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An increasing number of Model Predictive Control (MPC) tools have recently been developed. These MPC methods utilize as input a wide range of rainfall forecasts, which differ in their characteristics and prediction skills. Operators need to identify the most appropriate MPC and rainfall forecast for their needs. A domain-based framework is proposed to distinguish between four operational domains, which consider the magnitude of the forecasted rain and the expected status of the system. The framework is presented by using selected Danish case studies as examples, and it will provide a support to operators in the implementation of MPC strategies.

This article demonstrates the incorporation of stochastic grey-box models for urban runoff forecasting into a full-scale, system-wide control setup where setpoints are dynamically optimized considering forecast uncertainty and sensitivity of overflow locations in order to reduce combined sewer overflow risk. The stochastic control framework and the performance of the runoff forecasting models are tested in a case study in Copenhagen (76 km2 with 6 sub-catchments and 7 control points) using 2-h radar rainfall forecasts and inlet flows to control points computed from a variety of noisy/oscillating in-sewer measurements. Radar rainfall forecasts as model inputs yield considerably lower runoff forecast skills than “perfect” gauge-based rainfall observations (ex-post hindcasting). Nevertheless, the stochastic grey-box models clearly outperform benchmark forecast models based on exponential smoothing. Simulations demonstrate notable improvements of the control efficiency when considering forecast information and additionally when considering forecast uncertainty, compared with optimization based on current basin fillings only.

This paper provides guidance on how to identify and design the most suitable climate adaptation strategies for enhancing the liveability and flood resilience of urban catchments. It presents findings from a case study of Elwood, a coastal Melbourne suburb regularly affected by flooding. The research integrates social science, architecture and environmental engineering to co-develop technical, design and policy solutions that respond to the local community’s vision for the future and are robust under a range of future climate, population and urban development scenarios. The paper shows that ensuring a city’s flood resilience involves a range of measures to retreat from, adapt to and defend against flooding; this necessarily requires an integrated approach and interdisciplinary expertise to develop adaptation pathways that are grounded in community aspirations and priorities, inspired by novel design solutions and informed by modelling of performance, robustness and economic viability.

High quality on-line flow forecasts are useful for real-time operation of urban drainage systems and wastewater treatment plants. This requires computationally efficient models, which are continuously updated with observed data to provide good initial conditions for the forecasts. This paper presents a way of updating conceptual rainfall-runoff models using Maximum a Posteriori estimation to determine the most likely parameter constellation at the current point in time. This is done by combining information from prior parameter distributions and the model goodness of fit over a predefined period of time that precedes the forecast. The method is illustrated for an urban catchment, where flow forecasts of 0–4 h are generated by applying a lumped linear reservoir model with three cascading reservoirs. Radar rainfall observations are used as input to the model. The effects of different prior standard deviations and lengths of the auto-calibration period on the resulting flow forecast performance are evaluated. We were able to demonstrate that, if properly tuned, the method leads to a significant increase in forecasting performance compared to a model without continuous auto-calibration. Delayed responses and erratic behaviour in the parameter variations are, however, observed and the choice of prior distributions and length of auto-calibration period is not straightforward.

This study evaluated methods for automated classification of rain events into groups of "high" and "low" spatial and temporal variability in offline and online situations. The applied classification techniques are fast and based on rainfall data only, and can thus be applied by, e.g., water system operators to change modes of control of their facilities. A k-means clustering technique was applied to group events retrospectively and was able to distinguish events with clearly different temporal and spatial correlation properties. For online applications, techniques based on k-means clustering and quadratic discriminant analysis both provided a fast and reliable identification of rain events of "high" variability, while the k-means provided the smallest number of rain events falsely identified as being of "high" variability (false hits). A simple classification method based on a threshold for the observed rainfall intensity yielded a large number of false hits and was thus outperformed by the other two methods.

We have developed a setup that couples the urban development model DANCE4WATER with the 1D-2D hydraulic model MIKE FLOOD. The setup makes it possible to assess the impact of urban development and infrastructural change scenarios on flood risk in an automated manner. In addition, it permits us to use the results of the hydraulic simulation to condition DANCE4WATER and to account for flood risk in the simulated urban development. In an Australian case study, we demonstrate that future flood risk can be significantly reduced while maintaining the overall speed of urban development.

Flood risk in cities is strongly affected by the development of the city itself. Many studies focus on changes in the flood hazard as a result of, for example, changed degrees of sealing in the catchment or climatic changes. However, urban developments in flood prone areas can affect the exposure to the hazard and thus have large impacts on flood risk. Different urban socio-economic development scenarios, rainfall inputs and options for the mitigation of flood risk, quickly lead to a large number of scenarios that need to be considered in the planning of the development of a city. This calls for automated analyses that allow the planner to quickly identify if, when and how infrastructure should be modified. Such analysis, which accounts for the two-way interactions between city development and flood risk, is possible only to a limited extent in existing tools. We have developed a software framework that combines a model for the socio-economic development of cities (DANCE4WATER) with an urban flood model. The urban flood model is a 1D-2D spatially distributed hydrologic and hydraulic model that, for a given urban layout, simulates flow in the sewer system and the surface flow in the catchment (MIKE FLOOD). The socio-economic model computes urban layouts that are transferred to the hydraulic model in the form of changes of impervious area and potential flow paths on the surface. Estimates of flood prone areas, as well as the expected annual damage due to flooding, are returned to the socio-economic model as an input for further refinement of the scenarios for the urban development. Our results in an Australian case study suggest that urban development is a major driver for flood risk and vice versa that flood risk can be significantly reduced if it is accounted for in the development of the cities. In particular, flood risk in a scenario with strong urban growth and almost a doubling of the amount of sealed area in the catchment was found to remain almost unchange

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.

This article presents selected examples of model-based prediction and control of integrated sewer-wastewater treatment systems, developed within the framework of the Storm- and Wastewater Informatics project (SWI). By exploiting all the available on-line information (e.g. radar based rainfall measurements, flow and water levels, operational parameters at treatment plants) it is possible to dynamically optimize the integrated storm- and wastewater systems according to different criteria (e.g. utilizing the system fully at all times and reducing volumes of combined sewer overflows, loads of pollutants discharged from treatment plants, etc.). The tools developed in the SWI project include (but are not limited to (i) rainfall nowcasting based on radar measurements, (ii) probabilistic flow forecasting based on data assimilation and stochastic models, (iii) prediction and optimization of wet-weather performance of wastewater treatment plants, and (iv) integrated control of the different elements of the integrated wastewater systems. Full-scale testing of these tools in different catchment located in Denmark ensure that the developed tools can represent an important step forwards for on-line operation of combined sewer networks and wastewater treatment plants.

Real time control is considered a mean to efficiently improve the performance of urban drainage systems. A globally optimal utilisation of e.g. storage volume in basins can best be achieved by considering runoff forecasts in the decision setup. These forecasts, however, are subject to significant uncertainty. This uncertainty should be considered in the decision making. An approach that incorporates stochastic multistep runoff predictions from so-called greybox models into a real time control setup is presented. These models provide a dynamic description of forecast uncertainties and they simultaneously allow a continuous adaption of the model states to observed runoff. Methods for generating stochastic forecasts and incorporating these into the decision making framework are described. Using two sample events, the forecast quality is compared to state-of-the-art deterministic forecasting models and the effect on control decisions and the resulting overflow volume is evaluated. We can demonstrate potential of the stochastic models but identify a need for model adaptivity and modified model structures that allow for a more general modelling of forecast uncertainties.

We present stochastic flow forecasts to be used in a real-time control setup for urban drainage systems. The forecasts are generated using greybox models with rain gauge and radar rainfall observations as input. Predictions are evaluated as intervals rather than just mean values. We obtain satisfactory predictions for the smaller catchment but rather large uncertainties for the bigger catchment where the applied storage cascade seems too simple. Radar rainfall introduces more uncertainty into the flow forecast model estimation. However, the radar rainfall forecasts also result in a slightly improved point prediction of flows which we aim to exploit with a modified estimation approach in the future.

Merging of radar rainfall data with rain gauge measurements is a common approach to overcome problems in deriving rain intensities from radar measurements. We extend an existing approach for adjustment of C-band radar data using state-space models and use the resulting rainfall intensities as input for forecasting outflow from two catchments in the Copenhagen area. Stochastic greybox models are applied to create the runoff forecasts, providing us with not only a point forecast but also a quantification of the forecast uncertainty. Evaluating the results, we can show that using the adjusted radar data improves runoff forecasts compared to using the original radar data and that rain gauge measurements as forecast input are also outperformed. Combining the data merging approach with short term rainfall forecasting algorithms may result in further improved runoff forecasts that can be used in real time control.