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Snow-slurry lahars, containing a mixture of water, snow, ice and debris are highly mobile for their typical volumes and occur at a rate much higher than the geologic record indicates. This hazard may cause significant proximal impacts at snow-clad volcanoes even at small volumes. However, the occurrence, footprint and intensity of snow slurry lahar hazards are generally ill-quantified due to the lack of a reliable geologic record, as most evidence disappears after melting. A potential avenue to hazard quantification is through numerical modelling. Here, we quantify snow slurry lahar hazard at Mt. Ruapehu, New Zealand through simulations to obtain lahar intensities and footprints.The most recently active vent at Ruapehu is located underneath the crater lake, which has resulted in a predisposition towards generating snow slurry lahars (e.g. in 2007, 1995, 1975, 1969). The volcano is located within a popular national park and has several ski fields on its slopes, with many assets and visitors potentially impacted by lahars. We applied a multi-phase numerical model to simulate the snow slurry lahars with a high-resolution DEM to estimate the impact of lahars on Ruapehu. Calibration and benchmarking of the model and parameters against 2007 and 1995 snow slurry lahars was used to measure simulation performance, as well as providing insights into lahar initiation and dynamics. Simulations of lahar scenarios using the calibrated parameters were then used to analyse potential impacts at Ruapehu though aggregated lahar pathways, impact footprints and measures of lahar pressure and velocity., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

BioWhere aims to develop techniques that can be harnessed to map large volumes of biota specimens from all over New Zealand and Antarctica. With over 12 million records, these specimens along with their locations are georeferenced textually, usually with complex natural language descriptions, across various scientific publications and specimen collections held by museums and other institutions. However, only a small fraction of these specimens has been mapped owing to the large volume of the data and the amount of labour the process of converting textual descriptions into coordinates demands. The challenge is amplified further due to the indefinite, vague nature of natural language used to textually describe the locations of these specimens. The automated tools currently in use fall short of effectively mapping the specimens because they rely on incomplete gazetteers and ignore spatial language. The BioWhere project will explore the latest techniques in natural language processing and develop methods that can effectively overcome the aforementioned shortcomings using computational georeferencing methods to translate human language descriptions of locations to geographical coordinates. Our machine learning models will incorporate environmental factors, linguistic context, and the characteristics of the named place in building a self-learning gazetteer. Incorporating physical, historical, and cultural context will enrich the gazetteer with Māori knowledge including the origin, narrative and meaning of Māori place names. This will unlock vast amounts of structured scientific knowledge that are currently inaccessible. Furthermore, methods developed in the project will find further applications in a range of domains including disaster response, cultural heritage, and health.

Recent years have seen concerted efforts to revitalise New Zealand Māori, the indigenous language of Aotearoa New Zealand, after earlier attempts at suppression during colonisation. Automated methods for natural language processing together with the increasing availability of written Māori language resources have great potential for extracting knowledge from text to increase understanding of current and historical Māori worldviews. In the geographic domain, these methods can be used to increase knowledge of Māori conceptualisations of landscape and to enable information retrieval for purposes such as mapping of species distribution and disaster events. However, most existing tools are based on the form and syntax of English and other well-resourced languages and pose challenges when applied to Māori, including lack of annotated data, inappropriate grammatical assumptions and high levels of polysemy. We discuss these challenges as discovered during (1) the creation of a large Māori corpus through the amalgamation of multiple other language resources; and (2) the comparison of five rule-based and machine learning bag of words methods to identify geographic senses of a collection of 11 geographic feature type words, many of which have multiple other meanings.

BioWhere aims to develop techniques that can be harnessed to map large volumes of biota specimens from all over New Zealand and Antarctica. With over 12 million records, these specimens along with their locations are georeferenced textually, usually with complex natural language descriptions, across various scientific publications and specimen collections held by museums and other institutions. However, only a small fraction of these specimens has been mapped owing to the large volume of the data and the amount of labour the process of converting textual descriptions into coordinates demands. The challenge is amplified further due to the indefinite, vague nature of natural language used to textually describe the locations of these specimens. The automated tools currently in use fall short of effectively mapping the specimens because they rely on incomplete gazetteers and ignore spatial language. The BioWhere project will explore the latest techniques in natural language processing and develop methods that can effectively overcome the aforementioned shortcomings using computational georeferencing methods to translate human language descriptions of locations to geographical coordinates. Our machine learning models will incorporate environmental factors, linguistic context, and the characteristics of the named place in building a self-learning gazetteer. Incorporating physical, historical, and cultural context will enrich the gazetteer with Māori knowledge including the origin, narrative and meaning of Māori place names. This will unlock vast amounts of structured scientific knowledge that are currently inaccessible. Furthermore, methods developed in the project will find further applications in a range of domains including disaster response, cultural heritage, and health.

This book is the accomplishment of three young, enthusiastic, and highly qualified scientists, whom I wish to congratulate: They have ably put this fascinating yet catastrophic natural process in the spotlight. Although poorly known before the 1980 Mount St. Helens eruption, debris avalanches are now recognized as common events in the lifespan of stratovolcanoes. The editors assembled an amazing pool of scientists, who have devoted most (if not all) of their academic careers to understanding this awesome volcanic process. Given our growing awareness of the hazard that volcanoes pose on their surroundings, this contribution constitutes a very useful tool to volcanic monitoring, inasmuch as it encompasses our current understanding on debris avalanches as well as the unknowns that should be the focus of future studies.