Estudio de persistencia de la sequía en el norte y centro de México

ISRAEL VELASCO VELASCO Eduardo Alexis Cervantes Carretero DAVID ORTEGA GAUCIN (2013)

Tabla de contenido: Introducción – Antecedentes – Conceptos y enfoques de la proyección hidrológica a futuro: modelo autorregresivo, modelo de medias móviles, modelo autorregresivo de media móvil – Índices de estado – Índice hidrológico de sequía – Resultados – Conclusiones y recomendaciones.

En el acontecer natural hidrometeorológico, la estimación de eventos futuros tiene un elevado nivel de incertidumbre, tanto más grande en cuanto más a futuro. Sin embargo, algunos de estos fenómenos –la lluvia y el escurrimiento superficial-, pueden mostrar un cierto nivel de persistencia, entendido el término como la continuación de condiciones iguales o similares o del mismo tipo, lo cual se puede tratar con algunas técnicas estadístico-matemáticas, para intentar estimar su comportamiento futuro. Este trabajo incursiona sobre la estimación de la persistencia hidrológica, como un elemento de posible aplicación para apoyar la formulación de escenarios de sequía. Dicho trabajo tiene como fin estudiar, bajo diversos enfoques (Hurst, índices de severidad...), el fenómeno de la persistencia de las sequías y aplicarla a series hidrometeorológicas en alguna cuenca del norte y centro de México.

Introducción – Antecedentes – Conceptos y enfoques de la proyección hidrológica a futuro: modelo autorregresivo, modelo de medias móviles, modelo autorregresivo de media móvil – Índices de estado – Índice hidrológico de sequía – Resultados – Conclusiones y recomendaciones.

Working paper

Sequía Fenómeno de El Niño Corrientes cálidas Corrientes frías Series hidrometeorológicas Informes de proyectos Presa Lázaro Cárdenas, Durango CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA

Offshore wind energy climate projection using UPSCALE climate data under the RCP8.5 emission scenario

MARKUS SEBASTIAN GROSS (2016)

In previous work, the authors demonstrated how data from climate simulations can be utilized to estimate regional wind power densities. In particular, it was shown that the quality of wind power densities, estimated from the UPSCALE global dataset in offshore regions of Mexico, compared well with regional high resolution studies. Additionally, a link between surface temperature and moist air density in the estimates was presented. UPSCALE is an acronym for UK on PRACE (the Partnership for Advanced Computing in Europe)-weather-resolving Simulations of Climate for globAL Environmental risk. The UPSCALE experiment was performed in 2012 by NCAS (National Centre for Atmospheric Science)- Climate, at the University of Reading and the UK Met Office Hadley Centre. The study included a 25.6-year, five-member ensemble simulation of the HadGEM3 global atmosphere, at 25km resolution for present climate conditions. The initial conditions for the ensemble runs were taken from consecutive days of a test configuration. In the present paper, the emphasis is placed on the single climate run for a potential future climate scenario in the UPSCALE experiment dataset, using the Representation Concentrations Pathways (RCP) 8.5 climate change scenario. Firstly, some tests were performed to ensure that the results using only one instantiation of the current climate dataset are as robust as possible within the constraints of the available data. In order to achieve this, an artificial time series over a longer sampling period was created. Then, it was shown that these longer time series provided almost the same results than the short ones, thus leading to the argument that the short time series is sufficient to capture the climate. Finally, with the confidence that one instantiation is sufficient, the future climate dataset was analysed to provide, for the first time, a projection of future changes in wind power resources using the UPSCALE dataset. It is hoped that this, in turn, will provide some guidance for wind power developers and policy makers to prepare and adapt for climate change impacts on wind energy production. Although offshore locations around Mexico were used as a case study, the dataset is global and hence the methodology presented can be readily applied at any desired location. © Copyright 2016 Gross, Magar. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reprod

Article

atmosphere, climate change, Europe, Mexico, sampling, time series analysis, university, weather, wind power, climate, risk, theoretical model, wind, Climate, Models, Theoretical, Risk, Wind CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA CIENCIAS DE LA TIERRA Y DEL ESPACIO OCEANOGRAFÍA OCEANOGRAFÍA