Parameter Uncertainty in Shallow Rainfall-triggered Landslide Modeling at Basin Scale: A Probabilistic Approach

Arnone, Elisa; Dialynas, Y. G.; Noto, Leonardo; Bras, Rafael L.

doi:10.1016/j.proeps.2014.06.003

Cited by 24 publications

(27 citation statements)

References 40 publications

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“…Uncertainties in hydroclimatic variables, such as precipitation, air temperature, and resulting hydrologic fluxes, are particularly pronounced in steep, high mountain regions due to lack of observations to capture complex atmospheric processes (Roe, 2005;Wayland et al, 2016). Designating landslide hazard as a probability, rather than an index, systematically accounts for uncertainty and variability in stability analysis (Hammond et al, 1992;Simoni et al, 2008;Arnone et al, 2014) and more appropriately represents complex systems (Berti et al, 2012). Recently, some promising advances have been made in process-based models accounting for data uncertainty in landslide hazard mapping (e.g., Raia et al, 2014;Arnone et al, 2016a).…”

Section: Geomorphology and Modeling Backgroundmentioning

confidence: 99%

A hydroclimatological approach to predicting regional landslide probability using Landlab

et al. 2018

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Abstract. We develop a hydroclimatological approach to the modeling of regional shallow landslide initiation that integrates spatial and temporal dimensions of parameter uncertainty to estimate an annual probability of landslide initiation based on Monte Carlo simulations. The physically based model couples the infinite-slope stability model with a steady-state subsurface flow representation and operates in a digital elevation model. Spatially distributed gridded data for soil properties and vegetation classification are used for parameter estimation of probability distributions that characterize model input uncertainty. Hydrologic forcing to the model is through annual maximum daily recharge to subsurface flow obtained from a macroscale hydrologic model. We demonstrate the model in a steep mountainous region in northern Washington, USA, over 2700 km 2 . The influence of soil depth on the probability of landslide initiation is investigated through comparisons among model output produced using three different soil depth scenarios reflecting the uncertainty of soil depth and its potential long-term variability. We found elevation-dependent patterns in probability of landslide initiation that showed the stabilizing effects of forests at low elevations, an increased landslide probability with forest decline at midelevations (1400 to 2400 m), and soil limitation and steep topographic controls at high alpine elevations and in post-glacial landscapes. These dominant controls manifest themselves in a bimodal distribution of spatial annual landslide probability. Model testing with limited observations revealed similarly moderate model confidence for the three hazard maps, suggesting suitable use as relative hazard products. The model is available as a component in Landlab, an open-source, Python-based landscape earth systems modeling environment, and is designed to be easily reproduced utilizing HydroShare cyberinfrastructure.

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Section: Geomorphology and Modeling Backgroundmentioning

confidence: 99%

A hydroclimatological approach to predicting regional landslide probability using Landlab

et al. 2018

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“…The slope stability model explicitly accounts for the spatial heterogeneity of factors that control shallow landslides, including mechanical and hydrological soil properties (Arnone et al, , ), local topographic characteristics, and the temporal variation of soil moisture in response to precipitation events, which controls the temporal FS dynamics at the watershed scale (Simoni et al, ; Arnone et al, ; Lepore et al, ; Formetta et al, ). Possible landslide deposition paths are estimated based on the concept of run‐out distance (Bathurst et al, ).…”

Section: Methodsmentioning

confidence: 99%

Hydro‐geomorphic behavior of contrasting tropical landscapes and critical zone response to changing climate

Dialynas

Bras

2018

Earth Surf Processes Landf

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The potentially important influence of climate change on landscape evolution and on critical zone processes is not sufficiently understood. The relative contribution of hydro‐climatic factors on hillslope erosion rates may significantly vary with topography at the watershed scale. The objective of this study is to quantify the hydro‐geomorphic behavior of two contrasting landscapes in response to different climate change scenarios in the Luquillo Critical Zone Observatory, a site of particular geomorphological interest, in terms of hillslope erosion and rainfall‐triggered landslides. We investigate the extent to which hillslope erosion and landslide occurrence remain relatively invariant with future hydro‐climatic perturbations. The adjacent Mameyes and Icacos watersheds are studied, which are underlain by contrasting lithologies. A high resolution coupled hydro‐geomorphic model based on tRIBS (Triangulated Irregular Network‐based Real‐time Integrated Basin Simulator) is used. Observations of landslide activity and hillslope erosion are used to evaluate the model performance. The process‐based model quantifies feedbacks among different hydrologic processes, landslide occurrence, and topsoil erosion and deposition. Simulations suggest that the propensity for landslide occurrence in the Luquillo Mountains is controlled by tropical storms, subsurface water flow, and by non‐climatic factors, and is expected to remain significant through 2099. The Icacos watershed, which is underlain by quartz diorite, is dominated by relatively large landslides. The relative frequency of smaller landslides is higher at the Mameyes watershed, which is underlain by volcaniclastic rock. While projections of precipitation decrease at the study site may lead to moderate decline in hillslope erosion rates, the simulated erosional potential of the two diverse landscapes likely remains significant. © 2018 John Wiley & Sons, Ltd.

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“…In landslide research, a number of attempts have been reported that explicitly address ensemble techniques as a means of overcoming limitations from purely deterministic approaches or by increasing the predictive performance of statistically based landslide susceptibility mapping (e.g., Arnone et al, 2014;Cho, 2007;Haneberg, 2004;Melchiorre and Frattini, 2012;Rubio et al, 2004). None of them, however, incorporate ensemble techniques in real-time applications.…”

Section: Benefits and Types Of Probabilistic Approachesmentioning

confidence: 99%

Probabilistic landslide ensemble prediction systems: lessons to be learned from hydrology

Canli

Mergili

Thiebes

et al. 2018

Nat. Hazards Earth Syst. Sci.

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Abstract. Landslide forecasting and early warning has a long tradition in landslide research and is primarily carried out based on empirical and statistical approaches, e.g., landslidetriggering rainfall thresholds. In the last decade, flood forecasting started the operational mode of so-called ensemble prediction systems following the success of the use of ensembles for weather forecasting. These probabilistic approaches acknowledge the presence of unavoidable variability and uncertainty when larger areas are considered and explicitly introduce them into the model results. Now that highly detailed numerical weather predictions and high-performance computing are becoming more common, physically based landslide forecasting for larger areas is becoming feasible, and the landslide research community could benefit from the experiences that have been reported from flood forecasting using ensemble predictions. This paper reviews and summarizes concepts of ensemble prediction in hydrology and discusses how these could facilitate improved landslide forecasting. In addition, a prototype landslide forecasting system utilizing the physically based TRIGRS (Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability) model is presented to highlight how such forecasting systems could be implemented. The paper concludes with a discussion of challenges related to parameter variability and uncertainty, calibration and validation, and computational concerns.

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Parameter Uncertainty in Shallow Rainfall-triggered Landslide Modeling at Basin Scale: A Probabilistic Approach

Cited by 24 publications

References 40 publications

A hydroclimatological approach to predicting regional landslide probability using Landlab

A hydroclimatological approach to predicting regional landslide probability using Landlab

Hydro‐geomorphic behavior of contrasting tropical landscapes and critical zone response to changing climate

Probabilistic landslide ensemble prediction systems: lessons to be learned from hydrology

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