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.

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Assessing uncertainties in measurements must become a standard practice in the field of urban drainage andstormwater management. This chapter presents three standard methods to estimate uncertainties: the Type Amethod (repeated measurements), the Type B method (law of propagation of uncertainties) and the MCmethod (Monte Carlo method). Each method is described with its fundamental principles and equations,various examples are presented in detail and Matlab® codes are given to facilitate the calculations forroutine applications. An advanced method to account for partial autocorrelation in time series ispresented. Lastly, typical orders of magnitude of standard uncertainties for usual sensors used in urbandrainage and stormwater management are given.@en

The knowledge of water levels and discharges in urban drainage and stormwater management (UDSM) systems is of key importance to understand their functioning and processes, to evaluate their performance, and to provide data for modelling. In this chapter, devoted mainly to underground combined and separate sewer pipe systems, various methods and technologies are described and discussed. After an introduction to important aspects to deal with when measuring discharges in sewer systems, the following parts are presented successively: (i) measurement of water level with rulers, and pressure, ultrasonic and radar sensors, (ii) measurement of flow velocity with ultrasonic, Doppler, velocity profiler, free surface, and electromagnetic sensors, (iii) direct measurement of discharge with pre-calibrated devices, physical scale models, computational fluid dynamics modelling and use of pumping stations, and (iv) detection and/or measurement of infiltration into and exfiltration from sewers, with flow or pressure measurements, tracer experiments, distributed temperature sensing and geophysical methods.@en

This introductory chapter indicates why well-defined, high-standard, and reliable monitoring is a key aspect in the necessary evolution of urban drainage and stormwater management and why it should become routine practice. It provides a framework, guidelines, and recommendations to define monitoring objectives and means. It also presents the structure and the chapters of the rest of the book.@en

UV/Vis spectrophotometers have been used for one decade to monitor water quality in various locations: sewers, rivers, wastewater treatment plants (WWTPs), tap water networks, etc. Resulting equivalent concentrations of interest can be estimated by three ways: i) by manufacturer global calibration; ii) by local calibration based on the provided global calibration and grab sampling; iii) by advanced calibration looking for relations between UV/Vis spectra and corresponding concentrations from grab sampling. However, no study has compared the applied methods so far. This collaborative work presents a comparison between five different methods. A Linear Regression (LR), Support Vector Machine (SVM), EVOlutionary algorithm method (EVO) and Partial Least Squares (PLS) have been applied on various data sets (sewers, rivers, WWTPs under dry, wet and all weather conditions) and for three water quality parameters: TSS, COD total and dissolved. Two criteria (r² and Root Mean Square Error RMSE) have been calculated - on calibration and verification data subsets - to evaluate accuracy and robustness of the applied methods. Values of criteria have then been statistically analysed for all and separated data sets. Non-consistent outcomes come through this study. According to the Kruskal-Wallis test and RMSEs, PLS and SVM seem to be the best methods. According to uncertainties in laboratory analysis and ranking of methods, LR and EVO appear more robust and sustainable for concentration estimations. Conclusions are mostly independent of water matrices, weather conditions or concentrations investigated.

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Monitoring sewer sediments is necessary to better understand sedimentation and erosion processes. Sonar is one of the available techniques to proceed to sewer sediment measurements. Extraction of numerical data, implementation of a new algorithm to identify the water-sediment interface, laboratory and field experiments have been done to evaluate the device, to quantify uncertainties and to test the sonar under various conditions. Results demonstrate that: 1) uncertainties in repeatability are less than 4%, 2) the sonar delivers accurate results under various conditions (small to large sewers and grit chambers), and 3) in situ measurements are affected by uncertainties, mainly due to the fact that the sensor is not in a fixed position but is floating on the free surface in the sewer. This device is useful and accurate for semi-automatic measurement but further research and improvements should be done to better know the position of the device in the section.

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