Event-Based Landslide Modeling in the Styrian Basin, Austria: Accounting for Time-Varying Rainfall and Land Cover

Knevels, Raphael; Petschko, Helene; Proske, Herwig; Leopold, Philip; Maraun, Douglas; Brenning, Alexander

doi:10.3390/geosciences10060217

Cited by 32 publications

(44 citation statements)

References 68 publications

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“…Again, this is different from the work of Domingo-Santos et al (2011), where the solid angle refers to the pixel size. We maintain that choosing the target object size is quite relevant as it allows us to delineate target-size specific 'effective survey areas' (Bornaetxea et al 2018, Knevels et al 2020; that is, the portion of the territory where an observer is actually able to detect target objects. The 'effective survey area' can be defined by thresholding the solid angle map based on a value that depends, for example, on human visual acuity.…”

Section: Discussionmentioning

confidence: 99%

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r.survey: a tool for calculating visibility of variable-size objects based on orientation

Bornaetxea

Marchesini

2021

International Journal of Geographical Information Science

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Identification of terrain surface features can be done using approaches such as visual observation or remote sensing image processing. Accurate detection of survey targets at the ground level primarily depends on human visual acuity or sensor resolution, and then on acquisition geometry (i.e. the relative position and orientation between the surveyor and the terrain). Further, the delimitation of the observer's viewshed boundary or of the sensor's ground footprint is sometimes insufficient to ensure that all enclosed targets can be correctly detected. Size and orientation can hamper ground target visibility. In this paper we describe a new release of r.survey, an open-source spatial analysis tool for terrain survey assessment. This tool offers the necessary information to assess how terrain morphology is perceived by observers and/or sensors by means of three basic visibility metrics: 3D distance, view angle, and solid angle. It is also fully customizable, allowing single or multiple observation points, ground or aerial point of view, and size setting of the observed target, making it useful for many different purposes.

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Section: Discussionmentioning

confidence: 99%

“…For this work, the authors designed a specific GRASS GIS python module that automatically delineates the 'effective surveyed area,' referred to as r.survey. Recently, Knevels et al (2020) transferred the original GRASS GIS-based python tool into R. In this paper we go further and present the new release of the Python version of r.survey.…”

Section: Introductionmentioning

confidence: 99%

r.survey: a tool for calculating visibility of variable-size objects based on orientation

Bornaetxea

Marchesini

2021

International Journal of Geographical Information Science

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“…As a measure of variable importance, we extracted for each variable the mean decrease in deviance explained (mDD, %) under the consideration of all SpCV models. The mDD indicates the explanatory contribution of a variable to the overall explained deviance of the corresponding model [72,73]. The higher the mDD value, the greater is the contribution of a variable, and thus its importance.…”

Section: Assessment Of the Effect Of Land Use Legacymentioning

confidence: 99%

Towards the Use of Land Use Legacies in Landslide Modeling: Current Challenges and Future Perspectives in an Austrian Case Study

Knevels

Brenning

Gingrich

et al. 2021

Land

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Land use/land cover (LULC) changes may alter the risk of landslide occurrence. While LULC has often been considered as a static factor representing present-day LULC, historical LULC dynamics have recently begun to attract more attention. The study objective was to assess the effect of LULC legacies of nearly 200 years on landslide susceptibility models in two Austrian municipalities (Waidhofen an der Ybbs and Paldau). We mapped three cuts of LULC patterns from historical cartographic documents in addition to remote-sensing products. Agricultural archival sources were explored to provide also a predictor on cumulative biomass extraction as an indicator of historical land use intensity. We use historical landslide inventories derived from high-resolution digital terrain models (HRDTM) generated using airborne light detection and ranging (LiDAR), which are reported to have a biased landslide distribution on present-day forested areas and agricultural land. We asked (i) if long-term LULC legacies are important and reliable predictors and (ii) if possible inventory biases may be mitigated by LULC legacies. For the assessment of the LULC legacy effect on landslide occurrences, we used generalized additive models (GAM) within a suitable modeling framework considering various settings of LULC as predictor, and evaluated the effect with well-established diagnostic tools. For both municipalities, we identified a high density of landslides on present-day forested areas, confirming the reported drawbacks. With the use of LULC legacy as an additional predictor, it was not only possible to account for this bias, but also to improve model performances.

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“…with a relatively small set of predictors, which are primarily DEM-derived terrain attributes (Blahut et al 2010b;Heckmann et al 2014;Goetz et al 2015a). Additionally, by fitting our models with multi-temporal data, we may be more likely to achieve better transferability in time (Tuanmu et al 2011;Knevels et al 2020). Event-specific inventories may not be large enough for regional optimization and risk the potential of overfitting source conditions spatially varying conditions of that event (e.g., precipitation and snowmelt patterns).…”

Section: Source Area Modellingmentioning

confidence: 99%

“…For spatially distributed models, spatial transferability can be assessed by exploring model parameter selection and performance under different spatial partitioning scenarios of training and test data (Wenger and Olden 2012;Brenning 2012;Schratz et al 2019;Mergili et al 2019). Although spatial transferability has been well explored for regional landslide susceptibility models (Brenning 2005;Lombardo et al 2014;Petschko et al 2014;Goetz et al 2015a;Cama et al 2017;Knevels et al 2020), such analysis is not common for regional runout modelling.…”

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confidence: 99%

Comment on nhess-2021-22

2021

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Knowing the source and runout of debris-flows can help in planning strategies aimed at mitigating these hazards.Our research in this paper focuses on developing a novel approach for optimizing runout models for regional susceptibility modelling, with a case study in the upper Maipo river basin in the Andes of Santiago, Chile. We propose a two-stage optimization approach for automatically selecting parameters for estimating runout path and distance. This approach optimizes the random walk and Perla's two-parameter modelling components of the open-source Gravitational Process Path (GPP) modelling framework. To validate model performance, we assess the spatial transferability of the optimized runout model using spatial cross-validation, including exploring the model's sensitivity to sample size. We also present diagnostic tools for visualizing uncertainties in parameter selection and model performance. Although there was considerable variation in optimal parameters for individual events, we found our runout modelling approach performed well at regional prediction of potential runout areas. We also found that although a relatively small sample size was sufficient to achieve generally good runout modelling performance; larger samples sizes (i.e. ≥80) had higher model performances and lower uncertainties for estimating runout distances at unknown locations. We anticipate that this automated approach using open-source software R and SAGA-GIS will make process-based debris-flow models more readily accessible and thus enable researchers and spatial planners to improve regional-scale hazard assessments. IntroductionKnowledge of where debris flows are initiated and how far they travel is crucial for assessing their impact over large regions (Aleotti and Chowdhury 1999;van Westen et al. 2006). Commonly, debris-flow runout modelling for large areas is performed by first delineating source areas and then applying empirical-statistical or process-based numerical methods for simulating the runout characteristics (Blahut et al. 2010a;Horton et al. 2013;Mergili et al. 2019). There is a wide selection of heuristic, statistical and machine learning methods suitable for predicting sources areas in large regions (Chung and Fabbri 1999;Carrara

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Event-Based Landslide Modeling in the Styrian Basin, Austria: Accounting for Time-Varying Rainfall and Land Cover

Cited by 32 publications

References 68 publications

r.survey: a tool for calculating visibility of variable-size objects based on orientation

r.survey: a tool for calculating visibility of variable-size objects based on orientation

Towards the Use of Land Use Legacies in Landslide Modeling: Current Challenges and Future Perspectives in an Austrian Case Study

Comment on nhess-2021-22

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