Corso di Dottorato Regionale in Scienze della Terra
PhD Course in Earth Sciences
XXX Ciclo – 30
thCycle
2014 – 2017
Michele D’Ambrosio
Analysis of slope deposits for applications in the modelling
of shallow mass movements
Tutore - Supervisor: Prof. Filippo Catani
Coordinatore del Corso di Dottorato
Prof. Carlo Baroni
1
CONTENTS
RIASSUNTO………...…..…4
1
INTRODUCTION ... 7
2
STATE OF THE ART ... 10
2.1 HILLSLOPE: FORM AND PROCESS ... 10
2.2 HILLSLOPE DEPOSITS: CHARACTERISTICS AND PROPERTIES ... 15
2.3 SHALLOW MASS MOVEMENTS ... 20
2.4 MAIN FORECASTING MODELS ... 25
3
MATERIALS AND METHODS ... 32
3.1 GEOTECHNICAL-HYDROLOGICAL CHARACTERIZATION ... 32
3.1.1 Soils sampling and survey procedure... 32
3.1.2 Field tests ... 33
3.1.3 Laboratory test ... 43
3.2 THE SLOPE STABILITY SIMULATOR: HIRESSS ... 49
3.2.1 Hydrological model ... 49
3.2.2 Geotechnical model ... 52
3.2.3 Monte Carlo simulation ... 53
3.3 MODELS INPUT DATA ... 54
4
TEST SITES AND RESULTS ... 56
4.1 TOSCANA REGION ... 56
4.1.1 Geological and geomorphological settings ... 56
4.1.2 Lithological classification and land use of survey points ... 61
4.1.3 Soils classification, grain size distribution ... 63
4.1.4 Phase relations and Atterberg limits ... 67
4.1.5 Shear strength parameters, matric suction and saturated hydraulic conductivity ... 68
2
4.2 VALLE D’AOSTA REGION ... 73
4.2.1 Geological and geomorphological settings ... 73
4.2.2 Soils classification, grain size distribution ... 77
4.2.3 Phase relations and Atterberg limits ... 78
4.2.4 Shear strength parameters, matric suction and saturated hydraulic conductivity... 80
4.2.5 Root reinforcement ... 81
5
DISCUSSION AND HYDROGEOLOGICAL APPLICATIONS ... 84
5.1 TOSCANA REGION ... 86
5.2 VALLE D’AOSTA REGION ... 100
6
CONCLUSIONS ... 112
7
REFERENCES ... 114
8
PUBLICATIONS ... 129
4
RIASSUNTO: Analisi dei terreni di copertura ai fini
dell’impiego in modelli di previsione delle frane superficiali
Il paesaggio collinare è fortemente influenzato da movimenti di massa superficiali. Lo strato più superficiale, costituito da depositi di versante o regolite, è la parte più esposta e quindi soggetta a processi morfogenetici. La disponibilità di misure omogenee e affidabili dei parametri ambientali di questi depositi di versante è un aspetto importante delle moderne Scienze della Terra. Tuttavia, l'aspetto più problematico quando si cerca di usare modelli di previsione di frane a scala di bacino o regionale è la disponibilità di parametri di input e la loro variazione spaziale (Khazai e Sitar 2000, Guzzetti et al., 2007). Infatti, le analisi delle proprietà geotecniche di terreni e depositi superficiali sono, sorprendentemente, ancora mancanti a livello regionale.L'obiettivo principale di questa tesi di dottorato è quello di fornire un insieme omogeneo di dati sulle proprietà principali dei terreni e dei depositi necessari per definire le loro caratteristiche geotecniche ed idrologiche mediante una serie di estensive campagne di misure in situ e misure di laboratorio. Questi tipi di dati in un contesto regionale sono utili per: i) determinare gli intervalli di variazione dei parametri geotecnici e idrogeologici che controllano i meccanismi di innesco di frana; ii) identificare successivamente le aree in cui le situazioni critiche di rischio idrogeologico potrebbero aumentare. I dati ottenuti sono stati studiati per valutare le relazioni tra i diversi parametri e la litologia del bedrock. Inoltre, un ulteriore obiettivo è quello di fornire dati di input per i modelli fisici volti a prevedere le frane superficiali indotte dalle piogge.
L’attività di caratterizzazione ha riguardato due aree di studio: la Regione Toscana e una parte della regione Val d’Aosta, in modo da analizzare differenti contesti geomorfologici e geolitologici. Complessivamente sono stati selezionati 129 siti di indagine sul territorio toscano e 12 nell’area della Val d’Aosta. I siti sono stati selezionati in base alle caratteristiche fisiografiche (quota e pendenza), alla localizzazione di fenomeni franosi (es. progetto DIANA, Casagli et al., 2013a, b) e alle caratteristiche geo-litologiche del substrato affiorante (es. progetto CARG), tenendo in considerazione anche la disposizione dei siti di indagine esistenti e ricavati dalla letteratura. Per ogni sito di indagine, sono state condotte le seguenti analisi, ad una distanza di pochi metri l’una dall’altra: 1) Registrazione della posizione geografica mediante GPS e documentazione fotografica delle caratteristiche del sito (morfologia e vegetazione); 2) Misura dei parametri di resistenza al taglio del terreno tramite apparecchio di taglio in foro (“Borehole Shear Test”, BST); 3) Misura della suzione o tensione matriciale del
5 terreno tramite il tensiometro; 4) Misura della permeabilità satura (K-sat) tramite il permeametro a carico costante Amoozemeter; 5) Prelievo di un’aliquota di circa 3 kg di campione per le prove di laboratorio.
I campioni raccolti in situ (prelevati direttamente dal foro scavato per le misure BST) sono stati analizzati in laboratorio per definire un’ampia serie di parametri e le principali proprietà indice per caratterizzare in maniera più completa i terreni. In particolare sono state svolte: 1) Analisi granulometrica e classificazione dei terreni (determinazione della curva granulometrica per setacciatura e sedimentazione); 2) Determinazione delle principali proprietà indice (contenuto d’acqua naturale wn, peso di volume naturale γ e secco γd); 3)
Determinazioni dei limiti di Atterberg (limite liquido LL e limite plastico PL, e dell’indice di plasticità IP); 4) Prove di taglio diretto (determinazione dell’angolo di attrito interno ɸ’ e della coesione c’ efficaci).
L'analisi di queste misure ha consentito di evidenziare l'estrema eterogeneità della granulometria dei depositi di versante. Infatti, ci sono importanti variazioni nella frazione di ghiaia e argilla. Queste variazioni sono probabilmente responsabili delle variazioni nelle proprietà geotecniche (resistenza al taglio e permeabilità) presenti in questi terreni. Tuttavia, nonostante variazioni significative, è stato possibile associare modelli di distribuzione statistica (ad es. modelli normali o gaussiani) che descrivono tali variazioni nelle proprietà di questi depositi. I risultati sono stati quindi trattati statisticamente e spazializzati secondo le litologie principali nelle due aree di studio. Questi dati sono stati usati come dati di input di un modello fisicamente basato (HIRESSS) e due modelli deterministici (SHALSTAB e TRIGRS) per la previsione di frane superficiali.
Per quanto riguarda la regione Toscana, è stato selezionato un evento meteorico passato per analizzare ulteriormente la capacità del modello nella previsione di frane a livello regionale, utilizzando i parametri geotecnici e idrologici raccolti. Questo evento, avvenuto nell'ottobre 2010, ha innescato circa 50 frane nell’area di studio. La validazione qualitativa ha dimostrato che una caratterizzazione dettagliata dei parametri dei suoli e la loro spazializzazione possono aumentare la capacità del modello di previsione per identificare correttamente le aree con maggiore potenziale di instabilità durante l'evento meteorico. L'utilizzo di dati reali raccolti in situ come input per il modello HIRESSS fornisce risultati migliori rispetto all'utilizzo dei dati della letteratura in termini di prevedibilità del meccanismo di instabilità del pendio.
Nelle simulazioni condotte con i dati riguardanti la Valle d'Aosta, il modello HIRESSS è stato validato sia a livello temporale che spaziale su due eventi storici di intense precipitazioni
6 che hanno colpito la regione Val d’Aosta tra gli anni 2008 e 2009 provocando l’innesco di molti fenomeni franosi a cinematica rapida superficiale. La validazione dei risultati ottenuti può essere effettuata considerando la correlazione temporale tra i dati calcolati dal modello numerico e quelli descritti relativi all’evento realmente accaduto, oppure considerando la correlazione spaziale. La validazione temporale considera l’evento pluviometrico nella sua totalità dai giorni precedenti al momento della massima allerta, dove le precipitazioni registrate sono scarse o nulle, fino al giorno in cui viene registrata la massima intensità di pioggia. In questo modo il modello numerico HIRESSS calcola una bassa probabilità per i giorni nei quali non vengono registrate precipitazioni fino ad arrivare a calcolare una probabilità massima nei giorni in cui avvengono le precipitazioni più intense.
La validazione spaziale viene effettuata seguendo il metodo Pixel by Pixel; questo metodo consiste nel comparare la probabilità d’instabilità di ogni pixel con i pixel coinvolti nell’evento franoso realmente accaduto. Questa validazione comporta molta incertezza nei risultati poiché le segnalazioni di eventi franosi possono avere degli errori sulla precisa localizzazione spaziale e sulla dimensione del fenomeno.
Nel caso di studio, a causa di errori relativi alla precisione della localizzazione spaziale degli eventi, è stato deciso di considerare un'area di influenza intorno al punto di frana di circa 1 km2, entro il quale è stato calcolato il numero di pixel sopra il 75% della probabilità di frana.
La correlazione temporale tra la simulazione e gli eventi reali è quasi perfetta in quanto il picco di pioggia corrisponde ad un aumento della probabilità di frana. La validazione spaziale ha mostrato una correlazione più difficile con i dati osservati. Con la validazione pixel-by-pixel, si può vedere che il numero di pixel con una elevata probabilità di frana difficilmente supera il 15% a causa della presenza di numerose aree a bassa probabilità di frana all'interno dell'area di influenza. Da queste simulazioni è stata dimostrata una buona correlazione dal punto di vista temporale e abbastanza dal punto di vista spaziale del modello HIRESSS rispetto agli eventi reali. Per migliorare la validazione spaziale, è necessario aumentare il numero di dati di partenza per ottenere, mediante l'interpolazione, un modello il più vicino possibile alla realtà.
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1
INTRODUCTION
Most of the Italy’s surface is occupied by hillslopes, whose are heavily affected by shallow mass movement. The top layer, consisting of regolith or slope deposits, is the most exposed part and therefore subject to morphogenetic processes. The availability of homogenous, reliable measurements of environmental parameters of this slope deposits is an important feature of the modern Earth Sciences. Nevertheless, the most problematic aspect when trying to model shallow landslide at basin or larger scale is the availability of input parameters and their spatial variability (Khazai and Sitar 2000; Guzzetti et al. 2007). Indeed, regionally diffused measurements concerning soils and shallow deposits geotechnical properties are, surprisingly, still lacking.
The main aim of this PhD thesis is to provide a homogenous set of data concerning the principal properties of soils and deposits that are needed in order to define their geotechnical and hydrological characteristics, by means of an extensive field campaign of in situ measurements and laboratory measures. These kinds of data in a regional context are useful in order to: i) determine the ranges of variation of the geotechnical and hydrogeological parameters that control landslide triggering mechanisms, ii) subsequently individuate the areas of the study sites where critical hydrogeological hazard situations should rise. The data obtained have been studied in order to assess the relationships existing among the different parameters and the bedrock lithology. In addition, a further objective is to provide constrains for physical models aimed to forecast rainfall induced shallow landslides.
Physically based approaches for modelling rainfall-induced shallow landslides are an intensely debated research topic among the earth sciences community, and many models have been presented thus far (Dietrich and Montgomery 1998; Simoni et al. 2008; Pack et al. 2001; Baum et al. 2002, 2010; Rossi et al. 2013; Lu and Godt 2008; Ren et al. 2010; Arnone et al. 2011). However, the application of models over large areas is hindered by a poor comprehension of the spatial organization of the required geotechnical and hydrological input parameters. In the last years, geotechnical and hydrological parameters have been proven to be more troublesome to manage because they are characterized by an inherent variability and their measurement is difficult, time-consuming, and expensive, especially when data are needed for large areas (Carrara et al. 2008; Baroni et al. 2010; Park et al. 2013).
Consequently, in reviewing the literature about providing distributed slope stability modelling with spatially variable geotechnical parameters, it is impossible to find an approach that is universally accepted and that can be used as a standard.
8 In many cases, for each geotechnical parameter, a constant value is used for the whole study area as averaged from in situ measurements (Jia et al. 2012) or derived from literature data. In some studies, a limited degree of spatial variability is ensured using a certain value for distinct geological, lithological, or engineering geological units, as derived from direct measurements (Segoni et al. 2009; Baum et al. 2010; Montrasio et al. 2011; Zizioli et al. 2013) or from existing databases and published data (Lepore et al. 2013; Ren et al. 2014; Tao and Barros 2014). The variability and uncertainty in geotechnical input parameters heavily reflect on the results when a deterministic approach is used in physically based models, and in recent years, the use of probabilistic approaches has widely increased as it allows a more proper consideration of uncertainties and inherent variability of the input data (Park et al. 2013). For instance, Santoso et al. (2011) used a probabilistic approach, even if limited to the characterization of the permeability, while many authors considered cohesion and friction angle as random variables using a probabilistic or stochastic approach (Griffiths et al. 2011; Park et al. 2013; Mercogliano et al. 2013; Chen and Zhang 2014; Fanelli et al., 2016; Salciarini et al. 2017).
Starting from this state of the art, the present PhD thesis shows two regional scale applications of some distributed slope stability models, in which the results of geotechnical and hydrological characterization are implemented. Soil properties have been statistically characterized in order to define the input parameters in the physical models, with the final aim of testing the ability of the models to predict shallow landslide occurrence in response of an intense meteoric precipitation.
The two study areas correspond to Toscana region (central Italy) and a part of Valle d’Aosta region (north Italy). The physically based distributed stability model (HIRESSS) used is developed by Rossi et al. (2013). Further simulations (smaller than the previous ones in terms of extension of the study area) were performed using TRIGRS (Baum et al., 2002) and SHALSTAB (Dietrich and Montgomery, 1998) models regarding to Toscana region only.
This PhD thesis is part of two regional project: ▪ GeoDBTerre, regarding to Toscana region,
▪ Scientific collaboration between DST-UNIFI and Valle d’Aosta region.
Within the PhD programme, a three-month’s research period was carried out in South Korea, at Sejong University (Engineering Geology and Geohazard laboratory in Department of Geoinformation Engineering), where, together with prof. Park team, we worked to analyse the landslide susceptibility analysis in Toscana area of Italy using physically based model and
9 submitted a conference paper for 2017 Spring conference of Korean Society of Engineering Geology (KSEG 2017).
In addition to the Introduction (Chapter 1), this thesis includes a total of eight chapters, structured as follows:
Chapter 2: Contains an overview of the geomorphological settings of hillslope and their form and process; it also includes an overview of the shallow mass movements and the main existing forecasting models.
Chapter 3: Contains the detailed description of the methods used to geotechnical-hydrological characterization; it also includes a description of the physically based model (HIRESSS) used to simulations.
Chapter 4: Contains the results of geotechnical-hydrological characterization performed in two cases studies.
Chapter 5: Contains a discussion of the results and their hydrogeological applications using forecasting models.
Chapter 6: Summarizes the main findings of the thesis.
Chapter 7: Contains the main references of this work, listed in alphabetical order. Chapter 8: Consist of a list of the paper written during the three years PhD course. Chapter 9: The appendix contains a table of geotechnical properties of the sites investigated in Toscana region.
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2
STATE OF THE ART
2.1 HILLSLOPE: FORM AND PROCESS
Hillslopes constitute a basic element of all landscapes (Finlayson and Statham 1980) and a fundamental component of geomorphologic systems. However, there is an ‘amazing absence of any precise definition’ of hillslopes (Schumm and Mosley 1973; Dehn et al. 2001). Hillslopes have a very large variety of sizes and forms, being continuously subjected to constant changes (Figure 1); and several synonymous terms are used to describe the phenomenon hillslope, e.g. valley slope, hillside slope, mountain flank.
Figure 1 An example of landscape occupied by hillslope (Tuscany, original photo)
Generally, a hillslope is a landform unit, that is, a part of the Earth’s surface, with specific characteristics (Anderson and Anderson, 2010; Gutiérrez et al. 2016). As a basic characterization, a hillslope can be defined as an inclined landform unit with a slope angle larger than a lower threshold βmin (delimiting hillslopes from plains) and smaller than a higher
threshold βmax (delimiting hillslopes from vertical walls like cliffs or overhangs), which is
11 According to Young (1972) and Parsons (1988) a hillslope profile can be defined as a line on a land surface, connecting a starting point at the drainage divide with an end point at the thalweg, following the direction of the steepest slope. Hillslope profiles have been used to characterize several types of terrain using typical distribution of slope angles. Differences in the frequency distributions of slope angle are related to lithology (material resistance), climate (stress through rainfall, temperature), and evolutionary state of the slope (see limiting angles above). Hillslope profiles usually cover several process domains. Often the upper section of a slope is characterized by erosion, the middle section by transport and the basal section by deposition.
Complete hillslopes are often represented by hillslope profiles (Figure 2). According to the model of Dalrymple (1968), it can be subdivided a slope in the following nine particular unit:
• interfluve: divided area characterized by largely vertical subsurface water and soil movement
• seepage slope: gently dipping portion dominated by downward percolation • convex creep slope: upper convex zone characterized by creep and terracette
formation
• fall face: Cliff face characterized by rapid detachment of material or bedrock (weathering limited) exposure.
• transportational mid-slope: Active region characterized by mass movement, terracette formation, slope wash and subsurface water action
• colluvial foot slope: Depositional region. Material is further transported down slope by creep, slopewash and subsurface flow.
• Alluvial toe-slope: region of alluvial deposition (e.g. levee deposits) • Channel wall: removal by corrosion, slumping, fall etc.
• Channel bed: Downstream transport of material
In profile, hillslopes may be convex, straight, or concave (Abrahams, 1986). In humid and temperate regions, soil-mantled slopes generally steepen downslope from convex ridge tops at drainage divides. They also typically have a planar midslope segment with a constant angle, and a concave basal zone at the bottom of the slope. Straight portions of slope profiles are typically more pronounced in steep terrain where frequent landslides plane off topography in the mid-slope zone. In contrast, arid slopes often have a vertical cliff face downslope of the ridgetop, and exhibit slope variations that are controlled by the relative strength of the bedrock, with harder rocks holding up steeper slopes and weaker rocks supporting gentler slopes.
12 Figure 2 The nine-unit slope model of Dalrymple et al. (1969).
In map view, slopes are described as convergent, divergent, or planar, depending on the predominant erosional and depositional mechanisms. On convergent slopes, flow lines converge downslope, and divergent slopes are those where flow lines diverge downslope. Two adjacent balls rolling down a convergent slope would be at risk of colliding. They would move farther apart rolling down a divergent slope. Likewise, sediment moving down slopes tends to accumulate in convergent valleys over time. In general, ridgelines are divergent, and valleys are convergent. Like inclined planes, planform planar slopes are neither convergent nor divergent.
The diffusion-like action of creep and sheetwash creates zones of hilltop convexity and divergent noses on soil-covered hillsides. Long, planar slope segments common in steep, rapidly eroding terrain reflect the influence of landslides, but landslides also create relief where they incise mountainsides and excavate new valleys. The mix of convergent, planar, and
13 divergent terrain in a landscape reflects aspects of its geomorphic setting. Narrow hilltop convexities and long planar slopes are typical in rapidly uplifting and eroding terrain. Broad, rolling hills generally reflect slower uplift and erosion rates.
Hillslopes can be thought of as being composed of a mosaic of slope types, ranging from steep mountains and cliffs to almost flat plains. Slopes made of loose, unconsolidated sediment, mantled by soil, and that expose bedrock each offer substantially different resistance to erosion and gravity-induced failure. Because of this, the material that makes up a slope strongly influences the processes that determine its evolution and morphology and then reflect the processes shaping them. On most hillslopes, large quantities of soil and sediment are moved over time by air, water, and ice often under the direct influence of gravity.
It is possible to consider hillslopes as a process-response system (Pidwirny, 2006). The hillslope system receives inputs of solar radiation, precipitation, solid and dissolved substances from the atmosphere, and unconsolidated sediment derived from the weathering of bedrock. The inputs of unconsolidated sediment are controlled by weathering rates. Outputs to hillslopes occur by evapotranspiration, by percolation of water and the movement of dissolved substances into the bedrock, and by removal of sediment by streams by glaciers or by ocean waves and currents. Outputs of debris or sediment from hillslope systems are controlled primarily by the availability of erosional mechanisms to transport material that accumulates at the slope's surface and base. For example, the presence of a stream at the base of a hillslope encourages removal of sediment that moves downslope. If the stream's discharge is too small to handle the debris input, sediment will accumulate at the base of the slope. The degree of hillslope inputs and outputs depends upon several factors, including bedrock geology, climate, and the nature of the slope to the broader landscape. The balance between inputs and outputs from the hillslope system exerts a major control over the form of the developing slope. In situations where inputs are the controlling factor, the slope is said to be weathering limited because outputs quickly remove any accumulating debris. Where the potential for weathering is high but outputs are restrained, the hillslope system is classified as being transport limited. Landscapes that are transport limited are easily recognized by the presence of a deep soil profile.
Weathering and transport limitations have been used as an important concept in geomorphology and are basic to an understanding of hillslope development. The two terms were originally introduced to geomorphology by G.K. Gilbert in his monograph on the geology of the Henry Mountains (Gilbert 1877). The same concept was revisited by Alfred Jahn in 1954, which uses the denudational balance of slopes to classify slope processes.
14 Kirkby (Carson and Kirkby 1972) first coined the terms weathering-limited and transport-limited. At one extreme, weathering-limited slopes (also called production-limited slopes) have net rates of sediment transport that are determined by the rate at which weathering provides new material. On these slopes, soil erosion rates generally match rates of soil production, so weathered material is transported downslope about as rapidly as it is produced. These slopes tend to be bedrock slopes with thin soils. The steepness of weathering-limited slopes is generally controlled by rock mass strength, and slope morphology is often closely related to the underlying rock type. Weathering-limited slopes are commonly found in arid regions where weathering and rock detachment rates are slow. Transport-limited slopes have soil production rates that equal or exceed rates of sediment transport, a condition that results in the development of a persistent soil mantle. There is enough transportable material available on these slopes that the transport capacity of erosional processes governs the rate at which sediment leaves the slope. Transport-limited slopes are less influenced by the properties of the underlying bedrock and more strongly controlled by soil properties. Consequently, the smoothly convex to planar form of soil-mantled slopes typically masks variations in the underlying rock type as well as bedrock structures like folds, joint patterns, and fault scarps. The distinction between weathering-limited and transport-limited slopes generally corresponds to differences between bedrock and soil-mantled slopes, respectively.
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2.2 HILLSLOPE DEPOSITS: CHARACTERISTICS AND
PROPERTIES
In literature, the word "soil" (an engineering term) is used to designate unconsolidated shallow material.
Many authors talk about “Regolith”; the term was coined by Merrill (1897) to describe an ‘incoherent mass of varying thickness composed of materials essentially the same as make up the rocks themselves, but in greatly varying conditions of mechanical aggradation and chemical combination’.
Jackson (1997) defines regolith as a term for ‘the layer or mantle of fragmental and unconsolidated material, whether residual or transported and highly varied in character, that nearly everywhere forms the surface of the land and overlies the bedrock’.
The regolith, or hillslope deposits, (Blikra et al. 1998), are slope-waste material, typically immature with highly variable granulometry. It is “the entire unconsolidated or secondarily re-cemented cover that overlies more coherent bedrock” and which “has been formed by weathering, erosion, transport and/or deposition of the older material” (Eggleton, 2001). Hillslope deposits may derive from different sources and processes, depending on the physical agents and/or the chemical processes that have acted on it, breaking up and altering the bedrock. In addition, colluvial deposits can be originated from human activities such as agriculture, sheep farming and deforestation.
Typically, the thickness of the regolith change along the hillslope profile according to the position (Figure 3).
It consists of physically broken and, generally, chemically altered rocks, formed by fragments of bedrock (parent material) highly altered until their degradation to clay, silt and sand (Strahler, 1984), and lies beneath of a bedrock, made up of unaltered consolidated, rocks. The altered and the parent materials can have similar geotechnical property.
Rocks, when moderately to intensively chemically altered in situ, form profiles that consist of progressively more altered bedrock towards the surface – an in-situ weathering profile. Weathered debris may be moved by surface erosion or moved below the surface – in solution or physically – by groundwater and biota. Such eroded components may be deposited to form transported regolith elsewhere in the landscape (Scott, 2009).
16 Figure 3 An example of hillslope (from Strahler, 1984).
The top layer of the regolith, the soil, is a mixture of minerals and organic matter and it is the seat of the main chemical and biological process. The chemical interaction starts with dissolution of components from the minerals in the rock and oxidation of readily oxidable ions, such as ferrous iron.
In situ regolith weathering profile often have a characteristic sequence of materials
developed in them. Taylor and Eggleton (2001) provide detailed descriptions and interpretations of weathering profiles (Figure 4).
Essentially, the sequence is: • soil
• ferruginous and/or aluminous lag
• collapsed saprolite (may be mottled by ferric oxihydroxides) • saprolite mottled by ferric oxihydroxides
• bleached saprolite (composed of kaolinite and/or quartz grading downward into more complex clay minerals and quartz ± other primary minerals)
• saprock
17 Hillslope deposit can have different genesis, depending on physical agents and/or chemical processes that have acted on it, breaking up and altering the bedrock (Strahler, 1984).
Depending on the source and the process of accumulation, the different kind of deposits can be distinguished in:
• eluvium, that is the material formed in situ due to weathering process of the underlying rock, intensively affected by transport or leaching;
• colluvium, which is the coverage that accumulates at the foot of slopes through slope processes and is constituted by rocks and materials which can be different from those underlying;
• alluvium, which is the finest part of soil, transported and deposited primarily by fluvial agency.
18 Colluvium is the unsorted, unstratified hillslope material that overlies bedrock. It generally forms by processes that do not involve water or wind — gravity-driven mass wasting, frost wedging, and burrowing activity. Hillslope soils tend to be colluvial because they are produced by weathering of underlying bedrock and are gradually transported downhill under the force of gravity.
The colluvium structure is massive and lacks sedimentary structures (i.e. layers), except for a rare coarse stratification; it often contains palaeosols, which represent halts in deposition, crude bedding downslope, and a large range of grain sizes and fabrics (Bertram et al. 1997). Owing to its grain size, colluvium is characterized by isolated pebbles and gravel clasts dispersed in a silty matrix. Lithology of the clasts could be the same of the underlying bedrock or clasts could be inherited as residual elements derived from the weathering and transport of others source rocks. The fine material derives from the alteration and the erosion of the bedrock. The thickness of the colluvium is generally poor (some cm), but in some cases, it can be considerable (up to several m), with rapid lateral variations, and can infill bedrock depressions (Crozier et al. 1990). The top layer is the most exposed part and therefore affected by slope modelling process.
Because regolith can be the product of many different geological processes, its physical characteristics (for example, density, permeability, porosity and strength) can be highly variable over short distances. Rock type determines the rate at which the rock weathers and the possible products of weathering. The products of weathering also depend on the degree to which the rock has been weathered. The extent to which the weathering processes continue determines the actual character of the regolith profile (Scott, 2009).
Water plays a critical role in regolith development and surface and sub-surface flow are themselves modified by the structural make-up of the regolith (Scott, 2009). For example, groundwater may flow through both in situ and transported regolith at variable depths (Figure 5).
Characterization of the physical properties of regolith is of paramount importance in many engineering and environmental projects. In some cases, regolith must be removed in order to construct foundations on bedrock. In other cases, it is the regolith itself in which structures are anchored or which constitutes the source of material for embankments and other earthworks. Regolith can also be a locally significant aquifer that yields groundwater relatively easily and inexpensively.
19 Figure 5 The effect of groundwater in regolith processes (from Taylor and Eggleton 2001).
The geomorphological and geotechnical study of slope and covers is of great importance in order to assess whether the slope is stable or not and which terrain types and properties are involved in such phenomena. The material properties of soils and weathered rock often strongly influence the morphology of soil-mantled slopes, because of their poor geotechnical properties and water content.
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2.3 SHALLOW MASS MOVEMENTS
Shallow mass movements are a small part of a very large family of movements caused by gravity and defined as Landslides. They involve different materials, geometry and dynamics of collapsed material.
A landslide is a downslope movement of rock or soil, or both, occurring on the failure plane—either curved (rotational slide) or planar (translational slide) rupture—in which much of the material often moves as a coherent or semicoherent mass with little internal deformation (Highland et al., 2008). Material is mobilized when the shear stress imposed on a surface exceeds the shear strength.
Some classifications have been proposed by Varnes (1958), Hutchinson (1988) Hungr et al. (2001) but the most widely used classification of slope movement is that which was again modified by Varnes in 1978 and then revised in 1996. Landslides are classified into five types of movement and differentiated by two classes of material involved. The five movement types are: falls, topples, slides (rotational and translational) lateral spreads and flows (Figure 6). A sixth type, the complex movement, is defined as a combination of two or more basic types of movement. The two classes of materials are: rocks and soils. Soils can be subdivided in two classes: predominantly fine soils and predominantly coarse soils (Cruden and Varnes, 1996). When slopes are composed of predominantly coarse sediments it is common to identify landform features like alluvial fans, screes, talus cones, sand dunes, and glacial outwash deposits (Abrahams, 1986; Carson and Kirkby, 1972).
On slopes of this type, mass movement often occurs through the sliding or rolling of a small number of particles as localized instabilities develop. In some cases, these movements can organize themselves into larger avalanches through a domino effect. Mass movement on non-cohesive materials can also occur by way of shallow sliding. Shallow sliding occurs when planes of weakness develop just beneath the surface of the slope. Planes of weakness develop where horizontal layering occurs in the sediment. This layering can be caused by the nature of sediment deposition, percolation of water, or by the presence of subsurface soil, sediment or rock layers (Easterbrook, 1999).
When the mass movement processes operate on fine sediments, they occur over very long-time spans. One of the most widespread of these processes is soil creep (Cruden and Varnes, 1996). Soil creep is the downslope movement of soil and sediment under the influence of gravity by processes that are too slow to see without instrumental measurement or other indicators of long-term movement. Soil creep involves the movement of slope sediments in a
21 series of numerous cyclical steps. This process is often caused by the cyclical effects of temperature fluctuations, variations in moisture, gravity on inclined soil sediments, seasonal heave from ice or expanding clays, downslope movement of soil from the burrowing activity of animals, and material displaced downhill by uprooted trees.
22 Downslope creep rates are quite variable, and depend on slope angle, climate, soil moisture content, and particle size, but they rarely exceed a few millimetres per year (Carson and Kirkby, 1972). Creep rates typically decrease with depth below the ground surface, and most displacement happens within about a half meter of the surface. Evidence of active soil creep often includes such indicators of net downslope movement as tilted fence posts, pistol-butted trees, cracked building foundations, and accumulation of soil on the upslope side of fixed obstructions. Slow, gravity-driven deformation of mechanically over- steepened slopes can lead to rock creep and downslope bending of near-surface bedrock layers.
In cold regions with permafrost, seasonal thawing of the surficial layers can result in Solifluction, creep of saturated soils over impermeable, frozen ground (Cruden and Varnes, 1996). Solifluction is the slow movement of soil caused by freeze-thaw action. This process is a widespread in polar and sub-polar regions where permafrost exists. Solifluction occurs when seasonal or daily fluctuations of temperature are above freezing. At these temperatures, the upper portion of the soil surface and permafrost thaw creating waterlogged mass because subsurface ice prevents drainage. The waterlogged mass then flows downslope as lobes of sediment and surface vegetation.
A common feature among this kind of mass movement is the lower thickness of the sediments. According to the classification of Cruden and Varnes (1996), if a slide, flow or a complex landslide involves only a few meters of a soil depth, it is defined as a shallow landslide. As reported in landslides classifications proposed by Varnes, a shallow landslide can be described as a slope movement of a few meters soil depth. Shallow landslides get their name to scant depth of the surface movement (from about 10 cm up to approximately 1.5 m) and to small spatial dimensions of about 2 to 200 m². They have instant evolution (don't give warning signs) and occur primarily on slopes with gradients ranging between 18° and 45°, resulting in soil loss and damage to the landscape. Although the dimensions are often contained, the consequences of these phenomena are relevant, such as for agriculture, tourism and infrastructure (roads) and usually occur in clusters on a single slope (Wiegand and Geitner 2010).
Flows of soils are one of the most dangerous shallow landslides: their threat is attributed to the high velocity that they can reach during the runout and to the nearly total absence of premonitory signals. Shallow landslides are triggered by heavy rainfalls as well as by generous snow melt, particularly in hilly areas (Stoffel et al., 2014). Moreover, small and apparently harmless debris flows, triggered by a small zone of instability slope, can group together from different sources in channels greatly increasing mass displacement and destructive power
23 reaching speeds up to 20 m/s. High kinetic energy, due to high runout velocities, is extremely dangerous also for buildings and infrastructures. Even a soil slip is considered a shallow landslide, but, most of the time this type of landslide is the beginning of a soil flow: the soil collapses and can evolve into a flow after the involved material liquefaction due to the increasing pore pressure along the slip surface.
Another common type of shallow mass movements in cohesive materials are mudflows. It can be from very rapid to extremely rapid flows of saturated plastic debris in a channel, involving significantly greater water content related to the source material. Clay generates longer runout due to the dilution delay by water and drainage (Scott et al., 1992). These processes occur over very short time periods when slope materials become so saturated that the cohesive bonds between particles is lost.
It is a complex task to assess a critical state with respect to slope instability, since the relationship between precipitation and landslide susceptibility is not entirely clear, and the hillslope is constituted by a compound system (bedrock, regolith, weathered and bioturbated layers, vegetation cover) with different geotechnical properties.
Shallow landslides usually develop on steep terrain and in areas characterized by no woodland or with brush and small trees. This triggering usually occurs in the upper part of the slope, often in relation to an abrupt slope angle change or along the edge of a natural or artificial escarpment. There are many causes that can lead to the triggering of a flow in soil. The most common is a change in the interstitial water pressure system due to rainfall: as the soil gradually saturates, pore-water pressures increase and shear strengths decrease (Sidle et al, 1982). Another cause for triggering can be the loss of the apparent cohesion component during intense rainfall (Fredlund, 1987). Other causes can be a variation to the external force system due to an earthquake or to natural erosion or to anthropic activity. However, the key cause remains strongly connected to heavy rainfall.
The predisposing factors should be distinguished in three main groups:
• Hydrology: the initial moisture condition of a soil affects the slope stability and the movement triggering timing. The rise of the water table, variations in groundwater seepage or change in flow direction from recharge to discharge areas are hydrological factors that can trigger a shallow landslide (Zêzere et al., 1999, 2005; Tsai & Yang, 2006; Tsai, 2008);
• Lithology and geology: soil properties like cohesion, internal friction angle and soil unit weight affect the slope stability because they directly influence the mechanical
24 failure strength. Permeability is one of the most important factors that controls the surface and underground hydraulic circulation. The time needed to completely saturate a soil is strictly related to the permeability thus affecting the probability of reaching critical pore pressure. In addition, the stratigraphy of a terrain is important because the presence of one or more impermeable layers can cause a rapid saturation of the upper layers reaching the critical pore pressure which triggers the landslide (Iverson & LaHusen, 1989; Iverson et al., 1997; Iverson, 1997; Iverson et al., 2000; Takahashi, 1978, 1981);
• Morphology and topography: morphological features like the slope gradient greatly affect the stability of soil and the triggering threshold. Superficial and bedrock topography can control the superficial and groundwater flow and affect the moisture condition of soil (Pierson, 1980; Renau & Dietrich, 1997; Montgomery & Dietrich, 1994). A steep slope can reach instability sooner than a gradual one, but rainfall infiltration is more difficult due to major runoff probability and even water discharge can be faster leading to a more favourable initial moisture condition.
Slope saturation by water is a primary cause of landslides. Saturation can occur in the form of intense rainfall, snowmelt, changes in ground-water levels, and surface-water level changes along coastlines, earth dams, and in the banks of lakes, reservoirs, canals, and rivers (Highland et al., 2008). Landslides and flooding are closely associated because both are related to precipitation, runoff, and the saturation of ground by water. Flooding may cause landslides by undercutting banks of streams and rivers and by saturation of slopes by surface water (overland flow). In addition, debris flows and mudflows usually occur in small, steep stream channels and commonly are mistaken for floods; in fact, these two events often occur simultaneously in the same area. Conversely, landslides also can cause flooding when sliding rock and debris block stream channels and other waterways, allowing large volumes of water to back up behind such dams. This causes backwater flooding and, if the dam fails, subsequent downstream flooding. Moreover, solid landslide debris can “bulk” or add volume and density to otherwise normal streamflow or cause channel blockages and diversions, creating flood conditions or localized erosion (Highland et al., 2008).
Landslides also can cause tsunamis (seiches), overtopping of reservoirs, and (or) reduced capacity of reservoirs to store water. Steep wildfire-burned slopes often are landslide-prone due to a combination of the burning and resultant denudation of vegetation on slopes, a change in soil chemistry due to burning, and a subsequent saturation of slopes by water from various sources, such as rainfall.
25
2.4 MAIN FORECASTING MODELS
Shallow landslides, as we saw in the previous paragraph, are extremely dangerous because of their enormous destructive potential and because they do not have easily detectable premonitory warning signs. The main cause for forecasting difficulty is the soil slip and the flows in soil which are usually triggered by an intense rainfall or by a complex sequence of them.
Physically based approaches for modelling rainfall-induced shallow landslides are an intensely debated research topic among the earth sciences community, and many models have been presented thus far (Dietrich and Montgomery 1998; Simoni et al. 2008; Pack et al. 2001; Baum et al. 2002, 2010; Rossi et al. 2013; Lu and Godt 2008; Ren et al. 2010; Arnone et al. 2011). These types of models describe the phenomena by the equations of continuity and motion, the mathematical representation being based on the conservation of mass, momentum and energy (Wagener, 2004). However, the application of models over large areas is hindered by a poor comprehension of the spatial organization of the required geotechnical and hydrological input parameters. The performance of a model can be strongly influenced by the errors or uncertainties in the input parameters (Segoni et al. 2009; Jiang et al. 2013).
In recent years, spatially variable soil thickness maps have frequently been incorporated in distributed slope stability modelling (Segoni et al. 2009; Jia et al. 2012; Mercogliano et al. 2013), but geotechnical and hydrological parameters have been proven to be more troublesome to manage because they are characterized by an inherent variability and their measurement is difficult, time-consuming, and expensive, especially when data are needed for large areas (Carrara et al. 2008; Baroni et al. 2010; Park et al. 2013). As a consequence, in reviewing the literature about feeding distributed slope stability modelling with spatially variable geotechnical parameters, it is impossible to find an approach that is universally accepted and that can be used as a standard.
In many cases, for each geotechnical parameter, a constant value is used for the whole study area as averaged from in situ measurements (Jia et al. 2012) or derived from literature data. In some studies, a limited degree of spatial variability is ensured using a certain value for distinct geological, lithological, or engineering geological units, as derived from direct measurements (Segoni et al. 2009; Baum et al. 2010; Montrasio et al. 2011; Zizioli et al. 2013) or from existing databases and published data (Lepore et al. 2013; Ren et al. 2014; Tao and Barros 2014).
26 There are two different approaches when we handle these kind of data: statistical empirical models and deterministic models.
The Statistical empirical models are also called black box models: these models try to find a rainfall’s intensity threshold through statistical analysis of meteorological events that have mobilized landslides in a given area. The name black box derives from the fact that the entire connection between rainfall and landslide warning status is “hidden” by the statistical analysis: you do not analyse the mechanism of instability but only the cause and effect statistics.
Literature offers many examples of black box models that require only a collection of basic data such as dated landslides and rainfall records and an analysis of weather conditions that trigger the landslide. Then rainfall real time or forecasted data is plotted on a chart with thresholds and is continuously compared. Usually the thresholds are traced graphically without mathematical, physical or statistical criterion (Guzzetti et al., 2008). Where rainfall path records are available that have shown not to trigger landslides, the thresholds are defined as the best separator between the conditions that have triggered landslides and those that don’t (Jibson, 1989; Giannecchini et al. 2005).
The deterministic slope stability models can improve the level of spatial and temporal detail of the statistical empirical methods. This is possible because they are physically based: the processes involving the stability of a slope are described by mathematical relationships which link geotechnical, hydrological and morphometric characteristics of the slope portion that is analysed. These typology models are also called “white box” models because the cause, rainfall or other destabilizing factors, and the effect, landslide triggering, relate to an assumed physical modelled mechanism. The physically based method allows us to apply the calculation and obtain a specific result at any point in the studied area which means the model can be used in a spatially distributed manner. Therefore, a deterministic model can get an estimate of the stability of a slope even over large area at spatial resolution proportional to the input physical parameters resolution (Fanelli et al. 2016; Godt et al. 2008; Salciarini et al. 2006, 2012, 2017). We can find many models in literature that attempt to describe the triggering of shallow landslides and debris flows at various approximation levels. The most widely used approximation is the infinite slope of isotropic and homogeneous soil: it is assumed that the depth to bedrock is smaller than the length of the slope analysed.
Johnson and Rodine (1984) proposed one of the most well-known hypothesis for mobilization of debris flows and shallow landslides which is known as the Bingham model.
27 This model assumes that triggering can occur only if the shear stress exceeds the Coulomb strength, and then when a soil with a particular water content exceeds a critical thickness.
As mentioned in the previous paragraph, the main triggering factor for shallow landslides is the increase in groundwater pore pressure in response to heavy rainfall.
According to Iverson (1997), the infiltrating water adds weight which plays a mechanical role especially where the cohesion contributes significantly to the Coulomb soil strength. The pore pressure increase in a slope can occur in two ways: by direct infiltration of water at the slope surface and by groundwater flow from adjacent portions of the slope. The pore pressure increase can occur also when the infiltrating or flowing water elevates the regional water table up to a shallow soil depth.
Many authors use different solutions to the Richards equation (Richards, 1931) to represent the movement of water in unsaturated soils and to assess the effect of transient rainfall on the timing and location of landslides (Iverson, 2000; Crosta et al., 2003; Simoni et al., 2008).
Topography plays an important role in driving surface and groundwater flows and Montgomery & Dietrich (1994) proposed a model that explicitly considers the topographic influence on soil saturation and slope stability. They use the hydrologic model TOPOG (O’Loughlin, 1986) to predict 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 variability and uncertainty in geotechnical input parameters heavily reflect on the results when a deterministic approach is used in physically based models, and in recent years, the use of probabilistic approaches has widely increased as it allows a more proper consideration of uncertainties and inherent variability of the input data (Park et al. 2013). For instance, Santoso et al. (2011) used a probabilistic approach, even if limited to the characterization of the permeability, while many authors considered cohesion and friction angle as random variables using a probabilistic or stochastic approach (Park et al. 2013; Griffiths et al. 2011; Chen and Zhang 2014; Mercogliano et al. 2013).
Many software has been developed to handle this large number of parameters to apply stability models on a large scale and to visualize the results in many ways; all these software manage simpler versions of general forms of physical model equations introducing some approximations.
SHALSTAB, SHAllow Landslide STABility model, is a popular distributed slope stability analysis software (Dietrich and Montgomery, 1998). It has a physical core based on a
28 distributed steady state description of the hydrological fluxes coupled with an infinite slope analysis. In this model, runoff is assumed to be generated by shallow subsurface flow and saturation overland flow (Dunne and Leopold, 1978) which is perched on the potential failure plane, and driven downslope by a head gradient equivalent to the local surface topography (Dietrich et al., 1998). Because saturated conductivity tends to decline exponentially with depth (e.g., Wilson and Dietrich 1987, Montgomery et al. 1997) and the mechanically weak and dilated colluvial soil is generally more conductive than the underlying weathered bedrock, these assumptions represent reasonable approximations of the runoff process (Dietrich et al., 1998). In the infinite slope stability model, cohesion is zero in order to avoid having to assign values to spatially and temporally varying soil strength. To partially compensate for the elimination of cohesion, the friction angle is set at 45 degrees, which is relatively high. Coupling the runoff and slope stability models leads to the following expression for the ratio of effective precipitation (q) to the soil transmissivity (T) for slope instability to occur:
𝑞 = 𝑇 sin 𝜃𝑏 𝐴[ 𝜌𝑠 𝜌𝑤 (1 − ( 𝐶 sin 𝜃 cos 𝜃) tan 𝜃 tan ϕ)]
This equation is the SHALSTAB model. Here, A is the drainage area that contributes subsurface flow across a hillslope width, b is typically equal to the width of the landslide scar or the size of a grid cell in a digital terrain model, θ is the local gradient of the ground surface (presumed equal to the failure plane surface), tanϕ is the angle of internal friction of the soil,
C is the soil cohesion and ρs and ρw are the bulk density of the soil and water, respectively.
Note that if tanθ > tanϕ, no rainfall is necessary for instability, hence such sites if they have some soil on them are expected to be most prone to instability (Dietrich et al., 1998). The hillslope is predicted to be stable for all slopes in which tan 𝜃 < (𝜌𝑠− 𝜌𝑤 / 𝜌𝑠) tan 𝜙. This is because on such low slopes pore pressures in excess of that which can be obtained at full saturation is required for instability to arise. While excessive pore pressures may, in fact. arise due to exfiltration gradients set up by fracture flow in bedrock, (e.g., Wilson and Dietrich 1987, Montgomery et al. 1997), these effects are not accounted for in this model (the subsurface flow is assumed to be parallel to the ground surface). The basic tool is a grid-based model, a combination of C++ programs and ARC/INFO AML scripts intended to be used within an ESRI-ArcGIS software environment. This model has been classified as spatially predictive because it is not suited to forecast the timing of landslide triggering (Simoni et al., 2008).
29 SINMAP, Stability Index MAPping, and SINMAP 2 are other add-on tools for the ESRI-ArcGIS software. These have their theoretical basis in the infinite slope stability model with groundwater pore pressures obtained from a topographically based steady state model of hydrology (Pack et al., 1998, 2001). The input information (slope and specific catchment area) is obtained from the analysis of Digital elevation models (DEM). These parameters can be adjusted and calibrated with an interactive visual procedure that adjusts them based upon observed landslides. SINMAP allows an uncertainty of the variables through the specification of lower and upper bounds that define uniform probability distributions. Between these boundaries the parameters are assumed to vary at random in respect to the probability distribution.
Other software has a more complex approach to the hydrological modelling of the groundwater flow and require longer computational time. For example, SEEP/W (Geo-Slope, 2003a) is a stand-alone finite element software that resolves the Richards equations to account for transient groundwater flow within a slope. This software analyses groundwater seepage and excess pore-water pressure dissipation within porous materials and can model both saturated and unsaturated flow (Krahn, 2004). SEEP/ W is very efficient in resolving saturated-unsaturated and time-dependent problems and combining with the software SLOPE/W (Geo-Slope, 2003b) it performs the slope stability analysis adopting the limit equilibrium method. This software works very well for single slope stability analysis (Tofani et al., 2006) but is not suited to be applied to a distributed analysis.
TRIGRS, Transient Rainfall Infiltration and Grid based Regional Slope stability model, is a software developed in Fortran language, for computing the transient pore pressure distribution due to rainfall infiltration using the method proposed by Iverson (Baum et al., 2002). The results are stored in a distributed map of the factor of safety. Baum et al. (2002) have extended Iverson's method by adding a solution for an impermeable basal boundary at a finite depth and adding a simple runoff-routing scheme to disperse excess water from cells where the application rate (rainfall intensity) exceeds the infiltrability (infiltration capacity). The program allows the following input parameters to vary from cell to cell throughout the model:
• precipitation intensity, • slope,
• soil depth,
• initial water-table depth,
30 • hydraulic diffusivity,
• cohesion for effective stress,
• angle of internal friction for effective stress, • total unit weight of soil.
The application program TRIGRS combines models for infiltration and subsurface flow of storm water, routing of runoff, and slope stability to calculate the effects of storms on the stability of slopes over large areas. The infiltration models in TRIGRS are based on Iverson’s (2000) linearized solution of Richard’s Equation and the extensions of Baum et al. (2002) to that solution. Iverson’s solution consists of a steady component and a transient component. The steady component allows flow in an arbitrary direction determined by the steady infiltration rate and the slope angle. The transient component assumes one-dimensional, vertical downward flow, with a simple specified time-varying flux of fixed duration and intensity at the ground surface and a zero-flux condition for times greater than the starting time at an infinitely deep basal boundary. The infiltration models apply to saturated or tension-saturated initial conditions, so that flow is in the linear range for Darcy’s law and the hydraulic diffusivity is approximately constant. Baum et al. (2002) have generalized Iverson’ s (2000) original solution for the case of a time-varying sequence of surface fluxes of variable intensities and durations. TRIGRS uses a simple method for routing of surface runoff from cells that have excess surface water to adjacent downslope cells where it can either infiltrate or flow farther downslope. Baum et al. (2002) assume that runoff occurs when the precipitation and runoff supplied to a cell exceed its infiltrability. The saturated hydraulic conductivity generally equals the infiltrability for saturated and tension-saturated soils (Hillel, 1982; Iverson, 2000). Following Iverson (2000), Baum et al. (2002) model slope stability using an infinite-slope stability analysis. In his analysis, failure of an infinite slope is characterized by the ratio of resisting basal Coulomb friction to gravitationally induced downslope basal driving stress.
The factor of safety, FS, is calculated for transient pressure heads at multiple depths Z as follows: 𝐹𝑆 =tan 𝜃′ tan 𝛽+ 𝑐′− 𝜓(𝑍, 𝑡)𝛾 𝑤tan 𝜙 𝛾𝑠𝑍 sin 𝛽 cos 𝛽
31 where c’ is the effective soil cohesion, ϕ’ is the soil shear strength angle, ψ is the ground-water pressure head as a function of depth Z and time t, β is the slope angle, and γw and γs are
the unit weights of water and soil, respectively.
TRIGRS, freely distributed both as source code and executable files, is widely used by many authors for regional landslide hazard assessment (Baum et al., 2005; Salciarini et al., 2006; Chien-Yuan et al., 2005) and analysis under the approximation of nearly saturated soil, presence of flow field and isotropic, and homogeneous hydrologic properties (Baum et al., 2002). TRIGRS is very sensitive to initial conditions, therefore, if the initial water table depth is poorly constrained, it may produce questionable results.
GEOtop-FS is one of the most advanced models for distributed slope stability and was recently proposed by Simoni (2008). This model uses the hydrological distributed model GEOtop (Rigon et al., 2006) to compute pore pressure distribution by an approximate solution of the Richards equation and an infinite slope stability analysis to compute the distributed factor of safety. The approximate solution of Richards equation used by the software works in saturated soil conditions. The factor of safety of GEOtop-FS is computed in a probabilistic approach assigning statistical distributions to soil parameters instead of a single deterministic value and analysing the error propagation.
A problem when using these codes (e.g. SHALSTAB, Montgomery and Dietrich, 1994; TRIGRS, Baum et al., 2002; GEOtop-SF, Simoni et al., 2008), for the modelling of shallow rainfall-induced landslides over large areas resides in the difficulty (or operational impossibility) of obtaining sufficient, spatially distributed information on the mechanical and hydrological properties of the terrain. Moreover, the computational time take days for a relatively small area at high spatial and temporal resolution. It is impossible to use these software, even if they are the state of the art, in real time and for warning system purposes.
To solve these problems, the physically based distributed stability model developed by Rossi et al. (2013) has been used, and the main geotechnical and hydrological parameters controlling the shear strength and permeability of soils have been determined by in situ measurements integrated with laboratory analyses.
32
3
MATERIALS AND METHODS
3.1 GEOTECHNICAL-HYDROLOGICAL
CHARACTERIZATION
An accurate characterization of in situ soil behaviour is currently one of the most important areas for advancing the state of knowledge in geotechnical engineering. Several analytical and computational models have been developed that can calculate soil response with greater precision than that with which the soil parameters can be measured. However, the costs related to subsurface investigations can quickly outweigh the benefits gained from accurate soil parameter determinations.
Therefore, the development of improved testing methods needs to include simplicity and efficiency as primary considerations. By utilizing simple and efficient methods, in situ soil parameters can be quickly and accurately determined by an engineer or technician with a reduced chance of error, and the costs related to an individual soil investigation can be reduced. In situ soil testing methods have been proven to increase the accuracy and economy of a variety of work purposes that require knowledge of soil parameters. Since in situ tests measure soil properties in place, the costs and efforts associated with collecting, transporting, and preserving a soil sample and testing it in a laboratory can be reduced or eliminated. Field tests are more difficult to manage and control than laboratory tests, but they are considered to give a more direct and representative measurement of the real in situ soil properties (Baroni et al. 2010). In addition, by measuring soil properties in situ, the effects of soil disturbance can be reduced, giving soil properties that more closely model actual soil behaviour. A thorough geotechnical investigation can therefore combine laboratory testing with in situ testing to increase the accuracy of the soil parameters.
3.1.1 Soils sampling and survey procedure
In this paragraph, the procedure for sampling and analysis is presented. The procedure has been developed to make an extensive campaign of on site and laboratory investigations, repeated at different points representative of the main types of soil found in hillslopes. On average, each point of survey required 1 day of work in the field and two in the laboratory to carry out the measures of investigation.
33 The selected sites have been chosen based on physiographic features (height and slope), localization of landslides and geo-lithological characteristics of the bedrock (derived from the CARG project, www.isprambiente.gov.it), considering the localization of existing survey sites.
For each site, the following field tests were carried out, at a distance of a few meters from each other:
1. recording of geographical position using GPS and photographic documentation of the site characteristics (morphology and vegetation);
2. measurement of drained shear strength parameters of soil by means of the Borehole Shear Test (BST; Lutenegger and Halberg, 1981);
3. measurement of matric suction of the soil obtained with a tensiometer; 4. measurement of soil thickness;
5. measurement of saturated hydraulic conductivity (K-sat) by means of the constant-head well permeameter Amoozemeter (Amoozegar, 1989);
6. sampling of two aliquots (~2 kg each) of the material.
The soils have been sampled at depths ranging from 0.4 to 0.8 m below the ground level (b.g.l.). The depth of the soil samples can be considered significant to characterize the soil material involved in landsliding. As pointed out in Giannecchini (2006), Giannecchini et al. (2007), D’Amato Avanzi et al. (2009), and D’Amato Avanzi et al. (2013), the depth of the sliding surface of shallow landslides that usually occur in the study area is around 1 m deep.
During fieldwork, direct measurements of soil depth were made from pre-existing or newly excavated soil profiles or in borehole during BST test.
3.1.2 Field tests
The geotechnical parameters of soils were determined by a series of field tests including the Borehole Shear Test (BST; Lutenegger and Halberg 1981), which provides the shear strength parameters under natural conditions without disturbing the soil samples, matric suction measurements with a Tensiometer, and a constant head permeameter test performed with an Amoozemeter (Amoozegar 1989).
The BST test (Lutenegger and Hallberg, 1981) (Figure 7) was developed in order to ensure a fast and direct in situ measurement of shear strength of soil. It is similar to a laboratory direct shear test with the sides of the borehole being sheared. BST measurements were carried out at
34 a depth of 0.4-0.6 m from the ground surface, where the soils are at variable conditions, from unsaturated to completely saturated, depending on the seasonal variations of temperature and rainfall. In unsaturated soils, the pore water pressure uw is lower than air pressure ua.
Figure 7 The BST placed in the hole for shear test on site.
The difference between the two parameters (ua-uw) is represented by the soil matric
suction, which is measured by the tensiometer. Matric suction increases the shear strength in unsaturated soils; however, an increase of pore water pressure (often promoted by rainwater infiltrating in the soils) is able to rapidly decrease matric suction so that soil condition changes to saturated.
The BST is composed by the following components (Figure 8):
1. Shear head: this is the down-hole part of the apparatus and consists of a horizontal double-acting stainless steel pressure cylinder with opposing 50.8 x 63.5 mm sharply grooved shear plates. The cylinder is held in a pulling yoke and is opened and closed with gas pressure carried through shielded Nylon pressure lines. The free ends of the lines are color-coded and attached to male quick connectors (Handy, 2014).
