National strategy for landslide loss reduction

Godt, Jonathan W.; Wood, Nathan J.; Pennaz, Alice; Dacey, Connor M.; Mirus, Benjamin B.; Schaefer, Lauren N.; Slaughter, Stephen L.

doi:10.3133/ofr20221075

Cited by 4 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As such, resources from many countries have been devoted to studying landslides and mitigating future losses. Products from these studies include hazards maps (e.g., Kirschbaum & Stanley, 2018; Micu et al., 2023; Nowicki Jessee et al., 2018), susceptibility maps (e.g., Crawford et al., 2021; Huang et al., 2020; Hughes & Schulz, 2020), early warning systems (e.g., Baum & Godt, 2010; Guzzetti et al., 2020), and emergency response plans (e.g., Godt et al., 2022; Wooten et al., 2017). Although these efforts have improved our knowledge and response to landslides, additional work would be beneficial to further reduce landslide effects.…”

Section: Introductionmentioning

confidence: 99%

“…In contrast, hazard maps quantify the timing and magnitude of landslides and risk maps measure the expected losses from landslides. Regional‐scale or larger (>1,000 km 2 ) maps are fundamental for mitigating future losses from landslides by providing uniform information across scales relevant for land management, planning, infrastructure, and emergency response decisions (Godt et al., 2022). Several methods for categorizing landslide susceptibility have been used including geomorphic mapping, heuristic methods, physically based methods, and data‐driven statistical models (Reichenbach et al., 2018).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Mapping Landslide Susceptibility Over Large Regions With Limited Data

et al. 2023

Self Cite

View full text Add to dashboard Cite

Landslide susceptibility maps indicate the spatial distribution of landslide likelihood. Modeling susceptibility over large or diverse terrains remains a challenge due to the sparsity of landslide data (mapped extent of known landslides) and the variability in triggering conditions. Several different data sampling strategies of landslide locations used to train a susceptibility model are used to mitigate this challenge. However, to our knowledge, no study has systematically evaluated how different sampling strategies alter a model's predictor effects (i.e., how a predictor value influences the susceptibility output) critical to explaining differences in model outputs. Here, we introduce a statistical framework that examines the variation in predictor effects and the model accuracy (measured using receiver operator characteristics) to highlight why certain sampling strategies are more effective than others. Specifically, we apply our framework to an array of logistic regression models trained on landslide inventories collected at sub‐regional scales over four terrains across the United States. Results show significant variations in predictor effects depending on the inventory used to train the models. The inconsistent predictor effects cause low accuracies when testing models on inventories outside the domain of the training data. Grouping test and training sets according to physiographic and ecological characteristics, which are thought to share similar triggering mechanisms, does not improve model accuracy. We also show that using limited landslide data distributed uniformly over the entire modeling domain is better than using dense but spatially isolated data to train a model for applications over large regions.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mapping Landslide Susceptibility Over Large Regions With Limited Data

et al. 2023

Self Cite

View full text Add to dashboard Cite

show abstract

Landslides of China's Qinling

Feng,

Xu,

Tian

et al. 2024

Geoscience Data Journal

View full text Add to dashboard Cite

The Qinling Mountains in China frequently experience geological disasters, with large‐scale landslides being particularly prominent, causing severe economic losses to the local area. To gain a comprehensive understanding of the geological disaters distribution in the region, we conducted extensive research on the entire Qinling Mountains, covering an area of approximately 380,000 km2. By employing methods such as literature review, data collection, and interpretation of remote sensing images, we have successfully created a database of landslides. The inventory of landslides includes a total of 169,888 large‐scale landslides, covering a combined area of approximately 1575 km2. The average size of these landslides is approximately 92,734 m2. The scale of these landslides varies widely, with the smallest individual landslide covering an area of 166.25 m2 and the largest reaching 12.9 km2. Upon examining areas with frequent landslides, it was observed that landslides are usually densely distributed along riverbanks or within valleys. Landslide development is also dense in areas prone to frequent historical earthquakes. This comprehensive database provides essential data to support the analysis of spatial distribution patterns of large‐scale landslides in the Qinling Mountains. It also facilitates landslide assessments and serves as a reference for the prevention and control of landslide disasters in the area.

show abstract

Parsimonious High‐Resolution Landslide Susceptibility Modeling at Continental Scales

Mirus,

Belair,

Wood

et al. 2024

AGU Advances

View full text Add to dashboard Cite

Landslide susceptibility maps are fundamental tools for risk reduction, but the coarse resolution of current continental‐scale models is insufficient for local application. Complex relations between topographic and environmental attributes characterizing landslide susceptibility at local scales are not transferrable across areas without landslide data. Existing maps with multiple susceptibility classifications under‐represent landslide potential in moderate and gently sloping terrain. We leverage an extensive landslide database (N = 613,724), a high‐resolution digital elevation model (10‐m), and high‐performance computing resources, to develop a new nationwide susceptibility map for the contiguous United States, Hawaii, Alaska, and Puerto Rico. We calculate four alternative linear and nonlinear thresholds of topographic slope and relief using an objective split‐sample calibration. We down‐sample our results to a 90‐m grid to account for uncertainty in the digital elevation model and landslide position, and evaluate these thresholds' ability to differentiate areas of greater susceptibility. The less conservative nonlinear model optimally balances our priorities of capturing observed landslides (99%) while minimizing area covered by susceptible terrain (43%). Independent evaluation with four statewide landslide inventories (N = 172,367) reinforces our model selection but highlights spatially variable performance. Therefore, we propose a novel approach to susceptibility classification using the concentration of landslide‐prone terrain within each down‐sampled grid. While landslides are possible within any cells containing susceptible terrain, those with the highest concentration capture the majority of observed landslides. Our new map characterizes landside susceptibility more consistently than prior models; our transparent classification approach also provides flexibility for accommodating different tolerances in risk reduction measures.

show abstract

National strategy for landslide loss reduction

Abstract: Cover. Compilation of photographs related to landslides, including (1) landslide damage after

Cited by 4 publications

References 10 publications

Mapping Landslide Susceptibility Over Large Regions With Limited Data

Mapping Landslide Susceptibility Over Large Regions With Limited Data

Landslides of China's Qinling

Parsimonious High‐Resolution Landslide Susceptibility Modeling at Continental Scales

Contact Info

Product

Resources

About