Performance assessment of spatio-temporal regression kriging with GAMLSS models as trends

Abstract

The main objective of this study is to propose different probabilistic models for adjusting the trend component, since it significantly influences the quality of the spatiotemporal interpolation of rainfalls. We used the monthly total precipitation data of the São Francisco River Basin (SFRB) for the period of 31 years, 1989-2019. The SFRB occupies 8% of the whole Brazilian territory, mostly located in the Northeast Brazilian region. For the trend component, we propose the fitted GAMLSS models by comparing different probability distribution families, which in most cases include the characteristics of these data. The results indicate the existence of a spatio-temporal pattern of the residues obtained from the adjustment of the trend with zero adjusted Gamma distribution for the accumulated monthly precipitation. The adjustment revealed a spatial dependence of up to 873 km between the pluviometric stations and temporal autocorrelation of approximately 1.6 months. The methodology used in this study enabled us to create rainfall maps, interpolating unobserved locations in differences years. The projection of these maps to the SFRB is considered extremely important for planning and implementing activities related to water resources across the river basin.

Research Progress on Spatiotemporal Interpolation Methods for Meteorological Elements

“…If the residuals of the regression model have spatiotemporal autocorrelation, a spatiotemporal variability function is built for them and a spatiotemporal kriging estimation is performed, and the final estimate of the meteorological factors is the sum of the residuals' predicted values, the trend component, and the seasonal cycle component [35]. Among the regression models can be multivariate linear regression, locally weighted linear regression (LWLR) [36], and generalized additive model GAMLSS based on location, range, and shape [37,38]. Compared to STUK, STRK can introduce more auxiliary variables to fit the trend part of the variables, so satellite data can be used as auxiliary variables in meteorological studies to fuse the observation data from the observatory, which can help to accurately estimate the amount of precipitation at unknown points [39].…”