Investigating the relationships among vegetation characters, saturated hydraulic conductivity and surface morphology at catchment scale by integrating new field data and morphometric analysis

Shallow landslides susceptibility assessment by physically based methods relies on the parametrization of both hydraulic and geotechnical properties of soils, which in turn depend on the conditions of root structures and vegetation cover. Vegetation roots contribute to the shear strength of soils, but their quantitative contribution is currently uncertain. Saturated hydraulic conductivity (Ks) is also relevant for slope stability as it influences infiltration rates and runoff. While the literature clearly shows the dependence of Ks on soil texture, there is a general understatement of the role of root structures on this parameter. Moreover, the distribution patterns of vegetation follow relations with surface morphologies which are not fully understood and therefore, are worthy of further investigations. For these reasons, this work focuses on the quantitative assessment of the influence of vegetation on shear strength for shallow landsliding and the investigation of the relationships between vegetation characters, saturated hydraulic conductivity and topographic parameters. Study areas affected by shallow landslides are chosen in the Garfagnana and Alpi Apuane regions (Northern Apennines, Italy), as well as in the Mt. Amiata volcano area (Southern Tuscany, Italy), where field measurements of below-ground vegetation (Root Area Ratio - RAR), above-ground vegetation (Leaf Area Index - LAI and vegetation load) and Ks are acquired inside, in the neighbour and far from shallow landslide sites. To this aim, a multitemporal landslide inventory is already available for the study area. Below-ground data are collected in trench profiles, while above-ground data are acquired by using a digital relascope as well as implementing vegetation cover photography methods. Measurements of Ks are carried out by means of both constant and falling head approaches. The morphometric analysis is performed by using some morphometric variables (eg. slope and hillslope curvatures) derived from a digital elevation model with cell size of 10 m. Morphometric clustering of these variables allows us to extract a set of land units where the distribution of vegetation characters and Ks are assessed. First results show that: a) root reinforcement to soil in terms of root-related cohesion plays a relevant role within the soil depths involved in shallow landslides; b) the weight of above-ground vegetation plays a “mild” negative role on slope stability; c) Ks is correlated with both RAR and soil depth, suggesting possible criteria for the straightforward parametrization of input parameters

Spatial analysis of hydraulic conductivity for slope deposits at catchment scale in Northern Tuscany, Italy

Hydraulic conductivity (K) is a relevant engineering geology property of slope deposits (SD) overlying the geological bedrock. This parameter is relevant at the field scale to simulate infiltration and runoff processes, hillslope stability numerical analysis, hydrological studies and environmental issues. Direct measurements (field and laboratory tests), as well as indirect estimations (e:g. correlations from grain size distribution, pedotransfer functions) are available in the literature for estimating K. Many measurements are required to obtain significant results since K depends on many factors such as grain size distribution, bulk density, organic matter, etc. A big set (about 750) of K field measurements in the vadose zone of SD in Northern Tuscany (Italy) has been performed by means of constant and/or falling head permeameter. For each test site (a total of 150 locations), other engineering geology properties of SD such as depth, texture, bulk density, Atterberg limits and grain size distribution have been determined. In this work the local variability of K has been estimated thanks to a statistical analysis of K for each test site. Moreover geostatistical techniques have been applied to infer the spatial correlation of K at the catchment scale. The results show that K varies across the SD profile and in the geographic neighborhood of the test site exhibiting high spatial variability within the study area. The new pedotransfer function, that has been developed with satisfactory results (the determination coefficient R2 = 0.84), suggests that the depth of SD and d20 (is the diameter corresponding to 20% finer in the particle-size distribution) play a relevant role in the prediction of K:These parameters can be considered with profit in spatial analysis of K for SD allowing to produce K maps in the study area

Comparison between direct measurements and indirect estimations of hydraulic conductivity for slope deposits of the North-Western Tuscany, Italy

Hydraulic conductivity (K) is a relevant engineering geology property of deposits that cover the geological bedrock (Slope Deposits – SD). This parameter is useful for many applications fields such as: simulations of both infiltration and runoff processes, hillslope stability numerical analysis, hydrological studies, soil science and environmental problems. A wide range of methods are available in the literature in order to estimate K. Anyhow, they can be divided into direct measurement (field and laboratory test) and indirect estimations (eg. correlation from grain size, pedotransfer functions). However, many factors (eg. SD grain size, bulk density, organic matter, etc.) can affect the K value hence the determination of K within SD is often a challenge. Moreover, the value of K generally shows an high spatial variability requiring a large number of direct measurements to obtain robust spatial estimations. Indirect methods may be used alternatively or in pair with direct methods. However, relations between K and other soil physical properties are generally suitable only for specific type of soils, therefore, the application of those relations are constrained. In this work a wide (about 200) set of field measurements were performed in North-Western Tuscany in order to assess the variability of K in the vadose zone for SD characterized by different grain size composition. Measurements were carried out by means of both constant and falling head permeameters, as well as double ring infiltrometer. In the test sites engineering geology properties of SD such as bulk density and depth have been collected, moreover, samples have been collected for laboratory analysis. A statistical analysis of the K value has been performed for SD characterized by different grain size distribution and geological bedrock. Moreover, a comparison between the field methods have been also performed. Finally, a comparison between measured and estimated values of K has been done in order to assess the reliability of different equations to predict K. The results show that the K value varies across: different geological settings, the SD profile and the geographic neighborhood of the test site. Moreover, the results highlight that the indirect methods used in this work have to be used carefully in our study area

The new engineering geological map (carta litotecnica) of Tuscany (Italy)

Municipal administrations in Italy must be provided with thematic maps and documentation which describe the geological, geomorphological, lithological, hydrogeological and hydraulic characters useful to manage spatial planning issues. Among these documents, a “Lithotechnical” (or “Lithological-Technical”) map is drawn up, generally at the scale of 1:10,000, by organizing the geological formations into lithotechnical units according to their lithological and physical-mechanical properties. Often, this map also integrates the results of previous field and borehole investigations. However, this map is characterized by a certain degree of subjectivity because it is supported by few specific quantitative data. We present a new method for the regional scale engineering geological classification of sub-surface rock and soil masses obtained by integrating the geological map at the scale of 1:10,000 as a reference document, with a large set of data obtained through the collection and processing of new lithological and physical-mechanical observations and measurements of the outcropping geological formations. The adopted procedure involves both the extensive in situ use of the Schmidt's hammer and the execution of laboratory tests, such as the Slake Durability Test (Franklin & Chandra, 1972) and the determination of the rock unit weight. These tools and tests allow us to acquire a large set of quantitative in situ and laboratory data with known repeatability to obtain a regional scale GIS database providing the classification of the lithological and physical-mechanical characteristics of a wide range of geological formations. As a first step, each outcrop is classified according to a new engineering geological nomenclature system described by the code XXv[y]_[Z] whose values are obtained by integrating: i) a lithological parameter XXv evaluated from both typical characters of the geological formations under analysis and outcrop observations; ii) an engineering geological parameter [y] obtained by the results of the Slake Durability Test; iii) an engineering geological parameter [Z] (Rockmass Quality Index - RQI) evaluated at the outcrop scale on the basis of a large set of sclerometric measurements. The results of outcrop classification are stored into a point topology GIS dataset and are then processed and spatialized in order to assign the XXv[y]_[Z] code to the geological formations, thus obtaining the new engineering geological map. Within the framework of research agreements among Regione Toscana administration, the Consorzio LaMMA, the CNRIGG and the Department of Earth, Environmental and Physical Sciences of the University of Siena, the latter being the leader for their implementation, more than 300 geological formations were analysed and classified, and the new engineering geological GIS map was realized in Tuscany for the provinces of Arezzo, Florence, Lucca, Massa-Carrara, Pistoia, Prato and Siena (ca. 15,000 km2)

Caratterizzazione, modellazione predittiva e studio della variabilità locale e regionale delle proprietà idrologiche dei depositi di versante

Landslides are one of the widespread natural hazards and among these ones there are shallow landslides: landslides that mainly involve the soils (slope deposits, SD) that unconformably cover the geological substratum. A method of prevention that is currently promoted by scientific research is the realization of shallow landslide susceptibility maps which require the knowledge, among different factor, of the engineering-geological properties of the soils. Among the latter, hydrological properties and in particular hydraulic conductivity (K) of the SD, has a relevant weight being the major factor controlling the distribution and movement of the water in the subsoil. So, the requirement of characterizing of the engineering-geological properties of the SD, for which literature is still rather limited, is clear. Specifically, this thesis has as its first objective the characterization of the hydrological properties of the SD, focusing mainly on K. Another important aspect is represented by the fact that the engineering-geological properties of the SD (including K) are not currently well-known due to the high cost (both in terms of time and money) associated with data collection, laboratory and in situ tests. In this regard, this PhD thesis addresses two other aspects. Firstly, to investigate the predictive capacity of different techniques to estimate K from indirect methods. Finally, the local and regional variability of K of the SD was analyzed in order to obtain continuous maps of K. During the first 15 months, an intense field survey was carried out in the study area which has an extension of 420 km2, by means of 150 sampling sites and 720 hydraulic conductivity in situ tests (Ktests) and the collection of 146 samples for laboratory tests. Concerning first topic of this thesis, some of the engineering-geological properties (% gravel, % sand, % fines and Atterberg limits) show a tendency to distribute in different ways in relation to the lithology of the geological substratum. As regards K of the SD, the uncertainty of the Ktests was first estimated (coefficient of variation = 2 – 3%). All Ktests have been divided into 4 horizons according to the depth within which the measurements were performed. A high relationship was observed between log K and the depth of Ktests (R – Pearson = – 0.79). Some Ktests have been performed within shallow landslides it has been observed that K measured inside the landslide is about 4 times lower than that K measured inside landslides. Then, the correlations between K and engineering-geological properties of SD were realized through bi and multivariate statistical analysis: an overall weak correlation among log K and these properties is exhibited. Within the same textural classes and same grain size curves, it emerged that the effect of the lithology of the geological substratum can be considered negligible with respect to the K of the SD. Once the engineering-geological characterization of the SD was completed, another research topic was to evaluate and quantify the predictive efficacy of indirect methods to estimate K. This process was carried out by applying 31 empirical correlations (or pedo-functions, PTF) which are present in the literature and they showed very poor accuracy to predict K of SD. So it was decided to apply two methods for K prediction: multilinear regression and artificial neural networks. Through multilinear regression a new PTF was obtained which proved to be highly effective in predicting K (R2 = 0.82), as well as valid and robust from a statistical point of view. Similar results have been obtained through the implementation of artificial neural networks (R2 = 0.85 – 0.86). So, multilinear regression and neural networks prove to be quite efficient methods in predicting K of the SD. The last topic of this thesis was the analysis of the spatial distribution of K at site and regional scale. For each site the variability of K (range and interquartile range of log K is equal to 2.0 and 0.8 respectively). The spatial analysis at regional scale was performed with two different approaches: in the first case the Ktests were divided into four horizons according to the depth within which the Ktest was performed, in the second approach the entire dataset was previously normalized for the effect of depth. For each of the two approaches, exploratory geostatistical analysis were performed in order to verify the assumptions of normality, stationary and absence of trend, necessary for the correct execution of geostatistical methods. The algorithms of the Ordinary Kriging, Inverse Distance Weighted and Empirical Bayesian Kriging have been implemented which have allowed to generate the first maps of the continuous values of log K at regional scale in the study area. The different algorithms have provided similar results in terms of log K values (for the different approaches considered) obtaining accuracies (NRMSE = 10 – 20%) which are in good agreement with other examples of the literature

Influence of digital terrain model on mapping of slope deposits depth

In this work the influence of the characteristics of different Digital Terrain Models (DTMs) on Slope Deposits depth (dSD) maps is evaluated. dSD data collected during fieldwork have been mapped through GPS. Two different DTMs have been used for the analysis: one derived from LiDAR survey (1 m pixel size), the other extracted by the topographic maps of Regione Toscana (10 m pixel size). dSD maps have been extracted by integrating cluster analysis of morphometric variables with dSD data. In order to investigate the effects of spatial resolution, the LiDAR DTM has been down-sampled to both 5 m and 10 m. The weight of GPS positioning uncertainty has been assessed by performing a statistical analysis on the morphometric clusters falling into buffer areas of sampling points. Comparisons among outputs have been performed through map accuracy estimations, as well as by spatial distribution visual check and extension assessment of dSD classes. Results show that by increasing spatial resolution of input DTMs the statistical quality of the dSD maps generally reduces. Concerning the effect of buffer size around sampling points, as a general rule, the larger the buffer, the lower the map accuracy. Only the map obtained by 1 m LiDAR DTM is insensitive to buffer size

Seismic characterization and reconstruction of reference ground motion at accelerometric sites of the Italian national accelerometric network (RAN)

We present a field procedure that has been extensively used in Italy to characterize local seismic response at accelerometric sites and to retrieve ground motion at reference soil conditions by deconvolution analysis. To allow a generalized application to large areas where borehole data are generally lacking or inadequate for the seismic characterization for soils down to the reference seismic bedrock, cost-effectiveness of the considered procedures is a main issue. Thus, major efforts have been devoted to optimize available information and exploit fast and cheap surface geophysical prospecting. In particular, geological/geomorphological survey and passive seismic prospecting (both in single- and multi-station configurations) were jointly considered to reconstruct seismo-stratigraphical site conditions. This information was then used to feed numerical modeling aiming at computing the local seismic response and performing a deconvolution analysis to reconstruct ground motion at reference soil conditions. Major attention was devoted to evaluate and manage uncertainty involved in the procedure and to quantify its effect on final outcomes. An application of this procedure to a set of sites included in the Italian Accelerometric Network is presented