Comparative performance of empirical methods to estimate the reference evapotranspiration in Aquidauana, MS, Brazil

Moraes, Taynara da Silva; Costa, Carla Deisiane de Oliveira; Lopes, Adriano da Silva; Júnior, Eder Duarte Fanaya; Pereira, Vitor Gabriel Marinho de Faria; Cherri, Uilhiam

doi:10.15361/1984-5529.2018v46n2p143-150

Cited by 4 publications

(1 citation statement)

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“…The modified Willmott's index of agreement shows that the co-ordinary Kriging approach is more appropriate in all predictions evaluated, unless in the case of the maximum, as md < 0.6, as pointed out by Da Silva Moraes et al [49], for all approaches.…”

Section: Resultsmentioning

confidence: 66%

The assessment of annual rainfall field by applying different interpolation methods in the state of Rio Grande do Sul, Brazil

et al. 2021

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An accurate analysis of spatial rainfall distribution is of great importance for managing watershed water resources, in addition to giving support to meteorological studies and agricultural planning. This work compares the performance of two interpolation methods: Inverse distance weighted (IDW) and Kriging, in the analysis of annual rainfall spatial distribution. We use annual rainfall data for the state of Rio Grande do Sul (Brazil) from 1961 to 2017. To determine which proportion of the sample results in more accurate rainfall distribution maps, we use a certain amount of points close to the estimated point. We use mean squared error (MSE), coefficient of determination (R2), root mean squared error (RMSE) and modified Willmott's concordance index (md). We conduct random fields simulations study, and the performance of the geostatistics and classic methods for the exposed case was evaluated in terms of precision and accuracy obtained by Monte Carlo simulation to support the results. The results indicate that the co-ordinary Kriging interpolator showed better goodness of fit, assuming altitude as a covariate. We concluded that the geostatistical method of Kriging using nine closer points (50% of nearest neighbors) was the one that better represented annual rainfall spatial distribution in the state of Rio Grande do Sul.

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Section: Resultsmentioning

confidence: 66%