On the prediction of channel heads in a complex alpine terrain using gridded elevation data

Abstract

[1] Threshold conditions for channel initiation are evaluated by using gridded elevation data derived from a lidar survey, a reliable algorithm for the determination of surface flow paths, and field observations of channel heads for a study area located in the eastern Italian Alps. These threshold conditions are determined by considering the channel heads observed across a portion of the study area and computing the related values of (1) drainage area A, (2) area-slope function AS 2 , with S being the local slope, and (3) Strahler order ! Ã of surface flow paths extracted from gridded elevation data. Attention is focused on the dependence of the obtained threshold values on the size of grid cells involved and on the ability of the identified threshold conditions to provide reliable predictions of channel heads across the entire study area. The results indicate that the threshold values of A, AS 2 , and ! Ã are all significantly dependent on grid cell size, and the uncertainty in the determination of threshold values of ! Ã is significantly smaller than that affecting the determination of threshold values of A and AS 2 . The comparison between predicted and observed channel heads indicates that the considered methods display variable reliability and sensitivity over different drainage basins and grid cell sizes, with a general tendency to predict more channel heads than can be observed in the field. Acceptable predictions are normally obtained where channel heads are formed essentially by surface runoff. More comprehensive methods seem, however, to be needed to predict channel heads affected by groundwater seeping upward.

How does grid-resolution modulate the topographic expression of geomorphic processes?

“…Many authors have made use of field-mapped channel heads both as a basis for geomorphic analysis and as a method for evaluating channel extraction methods (Montgomery and Dietrich, 1989;Orlandini et al, 2011;Julian et al, 2012;Jefferson and McGee, 2013;Clubb et al, 2014). Prior to the availability of high-resolution topographic data, contributing area and slope-area scaling thresholds were commonly used to define the location of channel heads directly from DEMs (Mark, 1984;O'Callaghan and Mark, 1984;Montgomery and Dietrich, 1989;Tarboton et al, 1991;Dietrich et al, 1992Dietrich et al, , 1993.…”

“…Extracting channels from topography is a common requirement of many analyses, and it is expected that the accuracy of extracted channel networks will be affected by increasing grid resolution (Orlandini et al, 2011). Roering et al (2007), Hurst et al (2013b), and Grieve et al (2016b) used measurements of hillslope length and relief to identify signals of landscape transience.…”

Terrestrial Laser Scanner Data to Support Coastal Erosion Analysis: The Conero Case Study

“…Airborne LiDAR (ALS) and Terrestrial Laser Scanner (TLS) derived high-resolution Digital Terrain Models (DTMs) have opened avenues for hydrologic and geomorphologic studies (Slatton et al, 2007;Tarolli et al, 2009). Several researches demonstrated the opportunities and challenges of high resolution topography in many topics, including landsliding (McKean and Roering, 2004;Booth et al, 2009;Kasai et al, 2009;Tarolli et al, 2012;Lin et al, 2013), hillslope environments (Lashermes et al, 2007;Tarolli and Dalla Fontana, 2009;Passalacqua et al, 2010;Orlandini et al, 2011), fluvial environments (Hilldale and Raff, 2008;Jones et al, 2007;Cavalli et al, 2008;Notebaert et al, 2009;Cavalli and Tarolli, 2011;Legleiter, 2012), forestry (Pirotti et al, 2013a), and tectonics (Hilley and Arrowsmith, 2008;Kondo et al, 2008). In general, all the main surface processes signatures are correctly recognized using a DTM with cell sizes of 1 m.…”

Detecting and Characterizing Active Thrust Fault and Deep-Seated Landslides in Dense Forest Areas of Southern Taiwan Using Airborne LiDAR DEM

“…The processed airborne LiDAR data were further interpolated to derive a grid of digital surface models (DSMs) and DEMs with vegetation and buildings removed. The LiDAR bare ground data set was used to generate a 1-m-resolution DEM using the natural neighbor interpolator, which has proven to be useful for geomorphic analysis in previous studies [37].…”