15 Dec 2021

15 Dec 2021

Review status: this preprint is currently under review for the journal NHESS.

Impact of spatial data uncertainty in debris flow susceptibility analysis

Laurie Jayne Kurilla and Giandomenico Fubelli Laurie Jayne Kurilla and Giandomenico Fubelli
  • University of Turin, Department of Earth Sciences

Abstract. In a study of debris flow susceptibility on the European continent, an analysis of the impact between known location and a location accuracy offset for 99 debris flows, demonstrates the impact of uncertainty in defining appropriate predisposing factors, and consequent analysis for areas of susceptibility.

The dominant predisposing environmental factors, as determined through Maximum Entropy modeling, are presented, and analyzed with respect to the values found at debris flow event points versus a buffered distance of locational uncertainty around each point.

Five Maximum Entropy susceptibility models are developed utilizing the original debris flow inventory of points, randomly generated points, and two models utilizing a subset of points with an uncertainty of 5 km, 1 km, and a model utilizing only points with a known location of “exact”. The AUCs are 0.891, 0.893, 0.896, 0.921, and 0.93, respectively. The “exact” model, with the highest AUC, is ignored in final analyses due to the small number of points, and localized distribution, and hence susceptibility results likely non-representational of the continent.

Each model is analyzed with respect to the AUC, highest contributing factors, factor classes, susceptibility impact, and comparisons of the susceptibility distributions and susceptibility value differences.

Based on model comparisons, geographic extent and context of this study, the models utilizing points with a location uncertainty of less than or equal to 5 km best represent debris flow susceptibility of the continent of Europe. A novel representation of the uncertainty is expressed, and included in a final susceptibility map, as an overlay of standard deviation and mean of susceptibility values for the two best models, providing additional insight for subsequent action.

Laurie Jayne Kurilla and Giandomenico Fubelli

Status: open (until 09 Feb 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2021-364', Anonymous Referee #1, 17 Jan 2022 reply
    • AC1: 'Reply on RC1', Laurie Kurilla, 17 Jan 2022 reply

Laurie Jayne Kurilla and Giandomenico Fubelli

Laurie Jayne Kurilla and Giandomenico Fubelli


Total article views: 259 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
216 35 8 259 7 8
  • HTML: 216
  • PDF: 35
  • XML: 8
  • Total: 259
  • BibTeX: 7
  • EndNote: 8
Views and downloads (calculated since 15 Dec 2021)
Cumulative views and downloads (calculated since 15 Dec 2021)

Viewed (geographical distribution)

Total article views: 245 (including HTML, PDF, and XML) Thereof 245 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 18 Jan 2022
Short summary
Debris flow research, at broader geographic coverages, requires the use of inventories of past events. Such information may not have precise event locations, resulting in current and future susceptibility models with a lower confidence level. This research showcases the problems associated with inaccurate locations in identifying the conditions which predispose an area to debris flows and provides a novel approach to presenting such uncertainties to the users of the resulting models.