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Infilling missing precipitation records – A comparison of a new copula-based method with other techniques

Bárdossy, András ; Pegram, Geoffrey

Journal of hydrology (Amsterdam), 2014-11, Vol.519, p.1162-1170 [Periódico revisado por pares]

Kidlington: Elsevier B.V

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  • Título:
    Infilling missing precipitation records – A comparison of a new copula-based method with other techniques
  • Autor: Bárdossy, András ; Pegram, Geoffrey
  • Assuntos: Comparison ; Copula ; Earth sciences ; Earth, ocean, space ; Exact sciences and technology ; Hydrology. Hydrogeology ; Kriging ; Missing values
  • É parte de: Journal of hydrology (Amsterdam), 2014-11, Vol.519, p.1162-1170
  • Descrição: •We developed a new copula based method for infilling missing data.•We compared it against a comprehensive range of methods of interpolation from Nearest Neighbors to EM.•We found copula-based methods are superior to the others for estimating expected missing values.•The EM multiple regression algorithm is suitable for infilling monthly or annual data but not highly skewed daily data.•The copula-based method has the added advantage of providing distributions of infilled values. Infilling missing data might be an unpleasant and tedious task, but is necessary for analysis and water resources management, so it should not be done in a lackadaisical manner. The important thing about the infilled values is that they need to be as good as possible, because poor infilling is likely to lead to poor decisions. Traditionally, a range of methods is routinely employed, e.g. Nearest Neighbor substitution through to Kriging, but few methods attach a quality estimate to the infilled values. In this paper a new copula based method is developed for infilling missing daily and monthly rain gauge data and is compared against six other commonly used methods, in a semi-arid environment with a range of rain-rates and interstation distances, in the Southern Cape region of South Africa. For daily data it is clear that the copula-based methods are superior to the others in terms of point estimation and have the added benefit of providing an estimate of the precision of the interpolation, not provided by the others. In our case, the addition of atmospheric circulation patterns designed to add information for infilling has a relatively small positive effect on the quality of the estimation. The main reason for this is that a small number of wet days does not allow a good estimation of the conditional distribution of precipitation amounts; we note that the average probability of a dry day in this region is 86%. An improvement of the estimate of the probability of a dry day was however observed. In other regions, with a higher number of wet days, an atmospheric Circulation Pattern (CP) based method is likely to lead to further improvements. Using copula-based methods, the estimated probabilities of a dry day correspond well to the observed frequencies of dry days. The monthly data yield the same conclusion, with the qualification that the Expectation Maximisation [EM] algorithm performs as well as the copula method (because of the low count of dry months in this region) but its relative disadvantage is that it does not offer as valuable a precision estimate.
  • Editor: Kidlington: Elsevier B.V
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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