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An integrated model for predicting rainfall induced Landslides

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An integrated model for predicting rainfall induced Landslides

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Abstract

This study proposes a novel method that combines a deterministic slope stability model and a statistical model for predicting rainfall-induced landslides. The method first uses the deterministic model to derive the rainfall rate critical to induce slope failure for each land unit. Then it calculates the difference between the critical rainfall threshold and estimated rainfall intensity. Using the difference and estimated rainfall duration as explanatory variables, the method derives a logit (integrated) model to compute landslide occurrence probabilities. To demonstrate the effectiveness of this method, the study used radar rainfall estimates and landslides associated with a typhoon (tropical cyclone) to develop the integrated model and the same types of data associated with another typhoon to validate the model. The model had a modified success rate of 84.0% for predicting landslides and stable areas, and model validation yielded a modified success rate of 87.4%. Both rates were better than those from the critical rainfall model. The main advantage of the integrated model lies in its use of rainfall variables that are not included in calculating the critical rainfall. Also, as a probabilistic model, the integrated model is better suited for decision-making in watershed management. This study has advanced the method for predicting rainfall-triggered landslides.

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... The approach of rainfall threshold to predict the landslides has been found effective. For this reason many studies are found in other part of the world (Chang and Chiang, 2009;Crosta and Frattini, 2003;Dahal, 2012;Guzzetti et al., 2007) that deals with the rainfall induced landslides and rainfall threshold for landslides. In Nepal, however research in rainfall threshold for landslides forecasting is very limited. ...
... The causal factors (Figure 1) could be the underlying geology, topography, soil properties, soil depth to best rock, anthropogenic causes such as unplanned developmental works (e. g. rural roads in Nepal), whereas the intense rainfall and earthquake are the triggering factors (Jaboyedoff et al., 2016;Petley et al., 2007). There are many researchers and scientist around the globe, devoting their time and knowledge and exploring the rainfall threshold model that could represent the landslides as a function of rainfall (Borga et al., 2002;Chang and Chiang, 2009;Dahal and Hasegawa, 2008;Guzzetti et al., 2007;Martelloni et al., 2012;Montgomery and Dietrich, 1994). (Popescu, 2002) Development of rainfall threshold model for landslides is a data driven process, requires long term historical dates of the landslides and fine resolution rainfall data (i. ...
... In order to establish the rainfall threshold model for the data poor situation, the only available daily rainfall data and historical RS images are the data sources for this research. In order to establish rainfall threshold for the landslides, different models are available (Guzzetti et al., 2007;Dahal and Hasegawa, 2008;Chang and Chiang, 2009). This research is implementing a methodology that based on antecedence rain of five days. ...
Preprint
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Landslides are common in the hills of Nepal where the terrain slopes are steep and consist of fragile geo-morphology. In Nepal the causal and triggering factors of the landslides are respectively the underlying geology and rainfall is highly recognized, which is however less known and limited in study. Establishment of rainfall threshold for landslides is highly data driven technique, which is scared in the context of Nepal. The only available long term daily rainfall and sparsely available landslides date has been used to develop the rainfall threshold model for the Panchase region in the Central-Western hills of Nepal, where the annual rainfall is said to be the highest in the country. Historical daily (monsoonal) rainfall data of over four decades (1985-2015) were analyzed available from the Department of Hydrology and Meteorology (DHM) and five days antecedent rain was calculated. With the limitedly available landslides date, a rainfall threshold model was developed (155-0.795R 5adr) in spread-sheet for the region. Utilizing the five days antecedent rain fitted into the model, results the threshold rainfall (RT). Deducting the daily rainfall to the RT describe the threshold exceedance (R) for the landslides. The model can be plotted in simple spreadsheet (landslides date in X-axis and threshold exceedance R in Y-axis) to visualize how the threshold exceedance is varying over the period, whenever the threshold exceedance progressively and rapidly increased and crosses the zero threshold boundary line and reaches to the positive (> 0) zone, the spread sheet plots allows for the landslides early warnings. In case of the threshold exceedance is further increased there is likely to have landslides in the region. Linking this model to the proper landslide hazard map, landslide early warning can be made rigorously. The model was validated with the limited landslide dates documented in monsoon 2014 and 2015. All-to-gather 35 dated landslides were used to validate the model. The result indicated that the model is able to capture the landslides in the Panchase region during the period of 2014 and 2015 are mostly triggered by the rainfall. The model however still can be improved for better performance whenever there is higher resolution real time-rainfall is available. Due to the simplicity and at the data poor situation, the model is found to be useful to forecast the landslides during the monsoon season in the region.
... The advantage of logistic regression is that the dependent variable y is binary, e.g., landslide occurrence (y = 1) or nonoccurrence (y = 0) in this study, and the independent (or called explanatory) variables are x, the environmental factors possibly to affect the landslide occurrence [36,39]. The logistic regression model has been successfully applied to estimate the probability of landslide occurrence [38,40,41]. Some detailed verification of applying the logistic regression model to landslide prediction can refer to literature [38,40,41]. ...
... The logistic regression model has been successfully applied to estimate the probability of landslide occurrence [38,40,41]. Some detailed verification of applying the logistic regression model to landslide prediction can refer to literature [38,40,41]. The logit model from a logistic regression has the following form: ...
Article
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Accurate and reliable estimates of sediment yields from a watershed and identification of unstable stream reaches due to sediment-related disaster are crucial for watershed management, disaster prevention, and hazard mitigation purposes. In this study, we added hydrodynamic and sediment transport modules in a recently developed model to estimate sediment yields and identify the unstable stream reaches in a large-scale watershed (> 100km2). The calibrated and verified models can well reproduce the flow discharge and sediment discharge at the study site, the Shihmen Reservoir Watershed in Taiwan, during several typhoon events. For the scenario applications, the results revealed that the contribution (> 96%) of landslides on sediment supply is much more significant than compared to soil erosion (< 4%). The sediment contribution from the upstream of the hydrological station-Yufeng is approximately 36–55% of the total sediment supply for the rainfall events of 25, 50, 100, and 200 years return period. It also indicates that 22–52% of sediment still remain at foot of the slope and the streams, which become a potential source for sediment hazards in the future. Combining with the bed erosion and deposition depths, flow-induced shear stress from the SRH-2D model, and probability of slope failure within 250 m of stream reaches, the relatively stability of stream reaches can be identified. The results could provide the water resource authorities for reference to take precautionary measures in advance on the stream reaches with high-degree instability.
... These rainfall-induced landslides not only cause considerable financial losses but also ecological and environmental problems, such as increased soil erosion rate and downstream sediment load (Anderson and Sitar 1995;Hovius et al. 1997;Claessens et al. 2007;Peng et al. 2015Peng et al. , 2017Wu et al. 2018a). Therefore, studies on rainfall-induced landslides have become an important research topic in recent decades, which have led to the reporting of many rainfallinduced catastrophes worldwide (Crozier 1999;Tsaparas et al. 2002;Chen and Lee 2003;Collins and Znidarcic 2004;Godt et al. 2006;Chang and Chiang 2009;Evans et al. 2009;Tsai 2011;Xu et al. 2011Xu et al. , 2012Ali et al. 2014;Wang et al. 2014). ...
Article
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In order to effectively reduce the impact of rainfall-induced landslides on properties and life, it is important to understand rainfall-caused landslides and their sliding mechanism. The objective of this paper is to study the effects of different rainfall patterns and different slope structure on the deformation and failure process of shallow loess slopes. To achieve the objective, three categories of indoor physical model experiments of a loess slope with and without a vertical joint were implemented under different rainfall patterns. Three kinds of sensors including volumetric water content, matric suction, and pore-water pressure sensors were buried in the model slopes to record the internal changes driving deformation. Analyses of the sensor records and the associated deformational changes, and the experimental results under different conditions show that the matric suction in loess slopes decreased gradually. Loess strength reduced with the continuous increase of volumetric water content. After excess pore-water pressure was generated by the slope deformation and poor drainage of the loess, it decreased the effective stress and the loess strength, which resulted in landslides. In addition, it was observed that the influence of slope structure on stability was greater than that of rainfall patterns. This paper attempts to explain the failure mode and triggering mechanisms of shallow loess landslides induced by rainfall.
... In order to improve the I-D thresholds, especially for warning system purposes, it would be advisable to apply validation approaches, that allow to assess the level of uncertainty and the confidence levels of the prediction and forecasts (Guzzetti et al., 2008;Chang and Chiang, 2009;Brunetti et al., 2010;Berti, 2012;Peruccacci et al., 2012;Melillo et al., 2016). ...
Preprint
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Abstract. The Deba area is intensely affected by frequent shallow landslides triggered by rainfall. Relationships between rainfall and landslides in northern Spain, particularly for rainfall events driving multiple movements simultaneously, have not been explored in depth so far. This contribution explores the role of rainfall in landslide activity during a quite long time span, (60 years), from a large network of rainfall gauges and a complete inventory of landslides, and utilizing three different strategies of analysis. 1,180 landslides have been inventoried, and 3,241 rainfall episodes automatically recognized and characterized in terms of rainfall amount, duration and intensity. Antecedent rainfall has also been considered. Six episodes of intense rainfall, which have produced multiple landslides (> 50 % of the recent past occurrences) have been identified. The analysis provides different results: the extraordinary character of the triggering rainfall has been assessed, the meteorological conditions associated to those extreme episodes have been recognized and empirical rainfall threshold producing multiple landslides has been found (I = 7.7D-0.428) and compared with others described in literature. Results show that multiple landslide occurrences are triggered by extreme convective rainfall, intense, short and with limited horizontal extent, as well as a marked summer-autumn seasonality, characteristic of Mediterranean climate.
... Cumulative rainfall amounts have been widely used 26,30,31 for defining rainfall thresholds. However, Chang and Chiang 45 have shown that not all rainfall processes can affect landslide displacement, but only a certain amount of rainfall is able to induce landslide movement. To determine this critical rainfall amount, all earth slides in the study area were selected, and their generalized geological models were established by Geostudio 2012 software (http://www.geoslope.com). ...
Article
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Establishing an efficient regional landslide rainfall warning system plays an important role in landslide prevention. To forecast the performance of landslides with creep deformation at a regional scale, a black box model based on statistical analysis was proposed and was applied to Yunyang County in the Three Gorges Reservoir area (TGRA), China. The data samples were selected according to the characteristics of the landslide displacement monitoring data. Then, the rainfall criteria applied to different time periods were determined by correlation analysis between rainfall events and landslides and by numerical simulation on landslide movement under certain rainfall conditions. The cumulative rainfall thresholds that were determined relied on the displacement ratio model, which considered landslide scale characteristics and the statistical relationship between daily rainfall data and monthly displacement data. These thresholds were then applied to a warning system to determine a five-level warning partition of landslides with creep deformation in Yunyang County. Finally, landslide cases and displacement monitoring data were used to validate the accuracy of the model. The validation procedure showed that the warning results of the model fit well with actual conditions and that this model could provide the basis for early warning of landslides with creep deformation.
... Para la determinación de la lluvia asociada al evento comúnmente se utilizan estaciones de lluvia cerca del sitio de ocurrencia o representativas de la región de análisis (Aleotti, 2004;Capparelli and Tiranti, 2010;Segoni, Piciullo and Gariano, 2018), lo cual representa una gran limitante considerando la variabilidad espacial de la lluvia en terrenos montañosos, como los Andes Colombianos (Poveda et al., 2005). Otras aproximaciones disponibles en los últimos años son los estimados a partir de sensores remotos activos como los radares meteorológicos (Chang and Chiang, 2009;Crosta and Frattini, 2010) y pasivos como los sensores satelitales (Hong and Nasa, 2007;Kirschbaum, Stanley and Simmons, 2015;Cullen, Al-Suhili and Khanbilvardi, 2016), los cuales representan la distribución espacial a resoluciones gruesas sin embargo con mayor representatividad que las estaciones de lluvia, y a escalas temporales con gran incertidumbre para resoluciones diarias o de mayor detalle, las cuales son las que generalmente se requieren en este tipo de análisis. Finalmente es importante también considerar que los valores obtenidos de dichos sensores remotos no son mediciones directas, sino que corresponden a estimaciones a partir de la reflectividad medida o de la temperatura de brillo. ...
Article
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En Colombia entre los años 1914 y 2015, se han presentado 1.139 avenidas torrenciales con un saldo trágico de 2.195 víctimas mortales. Eventos como los ocurridos en Salgar y Mocoa señalan la necesidad de implementar sistemas de alerta temprana en aquellas áreas susceptibles que han sido densamente pobladas. En el presente trabajo se estudia el caso del valle de Aburrá; se proponen umbrales de lluvia críticos como detonantes de avenidas torrenciales para un sistema de alerta temprana. Para la definición de umbrales se utilizó el método del RTI (Índice de Lluvia Detonante), desarrollado en Taiwán. El método utiliza datos de lluvia horarios de 15 estaciones localizadas dentro del Valle y el inventario de avenidas registradas en el DesInventar. A partir de estas series de datos se encontraron 1.784 eventos de lluvia detonantes potenciales entre 1994-2016, con un promedio de intensidad máxima de 31,2 mm/h. Los resultados arrojan valores de RTI críticos de 2.268, 2.734, 3.128, 3.337 mm2/h para 1, 3, 7 y 15 días de lluvia antecedente respectivamente; con la intensidad máxima promedio se obtuvo un umbral critico de lluvia antecedente acumulada de 76, 91, 104, 111 mm para 1, 3, 7 y 15 días de lluvia antecedente, respectivamente.
... The results found to be ninety percent accurate. Chang and Chiang (2009) also performed investigation in the line of landslide studies. In their study, an integrated model combining deterministic, statistical and rainfall threshold mode for the landslide susceptibility was proposed, and the model was demonstrated for the typhoon-induced landslides in Taiwan. ...
Chapter
Data mining techniques have potential to unveil the complexity of an event and yields knowledge that can create a difference. They can be employed to investigate natural phenomena; since these events are complex in nature and are difficult to characterize as there are elements of uncertainty involved in their functionality. Therefore, techniques that are compatible with uncertain elements can be employed to study them. This chapter explains the concepts of data mining and discusses at length about the landslide event. Further, the utility of data mining techniques in disaster management using a previous work was explained and provides a brief note on the efficiency of web mining in creating awareness about natural hazard by providing refined information. Finally, a conceptual framework for landslide hazard assessment using data mining techniques such as Artificial Neural Network (ANN), Fuzzy Geometric Mean Model (FGMM), etc. were chosen for description. It was quite clear from the study that data mining techniques are useful in assessing and modelling different aspects of landslide event.
... It encompasses two major approaches: statistical and physical. The common statistical approach employs a regression model, such as binary regression, to identify a set of maximum likelihood parameters based on historical data to predict the landslide distribution (Chang and Chiang, 2009). In the physical approach, the infinite slope stability theory is applied to calculate the safety factor and predict the potential landslide area, such as the Transient Rainfall Infiltration and Grid-based Regional Slope-Stability model (TRIGRS) (Baum et al., 2008) and a digital terrain model for mapping the pattern of po-tential shallow slope instability (SHALSTAB) (Montgomery and Dietrich, 1994). ...
Article
Full-text available
The production and transportation of sediment in mountainous areas caused by extreme rainfall events that are triggered by climate change is a challenging problem, especially in watersheds. To investigate this issue, the present study adopted the scenario approach coupled with simulations using various models. Upon careful model selection, the simulation of projected rainfall, landslide, debris flow, and loss assessment was integrated by connecting the models’ input and output. The Xindian watershed upstream from Taipei, Taiwan, was identified and two extreme rainfall scenarios from the late 20th and 21st centuries were selected to compare the effects of climate change. Using sequence simulations, the chain reaction and compounded disaster were analysed. Moreover, the potential effects of slope land hazards were compared for the present and future, and the likely impacts in the selected watershed areas were discussed with respect to extreme climate. The results established that the unstable sediment volume would increase by 28.81% in terms of the projected extreme event. The total economic losses caused by the chain impacts of slope land disasters under climate change would be increased to USD 358.25 million. Owing to the geographical environment of the Taipei metropolitan area, the indirect losses of a water supply shortage caused by slope land disasters would be more serious than direct losses. In particular, avenues to ensure the availability of the water supply will be the most critical disaster prevention topic in the event of a future slope land disaster. The results obtained from this study are expected to be beneficial because they provide critical information for devising long-term strategies to combat the impacts of slope land disasters.
... Logistic regression analysis, also called binary regression analysis, is a widely used statistical model when the dependent variable is binary, e.g., landslide occurrence or nonoccurrence, and independent variables are in the numerical type (e.g., slope), in the categorical type (e.g., lithology), or in both types (e.g., [44]). As it is beyond the present paper's scope, some detailed verification of applying the logistic regression model to landslide prediction can be referred to in the literature (e.g., [45][46][47]). ...
Article
Full-text available
Qualifying sediment dynamic in a reservoir watershed is essential for water resource management. This study proposed an integrated model of Grid-based Sediment Production and Transport Model (GSPTM) at watershed scale to evaluate the dynamic of sediment production and transport in the Shihmen Reservoir watershed in Taiwan. The GSPTM integrates several models, revealing landslide susceptibility and processes of rainfall-runoff, sediment production from landslide and soil erosion, debris flow and mass movement, and sediment transport. For modeling rainfall-runoff process, the tanks model gives surface runoff volume and soil water index as a hydrological parameter for a logistic regression-based landslide susceptibility model. Then, applying landslide model with a scaling relation of volume and area predicts landslide occurrence. The Universal Soil Loss Equation is then used for calculating soil erosion volume. Finally, incorporating runoff-routing algorithm and the Hunt's model achieves the dynamical modeling of sediment transport. The landslide module was calibrated using a well-documented inventory during 10 heavy rainfall or typhoon events since 2004. A simulation of Typhoon Morakot event was performed to evaluate model's performance. The results show the simulation agrees with the tendency of runoff and sediment discharge evolution with an acceptable overestimation of peak runoff, and predicts more precise sediment discharge than rating methods do. In addition, with clear distribution of sediment mass trapped in the mountainous area, the GSPTM also showed a sediment delivery ratio of 30% to quantify how much mass produced by landslide and soil erosion is still trapped in mountainous area. The GSPTM is verified to be useful and capable of modeling the dynamic of sediment production and transport at watershed level, and can provide useful information for sustainable development of Shihmen Reservoir watershed.
... Located at the junction of the Eurasian plate and the Philippine Sea plate, Taiwan has frequent tectonic activity (Ho, 1986;Yu et al., 1997;Willett et al., 2003). Fractured rock mass, a warm and humid climate, and an average of three to five typhoon events per year contribute to the high frequency of slope failures in mountainous areas in Taiwan (Wang and Ho, 2002;Shieh, 2000;Dadson et al., 2004;Chang and Chiang, 2009;Chen, 2011). The high coverage of the seismic network and rain gauge stations in Taiwan and the high occurrence frequency of landslides make the island a suitable area for examining the use of seismic observations to identify landslide times and thus the rainfall factors contributing to landslide events. ...
Article
Full-text available
One purpose of landslide research is to establish early warning thresholds for rainfall-induced landslides. Insufficient observations of past events have inhibited the analysis of critical rainfall conditions triggering landslides. This difficulty may be resolved by extracting the timing of landslide occurrences through analysis of seismic signals. In this study, seismic records of the Broadband Array in Taiwan for Seismology were examined to identify ground motion triggered by large landslides that occurred in the years 2005 to 2014. A total of 62 landslide-induced seismic signals were identified. The seismic signals were analyzed to determine the timing of landslide occurrences, and the rainfall conditions at those times – including rainfall intensity (I), duration (D), and effective rainfall (Rt) – were assessed. Three common rainfall threshold models (I–D, I–Rt, and Rt–D) were compared, and the crucial factors of a forecast warning model were found to be duration and effective rainfall. In addition, rainfall information related to the 62 landslides was analyzed to establish a critical height of water model, (I − 1.5) ⋅ D = 430.2. The critical height of water model was applied to data from Typhoon Soudelor of 2015, and the model issued a large landslide warning for southern Taiwan.
... Apart from experimental works, predictive (e.g., Guzzetti et al., 2008, Wu et al., 2015, Sasahara, 2017, statistical (e.g., Ibsen and Casagli, 2004, Chang and Chiang, 2009, Kristo et al., 2017, probabilistic (e.g., Zhang et al., 2010, Ering andBabu, 2016), and numerical methods (e.g., Cai and Ugai, 2004, Yoo and Jung, 2006, Davies et al., 2014, Leshchinsky et al., 2015 have been used to assess the stability of unsaturated slopes, and to provide insights into the failure mechanism. ...
Article
Full-text available
In this paper, a new fully-coupled Smoothed Particle Hydrodynamics (SPH) formulation for unsaturated soils is developed to study the influence of rainfall infiltration on slope stability. The single-layer two-phase formulation is investigated in SPH for the first time to simulate the response of unsaturated soils. The use of a single set of particles improves the computational efficiency and facilitates the implementation of infiltration boundary conditions. The Drucker–Prager strain-softening model, with the use of Bishop's effective stress, is adopted as the soil’s constitutive model. New extensions of a first-order consistent wall boundary treatment are proposed for the coupled hydro-mechanical problem to enforce non-slip/free-slip conditions for the soil phase and water phase. A novel stress diffusion algorithm for general application is introduced to smooth out the numerical noise in the stress field under large deformation. The accuracy of the formulated SPH model is examined with available analytical solutions and experimental data. The proposed numerical scheme is finally applied to the simulation of rainfall-induced slope collapse of an unsaturated slope with two different bedrock geometries. Results demonstrate that the geometry of the bedrock is shown to play an important role in the failure initiation and propagation of the collapse. It is found that the proposed model allows the investigation of both triggering and post-failure mechanism, providing a smooth stress field even at large deformations.
... Location and timing of the forecasted landslides can be determined by physicalbased models; because of this, they can be successfully applied in the early warning systems for landslide prediction [32]. The performance of the physical-based models depends upon their coupling with hydrological models for stabilizing the slopes [33]. ...
Conference Paper
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In different physiographic and climatic regions worldwide, rainfall is recognized as one of the most common triggering factor for landslides causing severe damage to property and lives of large number of people every year. Urbanization in case of hilly areas has led to the need of detailed study and research in the field of landslides triggered by rainfall. Rainfall thresholds are statistical approximation of minimum rainfall conditions that trigger landslides for a particular mix of geologic, hydrologic, and topographic variables in a particular area. In the hazard-prone areas, the assessment of landslide-triggering rainfall thresholds is useful for development of early warning system. A lot many studies are available on this topic, which determine and estimate the amount of rainfall causing landslides. This paper aims at presenting a current state-of-the-art on the application of rainfall thresholds concepts, techniques, and methods for landslide occurrence with a focus on recent papers (after 2000) published in peer-reviewed journals.
... to identify a set of maximum likelihood parameters based on historical data to predict the landslide distribution (Chang and Chiang, 2009). In the physical approach, the infinite slope stability theory is applied to calculate the safety factor and predict the potential landslide area, such as the transient rainfall infiltration and grid-based regional slope-stability model (TRIGRS) ( Baum et al., 2008) and digital terrain model for mapping the pattern of potential shallow slope instability (SHALSTAB) (Montgomery and Dietrich, 1994). ...
Article
Full-text available
The production and transportation of sediment in mountainous areas caused by extreme rainfall events triggered by climate change is a challenging problem, especially in watersheds. To investigate this issue, the present study adopted the scenario approach coupled with simulations using various models. Upon careful model selection, the simulation of projected rainfall, landslide, debris flow, and loss assessment were integrated by connecting the models' input and output. The Xindian watershed upstream from Taipei, Taiwan, was identified and two extreme rainfall scenarios from the late 20th and 21st centuries were selected to compare the effects of climate change. Using sequence simulations, the chain reaction and compounded disaster were analysed. Moreover, the potential effects of slope land hazards were compared between the present and future, and the likely impacts in the selected watershed areas were discussed with respect to extreme climate. The results established that the unstable sediment volume would increase by 28.81 % in terms of the projected extreme event. The total economic losses caused by the chain impacts of slope land disasters under climate change would be increased to US$ 358.25 million. Owing to the geographical environment of the Taipei metropolitan area, the indirect losses of water supply shortage caused by slope land disasters would be more serious than direct losses. In particular, avenues to ensure the availability of water supply will be the most critical disaster prevention topic in the event of a future slope land disaster. The results obtained from this study are expected to be beneficial, because they provide critical information for devising long-term strategies to combat the impacts of slope land disasters.
... The catastrophic and hardly-predictable dynamics of landslides has encouraged authors to consider landslides from a risk mangement point of view and enhanced the development of predicting tools (e.g. Guzzetti et al., 1999;Liu and Wu, 2008;Chang and Chiang, 2009). ...
Thesis
L'évolution des paysages est au coeur d'un système complexe d'interactions entre les phénomènes tectoniques, climatiques et érosifs. Si le contrôle principal du climat sur les taux d'érosion est encore sujet à débat, les régions montagneuses restent un milieu particulièrement sensible aux modifications climatiques. Pour mieux appréhender ces liens en milieu montagneux et le contrôle des processus d'érosion, il est nécessaire de comprendre et de quantifier l'importance de chaque phénomène érosif dans l'évolution des paysages. Parmi ceux-ci, les glissements de terrain sont un phénomène brusque, imprévisible et souvent catastrophique pouvant mobiliser des volumes rocheux considérables. De nombreux travaux suggèrent d'ailleurs que les glissements constituent le principal agent de l'érosion des pentes dans les vallées non englacées de l'Himalaya. L'objectif de cette thèse est de mieux appréhender l'évolution et la dynamique des glissements de terrain de l'Himalaya central et leur rôle dans l'érosion de cette chaîne de montagnes, sur une large échelle de temps. Ces travaux ont été menés selon 3 axes principaux imbriqués spatialement et temporellement. Le bassin de la Khudi Khola, au Népal central, présente un large glissement de terrain, actif depuis plusieurs décennies. Cette particularité nous a permis d'étudier en détail ce glissement, dans un contexte d'érosion intense, au jour le jour, à l'échelle d'une mousson et sur plusieurs décennies. L'histoire du glissement de Saituti a été reconstituée grâce à l'analyse d'images satellite et aériennes. Une activité continue, bien que variable, du glissement depuis près d'un demi siècle a pu être observée. L'estimation des volumes de sédiments produits par le glissement a permis de mettre en évidence la place prépondérante de cette structure érosive dans l'érosion totale du bassin au cours des dernières années, voire des dernières décennies. La dynamique quotidienne des mouvements au sein du glissement associée à l'export des sédiments par le réseau de drainage ont également été observés. Il apparaît un découplage entre les mouvements de terrain, donc la production de sédiments, qui sont contrôlés par le niveau de nappe, et l'export du matériel par la rivière, dépendant du débit de surface. Une fois initiés, les mouvements se poursuivent durant toute la période de mousson, mais seuls les épisodes pluvieux importants permettent un transport efficace du matériel produit à la rivière. Les flux annuels de matière en suspension dans la rivière ont également pu être estimés et s'accordent au premier ordre avec les volumes créés par le glissement. Ces résultats suggèrent également le rôle principal du glissement de Saituti dans l'érosion de la vallée. A l'échelle de l'Himalaya central, l'activité des glissements au cours de la dernière décennie témoigne d'une domination de l'érosion par des événements majeurs, de l'ordre de plusieurs millions de mètres cubes, similaires à celui de Saituti. Cette étude montre qu'à moyen terme, de tels glissements peuvent influencer très fortement les concentrations en isotopes cosmogéniques des sables de rivières dans les bassins versants de taille intermédiaire (quelques centaines de km2). La concentration de ces sables apparaît principalement dépendante de la date et de l'amplitude du dernier événement majeur de glissement. Dans ces environnements, les taux de dénudation déterminés par l'utilisation des isotopes cosmogéniques doivent être interprétés avec beaucoup de précaution. Ainsi, l'activité, possiblement continue, de quelques glissements de terrain peut exercer une influence majeure sur l'érosion des vallées de l'Himalaya central. Ce facteur doit être pris en compte dans l'analyse des processus érosifs ainsi que dans les modèles d'évolution des paysages, à court et moyen terme
... Additionally, being data-driven, a statistical model built up for one region cannot readily be extrapolated to the neighbouring area. On the contrary, the main drawback of physically based modeling is the difficulty to gather the input parameters over large and complex areas (Carrara et al. 2008;Chang and Chiang 2009). However, a possible solution is to calibrate the parameter values through back-analysis of preceding major landslide events, after which event-based landslide inventories are generally available (Casadei et al. 2003;Li et al. 2011). ...
Article
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In the last years, great efforts have been made to improve the assessment of the temporal and spatial occurrence of rainfall-induced shallow landslides. Therefore, in this paper we used a physically based stability model (TRIGRS) in order to reproduce the landslide event occurred in the Monterosso catchment (Cinque Terre, Eastern Liguria, Italy) on 25 October 2011. The input parameters of the numerical model have been evaluated taking into account the land-use setting and paying specific attention to the evaluation of the spatial variation of soil thickness on terraced areas. The resulting safety factor maps have been compared with the inventory map of the landslides triggered during the event. The simulation results, which have been obtained also considering four different spatial resolutions of the digital terrain model, emphasize the influence of land use in shallow landslide occurrence and indicate the importance of a realistic spatial variation of soil thickness to enhance the reliability of the model. Finally, different triggering scenarios have been defined using hourly rainfall values statistically derived from historical data. The results indicate the proneness of the area to shallow landsliding, given that rainfall events with a relatively low return period (e.g. 25 years) can trigger numerous slope failures.
... In the past decades, many models have proposed for simulation of landslides, debris flows, sediment transport process individually. For landslide, researchers have used empirical or physical models to evaluate the landslide susceptibility (e.g., Guzzetti et al. 2005;Reichenbach et al. 2005;Gabet and Mudd 2006;Chen et al. 2006;Chang and Chiang 2009) and mobilization (Iverson et al. 1997). To estimate landslide volume, the volume-area relation using statistical approach gave reasonable predictions (e.g., Guzzetti et al. 2009;Klar et al. 2011;Parker et al. 2011;Chen et al. 2012). ...
Chapter
We highlight a methodology of simulation of large-scale watershed mass transport, including landslide, debris flow, and sediment transport. A case study of Tsengwen reservoir watershed under the extreme rainfall triggered by typhoon Morakot is demonstrated. This approach starts with volume-area relation with landslide inventory method to predict temporal and regional landslide volume production and distribution. Then, debris flow model, Debris-2D, is applied to simulate the mass transport from hillslope to fluvial channel. Finally, a sediment transport model, NETSTARS, is used for hydraulic and sediment routing in river and reservoir. Near the water intake at the reservoir dam, the simulated sediment concentration is in good agreement with the measured one. The proposed approach gives good prediction and should help the management of reservoir operation and disaster prevention. © Springer International Publishing AG 2018. All rights are reserved.
... Taiwan is an area extremely susceptible to landslides because of the steep mountainous topography, complex geological conditions, frequent heavy rainfall, and frequent earthquakes. Thus, many studies have been performed on landslides in Taiwan (Jan and Chen, 2005;Chang and Chiang, 2009;Lin and Chen, 2012;Chen et al., 2015b). Landslides triggered by rainfall and earthquakes are the major source of coarse and fine sediments in channels and rivers in Taiwan. ...
Article
Debris sourced from landslides will result in environmental problems such as increased sediment discharge in rivers. This study analyzed the sediment discharge of 17 main rivers in Taiwan during 14 typhoon events, selected from the catchment area and river length, that caused landslides according to government reports. The measured suspended sediment and water discharge, collected from hydrometric stations of the Water Resources Agency of Taiwan, were used to establish rating-curve relationships, a power-law relation between them. Then sediment discharge during typhoon events was estimated using the rating-curve method and the measured data of daily water discharge. Positive correlations between sediment discharge and rainfall conditions for each river indicate that sediment discharge increases when a greater amount of rainfall or a higher intensity of rainfall falls during a typhoon event. In addition, the amount of sediment discharge during a typhoon event is mainly controlled by the total amount of rainfall, not by peak rainfall. Differences in correlation equations among the rivers suggest that catchments with larger areas produce more sediment. Catchments with relatively low sediment discharge show more distinct increases in sediment discharge in response to increases in rainfall, owing to the little opportunity for deposition in small catchments with high connectivity to rivers and the transportation of the majority of landslide debris to rivers during typhoon events. Also, differences in geomorphic and geologic conditions among catchments around Taiwan lead to a variety of suspended sediment dynamics and the sediment budget. Positive correlation between average sediment discharge and average area of landslides during typhoon events indicates that when larger landslides are caused by heavier rainfall during a typhoon event, more loose materials from the most recent landslide debris are flushed into rivers, resulting in higher sediment discharge. The high proportion of large landslides in Taiwan contributes significantly to the high annual sediment yield, which is among the world's highest despite the small area of Taiwan.
... Furthermore, NWP models are not very efficient in predicting heavy rainfall events (Březková et al., 2010; Hong and Lee 2009; Khaladkar et al., 2007; Selvam 2011). To overcome this limitation, the mesoscale atmospheric models are used on finer spatial resolution of 0.5–1 km incorporating land surface processes; they are effective in predicting extreme heavy rainfall events (Dodla and Ratna 2010, Chang and Chiang 2009). These high resolution NWP models demand huge computational requirement. ...
Article
Forecasting of extreme precipitation events at a regional scale is of high importance due to their severe impacts on society. The impacts are stronger in urban regions due to high flood potential as well high population density leading to high vulnerability. Though significant scientific improvements took place in the global models for weather forecasting, still they are not adequate at a regional scale (e.g., for an urban region) with high false alarms and low detection. There has been a need to improve the weather forecast skill at a local scale with probabilistic outcome. Here we develop a methodology with quantile regression, where the reliably simulated variables from Global Forecasts Systems (GFS) are used as predictors and different quantiles of rainfall are generated corresponding to that set of predictors. We apply this method to a flood prone coastal city of India, Mumbai, which has experienced severe floods in recent years. We find significant improvements in the forecast with high detection and skill scores. We apply the methodology to 10 ensemble members of Global Ensemble Forecast System (GEFS) and find a reduction in ensemble uncertainty of precipitation across realizations with respect to that of original precipitation forecasts. We validate our model for the monsoon season of 2006 and 2007, which are independent of the training/ calibration data set used in the study. We find promising results and emphasize to implement such data driven methods for a better probabilistic forecast at an urban scale primarily for an early flood warning.
... Taiwan represents an area extremely susceptible to landslides because of the steep mountainous topography, complex geological conditions, frequent heavy rainfall, and frequent earthquakes. Thus, many studies have been performed on landslides in Taiwan (Jan and Chen 2005;Chang and Chiang 2009;Lin and Chen 2012). However, these studies typically examined specific events or locations. ...
Article
This study analyzed the size of 172 rainfall-induced landslides in Taiwan during 2006–2012. Comparing the landslide size with rainfall conditions, this study found that large and deep landslides usually occurred due to long-duration and moderate-intensity rainfall (11.5–31.0 mm/h; 26.5–62.5 h), whereas small and shallow landslides occurred in a wide range of rainfall intensity and duration (8.5–31.0 mm/h; 4.0–62.5 h). This observation is ascribable to the fact that large and deep landslides need a high ground water level caused by a prolonged rainfall. Concerning the area of landslides, their frequency–area distribution correlates well with a power law relation having an exponent of −1.1 ± 0.07, over the range 6.3 × 102 to 3.1 × 106 m2. The slope of the power law relation for the size–frequency distribution of landslides in Taiwan is lower than those for other areas around the world. This indicates that for the same total area or total number of landslides, the proportion of large landslides is higher in Taiwan than in other areas.
... The comparison between different methods to assess landslide susceptibility is not a new research topic when performed exclusively between different empirically-based statistical methods (Gorsevski et al., 2003;Süzen and Doyuran, 2004;Brenning, 2005;Davis et al., 2006;Lee et al., 2007;Felicísimo et al., 2013;Bui et al., 2016) or between different physicallybased methods (Zizioli et al., 2013;Formetta et al., 2014;Pradham and Kim, 2015;Teixeira et al., 2015). Regarding the 15 comparison of the predictive capacity between empirically-based and physical-based methods, a few number of works exist (Crosta et al., 2006;Carrara et al., 2008;Frattini et al., 2008;Yilmaz and Keskin, 2009;Cervi et al., 2010;Goetz et al., 2011) and from those only a limited number of studies have combined the results obtained with empirically-based and physicallybased approaches (Chang and Chiang, 2009;Goetz et al., 2011). According to Zizioli et al. (2013) the different methods used to assess shallow slides susceptibility are not mutually exclusive. ...
Article
Full-text available
Approaches used to assess shallow slides susceptibility at the basin scale are conceptually different depending on the use of empirically-based or physically-based methods. The former are sustained by the assumption that the same causes are more likely to produce the same effects, whereas the latter are based on the comparison between forces which tend to promote movement along the slope and the opposing forces that promote resistance to movement. Within this general framework, this work tests two hypotheses: (i) although conceptually and methodological distinct, the statistic and deterministic methods generate similar shallow slides susceptibility results regarding the model’s predictive capacity and spatial agreement; and (ii) the combination of shallow slides susceptibility maps obtained with empirically-based and physically-based methods, for the same study area, generate a more reliable susceptibility model for shallow slides occurrence. These hypotheses were tested in a small test site (13.9 km²) located north of Lisbon (Portugal), using a empirically-based method (the Information Value method) and a physically-based method (the Infinite Slope method). The landslide susceptibility maps produced with the statistic and deterministic methods were combined into a new landslide susceptibility map. The latter was based on a set of integration rules defined by the cross-tabulation of the susceptibility classes of both maps and analysis of the corresponding contingency tables. The results demonstrate a higher predictive capacity of the new shallow slides susceptibility map, which combines the independent results obtained with empirically-based and physically-based models. Moreover the combination of the two models allowed the identification of areas where the results of the Information Value and the Infinite Slope methods are contradictory. Thus, these areas were classified as uncertain and deserve additional investigation at a more detailed scale.
... The literature about landslide risk assessment reveals various GIS-based methodologies that can be broadly categorized as qualitative and quantitative approaches. The quantitative approach comprises of Analytical Hierarchy Process (Ayalew and Yamagishi 2005;Basa et al. 2016;Mashhadifarahani 2015;Mondal and Maiti 2012;Yalcin 2008), Fuzzy logic approach (Champatiray 2000;Saboya Jr et al. 2006), logistic regression (Ayalew and Yamagishi 2005;Chang and Chiang 2009;Guzzetti et al. 1999;Xu et al. 2012), multivariate statistical models (Kanungo et al. 2012), artificial neural network approach (Ercanoglu 2005;Pradhan and Lee 2010) and weighted overlay methods (Ayalew et al. 2004;Cardinali et al. 2002;Preuth et al. 2010). The qualitative approach usually combines expert knowledge to monitor the geomorphological and geological features prone to slope failures. ...
Article
Landslides are prevalent, regular, and expensive hazards in the Karakoram Highway (KKH) region. The KKH connects Pakistan with China in the present China-Pakistan Economic Corridor (CPEC) context. This region has not only immense economic importance but also ecological significance. The purpose of the study was to map the landslide-prone areas along KKH using two different techniques-Analytical Hierarchy Process (AHP) and Scoops 3D model. The causative parameters for running AHP include the lithology, presence of thrust, land use land cover, precipitation, and Digital Elevation Model (DEM) derived variables (slope, curvature, aspect, and elevation). The AHP derived final landslide susceptibility map was classified into four zones, i.e., low, moderate, high, and extremely high. Over 80% of the study area falls under the moderate (43%) and high (40%) landslide susceptible zones. To assess the slope stability of the study area, the Scoops 3D model was used by integrating with the earthquake loading data. The results of the limit equilibrium analysis categorized the area into four groups (low, moderate, high, and extremely high mass) of slope failure. The areas around Main Mantle Thrust (MMT) including Dubair, Jijal, and Kohistan regions, had high volumes of potential slope failures. The results from AHP and Scoops 3D techniques were validated with the landslides inventory record of the Geological Survey of Pakistan and Google Earth. The results from both the techniques showed similar output that coincides with the known landslides areas. However, Scoops 3D provides not only susceptible zones but also the range of volume of the potential slope failures. Further, these techniques could be used in other mountainous areas, which could help in the landslide mitigation measures.
... Landslides are devastating natural disasters occurring worldwide every year, causing immense damage and loss of life (Sassa, 1974(Sassa, , 1984Fourie, 1996;Corominas and Moya, 1999;Collins and Znidarcic, 2004;Chang and Chiang, 2009;Saito, 2010;Kim et al. 2013;Sassa et al, 2013;Shokouhi et al. 2013;Kim et al. 2014a, b;Peruccacci, 2017;Ravindran, 2018). An implemented method to understand the causes and mechanisms of failure for landslides is by means of a flume test. ...
Article
Full-text available
Landslide initiation has multiple preconditional, preparatory and triggering factors, including rainfall intensity, slope angle and slope moisture content. Previous literature only considers a singular variable in effecting failure. This study shows trends typical of previous literature whilst considering how an amalgamation of assorted variables collaborate to effect failure. To better understand the influences of these factors, a series of tests were conducted using a flume device, employed in the generation of modelled single soil layer slope failures. Experiments were performed in 3 series, determined by rainfall intensity (40, 70 and 100 mm/h) and within these series, alterations were made between slope angle (45–55°) and initial moisture content (5–12%). Failure times occurred once pore water pressure had peaked at positive values, as well as, moisture content equalised throughout the slope. Variations in failure time occurred when altering slope angle and initial moisture content. Increasing the initial moisture content created faster failures whilst slopes inclined 45° failed faster with the exception of 100 mm/h intensity experiments. Initial failure times were summarised and used to develop an intensity-duration threshold function of I = 80.065D−0.596.
... To alleviate these drawbacks, back-analyses on a failed (or a failing) slope are frequently performed. Commonly used tools of back-analysis are limit-equilibrium-based and/or limit-analysis-based methods [1][2][3]6,7,9,36] which render back-calculated c-φ relationships (c: cohesion intercept; φ: internal friction angle) as the output. However, back-analyses using a limit-equilibrium or a limit-analysis-based method are intrinsic with a major disadvantage that no displacementrelated information can be obtained. ...
Article
A force-equilibrium-based finite displacement method (FFDM) incorporating Mohr-Coulomb (M-C) and Hoek-Brown (H-B) failure criteria is used to compute displacements of a monitored slope undergoing rainfall-induced groundwater table (GWT) fluctuations. The analytical method consists of back-calculating strength and displacement parameters using recorded displacements and groundwater table (GWT) in the 1st event of a series of rainfall-induced GWT changes and predicting slope displacements for other events of GWT changes. Results of analyses for the studied slope indicate that (1) despite various sets of back-calculated strength and displacement parameters are obtained, the accuracy of slope displacement prediction is not affected by using various sets of back-calculated parameters; (2) the accuracy of slope displacement prediction is not affected by the use of different failure criteria (M-C or H-B). To simulate a sliding with variable displacements along the slip surface, a displacement diagram associated with an operational dilatancy angle (ψ) is used, i.e., a negative value of ψ is selected to approximate a sliding mass undergoing a volume contraction. Results of a comparative study preliminarily shows good agreement between the calculated and the measured displacements of the slope. However, further validation for this technique using more observed data are necessary.
... Such models include SHALSTAB (Dietrich and Montgomery, 1998), dSLAM (Wu and Sidle, 1995), TRIGRS (Baum et al., 2008), SINMAP (Pack et al., 2001), HIRESS (Rossi et al., 2013;Salvatici et al., 2018) as well as many others (e.g. Chang and Chiang, 2009). However, the ability of physically-based models for shallow landslide hazard analysis has been questioned (Zieher et al., 2017) but the approach is considered feasible for computing a regional overview of slope stability and may oversimplify at the local scale, where slope-based geotechnical modelling may prove more fruitful. ...
Article
Shallow, rainfall-triggered landslides are an important catchment process that affect the rate and calibre of sediment within river networks and create a significant hazard, particularly when shallow landslides transform into rapidly moving debris flows. Forests and trees modify the magnitude and rate of shallow landsliding and have been used by land managers for centuries to mitigate their effects. We understand that at the tree and slope scale root reinforcement provides a significant role in stabilising slopes, but at the catchment scale root reinforcement models only partially explain where shallow landslides are likely to occur due to the complexity of subsurface material properties and hydrology. The challenge of scaling from slopes to catchments (from 1-D to 2-D) reflects the scale gap between geomorphic process understanding and modelling, and temporal evolution of material properties. Hence, our understanding does not, as yet, provide the necessary tools to allow vegetation to be targeted most effectively for landslide reduction. This paper aims to provide a perspective on the science underpinning the challenges land and catchment managers face in trying to reduce shallow landslide hazard, manage catchment sediment budgets, and develop tools for catchment targeting of vegetation. We use our understanding of rainfall-triggered shallow landslides in New Zealand and how vegetation has been used as a tool to reduce their incidence to demonstrate key points.
... Rainfall is the key external driving force causing instability of soil or parent rocks and transporting materials for most erosions. Thresholds of rainfall intensity triggering landslides had been estimated around the world (Aleotti, 2004;Chang and Chiang, 2009;Crosta et al., 2017;Damiano et al., 2012;Dou et al., 2014;Guo et al., 2019;Guo et al., 2020;Sarkar et al., 2016;Segoni et al., 2018), so the rainfall intensity thresholds causing Benggang wall collapses would be a useful indicator of stability. ...
Article
Benggang, a unique and severe form of gully erosion, is widely distributed in weatherd granite crust regions of South China and causes great harms to ecosystem and human. In the recent decades, large investments had been to restore the collapsing Benggang units. In the comprehensive benefit assessment protocol for soil and water conservation effectiveness of the Benggang restoration, stability assessment of the restored Benggang units lacks. This study developed a protocol to assess the stability of the restored Benggang units in a weathered granite crust region via diagnosing sedimentary layers in a soil profile (a meter away from its debris dam). Characteristic sedimentary layers formed by collapse, or strong or weak surface erosion were defined by biplotting ratios of coarse particles (> 1 mm) (PR) and quartz proportion (QR) in each sedimentary layer to those in the collapsing wall (original soil). Among thirty restored Benggang units investigated after 2–10 years of restoration in Anxi and Changting counties in Fujian province, 27 units were still collapsing or collapsed, consisting of 12 very active ones, while only 3 units were fully stabilized. Rainfall intensity threshold causing collapse (RITC) of the units was identified via matching local rainfall events with the characteristic sedimentary layers of collapsion in the dam-front soil profile, significantly higher RITCs in the stable or collapsing-to-stable units than the continuously or late collapsing units (p < 0.05). Based on the estimated RITCs, no significant differences in stability of the restored units were observed between Changting strategy (ecological restoration only with vegetation) and Anxi strategy (restoration with cashcrop plantation). The findings of this study suggested that the characteristic sedimentary layers in the dam-front soil profile determined by the PR-QR biplotwas feasible for stability assessment of the restored Benggang units and provided a quantitative RITC estimation.
... Rainfall is the most important factor inducing landslides. At present, there are two main rainfall threshold models, a physical rainfall threshold and an empirical rainfall threshold [23][24][25]; the empirical threshold is based on the relationship between a large number of landslides and rainfall data, and is currently the most commonly used rainfall threshold model. Rainfall intensity and accumulation are the two main aspects that affect the rainfall threshold. ...
Article
Full-text available
Landslides are one of the most severe and common geological hazards in the world. The purpose of this research is to establish a coupled landslide warning model based on random forest susceptibility zoning and precipitation. The 1520 landslide events in Fengjie County, Chongqing, China, before 2016 are taken as research cases. We adapt the random forest model to build a landslide susceptibility model. The antecedent effective precipitation model, based on the fractal relationship , is used to calculate the antecedent effective precipitation in the 10 days before the landslide event. Based on different susceptibility zones, the effective precipitation corresponding to different cumulative frequencies is counted as the threshold, and the threshold is adjusted according to the fitted curve. Finally, according to the daily precipitation, the rain warning levels in susceptibility zones are further adjusted, and the final prewarning model of the susceptibility zoning and precipitation coupling is obtained. The results show that the random forest model has good prediction ability for landslide susceptibility zoning, and the precipitation warning model that couples landslide susceptibility, antecedent effective precipitation, and the daily precipitation threshold has high early warning ability. At the same time, it was found that the precipitation warning model coupled with antecedent effective precipitation and the daily precipitation threshold has more accurate precipitation warning ability than the precipitation warning model coupled with the antecedent effective precipitation only; the coupling of the two can complement each other to better characterize the occurrence of landslides triggered by rainfall. The proposed coupled landslide early warning model based on random forest susceptibility and rainfall inducing factors can provide scientific guidance for landslide early warning and prediction, and improve the manageability of landslide risk.
... Hence, Chang et al. (2007) generated typhoon-induced landslide and earthquake-induced landslide models using LR in Taiwan. Chang and Chiang (2009) volume for calculating the probability of magnitude prediction of landslide hazard. Further, they estimated the probabilities for spatial and temporal prediction using LR and Poisson models, respectively and combined magnitude probability to obtain the joint probability assuming all three probabilities to be mutually independent. ...
Article
Full-text available
Over the last few decades, several landslide susceptibility and hazard mapping (LSHM) techniques have been developed. Maps for the same region have also been generated by different individuals following dissimilar approaches, which can be grouped into qualitative, semi-quantitative and quantitative approaches. As all these techniques have their pros and cons, hence no one technique is standardized for effective analysis of landslide hazards. One issue is the inconsistency in adopting common terminologies for LSHM, that has unavoidably led to many misperceptions. Many authors use susceptibility as a synonym of hazard in landslide zonation. However, Landslide Susceptibility Mapping (LSM) or spatial prediction is just one of the three components of Landslide Hazard Mapping (LHM). The other two components are temporal and magnitude prediction. Many authors have shown their concern regarding the use of hazard and susceptibility terms as synonyms, but none has reviewed those articles and classified them. We reviewed 367 articles from 1972 to 2021, out of which 236 articles were reviewed in detail to prepare a literature database. From the analysis and graphical visualizations of the database, we found the most commonly used techniques for LSHM. We identified a clear geographical biasness in susceptibility analysis. Also, we have found that about 15% of the articles have mistakenly considered susceptibility and hazard terms as synonyms of each other. It constitutes a guideline for future studies and applications, particularly for LSHM. The paper also aims at addressing the gaps in the conversion of susceptibility maps into true hazard and risk maps.
... Simple landslide forecasting systems use statistical analysis based on empirical correlations of seasonal rainfall data. However, such an approach is not suitable for accurately predicting the time of failure as the variation in soil shear strength with suction is neglected (Fourie 1996;Chang and Chiang 2009;Manconi and Giordan 2016;Intrieri et al. 2019). Back analysis of failed slope based on the probabilistic approach (Gilbert et al. 1998;Juang et al. 2013;Ering and Babu 2016) and machine learning techniques (Kuradusenge et al. 2020) are also often used for the development of EWS. ...
Article
Studies on rainfall-induced landslides are essential for protecting the lives and property in hilly regions. Augmented numerical investigation considering geotechnical, geological, and environmental parameters of the slope for the past landslides and potential slip surfaces helps in identifying appropriate triggering mechanisms and preventive measures. In this work, numerical modelling was carried out for predicting the time of landslide occurrence of a shallow landslide based on the laboratory estimated soil water characteristic curve (SWCC) data, net rainfall infiltration, runoff, and geological characteristics of the study area. The accurate estimation of the SWCC was vital in the back-analysis of a landslide as SWCC directly governs the hydraulic and shear strength characteristics of the unsaturated slope. Instantaneously measured SWCC by the sensors severely overestimated the suction values for a given water content for the studied soil at different compaction densities. The back-analyzed rainfall-induced slope stability analysis of the case study showed that the estimated factor of safety variation with time by using the conventional SWCC estimation was inconsistent with the observed time of failure. A laboratory method was proposed to evaluate “equilibrium SWCC” data to account for the hydraulic equilibrium between the suction sensor and the field soil. The equilibrium SWCC data accurately predicted the time of landslide occurrence and slip surface. Further, the present study also highlighted the significance of the field density and net rainfall infiltration, considering climatic data, on forensic investigations through sensitivity analysis.
... The results found to be ninety percent accurate. Chang and Chiang (2009) also performed investigation in the line of landslide studies. In their study, an integrated model combining deterministic, statistical and rainfall threshold mode for the landslide susceptibility was proposed, and the model was demonstrated for the typhoon-induced landslides in Taiwan. ...
Chapter
Data mining techniques have potential to unveil the complexity of an event and yields knowledge that can create a difference. They can be employed to investigate natural phenomena; since these events are complex in nature and are difficult to characterize as there are elements of uncertainty involved in their functionality. Therefore, techniques that are compatible with uncertain elements can be employed to study them. This chapter explains the concepts of data mining and discusses at length about the landslide event. Further, the utility of data mining techniques in disaster management using a previous work was explained and provides a brief note on the efficiency of web mining in creating awareness about natural hazard by providing refined information. Finally, a conceptual framework for landslide hazard assessment using data mining techniques such as Artificial Neural Network (ANN), Fuzzy Geometric Mean Model (FGMM), etc. were chosen for description. It was quite clear from the study that data mining techniques are useful in assessing and modelling different aspects of landslide event.
... In the past decades, there are various models developed to simulate landslides, debris flows, sediment transport process individually. As for landslide, researchers has used empirical or physical models to evaluate the landslide susceptibility (Guzzetti et al., 2005 Chang andChiang, 2009) and estimate landslide volume (Khazai and Sitar, 2000;Guzzetti et al., 2009;Klar et al., 2011). For debrisǦflow simulation, several numerical models, i.e. ...
Conference Paper
Full-text available
In order to analyze susceptibilities to shallow landslide occurrence, a modeling of overburden soil depth and rainstorm occurrence is necessary, since both of them are controlling factors in the recurrence interval of shallow land sliding. Landslide hazard zoning mapping is a tool and one way solution to mitigate the landslide disaster. Shallow landslides are one of the most common types of failures occurring frequently in steep slopes, overburden soil, landscapes in different climatic zones. As for the effect of topography that slope angle, slope drainage, vicinity of road and infrastructure, overburden soil depth and geology are important factors for recurrence of shallow land sliding. Data, although insufficient in number, stimulated the debate about the effect of geology and topography on the susceptibility to shallow land sliding. An Analytical Hierarchical Process is applied in order to derive the weights associated with attribute map layers. And based on these weights, GIS datasets are combined by weighted Average Analysis (WAA) and the land slide susceptibility map of the study area created. The resulting information was compared With the land slide susceptibility map derived through the SINMAP model. Both outputs are useful for a better understanding of landslide susceptibility comparatively to a sensitive landslide disaster event and their origins and prioritization of efforts for the reduction and mitigation of future landslide hazards. Sensitivity of the both approaches was fine-tuned with the overburden soil strength parameters, geomorphological evidences and field verification techniques.
... These factors along with high population density pose an increased risk of vulnerable situations in developing countries like India (Kritikos and Davies 2015). These unpredictable and massive natural hazards result in property damages incurring financial losses in many parts of the world, impacting local and global economy (Suzen and Doyuran 2004;Chang and Chiang 2009;Cogan and Gratchev 2019;Lee et al. 2012;Nourani et al. 2014). About 15% (~0.42 million km 2 ) of India's total landmass falls under the landslide-prone hazardous zone (Kanungo and Sharma 2014;Harilal et al. 2019; National Disaster Management Authority 2019; Thennavan and Pattukandan Ganapathy 2020). ...
Article
Kerala is the third most densely populated state in India, with 860 persons per square kilometer. The uniqueness and diversity of the state’s topology make it highly vulnerable to natural hazards. Kerala State Emergency Operations Centre Kerala State Disaster Management Authority (2016). This study was initiated in the backdrop of landslides and floods in 2018, which had wreaked havoc in the region. Among the 4728 landslides reported in the state’s ten districts, Idukki was the worst affected with 2219 landslide occurrences. A statistically significant cluster of landslide hotspots was identified within the Idukki district using Getis-Ord Gi* statistics. Landslide susceptibility analysis was carried out using logistic regression (LR) and artificial neural network (ANN). Natural parameters influencing landslides such as slope, elevation, rainfall, geology, distance to drainage, and anthropogenic conditioning factors such as land use, road density, and quarry density were considered in this study. The results indicate that both natural and anthropogenic conditioning factors have a significant influence on landslide occurrences. According to the LR results, about 37.87% and 38.07% of the district’s total area is situated in high and medium landslide susceptibility zones. The results establish that ANN has better predictive performance compared with LR.
... Most of these approaches insert parameters of a rainfall event, collected through rain gauges or radar instruments, within the set of predictors of a data-driven algorithm to model the probability of occurrence of the triggered slope instabilities (Dai and Lee 2003;Ayalew and Yamagishi 2005;Wang and Sassa 2006;Chang et al. 2008;Chang and Chiang 2009;Capecchi et al. 2015;Lee et al. 2020). ...
Article
Full-text available
A combined method was developed to forecast the spatial and the temporal probability of occurrence of rainfall-induced shallow landslides over large areas. The method also allowed to estimate the dynamic change of this probability during a rainfall event. The model, developed through a data-driven approach basing on Multivariate Adaptive Regression Splines technique, was based on a joint probability between the spatial probability of occurrence (susceptibility) and the temporal one. The former was estimated on the basis of geological, geomorphological, and hydrological predictors. The latter was assessed considering short-term cumulative rainfall, antecedent rainfall, soil hydrological conditions, expressed as soil saturation degree, and bedrock geology. The predictive capability of the methodology was tested for past triggering events of shallow landslides occurred in representative catchments of Oltrepò Pavese, in northern Italian Apennines. The method provided excellently to outstanding performance for both the really unstable hillslopes (area under ROC curve until 0.92, true positives until 98.8%, true negatives higher than 80%) and the identification of the triggering time (area under ROC curve of 0.98, true positives of 96.2%, true negatives of 94.6%). The developed methodology allowed us to obtain feasible results using satellite-based rainfall products and data acquired by field rain gauges. Advantages and weak points of the method, in comparison also with traditional approaches for the forecast of shallow landslides, were also provided.
... In order to improve the thresholds here obtained, especially for warning system purposes, it would be advisable to apply validation approaches, that allow to assess the level of uncertainty of the prediction and forecasts (Berti, 2012;Brunetti et al., 2010;Chang & Chiang, 2009;Guzzetti et al., 2008;Melillo et al., 2016;Peruccacci et al., 2012). An updated database, with a greater number of new events, or independent databases for close areas, would be very useful to test the results obtained in Deba. ...
Article
Deba area is intensely affected by frequent shallow landslides triggered by rainfall. This contribution explores the role of rainfall in landslide activity during a quite long time span (60 years), from a large network of rainfall gauges and a complete inventory of landslides. Out of 1,180 landslides inventoried, more than 50% occurred simultaneously in 6 known dates, corresponding to 6 episodes triggering multiple landslides; 3,241 rainfall episodes have been automatically recognized and characterized in terms of rainfall amount and duration, providing a representative dataset that covers a wide range of movement types and behaviors.The relationship between rainfall episodes driving multiple movements simultaneously has not been explored in depth so far in northern Spain. The extraordinary character of the triggering rainfall has been assessed and empirical rainfall thresholds (total amount, and mean intensity), producing multiple landslides, have been found and compared with others described in the literature. Also, the meteorological conditions associated to those extreme events have been recognized: multiple landslide occurrences are triggered by extreme convective rainfall: intense, short and with limited horizontal extent, as well as a marked summer-autumn seasonality. This weather pattern is more characteristic of Mediterranean areas than of mild marine west-coast climates.
... Increased urbanization in mountainous terrain, particularly those that experience intense or prolonged rainfall, leads to a concurrent increase in the threat of landslides through landscape destabilization (Cascini et al. 2005;Chacón et al. 2006;Schlögl1 et al. 2019;Roccati et al. 2019). Consequences can be severe including human fatalities, infrastructure failure, financial loss and environmental degradation, such as surface disruption, soil erosion and an increased sediment load (Guzzetti et al. 1999;Chang and Chiang 2009;Sköld and Nyberg 2016). Modification of the landscape through human activity, for instance changing the nature of groundcover and rerouting paths of fluid flow, can alter the natural partitioning of infiltration and runoff, which potentially affects rates of erosion and sedimentation throughout the entire watershed (Morgan 1988). ...
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The Lago Maggiore catchment is characterized by medium to high altitude (up to 4633 m a.s.l. with a median of 1270 m a.s.l.), high precipitation (~ 1700 mm/yr), and brittle tectonic deformation of impermeable rocks, such as granite and gneiss, that are characterized by a predisposition to slope failure. We analysed daily rainfall data associated with 38 landslides that occurred between 1980 and 2017 from meteorological stations placed into four sub-basins. The purpose was to determine whether or not extreme rainfall events exceeded landslides thresholds reported by previous studies. A statistical analysis using the RClimDex package was done, to verify changes in extreme rainfall over time. A spatial approach using Inverse Distance Weighting (IDW) in QGIS was used to extrapolate rainfall data specific to landslide areas, as well as GIS techniques and processing tools to conduct geomorphic analyses. Finally, a multivariate analysis, (general linear model), was used to understand associations between variables (landslide types, lithology, valley, elevation, slope, land use, rainfall, and the presence of rivers, roads, paths, and buildings), known to affect the generation of landslides. Results show extreme rainfall events to be a secondary factor in the triggering of landslides, whereas the most significant factors are presence of building, proximity to rivers and lithology. It was found that intense rainfall is a concomitant cause to landslides in some instances but does not play a role in others.
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Landslides often cause deaths and severe economic losses. In general, forests play an important role in reducing landslide probability because of the stabilizing effect of the tree roots. Although fruit groves consist of trees, which are similar to forests, practical land management, such as the frequent trampling of fields by laborers and compression of the terrain, may cause such land to become prone to landslides compared with forests. Fruit groves are widely distributed in hilly regions, but few studies have examined their role in landslide initiation. This study aims at filling this gap evaluating the predisposing and triggering conditions for rainfall-triggering landslides in part of Uwajima City, Japan. A large number of landslides occurred due to a heavy rainfall event in July 2018, where citrus groves occupied about 50% of the study area. In this study, we combined geodata with a regression model to assess the landslide hazard of fruit groves in hilly regions. We developed maps for five conditioning factors: slope gradient, slope aspect, normalized difference vegetation index (NDVI), land use, and geology. Based on these five maps and a landslide inventory map, we found that the landslide area density in citrus groves was larger than in forests for the categories of slope gradient, slope aspect, NDVI, and geology. Ten logistic regression models along with different rainfall indices (i.e., 1-h, 3-h, 12-h, 24-h maximum rainfall and total rainfall) and different land use (forests or citrus groves) in addition to the other four conditioning factors were produced. The result revealed that “citrus grove” was a significant factor with a positive coefficient for all models, whereas “forest” was a negative coefficient. These results suggest that citrus groves have a higher probability of landslide initiation than forests in this study area. Similar studies targeting different sites with various types of fruit groves and several rainfall events are crucial to generalize the analysis of landslide hazard in fruit groves.
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Approaches used to assess shallow slide susceptibility at the basin scale are conceptually different depending on the use of statistical or physically based methods. The former are based on the assumption that the same causes are more likely to produce the same effects, whereas the latter are based on the comparison between forces which tend to promote movement along the slope and the counteracting forces that are resistant to motion. Within this general framework, this work tests two hypotheses: (i) although conceptually and methodologically distinct, the statistical and deterministic methods generate similar shallow slide susceptibility results regarding the model's predictive capacity and spatial agreement; and (ii) the combination of shallow slide susceptibility maps obtained with statistical and physically based methods, for the same study area, generate a more reliable susceptibility model for shallow slide occurrence. These hypotheses were tested at a small test site (13.9 km²) located north of Lisbon (Portugal), using a statistical method (the information value method, IV) and a physically based method (the infinite slope method, IS). The landslide susceptibility maps produced with the statistical and deterministic methods were combined into a new landslide susceptibility map. The latter was based on a set of integration rules defined by the cross tabulation of the susceptibility classes of both maps and analysis of the corresponding contingency tables. The results demonstrate a higher predictive capacity of the new shallow slide susceptibility map, which combines the independent results obtained with statistical and physically based models. Moreover, the combination of the two models allowed the identification of areas where the results of the information value and the infinite slope methods are contradictory. Thus, these areas were classified as uncertain and deserve additional investigation at a more detailed scale.
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A novel method called knowledge-guided spatio-temporal consistent correlation analysis (KSTCCA) was developed to discover reliable deformation features induced by multiple factors based on multimode landslide monitoring data. Compared to conventional approaches, KSTCCA integrates both temporal and spatial correlation analysis to improve the consistency of deformation patterns and capture the spatio-temporal heterogeneities in multimode monitoring data. KSTCCA considers both the landslide deformation mechanisms and the relationships between different influential factors as knowledge. Moreover, the method extracts the morphological structures of monitoring curves based on a seven-point approach and identifies knowledge rules using the k-means clustering method. Under the guidance of prior knowledge, a spatial correlation analysis is conducted based on support vector regression, and a temporal correlation analysis of the time lag is carried out based on the morphological structure features. Finally, three kinds of typical monitoring data, including deformation, rainfall, and reservoir water level data collected in the Baishuihe landslide area, China, are used for experimental analysis to verify the validity of the proposed method.
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Sedimentary produced and transported in mountainous area under extreme rainfall by climate change is a challenged issue in recent years, especially in a watershed scale. The scenario approach with coupled simulation by different models could be one of a solution for further discussion under warming climate. With properly model selection, the simulation of projected rainfall, landslide, and debris flow are integrated by fully connection between models. Moreover, a case in Xindian watershed upstream the capital of Taiwan is chose for studying, and two extreme scenarios in late 20th and late 21st century are selected for comparison on changing climate. With sequent simulation, the chain process and compounded disaster can be considered in our analysis. The potential effects of landslides and debris flows are compared between current and future, and the likely impact in selected watershed are discussed under climate extreme. Result shows the unstable sediment volume would enlarge 29 % in terms of projected extreme event. The river bed may have strong variation by serious debris flow and increase about 10 % elevation in main channel. These findings also highlight the increasing risk in stable water supply, isolated village effect, and other secondary disaster in this watershed. A practical reference could be provided by some critical information in our result for long-term adapted strategies.
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One of the purposes of slope disaster research is to establish an early warning method for rainfall-induced landslides. The insufficient observational records of the past, however, have inhibited the analysis of critical rainfall conditions. This dilemma may be resolved by extracting the times of landslide occurrences from the seismic signals recorded by adjacent seismic stations. In this study, the seismic records of the Broadband Array in Taiwan for Seismology (BATS) were examined to identify the ground motion triggered by large-scale landslides occurring from 2005 to 2014. After the signals from local and teleseismic earthquakes were eliminated, 62 landslide-induced seismic signals were identified. The seismic signals provided the occurrence times of the landslides for assessment of the rainfall conditions, including rainfall intensity (I, mm/h), duration (D, h), and cumulated rainfall (R, mm). Comparison of three common rainfall threshold models (I–D, I–R, and R–D) revealed duration and cumulated rainfall to be the crucial factors in developing a forecast warning model. In addition, a critical volume of water model, (I−1.04)·D=452mm, combining statistical and deterministic approaches was established through analysis of rainfall information from the 62 large-scale landslides that occurred.
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The effects of landslides have been exponentially increasing due to the rapid growth of urbanization and global climate change. The information gained from predictive models and landslide susceptibility analyses can be used to develop warning systems and mitigation measures. A comparative study was conducted to evaluate the effectiveness of landslide susceptibility analyses in a given area using three decision tree algorithms including Random Forest (RF), C4.5, and C5.0. Two sets of imagery datasets (raster and vector) were used and three combinations of 13 conditioning factors (including seven geotechnical properties of the soil) were determined by Information Gain, Gain Ratio, Chi-Squared Test, and Random Forest Importance. Datasets for the landslide conditioning factors were created based on the outcomes from the feature selection methods, in three different scenarios. In Scenario 1 the least important factors/features (as identified by information gain, chi-square, and gain ratio measures) were eliminated. In Scenario 2 only the most important factors (as identified by RF feature selection method evaluation) were kept. In Scenario 3, no factor was eliminated, using the data directly obtained from the sources without applying any feature selection method. The performances of the models were evaluated using statistical verification scores. C4.5 was found to have the highest performance when all 13 conditioning parameters (Scenario 3) were used for both the raster and vector data set. The RF model was the least effective in predicting the landslides in all three scenarios. However, the use of the balance vector dataset significantly increased the performance of the RF model. C4.5 and C5.0 had significantly better performance in handling extremely unbalance data in comparison to RF. Density, silt and clay content, and Atterberg’s limits (LL and PI) were the most important geotechnical conditioning factors in the performed landslide susceptibility analyses.
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A presente tese versa na avaliação da suscetibilidade à ocorrência de dois tipos de movimento de vertente: os deslizamentos superficiais e as escoadas de detritos. O trabalho desenvolvido foca-se na implementação de métodos estatísticos e determinísticos para a modelação das áreas de iniciação e propagação destes dois tipos de movimento de vertente. Na avaliação da suscetibilidade à ocorrência de deslizamentos superficiais, recorre-se à comparação entre um método estatístico (Regressão Logística) e um método determinístico (método do Talude Infinito). Na simulação das áreas de propagação, utiliza-se um modelo simples de autómatos celulares. Para a avaliação da suscetibilidade à iniciação e propagação de escoadas de detritos, em contexto de área ardida, estabelece-se a comparação entre um método estatístico bivariado (Valor Informativo) e um multivariado (Regressão Logística). Na simulação das áreas afetadas pela passagem e deposição do material transportado procede-se à comparação entre o modelo empírico Flow Path Assessment of Gravitational Hazards at a Regional Scale (Flow-R) e o algoritmo de direção de escoamento D-infinity downslope influence (DI). Aplica-se ainda um modelo dinâmico, a 2D, para a simulação da iniciação, erosão, propagação e deposição de escoadas de detritos à escala da bacia. A investigação realizada permite reconhecer a aplicabilidade dos modelos estatísticos de iniciação e propagação de deslizamentos superficiais e escoadas de detritos. Os resultados mostram que é possível obter modelos robustos e validáveis, partindo de dados incompletos e adquiridos a custo reduzido. Contudo, constata-se que os procedimentos de validação nem sempre excluem a necessidade de uma análise mais aprofundada, baseada em critérios geomorfológicos que atendem ao funcionamento dos processos físicos. Conclui-se, também, que o tipo de modelo (estatístico ou determinístico) deverá ser escolhido em função dos dados disponíveis; porém, a combinação dos diferentes métodos oferece resultados mais credíveis e permite identificar áreas classificadas como incertas, no que respeita à suscetibilidade, mas com elevado potencial para se instabilizarem, o que não é possível quando se utiliza um único modelo de suscetibilidade. Por fim, a aplicação de um modelo dinâmico, a 2D, possibilita a elaboração de cenários de propagação de escoadas de detritos, à escala da bacia, que se revelam consistentes com os registos históricos sobre a ocorrência de escoadas na área de estudo e viabilizam uma comparação com o edificado atualmente existente e, consequentemente, a contabilização dos edifícios em risco.
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—Rainfall-triggered landslides constitute a serious hazard and an important geomorphic process in many parts of the world. Attempts have been made at various scales in a number of countries to investigate triggering conditions in order to identify patterns in behaviour and, ultimately, to define or calculate landslide-triggering rainfall thresholds. This study was carried out in three landslide-prone regions in the North Island of New Zealand. Regional landslide-triggering rainfall thresholds were calculated using an empirical “Antecedent Daily Rainfall Model.” In this model, first introduced by, triggering rainfall conditions are represented by a combination of rainfall occurring in a period before the event (antecedent rainfall) and rainfall on the day of the event. A physically-based decay coefficient is derived for each region from the recessional behaviour of storm hydrographs and is used to produce an index for antecedent rainfall. Statistical techniques are employed to obtain the thresholds which best separate the rainfall conditions associated with landslide occurrence from those of non-occurrence or a given probability of occurrence.The resultant regional models are able to represent the probability of occurrence of landsliding events on the basis of rainfall conditions. The calculated thresholds show regional differences in susceptibility of a given landscape to landslide-triggering rainfall. These differences relate to both the landslide database and the difference of existing physical conditions between the regions.
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A detailed description of the operational WSR-88D rainfall estimation algorithm is presented. This algorithm, called the Precipitation Processing System, produces radar-derived rainfall products in real time for forecasters in support of the National Weather Service's warning and forecast missions. It transforms reflectivity factor measurements into rainfall accumulations and incorporates rain gauge data to improve the radar estimates. The products are used as guidance to issue flood watches and warnings to the public and as input into numerical hydrologic and atmospheric models. The processing steps to quality control and compute the rainfall estimates are described, and the current deficiencies and future plans for improvement are discussed.
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This study aims at assessing the potential of anomalous propagation conditions to occur, reviews past attempts to mitigate ground clutter contamination of radar data resulting from anomalous signal propagation, and presents a new algorithm for radar data quality control. Based on a 16-yr record of operational sounding data, the likelihood of atmospheric conditions to occur across the United States that potentially lead to anomalous propagation of radar signals is estimated. Anomalous signal propagation may lead to a significant contamination of radar data from ground echoes normally not seen by the radar, which could result in serious rainfall overestimates, if not recognized and treated appropriately. Many different approaches have been proposed to eliminate the problem of regular ground clutter close to the radar and temporary clutter resulting from anomalous signal propagation. None of the reported approaches, however, satisfactorily succeeds in the case of anomalous propagation ground returns embedded in precipitation echoes, a problem that remains a challenge today for radar data quality control. Taking strengths and weaknesses of past approaches into consideration, a new automated procedure has been developed that makes use of the three-dimensional reflectivity structure. In particular, the vertical extent of radar echoes, their spatial variability, and vertical gradient of intensity are evaluated by means of a decision tree. The new algorithm appears to work equally well in situations where anomalous propagation ground returns are either separated from or embedded within precipitation echoes. Moreover, sea clutter echoes are identified as not raining and successfully removed.
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A distributed, physically based slope stability model (dSLAM), based on an infinite slope model, a kinematic wave groundwater model, and a continuous change vegetation root strength model, is presented. It is integrated with a contour line-based topographic analysis and a geographic information system (GIS) for spatial data extraction and display. The model can be run with either individual rainfall events or long-term sequences of storms. These inputs can be either actual storm records or synthesized random events based on Monte Carlo simulation. The model is designed to analyze rapid, shallow landslides and the spatial distribution of safety factor (FS) in steep, forested areas. It can investigate the slope stability problem in both temporal and spatial dimensions, for example, the impact of timber harvesting on slope stability either at a given time or through an extended management period, the probability of landslide occurrence for a given year, and the delivery of landslide sediments to headwater streams. The dSLAM model was applied in a steep, forested drainage of Cedar Creek in the Oregon Coast Ranges using actual spatial patterns of timber harvesting and measured rainfall during a major storm which triggered widespread landslides in that area in 1975. Simulated volume and number of failures were 733 m(3) and 4, respectively. These values agreed closely with field measurements following the 1975 storm. However, the effect of parameter uncertainty may complicate this comparison. For example, when soil cohesion values of 2.0 and 3.0 kPa were used, the failure volume changed by factors of 2.04 and 0.41, respectively, compared with the average condition of 2.5 kPa used in the simulation. For soil depths 30% higher and lower than the standard condition, the failure volume changed by factors of 2.0 and 0.27, respectively. When maximum root cohesion changed from 12.5 kPa (average condition) to 10 kPa, the failure volume increased 1.73-fold; for the case of 15 kPa, the failure volume changed by a factor of 0.55. The simulated failures caused by the storm were mostly in hollows. The simulations show that the spatial distribution of FS is controlled mainly by topography and timber-harvesting patterns and is greatly affected by groundwater flow patterns during major rainstorms. Most areas with FS < 3.0 corresponded with the distribution of blocks clear-cut in 1968, and all elements with FS < 2.0 were in areas clear-cut in 1968. Areas with low FS (1.0-1.6) expanded dramatically during the rainstorm and decreased at a slow rate after the storm. Factors of safety in hollows declined sharply during the storm.
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A model for the simulation of shallow landsliding triggered by heavy rainstorms is analysed and discussed. The model is applied in two mountainous catchments in the Dolomites (Eastern Italian Alps): the Cordon catchment (5 km2) and the Vauz catchment (1·9 km2), where field surveys provided a description of hydraulic and geotechnical properties of soils and an inventory of landslide scars is available. The stability mapping procedure, which is similar to that proposed by Montgomery and Dietrich (1994 Water Resources Research30: 1153), combines steady-state hydrologic concepts with the infinite slope stability model. The model provides an estimate of the spatial distribution of the critical rainfall, which is the minimum steady-state rainfall predicted to cause instability. The comparison of the landslides observed in the study basins with model predictions shows that the distribution of critical rainfall obtained from the model provides a surrogate for failure initiation probability as a function of topographic location. Copyright © 2002 John Wiley & Sons, Ltd.
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In humid uplands landsliding is the dominant mass wasting process. In the western Southern Alps of New Zealand landslides are scale invariant and have a power-law magnitude frequency distribution. Independent studies from other regions suggest that this is a general property of landsliding. This observation is of critical importance to the evaluation of the impact of events of different length scales over different time intervals on landscape evolution. It is particularly useful when estimating regional geomorphic rates, because it constrains the frequency and overall significance of extreme events, which cannot otherwise be evaluated. By integrating the complete response of the system, we estimate the regional denudation rate due to landsliding to be 9 +/- 4 mm yr(-1). Sediment discharge from the western Southern Alps is dominated by landslide-derived material.
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Landslide susceptibility mapping is one of the most critical issues in Turkey. At present, geotechnical models appear to be useful only in areas of limited extent, because it is difficult to collect geotechnical data with appropriate resolution over larger regions. In addition, many of the physical variables that are necessary for running these models are not usually available, and their acquisition is often very costly. Conversely, statistical approaches are currently pursued to assess landslide hazard over large regions. However, these approaches cannot effectively model complicated landslide hazard problems, since there is a non-linear relationship between nature-based problems and their triggering factors. Most of the statistical methods are distribution-based and cannot handle multisource data that are commonly collected from nature. In this respect, logistic regression and neural networks provide the potential to overcome drawbacks and to satisfy more rigorous landslide susceptibility mapping requirements. In the Hendek region of Turkey, a segment of natural gas pipeline was damaged due to landslide. Re-routing of the pipeline is planned but it requires preparation of landslide susceptibility map. For this purpose, logistic regression analysis and neural networks are applied to prepare landslide susceptibility map of the problematic segment of the pipeline. At the end, comparative analysis is conducted on the strengths and weaknesses of both techniques. Based on the higher percentages of landslide bodies predicted in very high and high landslide susceptibility zones, and compatibility between field observations and the important factors obtained in the analyses, the result found by neural network is more realistic.
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As a first step forward in regional hazard management, multivariate statistical analysis in the form of logistic regression was used to produce a landslide susceptibility map in the Kakuda-Yahiko Mountains of Central Japan. There are different methods to prepare landslide susceptibility maps. The use of logistic regression in this study stemmed not only from the fact that this approach relaxes the strict assumptions required by other multivariate statistical methods, but also to demonstrate that it can be combined with bivariate statistical analyses (BSA) to simplify the interpretation of the model obtained at the end. In susceptibility mapping, the use of logistic regression is to find the best fitting function to describe the relationship between the presence or absence of landslides (dependent variable) and a set of independent parameters such as slope angle and lithology. Here, an inventory map of 87 landslides was used to produce a dependent variable, which takes a value of 0 for the absence and 1 for the presence of slope failures. Lithology, bed rock-slope relationship, lineaments, slope gradient, aspect, elevation and road network were taken as independent parameters. The effect of each parameter on landslide occurrence was assessed from the corresponding coefficient that appears in the logistic regression function. The interpretations of the coefficients showed that road network plays a major role in determining landslide occurrence and distribution. Among the geomorphological parameters, aspect and slope gradient have a more significant contribution than elevation, although field observations showed that the latter is a good estimator of the approximate location of slope cuts. Using a predicted map of probability, the study area was classified into five categories of landslide susceptibility: extremely low, very low, low, medium and high. The medium and high susceptibility zones make up 8.87% of the total study area and involve mid-altitude slopes in the eastern part of Kakuda Mountain and the central and southern parts of Yahiko Mountain.
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Mapping of landslide susceptibility in forested watersheds is important for management decisions. In forested watersheds, especially in mountainous areas, the spatial distribution of relevant parameters for landslide prediction is often unavailable. This paper presents a GIS-based modeling approach that includes representation of the uncertainty and variability inherent in parameters. In this approach, grid-based tools are used to integrate the Soil Moisture Routing (SMR) model and infinite slope model with probabilistic analysis. The SMR model is a daily water balance model that simulates the hydrology of forested watersheds by combining climate data, a digital elevation model, soil, and land use data. The infinite slope model is used for slope stability analysis and determining the factor of safety for a slope. Monte Carlo simulation is used to incorporate the variability of input parameters and account for uncertainties associated with the evaluation of landslide susceptibility. This integrated approach of dynamic slope stability analysis was applied to the 72-km2 Pete King watershed located in the Clearwater National Forest in north-central Idaho, USA, where landslides have occurred. A 30-year simulation was performed beginning with the existing vegetation covers that represented the watershed during the landslide year. Comparison of the GIS-based approach with existing models (FSmet and SHALSTAB) showed better precision of landslides based on the ratio of correctly identified landslides to susceptible areas. Analysis of landslide susceptibility showed that (1) the proportion of susceptible and non-susceptible cells changes spatially and temporally, (2) changed cells were a function of effective precipitation and soil storage amount, and (3) cell stability increased over time especially for clear-cut areas as root strength increased and vegetation transitioned to regenerated forest. Our modeling results showed that landslide susceptibility is strongly influenced by natural processes and human activities in space and time; while results from simulated outputs show the potential for decision-making in effective forest planning by using various management scenarios and controlling factors that influence landslide susceptibility. Such a process-based tool could be used to deal with real-dynamic systems to help decision-makers to answer complex landslide susceptibility questions.
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Landslides mapped in 14 watershed analyses in Oregon and Washington provide a regional test of a model for shallow landsliding. A total of 3224 landslides were mapped in watersheds covering 2993 km2 and underlain by a variety of lithologies, including Tertiary sedimentary rocks of the Coast Ranges, volcanic rocks of the Cascade Range and Quaternary glacial sediments in the Puget Lowlands. GIS (geographical information system) techniques were used to register each mapped landslide to critical rainfall values predicted from a theoretical model for the topographic control on shallow landsliding using 30 m DEMs (digital elevation models). A single set of parameter values appropriate for simulating slide hazards after forest clearing was used for all watersheds to assess the regional influence of topographic controls on shallow landsliding. Model performance varied widely between watersheds, with the best performance generally in steep watersheds underlain by shallow bedrock and the worst performance in generally low gradient watersheds underlain by thick glacial deposits. Landslide frequency (slides/km2) varied between physiographic provinces but yielded consistent patterns of higher slide frequency in areas with lower critical rainfall values. Simulations with variable effective cohesion predicted that high root strength effectively limits shallow landsliding to topographic hollows with deep soils and locations that experience excess pore pressures, but that low root strength leads to higher probabilities of failure across a greater proportion of the landscape.
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This paper deals with several aspects of the assessment of hazard and risk of landsliding. In recent years the interest in this topic has increased greatly and there are many technical papers dealing with this subject in the literature. This article presents a summary review and a classification of the main approaches that have been developed worldwide. The first step is the subdivision between qualitative and quantitative methods. The first group is mainly based on the site-specific experience of experts with the susceptibility/hazard determined directly in the field or by combining different index maps. The approaches of the second group are formally more rigorous. It is possible to distinguish between statistical analyses (bivariate or multivariate) and deterministic methods that involve the analysis of specific sites or slopes based on geo-engineering models. Such analyses can be deterministic or probabilistic. Among the quantitative methods discussed is the Neural Networks approach which has only recently been applied to engineering geology problems. Finally several considerations concerning the concept of acceptable risk and risk management are presented.
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Most debris flows in Japan are triggered by heavy rainfall. Most coincide with the rainfall peak, but some occur after the peak. Judging the timing of debris flow is critical for evacuation planning. We monitored rainfall and runoff in 11 small catchments underlain by granitic rock or Mesozoic sedimentary rock in central Japan to assess the use of runoff monitoring to predict the timing of debris flows. The runoff peak response time was longer in the sedimentary rock catchments. This result suggests that runoff processes are different between granitic and sedimentary rock catchments. The runoff in sedimentary rock catchments was likely delayed by passing through fractures in the bedrock, and debris flows in these catchments may be triggered by deep-seated landslides. Measuring rainfall runoff response by gauging multiple catchments could become an effective tool for preparing accurate evacuation plans.
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Of all the natural disasters occurring in Taiwan, tropical cyclones are the most serious. Over a 20-yr period, Taiwan was hit by an average of 3.7 typhoons per year. These storms can produce heavy rainfall and strong winds, leading to severe damage to agriculture and industry, and serious loss of human life. An outstanding example is Typhoon Herb, which made landfall in Taiwan on 31 July 1996. Typhoon Herb took 70 lives and caused an estimated $5 billion of damage to agriculture and property. Accurate prediction of the track, intensity, precipitation, and strong winds for typhoons affecting Taiwan is not an easy task. The lack of meteorological data over the vast Pacific Ocean arid the strong interaction between typhoon circulation and Taiwan's mesoscale Central Mountain range are two major factors that make the forecasting of typhoons in the vicinity of Taiwan highly challenging. Improved understanding of the dynamics of typhoon circulation and their interaction with the Taiwan terrain is needed for more accurate prediction. With this objective in mind, the National Science Council in Taiwan sponsored the Workshop on Typhoon Research in the Taiwan Area at Boulder, Colorado, on 17-18 May 1997. In this paper, the authors review the observational and numerical studies of typhoons affecting Taiwan, present some preliminary results from the study of Typhoon Herb, summarize the recommendations obtained from the workshop, and provide suggestions for future research.
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A GIS-based approach to modeling the spatial distribution of shallow debris slides combines a mechanistic infinite slope stability model with a steady-state hydrology model. The spatial distribution of a 'stability index' is governed primarily by specific catchment area (the upslope area per unit contour length) and slope. The model can be interactively calibrated within a GIS system to the unique characteristics of the topography, rainfall, and soils of a particular study area using simple parameters, graphs and maps. Once a landslide and terrain inventory is completed using aerial photographs, this approach is shown to have the capability of producing a stability classification map of a huge area in a very short time. An analysis of the Kilpala watershed of northern Vancouver Island, British Columbia is presented as an example.
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Landslides are triggered by factors such as heavy rainfall, seismic activity, and construction on hill-slopes. The leading cause of landslides in Puerto Rico is intense and/or prolonged rainfall. A rainfall threshold for rainfall-triggered landsliding is delimited by 256 storms that occurred between 1959 and 1991 in the central mountains of Puerto Rico, where mean annual rainfall is close to or in excess of 2,000 mm. Forty one of the 256 storms produced intense and/or prolonged rainfall that resulted in tens to hundreds of landslides. A threshold fitted to the lower boundary of the field defined by landslide-triggering storms is expressed as I = 91.46 D-0.82 where I is rainfall intensity in millimeters per hour, and D is duration in hours. Landslide-producing storms occurred at an average rate of 1.2 per year. In general the landslides triggered by short-duration, high-intensity rainfall events were mainly shallow soil slips and debris flows, while the long-duration, low-intensity rainfall produced larger, deeper debris avalanches and slumps. For storms that had durations of up to 10 h, landsliding did not occur until rainfall intensity was as much as three times as high as the rainfall intensity reported as sufficient to trigger landsliding in temperate regions. As storm durations approach 100 h, the rainfall conditions necessary to initiate landsliding in Puerto Rico converge with those defined for temperate regions. A comparison of the Puerto Rico threshold with rainfall data from other humid-tropical regions suggests that the threshold developed for Puerto Rico may be applicable to other similar environments throughout the world.
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Published records of the rainfall intensities and durations associated with shallow landsliding and debris flow activity suggests a limiting threshold for this type of slope instability. The limit has the general form: I = 14.82 D-0.39 and is best defined for rainfall durations between 10 minutes and 10 days.
Article
A model for the topographic influence on shallow landslide initiation is developed by coupling digital terrain data with near-surface through flow and slope stability models. The hydrologic model TOPOG (O'Loughlin, 1986) predicts the degree of soil saturation in response to a steady state rainfall for topographic elements defined by the intersection of contours and flow tube boundaries. The slope stability component uses this relative soil saturation to analyze the stability of each topographic element for the case of cohesionless soils of spatially constant thickness and saturated conductivity. The steady state rainfall predicted to cause instability in each topographic element provides a measure of the relative potential for shallow landsliding. The spatial distribution of critical rainfall values is compared with landslide locations mapped from aerial photographs and in the field for three study basins where high-resolution digital elevation data are available: Tennessee Valley in Marin County, California; Mettman Ridge in the Oregon Coast Range; and Split Creek on the Olympic Peninsula, Washington. Model predictions in each of these areas are consistent with spatial patterns of observed landslide scars, although hydrologic complexities not accounted for in the model (e.g., spatial variability of soil properties and bedrock flow) control specific sites and timing of debris flow initiation within areas of similar topographic control.
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Radar measurement uncertainties associated with storm top, cloud top, and other height measurements are well recognized; however, the authors feel the resulting impacts on the trends of storm features are not as well documented or understood by some users of the WSR-88D system. Detailed examination of radar-measured life cycles of thunderstorms occurring in Arizona indicates substantial limitations in the WSR-88D's capability to depict certain aspects of storm-height attribute evolution (i.e., life cycle) accurately. These inherent limitations are illustrated using a vertical reflectivity structure model for the life cycle of a simple, "single-pulse" thunderstorm. The life cycle of this simple storm is "scanned" at varying ranges and translation speeds. The results show that radar-determined trends are often substantially different from those of the model storm and that in extreme cases the radar-detected storm and the model storm can have trends in storm-top height of opposite sign. Caution is clearly required by both the operational and research users of some products derived from operational WSR-88D data.
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During the rainstorm of June 27, 1995, roughly 330–750 mm of rain fell within a 16-hour period, initiating floods and over 600 debris flows in a small area (130 km²) of Madison County, VA. We developed a distributed version of Iverson's transient response model for regional slope stability analysis for the Madison County debris flows. This version of the model evaluates pore-pressure head response and factor of safety on a regional scale in areas prone to rainfall-induced shallow (<2–3 m) landslides. These calculations used soil properties of shear strength and hydraulic conductivity from laboratory measurements of soil samples collected from field sites where debris flows initiated. Rainfall data collected by radar every 6 minutes provided a basis for calculating the temporal variation of slope stability during the storm. The results demonstrate that the spatial and temporal variation of the factor of safety correlates with the movement of the storm cell. When the rainstorm was treated as two separate rainfall events and a larger hydraulic conductivity and friction angle than the laboratory values were used, the timing and location of landslides predicted by the model were in closer agreement with eyewitness observations of debris flows. Application of spatially variable initial pre-storm water table depth and soil properties may improve both the spatial and temporal prediction of instability.
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Oklahoma thunderstorm data were used to determine how the estimation of area rainfall by radar can be improved by using one or several raingages. The radar data were collected between 1964 and 1968 with the WSR-57 radar at the National Severe Storms Laboratory, Norman, Okla. The rainfall data were obtained from the Agriculture Research Service's dense network of raingages near Chickasha, Okla. The improvement of area rainfall measurements by combining radar measurements with discrete raingage measurements is demonstrated. It is shown, for example, that the rms error of radar measurements of storm rainfall amount, for a 1000 mi² area, was reduced by 39% after the radar was calibrated with only one rain-gage. At least four uniformly spaced gages are required to measure storm rainfall amounts for the same area as accurately as the radar calibrated with only one gage. The present network of gages over the United States is approximately one gage per 1000 mi². The ability of radar to measure rainfall variability accurately has been demonstrated; therefore, it is possible to assess objectively whether a particular gage measurement will be useful for adjusting radar rainfall measurements. With the recent development of an effective system for automatically digitizing and communicating radar data in a form suitable for computer processing, these findings make possible the development of an operational system for measuring rainfall with an accuracy and timeliness never before achieved.
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The methods kriging with external drift (KED) and indicator kriging with external drift (IKED) are used for the spatial interpolation of hourly rainfall from rain gauges using additional information from radar, daily precipitation of a denser network, and elevation. The techniques are illustrated using data from the storm period of the 10th to the 13th of August 2002 that led to the extreme flood event in the Elbe river basin in Germany. Cross-validation is applied to compare the interpolation performance of the KED and IKED methods using different additional information with the univariate reference methods nearest neighbour (NN) or Thiessen polygons, inverse square distance weighting (IDW), ordinary kriging (OK) and ordinary indicator kriging (IK). Special attention is given to the analysis of the impact of the semivariogram estimation on the interpolation performance. Hourly and average semivariograms are inferred from daily, hourly and radar data considering either isotropic or anisotropic behaviour using automatic and manual fitting procedures. The multivariate methods KED and IKED clearly outperform the univariate ones with the most important additional information being radar, followed by precipitation from the daily network and elevation, which plays only a secondary role here. The best performance is achieved when all additional information are used simultaneously with KED. The indicator-based kriging methods provide, in some cases, smaller root mean square errors than the methods, which use the original data, but at the expense of a significant loss of variance. The impact of the semivariogram on interpolation performance is not very high. The best results are obtained using an automatic fitting procedure with isotropic variograms either from hourly or radar data.
Article
Debris flow is commonly initiated by torrential rain and its triggering is correlated to the hydrological, geological, and geomorphic conditions on site. In spite of the important effects of geology and topography, rainfall characteristic is the main external triggering factor to debris flow and is a predominant parameter for real-time monitoring. Due to the scarcity of sufficient spatial ground-based rainfall data in hill areas, quantitative precipitation estimation using remote-sensing techniques such as radar and satellite is needed for debris flow pre-warning. The QPESUMS (Quantitative Precipitation Estimation and Segregation Using Multiple Sensors) system was acquired to retrieve spatial rainfall data during the rainfall period from June 30 to July 6 in 2004 when Typhoon Mindulle and southwesterly flow struck Taiwan. The retrieved data were used for setting up the debris flow monitoring algorithm. With the aid of multiple platforms of meteorological observations, a rainfall threshold isohyet in a pilot area was mapped for debris flow monitoring. The rainfall monitoring algorithm based on QPESUMS provides more detailed information than the limited number of ground-based rainfall stations for interpreting the spatial distributions of rainfall events, and therefore is more suitable for debris-flow monitoring.
Article
A reflectivity‐rainfall rate (Z‐R) relationship is derived from Carvel radar and Edmonton rain gauge measurements. Our analysis indicates that the traditional point‐by‐point comparison method is not accurate for Alberta summertime precipitation due to timing errors in fast moving convective storms. The Window Probability Matching Method (WPMM) was superior and provided a robust Z‐R relationship in the form of Z = 32.5 R.
Article
Even though gauged rainfall data generally provide accurate depth measurements, sparsely spaced, gauging stations cannot effectively account for the spatial variability of precipitation at basin scale. On the other hand, radar data such as the WSR-88D stage III radar rainfall data can generally capture the spatial variability of rainfall fields, but tends to underestimate rainfall depth of stratiform storms, or both convective and stratiform storms if a storm is of low intensity. To take advantage of both the strength of radar data (mapping accurate spatial variability of rainfall) and that of gauge data (accurate depth measurements), the two data sets were merged together by the Statistical Objective Analysis (SOA) scheme. The event-based hydrologic experiments using a semi-distributed, physics-based hydrologic model (distributed physically based hydrologic model using remote sensing, DPHM-RS) revealed that WSR-88D Stage III radar rainfall data simulated more accurate runoff hydrographs than gauged data for convective storms but less accurate runoff hydrograph for stratiform storms, because radars measured slightly more rainfall than gauges for convective storms, but substantially less rainfall for stratiform storms. However, after merging WSR-88D stage III radar data with gauge data by SOA, the radar's underestimation of stratiform storm depth decreased substantially, but the adjustment could be counter productive for convective storms. Results show that rainfall spatial variability, depths, and hydrologic model resolution play a major role on the accuracy of simulated runoff volumes and peak flows. Copyright © 2006 John Wiley & Sons, Ltd.
Article
A rainfall-based landslide-triggering model, developed from previous landslide episodes in Wellington City, New Zealand, is tested for its ability to provide a 24-hour forecast of landslide occurrence. The model, referred to as the Antecedent Water Status Model, calculates an index of soil water, by running a daily water balance and applying a soil drainage factor to excess precipitation, over the preceding ten days. Together with the daily rainfall input, the soil water status has been used empirically to identify a threshold condition for landslide triggering.The prediction process provides a daily update of the soil water status and thereby the amount of rainfall required on the following day to equal or exceed the triggering threshold. The probability that this triggering rainfall will occur is then determined from the frequency/magnitude distribution of the local rainfall record. The model produces a satisfactory level of prediction, particularly for periods of concentrated landslide activity. Copyright © 1999 John Wiley & Sons, Ltd.
Article
Many landslides are triggered by rainfall. Previous studies of the relationship between landslides and rainfall have concentrated on deriving minimum rainfall thresholds that are likely to trigger landslides. Though useful, these minimum thresholds derived from a log–log plot do not offer any measure of confidence in a landslide monitoring or warning system. This study presents a new and innovative method for incorporating rainfall into landslide modelling and prediction. The method involves three steps: compiling radar reflectivity data in a QPESUMS (quantitative precipitation estimation and segregation using multiple sensors) system during a typhoon (tropical hurricane) event, estimating rainfall from radar data and using rainfall intensity and rainfall duration as explanatory variables to develop a landslide logit model. Given the logit model, this paper discusses ways in which the model can be used for computing probabilities of landslide occurrence for a real-time monitoring system or a warning system, and for delineating and mapping landslides. Copyright © 2007 John Wiley & Sons, Ltd.
Article
The growing availability of digital topographic data and the increased reliability of precipitation forecasts invite modelling efforts to predict the timing and location of shallow landslides in hilly and mountainous areas in order to reduce risk to an ever-expanding human population. Here, we exploit a rare data set to develop and test such a model. In a 1·7 km2 catchment a near-annual aerial photographic coverage records just three single storm events over a 45 year period that produced multiple landslides. Such data enable us to test model performance by running the entire rainfall time series and determine whether just those three storms are correctly detected. To do this, we link a dynamic and spatially distributed shallow subsurface runoff model (similar to TOPMODEL) to an infinite slope model to predict the spatial distribution of shallow landsliding. The spatial distribution of soil depth, a strong control on local landsliding, is predicted from a process-based model. Because of its common availability, daily rainfall data were used to drive the model. Topographic data were derived from digitized 1 : 24 000 US Geological Survey contour maps. Analysis of the landslides shows that 97 occurred in 1955, 37 in 1982 and five in 1998, although the heaviest rainfall was in 1982. Furthermore, intensity–duration analysis of available daily and hourly rainfall from the closest raingauges does not discriminate those three storms from others that did not generate failures. We explore the question of whether a mechanistic modelling approach is better able to identify landslide-producing storms. Landslide and soil production parameters were fixed from studies elsewhere. Four hydrologic parameters characterizing the saturated hydraulic conductivity of the soil and underlying bedrock and its decline with depth were first calibrated on the 1955 landslide record. Success was characterized as the most number of actual landslides predicted with the least amount of total area predicted to be unstable. Because landslide area was consistently overpredicted, a threshold catchment area of predicted slope instability was used to define whether a rainstorm was a significant landslide producer. Many combinations of the four hydrological parameters performed equally well for the 1955 event, but only one combination successfully identified the 1982 storm as the only landslide-producing storm during the period 1980–86. Application of this parameter combination to the entire 45 year record successfully identified the three events, but also predicted that two other landslide-producing events should have occurred. This performance is significantly better than the empirical intensity–duration threshold approach, but requires considerable calibration effort. Overprediction of instability, both for storms that produced landslides and for non-producing storms, appears to arise from at least four causes: (1) coarse rainfall data time scale and inability to document short rainfall bursts and predict pressure wave response; (2) absence of local rainfall data; (3) legacy effect of previous landslides; and (4) inaccurate topographic and soil property data. Greater resolution of spatial and rainfall data, as well as topographic data, coupled with systematic documentation of landslides to create time series to test models, should lead to significant improvements in shallow landslides forecasting. Copyright © 2003 John Wiley & Sons, Ltd.
Article
Shallow landsliding in the Seattle, Washington, area, has caused the occasional loss of human life and millions of dollars in damage to property. The effective management of the hazard requires an understanding of the rainfall conditions that result in landslides. We present an empirical approach to quantify the antecedent moisture conditions and rainstorm intensity and duration that have triggered shallow landsliding using 25 years of hourly rainfall data and a complementary record of landslide occurrence. Our approach combines a simple water balance to estimate the antecedent moisture conditions of hillslope materials and a rainfall intensity–duration threshold to identify periods when shallow landsliding can be expected. The water balance is calibrated with field-monitoring data and combined with the rainfall intensity–duration threshold using a decision tree. Results are cast in terms of a hypothetical landslide warning system. Two widespread landslide events are correctly identified by the warning scheme; however, it is less accurate for more isolated landsliding. Copyright © 2005 John Wiley & Sons, Ltd.
Article
Radar estimates of rainfall are being increasingly applied to flood forecasting applications. Errors are inherent both in the process of estimating rainfall from radar and in the modelling of the rainfall–runoff transformation. The study aims at building a framework for the assessment of uncertainty that is consistent with the limitations of the model and data available and that allows a direct quantitative comparison between model predictions obtained by using radar and raingauge rainfall inputs. The study uses radar data from a mountainous region in northern Italy where complex topography amplifies radar errors due to radar beam occlusion and variability of precipitation with height. These errors, together with other error sources, are adjusted by applying a radar rainfall estimation algorithm. Radar rainfall estimates, adjusted and not, are used as an input to TOPMODEL for flood simulation over the Posina catchment (116 km2). Hydrological model parameter uncertainty is explicitly accounted for by use of the GLUE (Generalized Likelihood Uncertainty Estimation). Statistics are proposed to evaluate both the wideness of the uncertainty limits and the percentage of observations which fall within the uncertainty bounds. Results show the critical importance of proper adjustment of radar estimates and the use of radar estimates as close to ground as possible. Uncertainties affecting runoff predictions from adjusted radar data are close to those obtained by using a dense raingauge network, at least for the lowest radar observations available. Copyright © 2004 John Wiley & Sons, Ltd.