A Geospatial Fabric (GF) for National Hydrological Modeling

Thursday, 18 December 2014
Roland Viger and Andrew Bock, USGS, Water Science Center, Lakewood, CO, United States
The US Geological Survey (USGS) Geospatial Fabric (GF) supports the USGS National Hydrologic Model (NHM) by defining a minimally sufficient, nationally consistent set of geographic information needed to simulate streamflow at almost 60,000 points of interest (POIs). POIs primarily are defined based on: (a) a high quality set of USGS stream gages (Gages-II), (b) National Weather Service forecast nodes, (c) the USGS National Water Quality Assessment’s modeling network, (d) at inlets and outlets of selected water bodies, and (e) at confluences. Each POI is associated with a stream segment which typically has two adjacent land surface areas, referred to as hydrologic response units (HRUs). Parameter tables, largely based on the National Land Cover Databases, the Soil Survey Geographic Database (SSURGO), and the geometry of the spatial data, have been derived for these features. Configurations of GF features and attribute tables are defined and made available through the USGS ScienceBase (https://www.sciencebase.gov/catalog/item/537b7327e4b0929ba496f66f). Data are organized into 20 ESRI file geodatabases, each covering a different region of the United States (https://www.sciencebase.gov/catalog/item/535edb4ae4b08e65d60fc837). Future releases will include additional realizations of NHM parameter tables. These will serve to assess the impact of alternate data sources and processing methodologies on simulated streamflows. Tools for dynamically subsetting geodatabases and model inputs based on custom watersheds are currently being prototyped. The GF is a versatile framework for data integration because it maintains feature-level indexing back to NHDPlus and the National Hydrography Dataset, which is used in many water resource studies. In addition, the GF will help to ensure a minimum initial quality of parameter information, reduce the time of developing hydrological modeling applications in the United States, and generally improve the accuracy and scientific impact of USGS hydrological modeling.