Seasonal precipitation interpolation at the Valencia region with multivariate methods using geographic and topographic information

• Published in: International journal of climatology Vol. 30; no. 10; pp. 1547 - 1563
• Main Authors: Portalés, Cristina, Boronat, Nuria, Pardo‐Pascual, Josep E., Balaguer‐Beser, Angel
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 01-08-2010; Wiley
• Subjects: digital elevation model; Earth, ocean, space; Exact sciences and technology; External geophysics; Geophysics. Techniques, methods, instrumentation and models; geostatistics; Meteorology; multiple regression; precipitation; topographic variables; Water in the atmosphere (humidity, clouds, evaporation, precipitation); Iberian Peninsula; Southern Europe; Valencia region; Europe; Mediterranean Basin; Spain; MED, Spain, Valencia; MED, Eastern Mediterranean; geostatistics; Precipitation intensity; atmospheric precipitation; digital elevation model; Spatial correlation; kriging; topography; digital elevation models; geographic information systems; Multiple regression; topographic variables; Multivariate interpolation; precipitation; Spatial distribution; Optimum interpolation; Rain gauge; Comparative study
• ISSN: 0899-8418; 1097-0088; 1097-0088
• DOI: 10.1002/joc.1988

The spatial pattern of precipitation is a complex variable that strongly depends on other geographic and topographic factors. As precipitation is usually known only at certain locations, interpolation procedures are needed in order to predict this variable in other regions. The use of multivariate interpolation methods is usually preferred, as secondary variables—generally derived using GIS tools—correlated with precipitation can be included. In this paper, a comparative study on different univariate and multivariate interpolation methodologies is presented. Our study area is centred in the region of Valencia, located to the eastern Spanish Mediterranean coast. The followed methodology can be divided in three steps. First, secondary variables having significant correlations with the precipitation were derived, where the hillsides were used as influence areas of certain variables. Secondly, precipitation was interpolated with different methodologies. Finally, the derived models were compared in terms of predicted errors. Models were achieved for seasonal scales, considering a total of 179 raingauges; data of another 45 raingauges were also used to predict errors. Results prove that there is no ideal method for all the cases but it will depend on one hand, on the number of geographical factors that influence the rainfall and, on the other hand, on the major or minor spatial correlation within the rainfall. Copyright © 2009 Royal Meteorological Society