Design, Implementation, and Evaulation of GIS-Based Learning Materials in an Introductory Geoscience Course

Little is known about how well GIS-based learning lives up to its potential for improving students' skills in problem solving, analysis, and spatial visualization. This article describes a study in which researchers determined ways to quantify student learning that occurred with a GIS-based module on plate tectonics and geologic hazards, and to improve the materials design with the use of classroom observations and field testing. The study found that student difficulties in working with GIS-based activities can be overcome by making some features of the GIS transparent to the user, that a lack of basic geography skills can interfere in the progression of a GIS-based activity, and that some conceptual difficulties can be overcome by providing guiding questions that help students interrogate visual data. In addition, it was noted that some misconceptions in interpretation of two-dimensional maps and three-dimensional block diagrams can persist even after direct instruction. In general, a positive correlation was noted between spatial thinking and GIS-based learning. Educational levels: Graduate or professional

Long-term probabilistic volcanic hazard assessment using open and non-open data: observations and current issues

Probabilistic volcanic hazard assessment (PVHA) has become the paradigm to quantify volcanic hazard over the last decades. Substantial aleatory and epistemic uncertainties in PVHA arise from complexity of physico-chemical processes, impossibility of their direct observation and, importantly, a severe scarcity of observables from past eruptions. One factor responsible for data scarcity is the infrequency of moderate/large eruptions; other factors include lack of discoverability and accessibility to volcanological data. Open-access databases can help alleviate data scarcity and have significantly contributed to long-term PVHA of eruption onset and size, while are less common for data required in other PVHA components (e.g., vent opening). Making datasets open is complicated by economical, technological, ethical and/or policy-related challenges. International synergies (e.g., Global Volcanism Program, WOVOdat, Global Volcano Model, EPOS) will be key to facilitate the creation and maintenance of open-access databases that support Next-Generation PVHA. Additionally, clarification of some misconceptions about PVHA can also help progress. Firstly, PVHA should be understood as an expansion of deterministic, scenario-based hazard assessments. Secondly, a successful PVHA should sometimes be evaluated by its ability to deliver useful and usable hazard-related messages that help mitigate volcanic risk. Thirdly, PVHA is not simply an end product but a driver for research: identifying the most relevant sources of epistemic uncertainty can guide future efforts to reduce the overall uncertainty. Broadening of the volcanological community expertise to statistics or engineering has already brought major breakthroughs in long-term PVHA. A vital next step is developing and maintaining more open-access datasets that support PVHA worldwide

Computational Strategies in Uncertainty Quantification for Hazard Mapping

There are many hazards associated with volcanic activities. Amongst them are Pyroclastic flows; a mixture of rock fragments, debris and hot gases that flow down the slope of actives volcanoes at high velocities. These flows have proven to be devastating, and at the same time more than 500 millions people in the world live within potential exposure to such a hazard. A few approaches have been used to try to mitigate the impact of volcanic hazard in general. These include remote sensing technology and developing hazard maps – a graphic representation of safe and risky zones for a given volcanic area. In this dissertation, we develop a workflow for fast creation of accurate hazard maps. We apply this workflow on the case of the Long Valley volcanic region in northern California (USA). We have also made a couple of contributions that, while pertinent to the problem at hand, also have merit in a wide range of applications. First, we develop a Hierarchical Bayesian model that combines data on Pyroclastic flow behavior from various volcanic sites into a ”global” dataset and reduces predictive uncertainty at volcanoes with sparse data. Of particular interest to us is the uncertainty in key input variables for computer simulations of Pyroclastic flows. Secondly, we develop a learn- ing algorithm for experimental resource allocation in the case where multiple objectives need to be achieved simultaneously. This algorithm allows us to compute probability of hazard for multiple locations at the same time, and vastly reduce the time it takes to create hazard maps. These two contributions form the basis of a tool for geo-scientists to rapidly assess risk spatially at a moment notice, and provide hazard maps that can be used as a teaching tool for communities at risk

Rapid methods of landslide hazard mapping : Fiji case study

A landslide hazard probability map can help planners (1) prepare for, and/or mitigate against, the effects of landsliding on communities and infrastructure, and (2) avoid or minimise the risks associated with new developments. The aims of the project were to establish, by means of studies in a few test areas, a generic method by which remote sensing and data analysis using a geographic information system (GIS) could provide a provisional landslide hazard zonation map. The provision of basic hazard information is an underpinning theme of the UN’s International Decade for Natural Disaster Reduction (IDNDR). It is an essential requirement for disaster preparedness and mitigation planning. This report forms part of BGS project 92/7 (R5554) ‘Rapid assessment of landslip hazards’ Carried out under the ODA/BGS Technology Development and Research Programme as part of the British Government’s provision of aid to developing countries. It provides a detailed technical account of work undertaken in a test area in Viti Levu in collaboration with Fiji Mineral Resources Department. The study represents a demonstration of a methodology that is applicable to many developing countries. The underlying principle is that relationships between past landsliding events, interpreted from remote sensing, and factors such as the geology, relief, soils etc provide the basis for modelling where future landslides are most likely to occur. This is achieved using a GIS by ‘weighting’ each class of each variable (e.g. each lithology ‘class’ of the variable ‘geology’) according to the proportion of landslides occurring within it compared to the regional average. Combinations of variables, produced by summing the weights in individual classes, provide ‘models’ of landslide probability. The approach is empirical but has the advantage of potentially being able to provide regional scale hazard maps over large areas quickly and cheaply; this is unlikely to be achieved using conventional ground-based geotechnical methods. In Fiji, landslides are usually triggered by intense rain storms commonly associated with tropical cyclones. However, the regional distribution of landslides has not been mapped nor is it known how far geology and landscape influence the location and severity of landsliding events. The report discusses the remote sensing and GIS methodology, and describes the results of the pilot study over an area of 713 km2 in south east Viti Levu. The landslide model uses geology, elevation, slope angle, slope aspect, soil type, and forest cover as inputs. The resulting provisional landslide hazard zonation map, divided into high, medium and low zones of landslide hazard probability, suggests that whilst rainfall is the immediate cause, others controls do exert a significant influence. It is recommended that consideration be given in Fiji to implementing the techniques as part of a national strategic plan for landslide hazard zonation mapping

The effects of vent location, event scale and time forecasts on pyroclastic density current hazard maps at Campi Flegrei caldera (Italy)

This study presents a new method for producing long-term hazard maps for pyroclastic density currents (PDC) originating at Campi Flegrei caldera. Such method is based on a doubly stochastic approach and is able to combine the uncertainty assessments on the spatial location of the volcanic vent, the size of the flow and the expected time of such an event. The results are obtained by using a Monte Carlo approach and adopting a simplified invasion model based on the box model integral approximation. Temporal assessments are modeled through a Cox-type process including self-excitement effects, based on the eruptive record of the last 15 kyr.Mean and percentilemaps of PDC invasion probability are produced, exploring their sensitivity to some sources of uncertainty and to the effects of the dependence between PDC scales and the caldera sector where they originated. Conditional maps representative of PDC originating inside limited zones of the caldera, or of PDC with a limited range of scales are also produced. Finally, the effect of assuming different time windows for the hazard estimates is explored, also including the potential occurrence of a sequence of multiple events. Assuming that the last eruption of Monte Nuovo (A.D. 1538) marked the beginning of a new epoch of activity similar to the previous ones, results of the statistical analysis indicate a mean probability of PDC invasion above 5% in the next 50 years on almost the entire caldera (with a probability peak of 25% in the central part of the caldera). In contrast, probability values reduce by a factor of about 3 if the entire eruptive record is considered over the last 15 kyr, i.e., including both eruptive epochs and quiescent periods

Event trees and epistemic uncertainty in long‐term volcanic hazard assessment of Rift Volcanoes: the example of Aluto (Central Ethiopia)

Aluto is a peralkaline rhyolitic caldera located in a highly populated area in central Ethiopia. Its postcaldera eruptive activity has mainly consisted of self‐similar, pumice‐cone‐building eruptions of varying size and vent location. These eruptions are explosive, generating hazardous phenomena that could impact proximal to distal areas from the vent. Volcanic hazard assessments in Ethiopia and the East African Rift are still limited in number. In this study, we develop an event tree model for Aluto volcano. The event tree is doubly useful: It facilitates the design of a conceptual model for the volcano and provides a framework to quantify volcanic hazard. We combine volcanological data from past and recent research at Aluto, and from a tool to objectively derive analog volcanoes (VOLCANS), to parameterize the event tree, including estimates of the substantial epistemic uncertainty. Results indicate that the probability of a silicic eruption in the next 50 years is highly uncertain, ranging from 2% to 35%. This epistemic uncertainty has a critical influence on event‐tree estimates for other volcanic events, like the probability of occurrence of pyroclastic density currents (PDCs) in the next 50 years. The 90% credible interval for the latter is 5–16%, considering only the epistemic uncertainty in conditional eruption size and PDC occurrence, but 2–23% when adding the epistemic uncertainty in the probability of eruption in 50 years. Despite some anticipated challenges, we envisage that our event tree could be translated to other rift volcanoes, making it an important tool to quantify volcanic hazard in Ethiopia and elsewhere

Decision Analysis for Management of Natural Hazards

Losses from natural hazards, including geophysical and hydrometeorological hazards, have been increasing worldwide. This review focuses on the process by which scientific evidence about natural hazards is applied to support decision making. Decision analysis typically involves estimating the probability of extreme events; assessing the potential impacts of those events from a variety of perspectives; and evaluating options to plan for, mitigate, or react to events. We consider issues that affect decisions made across a range of natural hazards, summarize decision methodologies, and provide examples of applications of decision analysis to the management of natural hazards. We conclude that there is potential for further exchange of ideas and experience between natural hazard research communities on decision analysis approaches. Broader application of decision methodologies to natural hazard management and evaluation of existing decision approaches can potentially lead to more efficient allocation of scarce resources and more efficient risk management