Groundwater bodies are important and valuable natural resources. To better understand the hydrological state of the environment and groundwater dynamics, data sets and measurements need to be made available and accessible to scientists, planners, and stakeholders to allow for proper decision making support. A common challenge in hydrogeological modelling is to discover available data and fit these correctly into required input formats for specific modelling tools. The results need be made available again as inputs for analyses, presentation, and for subsequent use in dependent modelling routines. Information exchange via the internet has become faster, but data sets remain scattered both in location and formats. Present research in hydrogeology and freshwater resources management can be significantly supported and accelerated by relating, reusing and combining existing data sets, models and simulations in a streamlined, computer-aided and networked fashion.

In this thesis Design Science Research (DSR), Grounded Theory (GT) and Case Studies are triangulated in a GIScience research framework in order to design a Spatial Data Infrastructure (SDI) concept that addresses the full data life cycle in the context of hydrogeology in New Zealand. The 'Hydrogeology Infrastructure' was designed as a distributed system of platform- and location-independent services. It describes which data formats, interfaces and services are required in order to integrate inter-organisational data management, processing, hydrogeological modelling, and visualisation. A series of networked and open standards-based prototypes of the components of the 'Hydrogeology Infrastructure' were implemented, tested, evaluated and discussed. A web-based user interface was developed that demonstrates the access to the distributed functions and services of the infrastructure.

Formerly disconnected and distributed data sets can now be used for hydrogeological data analysis, visualisation and modelling from within one user-facing application. Enabling interoperability of environmental data management tools, scientific modelling routines and data visualisation processes will improve natural resources management and produce better and reproducible environmental knowledge.