The present report summarizes the benefits of the eleven digital solutions demonstrated within DWC-WP2 in the form of fact sheets. The document aims to help cities and water utilities in finding appropriate solutions for their operational, environmental or public health deficits. The report is the final version which was submitted in Nov. 2022 after incorporating the recommendations and amendments by the EC.

The Data Management Plan (DMP) is a guidance document, which introduces a series of clear rules and procedures to improve data management during the project and foster the reuse of publications and data in open access.

This report describes the main functionalities the SMART-Control web-based tool T1B Quantitative microbial risk assessment. The tool helps to quantify the pathogen occurrence in source water and their removal by various treatment steps at MAR facilities by using a probabilistic approach. The interactive web-based QMRA tool supports the evidence-based risk assessment to minimize water-related infectious diseases.

Subsurface travel time from the area of recharge to the point of abstraction during MAR is a critical parameter to ensure sufficient attenuation for hygienic parameters and other undesired substances. A new simulation tool has been developed by the SMART-Control project partners KWB and TUD for determination of groundwater hydraulic residence time (HRT) using seasonal temperature fluctuations observed in recharge water and MAR recovery wells. This tool represents a proxy for quick, costs-effective and reliable control of travel time during aquifer passage. Time series of seasonal temperature measurements observed in surface water and abstraction wells can be fitted to sinusoidal functions. Peak values represented as local maxima and local minima and turning points from the fitted sinusoidal curves are used for the approximation of travel times between surface water and abstraction well. The calculated values are adjusted by a thermal retardation factor. The developed tool is userfriendly and offers the possibility to use existing hystorical temperature measurements as well as online sensor data. Data acquisition is resolved through the internal connectivity with other web-tools developed within the SMART-Control project, providing thus an integrated simulation environment.

Zum Forschungsdatenmanagement zählen alle Aktivitäten, die mit der Aufbereitung, Speicherung, Archivierung und Veröffentlichung von Forschungsdaten verbunden sind. Die Bedeutung des Forschungsdatenmanagements ist in den vergangenen Jahren immens gestiegen. Grund dafür sind die großen Datenmengen, die im Zuge der Digitalisierung und Automatisierung von Prozessen anfallen und neue Herausforderungen an deren Verwaltung und Verarbeitung stellen, die mit den bisherigen Werkzeugen schwer bewältigt werden können. Dies gilt auch für Daten in der Wasserforschung. Der nachhaltige Zugang zu Forschungsdaten und die Erstellung von Datenmanagementplänen werden zunehmend von Forschungsförderern verlangt. Am Kompetenzzentrum Wasser Berlin gGmbH (KWB) werden im Rahmen von Forschungsprojekten eine Vielzahl von Daten verarbeitet, die entweder selbst erhoben oder von Projektpartnern zur Verfügung gestellt werden. Dazu zählen Messdaten, Metadaten, Fotos/Videos, Bestands- und Zustandsdaten und verarbeitete Daten (z.B. Zeitreihen, aggregierte Werte, Ergebnisse aus Computersimulationen). Um solche Daten nachhaltig nutzbar zu machen, zu verwalten und zu verarbeiten, sind standardisierte Prozesse, Werkzeuge und Methoden zu entwickeln, die eine projektübergreifende Reproduzierbarkeit der Ergebnisse gewährleisten. Ziel des Projektes FAKIN (Forschungsdatenmanagement an kleinen Instituten) war es, ein solches Forschungsdatenmanagement (FDM) für das KWB in Zusammenarbeit mit den Projektwissenschaftlern zu erarbeiten und unternehmensweit zu etablieren. Damit sollte das Vorhaben als übertragbares Fallbeispiel für das FDM an kleinen, aber stark vernetzten außeruniversitären Forschungsinstituten dienen.

The Data Management Plan (DMP) is a guidance document which introduces a series of clear rules and procedures to improve data management during the project and foster the reuse of publications and data in open access. ; Version submitted to EU (v0.1.0)

This report summarizes the results of Life Cycle Assessment, Water footprinting, and quantitative microbial and chemical risk assessment for selected demosites of water reuse in Europe, measuring the potential impacts of different types of water reuse on environment and human health. The case studies show that water reuse is often preferable from an environmental point of view in areas with water scarcity problems if compared to other alternatives such as water import or seawater desalination. Potential risks of water reuse for ecosystems or human health can be adequately managed if suitable processes for reclaimed water treatment are used and operated correctly. However, the study also shows the trade-offs between a higher level of reclaimed water treatment and increased environmental impacts from associated efforts in energy, chemicals and infrastructure. This inherent trade-off requires a site-specific assessment of reuse schemes to choose an adequate treatment scheme for risk management with reasonable global environmental impacts.

The groundwater drawdown model WTAQ-2, provided by the United States Geological Survey for free, has been “wrapped” into an R package, which contains functions for writing input files, executing the model engine and reading output files. By calling the functions from the R package a sensitivity analysis, calibration or validation requiring multiple model runs can be performed in an automated way. Automation by means of programming improves and simplifies the modelling process by ensuring that the WTAQ-2 wrapper generates consistent model input files, runs the model engine and reads the output files without requiring the user to cope with the technical details of the communication with the model engine. In addition the WTAQ-2 wrapper automatically adapts cross-dependent input parameters correctly in case one is changed by the user. This assures the formal correctness of the input file and minimises the effort for the user, who normally has to consider all cross-dependencies for each input file modification manually by consulting the model documentation. Consequently the focus can be shifted on retrieving and preparing the data needed by the model. Modelling is described in the form of version controlled R scripts so that its methodology becomes transparent and modifications (e.g. error fixing) trackable. The code can be run repeatedly and will always produce the same results given the same inputs. The implementation in the form of program code further yields the advantage of inherently documenting the methodology. This leads to reproducible results which should be the basis for smart decision making.

Objective of this synthesis report is to summarise the main achievements of the OPTIWELLS-2 project. Based on a preparatory phase OPTIWELLS-1 (2011-2012), the main project phase OPTIWELLS-2 (2012-2015) included the development of two different optimisation modelling methodologies (data-driven, process-driven) for minimising a well field’s specific energy demand whilst satisfying both, water demand and water quality constraints. Chapter 2 gives a short overview on the technical background on pipe hydraulics and the general methodology used within the project. The general workflow of the testing and application for the three case study well fields investigated within OPTIWELLS-2 is summarised in Chapter 3. For the first two case studies (Chapter Fehler! Verweisquelle konnte nicht gefunden werden. and Fehler! Verweisquelle konnte nicht gefunden werden.), a process-driven modelling approach was used, which enabled the assessment of three different management strategies (smart well field management, pump renewal or a combination of both) on the specific energy demand. This approach was more time and data-demanding (Chapter 2.5) compared to the data-driven approach used for the third case study (Chapter Fehler! Verweisquelle konnte nicht gefunden werden.). The cross-case analysis (Chapter 4) showed, that the energetic prediction accuracy of process-driven modelling (Chapter 4.1.3) was improved significantly by using pump characteristics derived from audits instead of relying on manufacturer data, whilst including steady-state well drawdown compared to assuming a static water level in the production well was much less important. This can be explained by the fact, that well drawdown contributed to less than 3% of the required pump head (Chapter 4.1.1), whilst the offset between audit and manufacturer pump characteristics is much more relevant because of pump ageing during long usage periods (up to 40 years). The data-based modelling approach used for Site C has yielded energy consumption forecasts with a similar accuracy, but is more robust as it relies on operational data, thus requiring no calibration.