Spatial analysis and modeling of climate variables in the Cuitzeo Basin, Mexico
Climatic information with sufficient quality and spatially distributed is an essential requirement for developing research in several disciplines, such as Hydrology, Agronomy, Climatology and Ecology. In the present paper we attempt to reach to a model of the spatial distribution of precipitation an...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Universidad Nacional Autónoma de México
2010-09-01
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| Series: | Investigaciones Geográficas |
| Subjects: | |
| Online Access: | http://www.investigacionesgeograficas.unam.mx/index.php/rig/article/view/59226 |
| Summary: | Climatic information with sufficient quality and spatially distributed is an essential requirement for developing research in several disciplines, such as Hydrology, Agronomy, Climatology and Ecology. In the present paper we attempt to reach to a model of the spatial distribution of precipitation and temperature in the lake Cuitzeo basin, based on interpolation methods using climatic and geographic variables and supported by the application of correlation analysis, simple and multiple regression and the use of geographic information systems. Three models were developed: one including 17 stations within the basin (Basin model); a second including 24 stations located at less than 10 km from the basin’s water shed (Buffer 10 model); and a third using 30 stations located at less than 20 km from the catchment’s water divide (Buffer 20 model). Based on the results of confidence analysis, the final average temperature map was the regression map resulting from the Buffer 20 model corrected by the addition of the anomaly map, with R2=0.72 and RMSE of 0.64 oC. In precipitation maps, the highest confidence results were derived from the data from the Buffer 20 model. The final annual precipitation map was obtained from the regression map without correction by residuals, with R2=0.746 and RMSE=55.51 oC. Confidence analysis shows that both models had statistically significant determination coefficients (Prob. > F=0.05), however, models could be improved by the availability of more stations within the basin, given that the quantity and quality of data is a variable having an effect on the output of model application. The resulting final maps are relevant for modeling the spatial distribution of types of vegetation cover and of plant species, because climate, together with altitude, slope, exposure and other factors, is fundamental for determining the distribution of plant communities and of their component species. |
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| ISSN: | 0188-4611 2448-7279 |