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The analysis of longitudinal river profiles is an important tool for studying\udlandscape evolution. However, characterizing river profiles based on digital\udelevation models (DEMs) suffers from errors and artifacts that particularly\udprevail along valley bottoms. The aim of this study is to characterize\uduncertainties that arise from the analysis of river profiles derived from\uddifferent, near-globally available DEMs. We devised new algorithms –\udquantile carving and the CRS algorithm – that rely on quantile regression to\udenable hydrological correction and the uncertainty quantification of river\udprofiles. We find that globally available DEMs commonly overestimate river\udelevations in steep topography. The distributions of elevation errors become\udincreasingly wider and right skewed if adjacent hillslope gradients are\udsteep. Our analysis indicates that the AW3D DEM has the highest precision and\udlowest bias for the analysis of river profiles in mountainous topography. The\udnew 12 m resolution TanDEM-X DEM has a very low precision, most likely due to\udthe combined effect of steep valley walls and the presence of water surfaces\udin valley bottoms. Compared to the conventional approaches of carving and\udfilling, we find that our new approach is able to reduce the elevation bias\udand errors in longitudinal river profiles

Bumps in river profiles: uncertainty assessment and smoothing using quantile regression techniques