Environmetrics最新文献

{"title":"Spatial Approach of Peaks-Over-Thresholds in Trend Detection of Extremes","authors":"Béwentaoré Sawadogo,&nbsp;Diakarya Barro","doi":"10.1002/env.70086","DOIUrl":"https://doi.org/10.1002/env.70086","url":null,"abstract":"<div>\u0000 \u0000 <p>In this study, recent trends in extreme temperatures and rainfall are evaluated using the statistical theory of extreme values, in a non-stationary context. We extend the non-stationary pointwise threshold exceedances approach to spatial threshold exceedances. The temporal evolution of the extremes is handled pointwise by the peaks-over-threshold approach. First, evolution of the parameters of the distribution of exceedances and the intensity of extreme event occurrences for several meteorological variables are described as functions of time. Then, the spatial dependence structure attached to threshold exceedances is integrated using generalized <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>ℓ</mi>\u0000 </mrow>\u0000 <annotation>$$ ell $$</annotation>\u0000 </semantics></math>-Pareto processes in a non-stationary framework. In addition to better taking spatial dependence into account, our method allows us to simulate spatial exceedance processes at different dates and predict non-stationary return levels at desired times by extrapolating the trends identified in the marginal distributions and the estimated dependence structure. Our approach is applied to temperature and rainfall data in Burkina Faso.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 3","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147615059","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 Dynamic Model for Extreme Hourly Precipitation","authors":"Debbie J. Dupuis,&nbsp;Carlotta Pacifici,&nbsp;Luca Trapin","doi":"10.1002/env.70082","DOIUrl":"https://doi.org/10.1002/env.70082","url":null,"abstract":"<p>Despite the scarcity of comprehensive studies at a global scale, many regional analyses report increases in extreme hourly precipitation values. The growing interest in assessing trends in extreme hourly precipitation has outpaced the development of new statistical tools tailored to their features. Typical analyses employ Extreme Value Theory (EVT) in a regression framework, where the parameters of EVT distributions are modeled as functions of exogenous covariates. While this approach is common in daily precipitation analyses, it may be less suitable for the higher-frequency hourly data. We propose a dynamic EVT approach, where the distribution parameters evolve according to an autoregressive-type dynamic. Our model accommodates the high-frequency nature of the data without requiring arbitrary choices regarding the covariates as in the regression approach. Applied to a group of Midwest US cities experiencing increases in hourly extreme precipitation, our method reveals dynamics in extreme high quantiles and outperforms a reference model in the EVT regression approach.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 3","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70082","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147567322","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 Spatial Analysis of Misclassified Binary Data Incorporating Internal-Validation Studies","authors":"Yuhan Ma,&nbsp;Kyuhee Shin,&nbsp;GyuWon Lee,&nbsp;Joon Jin Song","doi":"10.1002/env.70088","DOIUrl":"https://doi.org/10.1002/env.70088","url":null,"abstract":"<div>\u0000 \u0000 <p>Accurate spatial classification is a challenging task, especially when binary outcomes are subject to measurement errors and misclassification. Motivated by a precipitation study in South Korea, we propose Bayesian spatial classification methods with misclassification correction using internal validation data. The prior distributions for the misclassification parameters are specified using internal validation data in the Bayesian spatial classification of the main study, where the gold-standard device is unavailable. A simulation study is conducted to compare the performance of the proposed methods with the naive method that ignores the misclassification. It is found that the proposed methods outperform the naïve model. The proposed methods are also illustrated with precipitation data from South Korea.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 3","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147566189","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":"What Seismic Phase is L'Aquila in Fifteen Years After the \u0000 \u0000 \u0000 \u0000 \u0000 M\u0000 \u0000 \u0000 w\u0000 \u0000 \u0000 \u0000 6\u0000 .\u0000 1\u0000 \u0000 $$ {M}_wkern0.3em 6.1 $$\u0000 Earthquake of April 6, 2009?","authors":"Elisa Varini,&nbsp;Renata Rotondi,&nbsp;Alex González Fuentes","doi":"10.1002/env.70087","DOIUrl":"https://doi.org/10.1002/env.70087","url":null,"abstract":"<p>On April 6, 2009, central Italy was hit by a <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mrow>\u0000 <mi>M</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mi>w</mi>\u0000 </mrow>\u0000 </msub>\u0000 <mspace></mspace>\u0000 <mn>6</mn>\u0000 <mo>.</mo>\u0000 <mn>1</mn>\u0000 </mrow>\u0000 <annotation>$$ {M}_wkern0.3em 6.1 $$</annotation>\u0000 </semantics></math> earthquake that caused 308 victims in the city and province of L'Aquila; subsequently, in 2016, two <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mrow>\u0000 <mi>M</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mi>w</mi>\u0000 </mrow>\u0000 </msub>\u0000 <mspace></mspace>\u0000 <mn>6</mn>\u0000 <mo>+</mo>\u0000 </mrow>\u0000 <annotation>$$ {M}_wkern0.3em 6+ $$</annotation>\u0000 </semantics></math> shocks were recorded in an area located a few dozen kilometers further north, respectively in Amatrice and Norcia. Since, like many of the physical phenomena we observe on the Earth, seismic generation processes are characterized by long-term dependence and governed by power laws, the magnitudes of the two seismic sequences associated with these strong events were analyzed separately in the framework of nonextensive statistical mechanics to examine the connection between the variations of the magnitude probability distribution and the phases of a seismic crisis. In this work, instead, we consider all the events recorded in the same area as a whole, starting from the beginning of the most complete part of the Italian catalog ISIDe, that is, from 2005 to 2024. The aim is to verify whether the variations observed in the Tsallis entropy and in its parameters before both L'Aquila and Amatrice-Norcia earthquakes are sufficient, as well as necessary, conditions for the occurrence of strong shocks, so that they can be considered as reliable seismic precursors. The same analysis is repeated for the corresponding data set drawn from the most recent HOmogenized instRUmental Seismic catalog (HORUS) released since July 2020. It turns out that the predictive capacity of such precursors increases when joint variations of more parameters are taken into account.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 3","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70087","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147566193","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":"Generalized Additive Model With Dynamic Coefficients for Spatiotemporal Ozone Predictions","authors":"Abdollah Jalilian,&nbsp;Claudia Cappello,&nbsp;Monica Palma,&nbsp;Sandra De Iaco","doi":"10.1002/env.70076","DOIUrl":"https://doi.org/10.1002/env.70076","url":null,"abstract":"<p>Accurate prediction of surface-level ozone concentrations is critical for air quality management and public health protection. This study develops a flexible spatiotemporal statistical modeling framework to predict daily mean O₃ concentrations across Italy by integrating satellite-derived ozone estimates with ground-based observations and high-resolution environmental predictors. The proposed model is based on a linear regression with dynamic intercept and slope that relate in situ O₃ measurements to satellite data, explicitly addressing additive (systematic shifts) and multiplicative (scaling) biases in satellite-derived ozone estimates. These spatiotemporally varying coefficients are modeled through a generalized additive model framework, allowing the capture of complex and potentially nonlinear relationships between ozone levels and environmental covariates. This unified and interpretable approach enables a detailed understanding of bias patterns in satellite data. Model diagnostics and crossvalidation demonstrate superior explanatory power and predictive performance compared to simpler models. The interpretability of the model is illustrated by revealing the influence of elevation, nitrogen dioxide concentrations, and seasonal variation on bias structures. Furthermore, the model's downscaling capability is demonstrated by producing fine-scale ozone concentration predictions over Italy and its surrounding regions. The proposed modeling framework offers an accurate, scalable, and interpretable tool for mapping surface-level ozone, supporting improved environmental monitoring and informing policy decisions.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 3","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70076","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147564562","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":"Statistical Downscaling of the Bulk and Tail of Simulated Precipitations","authors":"Gabriel Gobeil,&nbsp;Jonathan Jalbert,&nbsp;Philippe Roy","doi":"10.1002/env.70083","DOIUrl":"10.1002/env.70083","url":null,"abstract":"<p>Intense precipitation events are projected to become more frequent and severe in the future. Impact studies analyzing these changes typically rely on simulated precipitation data generated by climate models. However, these simulated datasets often exhibit biases and require post-processing before they can be effectively used in impact studies. Standard post-processing techniques aim to align the statistical distribution of simulated precipitation with that of the observed data. This involves making necessary adjustments to the simulated precipitation to ensure a consistent match between the two datasets. While the empirical cumulative distribution function (CDF) is frequently employed for this purpose, its accuracy diminishes in the tail of the distribution, making it unsuitable for extreme values. In this study, we propose an approach for post-processing simulated precipitation based on the use of the extended generalized Pareto (EGP) distribution for modeling the precipitation. Unlike the empirical CDF, the EGP distribution is capable of consistently modeling both the bulk and the tail of the distribution in accordance with the extreme value theory. To demonstrate the efficacy of our method, we apply it to post-process daily simulated precipitation data from two Canadian cities. Additionally, we have made the proposed method, along with other relevant techniques from existing literature, accessible through the open-source Julia package \u0000QuantileMatching.jl.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70083","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147562896","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":"Long Short-Term Memory Network and Statistical Time Series Analysis Forecast Models for 30 min Interval Wind Farm Power Output and Regional Price Variables","authors":"Luigi R. Cirocco,&nbsp;Joshua Chopin,&nbsp;Belinda Chiera,&nbsp;John W. Boland","doi":"10.1002/env.70084","DOIUrl":"10.1002/env.70084","url":null,"abstract":"<p>This study compares parametric statistical time series models, such as autoregressive moving average (ARMA), with nonparametric artificial neural networks, specifically long short-term memory (LSTM) models, for univariate forecasting. Two time series are analyzed separately: wind power output from the Clements Gap wind farm and the regional electricity price for South Australia. One-step-ahead forecast performance is evaluated using normalized mean bias error (NMBE), normalized mean absolute error (NMAE), and normalized root mean square error (NRMSE). Three LSTM models were examined: a manually tuned model, a structurally equivalent model implemented in a different library, and a model with automated hyperparameter tuning. While LSTM models achieved competitive performance, statistical models often performed equally well or better. For price forecasts, the manually tuned LSTM achieved the lowest NMBE (<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>7</mn>\u0000 <mo>×</mo>\u0000 <msup>\u0000 <mn>10</mn>\u0000 <mrow>\u0000 <mo>−</mo>\u0000 <mn>6</mn>\u0000 </mrow>\u0000 </msup>\u0000 </mrow>\u0000 <annotation>$$ 7times {10}^{-6} $$</annotation>\u0000 </semantics></math>), while ARMA(3,1) had the best NMAE (0.0253) and AR(6) the best NRMSE (0.220). For wind forecasts, the manually tuned LSTM again performed best overall (NMBE: 0.00034, NMAE: 0.143, NRMSE: 0.225), while the equivalent library model performed worst. These results highlight the need to subject nonparametric LSTM models to more rigorous and systematic evaluation relative to their parametric statistical counterparts.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70084","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147569334","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":"Estimating the Impact of Socioeconomic Drivers on Land Degradation in Italy via Spatio-Temporal Additive Expectile Regression","authors":"Luca Merlo,&nbsp;Beatrice Foroni,&nbsp;Ioannis Konaxis,&nbsp;Lea Petrella,&nbsp;Luca Salvati","doi":"10.1002/env.70085","DOIUrl":"10.1002/env.70085","url":null,"abstract":"<p>Climate changes, soil degradation, and desertification increasingly threaten the entire territory of Italy due to the complex interplay between natural processes and anthropogenic forces. Motivated by this pressing issue, this paper investigates how land-use and socioeconomic drivers have shaped desertification dynamics across Italian provinces between 1960 and 2020 using an additive expectile regression model for spatio-temporal data. The effects of continuous covariates are modeled as the sum of a linear part and a nonlinear smooth function using penalized B-splines, while tensor-product B-splines based on coordinate information are used to capture spatial dependencies. To promote sparsity in the model, estimation is carried out via component-wise boosting combined with a stability selection approach, retaining only the most dominant factors. The results identify industrialization and tourism development activities as the primary amplifiers of desertification risk in vulnerable regions. Our analysis also uncovers strong spatial heterogeneity of desertification processes related to province characteristics, which is particularly acute in southern Italy and in the major islands.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70085","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147569007","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":"Estimation of Parameters in Inhomogeneous Neyman-Scott Processes Using Presence/Absence Data","authors":"Magnus Ekström,&nbsp;Léna Gozé,&nbsp;Saskia Sandring,&nbsp;Bengt Gunnar Jonsson,&nbsp;Jörgen Wallerman,&nbsp;Göran Ståhl","doi":"10.1002/env.70080","DOIUrl":"10.1002/env.70080","url":null,"abstract":"<p>Environmental monitoring is of particular importance for studying biodiversity and ecosystems. Many environmental monitoring programs emphasize plant registrations as part of their inventory responsibilities. Among the various methods available for surveying plant communities, we focus on presence/absence (P/A) sampling due to its underutilized potential. P/A sampling offers several advantages over other methods, particularly its efficiency in terms of time and cost. However, interpreting direct information from this type of data can be challenging, as the results are heavily dependent on plot size and species distribution patterns. To overcome these difficulties, model-based assumptions are necessary. In this article, we propose a method for estimating parameters of an inhomogeneous Neyman-Scott point process, specifically a Matérn cluster process, using P/A data. The inhomogeneity is modeled by allowing the offspring process intensity to vary with environmental covariates. The proposed estimators and their corresponding confidence intervals are evaluated through Monte Carlo simulations and empirical data (P/A registrations for three plant species) collected by surveyors in Northern Sweden. The results indicate that the method generally produces nearly unbiased estimators, particularly when the sample size is sufficiently large. These parameter estimates from the underlying inhomogeneous Neyman-Scott point process can subsequently be used to compute local estimates of expected plant density.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70080","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146211385","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":"Accounting for Preferential Sampling in Geostatistical Inference","authors":"Rui Qiang,&nbsp;Peter F. Craigmile","doi":"10.1002/env.70081","DOIUrl":"10.1002/env.70081","url":null,"abstract":"<div>\u0000 \u0000 <p>In geostatistical inference, preferential sampling takes place when the locations of point-referenced data are related to the latent spatial process of interest. Traditional geostatistical models can lead to biased inferences and predictions under preferential sampling. We introduce an extended Bayesian hierarchical framework that models both the observed locations and the responses jointly, using a spatial point process for the locations and a geostatistical process for the responses. We illustrate extensions beyond the classical log-Gaussian Cox process for the sampling locations, combined with a Gaussian process for the responses. We also introduce simpler methods for accounting for preferential sampling that are less computationally demanding at the expense of prediction accuracy. We validate our models through simulation, demonstrating their effectiveness in correcting biases and improving prediction accuracy. We apply our models to decadal average temperature data from the Global Historical Climate Network in the Southwestern United States and show that preferential sampling could be present in some spatial regions.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146217243","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}