Abstract:Annual rainfall records from the island of Crete in Greece were used with the aid of a geographical information system (GIS) to study the temporal and spatial rainfall characteristics. The GIS was used to produce a digital elevation model, delineate watersheds and estimate the areal rainfall from a network of raingauges by using different interpolation schemes. The rainfall-elevation correlation was significant, suggesting an orographic type of precipitation for the island. The rainfall records for the majority of the stations were found to fit the normal distribution. Deviation from normal for the rest of the records was attributed to the wettest year of 1977 -1978 . The year 1989-1990 was the driest, and most rainfall records showed a decrease in rainfall over 30 years with higher negative rainfall gradients at the higher elevations. Frequency analysis of the rainfall records was used to estimate areal rainfall for the island of Crete and its main watersheds for return periods of 2, 5 and 10 years.
This 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.
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