Environmetrics最新文献

{"title":"Stratified, Spatially Balanced Cluster Sampling for Cost-Efficient Environmental Surveys","authors":"Juha Heikkinen,&nbsp;Helena M. Henttonen,&nbsp;Matti Katila,&nbsp;Sakari Tuominen","doi":"10.1002/env.70019","DOIUrl":"https://doi.org/10.1002/env.70019","url":null,"abstract":"<p>Large-scale environmental surveys relying on intensive fieldwork are expensive, but survey sampling methodology offers several options to improve their cost-efficiency. For example, sites selected for field assessments can be arranged in clusters to reduce the time spent moving between the sites, and auxiliary data can be utilized to stratify the survey region and sample less important strata less densely. Geographically balanced and well-spread sampling can yield further improvements since the target variables of environmental surveys tend to be spatially autocorrelated. A combination of these ideas was illustrated and evaluated in the context of a national forest inventory, and alternative methods of spatially balanced sampling were compared. The main findings were that (i) both the local pivotal method and the generalized random-tessellation stratified design guaranteed a clearly better spatial regularity than systematic sampling when applied to fragmented regions resulting from stratification and (ii) they also ensured better global balance in unstratified sampling. In our case study, where stratification and sample allocation were based on high-quality auxiliary data, stratified sampling was clearly more efficient than unstratified for the primary survey target parameter. However, our results also illustrate that highly nonproportional sample allocation can be dangerous in a multi-purpose survey.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70019","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144197385","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":"Learning From Limited Temporal Data: Dynamically Sparse Historical Functional Linear Models With Applications to Earth Science","authors":"Joseph Janssen,&nbsp;Shizhe Meng,&nbsp;Asad Haris,&nbsp;Stefan Schrunner,&nbsp;Jiguo Cao,&nbsp;William J. Welch,&nbsp;Nadja Kunz,&nbsp;Ali A. Ameli","doi":"10.1002/env.70018","DOIUrl":"https://doi.org/10.1002/env.70018","url":null,"abstract":"<p>Scientists and statisticians often seek to understand the complex relationships that connect two time-varying variables. Recent work on sparse functional historical linear models confirms that they are promising as a tool for obtaining complex and interpretable inferences, but several notable limitations exist. Most importantly, previous works have imposed sparsity on the historical coefficient function, but have not allowed the sparsity, hence lag, to vary with time. We simplify the framework of sparse functional historical linear models by using a rectangular coefficient structure along with Whittaker smoothing, then reduce the assumptions of the previous frameworks by estimating the dynamic time lag from a hierarchical coefficient structure. We motivate our study by aiming to extract the physical rainfall–runoff processes hidden within hydrological data. We show the promise and accuracy of our method using eight simulation studies, further justified by two real sets of hydrological data.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 4","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70018","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144074494","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":"On Tail Structural Change in U.S. Climate Data","authors":"Hanjun Lu,&nbsp;Alan P. Ker","doi":"10.1002/env.70016","DOIUrl":"https://doi.org/10.1002/env.70016","url":null,"abstract":"<p>While many studies on climate have focused on location shifts, none have specifically tested whether lower or upper tails of the climate data generating process have structurally changed over time. This manuscript applies a new test that can detect either distributional or tail structural change to various annual and daily U.S. climate measures. Notably, we find both distributional and tail structural change and, quite interestingly, tend to observe greater evidence in one tail versus the other for most climate measures. We also find the presence of multiple breaks. Our results imply that climate modeling, and specifically climate-crop yield modeling, should account for significant and asymmetric changes in climate distributions and not only location shifts.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 4","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70016","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143939422","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":"Evaluation of ETAS and STEP Forecasting Models for California Seismicity Using Point Process Residuals","authors":"Joshua Ward,&nbsp;Maximilian Werner,&nbsp;William Savran,&nbsp;Frederic Schoenberg","doi":"10.1002/env.70014","DOIUrl":"https://doi.org/10.1002/env.70014","url":null,"abstract":"<div>\u0000 \u0000 <p>Variants of the Epidemic-Type Aftershock Sequence (ETAS) and Short-Term Earthquake Probabilities (STEP) models have been used for earthquake forecasting and are entered as forecast models in the purely prospective Collaboratory Study for Earthquake Predictability (CSEP) experiment. Previous analyses have suggested the ETAS model offered the best forecast skill for the first several years of CSEP. Here, we evaluate the prospective forecasting ability of the ETAS and STEP one-day forecast models for California from 2013 to 2017, using super-thinned residuals and Voronoi residuals. We find very comparable performance of the two models, with slightly superior performance of the STEP model compared to ETAS according to most metrics.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 4","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143871834","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":"Comparative Analysis of Bootstrap Techniques for Confidence Interval Estimation in Spatial Covariance Parameters With Large Spatial Data","authors":"Zih-Bing Chen,&nbsp;Hao-Yun Huang,&nbsp;Cheng-Xin Yang","doi":"10.1002/env.70015","DOIUrl":"https://doi.org/10.1002/env.70015","url":null,"abstract":"<p>Inconsistent estimation issues in the Matérn covariance function pose significant challenges to constructing confidence intervals using traditional methods. This paper addresses these challenges by employing the bootstrap method and comparing two straightforward approaches: the percentile bootstrap (PB) and the reverse percentile interval (RPI). We assess their efficacy through coverage rates and interval scores, focusing on accuracy and breadth. Theoretically, we prove that PB outperforms RPI, a claim substantiated by simulation experiments showing its superior coverage accuracy and interval scores. Moreover, the simulation results show strongly interdependent phenomena between parameters. Accordingly, by exploring the micro-ergodic parameter's impact, the study provides insights into these findings' underlying factors, particularly relevant for large spatial datasets. In the empirical study, our approach exhibits greater reliability and effectiveness in confidence interval estimation for large datasets with uniformly and non-uniformly distributed locations, as compared to several other methods. Furthermore, we applied the method to sea surface temperature data, demonstrating its strong applicability for analysis. This study provides theoretical insight and practical guidance for constructing confidence intervals, particularly in mitigating inconsistent estimation issues, especially in the context of the Matérn covariance function.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 3","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70015","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836087","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":"Simple Yet Effective: A Comparative Study of Statistical Models for Yearly Hurricane Forecasting","authors":"Pietro Colombo,&nbsp;Raffaele Mattera,&nbsp;Philipp Otto","doi":"10.1002/env.70009","DOIUrl":"https://doi.org/10.1002/env.70009","url":null,"abstract":"<p>In this article, we study the problem of forecasting the next year's number of Atlantic hurricanes, which is relevant in many fields of applications such as land-use planning, hazard mitigation, reinsurance and long-term weather derivative market. Considering a set of well-known predictors, we compare the forecasting accuracy of both machine learning and classical statistical models, showing that the latter may be more adequate than the first. Quantile regression models, which are adopted for the first time for forecasting hurricane numbers, provide the best results. Moreover, we construct a new index showing good properties in anticipating the direction of the future number of hurricanes. We consider different evaluation metrics based on both magnitude forecasting errors and directional accuracy.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 3","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761960","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 Non-Parametric Estimation Method of the Population Size in Capture-Recapture Experiments With Right Censored Data","authors":"Anabel Blasco-Moreno,&nbsp;Pedro Puig","doi":"10.1002/env.70013","DOIUrl":"https://doi.org/10.1002/env.70013","url":null,"abstract":"<div>\u0000 \u0000 <p>We present a new non-parametric approach for estimating the total number of animals or species when we only have information on the number of animals or species \u0000 that have been observed once, twice, <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>…</mi>\u0000 </mrow>\u0000 <annotation>$$ dots $$</annotation>\u0000 </semantics></math>, and the number of animals or species that have been observed <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>r</mi>\u0000 </mrow>\u0000 <annotation>$$ r $$</annotation>\u0000 </semantics></math> and more than <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>r</mi>\u0000 </mrow>\u0000 <annotation>$$ r $$</annotation>\u0000 </semantics></math> times. The approach, like the Chao estimator, gives a lower bound on population size while also providing bootstrap confidence intervals. We conducted simulations to compare our estimator to other competing ones in special scenarios with <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>r</mi>\u0000 <mo>=</mo>\u0000 <mn>2</mn>\u0000 </mrow>\u0000 <annotation>$$ r=2 $$</annotation>\u0000 </semantics></math> and 3 and found that it performed quite well. In the case of uncensored samples, we analyze which censoring point is preferable in specific examples, as well as when censoring at <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>r</mi>\u0000 <mo>=</mo>\u0000 <mn>3</mn>\u0000 </mrow>\u0000 <annotation>$$ r=3 $$</annotation>\u0000 </semantics></math> is superior to censoring at <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>r</mi>\u0000 <mo>=</mo>\u0000 <mn>2</mn>\u0000 </mrow>\u0000 <annotation>$$ r=2 $$</annotation>\u0000 </semantics></math>.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 3","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761959","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":"From Climate Chat to Climate Shock: Non-Linear Impacts of Transition Risk in Energy CDS Markets","authors":"Emanuele Campiglio,&nbsp;Luca De Angelis,&nbsp;Paolo Neri,&nbsp;Ginevra Scalisi","doi":"10.1002/env.70012","DOIUrl":"https://doi.org/10.1002/env.70012","url":null,"abstract":"<p>It is still unclear to what extent transition risks are being internalized by financial investors. In this paper, we provide a novel investigation of the impact of media-based measures of transition risks on the credit risk of energy companies, as measured by their credit default swaps (CDS) indices. We include both European and North American markets in the 2010–2020 period. Using linear and non-linear local projections, we find that a transition risk shock affects CDS indices only when combined with tangible physical climate-related impacts. We also find evidence of non-linear cross-border effects, with North American energy companies particularly affected by European dynamics. We suggest that the public reaction in the wake of severe climate-related disasters, which might push policymakers to adopt more decisive climate action, contributes to making the transition-related debate salient in the eyes of credit market actors.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 3","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143749465","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":"Effects of Climate Change on House Prices in Outdoor Tourism Destinations: A Case Study of Southwestern Colorado","authors":"Kadie S. Clark,&nbsp;J. Isaac Miller","doi":"10.1002/env.70007","DOIUrl":"https://doi.org/10.1002/env.70007","url":null,"abstract":"<div>\u0000 \u0000 <p>We estimate the historical effects of climate change on real estate prices in the San Juan Mountain Region of Southwestern Colorado, an area strongly influenced by outdoor recreation-based tourism, and we use these estimates to make projections for future house prices in the region based on multiple anthropogenic climate forcing scenarios. We find that local warm-season minimum and cold-season temperature and local warm-season maximum temperature have significantly positive long-run relationships with global anthropogenic climate forcing. Moreover, once we control for non-climate factors that affect the housing market, we find that local cold-season precipitation and local warm-season maximum temperature have significant but opposite effects on local house prices. Scenario-based projections suggest that these two effects largely negate each other under any climate scenario, so that effects of climate change on house prices are expected to continue through the end of the century as they have over the past few decades.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 3","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143690027","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":"Joint Modeling of Wind Speed and Wind Direction Through a Conditional Approach","authors":"Eva Murphy,&nbsp;Whitney Huang,&nbsp;Julie Bessac,&nbsp;Jiali Wang,&nbsp;Rao Kotamarthi","doi":"10.1002/env.70011","DOIUrl":"https://doi.org/10.1002/env.70011","url":null,"abstract":"<p>Atmospheric near surface wind speed and wind direction play an important role in many applications, ranging from air quality modeling, building design, wind turbine placement to climate change research. It is therefore crucial to accurately estimate the joint probability distribution of wind speed and direction. In this work, we develop a conditional approach to model these two variables, where the joint distribution is decomposed into the product of the marginal distribution of wind direction and the conditional distribution of wind speed given wind direction. To accommodate the circular nature of wind direction, a von Mises mixture model is used; the conditional wind speed distribution is modeled as a directional dependent Weibull distribution via a two-stage estimation procedure, consisting of a directional binned Weibull parameter estimation, followed by a harmonic regression to estimate the dependence of the Weibull parameters on wind direction. A Monte Carlo simulation study indicates that our method outperforms two other approaches in estimation efficiency: one that utilizes periodic spline quantile regression and another that generates data from the commonly used Abe-Ley distribution for cylindrical data. We illustrate our method by using the output from a regional climate model to investigate how the joint distribution of wind speed and direction may change under some future climate scenarios. Our method indicates significant changes in the variation of wind speed with respect to some directions.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 3","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689690","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}