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.

In Germany 35% of the total energy consumption in water utilities is due to well pumping (Plath et al., 2010). Therefore, a more efficient abstraction, besides the reduction of the carbon footprint, will lead to economic benefits for the operator. Different strategies exist for energy saving both in the operation of well fields as well as with the use of adapted, energy-efficient technical equipment (pumps, pipes, etc.) (Madsen et al., 2009). The objective of this study is the development and testing of a well field optimization tool, which is based on a hydraulic pipe network model (EPANET) but also takes steady-state well drawdown into account. The optimizer, based on coupling EPANET with the programing language R, simulates automatically the different optimization strategies (e.g. smart well field management, pump renewal) and evaluates their impact on the energy demand. The developed well field model was tested for a case study in France and predicted the measured energy demand with an error of less than 2%. The identified energy saving potential found by the optimizer reaches up to 17% in case of implementing only smart well field management and close to 50% combining the latter option with pump renewal.

Last decades the concern about energy consumption has globally arisen due to awareness on climate change and the increase of energy prices. In the water field the nexus between water and energy has been extensively studied, however, there has been little discussion about energy-efficient specific approaches. This master thesis is part of the OPTIWELLS project which addresses to determine more energy efficient techniques for water supply operation, in particular for water abstraction well fields. One option to optimize a well field preserving its structure or components is the “smart well field management”, which maximize the time during which the pumps are performing on their best efficiency point, guaranteeing the water demand. The smart well field management is complex and accounts for various integrated processes. The aim of the project is to develop a prototype of a software tool able to cope with this complex optimisation problem. In particular, this master thesis deals with the modelling of a case study, applying methodologies that will be implemented in the OPTIWELLS prototype tool. Results and methods of data analysis for a well field, including a site audit, are described. The well field modelling was carried out with EPANET software by means of its Programmer’s Toolkit. No reliable data to validate the energy consumption estimation of the model were available. However, the report shows that observed hydraulic conditions of an abstraction well field can be accurately reproduced. The impact of different modelling approaches and amount of data available on energy evaluation is also drawn. Some insight into the well field current conditions (current pump curve, drawdown, water quality, specific energy demand,..) are discussed and recommendations or the operation of the case study site will be given.

Today, groundwater is one of the most important fresh water resources in big cities of the world. On one hand, the population growth and urban development and on the other hand, climate change and decreasing precipitation will increase the vital role of underground water resources to supply water for the cities, therefore an increase in the energy consumption in well fields has to be expected. It is becoming more difficult to ignore the cost of pumping energy for water stakeholders in Germany and Europe. In recent years, there has been an increasing interest in optimisation of energy consumption in different fields. The goal of this study is first to design a relational database to store the information of submersible pumps and second to develop a database management system for this pump database. The pump database is intended to be used in prototype model software aiming at the minimisation of a well field's pump energy demand. To this end, two approaches of assessing the necessary data for submersible pumps, and building a relational database are going to be discussed in this study. Finally, two applications with graphical user interfaces which have been developed by using the programming language “R” are presented for loading the data into the database, visualizing the database tables and plotting the pump curves.