Data and Model Integration Promoting Interdisciplinarity

Abstract:

It is very difficult to reflect accumulated subsystem knowledge into holistic knowledge. Knowledge about a whole system can rarely be introduced into a targeted subsystem. In many cases, knowledge in one discipline is inapplicable to other disciplines. We are far from resolving cross-disciplinary issues. It is critically important to establish interdisciplinarity so that scientific knowledge can transcend disciplines. We need to share information and develop knowledge interlinkages by building models and exchanging tools. We need to tackle a large increase in the volume and diversity of data from observing the Earth. The volume of data stored has exponentially increased. Previously, almost all of the large-volume data came from satellites, but model outputs occupy the largest volume in general. To address the large diversity of data, we should develop an ontology system for technical and geographical terms in coupling with a metadata design according to international standards.

In collaboration between Earth environment scientists and IT group, we should accelerate data archiving by including data loading, quality checking and metadata registration, and enrich data-searching capability. DIAS also enables us to perform integrated research and realize interdisciplinarity. For example, climate change should be addressed in collaboration between the climate models, integrated assessment models including energy, economy, agriculture, health, and the models of adaptation, vulnerability, and human settlement and infrastructure. These models identify water as central to these systems. If a water expert can develop an interrelated system including each component, the integrated crisis can be addressed by collaboration with various disciplines. To realize this purpose, we are developing a water-related data- and model-integration system called a water cycle integrator (WCI).