Search

Accumulation of fat, oil and grease (FOG) in the sumps of wastewater pumping stations is a common failure cause for these facilities. Floating solids are often not transported by the pump suction inlets and the individual solids can accumulate to stiff and thick FOG layers. The lack of data about the dynamics in FOG layer formation still hampers the design of effective measures towards its mitigation. In this article, we present a low-cost camera-based automated system for the observation of FOG layer dynamics in wastewater pumping stations at high-frequency (minutes) over extended time windows (months). Optical imagery is processed through a deep-learning computer vision routine that allows describing FOG layer dynamics (e.g. accumulation rate and changes in shape) and various hydraulic processes in the pump sump (e.g. the water level, surface flow velocity fields, vorticity, or circulation). Furthermore, the system can perform in-camera image processing, thus allowing the transfer of compressed-processed datasets when deployed in remote locations (Edge AI computing), which could be of great utility for the hydro-ecological monitoring community. In this study, the technology applied is illustrated with a dataset (six months, two-minute frequency) collected at a wastewater pumping station at the municipality of Rotterdam, The Netherlands. This monitoring system represents a source of information for the management of (waste)water pumping stations (e.g. detection of free-surface vortices and scheduling of sump cleaning operations) and facilitates the collection of standardized high-frequency FOG layer dynamics data for a detailed description of FOG build-up and transport processes., (Copyright © 2021. Published by Elsevier Ltd.)

Receiving water quality simulation in highly urbanised areas requires the integration of several processes occurring at different space-time scales. These integrated catchment models deliver results with a significant uncertainty level associated. Still, uncertainty analysis is seldom applied in practice and the relative contribution of the individual model elements is poorly understood. Often the available methods are applied to relatively small systems or individual sub-systems, due to limitations in organisational and computational resources. Consequently this work presents an uncertainty propagation and decomposition scheme of an integrated water quality modelling study for the evaluation of dissolved oxygen dynamics in a large-scale urbanised river catchment in the Netherlands. Forward propagation of the measured and elicited uncertainty input-parametric distributions was proposed and contrasted with monitoring data series. Prior ranges for river water quality-quantity parameters lead to high uncertainty in dissolved oxygen predictions, thus the need for formal calibration to adapt to the local dynamics is highlighted. After inferring the river process parameters with system measurements of flow and dissolved oxygen, combined sewer overflow pollution loads became the dominant uncertainty source along with rainfall variability. As a result, insights gained in this paper can help in planning and directing further monitoring and modelling efforts in the system. When comparing these modelling results to existing national guidelines it is shown that the commonly used concentration-duration-frequency tables should not be the only metric used to select mitigation alternatives and may need to be adapted in order to cope with uncertainties., (Copyright © 2019 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

This paper aims to stimulate discussion based on the experiences derived from the QUICS project (Quantifying Uncertainty in Integrated Catchment Studies). First it briefly discusses the current state of knowledge on uncertainties in sub-models of integrated catchment models and the existing frameworks for analysing uncertainty. Furthermore, it compares the relative approaches of both building and calibrating fully integrated models or linking separate sub-models. It also discusses the implications of model linkage on overall uncertainty and how to define an acceptable level of model complexity. This discussion includes, whether we should shift our attention from uncertainties due to linkage, when using linked models, to uncertainties in model structure by necessary simplification or by using more parameters. This discussion attempts to address the question as to whether there is an increase in uncertainty by linking these models or if a compensation effect could take place and that overall uncertainty in key water quality parameters actually decreases. Finally, challenges in the application of uncertainty analysis in integrated catchment water quality modelling, as encountered in this project, are discussed and recommendations for future research areas are highlighted., (Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.)

This work presents a method to emulate the flow dynamics of physically based hydrodynamic simulators under variations of time-dependent rainfall and parametric scenarios. Although surrogate modelling is often employed to deal with the computational burden of this type of simulators, common techniques used for model emulation as polynomial expansions or Gaussian processes cannot deal with large parameter space dimensionality. This restricts their applicability to a reduced number of static parameters under a fixed rainfall process. The technique presented combines the use of a modified Unit Hydrograph (UH) scheme and a polynomial chaos expansion (PCE) to emulate flow from physically based hydrodynamic models. The novel element of the proposed methodology is that the emulator compensates for the errors induced by the assumptions of proportionality and superposition of the UH theory when dealing with non-linear model structures, whereas it approximates properly the behaviour of a physically based simulator to new (spatially-uniform) rainfall time-series and parametric scenarios. The computational time is significantly reduced, which makes the practical use of the model feasible (e.g. real time control, flood warning schemes, hydraulic structures design, parametric inference etc.). The applicability of this methodology is demonstrated in three case studies, through the emulation of a simplified non-linear tank-in-series routing structure and of the 2D Shallow Water Equations (2D-SWE) solution (FLOW-R2D) in two computational domains. Results indicate that the proposed emulator can approximate with a high degree of accuracy the behaviour of the original models under a wide range of rainfall inputs and parametric values., (Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.)