Our research focuses on the integrated consideration of water quantity and water quality for surface and groundwater in the context of hydrological extremes and associated risks. We seek to deepen our understanding of the interactions between hydrological processes and human interventions. For this goal, we develop methods, simulation models and tools for the sustainable and flexible management of future water resources systems.
Global change - with aspects of climate change, demographic change, changes in land use, globalization of markets and other transformations - poses unprecedented challenges for sustainable development. From a water management perspective, for instance, hotter and drier summers and more frequent heavy rainfall increase the pressure on natural water resources and exacerbate the risk of hydrological extremes. In addition, due to the contamination of the groundwater, rivers and lakes, and the ongoing environmental degradation, the ecological condition of water bodies and the production and supply of drinking water are increasingly impaired.
Research at HydRiv contributes to these urgent, socially relevant questions with a particular focus on the following topics:
We apply and develop explorative data analysis and numerical modelling techniques for system analyses to gain a deeper understanding of interactions and chains of effects, especially in areas with different hydrological characteristics and land use structures such as urban and rural areas or surface and groundwater bodies.
We are working on concepts for system-oriented monitoring to close data gaps and optimize the collection of information on relevant variables and characteristics in meso-scale observation areas. Topics are i) the integration of new data sources (e.g. IoT sensors, crowd-sourced data, and ground-, air-, and space-based sensors) with data from established observation networks and measurement campaigns and ii) the use of data science methods for the management and exploration of data providing opportunities for scientific analysis and the more efficient use of information in decision contexts.
The improvement of modelling and predictive capabilities requires the further development of algorithms and modelling approaches that integrate hydrological human-environment sub-systems into a higher-level integrated system model, taking into account processes and interactions.
To this end, we develop hybrid approaches that combine numerical models with machine learning methods. We conduct our research activities both at the fundamental level and in application-related settings.
The topics of teaching cover the broad spectrum of hydrology and water management, hydrogeology and groundwater management, river basin management and water quality management. Content on hydrological and hydraulic modelling as well as flow and transport processes in groundwater and water quality complement the teaching offer.
Further courses impart specific knowledge on data science and geospatial methods as well as on measurement techniques for water quantity and quality, including practical methods of fieldwork. The focus is on methodological knowledge for data acquisition, data analysis, mathematical process modelling and subsequent computer-based processing with complex simulation models (such as FEFLOW [Finite Element Subsurface Flow & Transport simulation System], USGS´s modular hydrology model MODFLOW 6). Practical applications are a central element and are oriented towards the acquisition of competencies to meet current challenges for hydrology and water management: i) the development of sustainable adaptation options to cope with increasingly frequent and intense heat, drought and heavy rainfall events, ii) the long-term safeguarding of the quantity and quality of water resources and water bodies in the face of increasing demands, iii) the adaptation of water infrastructures and their management to ensure their long-term performance.
DAAD, DFG, BMBF, State of Lower Saxony, Tennet TSO GmbH, drinking water supplier (HWW GmbH, BS│ENERGY et al.), water board / associations
The topics of teaching cover the broad spectrum of hydrology and water management, hydrogeology and groundwater management, river basin management and water quality management. Content on hydrological and hydraulic modelling as well as flow and transport processes in groundwater and water quality complement the teaching offer. Further courses impart specific knowledge on data science and geospatial methods as well as on measurement techniques for water quantity and quality, including practical methods of fieldwork. The focus is on methodological knowledge for data acquisition, data analysis, mathematical process modelling and subsequent computer-based processing with complex simulation models.