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Titolo:
APPLICATION OF GEOSTATISTICS TO EVALUATE PARTIAL WEATHER STATION NETWORKS
Autore:
ASHRAF M; LOFTIS JC; HUBBARD KG;
Indirizzi:
COLORADO STATE UNIV,DEPT CHEM & BIORESOURCE ENGN FT COLLINS CO 80523 COLORADO STATE UNIV,DEPT CHEM & BIORESOURCE ENGN FT COLLINS CO 80523 UNIV NEBRASKA,DEPT AGR METEOROL LINCOLN NE 68583
Titolo Testata:
Agricultural and forest meteorology
fascicolo: 3-4, volume: 84, anno: 1997,
pagine: 255 - 271
SICI:
0168-1923(1997)84:3-4<255:AOGTEP>2.0.ZU;2-W
Fonte:
ISI
Lingua:
ENG
Keywords:
GEOSTATISTICS; WEATHER STATION NETWORKS; CROP WATER NETWORK; CLIMATIC DATA;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Science Citation Index Expanded
Science Citation Index Expanded
Science Citation Index Expanded
Citazioni:
14
Recensione:
Indirizzi per estratti:
Citazione:
M. Ashraf et al., "APPLICATION OF GEOSTATISTICS TO EVALUATE PARTIAL WEATHER STATION NETWORKS", Agricultural and forest meteorology, 84(3-4), 1997, pp. 255-271

Abstract

Climatic data are an essential input for the determination of crop water requirements. The density and location of weather stations are theimportant design variables for obtaining the required degree of accuracy of weather data. The planning of weather station networks should include economic considerations, and a mixture of full and partial weather stations could be a cost-effective alternative. A 'full' weather station is defined here as one in which all the weather Variables used in the modified Penman equation are measured, and a 'partial' weather station is one in which some, but not all, weather Variables are measured. The accuracy of reference evapotranspiration (Et-r) estimates forsites located some distance from surrounding stations is dependent onmeasurement 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-study data set of 2 years of daily climatic data (1989-1990) from 17 stations in the states of Nebraska, Kansas, and Colorado was used to compare alternative network designs and interpolation methods. Root mean squared interpolation error (RMSIE) values were the criteria for evaluating Et-r estimates and network performance. The kriging method gave the lowest RMSIE, followed by the inverse distance square method and the inverse distance method. Co-kriging improved the estimates still further. For a given level of performance, a mixture of full and partialweather stations would be more economical than full stations only. (C) 1997 Elsevier Science B.V.

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Documento generato il 03/04/20 alle ore 14:25:03