Environmetrics最新文献

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.

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.

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.

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.

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.

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.

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.