*1.1. Hydroinformatics—An Evolving Story*

The water cycle is a system characterized by inherent complexity, variation, and uncertainty due to interlinked social, natural and engineered subsystems. Hydroinformatics, as a scientific study of this complex system takes a deliberately interdisciplinary, sociotechnical approach [1], blurring the boundaries between water science, data science and computer science. Despite having its origins in computational hydraulics [2], it, however, does not only concern itself with modelling and decision support, as is often incorrectly assumed. The modern field of hydroinformatics also embraces the social dimension of water cycle management, e.g., social needs, concerns and consequences (including equity, data privacy, ethics, legal issues, etc.). Therefore, hydroinformatics should be viewed as having a horizontal role in integrating water sciences (i.e., hydrological, hydraulic and

environmental), data sciences (statistics, stochastics, data driven analytics), computer science and information and communication technologies (ICT) and society [3]. This also positions hydroinformatics as a cross-cutting field of study that underpins the transition of water authorities and utilities from reactive to proactive by leveraging technological advances to achieve to the so-called Water 4.0 state (also named Digital Water or Water Informatics) delivering sustainable and resilient water management.

As a dynamic field of research, hydroinformatics has evolved from the days of hydraulic/hydrologic modelling to an academic discipline with a thriving community of scientists, engineers and practitioners (organized around two professional organizations—the International Association for Hydro-Environment Engineering and Research, IAHR, and the International Water Association, IWA), with its own Journal [4], specialist groups and biannual international conferences. However, the discipline's network is not restricted to these institutions. It has grown around the world building strong communities and high-profile scientific journals, such as the International Environmental Modelling and Software Society (iEMSs) and their Journal [5] as well as the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) in the US and their Hydroinformatics Conferences. The discipline and its community run and contribute to educating new generations of hydroinformaticians through a number of professional and university degree courses o ffered all around the world.

Although it is beyond the scope of this paper to delve into the depths of hydroinformatics philosophy and approaches, the discipline can be thought of as a continuous process of developing and using water data, models and tools, to understand the environment, to engage all stakeholders, and help make decisions that improve society. This is a highly iterative process (Figure 1), because, as also stated in Vojinovi´c and Abbott [6], "*hydroinformatics integrates knowledges from the social and technical domains to create so-called conjunctive knowledges, that are concerned with an understanding of how technical interventions have social consequences and how the resulting social changes in turn generate new technical developments*". This evolving nature of hydroinformatics can also be viewed through the lens of changing communities attending the biannual Hydroinformatics conferences and consequently the transformation in the research focus over a period of 25 years. While the early years attracted mostly practitioners from the mature fields of computational hydraulics and hydrology and those involved in early applications of artificial intelligence methods, the later years' conferences can be viewed as a meeting place of a community of communities, encompassing various multi-disciplinary areas. This widening of disciplinary communities resulted in changes to the scope of the work presented at conferences, for example, from purely technical approaches to managing demand for water to socio-technical approaches where customer engagemen<sup>t</sup> is sought through, not only technical means, but also by combining behavioral and data science. Further examples of the changes include the proliferation of real-time modelling and decision methods due to increasing computing power and the availability of data through citizen science and ubiquities sensing. Together, with the drive to open science outputs to a wider audience (via open-source tools and data), to hybridize modelling systems (via integration of physical and data-driven models), and to better visualize data, processes and decisions (via serious gaming, virtual/augmented reality), the community is well-positioned to help humanity address a range of high-impact future real-world water challenges.
