Empirical Sewer Water Quality Model for Generating Influent Data for WWTP Modelling

Environmental Modelling & Software

et al. 2019

Self Cite

The description of intertwined ecological processes in surface waters requires a holistic approach that accounts for spatially distributed hydrological/water quality processes. This study describes a new approach to model dissolved oxygen (DO) based on linked hydrodynamic and closed nutrient cycle ecological models. Long term datasets from the River Dommel (Netherlands) are used to determine: 1) if this methodology is suitable for modelling DO concentrations, 2) the model sensitivity to various levels of nutrients input, and 3) the DO production and consumption processes and their response to nutrient input changes. Results show that seasonal dynamics of DO are well quantified at long timescales; the sensitivity of DO to different pollutant sources exhibits significant seasonal variation and the largest influences on DO are aeration and mineralization of organic material. The approach demonstrates an ability to consider the impacts of nutrient input and long term vegetation maintenance on ecological quality. Software Requirements • Wageningen Lowland Runoff Simulator (WALRUS) developed by Brauer et al.

Section: Wastewater Treatment Plant Water Quality Characterizationmentioning

confidence: 99%

Evaluation of a coupled hydrodynamic-closed ecological cycle approach for modelling dissolved oxygen in surface waters

Suarez

Brederveld²,

Environmental Modelling & Software

et al. 2019

Self Cite

“…Urban drainage flow was represented by 29 lumped rainfall-runoff and sewer transport schemes (Solvi 2006). Sewer water quality was represented by an influent generator at the WWTP (Langeveld et al 2017) and by an event mean concentration multiplier at the CSO-receiving water links (Moreno-Rodenas et al 2017b). The fully integrated model was implemented in the platform WEST (DHI).…”

Section: The Integrated Catchment Modelmentioning

confidence: 99%

Parametric emulation and inference in computationally expensive integrated urban water quality simulators

Langeveld

Clemens

2019

Environ Sci Pollut Res

Water quality environmental assessment often requires the joint simulation of several subsystems (e.g. wastewater treatment processes, urban drainage and receiving water bodies). The complexity of these integrated catchment models grows fast, leading to potentially over-parameterised and computationally expensive models. The receiving water body physical and biochemical parameters are often a dominant source of uncertainty when simulating dissolved oxygen depletion processes. Thus, the use of system observations to refine prior knowledge (from experts or literature) is usually required. Unfortunately, simulating realworld scale water quality processes results in a significant computational burden, for which the use of sampling intensive applications (e.g. parametric inference) is severely hampered. Data-driven emulation aims at creating an interpolation map between the parametric and output multidimensional spaces of a dynamic simulator, thus providing a fast approximation of the model response. In this study a large-scale integrated urban water quality model is used to simulate dissolved oxygen depletion processes in a sensitive river. A polynomial expansion emulator was proposed to approximate the link between four and eight river physical and biochemical river parameters and the dynamics of river flow and dissolved oxygen concentration during one year (at hourly frequency). The emulator scheme was used to perform a sensitivity analysis and a formal parametric inference using local system observations. The effect of different likelihood assumptions (e.g. heteroscedasticity, normality and autocorrelation) during the inference of dissolved oxygen processes is also discussed. This study shows how the use of datadriven emulators can facilitate the integration of formal uncertainty analysis schemes in the hydrological and water quality modelling community.

“…This model represents three biological lines and a storm-bypass section. The link of state variables at the boundary urban drainage-WWTP were produced using an stochastic generator [13]. A river sub-model, which is composed of a flow propagation scheme, was modelled through a hydrological storage-discharge model (tank-in-series scheme).…”

Section: Description Of the Model Structurementioning

confidence: 99%

“…Also, urban wastewater systems are often composed by several connected sewer networks across municipal areas (e.g., centralised wastewater treatment layouts), and the distances between those connected catchments are often beyond the de-correlation length of convective storm events [12]. This means that spatial and temporal scales of rainfall processes can affect in different manner the individual and the total catchment system [13].…”

Section: Introductionmentioning

confidence: 99%

Impact of Spatiotemporal Characteristics of Rainfall Inputs on Integrated Catchment Dissolved Oxygen Simulations

Cecinati

Langeveld

et al. 2017

Water

Self Cite

Integrated Catchment Modelling aims to simulate jointly urban drainage systems, wastewater treatment plant and rivers. The effect of rainfall input uncertainties in the modelling of individual urban drainage systems has been discussed in several studies already. However, this influence changes when simultaneously simulating several urban drainage subsystems and their impact on receiving water quality. This study investigates the effect of the characteristics of rainfall inputs on a large-scale integrated catchment simulator for dissolved oxygen predictions in the River Dommel (The Netherlands). Rainfall products were generated with varying time-aggregation (10, 30 and 60 min) deriving from different sources of data with increasing spatial information: (1) Homogeneous rainfall from a single rain gauge; (2) block kriging from 13 rain gauges; (3) averaged C-Band radar estimation and (4) kriging with external drift combining radar and rain gauge data with change of spatial support. The influence of the different rainfall inputs was observed at combined sewer overflows (CSO) and dissolved oxygen (DO) dynamics in the river. Comparison of the simulations with river monitoring data showed a low sensitivity to temporal aggregation of rainfall inputs and a relevant impact of the spatial scale with a link to the storm characteristics to CSO and DO concentration in the receiving water.