Geo-climatic hazards in the eastern subtropical Andes: Distribution, Climate Drivers and Trends

Detecting and understanding historical changes in the frequency of geo-climatic hazards (G-CHs) is crucial for the quantification of current hazards and project them into the future. Here we focus in the eastern subtropical Andes (32-33° S), using meteorological data and a century-long inventory of 553 G-CHs triggered by rainfall or snowfall. We first analyse their spatio-temporal distributions and the role of climate variability on the year-to-year changes in the number of days per season with G-CHs. Precipitation is positively correlated with the number of G-CHs across the region and year-round; mean temperature is negatively correlated with snowfall-driven hazards in the western (higher) half of the study region during winter and with rainfall-driven hazards in the eastern zone during summer. The trends of the G-CHs frequency since the mid-20th century were calculated taking cautions for their non-systematic monitoring. The G-CHs series for the different triggers, zones and seasons were generally stationary. Nonetheless, there is a small positive trend in rainfall-driven G-CHs in the eastern zone during summer congruent with a rainfall increase there. We also found a decrease in snowfall-driven G-CHs in the western zone since the late 1990?s onwards, most likely due to a reduction in winter precipitation rather than to an increase in temperature.Fil: Vergara Dal Pont, Iván Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto Andino Patagónico de Tecnologías Biológicas y Geoambientales. Universidad Nacional del Comahue. Instituto Andino Patagónico de Tecnologías Biológicas y Geoambientales; ArgentinaFil: Moreiras, Stella Maris. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Provincia de Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Universidad Nacional de Cuyo. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales; ArgentinaFil: Araneo, Diego Christian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Provincia de Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Universidad Nacional de Cuyo. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales; ArgentinaFil: Garreaud, René. Universidad de Chile; Chile. Centro de Ciencia del Clima y la Resiliencia; Chil

Assignment problems in wildfire suppression: a case study on control of flight resources

The phenomenon of wildfires has become one of the biggest problems our forests are suffering due to the high frequency and intensity that has acquired in recent decades. As the budget and fire resources are limited, it is essential to control these catastrophic fires by making efficient decisions. In this paper, we make use of operations research techniques that allow the optimal assignments of aircrafts to extinguishing wheels and to refueling points, which are two important tasks to be performed by the controller of aerial resources in a forest fire.The authors wish to thank the interesting proposals, comments, and computing support made by W. González-Manteiga, B. Pateiro-López, A. Riera-Álvarez, two referees, and an anonymous associate editor. This research received financial support from the Ministerio de Economía y Competitividad of Spain through grant MTM2014-53395-C3-2-P, MTM2016-76969-P, MTM2017-87197-C3-3-P, and from ITMATI, Technological Institute of Industrial Mathematics, Santiago de Compostela, Spain, through the Enjambre project, which are gratefully acknowledgeS

A test for directional-linear independence, with applications to wildfire orientation and size

Original PaperA nonparametric test for assessing the independence between a directional random variable (circular or spherical, as particular cases) and a linear one is proposed in this paper. The statistic is based on the squared distance between nonparametric kernel density estimates and its calibration is done by a permutation approach. The size and power characteristics of various variants of the test are investigated and compared with those for classical correlation-based tests of independence in an extensive simulation study. Finally, the best-performing variant of the new test is applied in the analysis of the relation between the orientation and size of Portuguese wildfire

An introduction to statistical methods for circular data

Angles, directions, events, occurrences along time... all of them can be viewed as data on a circle (circular data). The particular nature of this type of data requires specific and adapted inferential and modelling procedures. Although there are quite a few references on this topic, and despite circular data are quite common in many applied sciences, they are frequently overlooked. This brief introduction aims to give the reader just some basic ideas on circular data analysis (with some mentions to the general case of spherical or directional data), providing some relevant references and tools for their application in practiceS

Nonparametric estimation of directional highest density regions

Highest density regions (HDRs) are defined as level sets containing sample points of relatively high density. Although Euclidean HDR estimation from a random sample, generated from the underlying density, has been widely considered in the statistical literature, this problem has not been contemplated for directional data yet. In this work, directional HDRs are formally defined and plug-in estimators based on kernel smoothing and associated confidence regions are proposed. We also provide a new suitable bootstrap bandwidth selector for plug-in HDRs estimation based on the minimization of an error criteria that involves the Hausdorff distance between the boundaries of the theoretical and estimated HDRs. An extensive simulation study shows the performance of the resulting estimator for the circle and for the sphere. The methodology is applied to analyze two real data sets in animal orientation and seismologyOpen Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. R.M. Crujeiras and P. Saavedra-Nieves acknowledge the financial support of Ministerio de Economía y Competitividad and Ministerio de Ciencia e Innovación of the Spanish government under grants MTM2016-76969P, MTM2017-089422-P, PID2020-118101GB-I00 and PID2020-116587GB-I00 and ERDF. Authors also thank Elena Vázquez Abal for her help, Prof. Felicita Scapini for providing the sandhoppers data (collected under the support of the European Project ERB ICI8-CT98-0270), the computational resources of the CESGA Supercomputing Center and the referees for the constructive comments which have improved the paperS

Recent advances in directional statistics

Mainstream statistical methodology is generally applicable to data observed in Euclidean space. There are, however, numerous contexts of considerable scientific interest in which the natural supports for the data under consideration are Riemannian manifolds like the unit circle, torus, sphere and their extensions. Typically, such data can be represented using one or more directions, and directional statistics is the branch of statistics that deals with their analysis. In this paper we provide a review of the many recent developments in the field since the publication of Mardia and Jupp (1999), still the most comprehensive text on directional statistics. Many of those developments have been stimulated by interesting applications in fields as diverse as astronomy, medicine, genetics, neurology, aeronautics, acoustics, image analysis, text mining, environmetrics, and machine learning. We begin by considering developments for the exploratory analysis of directional data before progressing to distributional models, general approaches to inference, hypothesis testing, regression, nonparametric curve estimation, methods for dimension reduction, classification and clustering, and the modelling of time series, spatial and spatio-temporal data. An overview of currently available software for analysing directional data is also provided, and potential future developments discussed.Comment: 61 page