An Interdisciplinary approach to Finding and Using Data for Complex Environmental Modelling Problems:A Soil System Example
Environmental processes are often complex, as they involve non linear interactions between biological, chemical and physical processes across a range of temporal and spatial scales. Soil systems are one example of this complexity. Soils emerge from the interaction between geology, biology (plant and microbial processes) biogeochemistry, hydrology, and climate. Further complexity arises from the feedbacks between soil and human systems. Soils underpin society: they provide the vast majority of our food, the regulate water flows and quality, as a carbon store they are important for climate regulation. Through agriculture, land use change and pollution, human actions over the last have modified soils for centuries. Yet soil sustainability is often overlooked in policy and private sectors [1] To understand these complex systems, simulation models are needed to integrate the processes that shape soils and influence their sustainability and that of our ecosystems and societies. To date, much of the modelling of soils has existed within one domain (e.g. either carbon cycle models, soil water flow or quality, soil erosion or formation) We aim to develop a more comprehensive model of the soil system by coupling soil biogeochemistry (N14CP, see [2] with erosion and hydrological processes, to create a model that can be used to simulate soil functioning and response under changing environmental conditions. The model can then be used as part of an integrated risk assessment process (for an example of an integrated risk assessment approach used in the flood management domain see [3] to inform decision making, enabling sustainable soil management and ensuring continued provision of vital soil services such as food provision and carbon storage. Large, or even vast, amounts of data from diverse sources are required to not only act as model input, but also to determine initial conditions, parameter values and to calibrate and validate models. Searching for this data can be incredibly time consuming and takes time away from furthering our understanding within environmental science. Standard methods of data searching can often be unsuccessful and miss sources of useful data. We are interested in the potential of semantic web technologies to enable more efficient discovery and querying of data sources. This paper addresses the important and under researched area of how to manage data associated with such large scale integrated modelling experiments. In particular, we are adopting an interdisciplinary approach to determine how digital technologies might contribute to environmental science. The overall goal of the paper is to evaluate the potential effectiveness of semantic web technology in addressing the needs of complex environmental modelling.