Skip to contentSkip to footer
Academic departments

Hydroinformatics and Socio-Technical Innovation

The Hydroinformatics and Socio-Technical Innovation department focuses on catalysing change in society, technology and policy, and enhancing the use of enabling information and communication technologies (ICT), advanced data analytics and modelling, artificial intelligence towards sustainability and resilience.

Aims and ambitions

The department aims to catalyse change toward more sustainable and disaster-resilient societies, technologies and policies. To do so, it fosters multi-stakeholder participatory design of social innovations and the use of hydroinformatics and information and ICT for data, information and knowledge co-creation. In addition, it supports data analytics, modelling, decision-making and evidence-based implementation.

The department strives to further develop hydroinformatics by using ICT and artificial intelligence systems, data acquisition, advanced modelling, systems analysis, robust optimization, and multi-stakeholder decision support. With these efforts, it assists smart water and environmental management in uncertain conditions characterized by global change.  Such smart management is particularly important for disaster and climate resilience as well as sustainability and other highly relevant social issues.

In the department, we also aim to unleash the transformative potential of citizen science and social innovation with the help of co-design, novel forms of participation, enabling technologies and impact-focused approaches and incubation. With these approaches and actions, we stimulate sustainable development in different geographic, socio-economic, political and natural resource settings.

Research themes

The department addresses two interlinked themes: Hydroinformatics and Knowledge & Innovation Dynamics.

Hydroinformatics

Hydroinformatics concerns the use of information and communication technologies (ICTs) and modelling, and their application for resolving water-related problems in civil engineering, and information systems for integrated water management.

Hydroinformatics tools and methods can be applied, for example, in: 

  • Flood and drought simulation, forecasting, early warning, and risk management
  • Real-time control and anticipatory water management
  • Reservoir operation and optimization
  • Urban water systems operation and management
  • Water resources planning and management for groundwater and surface water
  • Water quality modelling and management
  • Integrated river basin management
  • Water information systems design, implementation and management (from digital information to internet networking)

Sub themes

  • Process-based modelling: quantity, quality and environmental modelling

    Process-based modelling involves  traditional computational hydraulics in combination  with newer developments in numerical analysis, computer science and communications technology. It includes the integration of information obtained from diverse sources, from field data to data from hydraulic and numerical models to data from non-engineering fields such as ecology, economy and social science. Hydrologic and hydraulic modelling are used  to provide accurate results to assist in the development of sustainable water-related solutions, such as flood and drought risk management, water resources planning and  environmental impact assessments. Research in this field helps communities understand the effects of flooding and droughts through process-based modelling for floods, droughts and other environmental problems. In addition, process-based modelling researchers provide risk management expertise.

  • Artificial intelligence and data-driven modelling

    Artificial intelligence (AI) technologies, applied to the enormous amounts of data collected about the Earth and its aquatic systems, make it possible to solve a wide spectrum of problems. AI can, for example, generate flood maps, identify water bodies and their dynamics, detect potentially dangerous hydrometeorological events such as hurricanes and floods, extend forecasts to ungauged basins, analyse social media messages about floods on X. Current and future research in this area includes extending the spectrum of promising AI techniques and digital innovations to water research and water management practice; and testing AI-based methods to forecast extreme events, develop surrogate hydrological models and Digital Twins of hydrological systems.

  • Systems engineering, optimization and collaborative decision support

    Systems analysis and engineering of water systems has long been a central theme in hydroinformatics. We use a systems approach to conceptualise water systems structure, to enable their better understanding and modelling, leading to better water management decisions. The department will continue research and development of such applications for both man-made water systems and for natural water environments that lend themselves to a systems analysis approach. In recent years, significant progress has been made in using system analysis together with multi-objective optimization approaches. This could reveal potential optimal solutions for different stakeholders concerned with a particular water system. Promising solutions derived from optimization are then analysed with detailed spatially-distributed simulation models to enable engagement of multiple collaborating stakeholders with different knowledge needs and interests. Future research will focus on further development of systems analysis and optimization approaches for more integrated problems where different fields (food, energy, ecosystems) are addressed simultaneously.

  • Disaster risk management under uncertainty

    Societies are increasingly becoming vulnerable to hazards and disasters. Model-based forecasting and  decision making is heavily influenced by the uncertainty of data and models. To address  the risk of decision making under uncertainty, we use data analysis, model building and uncertainty assessments, with ultimate goal of mitigating risks arising from natural hazards and disasters. The department’s research in this field particularly focuses on floods and droughts. Future hydroinformatics research  in this field will  focus on data integration, also known as data fusion or data merging, to  ensure optimal use of data from diverse sources in model-based decision making.

  • Hydrometeorological ensemble prediction

    Hydrometeorological ensemble prediction aims to contribute to climate and water management services, such as flood forecasting and early warning. Such services are increasingly benefitting from an ongoing paradigm shift from single (deterministic) prediction to ensemble (probabilistic) prediction. Ensemble prediction, which provides multiple predictions for the same time and place to account for uncertainty, is the most consistent and complete method currently available to produce reliable and accurate meteorological and hydrological forecasts. Ensemble prediction quantifies forecast uncertainty based on day-to-day observations, weather conditions, and hydrological states, rather than basing it only on statistics of past forecast performance. Research in this field includes further reducing the uncertainty of the forecasts while maintaining reliability, methods for assessing the performance of hydrometeorological predictions for users, and supporting interpretation and decision making for disaster risk reduction.

  • Monitoring, citizen science and control

    It is not possible to manage what is not measured. In hydrology and water resources in particular, monitoring is crucial to collect data and generate relevant information about past and current states, to ultimately assist informed decisions related to operational actions (such as  early warnings and control strategies of hydraulic structures), or planning activities (such as  land use change). This means that sensors should be strategically located  in every water system, ranging from river basins to water distribution networks. The design of monitoring networks, must consider relevant spatial and temporal scales of the measured variables, as well as  coverage, spacing and redundancy. Research in this field includes optimizing the design of monitoring networks with dynamic components in time and space.

  • Big data analytics and advanced computing

    Earth observation, including that of water-related processes, generates enormous amounts of data (so-called ‘big data’). Analysing such data can reveal hidden patterns that can be used in solving modelling and forecasting problems. The department has knowledge and experience in using resources on high-performance computers and it supports MSc and PhD studies by enabling access to these resources

Knowledge & Innovation dynamics

Research on knowledge and innovation dynamics aims to understand and shape their role in transformative change processes across all areas of water management and governance (water resources management, water services as well as flood and drought risk management).

Our research contributes to a deeper analysis and understanding of the objectives, purposes, processes and instruments of innovative, problem-driven forms of knowledge generation and application, as well as innovation processes. Through such work, particularly in the area of digital innovations, we support the water sector as it addresses complex societal needs.

Sub themes

  • The human and social dimensions of citizen science

    Citizen science offers a unique opportunity for a paradigm shift in the co-creation and application of knowledge. It can trigger shifts in the role of citizens and communities in environmental management and related decision-making, with significant  impacts on existing governance processes. We use both deductive approaches (informed by existing theories) and inductive approaches (case-based insights not framed/explained by existing theory) to advance the understanding of citizen science as a transformative process of data and knowledge co-creation and application that engages specific stakeholders or the general public. We focus in particular on substantiating the theoretical understanding of the human and social dimensions of citizen science.

  • Social Innovation and stakeholder engagement

    Social innovation refers to both the processes and the outcomes of addressing collective needs. Previous work has advanced the concept of social innovation as comprising the distinct but closely related dimensions of technology, capacity development, governance, interaction processes, and the creation of business opportunities. Interaction processes are key in bringing these dimensions together. All relevant stakeholders need to be engaged and involved from the start and all the way through to adaptation and upscaling. During different stages of a given social innovation process, providers of knowledge or solutions and potential users of these solutions need to interact to create common ground for the co-production of knowledge and innovation. Such interactions include an understanding of needs and the design, implementation, and use of knowledge and innovation. There are still major gaps in how such social innovation processes are implemented, especially in areas that are poor, remote and home to marginalised groups.

  • Water Innovation Dynamics

    In recent years, a major shift has taken place towards demand-driven, mission-oriented research and innovation to address the societal challenges of climate change, population growth and environmental degradation,  thereby progressing toward the Sustainable Development Goals (SDGs). At the same time, the water sector predominantly integrates technological and non-technological innovations from other sectors, benefiting from inputs and components that enable a high added value in terms of finance and services but at the cost of innovation path dependency on other sectors. In addition,  the Covid-19 pandemic sparked a crisis that radically changed research and development interactions. This forced partners in research and development projects and capacity development interventions to collaborate online. This sub-theme focuses on incubation as a means of fostering water innovation at an aggregate level, including innovations focused on strengthening monitoring and forecasting capacity by using citizen science and earth observation.