An automated method for producing synoptic regional maps of river gradient variation: Procedure, accuracy tests, and comparison with other knickpoint mapping methods

The study of abrupt changes in longitudinal river profiles, or knickpoints, is currently approached through an empirical power law: the slope–area relationship. Results based on digital elevation model (DEM) analyses and stream extractions are generally intended to determine crustal uplift rates and identify transient landscape conditions. In this article, we present an alternative geomorphometric method for locating knickpoints and knickzones based on local slope gradient and curvature attributes. Intended as a rapid, regional scale, automated knickpoint detection technique, the accuracy of this slope–curvature method is tested on two digital elevation grids, NASA's SRTM (ground resolution of 90 m, resampled here to 75 m) and the ASTER DEM (15 m) in the Sierra Nacimiento (New Mexico, USA), a basement-cored mountain range recently exhumed by waves of headward drainage integration in response to remote tectonic deformation in the adjacent Rio Grande rift. Out of every 10 gradient anomalies detected by the SRTM-derived numeric routine, up to 8 are certifiable knickpoints recognized among a population of georeferenced occurrences surveyed in the field. An independent comparison with the slope–area method provided a further accuracy test, which was particularly useful at sites that could not be validated in the field for practical reasons. Given the low tectonic activity of the study area, the majority of knickpoints was also found to coincide with lithologic boundaries, making it difficult without further geomorphological data to single out dynamic knickpoints directly caused by the upstream propagation of channel instabilities relating to base level change.

► A method for mapping stream gradients on digital elevation grids is presented.
► A routine using slope curvature properties detects river knickpoints automatically.
► Ground-truth validation in a New Mexico mountain range confirms good accuracy levels.
► The new approach is compared with Hack's slope–area knickzone detection method.
► In the chosen test area, the knickpoints mostly coincide with lithologic boundaries.