The strategy for PCGAP is based on a DIF (Dynamic Information Framework); essentially a Decision Support Framework, built around "earth system" models.
To address the issues posed by PCGAP, information from multiple diverse sources must be acquired and merged. "Models" (computer code representing the understanding of how the environment functions) must then assimilate that information, and compute possible outcomes of the combinations of landscape structure and climate. To keep track of all of these elements, it is useful to establish a “Dynamic Information Framework (DIF)," with the objective of providing a consistent theoretical basis, overall capability of integrating across sectors, and providing information using recent advances in cyberinformatics (including delivery of advanced visualizations, to enable a viewer to more readily assimilate the message being delivered). The DIF then provides the core for a DSS.
The core of a modeling environment is built by progressive information layers identified as the required inputs for the geospatial hydrology and landscape models, but can serve multiple purposes. For example, information on land cover classifications identify the biophysical attributes of vegetation needed for modeling can provide the basis for carbon inventories, regional zoning, and so on. The first layer of information is provided by topography, which defines the boundaries of a river basin. These data can be derived in many ways, from local maps to the Shuttle Radar Topography Mission (SRTM). The topography data is used to derive river networks, and grids about how flow is accumulated. Political boundaries can be superimposed on the basin, recognizing that such boundaries most frequently do not correspond to the basin itself, and leading to transboundary issues. Information on soils is needed, including soil type, depth, texture, and fertility. Such data are typically derived from local knowledge, or from global datasets. Land cover information, from regional surveys and different satellites, is critical for multiple purposes. An all-important “driver” of the land surface is climate, expressed as the minimum and maximum temperate, precipitation, and winds. These data can be derived from local weather station networks, and from regional and global data assimilation schemes and climate models.
Establishing the process to actually execute such models is not a trival process, for several reasons. The information required comes from multiple sources, from individual rain gauges to statistics on hydropower and grain yields, to glacier melting to rock types. The information required comes from multiple disciplines, which presents problems with even communication between specialists. Existing data holdings are not always readily obtainable, sometimes for institutional reasons. New field measurements, especially holistic and cross-boundaries, are challenging. Handling such diverse data and executing models is not straightforward. There are very real problems in converting data streams into useful information that go beyond a database. Perhaps most challenging is how to not only create such information, but how to get it into the hands of users of different levels, from the specialist to the local and regional decision makers to the local farmer or fisherman. Hence it is necessary to be clear and explicit about exactly what information is required by each stakeholder.
To meet these challenging criteria, the modeling effort draws on the emergence of a new generation of Earth System Science, based on the rapidly evolving capabilities for addressing global change issues. Earth System Science involves use of satellites, new generations of dynamic computer “models,” field measurements focused by model requirements covering wide areas, and, especially, a thinking and practice of “integrated systems.” Fundamental to these is a new class of open and publically accessible hydrology models, which can be regarded not only as hydrology models, but also as overall landscape models, because of the processes (and data layers) they represent. The requirements of the model dictate what data modules must be assembled, and the structure of the model allows the production of the output variables, which ultimately provide the DSS with the information to make decisions.
Constructing a model from “first principles” starts from physical laws known to govern the natural system, the laws represented as equations that describe the relationships amongst variables; where changes in the variable denote changes in the state of the system. The accuracy of the model is to test by comparing predictions with observed data.