Geoestadística para integrar mediciones de campo con estimaciones satelitales adecuados para escala local
DOI:
https://doi.org/10.24850/j-tyca-15-01-02Keywords:
Bajo Grijalva, cuenca tropical, desescalamiento geoestadístico de datos, precipitación satelital, regresión krigingAbstract
En paÃses como México hacen falta más estaciones de medición de lluvia. Además, en la cuenca Grijalva, datos de solo tres o menos estaciones se integran en productos satelitales de misiones como Tropical Rainfall Monitoring Mission (TRMM) o Global Precipitation Mission (GPM). Aunque las misiones satelitales permiten obtener estimaciones de lluvia a un espaciamiento constante (p. ej., 11 km para GPM), esta resolución no es adecuada para gestión local. La integración de una mayor cantidad de datos de pluviómetros con valores de satélite aumentados de escala puede ser útil para obtener datos de precipitación de escala local. En este trabajo se aplicó kriging ordinario (OK) a los datos satelitales de precipitación (GPM y TRMM) agregados anualmente y regresión kriging (RK) para integrar los datos resultantes con datos de todos los pluviómetros disponibles. Los resultados de esta integración se compararon con los resultados de la interpolación de datos de pluviómetros utilizando OK y kriging universal (UK). Una interpolación del inverso de la distancia al cuadrado (IDW) se consideró como criterio de bajo desempeño. Los métodos de evaluación y de definición de similaridad fueron validación cruzada (Lou-CV), análisis de componentes principales, matriz de correlación y mapa de calor con análisis de conglomerados. OK funcionó bien para desescalar las estimaciones satelitales de GPM. La integración RK de datos de pluviómetros con datos de GPM desescalados con OK obtuvo los mejores parámetros de validación en comparación con las interpolaciones de mediciones de pluviométros. Los métodos geoestadísticos son prometedores para desescalar las estimaciones satelitales e integrarlas con todos los datos disponibles de pluviómetros. Los resultados indican que la evaluación usando parámetros para evaluar la efectividad de la interpolación usando datos medidos debe complementarse con métodos para definir similaridad entre las capas espaciales obtenidas. Este enfoque permite obtener datos de precipitación útiles para modelado y manejo del agua a nivel local.
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