The potential of GIS modelling of gravity flows and slope instabilities

Abstract

The component of gravitational acceleration parallel to the slope of the local surface partly determines the state of slope stability and the kinematics of flow under gravity on that slope. Geographical information systems based on digital elevation models offer the potential to be able to map this variable and permit the modelling of a variety of stability criteria and surface processes including landslides, rock avalanches, pyroclastic flows and lava flows. Three types of models and the basic map operations required to run them are discussed. The models are as follows: (i) sites of potential shallow slope failure (e.g. landslides), (ii) maps of flow deposition based on energy balance calculations (e.g. rock avalanches) and (iii) finite difference, initial value type simulations of dynamic flow (e.g. lava flows). The potential value of these models to hazard assessment is great but their application in specific cases must be assessed with reference to the accuracy of the digital elevation model used.     

COMMUNICATIONS FROM READERS

We recommend use of the widely available computer package GLIM to analyze relationships involving categorical, or qualitative, variables. The package is flexible and fairly simple to use and readily produces data plots as well as information on parameter estimates and residual values. In order to explain how to fit log-linear and logistic-linear models using GLIM, we give two examples based on sample survey data from grocery shoppers in Oxford, England. In the first, we examine relationships between a categorical response to an attitude statement and categorical explanatory variables using the log-linear model and, in the second, relationships between use and a mixture of continuous and categorical explanatory variables using the logistic-linear (logit) model.     

Litho-tectonic and precipitation implications on landslides,Yamuna valley,NW Himalaya

ABSTRACT

An attempt is made to explain the relationship of landslides to litho-tectonic and precipitation regimes. The possible influence of these factors on the dimensional pattern of landslides is also inferred. The Yamuna River valley, NW Himalaya, which traverses the Higher Himalaya (HH) and Lesser Himalaya (LH) rock mass, endures disastrous landslides and hence is taken as the case for study. To achieve the objectives, proxies like stream length gradient, topographic profile, steepness index, and ratio of valley floor width to valley height were used to infer a spatially varying tectonic regime, whereas rainfall data and Normalized Difference Vegetation Index were used to determine spatial differences in precipitation and vegetation variability, respectively. Dimensional patterns of landslides utilized the landslide area and volume. The higher reaches of the HH and lowest part of the LH show rockfall dominance associated with relatively high tectonic activity, whereas most of the debris slides coincide with regional thrusts. Total area and volume occupied by the landslides are ~1.5 ± 0.16 × 10⁶ m² and ~4.7 ± 1.2 × 10⁶ m³, respectively. Dimensions of debris slides were found to be less influenced by the litho-tectonic and precipitation regimes, whereas the dimensions of rockfalls were found to be more sensitive to these conditions.     

Sources of Uncertainty in Shear Stress and Roughness Length Estimates Derived from Velocity Profiles∗

Shear stress is a fundamental parameter in many sediment transport expressions. It is commonly estimated from information contained in the velocity profile, specifically, shear velocity, u _?, and roughness length, z ₀ Under ideal conditions, the functional relationship between elevation above the bed and flow velocity is log-linear, as expressed by the “law of the wall.” Unless the field data conform exactly to this ideal relationship, there is uncertainty in estimates of u _? and z ₀. derived from velocity profiles. This uncertainty depends on the degree of scatter or deviation from the assumed log-linear model. Expressions are presented to quantify the error and to correct for it. Guidelines are suggested to minimize potential uncertainty, especially with regard to instrument deployment and velocity profile analysis. Calculation of confidence intervals around estimates of u _? and z ₀, as proposed by Wilkinson (1984), is necessary and recommended.     

Fundamental operations in computer-assisted map analysis

Abstract

Geographical information systems (GIS) provide capabilities for the mapping, management and analysis of cartographic information. Unlike most other disciplines, GIS technology was born from specialized applications. A comprehensive theory relating the various techniques used in these applications is only now emerging. By organizing the set of analytic methods into a mathematical structure, a generalized framework for cartographic modelling is developed. Within this framework, users logically order primitive operators on map variables in a manner analogous to traditional algebra and statistics. This paper describes the fundamental classes of operations used in computer-assisted map analysis. Several of the procedures are demonstrated using a fourth-generation computer language for personal computers.     

Logical data modelling for geographical applications

Abstract.

A formal, yet practical, GeoRelational Data Model (GRDM) is presented for the logical database design phase of the development of spatial information systems. Geographic applications are viewed in the context of information systems development. The generic needs of modelling spatial data are analyzed; it is concluded that they are not served satisfactorily by existing data models, so specifications of modelling tools for spatial application design are given. GRDM provides a set of representational constructs (relations and layers for the logical schema; virtual layers, object classes and spatial constraints for the user views) on top of well-established models. It constitutes part of a full, easily automated application design methodology. Extensive examples demonstrate the relevance, and ease-of-use of the platform-independent GRDM.     

The frequency and distribution of recent landslides in three montane tropical regions of Puerto Rico

Landslides are common in steep mountainous areas of Puerto Rico where mean annual rainfall and the frequency of intense storms are high. Each year, landslides cause extensive damage to property and occasionally result in loss of life. Average population density is high, 422 people/km², and is increasing. This increase in population density is accompanied by growing stress on the natural environment and physical infrastructure. As a result, human populations are more vulnerable to landslide hazards. The Blanco, Cibuco, and Coamo study areas range in surface area from 276 to 350 km² and represent the climatologic, geographic, and geologic conditions that typify Puerto Rico. Maps of recent landslides developed from 1:20,000-scale aerial photographs, in combination with a computerized geographic information system, were used to evaluate the frequency and distribution of shallow landslides in these areas. Several types of landslides were documented—rainfall-triggered debris flows, shallow soil slips, and slumps were most abundant. Hillslopes in the study area that have been anthropogenically modified, exceed 12° in gradient, are greater than 300 m in elevation, and face the east-northeast, are most prone to landsliding. A set of simplified matrices representing geographic conditions in the three study areas was developed and provides a basis for the estimation of the spatial controls on the frequency of landslides in Puerto Rico. This approach is an example of an analysis of the frequency of landslides that is computationally simple, and therefore, may be easily transferable to other settings.     

Predictive land value modelling in Guatemala City using a geostatistical approach and Space Syntax

ABSTRACT

Spatial information of land values is fundamental for planners and policy makers. Individual appraisals are costly, explaining the need for predictive modelling. Recent work has investigated using Space Syntax to analyse urban access and explain land values. However, the spatial dependence of urban land markets has not been addressed in such studies. Further, the selection of meaningful variables is commonly conducted under non-spatialized modelling conditions. The objective of this paper is to construct a land value map using a geostatistical approach using Space Syntax and a spatialized variable selection. The methodology is applied in Guatemala City. We used an existing dataset of residential land value appraisals and accessibility metrics. Regression-kriging was used to conduct variable selection and derive a model for spatial prediction. The prediction accuracy is compared with a multivariate regression. The results show that a spatialized variable selection yields a more parsimonious model with higher prediction accuracy. New insights were found on how Space Syntax explains land value variability when also modelling the spatial dependence. Space Syntax can contribute with relevant spatialized information for predictive land value modelling purposes. Finally, the spatial modelling framework facilitates the production of spatial information of land values that is relevant for planning practice.     

Spatial patterns of old, deep-seated landslides: A case-study in the northern Ethiopian highlands

During the last decade, slope failures were reported in a 500 km² study area in the Geba–Werei catchment, northern Ethiopia, a region where landslides were not considered an important hazard before. Field observations, however, revealed that many of the failures were actually reactivations of old deep-seated landslides after land use changes. Therefore, this study was conducted (1) to explore the importance of environmental factors controlling landslide occurrence and (2) to estimate future landslide susceptibility. A landslide inventory map of the study area derived from aerial photograph interpretation and field checks shows the location of 57 landslides and six zones with multiple landslides, mainly complex slides and debris flows. In total 14.8% of the area is affected by an old landslide. For the landslide susceptibility modelling, weights of evidence (WofE), was applied and five different models were produced. After comparison of the models and spatial validation using Receiver Operating Characteristic curves and Kappa values, a model combining data on elevation, hillslope gradient, aspect, geology and distance to faults was selected. This model confirmed our hypothesis that deep-seated landslides are located on hillslopes with a moderate slope gradient (i.e. 5°–13°). The depletion areas are expected on and along the border of plateaus where weathered basalts rich in smectite clays are found, and the landslide debris is expected to accumulate on the Amba Aradam sandstone and upper Antalo limestone. As future landslides are believed to occur on inherently unstable hillslopes similar to those where deep-seated landslides occurred, the classified landslide susceptibility map allows delineating zones where human interventions decreasing slope stability might cause slope failures. The results obtained demonstrate that the applied methodology could be used in similar areas where information on the location of landslides is essential for present-day hazard analysis.     

Landslides in blanket peat on Cuilcagh Mountain, northwest Ireland

The northern and eastern sides of the Cuilcagh Mountain upland, in northwest Ireland, are mantled with over 50 km² of blanket bog that has experienced an unusually high spatial and temporal frequency of peat mass movements. In all, 29 peaty-debris slides, nine bog slides, two peat slides and five more peat landslides of uncertain type have been recorded within this study area. More than 27 km² of this peatland has been afforded several levels of statutory protection as well as international recognition of its geo-environmental importance. Field and laboratory investigations of the peat at several of the more recent failure sites showed it to be typical of Irish and Pennine (northern England) blanket bogs in most physical and hydrological respects. Field geomorphological evidence and modelling of stability thresholds indicate that the particular susceptibility of the Cuilcagh Mountain blanket bog to failure arises from two local factors: (i) the attainment of threshold maximum peat depths on the East Cuilcagh plateau, and (ii) the unconformable deposition of thin layers of glacial till (in places) and blanket peat over the pre-existing topographic surface formed from the major shale formations that underlie the northern slopes. With two exceptions, there is no conclusive evidence that human activities and management strategies for the area have had any significant influence on the occurrence of the peat landslides. The high frequency of large rainfall events since 1961 that did not trigger landslides suggests that failures are unlikely to become more frequent in response to climate change effects because they are controlled by slowly changing internal thresholds.     

Size and spatial distribution of loess slides on the Chinese Loess Plateau

ABSTRACT

The size and spatial distribution of loess slides are important for estimating the yield of eroded materials and determining the landslide risk. While previous studies have investigated landslide size distributions, the spatial distribution pattern of landslides at different spatial scales is poorly understood. The results indicate that the loess slide distribution exhibits a power-law scaling across a range of the size distribution. The mean landslide size and size distribution in the different geomorphic types are different. The double Pareto and inverse gamma functions can coincide well with the empirical probability distribution of the loess slide areas and can quantitatively reveal the rollover location, maximum probability, and scaling exponents. The frequency of loess slides increases with mean monthly precipitation. Moreover, point distance analysis showed that > 80% of landslides are located < 3 km from other loess slides. We found that the loess slides at the two study sites (Zhidan and Luochuan County) in northern Shaanxi Province, China show a significant clustered distribution. Furthermore, analysis results of the correlated fractal dimension show that the landslides exhibit a dispersed distribution at smaller spatial scales and a clustered distribution at larger spatial scales.     

Work, persistence, and formative events: The geomorphic impact of landslides

Understanding the scale and frequency of physical processes that act upon and form the surface of the Earth is a fundamental goal of earth science. Here we determine the magnitudes of landslides that impact the landscape in terms of work, persistence, and formative events. A systematic analysis of rapid landsliding (the analysis did not consider creep and other slow semi-continuous processes) indicates that moderate-sized landslides do the most work transporting material on hillslopes. The work peak defines the moderate magnitude, and that magnitude varies based on local physiography and climate. Landslides that form the work peak are distinct from catastrophic landslides that are themselves formative and system resetting. The persistence time for debris slides/debris flows (P_DS) and rock slides/rock avalanches (P_RS) is calculated over six orders of magnitude. We consider an event catastrophic when it persists in the landscape, as described by a persistence ratio (P_F), an order of magnitude longer than the population of landslides that form the work peak.     

Predictive modelling of rainfall-induced landslide hazard in the Lesser Himalaya of Nepal based on weights-of-evidence

Landslide hazard mapping is a fundamental tool for disaster management activities in mountainous terrains. The main purpose of this study is to evaluate the predictive power of weights-of-evidence modelling in landslide hazard assessment in the Lesser Himalaya of Nepal. The modelling was performed within a geographical information system (GIS), to derive a landslide hazard map of the south-western marginal hills of the Kathmandu Valley. Thematic maps representing various factors (e.g., slope, aspect, relief, flow accumulation, distance to drainage, soil depth, engineering soil type, landuse, geology, distance to road and extreme one-day rainfall) that are related to landslide activity were generated, using field data and GIS techniques, at a scale of 1:10,000. Landslide events of the 1970s, 1980s, and 1990s were used to assess the Bayesian probability of landslides in each cell unit with respect to the causative factors. To assess the accuracy of the resulting landslide hazard map, it was correlated with a map of landslides triggered by the 2002 extreme rainfall events. The accuracy of the map was evaluated by various techniques, including the area under the curve, success rate and prediction rate. The resulting landslide hazard value calculated from the old landslide data showed a prediction accuracy of > 80%. The analysis suggests that geomorphological and human-related factors play significant roles in determining the probability value, while geological factors play only minor roles. Finally, after the rectification of the landslide hazard values of the new landslides using those of the old landslides, a landslide hazard map with > 88% prediction accuracy was prepared. The methodology appears to have extensive applicability to the Lesser Himalaya of Nepal, with the limitation that the model's performance is contingent on the availability of data from past landslides.     

Research related to geographical information systems at the Natural Environment Research Council's Unit for Thematic Information Systems

Abstract

The wide variety of research into aspects of geographical information systems (GIS) undertaken at the Natural Environment Research Council's Unit for Thematic Information Systems is described. This includes work on spatial data models, on the application of artificial intelligence to improve the quality of input of remotely-sensed data, on automation of map cataloguing, on improving display and on GIS modelling.     

Sources of Uncertainty in Shear Stress and Roughness Length Estimates Derived from Velocity Profiles*

Shear stress is a fundamental parameter in many sediment transport expressions. It is commonly estimated from information contained in the velocity profile, specifically, shear velocity, u_*, and roughness length, z₀ Under ideal conditions, the functional relationship between elevation above the bed and flow velocity is log-linear, as expressed by the “law of the wall.” Unless the field data conform exactly to this ideal relationship, there is uncertainty in estimates of u_* and z₀. derived from velocity profiles. This uncertainty depends on the degree of scatter or deviation from the assumed log-linear model. Expressions are presented to quantify the error and to correct for it. Guidelines are suggested to minimize potential uncertainty, especially with regard to instrument deployment and velocity profile analysis. Calculation of confidence intervals around estimates of u_* and z₀, as proposed by Wilkinson (1984), is necessary and recommended.     

Modelling spatially and temporally distributed phenomena: new methods and tools for GRASS GIS

Abstract

The concept of GRASS (Geographic Resources Analysis Support System) as an open system has created a favourable environment for integration of process based modelling and GIS. To support this integration a new generation of tools is being developed in the following areas: (a) interpolation from multidimensional scattered point data, (b) analysis of surfaces and hypersurfaces, (c) modelling of spatial processes and, (d) 3D dynamic visualization. Examples of two applications are given-spatial and temporal modelling of erosion and deposition, and multivariate interpolation and visualization of nitrogen concentrations in the Chesapeake Bay.     

香港岛地区滑坡灾害的时空分布模式

香港岛是香港特别行政区开发程度最高的区域，人工滑坡及自然滑坡时常发生。滑坡灾害在时间和空间的分布受多种因素的影响并呈现出一定的规律性，本文利用GEO发布的近十多年的滑坡资料对香港岛地区滑坡灾害的时间、空间和时空分布模式进行了分析。     

Modelling uncertainty and spatial dependence: Stochastic imaging

Abstract

The most vibrant area of research in geostatistics is stochastic imaging, that is, the modelling of spatial uncertainty through alternative, equiprobable, numerical representations (maps) of spatially distributed phenomena. These stochastic images are conditioned to a variety of data accounting for their specific measurement scale and reliability.

Any geostatistical prediction is built on a prior model of spatial correlation that ties data to unsampled values and, equally importantly, unsampled values at different locations together. Since a major goal in the exercise of mapping is to display organization in space, spatial correlation is a necessity. As for uncertainty it is so pervasive that it is imperative to account for it.     

An inductive modelling procedure based on Bayes' theorem for analysis of pattern in spatial data

Abstract

This paper describes an inductive modelling procedure integrated with a geographical information system for analysis of pattern within spatial data. The aim of the modelling procedure is to predict the distribution within one data set by combining a number of other data sets. Data set combination is carried out using Bayes’ theorem. Inputs to the theorem, in the form of conditional probabilities, are derived from an inductive learning process in which attributes of the data set to be modelled are compared with attributes of a variety of predictor data sets. This process is carried out on random subsets of the data to generate error bounds on inputs for analysis of error propagation associated with the use of Bayes’ theorem to combine data sets in the GIS. The statistical significance of model inputs is calculated as part of the inductive learning process. Use of the modelling procedure is illustrated through the analysis of the winter habitat relationships of red deer in Grampian Region, north-east Scotland. The distribution of red deer in Deer Management Group areas in Gordon and in Kincardine and Deeside Districts is used to develop a model which predicts the distribution throughout Grampian Region; this is tested against red deer distribution in Moray District. Habitat data sets used for constructing the model are accumulated frost and altitude, obtained from maps, and land cover, derived from satellite imagery. Errors resulting from the use of Bayes’ theorem to combine data sets within the GIS and introduced in generalizing output from 50 m pixel to 1 km grid squares resolution are analysed and presented in a series of maps. This analysis of error trains is an integral part of the implemented analytical procedure and provides support to the interpretation of the results of modelling. Potential applications of the modelling procedure are discussed.