Environmetrics最新文献

{"title":"Improving and Evaluating Resistant Alternatives to Procrustes Analysis for Multivariate Dataset Matching","authors":"Xiaozhuo Tang,&nbsp;Donald A. Jackson","doi":"10.1002/env.70073","DOIUrl":"https://doi.org/10.1002/env.70073","url":null,"abstract":"<p>Procrustes analysis (PA) is a widely used method for aligning and comparing two or more multivariate data matrices and has been applied across various fields. However, PA is based on the least sum of squares, which has long been recognized as highly sensitive to outliers. This study proposes two new resistant alternatives to PA, advances five previously developed resistant PA methods, and provides accompanying R code for their implementation. The performance of PA and seven resistant PA methods in matching datasets containing outliers was compared and evaluated through simulation studies. We simulated 180 scenarios, including 36 scenarios without outliers and 144 scenarios varying in error variability, proportion of outliers, magnitude of outliers, sample size, reflection, and correlation structure. Results showed that in the presence of outliers, PA performed poorly in identifying outliers and matching datasets across all scenarios, while resistant PA methods, especially our proposed Improved Least Trimmed Squares, demonstrated strong robustness and practical utility. Key factors affecting methods' performance included error variability, proportion of outliers, magnitude of outliers, and sample size. These resistant PA methods have broad applicability in fields such as ecology and evolution, where robust comparison of multivariate datasets is essential.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70073","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146096375","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":"Modeling the Spatial Interplay Between Migration and Environmental Conditions","authors":"Daniela Ghio,&nbsp;Sarah Hoyos-Hoyos,&nbsp;Gavin Liu,&nbsp;Emmanuel Kyeremeh,&nbsp;Robert McLeman,&nbsp;Gabby Resch,&nbsp;Ali Mazalek","doi":"10.1002/env.70070","DOIUrl":"https://doi.org/10.1002/env.70070","url":null,"abstract":"<p>The measurement and modeling of migration patterns and potential drivers are often limited by the quality of spatial data at local levels. Our aim is to address these constraints by advantaging of consolidated techniques in geospatial regression and emerging machine-learning approaches to explore how selected contextual socio-economic and environmental factors are related to migration trends. We define a three-step empirical strategy. First, we use the Geographically-Weighted-Regression to model the spatial variation at local scales. Second, we adopt the Multiscale-Geographically-Weighted-Regression to focus on the spatial heterogeneity across geographical scales. Third, we adopt the Geographically-Weighted-Random-Forest-Regression to validate variations across multiple local models. To illustrate, we apply the proposed methodology to selected environmental factors and gridded estimates of net migration patterns in Ghana, between 1985 and 2014. By the comparison of results, we argue the models' complementary explaining the contribution of each method to depict the spatial variability of migration environmental factors.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70070","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145969763","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":"Spatio-Temporal Analysis of Extreme PM\u0000 \u0000 \u0000 \u0000 \u0000 2\u0000 .\u0000 5\u0000 \u0000 \u0000 Levels in Taiwan","authors":"Bing-Ru Jhou,&nbsp;Nan-Jung Hsu,&nbsp;Hsin-Cheng Huang","doi":"10.1002/env.70068","DOIUrl":"https://doi.org/10.1002/env.70068","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 &lt;p&gt;We investigate extremes of PM&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mrow&gt;&lt;/mrow&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;mo&gt;.&lt;/mo&gt;\u0000 &lt;mn&gt;5&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ {}_{2.5} $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; in Taiwan using data from Environmental Protection Administration monitoring stations. The objective is to examine long-term trends in PM&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mrow&gt;&lt;/mrow&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;mo&gt;.&lt;/mo&gt;\u0000 &lt;mn&gt;5&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ {}_{2.5} $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; extremes and short-term spikes, measured by daily 1-hour maximums, as they relate to human exposure and acute health risks in daily life. To model daily 1-hour maximum PM&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mrow&gt;&lt;/mrow&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;mo&gt;.&lt;/mo&gt;\u0000 &lt;mn&gt;5&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ {}_{2.5} $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; concentrations across space and time, we adopt the generalized extreme value (GEV) distribution. Extending beyond location-specific temporal extreme-value models, we develop a spatio-temporal varying-coefficient GEV framework in which the distributional parameters evolve smoothly over space and time through spatio-temporal basis functions. This formulation leverages information from neighboring sites and adjacent times, enabling the model to capture both broad-scale structures and localized variability in extremes. The framework yields marginal distributions of extreme PM&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mrow&gt;&lt;/mrow&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;mo&gt;.&lt;/mo&gt;\u0000 &lt;mn&gt;5&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ {}_{2.5} $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; at any location and time, accounting for spatial heterogeneity, seasonal dynamics, and irregular data availability. The method is computationally efficient and can be implemented with existing R packages. Application to Taiwanese air-quality data reveals a general decline in extreme PM&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 ","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145891407","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":"A Bayesian Multisource Fusion Model for Spatiotemporal \u0000 \u0000 \u0000 \u0000 \u0000 PM\u0000 \u0000 \u0000 2\u0000 .\u0000 5\u0000 \u0000 \u0000 \u0000 $$ {mathrm{PM}}_{2.5} $$\u0000 in an Urban Setting","authors":"Abi I. Riley,&nbsp;Marta Blangiardo,&nbsp;Frédéric B. Piel,&nbsp;Andrew Beddows,&nbsp;Sean Beevers,&nbsp;Gary W. Fuller,&nbsp;Paul Agnew,&nbsp;Monica Pirani","doi":"10.1002/env.70065","DOIUrl":"https://doi.org/10.1002/env.70065","url":null,"abstract":"&lt;p&gt;Airborne particulate matter (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mtext&gt;PM&lt;/mtext&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;mo&gt;.&lt;/mo&gt;\u0000 &lt;mn&gt;5&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ {mathrm{PM}}_{2.5} $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;) is a major public health concern in urban environments, where population density and emission sources exacerbate exposure risks. We present a novel Bayesian spatiotemporal fusion model to estimate monthly &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mtext&gt;PM&lt;/mtext&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;mo&gt;.&lt;/mo&gt;\u0000 &lt;mn&gt;5&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ {mathrm{PM}}_{2.5} $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; concentrations over Greater London (2014–2019) at 1 km resolution. The model integrates multiple &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mtext&gt;PM&lt;/mtext&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;mo&gt;.&lt;/mo&gt;\u0000 &lt;mn&gt;5&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ {mathrm{PM}}_{2.5} $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; data sources, including outputs from two atmospheric air quality dispersion models, and predictive variables, such as vegetation and satellite aerosol optical depth, while explicitly modeling a latent spatiotemporal field. Spatial misalignment of the data is addressed through a hierarchical fusion and spatial interpolation approach to predict across the entire area. Building on stochastic partial differential equations (SPDE) within the integrated nested Laplace approximations (INLA) framework, our method introduces spatially- and temporally-varying coefficients to flexibly calibrate datasets and capture fine-scale variability. Model performance and complexity are balanced using predictive metrics such as the predictive model choice criterion and thorough cross-validation. The best performing model shows excellent fit and robust predictive performance, enabling reliable high-resolution spatiotemporal mapping of &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 ","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70065","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145824946","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":"Multiscale Multi-Type Spatial Bayesian Analysis for High-Dimensional Data With Application to Wildfires and Migration","authors":"Shijie Zhou,&nbsp;Jonathan R. Bradley","doi":"10.1002/env.70066","DOIUrl":"https://doi.org/10.1002/env.70066","url":null,"abstract":"<div>\u0000 \u0000 <p>Wildfires have significantly increased in the United States (U.S.), making certain areas harder to live in. This motivates us to jointly analyze active fires and population changes in the U.S. from July 2020 to June 2021. The available data are recorded on different scales (or spatial resolutions) and by different types of distributions (referred to as multi-type data). Moreover, wildfires are known to have a feedback mechanism that creates signal-to-noise dependence. We analyze point-referenced remote sensing fire data from the National Aeronautics and Space Administration (NASA) and county-level population change data provided by the U.S. Census Bureau's Population Estimates Program (PEP). We develop a multiscale multi-type spatial Bayesian model that assumes the average number of fires is zero-inflated normal, the incidence of fire is Bernoulli, and the percentage population change is normally distributed. This high-dimensional dataset makes Markov chain Monte Carlo (MCMC) implementation infeasible. We bypass MCMC by extending a recently introduced computationally efficient Bayesian framework to directly sample from the exact posterior distribution, which includes a term to model signal-to-noise dependence. Such signal-to-noise dependence is known to be present in wildfire data, but is commonly not accounted for. A simulation study is used to highlight the computational performance of our method. In our analysis, we obtained predictions of wildfire probabilities, identified several useful covariates, and found that regions with many fires were associated with population change.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145824945","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":"Trends in Rainfall and Temperature in the Murray Mallee and Upper Eyre Peninsula Region of South Australia—Connections to Climate and Land Use Change","authors":"Sachinda Ekanayake,&nbsp;John Boland,&nbsp;Baden Myers,&nbsp;Guna A. Hewa","doi":"10.1002/env.70064","DOIUrl":"https://doi.org/10.1002/env.70064","url":null,"abstract":"<div>\u0000 \u0000 <p>The Murray Mallee (MM) and Upper Eyre Peninsula (UEP) are two important regions to the South Australian agricultural economy. The UEP is dominated by agricultural land use with a semi-arid Mediterranean-type climate, with low, variable annual rainfall. The climate is similar, with significant farming production around the Murray Mallee, adjacent to the Murray River. When investigating rainfall trends in the UEP region, annual rainfall has decreased considerably in the last 73 years, from 1950 to 2023, at all rain monitoring stations. From the 15 stations in the MM, most of the locations' rainfall is decreasing, but three stations, including Callington, Murray Bridge, and Mannum, show an increasing trend in annual rainfall. Monarto Safari Park station had only recent rainfall records; however, it also shows an increasing trend. The records for MM, particularly for gauges where rainfall has seen an increasing trend, seem to correspond with a revegetation program on the Monarto Plateau during the 1970s, and this may have been significant enough to increase rainfall in the surrounding regions, including Callington and Murray Bridge. Each station's annual rainfall, mean minimum temperature, and mean maximum temperature data are analyzed separately using trend analysis with exponential smoothing and three robust change-point analysis methods in the MM and UEP regions. The significant change points observed in annual rainfall and temperature in each location and the overall reduction in rainfall may be attributed to industrial expansion, climate change, and shifts in land use patterns driven by urbanization, which have likely impacted water resources. This study investigated the relationship between land use and climate change that may impact South Australian rainfall patterns and increases in temperature over the last few decades.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145779480","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":"Return Period of Nonconcurrent Climate Compound Events: A Nonparametric Bivariate Generalized Pareto Approach","authors":"Grégoire Jacquemin,&nbsp;Denis Allard,&nbsp;Xavier Freulon,&nbsp;Mathieu Vrac","doi":"10.1002/env.70063","DOIUrl":"https://doi.org/10.1002/env.70063","url":null,"abstract":"<p>Compound events (CEs), commonly defined as the “combination of multiple drivers and/or hazards that contributes to societal or environmental risk”, often result in amplified impacts compared to individual hazards. In order to estimate the return period of bivariate CEs, a novel nonparametric approach employing bivariate Generalized Pareto distributions (bi-GPD) is proposed and compared to a copula-based approach. Special attention is given to account for temporal dependencies and nonconcurrent compound events. The latter are defined as excess of variables over a threshold at a relatively close time. The return period of such bivariate events is carefully defined and closed-form expressions are obtained for both approaches. Simulations reveal the bi-GPD approach is effective in case of positive asymptotic dependence and should be avoided in case of asymptotic independence. The novel approach is then applied to ERA5 reanalysis data to analyze two types of compound events: a spatial CE with simultaneous floods due to accumulated precipitation across two large watersheds in France and a preconditioned CE consisting of a devastating flood triggered by extreme precipitation over a saturated soil.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70063","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145779606","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":"WOMBAT v2.S: A Bayesian Inversion Framework for Attributing Global CO2 Flux Components From Multiprocess Data","authors":"Josh Jacobson,&nbsp;Michael Bertolacci,&nbsp;Andrew Zammit-Mangion,&nbsp;Andrew Schuh,&nbsp;Noel Cressie","doi":"10.1002/env.70052","DOIUrl":"https://doi.org/10.1002/env.70052","url":null,"abstract":"<div>\u0000 \u0000 <p>Contributions from photosynthesis and other natural components of the carbon cycle present the largest uncertainties in our understanding of carbon dioxide (CO₂) sources and sinks. While the spatiotemporal distribution of Earth's net surface flux (the sum of all contributions) can be inferred from atmospheric CO₂ concentrations through flux inversion, attributing the net flux to its individual components remains challenging. The advent of solar-induced fluorescence (SIF) satellite observations provides an opportunity to separate natural components by isolating gross primary productivity (GPP), the photosynthetic component of the net flux. Here, we introduce a novel statistical flux-inversion framework that simultaneously assimilates observations of SIF and CO₂ concentration, extending WOMBAT v2.0 (WOllongong Methodology for Bayesian Assimilation of Trace-gases, version 2.0) with a hierarchical model of spatiotemporal dependence between GPP and SIF processes. We call the new framework WOMBAT v2.S, and we apply it to SIF and CO₂ data from NASA's Orbiting Carbon Observatory-2 (OCO-2) satellite and other instruments to estimate natural fluxes over the globe during a recent six-year period. In a simulation experiment that matches OCO-2's retrieval characteristics, the inclusion of SIF improves the accuracy and uncertainty quantification of component flux estimates. Comparing estimates from WOMBAT v2.S, v2.0, and the independent FLUXCOM initiative, we observe that linking GPP to SIF has little effect on net flux, as expected, but leads to stronger seasonal cycles and altered zonal distributions in natural flux components.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 8","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145750798","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":"Non-Stationarities in Extreme Hourly Precipitation Over the Piave Basin, Northern Italy","authors":"Dáire Healy,&nbsp;Ilaria Prosdocimi,&nbsp;Isadora Antoniano-Villalobos","doi":"10.1002/env.70051","DOIUrl":"https://doi.org/10.1002/env.70051","url":null,"abstract":"<div>\u0000 \u0000 <p>We study the spatio-temporal features of extremal sub-daily precipitation data over the Piave river basin in northeast Italy using a rich database of observed hourly rainfall. Empirical evidence suggests that both the marginal and dependence structures for extreme precipitation in the area exhibit seasonal patterns, and spatial dependence appears to weaken as events become more extreme. We investigate factors affecting the marginal distributions, the spatial dependence and the interplay between them. Capturing these features is essential to provide a realistic description of extreme precipitation processes in order to better estimate their associated risks. With this aim, we identify various climatic covariates at different spatio-temporal scales and explore their usefulness. We go beyond existing literature by investigating and comparing the performance of recently proposed covariate-dependent models for both the marginal and dependence structures of extremes. Furthermore, a flexible max-id model, which encompasses both asymptotic dependence and independence, is used to learn about the spatio-temporal variability of rainfall processes at extreme levels. We find that modeling non-stationarity only at the marginal level does not fully capture the variability of precipitation extremes, and that it is important to also capture the seasonal variation of extremal dependence.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 8","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145686297","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":"Compactly-Supported Nonstationary Kernels for Computing Exact Gaussian Processes on Big Data","authors":"Mark D. Risser,&nbsp;Marcus M. Noack,&nbsp;Hengrui Luo,&nbsp;Ronald J. Pandolfi","doi":"10.1002/env.70054","DOIUrl":"https://doi.org/10.1002/env.70054","url":null,"abstract":"<p>The Gaussian process (GP) is a widely used method for analyzing large-scale data sets, including spatio-temporal measurements of nonlinear processes that are now commonplace in the environmental sciences. Traditional implementations of GPs involve stationary kernels (also termed covariance functions) that limit their flexibility, and exact methods for inference that prevent application to data sets with more than about 10,000 points. Modern approaches to address stationarity assumptions generally fail to accommodate large data sets, while all attempts to address scalability focus on approximating the Gaussian likelihood, which can involve subjectivity and lead to inaccuracies. In this work, we explicitly derive an alternative kernel that can discover and encode both sparsity and nonstationarity. We embed the kernel within a fully Bayesian GP model and leverage high-performance computing resources to enable the analysis of massive data sets. We demonstrate the favorable performance of our novel kernel relative to existing exact and approximate GP methods across a variety of synthetic data examples. Furthermore, we conduct space–time prediction based on more than 1 million measurements of daily maximum temperature and verify that our results outperform state-of-the-art methods in the Earth sciences. More broadly, having access to exact GPs that use ultra-scalable, sparsity-discovering, nonstationary kernels allows GP methods to truly compete with a wide variety of machine learning methods.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 8","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70054","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145626471","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}