An algorithm to extract more accurate stream longitudinal profiles from unfilled DEMs

Abstract Morphometric features observed from a stream longitudinal profile (SLP) reflect channel responses to lithological variation and changes in uplift or climate; therefore, they constitute essential indicators in the studies for the dynamics between tectonics, climate, and surface processes. The widespread availability of digital elevation models (DEMs) and their processing enable semi-automatic extraction of SLPs as well as additional stream profile parameters, thus reducing the time spent for extracting them and simultaneously allowing regional-scale studies of SLPs. However, careful consideration is required to extract SLPs directly from a DEM, because the DEM must be altered by depression filling process to ensure the continuity of flows across it. Such alteration inevitably introduces distortions to the SLP, such as stair steps, bias of elevation values, and inaccurate stream paths. This paper proposes a new algorithm, called maximum depth tracing algorithm (MDTA), to extract more accurate SLPs using depression-unfilled DEMs. The MDTA supposes that depressions in DEMs are not necessarily artifacts to be removed, and that elevation values within them are useful to represent more accurately the real landscape. To ensure the continuity of flows even across the unfilled DEM, the MDTA first determines the outlet of each depression and then reverses flow directions of the cells on the line of maximum depth within each depression, beginning from the outlet and toward the sink. It also calculates flow accumulation without disruption across the unfilled DEM. Comparative analysis with the profiles extracted by the hydrologic functions implemented in the ArcGIS™ was performed to illustrate the benefits from the MDTA. It shows that the MDTA provides more accurate stream paths on depression areas, and consequently reduces distortions of the SLPs derived from the paths, such as exaggerated elevation values and negatively biased slopes that are commonly observed in the SLPs built using the ArcGIS™. The algorithm proposed here, therefore, could aid all the studies requiring more reliable stream paths and SLPs from DEMs.

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