A note on geographical systems and maps of Montserrat

It is often critically important that geospatial data are measured and mapped accurately, particularly for quantitative analyses and numerical modelling applications. Defining a geographical coordinate system requires a non-unique combination of geodetic techniques (e.g. ellipsoids, projections and geoids). The choice of geographical system presents scope for ambiguity and confusion about geographical data, especially those archived without appropriate metadata. Experience has shown that these confusions have been a repeating source of either frustration or inadvertent error for those using geographical data from Montserrat. This is, in part, probably due to common usage of multiple datums and the existence of numerous topographical datasets recorded during the past 150 years. Here, we attempt to provide a brief introduction to geodetic principles and their application to Montserrat geographical data. The differences between common datums are illustrated and we describe variations in magnetic declination as they apply to field use of magnetic instruments. We include a record of the source of the large-scale mapping datasets that have been used and analysed ubiquitously in the literature. The descriptions here are intended as an introductory reference resource for those using geographical data from Montserrat

Real-time prediction of rain-triggered lahars: incorporating seasonality and catchment recovery

Rain-triggered lahars are a significant secondary hydrological and geomorphic hazard at volcanoes where unconsolidated pyroclastic material produced by explosive eruptions is exposed to intense rainfall, often occurring for years to decades after the initial eruptive activity. Previous studies have shown that secondary lahar initiation is a function of rainfall parameters, source material characteristics and time since eruptive activity. In this study, probabilistic rain-triggered lahar forecasting models are developed using the lahar occurrence and rainfall record of the Belham River valley at the Soufrière Hills volcano (SHV), Montserrat, collected between April 2010 and April 2012. In addition to the use of peak rainfall intensity (PRI) as a base forecasting parameter, considerations for the effects of rainfall seasonality and catchment evolution upon the initiation of rain-triggered lahars and the predictability of lahar generation are also incorporated into these models. Lahar probability increases with peak 1 h rainfall intensity throughout the 2-year dataset and is higher under given rainfall conditions in year 1 than year 2. The probability of lahars is also enhanced during the wet season, when large-scale synoptic weather systems (including tropical cyclones) are more common and antecedent rainfall and thus levels of deposit saturation are typically increased. The incorporation of antecedent conditions and catchment evolution into logistic-regression-based rain-triggered lahar probability estimation models is shown to enhance model performance and displays the potential for successful real-time prediction of lahars, even in areas featuring strongly seasonal climates and temporal catchment recovery

A framework for probabilistic weather forecast post-processing across models and lead times using machine learning

Forecasting the weather is an increasingly data intensive exercise. Numerical Weather Prediction (NWP) models are becoming more complex, with higher resolutions, and there are increasing numbers of different models in operation. While the forecasting skill of NWP models continues to improve, the number and complexity of these models poses a new challenge for the operational meteorologist: how should the information from all available models, each with their own unique biases and limitations, be combined in order to provide stakeholders with well-calibrated probabilistic forecasts to use in decision making? In this paper, we use a road surface temperature example to demonstrate a three-stage framework that uses machine learning to bridge the gap between sets of separate forecasts from NWP models and the 'ideal' forecast for decision support: probabilities of future weather outcomes. First, we use Quantile Regression Forests to learn the error profile of each numerical model, and use these to apply empirically-derived probability distributions to forecasts. Second, we combine these probabilistic forecasts using quantile averaging. Third, we interpolate between the aggregate quantiles in order to generate a full predictive distribution, which we demonstrate has properties suitable for decision support. Our results suggest that this approach provides an effective and operationally viable framework for the cohesive post-processing of weather forecasts across multiple models and lead times to produce a well-calibrated probabilistic output.Comment: 17 pages, 9 figures, to be published in Philosophical Transactions of the Royal Society

Coupled subdaily and multiweek cycles during the lava dome erupt of Soufriere Hills Volcano, Montserrat

Observations of volcanoes extruding andesitic lava to produce lava domes often reveal cyclic behaviour. At Soufriere Hills Volcano, Montserrat, cycles with sub-daily and multi-week periods have been recognised on many occasions. These two types of cycle have been modelled separately as stick-slip magma flow at the junction between a dyke and an overlying cylindrical conduit (Costa et al. 2012), and as the filling and discharge of magma through the elastic-walled dyke (Costa et al., 2007a) respectively. Here, we couple these two models to simulate the behaviour over a period of well-observed multi-week cycles, with accompanying sub-daily cycles, from 13 May to 21 September 1997. The coupled model captures well the asymmetrical first-order behaviour: the first 40% of the multi-week cycle consists of high rates of lava extrusion during short period/high amplitude sub-daily cycles as the dyke reservoir discharges itself. The remainder of the cycle involves increasing pressurization as more magma is stored, and extrusion rate falls, followed by a gradual increase in the period of the sub-daily cycles

Understanding causality and uncertainty in volcanic observations: an example of forecasting eruptive activity on Soufrière Hills Volcano, Montserrat

Following a cessation in eruptive activity it is important to understand how a volcano will behave in the future and when it may next erupt. Such an assessment can be based on the volcano's long-term pattern of behaviour and insights into its current state via monitoring observations. We present a Bayesian network that integrates these two strands of evidence to forecast future eruptive scenarios using expert elicitation. The Bayesian approach provides a framework to quantify the magmatic causes in terms of volcanic effects (i.e., eruption and unrest). In October 2013, an expert elicitation was performed to populate a Bayesian network designed to help forecast future eruptive (in-)activity at Soufrière Hills Volcano. The Bayesian network was devised to assess the state of the shallow magmatic system, as a means to forecast the future eruptive activity in the context of the long-term behaviour at similar dome-building volcanoes. The findings highlight coherence amongst experts when interpreting the current behaviour of the volcano, but reveal considerable ambiguity when relating this to longer patterns of volcanism at dome-building volcanoes, as a class. By asking questions in terms of magmatic causes, the Bayesian approach highlights the importance of using short-term unrest indicators from monitoring data as evidence in long-term forecasts at volcanoes. Furthermore, it highlights potential biases in the judgements of volcanologists and identifies sources of uncertainty in terms of magmatic causes rather than scenario-based outcomes

Bayesian deep learning for large scale environmental data modelling

Deep learning – machine learning using deep neural networks – is an efficient way to discover patterns in data that may be more complex than we could manually hypothesise. Here we learn spatio-temporal models that harness information from gridded auxiliary datasets, such as digital terrain models and satellite imagery, by learning task-relevant derivatives of these with no requirement for manual feature engineering. By operating within the Bayesian probabilistic framework, we can learn well-calibrated deep models that quantify epistemic and aleatoric uncertainties and avoid overfitting despite the capacity of deep models to do so.Engineering and Physical Sciences Research Council (EPSRC

Vulnerability of bridges to scour:Insights from an international expert elicitation workshop

Scour (localised erosion) during flood events is one of the most significant threats to bridges over rivers and estuaries, and has been the cause of numerous bridge failures, with damaging consequences. Mitigation of the risk of bridges being damaged by scour is therefore important to many infrastructure owners, and is supported by industry guidance. Even after mitigation, some residual risk remains, though its extent is difficult to quantify because of the uncertainties inherent in the prediction of scour and the assessment of the scour risk. This paper summarises findings from an international expert workshop on bridge scour risk assessment that explores uncertainties about the vulnerability of bridges to scour. Two specialised structured elicitation methods were applied to explore the factors that experts in the field consider important when assessing scour risk and to derive pooled expert judgements of bridge failure probabilities that are conditional on a range of assumed scenarios describing flood event severity, bridge and watercourse types and risk mitigation protocols. The experts' judgements broadly align with industry good practice, but indicate significant uncertainty about quantitative estimates of bridge failure probabilities, reflecting the difficulty in assessing the residual risk of failure. The data and findings presented here could provide a useful context for the development of generic scour fragility models and their associated uncertainties

A Framework for Probabilistic Multi-Hazard Assessment of Rain-Triggered Lahars Using Bayesian Belief Networks

Volcanic water-sediment flows, commonly known as lahars, can often pose a higher threat to population and infrastructure than primary volcanic hazardous processes such as tephra fallout and Pyroclastic Density Currents (PDCs). Lahars are volcaniclastic flows of water, volcanic debris and entrained sediments that can travel long distances from their source, causing severe damage by impact and burial. Lahars are frequently triggered by intense or prolonged rainfall occurring after explosive eruptions, and their occurrence depends on numerous factors including the spatio-temporal rainfall characteristics, the spatial distribution and hydraulic properties of the tephra deposit, and the pre- and post-eruption topography. Modeling (and forecasting) such a complex system requires the quantification of aleatory variability in the lahar triggering and propagation. To fulfill this goal, we develop a novel framework for probabilistic hazard assessment of lahars within a multi-hazard environment, based on coupling a versatile probabilistic model for lahar triggering (a Bayesian Belief Network: Multihaz) with a dynamic physical model for lahar propagation (LaharFlow). Multihaz allows us to estimate the probability of lahars of different volumes occurring by merging varied information about regional rainfall, scientific knowledge on lahar triggering mechanisms and, crucially, probabilistic assessment of available pyroclastic material from tephra fallout and PDCs. LaharFlow propagates the aleatory variability modeled by Multihaz into hazard footprints of lahars. We apply our framework to Somma-Vesuvius (Italy) because: (1) the volcano is strongly lahar-prone based on its previous activity, (2) there are many possible source areas for lahars, and (3) there is high density of population nearby. Our results indicate that the size of the eruption preceding the lahar occurrence and the spatial distribution of tephra accumulation have a paramount role in the lahar initiation and potential impact. For instance, lahars with initiation volume ≥105 m3 along the volcano flanks are almost 60% probable to occur after large-sized eruptions (~VEI ≥ 5) but 40% after medium-sized eruptions (~VEI4). Some simulated lahars can propagate for 15 km or reach combined flow depths of 2 m and speeds of 5–10 m/s, even over flat terrain. Probabilistic multi-hazard frameworks like the one presented here can be invaluable for volcanic hazard assessment worldwide

Dome growth, collapse, and valley fill at Soufrière Hills Volcano, Montserrat, from 1995 to 2013: Contributions from satellite radar measurements of topographic change

Frequent high-resolution measurements of topography at active volcanoes can provide important information for assessing the distribution and rate of emplacement of volcanic deposits and their influence on hazard. At dome-building volcanoes, monitoring techniques such as LiDAR and photogrammetry often provide a limited view of the area affected by the eruption. Here, we show the ability of satellite radar observations to image the lava dome and pyroclastic density current deposits that resulted from 15 years of eruptive activity at Soufrière Hills Volcano, Montserrat, from 1995 to 2010. We present the first geodetic measurements of the complete subaerial deposition field on Montserrat, including the lava dome. Synthetic aperture radar observations from the Advanced Land Observation Satellite (ALOS) and TanDEM-X mission are used to map the distribution and magnitude of elevation changes. We estimate a net dense-rock equivalent volume increase of 108 ± 15M m3 of the lava dome and 300 ± 220M m3 of talus and subaerial pyroclastic density current deposits. We also show variations in deposit distribution during different phases of the eruption, with greatest on-land deposition to the south and west, from 1995 to 2005, and the thickest deposits to the west and north after 2005. We conclude by assessing the potential of using radar-derived topographic measurements as a tool for monitoring and hazard assessment during eruptions at dome-building volcanoes