A Multiple linear regression GIS module using spatial variables to model orographic rainfallA Multiple linear regression GIS module using spatial variables to model orographic rainfall Peer-Reviewed Journal Publication Δημοσίευση σε Περιοδικό με Κριτές 2015-10-092004enThis paper aims to document the development of a new GIS-based spatial interpolation module that adopts a multiple linear regression technique. The functionality of the GIS module is illustrated through a test case represented by the island of Crete, Greece, where the models generated were applied to locations where estimates of annual precipitation were required. The response variable is ‘precipitation’ and the predictor variables are ‘elevation’, ‘longitude’ and ‘latitude’, or any combination of these. The module is capable of performing a sequence of tasks which will eventually lead to an estimation of mean areal precipitation and the total volume of precipitation. In addition, it can generate up to nine predictor variables and their parameters, and can estimate areal rainfall for a user-specified three-dimensional extent. The developed module performed satisfactorily. Precipitation estimates at ungauged locations were obtained using the multiple linear regression method in addition to some conventional spatial interpolation techniques (i.e. IDW, Spline, Kriging, etc.). The multiple linear regression models provided better estimates than the other spatial interpolation techniques. http://creativecommons.org/licenses/by/4.0/Journal of Hydroinformatics639-56 Tsanis Giannis Τσανης Γιαννης S. Naoum --Cases, clinical reports, statistics --Statistical data statistics cases clinical reports statistics statistical data