Environmetrics最新文献

{"title":"Hierarchical Bayesian Modeling of Total Column Ozone: Unraveling Equatorial Variability Over Ethiopia Using Satellite Data and Multisource Covariates","authors":"Yassin Tesfaw Abebe,&nbsp;Abdu Mohammed Seid,&nbsp;Lassi Roininen,&nbsp;U. Jaya Parakash Raju,&nbsp;Abebaw Bizuneh Alemu","doi":"10.1002/env.70115","DOIUrl":"https://doi.org/10.1002/env.70115","url":null,"abstract":"<p>Understanding the spatiotemporal dynamics of total column ozone (TCO) is critical for monitoring ultraviolet (UV) exposure and ozone trends, particularly in equatorial regions where variability remains underexplored. This study investigates monthly TCO over Ethiopia (2012–2022) using a Bayesian hierarchical model implemented via Integrated Nested Laplace Approximation (INLA). The model incorporates nine environmental covariates, capturing meteorological, stratospheric, and topographic influences alongside spatiotemporal random effects. Spatial dependence is modeled using the stochastic partial differential equation (SPDE) approach, while temporal autocorrelation is handled through an autoregressive structure. The model shows strong predictive accuracy, with correlation coefficients of 0.94 (training) and 0.91 (validation), and RMSE values of 3.91 DU and 4.45 DU, respectively. Solar radiation, stratospheric temperature, and the quasi-biennial oscillation are positively associated with TCO, whereas surface temperature, precipitation, humidity, water vapor, and altitude exhibit negative associations. Random effects highlight persistent regional clusters and seasonal peaks during summer. These findings provide new insights into regional ozone behavior over complex equatorial terrains, contributing to the understanding of the equatorial ozone paradox. The approach demonstrates the utility of combining satellite observations with environmental data in data-scarce regions, supporting improved UV risk monitoring and climate-informed policy planning.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 5","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70115","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148466878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bayesian Nonparametric Clustering for Spatiotemporal Data, With an Application to Air Pollution","authors":"Luca Aiello,&nbsp;Raffaele Argiento,&nbsp;Sirio Legramanti,&nbsp;Lucia Paci","doi":"10.1002/env.70118","DOIUrl":"https://doi.org/10.1002/env.70118","url":null,"abstract":"<div>\u0000 \u0000 <p>Air pollution is a major global health hazard, with fine particulate matter (PM10) linked to severe respiratory and cardiovascular diseases. Hence, analyzing and clustering spatiotemporal air quality data is crucial for understanding pollution dynamics and guiding policy interventions. This work illustrates Bayesian nonparametric clustering methods, with a particular focus on their application to spatiotemporal data, which are ubiquitous in environmental sciences. We first introduce key modeling approaches for point-referenced spatiotemporal data, highlighting their flexibility in capturing complex spatial and temporal dependencies. We then provide an overview of recent advancements in Bayesian nonparametric clustering, specifically focusing on spatial product partition models, which incorporate spatial structure into the partition. We illustrate the proposed methods on PM10 data from Northern Italy, demonstrating their ability to identify meaningful pollution patterns by clustering together monitoring stations with similar PM10 dynamics. This paper highlights the potential of Bayesian nonparametric methods for environmental data and offers insights into future research directions in the clustering of spatially referenced time-series data in environmental sciences.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 5","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148466880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hierarchical Bayesian Modeling of Total Column Ozone: Unraveling Equatorial Variability Over Ethiopia Using Satellite Data and Multisource Covariates","authors":"Yassin Tesfaw Abebe,&nbsp;Abdu Mohammed Seid,&nbsp;Lassi Roininen,&nbsp;U. Jaya Parakash Raju,&nbsp;Abebaw Bizuneh Alemu","doi":"10.1002/env.70115","DOIUrl":"https://doi.org/10.1002/env.70115","url":null,"abstract":"<p>Understanding the spatiotemporal dynamics of total column ozone (TCO) is critical for monitoring ultraviolet (UV) exposure and ozone trends, particularly in equatorial regions where variability remains underexplored. This study investigates monthly TCO over Ethiopia (2012–2022) using a Bayesian hierarchical model implemented via Integrated Nested Laplace Approximation (INLA). The model incorporates nine environmental covariates, capturing meteorological, stratospheric, and topographic influences alongside spatiotemporal random effects. Spatial dependence is modeled using the stochastic partial differential equation (SPDE) approach, while temporal autocorrelation is handled through an autoregressive structure. The model shows strong predictive accuracy, with correlation coefficients of 0.94 (training) and 0.91 (validation), and RMSE values of 3.91 DU and 4.45 DU, respectively. Solar radiation, stratospheric temperature, and the quasi-biennial oscillation are positively associated with TCO, whereas surface temperature, precipitation, humidity, water vapor, and altitude exhibit negative associations. Random effects highlight persistent regional clusters and seasonal peaks during summer. These findings provide new insights into regional ozone behavior over complex equatorial terrains, contributing to the understanding of the equatorial ozone paradox. The approach demonstrates the utility of combining satellite observations with environmental data in data-scarce regions, supporting improved UV risk monitoring and climate-informed policy planning.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 5","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70115","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148466881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatiotemporal Analysis of Extreme Winter Temperatures in Ireland","authors":"Dáire Healy,&nbsp;Jonathan A. Tawn,&nbsp;Peter Thorne,&nbsp;Andrew Parnell","doi":"10.1002/env.70112","DOIUrl":"https://doi.org/10.1002/env.70112","url":null,"abstract":"<p>We analyze synoptic observational data on extreme daily minimum temperatures in winter months over Ireland from 1950–2022. We model the tail of the marginal distributions of extreme winter minima using a generalized Pareto distribution capturing temporal and spatial sources of non-stationarity. We disentangle long-term climate trends from obfuscating shorter-term (month-to-winter long) large fluctuations, caused by, for example, anomalous behavior of the jet stream. We identify extreme spatial events with respect to a carefully chosen risk function and fit an <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>r</mi>\u0000 </mrow>\u0000 <annotation>$$ r $$</annotation>\u0000 </semantics></math>-Pareto process to extreme events exceeding a high-risk threshold. We show that long-term trends over Ireland of extremely cold winter temperatures are warming at a faster rate than both mean winter temperatures and summer extreme daily maximum temperatures. Critically, we show that if we did not account for large-scale, short-term climatic oscillations, we would incorrectly estimate that the extremely cold winter temperatures were getting colder. In terms of spatial extreme events, we find that in periods where the jet stream typically produces the coldest winter temperatures, the estimated rate at which temperatures could fall below the coldest value ever recorded has decreased by a factor of 100 over the study period.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 5","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70112","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148466727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatiotemporal Analysis of Extreme Winter Temperatures in Ireland","authors":"Dáire Healy,&nbsp;Jonathan A. Tawn,&nbsp;Peter Thorne,&nbsp;Andrew Parnell","doi":"10.1002/env.70112","DOIUrl":"https://doi.org/10.1002/env.70112","url":null,"abstract":"<p>We analyze synoptic observational data on extreme daily minimum temperatures in winter months over Ireland from 1950–2022. We model the tail of the marginal distributions of extreme winter minima using a generalized Pareto distribution capturing temporal and spatial sources of non-stationarity. We disentangle long-term climate trends from obfuscating shorter-term (month-to-winter long) large fluctuations, caused by, for example, anomalous behavior of the jet stream. We identify extreme spatial events with respect to a carefully chosen risk function and fit an <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>r</mi>\u0000 </mrow>\u0000 <annotation>$$ r $$</annotation>\u0000 </semantics></math>-Pareto process to extreme events exceeding a high-risk threshold. We show that long-term trends over Ireland of extremely cold winter temperatures are warming at a faster rate than both mean winter temperatures and summer extreme daily maximum temperatures. Critically, we show that if we did not account for large-scale, short-term climatic oscillations, we would incorrectly estimate that the extremely cold winter temperatures were getting colder. In terms of spatial extreme events, we find that in periods where the jet stream typically produces the coldest winter temperatures, the estimated rate at which temperatures could fall below the coldest value ever recorded has decreased by a factor of 100 over the study period.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 5","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70112","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148466661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Bayesian Approach and Vecchia Grouping for Estimating Spatial Covariances in Large Datasets","authors":"Fatemeh Ghasemi,&nbsp;Ali M. Mosammam,&nbsp;Jorge Mateu","doi":"10.1002/env.70114","DOIUrl":"10.1002/env.70114","url":null,"abstract":"<div>\u0000 \u0000 <p>Covariance matrices play a central role in the modeling, inference, and prediction of spatial data, but computations involving large covariance matrices become prohibitively expensive as the number of locations increases. This paper considers Bayesian nonparametric estimation of nonstationary spatial covariance structures for large datasets. The proposed approach is built on a Vecchia-type conditional representation of an ordered Gaussian process, which induces a sparse precision matrix and sparse Cholesky factorization and allows the high-dimensional model to be expressed as a sequence of Bayesian linear regressions. To further improve scalability, we apply a grouping strategy over the ordered locations, which removes weak dependencies, produces a block-structured sparse approximation, and substantially reduces computational cost. Distance-based ordering is also used to improve the quality of the approximation. Simulation studies show that, compared with the ungrouped version, the grouped approach yields favorable posterior interval performance while requiring less computational effort. In addition, real-data analysis demonstrates that the proposed method can serve as a computationally efficient approximation to the Kidd and Katzfuss-based posterior covariance structure in large-scale settings.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 5","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148381156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Capturing Aleatoric Uncertainty in Climate Models","authors":"Cornelia Gruber,&nbsp;Henri Funk,&nbsp;Magdalena Mittermeier,&nbsp;Helmut Küchenhoff,&nbsp;Göran Kauermann","doi":"10.1002/env.70108","DOIUrl":"https://doi.org/10.1002/env.70108","url":null,"abstract":"<p>Internal climate variability arises from the climate system's inherently chaotic dynamics. Quantifying it is essential for climate science, as it enables risk-based decision-making and differentiates between externally forced change and internal fluctuations. In statistical terms, natural variability corresponds to aleatoric uncertainty, that is, irreducible stochastic variability. Despite this close conceptual alignment, the link between internal climate variability and aleatoric uncertainty has not yet been formalized. We establish a theoretical link by showing that member-to-member differences in single-model large ensembles provide a direct representation of aleatoric uncertainty. To quantify the spatio-temporal structure of aleatoric uncertainty, we employ generalized additive models. The proposed framework is validated through comparison with ERA5-Land reanalysis data, demonstrating that ensemble-derived estimates reproduce key spatial and temporal patterns of real-world variability. Applied to the water balance over the Iberian Peninsula, our approach reveals coherent variability structures and pronounced regional heterogeneity. We find a decline in variability in drought-prone regions and seasons, a pattern that strengthens under +3°C global warming, implying an increased risk of persistent summer drought conditions. Beyond this application, the framework is climate-model agnostic and transferable to other variables and spatial scales, providing a statistical basis for quantifying internal climate variability as aleatoric uncertainty.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 5","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70108","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148324045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Capturing Aleatoric Uncertainty in Climate Models","authors":"Cornelia Gruber,&nbsp;Henri Funk,&nbsp;Magdalena Mittermeier,&nbsp;Helmut Küchenhoff,&nbsp;Göran Kauermann","doi":"10.1002/env.70108","DOIUrl":"https://doi.org/10.1002/env.70108","url":null,"abstract":"<p>Internal climate variability arises from the climate system's inherently chaotic dynamics. Quantifying it is essential for climate science, as it enables risk-based decision-making and differentiates between externally forced change and internal fluctuations. In statistical terms, natural variability corresponds to aleatoric uncertainty, that is, irreducible stochastic variability. Despite this close conceptual alignment, the link between internal climate variability and aleatoric uncertainty has not yet been formalized. We establish a theoretical link by showing that member-to-member differences in single-model large ensembles provide a direct representation of aleatoric uncertainty. To quantify the spatio-temporal structure of aleatoric uncertainty, we employ generalized additive models. The proposed framework is validated through comparison with ERA5-Land reanalysis data, demonstrating that ensemble-derived estimates reproduce key spatial and temporal patterns of real-world variability. Applied to the water balance over the Iberian Peninsula, our approach reveals coherent variability structures and pronounced regional heterogeneity. We find a decline in variability in drought-prone regions and seasons, a pattern that strengthens under +3°C global warming, implying an increased risk of persistent summer drought conditions. Beyond this application, the framework is climate-model agnostic and transferable to other variables and spatial scales, providing a statistical basis for quantifying internal climate variability as aleatoric uncertainty.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 5","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70108","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148324046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Scalable Bayesian Spatiotemporal Model for Water Level Predictions Using a Nearest Neighbor Gaussian Process Approach","authors":"Victor Hugo Nagahama,&nbsp;James Sweeney,&nbsp;Niamh Cahill","doi":"10.1002/env.70107","DOIUrl":"https://doi.org/10.1002/env.70107","url":null,"abstract":"<p>Obtaining accurate water level predictions is essential for water resource management and implementing flood mitigation strategies. Several data-driven models can be found in the literature. However, there has been limited research with regard to addressing the challenges posed by large spatio-temporally referenced hydrological datasets, in particular, the challenges of maintaining predictive performance and uncertainty quantification. Gaussian Processes (GPs) are commonly used to capture complex space-time interactions. However, GPs are computationally expensive and suffer from poor scaling as the number of locations increases, due to required covariance matrix inversions. To overcome the computational bottleneck, the Nearest Neighbor Gaussian Process (NNGP) introduces a sparse precision matrix providing scalability without compromising its inferential capabilities. In this work, we introduce an innovative model in the hydrology field, specifically designed to handle large datasets consisting of a large number of spatial points across multiple hydrological basins, with daily observations over an extended period. We investigate the application of a Bayesian spatiotemporal NNGP model to a rich dataset of daily water levels of rivers located in Ireland. The dataset comprises a network of 301 monitoring stations situated in various basins across Ireland, measured over a period of 90 days. The proposed approach allows predictions of water levels at future time points, as well as the prediction at unobserved locations through spatial interpolation. Furthermore, the Bayesian approach provides the benefits in terms of uncertainty propagation and quantification, which are of considerable importance to the hydrology field. Our findings demonstrate that the proposed model outperforms competing scalable approaches in terms of accuracy and precision. Moreover, the proposed model enables predictions across a broader range of water bodies with minimal trade-off in predictive performance.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 5","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70107","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148282396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Scalable Bayesian Spatiotemporal Model for Water Level Predictions Using a Nearest Neighbor Gaussian Process Approach","authors":"Victor Hugo Nagahama,&nbsp;James Sweeney,&nbsp;Niamh Cahill","doi":"10.1002/env.70107","DOIUrl":"https://doi.org/10.1002/env.70107","url":null,"abstract":"<p>Obtaining accurate water level predictions is essential for water resource management and implementing flood mitigation strategies. Several data-driven models can be found in the literature. However, there has been limited research with regard to addressing the challenges posed by large spatio-temporally referenced hydrological datasets, in particular, the challenges of maintaining predictive performance and uncertainty quantification. Gaussian Processes (GPs) are commonly used to capture complex space-time interactions. However, GPs are computationally expensive and suffer from poor scaling as the number of locations increases, due to required covariance matrix inversions. To overcome the computational bottleneck, the Nearest Neighbor Gaussian Process (NNGP) introduces a sparse precision matrix providing scalability without compromising its inferential capabilities. In this work, we introduce an innovative model in the hydrology field, specifically designed to handle large datasets consisting of a large number of spatial points across multiple hydrological basins, with daily observations over an extended period. We investigate the application of a Bayesian spatiotemporal NNGP model to a rich dataset of daily water levels of rivers located in Ireland. The dataset comprises a network of 301 monitoring stations situated in various basins across Ireland, measured over a period of 90 days. The proposed approach allows predictions of water levels at future time points, as well as the prediction at unobserved locations through spatial interpolation. Furthermore, the Bayesian approach provides the benefits in terms of uncertainty propagation and quantification, which are of considerable importance to the hydrology field. Our findings demonstrate that the proposed model outperforms competing scalable approaches in terms of accuracy and precision. Moreover, the proposed model enables predictions across a broader range of water bodies with minimal trade-off in predictive performance.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 5","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70107","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148282094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}