TY - JOUR
PB - ASABE
CY - St. Joseph, MI
JO - Applied Engineering in Agriculture
JA - Appl. Eng. Agric.
T2 - Applied Engineering in Agriculture
JF - Applied Engineering in Agriculture
SN - 0883-8542
AU - Rolim, J.
AU - Catalão, J.
AU - Teixeira, J.
TI - The Influence of Different Methods of Interpolating Spatial Meteorological Data on Calculated Irrigation Requirements
DO - https://doi.org/10.13031/2013.40625
PY - 2011
VL - 27
SP - 979
EP - 989
IS - 6
KW - Spatial interpolation
KW - Agro-meteorological variables
KW - Irrigation
KW - Nearest neighbor
KW - Inverse distance weighting
KW - Least squares collocation
UR - http://elibrary.asabe.org/abstract.asp?aid=40625&t=3
AB - Irrigation simulation models have become increasingly accurate in the estimation of irrigation requirements. Accurate input data for these models are needed to take full advantage of the increased accuracy. Climatic input data are often supplied by networks of meteorological stations that provide spatially distributed data that need to be interpolated for a given site. The main objective of this study was to evaluate the influence of the different methods of interpolation of the climate data on the estimated irrigation water requirements. Software was developed to perform the spatial interpolation of the meteorological data series recorded by a network of weather stations to the center of each plot. The interpolation methods studied were the nearest neighbor, inverse distance weighting, and least squares collocation methods. Accuracy assessment and comparative analyses were performed for a network of weather stations in southern Portugal for evapotranspiration, wind speed, relative humidity, and precipitation. The impact of the interpolated meteorological data on the accuracy of the calculated crop water requirements was determined from a soil water balance model (IrrigRotation). The accuracy assessment for evapotranspiration suggested that the relative error ranged between 10% and 15%. For precipitation, the relative error ranged between 44% and 60%, showing higher spatial variability and greater difficulty in interpolating this variable. The least squares collocation and inverse distance weighting methods yielded only a slight improvement in the accuracy of the interpolated meteorological data when compared with the most commonly used method, Thiessen polygons (the nearest neighbor method). For the irrigation requirements, the value of the relative error was, on average, 16%, 17%, and 14% for to the nearest neighbor, inverse distance weighting, and least squares collocation, respectively, which correspond to deviations in the irrigation requirements of 79, 86, and 67 mm, respectively. Depending on the irrigation method, this average deviation can represent the saving of some irrigation events. The maximum relative error value for the irrigation requirements was 45% (165 mm), and the minimum relative error was 5% (27 mm). Thus, the spatial variability of meteorological variables has a considerable impact on the accuracy of the calculation of irrigation requirements, with implications for the amount of water used in irrigation. The comparison between the least squares collocation and the inverse distance weighting methods showed identical interpolation performances; however, because the inverse distance weighting is a much simpler method, it can be recommended from the practical point of view.
ER -