Urban water modelling and the daily time step: issues for a realistic representation

Interest in modelling the total Urban Water Cycle is increasing, due to the realisation of the need for (high-level) flow integration to address issues of recycling, re-use and ultimately sustainability. Urban Water Cycle models are generally operating on a daily time step due to the inherent strategic/planning nature of such work. However, the choice of time step implies (more or less hidden) assumptions which may influence significantly the model’s performance. One such assumption – the way in which water tanks (e.g. rainwater, greywater, greenwater etc) are operated in terms of the sequence between tank overflow (spill) and water extracted from the tank for use (yield) is investigated in this paper. The two alternative sequences are termed here Yield After Spill (YAS) and Yield Before Spill (YBS). The Urban Water Optioneering Tool was used and advantages and disadvantages of these sequences were examined. The paper reviews the differences under a series of technological configurations and draws recommendations for modelling practice. It is suggested that YAS/YBS schemes have different impacts depending on the technological configuration of the case study under investigation, but that under normal operating conditions, daily time step simulations with YBS schemes tend to result in tank sizes that are (marginally) closer to sizes obtained by hourly time-steps. It is however suggested that YAS schemes should be preferred when the parameter of interest is runoff

WaterMet2 model functional requirements

© TRUST 2012This report specifies the functional requirements of the WaterMet2 Model that will be developed to quantify the generic Urban Water System (UWS) metabolism based performance model in the TRUST project (TRansitions to the Urban water Services of Tomorrow). The report is not a project deliverable but rather a work-in-progress to describe different aspects of the model and its functionality. This report addresses two main parts of the WaterMet2 Model functionality. The first part illustrates principal concepts of WaterMet2 modelling as a mass balance base model. Two main aspects of water modelling (i.e. quantity and quality modelling approaches) are described and analysed first. Modelling of the intended risk analysis as one of the purpose of TRUST project is demonstrated. Then, the spatial and temporal scales of the model are better described as well as a brief description of intervention modelling. Second part of the report presents the specific indicators of the WaterMet2 model in three parts: (1) performance indicators linked to all water related flows in the UWS; (2) risk indicators based on the current data received from WA32; and (3) cost indicators including capital and operational ones. For all introduced indicators, the relevant input data requirements are presented. Finally, the model calibration approach is briefly described. This document is based on the authors' current best understanding of the UWS metabolism concept and the associated performance related issues. Therefore, as WaterMet2 model progresses in more details, information presented in this report is likely to evolve and improv

Developing An Integrated Modelling System For Blue-Green Solutions

Blue-green interventions represent the next level of integration for sustainable cities: that of an integrated urban water and urban green design, operation and management. The key concept is that a more holistic approach would present a win-win scenario, in which urban green would be utilized as infrastructure for water services (e.g. mitigating urban floods) while urban water infrastructure would be used as a source of irrigation for urban green, increasing their performance in a range of services including amenities, reducing heat island effects and increasing ecosystem services. However, this focus on integration brings into sharp relief another need: that of developing models and tools able to investigate the interactions between different green and blue system elements and processes. This “ecosystem” of models and tools presents a challenge due to its scope, in terms of development, but also the challenge of model integration. This paper discusses these challenges and furthermore proposes a three level approach to building an integrated modelling system for this case, which is able to: (a) support in the choice of appropriate models; (b) facilitate their linking in runtime and (c) enable the homogenization of results from the different models into common views supporting decision making. The use of standards including OpenMI and WaterML are discussed in the light of the proposed approach. The concept is tested using a limited set of models developed for blue-green solutions design and the preliminary results are presented and critically discussed. The paper concludes with suggestions on the way forward in this work, while attempting to provide more generic insights into multi-model integration for decision support in the environmental domain. Acknowledgements: This work was supported by the Blue Green Dream (BGD) Climate-KIC/EIT Project

Quantitative UWS performance model: WaterMet2

© TRUST 2014The report presents a detailed description of the WaterMet2 methodology and tool as a quantitative urban water system (UWS) performance model. The WaterMet2 model is described in three distinct parts. Modelling concepts of different components in WaterMet2 are first described. It provides an overview of the principle flows/fluxes modelled in spatial and temporal scales in WaterMet2 and how they are modelled within the framework of mass balance equations in four subsystems (water supply, sub-catchment, wastewater and water resource recovery). The second part describes the WaterMet2 software. This consists of an overview of WaterMet2 on how input data are prepared, how to run a simulation and finally how to retrieve results in different formats. This part also introduces the WaterMet2 toolkit functions which can be used by other programming languages to call a WaterMet2 simulation model. In the third part, WaterMet2 is illustrated using the city of Oslo UWS as a generic reference model. This part first describes building and calibrating a WaterMet2 model for the existing UWS which faces water scarcity problems for a 30-year planning horizon starting from year 2011. Then, it examines two alternative intervention options (i.e. adding new water resource and water treatment options) which are supported by the WaterMet2. These options are examined for the UWS model and the improvements are compared to the business-as-usual case.European Union Seventh Framework Programme (FP7/2007-2013

SOURCE TO TAP URBAN WATER CYCLE MODELLING

This work was supported by TRUST (TRansitions to the Urban Water Services of Tomorrow) research project.The continuous expansion of urban areas is associated with increased water demand, both for domestic and non-domestic uses. To cover this additional demand, centralised infrastructure, such as water supply and distribution networks tend to become more and more complicated and are eventually over-extended with adverse effects on their reliability. To address this, there exist two main strategies: (a) Tools and algorithms are employed to optimise the operation of the external water supply system, in an effort to minimise risk of failure to cover the demand (either due to the limited availability of water resources or due to the limited capacity of the transmission system and treatment plants) and (b) demand management is employed to reduce the water demand per capita. Dedicated tools do exist to support the implementation of these two strategies separately. However, there is currently no tool capable of handling the complete urban water system, from source to tap, allowing for an investigation of these two strategies at the same time and thus exploring synergies between the two. This paper presents a new version of the UWOT model (Makropoulos et al., 2008), which adopts a metabolism modelling approach and is now capable of simulating the complete urban water cycle from source to tap and back again: the tool simulates the whole water supply network from the generation of demand at the household level to the water reservoirs and tracks wastewater generation from the household through the wastewater system and the treatment plants to the water bodies. UWOT functionality is demonstrated in the case of the water system of Athens and outputs are compared against the current operational tool used by the Water Company of Athens. Results are presented and discussed: The discussion highlights the conditions under which a single source-to-tap model is more advantageous than dedicated subsystem models.Rozos, E.; Makropoulos, C. (2013). SOURCE TO TAP URBAN WATER CYCLE MODELLING. Environmental Modelling & Software. 41:139-150. https://doi.org/10.1016/j.envsoft.2012.11.0151391504

Best practices for Sustainable Urban Water Cycle Systems

Aging infrastructure, demographic change, water scarcity, pollution, climate change and other megatrends create the need for a transition towards more sustainable urban water cycle services (UWCS) in the cities. This report "Best practices for Sustainable Urban Water Cycle Systems - an overview of and enabling and constraining factors for a transition to sustainable UWCSs" deals with and reviews best practices in the water sector. It is divided in four different themes in UWCS which are: demand management, reuse and recycling, leakage and loss reduction and the water-energy nexus. The report bundles some of the worldwide experiences in applying technologies and approaches, that support a transition to more sustainable water cycle systems and that have proven to work on the ground. These experiences are presented as inspiration for water professionals who want to engage in activities that contribute to a more sustainable urban water cycle system.Makropoulos, C.; Rozos, E.; Bruaset, S.; Frijns, J.; Van Der Zouwen, M. (2014). Best practices for Sustainable Urban Water Cycle Systems. http://hdl.handle.net/10251/3572

Quantitative UWS performance model: WaterMet2

The WaterMet2 tool encapsulates a conceptual, mass balance based model that enables quantifying the performance of an integrated urban water system (UWS) over a prolonged period of time (typically years) with a daily time step. The model allows quantifying the UWS performance, both water quantity and quality related, at different spatial scales and with particular focus on sustainability related performance. The WaterMet2 covers the full urban water cycle and provides means of evaluating the impact of different potential intervention strategies in the context of strategic level, long-term future decision making. It also provides basis for the assessment of various risks associated with the UWS performance.Behzadian, K.; Kapelan, Z.; Govindarajan, V.; Brattebø, H.; Sægrov, S.; Rozos, E.; Makropoulos, C. (2014). Quantitative UWS performance model: WaterMet2. http://hdl.handle.net/10251/4662

Guidance on evaluation and selection of sustainable water demand management technologies

This report presents guidance for the evaluation and selection of Water Demand Management (WDM) type technologies for the effective and efficient reduction of water consumption for different water stakeholders - householders, Water Service Providers (WSPs) and policy makers - in a technically sound, yet economically, environmentally and socially acceptable way for all stakeholders involved. The technical guidance is based on reviews of different WDM technologies and methodologies developed in Work Package 42 of the TRUST project. The WDM interventions considered in the report have been evaluated largely based on the their overall water saving potential, cost-effectiveness, water-related energy use as well as impact on the reliability of supply/demand balance of Water Distribution Systems (WDSs).Bello-Dambatta, A.; Kapelan, Z.; Butler, D.; Oertlé, E.; Wintgens, T.; Rozos, E.; Makropoulos, C.... (2014). Guidance on evaluation and selection of sustainable water demand management technologies. http://hdl.handle.net/10251/3534

Assessing Hydrological Simulations with Machine Learning and Statistical Models

Machine learning has been used in hydrological applications for decades, and recently, it was proven to be more efficient than sophisticated physically based modelling techniques. In addition, it has been used in hybrid frameworks that combine hydrological and machine learning models. The concept behind the latter is the use of machine learning as a filter that advances the performance of the hydrological model. In this study, we employed such a hybrid approach but with a different perspective and objective. Machine learning was used as a tool for analyzing the error of hydrological models in an effort to understand the source and the attributes of systematic modelling errors. Three hydrological models were applied to three different case studies. The results of these models were analyzed with a recurrent neural network and with the k-nearest neighbours algorithm. Most of the systematic errors were detected, but certain types of errors, including conditional systematic errors, passed unnoticed, leading to an overestimation of the confidence of some erroneously simulated values. This is an issue that needs to be considered when using machine learning as a filter in hybrid networks. The effect of conditional systematic errors can be reduced by naively combining the simulations (mean values) of two or more hydrological models. This simple technique reduces the magnitude of conditional systematic errors and makes them more discoverable to machine learning models

Machine Learning, Urban Water Resources Management and Operating Policy

Meticulously analyzing all contemporaneous conditions and available options before taking operations decisions regarding the management of the urban water resources is a necessary step owing to water scarcity. More often than not, this analysis is challenging because of the uncertainty regarding inflows to the system. The most common approach to account for this uncertainty is to combine the Bayesian decision theory with the dynamic programming optimization method. However, dynamic programming is plagued by the curse of dimensionality, that is, the complexity of the method is proportional to the number of discretized possible system states raised to the power of the number of reservoirs. Furthermore, classical statistics does not consistently represent the stochastic structure of the inflows (see persistence). To avoid these problems, this study will employ an appropriate stochastic model to produce synthetic time-series with long-term persistence, optimize the system employing a network flow programming modelling, and use the optimization results for training a feedforward neural network (FFN). This trained FFN alone can serve as a decision support tool that describes not only reservoir releases but also how to operate the entire water supply system. This methodology is applied in a simplified representation of the Athens water supply system, and the results suggest that the FFN is capable of successfully operating the system according to a predefined operating policy