Rainfall-induced landslide early warning system based on corrected mesoscale numerical models: an application for the southern Andes

Abstract

Abstract. Rainfall-induced landslides (RILs) are an issue in the southern Andes nowadays. RILs cause loss of life and damage to critical infrastructure. Rainfall-induced landslide early warning systems (RILEWSs) can reduce and mitigate economic and social damages related to RIL events. The southern Andes do not have an operational-scale RILEWS yet. In this contribution, we present a pre-operational RILEWS based on the Weather and Research Forecast (WRF) model and geomorphological features coupled to logistic models in the southern Andes. The models have been forced using precipitation simulations. We correct the precipitation derived from WRF using 12 weather stations through a bias correction approach. The models were trained using 57 well-characterized RILs and validated by ROC analysis. We show that WRF has strong limitations in representing the spatial variability in the precipitation. Therefore, accurate precipitation needs a bias correction in the study zone. We used accurate precipitation simulation and slope, demonstrating a high predicting capacity (area under the curve, AUC, of 0.80). We conclude that our proposal could be suitable at an operational level under determined conditions. A reliable RIL database and operational weather networks that allow real-time correction of the mesoscale model in the implemented zone are needed. The RILEWSs could become a support to decision-makers during extreme-precipitation events related to climate change in the south of the Andes.

Ideas and perspectives: Sensing energy and matter fluxes in a biota-dominated Patagonian landscape through environmental seismology – introducing the Pumalín Critical Zone Observatory

“…For all the detected events we calculated the spectrograms of all the seismic stations and located the event using the signal migration technique (Burtin et al, 2014) with 5-20 Hz filtered sig- We emphasize that this particular hillslope event occurred under presumably "dry" antecedent conditions as it happened during the transition from austral summer to austral fall (22 March 2022) and that landslide activity is concentrated during the rainy season (Morales et al, 2021). Our CZO may assist in improving rainfall duration-intensity thresholds (Guzzetti et al, 2008), thus providing -together with the wind data -a contribution for early warning, considering known limitations in the spatial variability of storms (Fustos-Toribio et al, 2022). Seismically derived landslide rates may further help shed light on hillslope-channel coupling and, thus, rates of carbon export from hillslopes (Croissant et al, 2021).…”

Rainfall-Induced Landslide Assessment under Different Precipitation Thresholds Using Remote Sensing Data: A Central Andes Case

“…Geomorphological characteristics were integrated using the SRTM (Shuttle Radar Topography Mission), forcing the spatial resolution at 30 m. In recent decades, SRTM products have been used by the geoscientific community to perform complex treatments of the spatial data [11,44,45]. SRTM is suitable to understand the geomorphological features of the terrain, becoming a useful support in landslide assessment.…”

“…Evaluating this issue requires utilizing historical global precipitation data and considering temporal and spatial coverage [9,10]. Additionally, low-income countries face inherent limitations regarding instrumentation for RIL's comprehensive monitoring and early warning systems [11]. The scarcity of precipitation data exacerbates the challenge of developing precise RIL susceptibility models.…”

“…Strategies have been generated in recent decades to define susceptibility to RIL using different approaches worldwide [14][15][16][17][18]. Mainly, susceptibility landslide models underestimate the geomorphological conditions, prioritizing the regional and local precipitation thresholds [11] and limiting their application in public policies [19]. Generally, methodological approaches are based on qualitative methods, semi-quantitative methods [20], statistical methods [18,21,22] and automatic-learning methods [21,23].…”

Mask R-CNN–Based Landslide Hazard Identification for 22.6 Extreme Rainfall Induced Landslides in the Beijiang River Basin, China

“…In addition, the complex geological conditions in the area are influenced by a variety of natural factors, resulting in a large number of hazards from mass landslides, including casualties, property damage, infrastructure damage, economic loss, ecological damage and many other aspects [4][5][6]. Such landslides induced by extreme rainfall are not uncommon around the world, such as in the Andean region [7], Hong Kong [8], America [9] and Malaysia [10], where rainfall-induced landslide disasters have caused severe human and economic losses.…”