A distributed hydrological–geotechnical model using satellite-derived rainfall estimates for shallow landslide prediction system at a catchment scale

This paper describes the potential applicability of a hydrological–geotechnical modeling system using satellite-based rainfall estimates for a shallow landslide prediction system. The physically based distributed model has been developed by integrating a grid-based distributed kinematic wave rainfall-runoff model with an infinite slope stability approach. The model was forced by the satellite-based near real-time half-hourly CMORPH global rainfall product prepared by NOAA-CPC. The method combines the following two model outputs necessary for identifying where and when shallow landslides may potentially occur in the catchment: (1) the time-invariant spatial distribution of areas susceptible to slope instability map, for which the river catchment is divided into stability classes according to the critical relative soil saturation; this output is designed to portray the effect of quasi-static land surface variables and soil strength properties on slope instability and (2) a produced map linked with spatiotemporally varying hydrologic properties to provide a time-varying estimate of susceptibility to slope movement in response to rainfall. The proposed hydrological model predicts the dynamic of soil saturation in each grid element. The stored water in each grid element is then used for updating the relative soil saturation and analyzing the slope stability. A grid of slope is defined to be unstable when the relative soil saturation becomes higher than the critical level and is the basis for issuing a shallow landslide warning. The method was applied to past landslides in the upper Citarum River catchment (2,310 km²), Indonesia; the resulting time-invariant landslide susceptibility map shows good agreement with the spatial patterns of documented historical landslides (1985–2008). Application of the model to two recent shallow landslides shows that the model can successfully predict the effect of rainfall movement and intensity on the spatiotemporal dynamic of hydrological variables that trigger shallow landslides. Several hours before the landslides, the model predicted unstable conditions in some grids over and near the grids at which the actual shallow landslides occurred. Overall, the results demonstrate the potential applicability of the modeling system for shallow landslide disaster predictions and warnings.     

The determination of statistical and deterministic hydrological landslide-triggering thresholds

 Hydrological landslide-triggering thresholds separate combinations of daily and antecedent rainfall or of rainfall intensity and duration that triggered landslides from those that failed to trigger landslides. They are required for the development of landslide early warning systems. When a large data set on rainfall and landslide occurrence is available, hydrological triggering thresholds are determined in a statistical way. When the data on landslide occurrence is limited, deterministic models have to be used. For shallow landslides directly triggered by percolating rainfall, triggering thresholds can be established by means of one-dimensional hydrological models linked to the infinite slope model. In the case of relatively deep landslides located in topographic hollows and triggered by a slow accumulation of water at the soil-bedrock contact, simple correlations between landslide occurrence and rainfall can no longer be established. Therefore real-time failure probabilities have to be determined using hydrological catchment models in combination with the infinite slope model. Received: 15 October 1997 · Accepted: 25 June 1997     

Numerical modeling of rainstorm-induced shallow landslides in saturated and unsaturated soils

For the assessment of shallow landslides triggered by rainfall, the physically based model coupling the infinite slope stability analysis with the hydrological modeling in nearly saturated soil has commonly been used due to its simplicity. However, in that model the rainfall infiltration in unsaturated soil could not be reliably simulated because a linear diffusion-type Richards’ equation rather than the complete Richards’ equation was used. In addition, the effect of matric suction on the shear strength of soil was not actually considered. Therefore, except the shallow landslide in saturated soil due to groundwater table rise, the shallow landslide induced by the loss in unsaturated shear strength due to the dissipation of matric suction could not be reliably assessed. In this study, a physically based model capable of assessing shallow landslides in variably saturated soils is developed by adopting the complete Richards’ equation with the effect of slope angle in the rainfall infiltration modeling and using the extended Mohr–Coulomb failure criterion to describe the unsaturated shear strength in the soil failure modeling. The influence of rainfall intensity and duration on shallow landslide is investigated using the developed model. The result shows that the rainfall intensity and duration seem to have similar influence on shallow landslides respectively triggered by the increase of positive pore water pressure in saturated soil and induced by the dissipation of matric suction in unsaturated soil. The rainfall duration threshold decreases with the increase in rainfall intensity, but remains constant for large rainfall intensity.     

A prototype system for space–time assessment of rainfall-induced shallow landslides in Italy

In the last decades, physically based distributed models turned out rather promising to achieve the space–time assessment of shallow landslides at large spatial scale. This technical note deals with the application of a physically based stability model named Shallow Landslides Instability Prediction (SLIP), which has been adopted by the Department of National Civil Protection of Italy as a prototype early warning system for rainfall-induced shallow landslides on national scale. The model is used as a main methodology to create space–time shallow landslide susceptibility maps based on a simple deterministic slope-stability approach, combined with high-resolution rainfall information and geographic information system-based geospatial datasets. The safety factor as an index to measure slope instability is modeled as function of topographic, geologic, geotechnical and hydrologic variables. Although the main aim of this work was to prove the operational viability of such model on a nationwide domain and some simplification are adopted at this stage, hind cast tests on some relevant case histories of shallow landslides occurred between October 2009 and October 2011 showed that the model has skill in representing both timing and location of those shallow landslides.     

Shallow landslides in pyroclastic soils: a distributed modelling approach for hazard assessment

An effective assessment of shallow landslide hazard requires spatially distributed modelling of triggering processes. This is possible by using physically based models that allow us to simulate the transient hydrological and geotechnical processes responsible for slope instability. Some simplifications are needed to address the lack of data and the difficulty of calibration over complex terrain at the catchment's scale. We applied two simple hydrological models, coupled with the infinite slope stability analysis, to the May 1998 landslide event in Sarno, Southern Italy. A quasi-dynamic model (Barling et al., 1994) was used to model the contribution to instability of lateral flow by simulating the time-dependent formation of a groundwater table in response to rainfall. A diffusion model [Water Resour. Res. 36 (2000) 1897] was used to model the role of vertical flux by simulating groundwater pressures that develop in response to heavy rainstorms. The quasi-dynamic model overestimated the slope instability over the whole area (more than 16%) but was able to predict correctly slope instability within zero order basins where landslides occurred and developed into large debris flows. The diffusion model simulated correctly the triggering time of more than 70% of landslides within an unstable area amounting to 7.3% of the study area. These results support the hypothesis that both vertical and lateral fluxes were responsible for landslide triggering during the Sarno event, and confirm the utility of such models as tools for hazard planning and land management.     

Modeling landslide recurrence in Seattle, Washington, USA

To manage the hazard associated with shallow landslides, decision makers need an understanding of where and when landslides may occur. A variety of approaches have been used to estimate the hazard from shallow, rainfall-triggered landslides, such as empirical rainfall threshold methods or probabilistic methods based on historical records. The wide availability of Geographic Information Systems (GIS) and digital topographic data has led to the development of analytic methods for landslide hazard estimation that couple steady-state hydrological models with slope stability calculations. Because these methods typically neglect the transient effects of infiltration on slope stability, results cannot be linked with historical or forecasted rainfall sequences. Estimates of the frequency of conditions likely to cause landslides are critical for quantitative risk and hazard assessments. We present results to demonstrate how a transient infiltration model coupled with an infinite slope stability calculation may be used to assess shallow landslide frequency in the City of Seattle, Washington, USA. A module called CRF (Critical RainFall) for estimating deterministic rainfall thresholds has been integrated in the TRIGRS (Transient Rainfall Infiltration and Grid-based Slope-Stability) model that combines a transient, one-dimensional analytic solution for pore-pressure response to rainfall infiltration with an infinite slope stability calculation. Input data for the extended model include topographic slope, colluvial thickness, initial water-table depth, material properties, and rainfall durations. This approach is combined with a statistical treatment of rainfall using a GEV (General Extreme Value) probabilistic distribution to produce maps showing the shallow landslide recurrence induced, on a spatially distributed basis, as a function of rainfall duration and hillslope characteristics.     

Assessing shallow landslide hazards using the TRIGRS and SHALSTAB models,Serra do Mar,Brazil

The hillslopes of the Serra do Mar, a system of escarpments and mountains that extend more than 1500 km along the southern and southeastern Brazilian coast, are regularly affected by heavy rainfall that generates widespread mass movements, causing large numbers of casualties and economic losses. This paper evaluates the efficiency of susceptibility mapping for shallow translational landslides in one basin in the Serra do Mar, using the physically based landslide susceptibility models SHALSTAB and TRIGRS. Two groups of scenarios were simulated using different geotechnical and hydrological soil parameters, and for each group of scenarios (A and B), three subgroups were created using soil thickness values of 1, 2, and 3 m. Simulation results were compared to the locations of 356 landslide scars from the 1985 event. The susceptibility maps for scenarios A1, A2, and A3 were similar between the models regarding the spatial distribution of susceptibility classes. Changes in soil cohesion and specific weight parameters caused changes in the area of predicted instability in the B scenarios. Both models were effective in predicting areas susceptible to shallow landslides through comparison of areas predicted to be unstable and locations of mapped landslides. Such models can be used to reduce costs or to define potentially unstable areas in regions like the Serra do Mar where field data are costly and difficult to obtain.     

FLaIR and SUSHI: two mathematical models for early warning of landslides induced by rainfall

The development of Early Warning Systems in recent years has assumed an increasingly important role in landslide risk mitigation. In this context, the main topic is the relationship between rainfall and the incidence of landslides. In this paper, we focus our attention on the analysis of mathematical models capable of simulating triggering conditions. These fall into two broad categories: hydrological models and complete models. Generally, hydrological models comprise simple empirical relationships linking antecedent precipitation to the time that the landslide occurs; the latter consist of more complex expressions that take several components into account, including specific site conditions, mechanical, hydraulic and physical soil properties, local seepage conditions, and the contribution of these to soil strength. In a review of the most important models proposed in the technical and international literature, we have outlined their most meaningful and salient aspects. In particular, the Forecasting of Landslides Induced by Rainfall (FLaIR) and the Saturated Unsaturated Simulation for Hillslope Instability (SUSHI) models, developed by the authors, are discussed. FLaIR is a hydrological model based on the identification of a mobility function dependent on landslide characteristics and antecedent rainfall, correlated to the probability of a slide occurring. SUSHI is a complete model for describing hydraulic phenomena at slope scale, incorporating Darcian saturated flow, with particular emphasis on spatial–temporal changes in subsoil pore pressure. It comprises a hydraulic module for analysing the circulation of water from rainfall infiltration in saturated and nonsaturated layers in non-stationary conditions and a geotechnical slope stability module based on Limit Equilibrium Methods. The paper also includes some examples of these models’ applications in the framework of early warning systems in Italy.     

Effects of climate change on shallow landslides in a small coastal catchment in southern Italy

In different areas of the world, shallow landslides represent a remarkable hazard inducing fatalities and economic damages. Then, the evaluation about potential variation in frequency of such hazard under the effect of climate changes should be a priority for defining reliable adaptation measurements. Unfortunately, current performances of climate models on sub-daily scales, relevant for heavy rainfall events triggering shallow landslides, are not reliable enough to be used directly for performing slope stability analysis. In an attempt to overcome the constrains by gap in time resolution between climate and hazard models, the paper presents an integrated suitable approach for estimating future variations in shallow landslide hazard and managing the uncertainties associated with climate and sub-daily downscaling models. The approach is tested on a small basin on Amalfi coast (southern Italy). Basing on available basin scale critical rainfall thresholds, the paper outlines how the projected changes in precipitation patterns could affect local slope stability magnitude scenarios with different relevances as effect of investigated time horizon and concentration scenario. The paper concludes with qualitative evaluations on the future effectiveness of the local operative warning system in a climate change framework.     

Prototyping an experimental early warning system for rainfall-induced landslides in Indonesia using satellite remote sensing and geospatial datasets

An early warning system has been developed to predict rainfall-induced shallow landslides over Java Island, Indonesia. The prototyped early warning system integrates three major components: (1) a susceptibility mapping and hotspot identification component based on a land surface geospatial database (topographical information, maps of soil properties, and local landslide inventory, etc.); (2) a satellite-based precipitation monitoring system () and a precipitation forecasting model (i.e., Weather Research Forecast); and (3) a physically based, rainfall-induced landslide prediction model SLIDE. The system utilizes the modified physical model to calculate a factor of safety that accounts for the contribution of rainfall infiltration and partial saturation to the shear strength of the soil in topographically complex terrains. In use, the land-surface “where” information will be integrated with the “when” rainfall triggers by the landslide prediction model to predict potential slope failures as a function of time and location. In this system, geomorphologic data are primarily based on 30-m Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data, digital elevation model (DEM), and 1-km soil maps. Precipitation forcing comes from both satellite-based, real-time National Aeronautics and Space Administration (NASA) Tropical Rainfall Measuring Mission (TRMM), and Weather Research Forecasting (WRF) model forecasts. The system’s prediction performance has been evaluated using a local landslide inventory, and results show that the system successfully predicted landslides in correspondence to the time of occurrence of the real landslide events. Integration of spatially distributed remote sensing precipitation products and in-situ datasets in this prototype system enables us to further develop a regional, early warning tool in the future for predicting rainfall-induced landslides in Indonesia.     

Predictive Power Evaluation of a Physically Based Model for Shallow Landslides in the Area of Oltrepò Pavese,Northern Italy

The use of real-time landslide early warning systems is attracting the attention of the scientific community, since it allows to assess “where” and “when” a shallow rainfall-induced landslide might occur by coupling rainfall amounts, hydrological models and slope-stability analysis. The paper deals with the main results of a back analysis, which refers to the application of a physically based stability model [Shallow Landslides Instability Prediction (SLIP)] on regional scale. The analysis concerns the occurrence of some recent rainfall-induced shallow landslides in the municipal territory of Broni, in the area of Oltrepò Pavese (Northern Italy). The study area is a hilly region 2.4 km² wide, where more than 40 % of the territory has slopes steeper than 15° and altitudes are between 90 and 250 m a.s.l. As regards the geologic setting, clayey-silty shallow colluvial deposits, with a maximum thickness of about 3 m, overlap a bedrock made of clayey shales, calcareous flysch and marls. The SLIP model is based on the limit equilibrium method applied to an infinite slope and on the Mohr–Coulomb strength criterion for the soil. By assuming that the main hydro-geotechnical process that leads to failure is the saturation of parts of the soil, the model allows to take into account the condition of partial saturation of the soil. The safety factor (F _S ) of a slope is also function of previous rainfalls. After the implementation of the model at territory scale, the input data have been introduced through a geographic information systems platform. In the current paper we mainly intend to evaluate the performance of SLIP at catchment scale, by comparison to (1) observed landslide events and (2) another well-established physically based model (TRIGRS). Further, we want to assess the suitability of the model as early warning tool. The results produced by the model are analyzed both in terms of safety factor maps, corresponding to some particular rainfall events, and in terms of the time-varying percentage of unstable areas over a 2-year span period. The paper shows the comparison between observed landslide localizations and model predictions. A quantitative comparison between the SLIP model and TRIGRS is presented, only for the most important event that occurred during the analyzed period. Overall, the results of the stability analyses based on observed rainfalls show the capability of the SLIP model to predict, in real-time and on a wide area, the occurrence of the analyzed phenomena.     

Integration of a limit-equilibrium model into a landslide early warning system

Landslides are a significant hazard in many parts of the world and exhibit a high, and often underestimated, damage potential. Deploying landslide early warning systems is one risk management strategy that, amongst others, can be used to protect local communities. In geotechnical applications, slope stability models play an important role in predicting slope behaviour as a result of external influences; however, they are only rarely incorporated into landslide early warning systems. In this study, the physically based slope stability model CHASM (Combined Hydrology and Stability Model) was initially applied to a reactivated landslide in the Swabian Alb to assess stability conditions and was subsequently integrated into a prototype of a semi-automated landslide early warning system. The results of the CHASM application demonstrate that for several potential shear surfaces the Factor of Safety is relatively low, and subsequent rainfall events could cause instability. To integrate and automate CHASM within an early warning system, international geospatial standards were employed to ensure the interoperability of system components and the transferability of the implemented system as a whole. The CHASM algorithm is automatically run as a web processing service, utilising fixed, predetermined input data, and variable input data including hydrological monitoring data and quantitative rainfall forecasts. Once pre-defined modelling or monitoring thresholds are exceeded, a web notification service distributes SMS and email messages to relevant experts, who then determine whether to issue an early warning to local and regional stakeholders, as well as providing appropriate action advice. This study successfully demonstrated the potential of this new approach to landslide early warning. To move from demonstration to active issuance of early warnings demands the future acquisition of high-quality data on mechanical properties and distributed pore water pressure regimes.     

Soil slip susceptibility assessment using mechanical–hydrological approach and GIS techniques: an application in the Apuan Alps (Italy)

This study aims at contributing to the soil slip susceptibility assessment in a typical basin of the southern Apuan Alps, Italy. On June 1996, this basin (Cardoso Torrent, 13 km² large) was hit by an extremely heavy rainstorm (maximum intensity of about 160 mm/h), which caused many landslides (debris slide–debris flows) and valley bottom flows (hyperconcentrated flows), destruction and deaths. Detailed surveys provided the characterization of the main factors (geological, geomorphologic, hydrological, hydrogeological and geotechnical) which contributed in triggering landslides. In order to evaluate the soil slip susceptibility in this area, a physically based model was applied and a GIS analysis of digital elevation model was performed. This approach couples a mechanical model based on an infinite slope form of the Mohr–Coulomb failure criterion, and a steady-state hydrological one (a modified version of Shalstab, which considers the cohesion of the debris material potentially involved in landsliding). GIS techniques allowed evaluating the effects of topographic convergence and drainage area on slope failure. In this way, based on the infiltration rate, the triggering of the June 1996 landslides was simulated and the critical rainfall thresholds assessed at about 200–250 mm/24 h.     

Application of the MoniFLaIR early warning system for rainfall-induced landslides in Piedmont region (Italy)

Landslides triggered by rainfall can be foreseen by modeling the relationship between the time occurrence of landslides and rainfall. This paper deals with the argument by adopting a hydrological model called Forecasting of Landslides Induced by Rainfall (FLaIR). The model is applicable for forecasting recurrent landslides and it is based on the identification of a mobility function Y(.) that links the occurrence of a slope movement to the antecedent rainfall. Once the mobility function is defined, it is possible to define its critical values, the exceeding of which indicates that new mobilizations could occur. The FLaIR model has been used to study some phenomena that happened in Lanzo Valleys, a Western Alps sector of the Piedmont region (Northern Italy) where slope debris flows are the predominant landslide type. The study has led to the development of an early warning system, called MoniFLaIR, for real-time monitoring and forecasting of slope hazard. This article describes some details of the system and its performance.     

Physically based modelling of shallow landslide sediment yield at a catchment scale

 A shallow landslide erosion and sediment yield component, applicable at the basin scale, has been incorporated into the physically based, spatially distributed, hydrological and sediment transport modelling system, SHETRAN. The component determines when and where landslides occur in a basin in response to time-varying rainfall and snowmelt, the volume of material eroded and released for onward transport, and the impact on basin sediment yield. Derived relationships are used to link the SHETRAN grid resolution (up to 1 km), at which the basin hydrology and final sediment yield is modelled, to a subgrid resolution (typically around 10–100 m) at which landslide occurrence and erosion is modelled. The subgrid discretization, landslide susceptibility and potential landslide impact are determined in advance using a geographic information system (GIS), with SHETRAN then providing information on temporal variation in the factors controlling landsliding. The ability to simulate landslide sediment yield is demonstrated by a hypothetical application based on a catchment in Scotland. Received: 30 October 1996 · Accepted: 25 June 1997     

Spatially distributed rainfall thresholds for the initiation of shallow landslides

The evaluation of the combined influence of rainfall patterns (in terms of mean intensity and duration) and the geomorphological and mechanical characteristics of hillslopes on their stability conditions is a major goal in the assessment of the shallow landslide triggering processes. Geographic Information Systems (GIS) represent an important tool to develop models that combine hydrological and geomechanical analyses for the evaluation of slope stability, as they allow to combine information concerning rainfall characteristics with topographic and mechanical properties of the slopes over wide areas. In this paper, a GIS-based code is developed to determine physically based intensity/duration rainfall thresholds at the local scale. Given the rainfall duration and the local geometric, hydrological and mechanical characteristics of the slopes, the code evaluates the spatial distribution of the minimum rainfall intensity that triggers shallow landslides and debris flows over a given area. The key feature of the code is the capability of evaluating the time t _p required to reach the peak pore pressure head on the failure surface and computing the corresponding critical intensity/duration thresholds based on post-event peak pore pressures. The reliability of the model is tested using a set of one-dimensional analyses, comparing the physically based thresholds obtained for three different slopes with some empirical rainfall thresholds. In a log–log scale, the thresholds provided by the model decrease linearly with increased rainfall duration and they are bracketed by the empirical thresholds considered. Finally, an example of application to a study area of the Umbria region in central Italy is presented, describing the capability of the model of providing site-specific thresholds for different rainfall scenarios.     

基于TRIGRS模型的浅层滑坡稳定性分析

采用基于网格的瞬态降雨入渗(TRIGRS)模型,以滑坡灾害频发的陕南安康市东部巴山东段白河县为研究区,探讨模型适用性及不同降雨条件下边坡稳定性空间分布规律。根据中国土壤分布图并结合已有研究,选取模拟所需的水土力学参数。将模拟所得研究区稳定性分布图与实际滑坡目录对比分析进行TRIGRS模型精度评估,分别模拟连阴雨和短时间强降雨两种降雨情景,探讨研究区边坡稳定性空间分布规律,结果表明:1)TRIGRS模型在模拟预测降雨诱发型浅层滑坡时,结合受试者特征ROC曲线进行精度评估,曲线下面积为0.752,说明此模型在白河县进行滑坡模拟时具有一定的合理性与准确性,能反应该地区滑坡灾害的空间分布特征;2)连阴雨情景模拟下,极不稳定区域主要集中在北部低山地貌区,以冷水镇和麻虎镇为主,随降雨历时增加向东部和南部增多,西部仓上镇、西营镇和双丰镇的极不稳定区域面积较少,能承受长时间连续性降雨。短时间强降雨对边坡稳定性的影响更为直接,极不稳定区域随降雨强度增大而增加,以冷水镇和麻虎镇为主要防范区域。结合地形分析,极陡峭区域边坡稳定性最差,无法承受持续性降雨和高强度降雨,较陡峭区域更易受到降雨历时和降雨强度的影响,而平缓区域则能承受长时间及高强度的降雨;3)TRIGRS模型根据不同降雨条件预测易发生滑坡灾害的区域,为滑坡实时预报警系统提供了新的可能方法。     

The influence of shallow landslides on sediment supply: A flume-based investigation using sandy soil

The impact of rainfall-induced shallow landslides on hillslope sediment discharge is not well understood. This paper reports experimental measurements of sediment discharge after water-induced shallow landslides are triggered on sandy soil in a flume under simulated rainfall. The principal aim of the research was to investigate how varying soil depth affects the location and occurrence of shallow slope failures, as well as how it affects sediment yields downslope. Four experiments were conducted using the same sandy soil and a 30° and 10° compound slope configuration under average rainfall intensity of 50 mm h^− 1 for up to 390 min. Soil depths were set to 200, 300, 400 and 500 mm. Engineering and geotechnical properties of the soil were examined. Sediment discharge and runoff were collected from the flume outlet at 15 minute intervals. Changes in the soil slope profiles after landslides and soil physical properties resulted from soil armouring, under continuous rainfall were also recorded. Results showed that sediment yields at the flume outlet, before landslides occurred, were very low and limited to the finer soil particles as would be expected for a sandy soil. However subsequent variations in sediment discharge were strongly related to failure events and their proximity to the outlet. Sediment yield was also affected by the original soil depth; the greater the depth, the higher the sediment yields. Post-failure reductions in sediment discharge were observed and attributed to post-failure slope stabilization under continuing rainfall and extensive soil armouring near the flume outlet. The results provide a clear linkage between landslides and sediment discharge due to hydrological processes occurring in the hillslope. This knowledge is being used to develop a model to predict sediment discharges from hillslopes following shallow landslide events.     

基于降雨响应的黄土丘陵区滑坡危险性预测研究——以宝鸡市麟游县为例

极端降雨易造成群发滑坡灾害,难以作为单体预测.为预测评估黄土丘陵区不同降雨强度诱发滑坡灾害危险性,论文在区域滑坡灾害特征研究的基础上,分析降雨强度特征及滑坡分布特征.以岭南滑坡为代表分析降雨诱发黄土-丘陵区滑坡的形成机制,介绍了无限斜坡模型原理、参数选取,利用GIS空间建模与分析功能,定量完成了无降雨、25 mm、50...     

Comparing the performance of TRIGRS and TiVaSS in spatial and temporal prediction of rainfall-induced shallow landslides

This study compares the performance of transient rainfall infiltration and grid-based regional slope stability (TRIGRS) model and time-variant slope stability (TiVaSS) model in the prediction of rainfall-induced shallow landslides. TRIGRS employs one-dimensional (1-D) subsurface flow to simulate the infiltration rate, whereas a three-dimensional (3-D) model is utilized in TiVaSS. The former has been widely used in landslide modeling, while the latter was developed only recently. Both programs are used for the spatiotemporal prediction of shallow landslides caused by rainfall. This study uses the July 2011 landslide event that occurred in Mt. Umyeon, Seoul, Korea, for validation. The performance of the two programs is evaluated by comparison with data of the actual landslides in both location and timing by using a landslide ratio for each factor of safety class (\({\text{LR}}_{\text{class}}\) index), which was developed for addressing point-like landslide locations. Moreover, the influence of surface flow on landslide initiation is assessed. The results show that the shallow landslides predicted by the two models are highly consistent with those of the observed sliding sites, although the performance of TiVaSS is slightly better. Overland flow affects the buildup of the pressure head and reduces the slope stability, although this influence was not significant in this case. A slight increase in the predicted unstable area from 19.30 to 19.93% was recorded when the overland flow was considered. It is concluded that both models are suitable for application in the study area. However, although it is a well-established model requiring less input data and shorter run times, TRIGRS produces less accurate results.