What is the Atlas?

We could define this Atlas as a “set of digital climatic maps of mean air temperature (minimum, mean and maximum), precipitation and solar radiation”. With these maps we are able to find, for every 200m of the Iberian Peninsula, which is the mean annual temperature, the precipitation of June or any of the variables mentioned before in climatic terms. In other words, you can query the mean value based on the average from all years where data is available. Furthermore, these values may be queried for the whole year (mean for temperature and solar radiation or accumulated for precipitation) or for a certain month.

Highlights:

-Completeness: 65 digital climatic maps of air mean temperature (minimum, mean and maximum), precipitation and solar radiation.

- High quality base data: 2285 meteorological stations have been used containing data from the 1951-1999 period (a range of 15 up to 50 years depending on the case) and a Digital Elevation Model with 200 m resolution.

- Detailed resolution (specially if we consider largeness of the Iberian Peninsula): spatial resolution of 200 m and monthly and annual temporal resolution.

- Integration of techniques: These maps have been processed using statistical techniques (such as multiple regression with residual correction), Geographic Information Systems (GIS) and spatial interpolation of meteorological stations data.

-Objectivity: Numerical quality (known error) computed and documented for every map. Error level and their spatial distribution are crucial aspects whether for cartography assessment or for assessment of models generated from this cartography.

-Interoperability: GIS enables these maps to be overlaid with other sources of information.

- Accessibility: Queriable and downloadable in GIS format freely on Internet.

Why has it been made?

It is well-known that the Iberian Peninsula presents interesting characteristics, as well as complex, in climatological and biogeographical terms. In addition, from geosciences and biosciences (hydrology, ecology, biogeography, etc) as well as from land managers (conservation agencies, etc) there is an obvious interest for climatology. Despite this interest, just data coming from meteorological stations is relevant enough. Even though, these stations present an irregular spatial distribution throughout the territory and hence, some questions arise: How making climatological maps? How generating information where no data is available? To sum up: How interpolating?

First approaches used expert knowledge and intuition for drawing maps but...

- Nobody can deal with several meteorological stations and variables (altitude, continentality, etc.) at the same time.
- Lack of objectivity in the resultant cartography
- Classical methods did not inform about quality parameters (relevant when modeling)
- Those maps did not offer a truly continuous variation over terrain
- Complex data, such as solar radiation, was not included

New technological (computers) and scientific progress (GIS), specially during last decade, make possible that today we are able to develop cartography based on numerical methods.

Who has developed the Atlas?

The Atlas started in 2000 as an initiative of the Unit of Botany in the BABVE Department (Animal Biology, Vegetal Biology and Ecology) as well as the Department of Geography, both in the Autonomous University of Barcelona.

A research was developed in order to deepen into issues relating to spatial interpolation of discrete climatological data spread over a territory (meteorological stations). First of all, this research was directed to climatological and vegetation data to obtain suitability maps of plant species, but later we focused our research in climatological aspects.

How has it been made?

The Atlas has been made through a combination of different techniques such us statistical analysis, spatial interpolation and Geographic information Systems.

As starting data we used 3528 meteorological stations for thermometric measures and 7293 stations for rain gauge measures. These stations were filtered in order to achieve a compromise between length of the series (temporal stability) and density (spatial coverage). Finally, following the statistical tests, we decided to work with series of 15 or more years for temperature data and 20 or more years for precipitation. Once this filtering process was finished, there remained 286 thermometric meteorological stations, 1217 rain gauge stations and 782
pluviothermometric stations.

In addition to these stations, it is important to state that we also used the Digital Elevation Model (DEM) of the Iberian Peninsula with a spatial resolution of 200m.

DEM and derived geographical variables (altitude, latitude, continentality, solar radiation and terrain curvature) have been introduced in statistical analysis to predict mean climatological values of temperature and precipitation.

This information has been completely implemented in a GIS (MiraMon) which enabled us to:

- Objective interpolation by considering geographic information
- Repeatability (new data, new models, etc)
- Data accessibility: through WMS Server based in OGC standards is possible to perform basic queries as well as download data for more complex analysis.
- High-quality print maps can be obtained
- Integration to other systems
- Process steps:

- filtering meteorological stations
- modeling geographical variables
- database enrichment
- map algebra
- statistical tests to assess map quality

In the methodology section of the booklet, there are reflected the technical aspects considered in the making of the Atlas. From here you can download the PDF file.

For further information over scientific matters and related techniques for the making of this Atlas, you can check Methodology section in this web or check References.

What are our future goals?

Our aim is to improve quality of present cartography and generate new derived maps (evapotranspiration for example). In order to get better maps we can follow two strategies:

1) Increase the quality of the current data. Basically, this may be achieved by increasing length series of the stations and also by increasing the number of meteorological stations, which is completely an institutional competence.

2) Improve the used methodology by testing new spatial interpolation techniques or incorporating new geographical variables.

ACKNOWLEDGEMENTS

Sergi Gumà, Núria Julià and Joan Masó for technical support.

Guadalupe Barea for the web design.

Pep Serra and Pedro Pinto for english and portuguese translations.

Roberto Vallejo (Inventario Forestal Nacional) for the supplied data.

Javier Martín Vide (Departament de Geografia Física i Anàlisi Geogràfica), Ángel M. Felicísimo (Departamento de Expresión Gráfica,UNEX) for althruistic academic support.