Abstract
Abstract. Surface roughness influences the release of avalanches and the dynamics of rockfall, avalanches and debris flow, but it is often not objectively implemented in natural hazard modelling. For two study areas, a treeline ecotone and a windthrow-disturbed forest landscape of the European Alps, we tested seven roughness algorithms using a photogrammetric digital surface model (DSM) with different resolutions (0.1, 0.5 and 1 m) and different moving-window areas (9, 25 and 49 m2). The vector ruggedness measure roughness algorithm performed best overall in distinguishing between roughness categories relevant for natural hazard modelling (including shrub forest, high forest, windthrow, snow and rocky land cover). The results with 1 m resolution were found to be suitable to distinguish between the roughness categories of interest, and the performance did not increase with higher resolution. In order to improve the roughness calculation along the hazard flow direction, we tested a directional roughness approach that improved the reliability of the surface roughness computation in channelised paths. We simulated avalanches on different elevation models (lidar-based) to observe a potential influence of a DSM and a digital terrain model (DTM) using the simulation tool Rapid Mass Movement Simulation (RAMMS). In this way, we accounted for the surface roughness based on a DSM instead of a DTM, which resulted in shorter simulated avalanche runouts by 16 %–27 % in the two study areas. Surface roughness above a treeline, which in comparison to the forest is not represented within the RAMMS, is therefore underestimated. We conclude that using DSM-based surface roughness in combination with DTM-based surface roughness and considering the directional roughness is promising for achieving better assessment of terrain in an alpine landscape, which might improve the natural hazard modelling.
Highlights
Surface roughness is a topographic parameter commonly used to identify and characterise surface features, such as different vegetation types (Stambaugh and Guyette, 2008) and geomorphological characteristics (Cavalli et al, 2008; McKean and Roering, 2004; Nguyen and Fenton, 2005)
We addressed the following research questions. (a) How well can different surface roughness categories be distinguished with the selected algorithms? (b) What is the influence of the digital surface model (DSM) resolution and moving-window area on the roughness classification? (c) Is it possible to improve the roughness calculation by introducing a directional roughness along the predominant mass flow direction? (d) How much can a mass flow simulation improve if roughness is properly taken into account?
There were important differences according to the spatial resolution and the moving-window area considered for the analysis
Summary
Surface roughness is a topographic parameter commonly used to identify and characterise surface features, such as different vegetation types (Stambaugh and Guyette, 2008) and geomorphological characteristics (Cavalli et al, 2008; McKean and Roering, 2004; Nguyen and Fenton, 2005). Quantifying surface roughness is central for the estimation of various biophysical characteristics and ecosystem services (Koponen et al, 2004; Wu et al, 2018). With the increasing availability of high-resolution remote sensing data, it is increasingly possible to quantify surface roughness over larger areas and to estimate how related ecosystem services and climate feedbacks change over time (Mina et al, 2017; MyersSmith et al, 2015; Nel et al, 2014; Palomo, 2017).
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