High Resolution Grids
High Resolution Grids
High- and very high- resolution products are increasingly demanded from many socioeconomic sectors (e.g. agriculture, energy or tourism, among others). Currently, the main tools used to develop these products are the geostatistical methods, which are the subset of statistics used to analyze and interpret geo-referenced data (e.g. climatic data). In order to reach the demanded resolution, these techniques use a digital elevation model (D.E.M.) which lets to define the derived variables of interest (e.g. blocks, continentality, slopes, etc.) for each particular application.
We have expertise applying these tecniques to climatic data (e.g. precipitation, temperature, etc.), developing very high-resolution daily gridded observational products which have been used in agro-climatic classification, climate change studies or climate envelope models, among others.
- Data collection: first of all, as much available data of the interesting variables on the target region should be collected. This observed data could come from different sources and, therefore, the different present data formats must be homogenized.
- Quality control: data collected may have many errors. Thus, in a second phase, statistical techniques for the detection and correction of such errors should be applied.
- Digital Elevation Model (D.E.M.): the second source of information necessary for the development of the grids is the D.E.M. and variables derived from it. After obtaining the D.E.M. for the target resolution, we derive auxiliary variables which can be used in the regression model to adjust the monthly values of the target variables.
- Grid development: once all the data needed for the development of the grids are available, geostatistical techniques are calibrated, validated and applied to obtain the final product and the uncertainty associated with that product. The final product will be provided in netCDF format unless a more appropriate alternative for the user was specified.
Case Study: Agroclimatic Characterization of Asturias
In the framework of the project "Caracterización Agroclimática de Asturias: Elaboración de la cartografía agroclimática del Principado de Asturias" funded by the Consejería de Medio Rural y Pesca del Principado de Asturias, a high resolution agroclimatic characterization of the region based on the climatic classification of Papadakis was developed for the period 1971-2000.
To this aim, a high resolution daily climatic database including precipitation and temperature is needed. It was developed using a quality controlled station network, covering the geographical domain and time period of interest, and the digital elevation model (D.E.M.) of the target horizontal resolution (GTOPO30). The grid developed has a regular 1x1 km horizontal spatial resolution.
The interpolation procedure includes a regression model of the monthly data, using as independent variables a set of D.E.M.-derived variables (blocking, slopes, continentality, etc.), and a second step applying an ordinary Kriging to the daily anomalies. Once the meteorological variables were built, we applied the algorithm defined by Papadakis to obtain the climatic class of each grid-point based on the winter and summer types, and the thermal and water regimes.
The methodologies used in this project are based on the following publications.Climatic Classification of Papadakis:
- F. Elías Castillo, L.Ruiz Beltrán. 1973. Clasificación agroclimática de España. Basada en la clasificación agroecológica de Papadakis. Instituto Nacional de Meteorología, Madrid.
- J. Papadakis. 1966. Climates of the world and their agricultural potentialities. Editado por el autor. Buenos Aires.
- J. Papadakis. 1970. Agricultural potentialities of the world climates. Edited by the author. Buenos Aires. 70 p.
- J. Papadakis. 1980. Ecología y manejo de cultivos, pasturas y suelos. Ed. Albatros. 304 p. Buenos Aires.
- Bedia, J., Herrera, S. y Gutiérrez, J.M. (2013) Dangers of using global bioclimatic datasets for ecological niche modeling. Limitations for future climate projections. Global and Planetary Change, 107, 1-12, 2013.