Abstract
Spatial interpolation of rain gauge data is important for ecohydrology study or modelling of land degradation. The monthly rainfall data recorded at 68 stations in Jornada basin was analysed to study the spatial patterns of rainfall. The inverse distance weighting spatial interpolation method was applied to model the spatial variability of rainfall for a wet (1992) and dry (1994) years. The rainfall interpolation was tested for the basin wide region and by constraining the rainfall-interpolation within the study area boundary. The accuracy of the interpolation result was measured by adopting the leave-one-out cross validation method. The result indicates that the rainfall displayed a strong spatial variability trend from the southwest to the northeast. The result from the CV analysis of the total data points for both year showed that the IDW interpolation method produced from data points within the study area boundary produced better fits compared to the CV result from all the data points within the Jornada basin. The result from constraining the rainfall-interpolation within the study-area boundary showed that the interpolation error from the edge effects can be minimised and result of the predicted values showed a closer fit to the measured values. DOI: 10.5901/mjss.2015.v6n4s3p108
Highlights
Spatial distribution of rainfall is important to many ecohydrological studies
The analysis presented here illustrates the performance of spatial interpolation methods in the Jornada basin which exemplify the semi-arid environment
The result from the cross validation (CV) analysis of the total data points for both year showed that the Inverse Distance Weighted (IDW) interpolation method produced a good fit model for the Jornada basin
Summary
Spatial distribution of rainfall is important to many ecohydrological studies. In semiarid environments, rainfall is the main source of water which determines the availability of drinking water and the level of soil moisture (Justiniano and Fredericksen, 2000; Schwinning et al, 2004). It is important to choose a suitable method to interpolate rainfall data from existing meteorological station points to large areas. Much research has been done on the issue of spatial interpolation using different interpolation methods on various climatic variables. Blöschl and Grayson (2000) suggested that, in choosing a particular interpolation model, the user must be aware that the quality of the interpolated surface is dependent on the accuracy of the original point data and how well the selected method reflects the underlining spatial structure. One of the most commonly used spatial interpolation method for interpolating rainfall distribution is inverse distance weighting. IDW method was used to interpolate rainfall data by many researchers, for example Goodale et al (1998) in Ireland, Dirks et al (1998) in Norfolk Island and Price et al (2000) in Canada. The study site’s simple topography and low relief negates the application of complex interpolation methods such as Kriging
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