This paper discusses a prototype infrastructure, HydroTerre, that provides researchers, educators and resource managers with seamless access to geospatial/geotemporal data for supporting physics-based numerical models. The prototype defines the supporting data as Essential Terrestrial Variables (ETV's) and includes data fusion tools necessary to predict and manage surface and groundwater resources that resolve important dynamics of upland stream networks. The evaluation of ecosystem and watershed services, such as the detection and attribution of the impact of climatic change, provides one of many examples of the pressing need for high resolution, spatially explicit resource assessments in upland catchments. However, the current infrastructure for supporting models and data anywhere in the continental USA (CONUS) must overcome important problems of: efficient accessibility to high resolution geospatial datasets from multiple sources, scalability of geospatial data in support of distributed models and data-intensive computation for multi-scale, multi-state simulations.We discuss data workflows for web access to ETV data processing in support of catchment modeling, as part of a larger strategy for consuming this data within a framework that enables hydrological modelers to build and test models with fast data access at a United States Geological Survey (USGS) National Hydrography Dataset Hydrological Unit Code (HUC) level-12 scale. Given the prospect of petabytes of existing high resolution environmental data (NRC, 2012), we limit our investigation to a limited set of ETV's necessary to provide the first level of support for model implementation anywhere in the CONUS, and that resolve important features of upland watersheds (e.g. hill slopes within 1st-2nd-3rd order streams). The paper demonstrates HydroTerre tools for fast ETV data access to web users, and describes the computational resources necessary for using ETV's as the basis for implementing spatially distributed models at scales approaching the native resolution of the data (≥30m). The Penn State Integrated Hydrologic Model (PIHM) serves as an example although other models are currently being considered.
All Science Journal Classification (ASJC) codes
- Environmental Engineering
- Ecological Modeling