APPLICATION OF GEOSTATISTICS TO EVALUATE PARTIAL WEATHER STATION NETWORKS

Climatic data are an essential input for the determination of crop wat er requirements. The density and location of weather stations are the important design variables for obtaining the required degree of accura cy of weather data. The planning of weather station networks should in clude economic considerations, and a mixture of full and partial weath er stations could be a cost-effective alternative. A 'full' weather st ation is defined here as one in which all the weather Variables used i n the modified Penman equation are measured, and a 'partial' weather s tation is one in which some, but not all, weather Variables are measur ed. The accuracy of reference evapotranspiration (Et-r) estimates for sites located some distance from surrounding stations is dependent on measurement error, error of the estimation equation, and interpolation error. The interpolation error is affected by the spatial correlation structure of weather variables and method of interpolation. A case-st udy data set of 2 years of daily climatic data (1989-1990) from 17 sta tions in the states of Nebraska, Kansas, and Colorado was used to comp are alternative network designs and interpolation methods. Root mean s quared interpolation error (RMSIE) values were the criteria for evalua ting Et-r estimates and network performance. The kriging method gave t he lowest RMSIE, followed by the inverse distance square method and th e inverse distance method. Co-kriging improved the estimates still fur ther. For a given level of performance, a mixture of full and partial weather stations would be more economical than full stations only. (C) 1997 Elsevier Science B.V.