Assessing distribution shifts and ecophysiological characteristics of the only Antarctic winged midge under climate change scenarios

Parts of Antarctica were amongst the most rapidly changing regions of the planet during the second half of the Twentieth Century. Even so, today, most of Antarctica remains in the grip of continental ice sheets, with only about 0.2% of its overall area being ice-free. The continent’s terrestrial fauna consists only of invertebrates, with just two native species of insects, the chironomid midges Parochlus steinenii and Belgica antarctica. We integrate ecophysiological information with the development of new high-resolution climatic layers for Antarctica, to better understand how the distribution of P. steinenii may respond to change over the next century under different IPCC climate change scenarios. We conclude that the species has the potential to expand its distribution to include parts of the west and east coasts of the Antarctic Peninsula and even coastal ice-free areas in parts of continental Antarctica. We propose P. steinenii as an effective native sentinel and indicator species of climate change in the Antarctic

Spatial characterization of climatic variables for Arica-Parinacota and Tarapacá, Chile using topoclimatic analysis

In the present study, models were developed to determine the monthly and annual spatio-temporal variation of temperature, precipitation, and solar radiation based on topoclimatic analysis of Arica-Parinacota and Tarapacá in northern Chile. To construct the equations of the topoclimatic model, the data from meteorological stations and physiographic factors (latitude, longitude, altitude, and distance to bodies of water) obtained from a digital terrain model with a resolution of 90 m were compiled in a database. The equations of the topoclimatic model were generated by a stepwise regression with a backward selection technique. The equations for average monthly temperature, precipitation, and solar radiation were determined by linear combinations. The results were statistically significant with coefficients of determination greater than 90%, in addition to being greater than the existing climate databases for this area

Un método simple para la estimación de los mapas medios mensuales de temperaturas mínimas y máximas del aire utilizando imágenes MODIS en la región de Murcia, España

[EN] Air temperature records are acquired by networks of weather stations which may be several kilometres apart. In complex topographies the representativeness of a meteorological station may be diminished in relation to a flatter valley, and the nearest station may have no relation to a place located near it. The present study shows a simple method to estimate the spatial distribution of minimum and maximum air temperatures from MODIS land surface temperature (LST) and normalized difference vegetation index (NDVI) images. Indeed, there is a strong correlation between MODIS day and night LST products and air temperature records from meteorological stations, which is obtained by using geographically weighted regression equations, and reliable results are found. Then, the results allow to spatially interpolate the coefficients of the local regressions using altitude and NDVI as descriptor variables, to obtain maps of the whole region for minimum and maximum air temperature. Most of the meteorological stations show air temperature estimates that do not have significant differences compared to the measured values. The results showed that the regression coefficients for the selected locations are strong for the correlations between minimum temperature with LSTnight (R2 = 0.69-0.82) and maximum temperature with LSTday (R2 = 0.70-0.87) at the 47 stations. The root mean square errors (RMSE) of the statistical models are 1.0 °C and 0.8 °C for night and daytime temperatures, respectively. Furthermore, the association between each pair of data is significant at the 95% level (p<0.01).[ES] Los registros de temperatura del aire son adquiridos por redes de estaciones meteorológicas las cuales podrían estar alejadas varios kilómetros entre sí. En topografías complejas la representatividad de una estación meteorológica podría verse disminuida en relación con un valle más plano, y la estación más cercana no tener relación con un lugar ubicado cerca de ella. El presente estudio, muestra un método simple para estimar la distribución espacial de las temperaturas mínimas y máximas del aire a partir de imágenes MODIS de temperatura de la superficie terrestre (LST) y el índice de vegetación de diferencia normalizada (NDVI). En efecto, existe una fuerte correlación entre los productos LST día y noche MODIS y los registros de temperatura del aire de las estaciones meteorológicas, lo que se obtiene al usar ecuaciones de regresión ponderadas geográficamente, encontrándose resultados confiables. Luego, los resultados permiten interpolar espacialmente los coeficientes de las regresiones locales usando como variable descriptora la altitud y el NDVI, para obtener mapas de la región completa para la temperatura del aire mínima y máxima. La mayoría de las estaciones meteorológicas muestran estimaciones de temperatura del aire que no tienen diferencias significativas en comparación con los valores medidos. Los resultados mostraron que los coeficientes de regresión para las ubicaciones seleccionadas son fuertes para las correlaciones entre temperatura mínima con LST noche (R2 = 0,69-0,82) y temperatura máxima con LST día (R2 = 0,70-0,87) en las 47 estaciones. Los errores cuadráticos medios (RMSE) de los modelos estadísticos son 1,0 °C y 0,80 °C para las temperaturas nocturna y diurna, respectivamente. Además, la asociación entre cada par de datos es significativa al nivel del 95% (p<0.01).This research was supported by the National Fund for Scientific and Technological Development (FONDECYT), Chile, project N° 1161809.Galdón-Ruíz, A.; Fuentes-Jaque, G.; Soto, J.; Morales-Salinas, L. (2023). A simple method for the estimation of minimum and maximum air temperature monthly mean maps using MODIS images in the region of Murcia, Spain. Revista de Teledetección. (61):59-71. https://doi.org/10.4995/raet.2023.1890959716

Modelo espacialmente explícito de estimación de las temperaturas extremas diarias en la Ciudad de Santiago, Chile, usando imágenes MODIS e información meteorológica

Ponencia presentada en: XI Congreso de la Asociación Española de Climatología celebrado en Cartagena entre el 17 y el 19 de octubre de 2018.[ES]En estudios relacionados para el monitoreo de la isla térmica en una ciudad es necesario el estimar la variabilidad espacial de la temperatura del aire. Este problema es muy importante cuando la densidad de estaciones meteorológicas presentes es de baja densidad, lo cual limita obtener campos térmicos confiables. El presente trabajo presenta un método para estimar las temperaturas extremas diarias a partir de datos de temperatura superficial diurna y nocturna obtenidos por el sensor MODIS (LST) a nivel de la Ciudad de Santiago de Chile. El método aplicado se basa en el uso de regresiones lineales espacialmente explícitas o regresiones ponderadas geográficamente (GWR), donde se estima la inestabilidad paramétrica a nivel de toda el área de estudio, donde las variables independientes usadas corresponden a la altitud y el índice de vegetación de diferencia normalizada (NDVI). Las regresiones fueron todas significativas, sin embargo, los mejores resultados del ajuste y evaluación de los modelos lineales para temperaturas máximas y mínimas se obtienen con datos de LST-MODIS día y noche en forma conjunta (mixtos) que por separado. Los resultados muestran que las regresiones espacialmente explícitas GWR presentan una buena precisión para la estimación de las temperaturas extremas diarias a partir de la temperatura superficial de noche y de día MODIS en comparación con OLS.[EN]In related studies for monitoring the thermal island in a city it is necessary to estimate the spatial variability of air temperature. This problem is very important when the density of meteorological stations present a low density, which limits to obtain reliable thermal fields. The present work shows a method to estimate the daily extreme temperatures from daytime and night surface temperature data obtained by the MODIS sensor (LST) at the level of the City of Santiago de Chile. The applied method is based on the use of spatially explicit linear regressions or geographically weighted regressions (GWR), where parametric instability is estimated at the level of the entire study area, where the independent variables used correspond to the altitude and the vegetation index of normalized difference (NDVI). The regressions were all significant, however, the best results of the adjustment and evaluation of the linear models for maximum and minimum air temperatures are obtained with day and night LST-MODIS data jointly than separately. The results show that the spatially explicit GWR regressions present good accuracy for the estimation of daily extreme temperatures from the MODIS day and night surface temperature compared to OLS.Esta investigación fue financiada por la Comisión Nacional de Investigación Científica y Tecnológica (CONICYT), por medio del proyecto FONDECYT 1161809

Spatio-Temporal Variation of the Urban Heat Island in Santiago, Chile during Summers 2005-2017

Urban heat islands (UHIs) can present significant risks to human health. Santiago, Chile has around 7 million residents, concentrated in an average density of 480 people/km(2). During the last few summer seasons, the highest extreme maximum temperatures in over 100 years have been recorded. Given the projections in temperature increase for this metropolitan region over the next 50 years, the Santiago UHI could have an important impact on the health and stress of the general population. We studied the presence and spatial variability of UHIs in Santiago during the summer seasons from 2005 to 2017 using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery and data from nine meteorological stations. Simple regression models, geographic weighted regression (GWR) models and geostatistical interpolations were used to find nocturnal thermal differences in UHIs of up to 9 degrees C, as well as increases in the magnitude and extension of the daytime heat island from summer 2014 to 2017. Understanding the behavior of the UHI of Santiago, Chile, is important for urban planners and local decision makers. Additionally, understanding the spatial pattern of the UHI could improve knowledge about how urban areas experience and could mitigate climate change.Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT) CONICYT FONDECYT 1161809 Publication Development Fund initiative of the Universidad Mayo

Evidence of Climate Change Based on Lake Surface Temperature Trends in South Central Chile

Lake temperature has proven to act as a good indicator of climate variability and change. Thus, a surface temperature analysis at different temporal scales is important, as this parameter influences the physical, chemical, and biological cycles of lakes. Here, we analyze monthly, seasonal, and annual surface temperature trends in south central Chilean lakes during the 2000–2016 period, using MODIS satellite imagery. To this end, 14 lakes with a surface area greater than 10 km2 were examined. Results show that 12 of the 14 lakes presented a statistically significant increase in surface temperature, with a rate of 0.10 °C/decade (0.01 °C/year) over the period. Furthermore, some of the lakes in the study present a significant upward trend in surface temperature, especially in spring, summer, and winter. In general, a significant increase in surface water temperature was found in lakes located at higher altitudes, such as Maule, Laja and Galletué lakes. These results contribute to the provision of useful data on Chilean lakes for managers and policymakers

Toward a Spectrophotometric Characterization of the Chilean Night Sky. A First Quantitative Assessment of ALAN across the Coquimbo Region

Light pollution is recognized as a global issue that, like other forms of anthropogenic pollution, has a significant impact on ecosystems and adverse effects on living organisms. Plentiful evidence suggests that it has been increasing at an unprecedented rate at all spatial scales. Chile—which, thanks to its unique environmental conditions, has become one of the most prominent astronomical hubs of the world—seems to be no exception. In this paper we present the results of the first observing campaign aimed at quantifying the effects of artificial lights at night on the brightness and colors of the Chilean sky. Through the analysis of photometrically calibrated all-sky images captured at four representative sites with an increasing degree of anthropization, and the comparison with state-of-the-art numerical models, we show that significant levels of light pollution have already altered the appearance of the natural sky even in remote areas. Our observations reveal that the light pollution level recorded in a small town of the Coquimbo Region is comparable with that of Flagstaff, Arizona, a ten times larger Dark Sky city, and that a mid-size urban area that is a gateway to the Atacama Desert displays photometric indicators of night sky quality that are typical of the most densely populated regions of Europe. Our results suggest that there is still much to be done in Chile to keep light pollution under control and thus preserve the darkness of its night sky—a natural and cultural heritage that it is our responsibility to protect

Assessment of atmospheric emissivity models for clear-sky conditions with reanalysis data

Abstract Atmospheric longwave downward radiation (L d) is one of the significant components of net radiation (Rn), and it drives several essential ecosystem processes. L d can be estimated with simple empirical methods using atmospheric emissivity (εa) submodels. In this study, eight global models for εa were evaluated, and the best-performing model was calibrated on a global scale using a parametric instability analysis approach. The climatic data were obtained from a dynamically consistent scale resolution of basic atmospheric quantities and computed parameters known as NCEP/NCAR reanalysis (NNR) data. The performance model was evaluated with monthly average values from the NNR data. The Brutsaert equation demonstrated the best performance, and then it was calibrated. The seasonal global trend of the Brutsaert equation calibrated coefficient ranged between 1.2 and 1.4, and the K-means analysis identified five homogeneous zones (clusters) with similar behavior. Finally, the calibrated Brutsaert equation improved the Rn estimation, with an error reduction, at the worldwide scale, of 64%. Meanwhile, the error reduction for each cluster ranged from 18 to 77%. Hence, Brutsaert’s equation coefficient should not be considered a constant value for use in εa estimation, nor in time or location