International Journal of Applied Earth Observation and Geoinformation 13 (2011) 396–408 Contents lists available at ScienceDirect International Journal of Applied Earth Observation and Geoinformation journal homepage: www.elsevier.com/locate/jag Comparative analysis of different techniques for spatial interpolation of rainfall data to create a serially complete monthly time series of precipitation for Sicily, Italy A. Di Piazza, F. Lo Conti, L.V. Noto ∗ , F. Viola, G. La Loggia Dipartimento di Ingegneria Civile, Ambientale e Aerospaziale, Università di Palermo, viale delle Scienze, Palermo 90128, Italy article info Article history: Received 5 November 2009 Accepted 27 January 2011 Keywords: Precipitation DEM Geostatistics Interpolation methods abstract The availability of good and reliable rainfall data is fundamental for most hydrological analyses and for the design and management of water resources systems. However, in practice, precipitation records often suffer from missing data values mainly due to malfunctioning of raingauge for specific time periods. This is an important issue in practical hydrology because it affects the continuity of rainfall data and ultimately influences the results of hydrologic studies which use rainfall as input. Many methods to estimate missing rainfall data have been proposed in literature and, among these, most are based on spatial interpolation algorithms. In this paper different spatial interpolation algorithms have been evaluated to produce a reasonably good continuous dataset bridging the gaps in the historical series. The algorithms used are deterministic methods such as inverse distance weighting, simple linear regression, multiple regression, geographically weighted regression and artificial neural networks, and geostatistical models such as ordinary kriging and residual ordinary kriging. In some of these methods, the elevation information, provided by a Digital Elevation Model, has been added to improve estimation of missing data. These algorithms have been applied to the mean annual and monthly rainfall data of Sicily (Italy), measured at 247 raingauges. Optimization of different settings of the various interpolation methods has been carried out using a subset of the available rainfall dataset (modeling set) while the remaining subset (validation set) has been used to compare the results obtained by the different algorithms. Validation results indicate that the univariate methods, neglecting the information of elevation, are characterized by the largest errors, which decrease when the elevation is taken into account. The ordinary kriging of residuals from linear regression between precipitation and elevation, which has provided the best performance at annual and monthly scale, has been used to complete the precipitation monthly time series in Sicily. Crown Copyright © 2011 Published by Elsevier B.V. All rights reserved. 1. Introduction The availability of a reliable source of rainfall and climate data is a fundamental prerequisite for the modeling of a wide variety of hydrological and environmental processes. While the nature and the structure of hydrological and environmental models may vary, most of them need a precipitation dataset that is complete and reli- able on a temporal and spatial basis. Unfortunately, measurement of hydrological variables (e.g. rainfall, temperature, streamflows, etc.) can suffer from systematic, random errors and gaps (missing data) (Larson and Peck, 1974; Vieux, 2001) and, among these, the missing data problem is probably the most important one. ∗ Corresponding author. E-mail address: valerio@idra.unipa.it (L.V. Noto). Generally there are two different approaches to treat the miss- ing data or data gaps: one possible approach consists of using only continuous records, ignoring the prior (or subsequent) events, while another approach suggests ignoring the gaps, assuming that the data are one continuous series of records. With the former approach many data are wasted and correct statistical inference cannot be made whereas the latter approach reduces the period of recorded events and overestimates the likelihood of occurrence of extreme events. On the other hand, the use of the dataset prone to missing data can result in errors that exhibit temporal and spatial patterns (Stooksbury et al., 1999). A valid alternative to the above mentioned approaches consists of filling the gaps in the rainfall time series by estimating the missing values. The reconstruction of serially incomplete data records has been the subject of a large number of scientific works where numerous techniques for esti- mating missing data values have been implemented and compared. 0303-2434/$ – see front matter. Crown Copyright © 2011 Published by Elsevier B.V. All rights reserved. doi:10.1016/j.jag.2011.01.005