An update of high‐resolution monthly climate surfaces for Mexico

Abstract

Climate surfaces are digital representations of climatic variables from a region in the planet estimated via geographical interpolation techniques. Climate surfaces have multiple applications in research planning, experimental design, and technology transfer. Although high-resolution climatologies have been developed worldwide, Mexico is one of the few countries that have developed several climatic surfaces. Here, we present an updated high-resolution (30 arc sec) climatic surfaces for Mexico for the average monthly climate period 1910-2009, corresponding to monthly values of precipitation, daily maximum, and minimum temperature, as well as 19 bioclimatic variables derived from the monthly precipitation and temperature values. To produce these surfaces we applied the thin-plate smoothing spline interpolation algorithm implemented in the ANUSPLIN software to nearly 5000 climate weather stations countrywide. As an additional product and unlike the previous efforts, we generated monthly standard error surfaces for the three climate parameters, which can be used for error assessment when using these climate surfaces. Our climate surface predicted slightly drier and cooler conditions than the previous ones. ANUSPLIN diagnostic statistics indicated that model fit was adequate. We implemented a more recent error assessment, a set of withheld stations to perform an independent evaluation of the model surfaces. We estimate the mean absolute error and mean error, with the withheld data and all the available data. Average RTGCV for monthly temperatures was of 1.26-1.12 • C and 24.67% for monthly precipitation, and a RTMSE of 0.48-0.56 • C and 11.11%. The main advantage of the surfaces presented here regarding the other three developed for the country is that ours cover practically the entire 20th century and almost the entire first decade of the 21st century. It is the most up to date high-resolution climatology for the country.

Nuevas localidades y formas florales del endemismo mexicano Stelis zootrophionoides (Orchidaceae), y evaluación de su riesgo de extinción

“…Como registros de presencia se utilizaron las cinco localidades hasta ahora conocidas para S. zootrophionoides (cuatro de ellas aquí reportadas como nuevas localidades para la especie), 19 variables ambientales actuales elaboradas a alta resolución para México (Cuervo-Robayo et al, 2014; http://idrisi.uaemex.mx/) y tres variables topográfi cas (U.S Geological Survey Hydro.1k; http://usgs.gov). Ambos conjuntos de coberturas (ambientales y topográfi cas; Tabla 1) se delimitaron para el estado de Chiapas.…”

Climatic adaptation and ecological descriptors of wild beans from Mexico

“…Agroecological characterization of wild/cultivated niches helps to determine the environmental requirements of a species, providing information on the adaptability and degree of tolerance to specific ecological conditions (Maxted, Dulloo, & Ford‐Lloyd, 2016; Pliscoff & Fuentes‐Castillo, 2011; Svenning, Fløjgaard, Marske, Nógues‐Bravo, & Normand, 2011; Wang et al., 2009). Ecological descriptors are environmental data from collection sites obtained after the standardization of map and layer construction using geographical information systems (GIS) (Cuervo‐Robayo et al., 2014; Suárez‐Venero, Soto‐Carreño, Garea‐Llanos, & Solano‐Ojeda, 2015; Wang et al., 2015). One classification system based on GIS aids in the development of a conservation strategy by enabling the retro‐classification of germplasm collections, facilitating efforts to focus on further exploration, and research in those regions with high probabilities of the presence of specific species or genotypes.…”

Temporal Trends and Spatial Patterns of Temperature and Its Extremes over the Beijing-Tianjin Sand Source Region (1960–2014), China

“…This is characterized by fitting a smooth and continuous surface with the observation points, and it does not require a preliminary estimate for the structure of a temporal variance and statistical hypothesis [52]. In comparison with other methods, previous studies, for example, those using Kriging [53,54] and those analyzing climatic and meteorological change [55][56][57][58], have shown the spline method to be more advantageous around topographic features. Spatial interpolation with the spline method was completed using ArcGIS software (Version 10.2).…”