Monthly precipitation mapping of the Iberian Peninsula using spatial interpolation tools implemented in a Geographic Information System
In this study, spatial interpolation techniques have been applied to develop an objective climatic cartography of precipitation in the Iberian Peninsula (583,551 km2). The resulting maps have a 200m spatial resolution and a monthly temporal resolution. Multiple regression, combined with a residual c...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2007 |
| País: | España |
| Institución: | Universitat Autònoma de Barcelona |
| Repositorio: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglés |
| OAI Identifier: | oai:ddd.uab.cat:67770 |
| Acceso en línea: | https://ddd.uab.cat/record/67770 https://dx.doi.org/urn:doi:10.1007/s00704-006-0264-2 |
| Access Level: | acceso abierto |
| Palabra clave: | PREI2008 |
| Sumario: | In this study, spatial interpolation techniques have been applied to develop an objective climatic cartography of precipitation in the Iberian Peninsula (583,551 km2). The resulting maps have a 200m spatial resolution and a monthly temporal resolution. Multiple regression, combined with a residual correction method, has been used to interpolate the observed data collected from the meteorological stations. This method is attractive as it takes into account geographic information (independent variables) to interpolate the climatic data (dependent variable). Several models have been developed using different independent variables, applying several interpolation techniques and grouping the observed data into different subsets (drainage basin models) or into a single set (global model). Each map is provided with its associated accuracy, which is obtained through a simple regression between independent observed data and predicted values. This validation has shown that the most accurate results are obtained when using the global model with multiple regression mixed with the splines interpolation of the residuals. In this optimum case, the average R2 (mean of all the months) is 0.85. The entire process has been implemented in a GIS (Geographic Information System) which has greatly facilitated the filtering, querying, mapping and distributing of the final cartography. |
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