Geospatial Data for Hydrology

Abstract

A key characteristic of distributed modeling is the spatially variable representation of the watershed in terms of topography, vegetative, or land use/cover, soils and impervious areas and the derivative model parameters that govern the hydrologic processes of infiltration, evapotranspiration, and runoff. Geospatial data exist that can be harnessed for model setup. Digital representation of topography, soils, land use/cover, and precipitation may be accomplished using widely available or special purpose GIS data sets. Each GIS data source has a characteristic data structure, which has implications for the hydrologic model. Major data structures are raster and vector. Raster data structures are characteristic of remotely sensed data with a single value representing a grid cell. Points, polygons, and lines are referred to generally as vector data. Multiple attributes can be associated with a point, line, area, or grid cell. Some data sources capture characteristics of the data in terms of measurement scale or sample volume. A rain gauge essentially measures rainfall at a point, whereas radar, satellites and other remote sensing techniques typically map the spatial variability over large geographic areas at resolutions ranging from meters to kilometers. Source data structures can have important consequences on the derived parameter and, therefore, model performance. Even after considerable processing, hydrologic parameters can continue to have some vestige of the original data structure, which is termed an artifact. This chapter addresses geospatial data structure, projection, scale, dimensionality, and source data for hydrologic applications.