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SPACE-TEMPORAL ANALYSIS OF URBAN HEAT ISLANDS

One of the main advantages of having various meteorological data points for given region, in this case the Metropolitan Area of ​​Buenos Aires (also known as AMBA), is the possibility to perform sampling and by its means acquire new patterns of behaviour.

Particularly, the analysis of time series of the maximum daily temperatures, allow us to detect the evolution and movement of a warm front in a spatial level. Consequently, it results in an excellent tool for identifying the onset and progress of urban heat islands. Accordingly, by identifying problematic areas, a mitigation plan for this phenomenon could be developed effectively (such as the installation of forested areas or bodies of water, among others).

It is known as “heat island” any closed isotherm enclosing a temperature zone greater than that of its surroundings. This concept is commonly used to refer to urban areas where the temperature is higher than in suburban peripheries. The referred differential temperature may vary between 3°C and 5°C but can reach higher values ​​if special conditions are presented.

In the video below you may be able to appreciate how the warm fronts in the AMBA region evolve and change in a given temporal window, from October 1, 2014 until January 31, 2015.

   

Figure 1. Video on the evolution of the fronts of heat in the city of Buenos Aires for a period of four months between the spring and the start of the summer.

Moreover, since the application of effective mitigation measures for this effect requires a detailed knowledge of the behavior of the spatio-temporal heat island, owning temperature georeferenced information allows the user to develop a methodology in order to generate a layout of the isotherms from the combination of a large number of weather stations present in the AMBA. Furthermore, if forecasts are made for different parts of the region and then curves from them are drawn, it is possible to predict the onset of heat islands, as shown in Figure 2, and consequently design planning measures prior to the occurrence of the event. These forecasts were made using a combination of models based on Genetic Programming and Artificial Neural Networks similar to those exemplified in other Climalabs.

Figure 2. Comparison of the isotherm between measurements and predictions made with different horizons of prognosis.

News about the heatwave in Buenos Aires are listed below to understand the relevance of develop strategies to deal with these phenomena:

- Heatwaves (Diario La Nación)

- Yellow alert for heat wave in Buenos Aires (Diario La Nación)

- Heatwave in Buenos Aires (Diario La Nación)

- Yellow alert in Buenos Aires (Diario La Nación)

At BerecoLabs we are developing these and other models to understand how these complex natural phenomena unfold and what are its impacts. Allowing us to create new management tools to reduce the vulnerability of the territories and make them more resilient.