Abstract
A joint spatiotemporal modeling approach, which capitalizes on the typically better informed time domain, is presented. The spatiotemporal process is modeled as a joint realization of a collection of spatially correlated time series. Temporal trends of known shape or period are modeled via deterministic functions of time, which are modulated by space-dependent coefficients. These coefficients are then regionalized in space, accounting for their cross-correlation. The proposed algorithm is applied for modeling the spatiotemporal trends of monthly averaged concentration values of sulphate deposition over Europe. The available data were recorded at the European Monitoring and Evaluation Program (EMEP) network and span a period of 9 years from January 1980 to December 1988. Stochastic simulation is performed for modeling joint space-time uncertainty regarding trend levels of sulphate concentration over Europe for that time period.
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Kyriakidis, P.C., Journel, A.G. (1999). Stochastic Modeling of Spatiotemporal Distributions: Application to Sulphate Deposition Trends Over Europe. In: Gómez-Hernández, J., Soares, A., Froidevaux, R. (eds) geoENV II — Geostatistics for Environmental Applications. Quantitative Geology and Geostatistics, vol 10. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-9297-0_8
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DOI: https://doi.org/10.1007/978-94-015-9297-0_8
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