## 4 Conclusions  
A major issue in WQMPs is ensuring that decisions related to planning, management, and optimization follow some standard procedure and that decision making is guided by a set of questions that lead to a certain standardization or at least render the decision steps transparent to the people working for the WQMP. In this paper, we have strived to demonstrate how an IDSS connected to a database (EnkiTM) can significantly contribute to the challenge of optimizing WQMPs. We were able to show this for several steps of the optimization process:* Understanding the initial WQMPs
* The questions asked by the IDSS are crucial to understanding the rationale of the WQMPs. Having a firm set of questions and documented answers contributes to transferring the information on the WQMP within an organization and communicating the information to the public, partners, and decision-makers. * Decision support in data validation and integration (storage) process (quality assessment and storage)
* The optimization of a WQMP is based on the data of the existing WQMP and the underlying decisions which led to the WQMP, as well as considerations such as possible changes that have occurred over time in the WQMP. All this information needs to be documented with the appropriate metadata for convenient retrieval when optimizing a WQMP. * The integration process of all the existing data of the case studies into the database Enki(tm) connected to the IDSS was key to understanding, documenting and relating information on sampling sites, sampling contexts, measured parameters, and geographical information. The integration process also showed where changes in the WQMP were made, and why. In several cases, some of these changes can affect the continuity in the available data series and the conclusions to be drawn from the data sets. On the other hand, it was very difficult to retrieve the information on why these changes were made since the staff in charge were not always available. Therefore, there is a need for a system to be able to support and document these decisions. * Selecting optimization procedures proposed in the literature
* The IDSS proposes specific optimization procedures (methods) proposed in the literature that correspond to specific optimization objectives. The IDSS was constructed to propose these methods for specific optimization objectives and provide support in applying the optimization procedures. * Contribution to applying the optimization procedures
* In the course of the application procedures, specific questions arise concerning the data sets. The IDSS connected to the database is able to provide a quick answer to these questions, such as variation in laboratories and data precision, changes in field protocols, changes in data series for a specific sampling site. * Decision support (replacing experts) to finalize the optimization procedure
* This step provides very crucial additional decision support. Indeed, in all optimization methods proposed in the literature, expert input is necessary to take final decisions regarding sampling. The IDSS was designed for this task and can be used, whether or not a statistical optimization method has been used. However, it was shown that the results of an optimization method provide a very good starting point for these questions and support the justification of the choices made. * Data-driven decision support to redesign WQMPs
* We were able to show that the database EnkiTM, the onboarding process and the decision support provided by the IDSS were instrumental in redesigning the WQMPs of our two case studies. A solid data and metadata management system are crucial to the IDSS, and cannot be separated without losing efficiency and accuracy in the process. To conclude, in this study we demonstrated that every WQMP needs a solid database for data management and decision making.