GIS-based construction of baseline climatologies for the Mediterranean using terrain variables

A GIS-based method for constructing high-resolution (in space) maps of mean seasonal temperature and precipitation is developed for the Mediterranean Basin. Terrain variables and geographical location are used as predictors of the climate variables at all points on a grid with a 1 km resolution, usi...

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Bibliographic Details
Published in:Climate research 2000-03, Vol.14 (2), p.115-127
Main Authors: Agnew, Maureen D., Palutikof, Jean P.
Format: Article
Language:English
Subjects:
Climate change
Climatology
Kriging
Modeling
Precipitation
Rain
Regression analysis
Seasons
Statistical variance
Topographical elevation
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Summary:A GIS-based method for constructing high-resolution (in space) maps of mean seasonal temperature and precipitation is developed for the Mediterranean Basin. Terrain variables and geographical location are used as predictors of the climate variables at all points on a grid with a 1 km resolution, using a regression-based approach. Variables used for model development include: longitude, latitude, elevation, distance from the nearest coast, direction to the nearest coast, slope, aspect, and the ratio of land to sea within given radii. Seasonal mean temperature and precipitation data, for the observation period 1952 to 1989, were assembled from 248 temperature sites and 285 precipitation sites in order to initialise the regression model. Temperature data from 36 stations and precipitation data from 35 stations were retained for model validation. Climate surfaces were constructed using the regression equations, and refined by kriging the residuals from the regression model and subtracting the result from the predicted 'observation' surface. Latitude, elevation and distance from the sea are found to be the most effective predictors of local seasonal climate. Validation determined that regression plus kriging predicts mean seasonal temperatures with a coefficient of determination (R²), between the expected and observed values, of 0.87 (summer) and 0.97 (winter), and mean seasonal precipitation with an R² of 0.46 (autumn) and 0.94 (summer). A simple regression model without kriging yields less accurate results in all seasons except for the temperature data in spring.
ISSN:0936-577X
1616-1572
DOI:10.3354/cr014115