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Open AccessArticle

Improving Mean Annual Precipitation Prediction Incorporating Elevation and Taking into Account Support Size

1
National Research Council of Italy—Institute for Agricultural and Forest Systems in the Mediterranean (ISAFOM), 87036 Rende (CS), Italy
2
National Research Council of Italy—Research Institute for Geo-Hydrological Protection (IRPI), 87036 Rende (CS), Italy
*
Author to whom correspondence should be addressed.
Academic Editor: Yves Tramblay
Water 2021, 13(6), 830; https://doi.org/10.3390/w13060830
Received: 22 February 2021 / Revised: 14 March 2021 / Accepted: 16 March 2021 / Published: 18 March 2021
(This article belongs to the Special Issue Modelling Precipitation in Space and Time)
Accounting for secondary exhaustive variables (such as elevation) in modelling the spatial distribution of precipitation can improve their estimate accuracy. However, elevation and precipitation data are associated with different support sizes and it is necessary to define methods to combine such different spatial data. The paper was aimed to compare block ordinary cokriging and block kriging with an external drift in estimating the annual precipitation using elevation as covariate. Block ordinary kriging was used as reference of a univariate geostatistical approach. In addition, the different support sizes associated with precipitation and elevation data were also taken into account. The study area was the Calabria region (southern Italy), which has a spatially variable Mediterranean climate because of its high orographic variability. Block kriging with elevation as external drift, compared to block ordinary kriging and block ordinary cokriging, was the most accurate approach for modelling the spatial distribution of annual mean precipitation. The three measures of accuracy (MAE, mean absolute error; RMSEP, root-mean-squared error of prediction; MRE, mean relative error) have the lowest values (MAE = 112.80 mm; RMSEP = 144.89 mm, and MRE = 0.11), whereas the goodness of prediction (G) has the highest value (75.67). The results clearly indicated that the use of an exhaustive secondary variable always improves the precipitation estimate, but in the case of areas with elevations below 120 m, block cokriging makes better use of secondary information in precipitation estimation than block kriging with external drift. At higher elevations, the opposite is always true: block kriging with external drift performs better than block cokriging. This approach takes into account the support size associated with precipitation and elevation data. Accounting for elevation allowed to obtain more detailed maps than using block ordinary kriging. However, block kriging with external drift produced a map with more local details than that of block ordinary cokriging because of the local re-evaluation of the linear regression of precipitation on block estimates. View Full-Text
Keywords: geostatistics; cokriging; kriging with external drift; change of support geostatistics; cokriging; kriging with external drift; change of support
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MDPI and ACS Style

Buttafuoco, G.; Conforti, M. Improving Mean Annual Precipitation Prediction Incorporating Elevation and Taking into Account Support Size. Water 2021, 13, 830. https://doi.org/10.3390/w13060830

AMA Style

Buttafuoco G, Conforti M. Improving Mean Annual Precipitation Prediction Incorporating Elevation and Taking into Account Support Size. Water. 2021; 13(6):830. https://doi.org/10.3390/w13060830

Chicago/Turabian Style

Buttafuoco, Gabriele; Conforti, Massimo. 2021. "Improving Mean Annual Precipitation Prediction Incorporating Elevation and Taking into Account Support Size" Water 13, no. 6: 830. https://doi.org/10.3390/w13060830

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