The accumulation of sediments in stormwater systems negatively affects their functioning. For example, the re-suspension of these sediments can lead to serious pollution of surface water bodies through combined sewer overflows (CSOs). In addition, the persistent accumulation of sediments reduces the storage and hydraulic capacity of stormwater systems, resulting in an increased risk of flooding. Stormwater managers spend considerable resources cleaning these systems, but we still lack reliable and easy-to-use monitoring methods to provide information on the location, volume and composition of sediments. This study explores the use of temperature sensors combined with the analysis of heat transfer processes to measure sediment depth in sand trap gully pots. To this end, a laboratory-scale experimental campaign was carried out using a 1 : 1 scale gully pot model, with different sediment types, hydrographs and inflow temperature conditions. The experiments were designed using field measurements to reproduce the temperature changes in gully pots and thus the heat transfer processes. The results showed maximum differences between reference measurements and estimated depths of less than 30 mm. Finally, the use of temperature sensors as a cost-effective solution for monitoring sediment accumulation is discussed.

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The adoption of increasingly restrictive water quality standards is directed to maintain natural ecosystems in a good status. Complying with such standards requires significant investments in water infrastructure and operations. Consequently, mathematical simulation is usually applied to assist in the decision-making process for such large-scale actuations. In particular, environmental models are proposed to represent the wastewater cycle in natural water bodies, such that the effect of different pollution mitigation alternatives can be estimated. Integrated catchment models (ICM) aim at simulating water quality dynamics by representing the link between urban drainage networks, wastewater treatment operations, rural hydrology and river physical-biochemical processes. However, these subsystems present dynamics acrossmultiple spatiotemporal scales and many relevant processes are still not fully understood. System observations are scarce and often insufficient to identify most model representations. As a result, ICM studies often produce significant output uncertainties.@en

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.@en

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 real-world 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 data-driven emulators can facilitate the integration of formal uncertainty analysis schemes in the hydrological and water quality modelling community.@en

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.@en

Vortices are a very common phenomenon to consider in many hydraulic engineering problems, e.g. when designing pump sumps or intake works for turbines. Until now, the focus is on avoiding the development of vortices because of induction of cavitation and air entrainment by a fully developed air core. However, vortices may also be used in a more positive manner e.g. by taking advantage of the capacity to transport (floating) particles to avoid the built up of scum layers in wastewater pump sumps. To the authors’ knowledge only little literature is available in this specific field of research. In order to obtain state-of-the art data on the dynamics of large particles in a free-surface vortex flow field and to examine the vortex transport capacity, a simple but novel 3D-PTV (Particle Tracking Velocimetry) method using 6 iPhones was designed, built, tested and employed to measure the 3D-motion of a large particle in the vortex flow. The experimental set-up, particle detection method and the post-processing of the raw data are described in detail along with some first experimental results. The presented 3D-PTV method showed to be able to obtain data that can be used for detailed analysis of the dynamics of large particles in the vortex flow. The overall standard uncertainty of the particle centre position is in the order of magnitude of 0.1 mm in each direction in a volume of a diameter of 0.6 m and a height of about 1 m, while recording at 240 fps.

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Environmental models often contain parameters, which are not measurable, yet conceptual descriptions of some physical process. The value of such parameters is often derived by measuring internal state model variables in the system and indirectly tuning/calibrating the value of the parameters so some degree of match is achieved. Bayesian inference is a widely used tool in which the modeller can transfer some prior beliefs about the parameter space, which is updated when additional knowledge on the system is acquired (e.g. more measurements are available). However, the amount of simulations required to perform a formal inference becomes prohibitive when using computationally expensive models. In this work the inference of the hydraulic and dissolved oxygen processes is presented for a large scale integrated catchment model. Two emulator structures were used to accelerate the sampling of the river flow and dissolved oxygen dynamics. Posterior parameter probability distributions were computed using one year of measured data in the river.@en

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.

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Under drought stress, Phytoseiulus persimilis females are able to lay drought-resistant eggs through an adaptive maternal effect. The mechanisms making these eggs drought resistant still remain to be investigated. For this purpose, we studied the physiological differences between drought-resistant and drought-sensitive eggs. We compared the volume and the surface-area-to-volume ratio (SA:V) of the eggs, their sex ratio, their chemical composition (by gas chromatography-mass spectrometry), their internal and external structure [by scanning electron microscope (SEM) and transmission electron microscope (TEM) images], and their developmental time. Our results show that drought-resistant and drought-sensitive eggs have a different chemical composition: drought-resistant eggs contain more compatible solutes (free amino acids and sugar alcohols) and saturated hydrocarbons than drought-sensitive eggs. This difference may contribute to reducing water loss in drought-resistant eggs. Moreover, drought-resistant eggs are on average 8.4% larger in volume, and have a 2.4% smaller SA:V than drought-sensitive eggs. This larger volume and smaller SA:V, probably the result of a higher water content, may make drought-resistant eggs less vulnerable to water loss. We did not find any difference in sex ratio, internal or external structure nor developmental time between drought-resistant and drought-sensitive eggs. These results mark the first step in the understanding of the strategies and the energetic costs involved in the production of drought-resistant eggs in P. persimilis females.

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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.

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In urban hydrological models, rainfall is the main input and one of the main sources of uncertainty. To reach sufficient spatial coverage and resolution, the integration of several rainfall data sources, including rain gauges and weather radars, is often necessary. The uncertainty associated with rain gauge measurements is dependent on rainfall intensity and on the characteristics of the devices. Common spatial interpolation methods do not account for rain gauge uncertainty variability. Kriging for Uncertain Data (KUD) allows the handling of the uncertainty of each rain gauge independently, modelling space- and time-variant errors. The applications of KUD to rain gauge interpolation and radar-gauge rainfall merging are studied and compared. First, the methodology is studied with synthetic experiments, to evaluate its performance varying rain gauge density, accuracy and rainfall field characteristics. Subsequently, the method is applied to a case study in the Dommel catchment, the Netherlands, where high-quality automatic gauges are complemented by lower-quality tipping-bucket gauges and radar composites. The case study and the synthetic experiments show that considering measurement uncertainty in rain gauge interpolation usually improves rainfall estimations, given a sufficient rain gauge density. Considering measurement uncertainty in radar-gauge merging consistently improved the estimates in the tested cases, thanks to the additional spatial information of radar rainfall data but should still be used cautiously for convective events and low-density rain gauge networks.

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A methodology is presented which can be used in the evaluation of parametric uncertainty in urban flooding simulation. Due to the fact that such simulations are time consuming, the following methodology is proposed: (a) simplification of the description of the physical process; (b) derivation of a training data set; (c) development of a data-driven surrogate model; (d) use of a forward uncertainty propagation scheme. The simplification comprises the following steps: (a) unit hydrograph derivation using a 2D hydrodynamic model; (b) calculation of the losses in order to determine the effective rainfall depth; (c) flood event simulation using the principle of the proportionality and superposition. The above methodology was implemented in an urban catchment located in the city of Athens, Greece. The model used for the first step of the simplification was FLOW-R2D, whereas the well-known SWMM software (US Environmental Protection Agency, Washington, DC, USA) was used for the second step of the simplification. For the training data set derivation, an ensemble of 100 Unit Hydrographs was derived with the FLOW-R2D model. The parameters which were modified in order to produce this ensemble were the Manning coefficients in the two friction zones (residential and urban open space areas). The surrogate model used to replicate the unit hydrograph derivation, using the Manning coefficients as an input, was based on the Polynomial Chaos Expansion technique. It was found that, although the uncertainties in the derived results have to be taken into account, the proposed methodology can be a fast and efficient way to cope with dynamic flood simulation in an urban catchment.@en

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.

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This study addresses the effect of spatial and temporal resolution of rainfall fields on the performance of a simplified integrated catchment model for predicting dissolved oxygen concentrations in a river. For that purpose we propose a procedure to generate rainfall products with increasing spatial information at different time step accumulations (10’, 30’ and 60’) at the spatial support of lumped urban catchment systems. Using a rain gauge network and single-polarization C-Band Radar data we generate 4 rainfall products; 1) Homogeneous rainfall from a single rain gauge, 2) Block kriging interpolation from a network of 13 rain gauges, 3) Averaged Radar estimation and 4) Universal block kriging from a rain gauge network and using Radar as an external covariate. Comparison of the model predictions with monitoring data in the river showed a low sensitivity to the temporal scales proposed and a relative improvement with increasing spatial information linked to the climatological characteristics of the storm period.@en