Hydroinformatics and Socio-Technical Innovation

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 (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 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.

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 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.

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.

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

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 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.

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.