Susan K. Greenlee

New land surface digital elevation model covers the Earth

Land surface elevation around the world is reaching new heights—as far as its description and measurement goes. A new global digital elevation model (DEM) is being cited as a significant improvement in the quality of topographic data available for Earth science studies. Land surface elevation is one of the Earths most fundamental geophysical properties, but the accuracy and detail with which it has been measured and described globally have been insufficient for many large-area studies. The new model, developed at the U.S. Geological Surveys (USGS) EROS Data Center (EDC), has changed all that.

Basin level statistical properties of topographic index for North America

Abstract For land–atmosphere interaction studies several Topmodel based land-surface schemes have been proposed. For the implementation of such models over the continental (and global) scales, statistical properties of the topographic indices are derived using GTOPO30 (30-arc-second; 1 km resolution) DEM data for North America. River basins and drainage network extracted using this dataset are overlaid on computed topographic indices for the continent and statistics are extracted for each basin. A total of 5020 basins are used to cover the entire continent with an average basin size of 3640 km 2 . Typically, the first three statistical moments of the distribution of the topographic indices for each basin are required for modeling. Departures of these statistical moments to those obtained using high resolution data have important implications for the prediction of soil-moisture states in the hydrologic models and consequently on the dynamics of the land–atmosphere interaction. It is found that a simple relationship between the statistics obtained at the 1 km and 90 m resolutions can be developed. The mean, standard deviation, skewness, L-scale and L-skewness all show approximate linear relationships between the two resolutions making it possible to use the moment estimates from the GTOPO30 data for hydrologic studies by applying a simple linear downscaling scheme. This significantly increases the utility value of the GTOPO30 datasets for hydrologic modeling studies.