than the input, BD inferred similarly unbiased parameters. This is not the case with LS and RM. During validation, SIP also delivers realistic uncertainty intervals for both rainfall and runoff. Thus, the technique presented is a significant step toward better quantifying input uncertainty in hydrological inference. As a next step, SIP will have to be combined with a technique addressing model structure uncertainty.

Für einen effektiven Betrieb eines Abwassersystems bietet sich vielerorts eine Abflussregelung an. Dazu empfiehlt sich ein regelbasierter, selbstadaptiver und hierarchischer Regelungsalgorithmus, der allgemein anwendbar ist. Bei der ARA Wohlen steigerte er die Leistung des Entwässerungsnetzes im Mittel um 10–20% und kann ca. 25% der Entlastungsereignisse verhindern.

Eighty-seven participants from 18 countries met to discuss the prospect for rainfall measurement and high-resolution mapping based on commercial microwave links in Africa. Experts from Europe and Israel provided training to African students, scientists, and meteorologists on this innovative method.

The hydraulic behavior and the capacity of sewer systems are assessed through hydrodynamic modeling. They are widely used in drainage planning, although their parameters are mostly estimated and the models are only checked for general plausibility. The Master‘s Thesis assessed the effect of calibration on the model reliability on the basis of a case study, namely Fehraltorf.
Fehraltorf is located in the agglomeration of Zurich and Winterthur and is inhabited by 6’292 people. Most of the village of Russikon and Fehraltorf are connected to the WWTP of Fehraltorf. The DWF amounts up to 66 l/s, whereas the residual flow Q347 of the main receiving water (Luppmen) is 72 l/s. The infiltration of the groundwater is 30.1 l/s, as a significant share of the drainage system was built below the average groundwater table.
During the thesis the conversion of the Mike Urban model to SWMM was revised and further adjustments were implemented. The data for input and calibration was chosen depending on its reliability and further processed if necessary. Rain events were selected and split for calibration and validation. Parameters known to be changed in the calibrations were selected and in the sensitivity analysis the most sensitive, and thus, important parameters were determined. Then, the calibration was conducted manually as well as automatically with simulated annealing. The effect of calibration was assessed for the calibration and validation events visually and with various objective functions, viz. Nash-Sutcliffe Efficiency, total volume, peak flow and time to peak. The hydraulic performance of the sewer system was tested by simulating a 10-year design rainfall event in the uncalibrated and the calibrated model.
For the simulations, the data of the rain gauge on the WWTP had to be discarded because it showed high deviations to the ones installed by EAWAG. The rainfall data and flow data (of the FloDars) collected by EAWAG were assessed to be reliable, but as the measurements were installed recently, only four rain events were available for calibration and validation. The objective functions could be raised to a satisfying level in the manual calibration and were further improved in the automatic calibration. The validation confirmed that the reliability of the model could be increased with calibration. Furthermore, the assessment of the sewer system revealed that there are less capacity limits in the calibrated model then in the uncalibrated one, but further adjustments are needed due to numerical instabilities.
Even though the validation suggests that manual as well as automatic calibration enhance the reliability of the model because of improved goodness of fit assessed by different objective functions, further input data and reliable calibration data is needed to perform the calibration once more.
After all, calibration is strongly recommended by the author of this thesis to not only improve parameter values which were only estimated in the uncalibrated models but also to detect systematic errors in the data and the model structure.

Precise and detailed digital elevation models (DEMs) are essential to accurately predict overland flow in urban areas. Unfortunately, traditional sources of DEM, such as airplane light detection and ranging (lidar) DEMs and point and contour maps, remain a bottleneck for detailed and reliable overland flow models, because the resulting DEMs are too coarse to provide DEMs of sufficient detail to inform urban overland flows. Interestingly, technological developments of unmanned aerial vehicles (UAVs) suggest that they have matured enough to be a competitive alternative to satellites or airplanes. However, this has not been tested so far. In this study we therefore evaluated whether DEMs generated from UAV imagery are suitable for urban drainage overland flow modelling. Specifically, 14 UAV flights were conducted to assess the influence of four different flight parameters on the quality of generated DEMs: (i) flight altitude, (ii) image overlapping, (iii) camera pitch, and (iv) weather conditions. In addition, we compared the best-quality UAV DEM to a conventional lidar-based DEM. To evaluate both the quality of the UAV DEMs and the comparison to lidar-based DEMs, we performed regression analysis on several qualitative and quantitative metrics, such as elevation accuracy, quality of object representation (e.g. buildings, walls and trees) in the DEM, which were specifically tailored to assess overland flow modelling performance, using the flight parameters as explanatory variables. Our results suggested that, first, as expected, flight altitude influenced the DEM quality most, where lower flights produce better DEMs; in a similar fashion, overcast weather conditions are preferable, but weather conditions and other factors influence DEM quality much less. Second, we found that for urban overland flow modelling, the UAV DEMs performed competitively in comparison to a traditional lidar-based DEM. An important advantage of using UAVs to generate DEMs in urban areas is their flexibility that enables more frequent, local, and affordable elevation data updates, allowing, for example, to capture different tree foliage conditions.

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.

In der schönen Bergwelt von Rigi Kaltbad fand die von Eawag und VSA organisierte Aqua Urbanica 2016 statt. Jörg Rieckermann (Eawag) und seine Mitorganisatoren Günter Gruber (TU Graz), Ulrich Dittmer (Universität Stuttgart) und Michael Burkhardt (HSR Hochschule für Technik Rapperswil) diskutieren im Interview die Themen und Hintergründe der Tagung. Unter anderem unterhalten sie sich über den Wert von guten Messdaten und Modellen, den Umgang mit Spurenstoffen und Unsicherheiten in der Planung sowie die fortschreitende Digitalisierung der Siedlungsentwässerung.

A case study for an integrated real time control (RTC) in the catchment Wohlen-Villmergen, Switzerland is introduced. The RTC-based control was simulated for the inflows to the wastewater treatment plant Wohlen and the connecting sewer system using a conceptual model in CityDrain II. For evaluating the model performance, high-resolution routine data were available for the entire year of 2015.
The main goal of this thesis was to control the system with an aim to improve the receiving water quality. In urban drainage systems, combined sewer overflows (CSO) represent the main pollutant source. A load-based approach was introduced to further characterize CSO. Then, different static and dynamic control strategies were compared for reducing CSO number, volume and load.
It was found that a dynamically controlled RTC can avoid 25 % of CSO events that would have happened in a static controlled system in 2015. However, the reduction was much smaller for overall CSO volume (7 %) and load (9 %).

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.

Hydrological or drainage models can be valuable tools to support water management in urban environments. They can help to assess the characteristics of a catchment and to make predictions about its response (e.g. discharge). Unfortunately, parameters and results of these models are not perfect. These inaccuracies manifest themselves as model discrepancy, the so-called bias. Model bias represents the systematic deviation between model output and the real system response. There are two main causes of bias. First, there are errors in the input estimates, e.g. due to the insufficient coverage of pluviometers. Second, there are deficits in the model structure, e.g. due to the use of oversimplified empirical equations. Besides model bias, there are output measurement errors, e.g. due to imprecise flowmeters.
Until now, a large proportion of hydrological studies implicitly neglected input and structural errors. Neglecting these can lead to: i) misrepresented output measurement errors, ii) incorrect parameter estimates which compensate for model discrepancy, and iii) predictions which underestimate the uncertainty. Furthermore, these approaches do not provide guidance for finding the reasons for bias. Therefore, they cannot support its reduction. This inappropriate error consideration can finally lead to faulty risk assessment, flawed evaluation of decision alternatives, and, consequently, impaired urban water management. More advanced error assessment techniques are therefore called for.
The main goal of this thesis is to better represent input, structural, and output measurement errors. This can meliorate parameter estimation and prediction generation. The main thesis contributions are two. The first is a realistic description of the uncertainties in stormwater, wastewater, and sediment transport predictions. The second is a sound representation of errors in the rainfall inputs. Additionally, we discuss and compare different statistical methods used for hydrological inference. Finally, we also present a new tool to improve sewer flow monitoring. [...]

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.

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.

Urban storm water management requires rainfall data of high spatial and temporal resolution which are often not available (Schilling, 1991). Path-integrated rain rate information from cellular microwave links (MWLs) has potential to aid in this regard (Messer et al., 2006) as MWL network are very dense in urban areas and rain rate information can be at virtually no cost polled from each single MWL with sub minute temporal resolution. Unfortunately MWL rainfall is often significantly biased (Fencl et al., 2015). In this investigation we proposed method to dynamically correct MWL rain rates by remote rain gauges (RGs) with hourly temporal resolution, i.e. RGs often operated by national weather services. Our results show that although these RGs have unsufficient resolution for most of the urban storm water management applications they can be conveniently used to calibrate MWL attenuation-rain rate model. Such calibrated model provides unbiased high-res quantitative precipitation estimates.

Well-planned urban infrastructure should meet critical loads during its design lifetime. In order to proceed with design, engineers are forced to make numerous assumptions with very little supporting information about the development of various drivers. For the wastewater sector, these drivers include the future amount and composition of the generated wastewater, effluent requirements, technologies, prices of inputs such as energy or chemicals, and the value of outputs produced such as nutrients for fertilizer use. When planning wastewater systems, there is a lack of methods to address discrepancies between the timescales at which fundamental changes in these drivers can occur, and the long physical life expectancy of infrastructure (on the order of 25–80 years). To explore these discrepancies, we take a hindsight perspective of the long-term development of wastewater infrastructure and assess the stability of assumptions made during previous designs. Repeatedly we find that the drivers influencing wastewater loads, environmental requirements or technological innovation can change at smaller timescales than the infrastructure design lifetime, often in less than a decade. Our analysis shows that i) built infrastructure is continuously confronted with challenges it was not conceived for, ii) significant adaptation occurs during a structure's lifetime, and iii) "muddling-through" is the pre-dominant strategy for adaptive management. As a consequence, we argue, there is a need to explore robust design strategies which require the systematic use of scenario planning methods and instruments to increase operational, structural, managerial, institutional and financial flexibility. Hindsight studies, such as this one, may inform the development of robust design strategies and assist in the transition to more explicit forms of adaptive management for urban infrastructures.

The concentration of total suspended solids (TSS) in surface waters is a commonly used indicator of water quality impairments. Its accurate prediction remains, however, problematic because: i) TSS build-up, erosion, and wash-off are not easily identifiable; ii) calibrating a TSS model requires observations of sediment loads, which are rare, and streamflow observations to calculate concentrations; iii) predicted TSS usually deviate systematically from observations, an effect which is commonly neglected. Ignoring systematic errors during calibration can lead to overconfident (i.e. unreliable) uncertainty estimates during predictions. In this paper, we therefore investigate whether a statistical description of systematic model errors makes it possible to generate reliable predictions for TSS. In addition, we explore how the reliability of TSS predictions increases when streamflow data are additionally used in model calibration. A key aspect of our study is that we use a Bayesian multi-output calibration and a novel autoregressive error model, which describes the model predictive error as a sum of independent random noise and autocorrelated bias. Our results show that using a statistical description of model bias provides more reliable uncertainty estimates of TSS than before and including streamflow data into calibration makes TSS predictions more precise. For a case study of a small ungauged catchment, this improvement was as much as 15%. Our approach can be easily implemented for other water quality variables which are dependent on streamflow.

Modelling rainfall–runoff in urban areas is increasingly applied to support flood risk assessment, particularly against the background of a changing climate and an increasing urbanization. These models typically rely on high-quality data for rainfall and surface characteristics of the catchment area as model input.
While recent research in urban drainage has been focusing on providing spatially detailed rainfall data, the technological advances in remote sensing that ease the acquisition of detailed land-use information are less prominently discussed within the community. The relevance of such methods increases as in many parts of the globe, accurate land-use information is generally lacking, because detailed image data are often unavailable. Modern unmanned aerial vehicles (UAVs) allow one to acquire high-resolution images on a local level at comparably lower cost, performing on-demand repetitive measurements and obtaining a degree of detail tailored for the purpose of the study.
In this study, we investigate for the first time the possibility of deriving high-resolution imperviousness maps for urban areas from UAV imagery and of using this information as input for urban drainage models. To do so, an automatic processing pipeline with a modern classification method is proposed and evaluated in a state-of-the-art urban drainage modelling exercise. In a real-life case study (Lucerne, Switzerland), we compare imperviousness maps generated using a fixed-wing consumer micro-UAV and standard large-format aerial images acquired by the Swiss national mapping agency (swisstopo). After assessing their overall accuracy, we perform an end-to-end comparison, in which they are used as an input for an urban drainage model. Then, we evaluate the influence which different image data sources and their processing methods have on hydrological and hydraulic model performance. We analyse the surface runoff of the 307 individual subcatchments regarding relevant attributes, such as peak runoff and runoff volume. Finally, we evaluate the model's channel flow prediction performance through a cross-comparison with reference flow measured at the catchment outlet.
We show that imperviousness maps generated from UAV images processed with modern classification methods achieve an accuracy comparable to standard, off-the-shelf aerial imagery. In the examined case study, we find that the different imperviousness maps only have a limited influence on predicted surface runoff and pipe flows, when traditional workflows are used. We expect that they will have a substantial influence when more detailed modelling approaches are employed to characterize land use and to predict surface runoff. We conclude that UAV imagery represents a valuable alternative data source for urban drainage model applications due to the possibility of flexibly acquiring up-to-date aerial images at a quality compared with off-the-shelf image products and a competitive price at the same time. We believe that in the future, urban drainage models representing a higher degree of spatial detail will fully benefit from the strengths of UAV imagery.

Vor dem Hintergrund von zunehmender Urbanisierung, Klimawandel und alternder Infrastrukturen gewinnt die Analyse und Bewertung von Überflutungen im urbanen Raum aufgrund von Starkregenereignissen zunehmend an Bedeutung. Dazu sind Modelle erforderlich, die das Niederschlag-Abflussverhalten in einem angemessenen zeitlichen und räumlichen Detail beschreiben. Neben Daten zur Menge und Verteilung des Niederschlags gehen in die Modellierung auch Oberflächeneigenschaften ein, insbesondere der Versiegelungsgrad und die Flächennutzung. In dieser Studie untersuchen wir erstmals die Möglichkeit, hochauflösende Landnutzungskarten städtischer Gebiete aus Drohnen(UAV)-Bildern abzuleiten und die Eignung solcher Karten als Datengrundlage für Modellanwendungen in der Siedlungswasserwirtschaft. Wir zeigen, dass die aus UAV-Bildern (automatisch) abgeleiteten Karten zum Versiegelungsgrad hinsichtlich ihrer Genauigkeit mit Karten auf Basis konventioneller Luftbilder vergleichbar sind. Darüber hinaus zeigt sich in unseren Experimenten, dass sich die Genauigkeit der Versiegelungskarten bedeutend steigern lässt, indem man diskriminative Lernverfahren in Verbindung mit Bildinformation aus einer grösseren Nachbarschaft benutzt.

Aims. Quantifying illicit drug loads through wastewater analysis (WWA) is an alternative approach to estimating population drug use. This study investigated the variability of daily drug loads in wastewater and their relationships to environmental factors over an extended period to: (i) explore the suitability of WWA in small populations and (ii) optimize the monitoring design for future studies.
Design, Setting, Participants. Daily wastewater samples (n = 1369 consecutive days) from a German village with approximately 7160 inhabitants.
Measurements. Samples were analysed for cocaine and benzoylecgonine with liquid chromatography-tandem mass spectrometry. Time-series analysis was used to explore the effects of weather and other factors on daily cocaine loads. Subsampling was used to assess monitoring design.
Findings. Cocaine loads [mean = 652 mg_COC/day, standard deviation (SD) = 498 mg_COC/day] increased over the study period, with higher values during winter and spring. Despite high day-to-day variation, loads were significantly higher during weekends [+161 mg_COC/day, 95% confidence interval (CI) = 115–207 mg_COC/day, P < 10⁻⁴] and days with frost (+114 mg_COC/day, 95% CI = 6–223 mg_COC/day, P = 0.039) or snow (+150 mg_COC/day, 95% CI = 46–253 mg_COC/day, P = 0.005). Annual means estimated from 1-week periods were subject to approximately 60% relative error. Increasing sample size and changing sampling from consecutive days to stratified random decreased this uncertainty.
Conclusions. Day-to-day variation and seasonality of drug loads from the few long-term wastewater studies available to date suggest that up to 56 stratified random samples are required to obtain reliable (expected uncertainty around 10%) annual estimates of drug loads. Successfully assessing changes in consumption patterns or relationships to external factors requires larger sample sizes than estimating annual means, which holds true for high-prevalence drugs in small communities and low-prevalence drugs in big cities.

If asked whether you had consumed illicit drugs recently, would you admit it? If yes, could you precisely recall types of drug, times and amounts used? If you were the person commissioned with the task of quantifying drug use, what approach would you use given the social stigma attached with such behavior? We measure drug residues in sewage, which represents urine of entire populations, to provide an objective estimate of total drug use in a region. In transdisciplinary projects, sewage-based results provide valuable information at unrivaled spatiotemporal resolution complementing traditional data.

Accurate rain rate measurements are essential for many hydrological applications. Although rain gauge remains the reference instrument for the measurement of rain rate, the strong spatial and temporal variability of rainfall makes it difficult to spot faulty rain gauges. Due to the poor spatial representativeness of the point rainfall measurements, this is particularly difficult where their density is low. Taking advantage of the high density of telecommunication microwave links in urban areas, a consistency check is proposed to identify faulty rain gauges using nearby microwave links. The methodology is tested on a data set from operational rain gauges and microwave links, in Zürich (Switzerland). The malfunctioning of rain gauges leading to errors in the occurrence of dry/rainy periods are well identified. In addition, the gross errors affecting quantitative rain gauge measurements during rainy periods, such as blocking at a constant value, random noise and systematic bias, can be detected. The proposed approach can be implemented in real time.

Hydrodynamische Kanalnetzmodelle sind für die Entwässerungsplanung unersetzlich.Obwohl sie den komplexen Niederschlags-Abfluss Prozess niemals exakt abbilden können, liefern sie oft akzeptable Ergebnisse. Es ist jedoch schwierig, plausible Schätzungen der Unsicherheit von Kanalnetzberechnungen zu erhalten, da die gängigen statistischen Verfahren auf sehr starken Annahmen über die Fehlerverteilung beruhen, wie z.B. unabhängige, und identisch verteilte Zufallseffekte, welche praktisch nie erfüllt sind. In diesem Artikel stellen wir deshalb eine Methode vor, um i) zuverlässigere Schätzungen der Unsicherheit von Kanalnetzsimulationen zu berechnen und ii) die Vorhersageunsicherheit in einzelne Komponenten zu trennen. Zu diesem Zweck werden neben dem deterministischen Modell und zufälligen Messfehlern auch systematische Modellabweichungen berücksichtigt. Diese systematischen Modellabweichungen werden durch einen autoregressiven Zufallsprozess beschrieben und erfassen die Effekte von Unsicherheiten in den Eingangsdaten und der Modellstruktur. Zur Kalibration wird Bayessche Parameterschätzung verwendet. Wir zeigen an einem Fallbeispiel, dass die Berücksichtigung der Modellabweichung die Zuverlässigkeit der Berechnungsergebnisse stark verbessert. Ausserdem wird deutlich, dass systematische Modellabweichungen die Vorhersageunsicherheit dominieren und weit grösser sind, als Unsicherheit in den Modellparametern oder Messfehler. Abgesehen von den langen Rechenzeiten ergeben sich gewisse Limitierungen aus der subjektiven Formulierung des Vorwissens über die Parameter des Fehlermodells. Die statistische Berücksichtigung von systematischen Modellabweichungen liefert jedoch eine zuverlässige Schätzungen der Unsicherheit als herkömmliche Methoden und scheint daher für Kanalnetzsimulationen sehr interessant.

Hydrodynamic models are useful tools for urban water management. Unfortunately, it is still challenging to obtain accurate results and plausible uncertainty estimates when using these models. In particular, with the currently applied statistical techniques, flow predictions are usually overconfident and biased. In this study, we present a flexible and relatively efficient methodology (i) to obtain more reliable hydrological simulations in terms of coverage of validation data by the uncertainty bands and (ii) to separate prediction uncertainty into its components. Our approach acknowledges that urban drainage predictions are biased. This is mostly due to input errors and structural deficits of the model. We address this issue by describing model bias in a Bayesian framework. The bias becomes an autoregressive term additional to white measurement noise, the only error type accounted for in traditional uncertainty analysis. To allow for bigger discrepancies during wet weather, we make the variance of bias dependent on the input (rainfall) or/and output (runoff) of the system. Specifically, we present a structured approach to select, among five variants, the optimal bias description for a given urban or natural case study. We tested the methodology in a small monitored stormwater system described with a parsimonious model. Our results clearly show that flow simulations are much more reliable when bias is accounted for than when it is neglected. Furthermore, our probabilistic predictions can discriminate between three uncertainty contributions: parametric uncertainty, bias, and measurement errors. In our case study, the best performing bias description is the output-dependent bias using a log-sinh transformation of data and model results. The limitations of the framework presented are some ambiguity due to the subjective choice of priors for bias parameters and its inability to address the causes of model discrepancies. Further research should focus on quantifying and reducing the causes of bias by improving the model structure and propagating input uncertainty.

Successful management and control of wastewater and storm water systems requires accurate sewer flow measurements. Unfortunately, the harsh sewer environment and insufficient flow meter calibration often lead to inaccurate and biased data. In this paper, we improve sewer flow monitoring by creating redundant information on sewer velocity from natural wastewater tracers. Continuous water quality measurements upstream and downstream of a sewer section are used to estimate the travel time based on i) cross-correlation (XCORR) and ii) dynamic time warping (DTW). DTW is a modern data mining technique that warps two measured time series non-linearly in the time domain so that the dissimilarity between the two is minimized. It has not been applied in this context before. From numerical experiments we can show that DTW outperforms XCORR, because it provides more accurate velocity estimates, with an error of about 7% under typical conditions, at a higher temporal resolution. In addition, we can show that pre-processing of the data is important and that tracer reaction in the sewer reach is critical. As dispersion is generally small, the distance between the sensors is less influential if it is known precisely. Considering these findings, we tested the methods on a real-world sewer to check the performance of two different sewer flow meters based on temperature measurements. Here, we were able to detect that one of two flow meters was not performing satisfactorily under a variety of flow conditions. Although theoretical analyses show that XCORR and DTW velocity estimates contain systematic errors due to dispersion and reaction processes, these are usually small and do not limit the applicability of the approach.

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.

Data from a new experimental setup deployed in Dübendorf (Switzerland) are used to quantify and model the magnitude and dynamics of the wet-antenna attenuation (WAA) affecting a 1.85-km commercial microwave link at 38 GHz. The results show that the WAA exhibits the following properties: 1) It is bounded by a maximum value of about 2.3 dB; 2) it increases exponentially toward 2.3 dB during the first 5-20 min of rainfall; and 3) it decreases exponentially as soon as the rain stops. A new dynamic WAA model that reproduces these three features and can be calibrated using solely link measurements is proposed. Its performance is evaluated at different temporal resolutions and compared with other wet-antenna models from the literature. The results show that the dynamic model outperforms all other models and significantly reduces the uncertainty of the retrieved path-averaged rain rates.

Streamflow cannot be measured directly and is typically derived with a rating curve model. Unfortunately, this causes uncertainties in the streamflow data and also influences the calibration of rainfall-runoff models if they are conditioned on such data. However, it is currently unknown to what extent these uncertainties propagate to rainfall-runoff predictions. This study therefore presents a quantitative approach to rigorously consider the impact of the rating curve on the prediction uncertainty of water levels. The uncertainty analysis is performed within a formal Bayesian framework and the contributions of rating curve versus rainfall-runoff model parameters to the total predictive uncertainty are addressed. A major benefit of the approach is its independence from the applied rainfall-runoff model and rating curve. In addition, it only requires already existing hydrometric data. The approach was successfully demonstrated on a small catchment in Poland, where a dedicated monitoring campaign was performed in 2011. The results of our case study indicate that the uncertainty in calibration data derived by the rating curve method may be of the same relevance as rainfall-runoff model parameters themselves. A conceptual limitation of the approach presented is that it is limited to water level predictions. Nevertheless, regarding flood level predictions, the Bayesian framework seems very promising because it (i) enables the modeler to incorporate informal knowledge from easily accessible information and (ii) better assesses the individual error contributions. Especially the latter is important to improve the predictive capability of hydrological models.

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.

Successful supervision and control of wastewater and storm water systems requires accurate sewer flow measurements. Unfortunately, the harsh sewer environment and missing flow meter calibration often lead to inaccurate and biased data. In this manuscript, we improve sewer flow monitoring by creating redundant information on sewer velocity from wastewater quality measurements. Continuous observations upstream and downstream of a sewer section are used to estimate the travel time based on dynamic time warping (DTW). DTW is a modern data mining technique that warps two measured time series non-linearly in the time domain so that the dissimilarity between the two is minimized. We first explore the characteristics of the method using numerical experiments. Our results demonstrate that the error in velocity estimates from DTW under typical conditions is about 7%. Second, we apply DTW in a case study to check the performance of two different sewer flow meters using temperature measurements. This enables us to detect that one of two flow meters was not performing satisfactorily under a variety of flow conditions. Although theoretical analyses show that DTW velocity estimates contain systematic errors due to dispersion and reaction processes, these are usually small and do not limit the applicability of the approach.

Incomplete knowledge about spatio-temporal rainfall dynamics causes input uncertainty in rainfall - runoff modelling. This is especially critical in urban areas, where subcatchments are small and runoff is generated extremely fast on the impervious areas. Networks of telecommunication microwave links (MWL) are very dense in urban areas and therefore could provide novel rainfall information which has the potential to reduce input uncertainty in urban drainage modelling. In this study, we therefore investigate how the better information on spatio-temporal rainfall variability from MWL observations improves pipe flow predictions. Specifically, we perform numerical experiments with virtual rainfall fields and compare the results of MWL rainfall reconstructions to those of rain gauge observations. For a case study of a suburb in Prague, Czech Republic, we are able to show that MWL networks in urban areas are sufficiently dense to provide good information on spatio-temporal rainfall variability. Total rainfall volumes are reproduced very well, with errors of about 6 %. Although peak rainfall intensities are still systematically underestimated by approximately 30%, this clearly outperforms rain gauge observations. Also, we find that peak flows from MWL observations are only biased by -6 %, whereas rain gauge observations cause a bias of 16 %. In our study, we did not include effects of uncertainties in both MWL and rain gauge measurements, which, arguably, can be high. Nevertheless, MWL networks could provide hundreds of rainfall sensors, which would be available at virtually no cost. As demonstrated, MWLs can significantly reduce input uncertainties in future rainfall-runoff modeling and thus improve discharge predictions.

In this paper we investigate potential of commercial microwave links (MWL) to capture the spatio-temporal rainfall dynamics and thus improve rainfall-runoff modelling in urban areas. Specifically, we perform numerical experiments with virtual rainfall fields and compare the results of MWL rainfall reconstructions to those of rain gauge (RG) observations. For a case study of a small sewer system, 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. This is especially beneficial for high-intensity rainfalls, which usually have a high spatial variability that cannot be accurately captured by RG point measurements.

In this paper, we investigate how rainfall observations from telecommunication microwave links improve when the link is adjusted against different reference rainfall data from rain gauges or distrometers. Based on field data and numerical experiments we assess the performance of four different rainfall retrieval models, which require different levels of precipitation information.

Recently, the rain-induced attenuation of microwave links (MWL) has been suggested as a novel tool to monitor rainfall in urban areas. However, this requires robust methods to identify and remove baseline attenuation during dry weather, which is not trivial. In this paper, we present a novel statistical method to identify the baseline attenuation based on robust local regression (REBS) offline and in real-time. To test the correct baseline removal, we were able to test the algorithm on an extraordinary dataset of a dense network of rain gauges and disdrometers that provides an unrivalled ground truth reference rain fall and avoids uncertainties due to scaling effect between the path-average MWL measurements and point rain gauge observations. Our results show that the robust regression algorithms predict the path-average rainfall intensities from MWL signal levels that are practically unbiased and match the reference rainfall well. Although the scatter for 1-min rainfall intensities can vary up to 80% of the reference measurements, this it reduced to about 20% for 10min average intensities.

Urbanization and the resulting land-use change strongly affect the water cycle and runoff-processes in watersheds. Unfortunately, small urban watersheds, which are most affected by urban sprawl, are mostly ungauged. This makes it intrinsically difficult to assess the consequences of urbanization. Most of all, it is unclear how to reliably assess the predictive uncertainty given the structural deficits of the applied models. In this study, we therefore investigate the uncertainty of flood predictions in ungauged urban basins from structurally uncertain rainfall-runoff models. To this end, we suggest a procedure to explicitly account for input uncertainty and model structure deficits using Bayesian statistics with a continuous-time autoregressive error model. In addition, we propose a concise procedure to derive prior parameter distributions from base data and successfully apply the methodology to an urban catchment in Warsaw, Poland. Based on our results, we are able to demonstrate that the autoregressive error model greatly helps to meet the statistical assumptions and to compute reliable prediction intervals. In our study, we found that predicted peak flows were up to 7 times higher than observations. This was reduced to 5 times with Bayesian updating, using only few discharge measurements. In addition, our analysis suggests that imprecise rainfall information and model structure deficits contribute mostly to the total prediction uncertainty. In the future, flood predictions in ungauged basins will become more important due to ongoing urbanization as well as anthropogenic and climatic changes. Thus, providing reliable measures of uncertainty is crucial to support decision making.

Micropollutants in urban receiving waters typically origin from many sources, such as wastewater effluents, combined sewer overflows, stormwater discharges and diffuse inputs from arable land. To design effective pollution management strategies in watersheds with mixed land use, we suggest a screening method that pinpoints the origin of pollutants and identifies relevant flow paths. This takes advantage of the fact, that medium polar micropollutants sorb to sediments. The core of the approach is an expert system based on Bayesian probabilities that updates the experts’ knowledge on potential micropollutant sources in a catchment using monitoring data from a single sediment sample. We are able to demonstrate the feasibility of the approach on a real-world case study in the Mönchaltorfer Aa catchment, where micropollutants most probably origin from wastewater and stormwater influents and not from agriculture. Limitations arise from the analytical cost and labour-intensive analysis, which limits the number of samples and, inscreening mode, only provides qualitative information on the observed compounds.

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.

Inflow and infiltration (I/I) have a number of negative effects on the operation of drainage systems. Measures to reduce I/I are applied in many municipalities. However, the effect is only assessed on rare occasions. While a number of guidelines and studies exist on how to quantify I/I, only little is known about the effect of the measures. To one part this results from limiting the quality assurance to the actual measure, e.g. leak-tightness of rehabilitated sewers. For a second part, this is due to lacking methods to separate the effect from external factors ike rainfall or hydrological conditions. This study presents the paired use of a reference catchment and regression analysis to illicit the effect of rehabilitation on groundwater infiltration. Comparing the results of the novel approach to the performance assessment in an ad-hoc manner revealed for the case study a crude overestimation of the effect for the traditional approach. While the regression analysis evaluated the effect to 24%, the traditional approach assesses the performance in the range between 38% and 61% depending on the season during the monitoring.

The analysis of sewage for urinary biomarkers of illicit drugs is a promising and complementary approach for estimating the use of these substances in the general population. For the first time, this approach was simultaneously applied in 19 European cities, making it possible to directly compare illicit drug loads in Europe over a 1-week period. An inter-laboratory comparison study was performed to evaluate the analytical performance of the participating laboratories. Raw 24-hour composite sewage samples were collected from 19 European cities during a single week in March 2011 and analyzed for the urinary biomarkers of cocaine, amphetamine, ecstasy, methamphetamine and cannabis using in-house optimized and validated analytical methods. The load of each substance used in each city was back-calculated from the measured concentrations. The data show distinct temporal and spatial patterns in drug use across Europe. Cocaine use was higher in Western and Central Europe and lower in Northern and Eastern Europe. The extrapolated total daily use of cocaine in Europe during the study period was equivalent to 356. kg/day. High per capita ecstasy loads were observed in Dutch cities, as well as in Antwerp and London. In general, cocaine and ecstasy loads were significantly elevated during the weekend compared to weekdays. Per-capita loads of methamphetamine were highest in Helsinki and Turku, Oslo and Budweis, while the per capita loads of cannabis were similar throughout Europe. This study shows that a standardized analysis for illicit drug urinary biomarkers in sewage can be applied to estimate and compare the use of these substances at local and international scales. This approach has the potential to deliver important information on drug markets (supply indicator).

A Markov switching algorithm is introduced to classify attenuation measurements from telecommunication microwave links into dry and rainy periods. It is based on a simple state-space model and has the advantage of not relying on empirically estimated threshold parameters. The algorithm is applied to data collected using a new and original experimental set-up in the vicinity of Zürich, Switzerland. The false dry and false rain detection rates of the algorithm are evaluated and compared to 3 other algorithms from the literature. The results show that, on average, the Markov switching model outperforms the other algorithms. It is also shown that the classification performance can be further improved if redundant information from multiple channels is used.

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.

Wastewater production, like many other engineered and environmental processes, is inherent stochastic in nature and requires the use of complex stochastic models, for example, to predict realistic patterns of down-the-drain chemicals or pharmaceuticals and personal care products. Up until now, a formal method of statistical inference has been lacking for many of those models, where explicit likelihood functions were intractable. In this Article, we investigate Approximate Bayesian Computation (ABC) methods to infer important parameters of stochastic environmental models. ABC methods have been recently suggested to perform model-based inference in a Bayesian setting when model likelihoods are analytically or computationally intractable and have not been applied to environmental systems analysis or water quality modeling before. In a case study, we investigate the performance of three different algorithms to infer the number of wastewater pulses contained in three high-resolution data series of benzotriazole and total nitrogen loads in sewers. We find that all algorithms perform well and that the uncertainty in the inferred number of corresponding wastewater pulses varies between 6% and 28%. In our case, the results are more sensitive to substance characteristics than to catchment properties. Although the application of ABC methods requires careful tuning and attention to detail, they have a great general potential to update stochastic model parameters with monitoring data and improve their predictive capabilities.

Anaccurate description of aging and deterioration of urban drainage systemsis 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.

The analysis of 87 peer-reviewed journal articles reveals that sampling for pharmaceuticals and personal care products (PPCPs) and illicit drugs in sewers and sewage treatment plant influents is mostly carried out according to existing tradition or standard laboratory protocols. Less than 5% of all studies explicitly consider internationally acknowledged guidelines or methods for the experimental design of monitoring campaigns. In the absence of a proper analysis of the system under investigation, the importance of short-term pollutant variations was typically not addressed. Therefore, due to relatively long sampling intervals, potentially inadequate sampling modes, or insufficient documentation, it remains unclear for the majority of reviewed studies whether observed variations can be attributed to “real” variations or if they simply reflect sampling artifacts. Based on results from previous and current work, the present paper demonstrates that sampling errors can lead to overinterpretation of measured data and ultimately, wrong conclusions. Depending on catchment size, sewer type, sampling setup, substance of interest, and accuracy of analytical method, avoidable sampling artifacts can range from “not significant” to “100% or more” for different compounds even within the same study. However, in most situations sampling errors can be reduced greatly, and sampling biases can be eliminated completely, by choosing an appropriate sampling mode and frequency. This is crucial, because proper sampling will help to maximize the value of measured data for the experimental assessment of the fate of PPCPs as well as for the formulation and validation of mathematical models. The trend from reporting presence or absence of a compound in “clean” water samples toward the quantification of PPCPs in raw wastewater requires not only sophisticated analytical methods but also adapted sampling methods. With increasing accuracy of chemical analyses, inappropriate sampling increasingly represents the major source of inaccuracy. A condensed step-by-step Sampling Guide is proposed as a starting point for future studies.

Despite more than a decade of research, the magnitude of wastewater leakage from defective sewer systems into groundwater supplies is still largely unknown, partly because reliable measurement methods are lacking. Although recently suggested in-sewer tracer studies present a promising solution, it is unclear how to optimally design such studies in network settings. In this study we present a formal experimental design procedure, which uses Bayesian data analysis to improve the diagnosis of sewer leakage by combining tracer test data with prior knowledge on network topology and condition. From a simulation study, we show that (1) if a single sewer section is expected to have high leakage, that section should be distinguished in measurement layouts through isolated tests or appropriate overlapping of multiple tests; (2) if multiple sections are expected to have high leakage, layouts with tests that cover more than one high-leakage section should be avoided; and (3) if a robust experimental design is desired, a balanced layout of tests that overlap multiple sections of high leakage, yet minimizes stretch length, should be chosen. This design will have the additional benefit of inducing covariance in the posterior distribution of exfiltration estimates, which can be used to advantage in subsequent studies. We apply these guidelines to a case study of a catchment in Zurich, Switzerland, and find that optimal layout design can improve the anticipated gain of information substantially relative to designs based on practical considerations alone. Remaining concerns regarding the procedure include (1) the generally poor understanding of the mechanisms governing sewer leakage, which can hamper reliable prior information on exfiltration; (2) the currently low measurement precision of sewer tracer studies, which might only allow for the detection of large leaks; and (3) the need for numerical implementation of the Bayesian inference procedure, which requires careful tuning and long computation times. In general, we were able to demonstrate that the incorporation of prior information through a Bayesian procedure adds significant value to experimental design, especially in situations with few "hard" data but good site-specific knowledge, which is common in water resources research.

Nowadays, mobile phone antennas can be found almost everywhere. We can see many of them in urban regions and also in very remote areas. Where the service provider is lacking a sufficient cable network, point-to-point radio systems are normally used to connect one antenna to the next or to the a nearby access point of the telephone network. The network of radio links has become very dense in the last years, especially in urban areas. But this kind of transmission is only possible if there is direct line of sight between the sender and the receiver. As soon as it starts to rain, the signal strength is lowered because the rain drops hamper the propagation of the electromagnetic waves. This attenuation of the signal depends on the intensity of the rainfall and can be measured at the receiver. By means of a widely accepted empirical relationship it is possible to estimate the average rain rate along the link from the attenuation measurements at the receiver.
An urban area of about 150km² in Greater Zurich is subject to this thesis. Measurements from 23 microwave links are provided by a local network operator and reference data from 13 rain gauges, two disdrometers and a weather radar were available. A total of 128 calibrations were conducted to estimate the parameters for the empirical relationship and two different types of the necessary pre-processing of the data were compared to each other. It could be shown that 50 per cent of the predictions made by means of a constant parameter set, are afflicted with a mean error of less than 1.2mm/h. Differences in the drop size distribution of rain events and the quality of the received signal were identified as important influence factors for reliable model predictions.

Mobilfunkantennen findet man heutzutage fast überall. Wir begegnen vielen davon in den städtischen Gebieten aber auch in abgelegenen Regionen. Überall dort, wo es den Mobilfunkanbietern an einem ausreichend dichten Kabelnetz mangelt, wird Richtfunk eingesetzt um die einzelnen Antennen mit einander zu verbinden und ans Telefonnetz anzuschliessen. Dieses Netz von Richtstrahlverbindungen ist in den letzten Jahren vor allem in den städtischen Gebieten immer dichter geworden. Diese Art der Informationsübertragung ist jedoch nur möglich, wenn zwischen dem Sender und dem Empfänger eine direkte Sichtverbindung besteht. Sobald es zu regnen beginnt, wird das Signal abgeschwächt, da die Regentropfen die Ausbreitung der elektromagnetischen Wellen behindern. Diese Dämpfung des Signals hängt von der Niederschlagsintensität ab und kann am Empfänger gemessen werden. Mithilfe einer gängigen empirischen Beziehung ist es möglich, aus den Dämpfungsmessungen am Empfänger die mittlere Regenintensität entlang der Verbindung zu bestimmen.
Diese Arbeit bezieht sich auf ein ungefähr 150km² grosses städtisches Gebiet im Grossraum Zürich. Es wurden Messungen von 23 Mikrowellenverbindungen eines lokalen Mobilfunkanbieters zur Verfügung gestellt und es waren Referenzdaten von 13 Regenschreibern, zwei Tropfenspektrometern und einem Wetterradar verfügbar. Insgesamt wurden 128 Kalibrierungen durchgeführt um geeignete Parameter für die empirische Beziehung zu schätzen und es wurden zwei unterschiedliche Varianten der nötigen Vorverarbeitung der Daten miteinander verglichen. Es konnte gezeigt werden, dass 50 Prozent der Vorhersagen, die mit einem konstanten Parameterset durchgeführt wurden, mit einem mittleren Fehler von weniger als 1.2mm/h behaftet sind. Unterschiede in den Tropfengrössenverteilung von Regenereignissen und die Qualität des gemessenen Signals wurden als wichtige Einflussfaktoren für zuverlässige Ergebnisse der Modellierung identifiziert.

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.

Direct measurements of sewer leakage with continuous dosing of tracers are often considered too imprecise for practical applications. However, no mathematical framework for data analysis is reported in literature. In this paper, we present an improved experimental design and data analysis procedure together with a comprehensive framework for uncertainty assessment. Test runs in a 700 m-long watertight sewer showed no significant bias and a very high precision of the methodology. The standard error in the results was assessed to 2.6% of the labeled flow with a simplified model. It could be reduced to 1.2% when a dynamic data analysis procedure was applied. The major error contribution was caused by transient transport phenomena, which suggests that careful choosing of the experimental time is more important than the choice of a very specific tracer substance. Although the method is not intended to replace traditional CCTV inspections, it can provide complementary information for rational rehabilitation planning.

In this article, we study the performance of international water management at Lake Titicaca in order to assess the empirical applicability of a new concept for measuring policy performance. This performance measurement concept (PER) is a function of three variables: optimum performance (OP), actual performance (AP), and counterfactual performance (CP). In examining the joint management of Lake Titicaca by Bolivia and Peru, we identify practical difficulties in applying PER and suggest extensions of the concept to assess uncertainty. We find that this measurement concept has several advantages: it takes into account counterfactual performance; the analysis can be performed in a structured, standardized, and transparent manner; and the formal framework allows for an assessment of uncertainty. Problems in applying the concept include: choice of attributes, identification of optimum performance, time-scale, and subjectivity of information. We conclude that, on balance, this approach is superior to other policy performance assessment measures developed to date.

The tracer methods developed to assess exfiltration from sewers in the European project APUSS (assessment of the performance of sewer systems) have a high degree of freedom with regard to the choice of tracer and the dosing strategy. These can lead to very different degrees of uncertainty in the measured exfiltration ratio. In this study, we demonstrate how to select an optimal experimental design using decision analysis, which accounts for this uncertainty and its associated costs. Although the results are site-specific, we can conclude generally that, when NaCl is used as the tracer, the accuracy of the exfiltration estimate is most sensitive to the amount of tracer used and the starting time of the experiment.

One of the goals of the APUSS project (assessing infiltration and exfiltration on the performance of urban sewer systems) is to assess sewer exfiltration, in order to support cities and operators to define problem-oriented rehabilitation strategies. In this paper, we review various methods currently used for the estimation of exfiltration and discuss data needs and applicability for rehabilitation planning. Although each approach has its individual advantages and drawbacks, we identified pressure tests and tracer methods to have the highest potential for decision support in rehabilitation planning. With regard to future challenges (e.g. micropollutants) such methods might play a key role in integrated sewer management.

A novel method is presented to estimate exfiltration from sewer systems using artificial tracers. The method relies upon use of an upstream indicator signal and a downstream reference signal to eliminate the dependence of exfiltration estimates on the accuracy of discharge measurement. An experimental design, a data analysis procedure, and an uncertainty assessment process are described and illustrated by a case study. In a 2-km reach of unknown condition, exfiltration was estimated at 9.9 ± 2.7%. Uncertainty in this estimate was primarily due to the use of sodium chloride (NaCl) as the tracer substance. NaCl is measured using conductivity, which is present at nonnegligible levels in wastewater, thus confounding accurate identification of tracer peaks. As estimates of exfiltration should have as low a measurement error as possible, future development of the method will concentrate on improved experimental design and tracer selection. Although the method is not intended to replace traditional CCTV inspections, it can provide additional information to urban water managers for rational rehabilitation planning.

In this paper, 60 tracer experiments in 37 different sewer reaches have been analyzed for longitudinal dispersion under dry weather flow conditions. It was found that dispersion coefficients of sewers are two to three orders of magnitude smaller than those measured in rivers and do not differ much from system to system. Suitable equations were identified to predict reasonable dispersion coefficients in sewer reaches with uniform geometry and stable flow conditions, For engineering applications that require a high degree of accuracy the performance of tracer measurements is recommended.

Urban sewer systems are subjected to constant stress by traffic and soil movement. Combined with natural fatigue of materials, these conditions lead to damage to underground pipes, causing exfiltration of sewage into the ground water as well as infiltration of ground water into the sewer pipes. EAWAG is currently developing a new method to quantify these processes by using both natural and artificial tracers, which will help us in planning more efficient remedial action.

Les réseaux d’égouts urbains sont constamment soumis à des contraintes liées à la circulation automobile et aux mouvements du sol. Se surajoutant à la fatigue naturelle des matériaux, elles causent des dommages coûteux à l’infrastructure souterraine provoquant des exfiltrations d’eaux usées et des infiltrations d’eaux souterraines. L’EAWAG est actuellement en train de développer de nouvelles méthodes permettant de quantifier ces processus indésirables à l’aide de traceurs naturels et artificiels. Les informations obtenues sur l’étendue des infiltrations et exfiltrations doivent permettre d’établir un plan de réhabilitation plus efficace des égouts urbains.

In this paper the authors present a method to quantify the exfiltration from sewers with artificial tracers. In an application study Lithium and Bromide were used as ionic tracers. The results from an error analysis indicate that the main problems with the application of the method originate from the time-varying sewer flow. Accurate information on exfiltration could be used in innovative sewer operation and maintenance strategies.

Städtische Kanalnetze sind ständigen Belastungen durch Verkehr und Bodenbewegungen ausgesetzt. In Kombination mit der natürlichen Ermüdung des Materials entstehen kostspielige Schäden an der unterirdischen Infrastruktur, die die Exfiltration von Abwasser sowie die Infiltration von Grundwasser zur Folge haben. Die EAWAG entwickelt zurzeit neue Methoden, die es ermöglichen, diese unerwünschten Prozesse mit künstlichen und natürlichen Tracern zu quantifizieren. Die Informationen über das Ausmass von Ex- und Infiltration sollen eine effiziente Sanierungsplanung ermöglichen.