spTimer: Spatio-temporal Bayesian modelling using R

Hierarchical Bayesian modeling of large point-referenced space-time data is increasingly becoming feasible in many environmental applications due to the recent advances in both statistical methodology and computation power. Implementation of these methods using the Markov chain Monte Carlo (MCMC) computational techniques, however, requires development of problem-specific and user-written computer code, possibly in a low-level language. This programming requirement is hindering the widespread use of the Bayesian model-based methods among practitioners and, hence there is an urgent need to develop high-level software that can analyze large data sets rich in both space and time.This paper develops the package spTimer for hierarchical Bayesian modeling of stylized environmental space-time monitoring data as a contributed software package in the R language that is fast becoming a very popular statistical computing platform. The package is able to fit, spatially and temporally predict large amounts of space-time data using three recently developed Bayesian models. The user is given control over many options regarding covariance function selection, distance calculation, prior selection and tuningof the implemented MCMC algorithms, although suitable defaults are provided. The package has many other attractive features such as on the fly transformations and an ability to spatially predict temporally aggregated summaries on the original scale, which saves the problem of storage when using MCMC methods for large datasets. A simulation example, with more than a million observations, and a real life data example are used to validate the underlying code and to illustrate the software capabilities

A spatiodynamic model for assessing frost risk in south-eastern Australia

2015 Royal Statistical Society. Previous climate research concluded that causal influences which have contributed to changes in frost risk in south-eastern Australia include greenhouse gas concentration, El-Niño southern oscillation and other effects. Some of the climatic indices representing these effects have spatiotemporal misalignment and may have a spatially and temporally varying effect on observed data. Other indices are constructed from grid-referenced physical models, which creates a point-to-area problem. To address these issues we use a spatiodynamic model, which comprises a blending of spatially varying and temporally dynamic parameters. For the data that we examine the model proposed performs well in out-of-sample validation compared with a spatiotemporal model

Hierarchical spatially varying coefficient and temporal dynamic process models using spTDyn

Bayesian hierarchical spatio-temporal models are becoming increasingly important due to the increasing availability of space-time data in various domains. In this paper we develop a user friendly R package, spTDyn, for spatio-temporal modelling. It can be used to fit models with spatially varying and temporally dynamic coefficients. The former is used for modelling the spatially varying impact of explanatory variables on the response caused by spatial misalignment. This issue can arise when the covariates only vary over time, or when they are measured over a grid and hence do not match the locations of the response point-level data. The latter is to examine the temporally varying impact of explanatory variables in space-time data due, for example, to seasonality or other time-varying effects. The spTDyn package uses Markov chain Monte Carlo sampling written in C, which makes computations highly efficient, and the interface is written in R making these sophisticated modelling techniques easily accessible to statistical analysts. The models and software, and their advantages, are illustrated using temperature and ozone space-time data

Spatio-temporal Bayesian modeling of precipitation using rain gauge data from the Hubbard Brook Experimental Forest, New Hampshire, USA

Estimating precipitation volume over space and time is essential for many reasons such as evaluating air quality, determining the risk of flood and drought, making forest management decisions, and developing strategies for municipal water supplies. It is imperative to employ sound statistical methods for modeling data from a network of sparsely located rain gauges with known confidence. This paper proposes a spatio-temporal Bayesian model for estimating precipitation volumes using observations from a network of gauges. Based on Gaussian processes, the Bayesian model is able to interpolate at a high spatial resolution at each time point. Such interpolations are used to obtain various spatio-temporally aggregated statistics, such as annual precipitation volume in a local area. Markov chain Monte Carlo based model fitting, employed here, allows estimation of uncertainty that can be used in decision making. These methods are applied to a large data set of weekly precipitation volumes collected over the years 1997-2015 at the Hubbard Brook Experimental Forest (HBEF) in New Hampshire, USA. Using the proposed methodology we estimate trends in annual precipitation volumes spatially aggregated over nine gauged watersheds in the HBEF. The proposed modeling is also used to demonstrate a method for determining how to downsize a rain gauge network

Possible future changes in South East Australian frost frequency: an inter-comparison of statistical downscaling approaches

Anthropogenic climate change has already been shown to effect the frequency, intensity, spatial extent, duration and seasonality of extreme climate events. Understanding these changes is an important step in determining exposure, vulnerability and focus for adaptation. In an attempt to support adaptation decision-making we have examined statistical modelling techniques to improve the representation of global climate model (GCM) derived projections of minimum temperature extremes (frosts) in Australia. We examine the spatial changes in minimum temperature extreme metrics (e.g. monthly and seasonal frost frequency etc.), for a region exhibiting the strongest station trends in Australia, and compare these changes with minimum temperature extreme metrics derived from 10 GCMs, from the Coupled Model Inter-comparison Project Phase 5 (CMIP 5) datasets, and via statistical downscaling. We compare the observed trends with those derived from the “raw” GCM minimum temperature data as well as examine whether quantile matching (QM) or spatio-temporal (spTimerQM) modelling with Quantile Matching can be used to improve the correlation between observed and simulated extreme minimum temperatures. We demonstrate, that the spTimerQM modelling approach provides correlations with observed daily minimum temperatures for the period August to November of 0.22. This represents an almost fourfold improvement over either the “raw” GCM or QM results. The spTimerQM modelling approach also improves correlations with observed monthly frost frequency statistics to 0.84 as opposed to 0.37 and 0.81 for the “raw” GCM and QM results respectively. We apply the spatio-temporal model to examine future extreme minimum temperature projections for the period 2016 to 2048. The spTimerQM modelling results suggest the persistence of current levels of frost risk out to 2030, with the evidence of continuing decadal variation.this research was made possible via financial support from the Australian Grains Research and Development Corporation (GRD

Bayesian space-time model to analyse frost risk for agriculture in Southeast Australia

Despite a broad pattern of warming in minimum temperatures over the past 50 years, regions of southeastern Australia have experienced increases in frost frequency in recent decades, and more broadly across southern Australia, an extension of the frost window due to an earlier onset and later cessation. Consistent across southern Australia is a later cessation of frosts, with some areas of southeastern Australia experiencing the last frost an average 4 weeks later than in the 1960s (i.e. mean date of last frost for the period 1960–1970 was 19 September versus 22 October for the period 2000–2009). We seek to model the spatial changes in frosts for a region exhibiting the strongest individual station trends, i.e. northern Victoria and southern New South Wales. We identify statistically significant trends at low-lying stations for the month of August and construct and validate a Bayesian space–time model of minimum temperatures, using rates of greenhouse gas (GHG) emissions, as well as other well-understood causal factors including solar radiation, the El Niño Southern Oscillation (ENSO 3.4) and times series data relating to the position (STRP) and intensity (STRI) of subtropical highs and blocking high pressure systems. We assess the performance of this modelling approach against observational records as well as against additive and linear regression modelling approaches using root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) as well as false alarm and hit rate metrics. The spatiotemporal modelling approach demonstrated considerably better predictive skill than the others, with enhanced performance across all the metrics analysed. This enhanced performance was consistent across each decade and for temperature extremes below 2 °C

A method to identify drivers of societal change likely to affect natural assets in the future, illustrated with Australia's native biodiversity

Human society has a profound adverse effect on natural assets as human populations increase and as global climate changes. We need to envisage different futures that encompass plausible human responses to threats and change, and become more mindful of their likely impacts on natural assets. We describe a method for developing a set of future scenarios for a natural asset at national scale under ongoing human population growth and climate change. The method involves expansive consideration of potential drivers of societal change, a reduction of these to form a small set of key drivers to which contrasting settings are assigned, which we use to develop a set of different scenarios. We use Australia's native biodiversity as the focus to illustrate the method