Environmetrics最新文献

Changes to the environment surrounding a temperature measuring station can cause local changes to the recorded temperature that deviate from regional temperature behavior. This phenomenon—often caused by construction or urbanization—occurs at a local level. If these local changes are assumed to represent regional or global processes it can have significant impacts on historical data analyses. These changes or deviations are generally gradual, but can be abrupt, and arise as construction or other environmental changes occur near a recording station. We propose a methodology to examine if changes in temperature behavior at a point in time exist at a local level at various locations in a region assuming that regional or global processes are correlated among nearby stations. Specifically, we propose a Bayesian change point model for spatio-temporally dependent data where we select the number of change points at each location using a “forward” selection process using deviance information criterion. We then fit the selected version of the model and examine the linear slopes across time to quantify the local changes in long-term temperature behavior. We show the utility of this model and method using both synthetic data and observed temperature measurements from eight stations in Utah consisting of daily temperature data for 60 years.

We introduce a bivariate bidimensional mixed-effects regression model, motivated by the analysis of $��{�\text{CO}�}_{�2�}�$ emission levels and growth on OECD countries from 1990 to 2018. The model is able to capture heterogeneity across countries and allows for a full association structure among outcomes, assuming a discrete distribution for the random terms with a possibly different number of support points in each univariate profile. We test the behavior of the proposed approach via a simulation study, considering several factors such as the number of observed units, times, and levels of heterogeneity in the data. Empirically, we define an extended version of the STIRPAT model where all model parameters, and not only the mean, vary according to a regression model. Our empirical findings provide evidence of heterogeneous behaviors across countries and suggest the need of a flexible approach to properly reflect the heterogeneity in both the emission levels and the growth processes.

The cover image is based on the Research Article Large-scale environmental data science with ExaGeoStatR by Sameh Abdulah et al., https://doi.org/10.1002/env.2770. Image Credit: Xavier Pita, KAUST.

Digital twins are increasingly important in many domains, including for understanding and managing the natural environment. Digital twins of the natural environment are fueled by the unprecedented amounts of environmental data now available from a variety of sources from remote sensing to potentially dense deployment of earth-based sensors. Because of this, data science techniques inevitably have a crucial role to play in making sense of this complex, highly heterogeneous data. This short article reflects on the role of data science in digital twins of the natural environment, with particular attention on how resultant data models can work alongside the rich legacy of process models that exist in this domain. We seek to unpick the complex two-way relationship between data and process understanding. By focusing on the interactions, we end up with a template for digital twins that incorporates a rich, highly dynamic learning process with the potential to handle the complexities and emergent behaviors of this important area.

In geostatistics, the theory of complex-valued random fields is often used to provide an appropriate characterization of vector data with two components. In this context, constructing new classes of complex covariance models to be used in structural analysis and, then for stochastic interpolation or simulation, represents a focus of particular interest in the scientific community and in many areas of applied sciences, such as in electrical engineering, oceanography, or meteorology. In this article, after a review of the theoretical background of a random field in a complex domain, the construction of new classes of complex-valued covariance models is proposed. In particular, the complex-valued covariance models obtained by the convolution of the real component are generalized and wide new classes of models are generated through integration. These families include even non-integrable real and imaginary components of the resulting complex covariance models. It is also illustrated how to fit the real and imaginary components of the complex models together with the density function used in the integration. The procedure is clarified through a case study with oceanographic data.

We propose a Bayesian model which produces probabilistic reconstructions of hydroclimatic variability in Queensland Australia. The model provides a standardized approach to hydroclimate reconstruction using multiple palaeoclimate proxy records derived from natural archives such as speleothems, ice cores and tree rings. The method combines time-series modeling with inverse prediction to quantify the relationships between a given hydroclimate index and relevant proxies over an instrumental period and subsequently reconstruct the hydroclimate back through time. We present case studies for Brisbane and Fitzroy catchments focusing on two hydroclimate indices, the Rainfall Index (RFI) and the Standardized Precipitation-Evapotranspiration Index (SPEI). The probabilistic nature of the reconstructions allows us to estimate the probability that a hydroclimate index in any reconstruction year was lower (higher) than the minimum (maximum) value observed over the instrumental period. In Brisbane, the RFI is unlikely (probabilities < 5%) to have exhibited extremes beyond the minimum/maximum values observed between 1889 and 2019. However, in Fitzroy there are several years during the reconstruction period where the RFI is likely (>50% probability) to have exhibited behavior beyond the minimum/maximum of what has been observed, during the instrumental period. For SPEI, the probability of observing such extremes prior to the beginning of the instrumental period in 1889 doesn't exceed 30% in any reconstruction year in Brisbane, but exceeds 50% in multiple years in Fitzroy.

Biomonitoring studies evaluating air quality via airborne element accumulation patterns in lichens typically control variability by focusing on narrow geographic regions and short time windows. Using samples of the widespread “rock-posy” lichen sampled across the Intermountain Region of the United States, we investigate whether accumulation patterns of generic pollution sources are detectable on broad geographic and temporal scales. We develop a novel Bayesian multivariate receptor modeling (BMRM) approach that sharpens detection and discrimination of candidate pollution sources through (i) regularization of source contributions to each sample and (ii) incorporating estimated lichen secondary chemistry as a factor. Through a simulation study, we demonstrate a distinct advantage in shrinking contributions when they are truly sparse, as would be expected with heterogeneous samples from dispersed collection sites. We contrast analyses employing both standard and sparse BMRMs, and positive matrix factorization (PMF). The sparse model better maintains source identity, as specified though informative prior distributions on elemental profiles. We advocate quantitative profile matching, which reveals that PMF primarily captures variations of the baseline profile for lichen secondary chemistry. Both PMF and BMRM results suggest that the most detectable signatures relate to aeolian dust deposition, while spatial patterns hint at sporadic anthropogenic influence.