Soil moisture and precipitation thresholds for real-time landslide prediction in El Salvador

Abstract

Described is the development of a regional forecasting system for landslide hazard threat level, suitable for use operationally by forecasting and disaster management agencies. The system utilizes spatially distributed operational hydrologic models to estimate depth-integrated soil moisture on basin scales of order 160 km 2 , with forcing of remotely sensed and on-site precipitation data. The depth-integrated soil moisture data and the precipitation forcing are used together with regional databases of landslide occurrence to develop threshold curves in the precipitation/soil moisture space that allow the prediction of landslide hazard threat level on satellite-derived rainfall pixel scales. Predetermined susceptibility maps may then be used together with the real-time prediction of hazard threat level for a particular rainfall pixel to determine the slopes within the pixel that are more likely to fail in real time and to characterize a given pixel as susceptible or nonsusceptible to landsliding for real-time prediction. The operational system development requires global satellite precipitation estimates with short latency, real-time precipitation data from sparse rain gauges in the region, and a regional database of historical landslide events with location and timing information. Parametric databases that support the operational hydrologic model consist of soil texture by depth and land-use/land-cover information. The case study presented is for the country of El Salvador. The study shows the feasibility of the regional system development and the validation of the assumed existence of a threshold curve in twodimensional space consisting of the depth-integrated soil moisture and of the forcing precipitation. The resulting threshold curve, when examined with data from the period 2006-2011 in El Salvador, resulted in warnings of landslide occurrence with frequency that spanned the range between 1 and 5 % of the days for the basins identified to be susceptible to landsliding.

Basic features of the predictive tools of early warning systems for water-related natural hazards: examples for shallow landslides

“…Usually, only a few landslides occur at a site during an observation period of typically some decades, so that probabilistic landslide initiation thresholds are mostly defined at regional scale, so as to have a rich data set of observed landslides (e.g., Terlien, 1998;Guzzetti et al, 2007;Jakob et al, 2012;Ponziani et al, 2012;Segoni et al, 2015;Iadanza et al, 2016). The use of physically based models of infiltration and slope stability can help in the prediction of slope response under conditions different from those actually encountered during the observation period, thus allowing the definition of site-specific landslide initiation thresholds (e.g., Arnone et al, 2011;Ruiz-Villanueva et al, 2011;Tarolli et al, 2011;Papa et al, 2013;Peres and Cancelliere, 2014;Posner and Georgakakos, 2015;Greco and Bogaard, 2016), which can be useful for carrying out stochastic predictions. However, the application of such physically based approaches in operational EWS is difficult due to the computational burden involved, which makes carrying out the calculations required for landslide probability assessment difficult in real time.…”

Assessment of simulated soil moisture from WRF Noah, Noah-MP, and CLM land surface schemes for landslide hazard application

“…One of the most widely adopted methods for landslide prediction is based on rainfall threshold and relies on building the rainfall intensity-duration curve using the information from past landslide events (Chae et al, 2017). However, such a method is in many cases insufficient for landslide hazard assessment (Posner and Georgakakos, 2015), because in addition to rainfall, the initial soil moisture condition is one of the main triggering factors of the events (Glade et al, 2000;Crozier, 1999;Tsai and Chen, 2010;Hawke and McConchie, 2011;Bittelli et al, 2012;Segoni et al, 2018b;Valenzuela et al, 2018;Bogaard and Greco, 2018).…”

Evaluation of Remotely Sensed Soil Moisture for Landslide Hazard Assessment

“…Unfortunately, in many remote areas, soil moisture stations are not available or only are sparsely distributed in those non-hazardous areas, due to high installation and maintenance cost (e.g., in our study area, although there is a total of 19 in-situ soil moisture sensors installed, nearly all of them are installed in the plain areas where landslides never occurred). Another technique to obtain continuous soil moisture variations relies on land surface /hydrological modelling [7,[24][25][26][27]]. However, model-based methods tend to suffer from time drifts problem (e.g., error accumulation over times), require a large number of accurate data inputs and are normally computationally intensive particularly for large monitoring areas.…”