title: Spatiotemporal Interpolation for Environmental Modelling.
abstract: A variation of the reduction-based approach to spatiotemporal interpolation (STI), in which time is treated independently from the spatial dimensions, is proposed in this paper. We reviewed and compared three widely-used spatial interpolation techniques: ordinary kriging, inverse distance weighting and the triangular irregular network. We also proposed a new distribution-based distance weighting (DDW) spatial interpolation method. In this study, we utilised one year of Tasmania's South Esk Hydrology model developed by CSIRO. Root mean squared error statistical methods were performed for performance evaluations. Our results show that the proposed reduction approach is superior to the extension approach to STI. However, the proposed DDW provides little benefit compared to the conventional inverse distance weighting (IDW) method. We suggest that the improved IDW technique, with the reduction approach used for the temporal dimension, is the optimal combination for large-scale spatiotemporal interpolation within environmental modelling applications.
authors: Data61, CSIRO, College Road, Sandy Bay TAS 7005, Australia. Ferry.Susanto@data61.csiro.au.|College of Engineering and Science, Victoria University, Footscray VIC 3011, Australia. Ferry.Susanto@data61.csiro.au.
authors: Ferry
authors: F
authors: Susanto
authors: Data61, CSIRO, College Road, Sandy Bay TAS 7005, Australia. Paulo.desouza@data61.csiro.au.
authors: Paulo
authors: P
authors: de Souza
authors: College of Engineering and Science, Victoria University, Footscray VIC 3011, Australia. Jing.He@vu.edu.au.
authors: Jing
authors: J
authors: He
mesh_terms: D004777:Environment; D004784:Environmental Monitoring / Q000379:methods*; D062070:Hydrology / Q000379:methods*; D008962:Models, Theoretical; D062206:Spatial Analysis
keywords: distribution-based distance weighting; inverse distance weighting; ordinary kriging; spatiotemporal interpolation; triangular irregular network