A multiple threshold method for fitting the generalized Pareto distribution to rainfall time series

By R. Deidda. 2010

70 citations recorded by the source corpus.

Abstract. Previous studies indicate the generalized Pareto distribution (GPD) as a suitable distribution function to reliably describe the exceedances of daily rainfall records above a proper optimum threshold, which should be selected as small as possible to retain the largest sample while assuring an acceptable fitting. Such an optimum threshold may differ from site to site, affecting consequently not only the GPD scale parameter, but also the probability of threshold exceedance. Thus a first objective of this paper is to derive some expressions to parameterize a simple threshold-invariant three-parameter distribution function which assures a perfect overlapping with the GPD fitted on the exceedances over any threshold larger than the optimum one. Since the proposed distribution does not depend on the local thresholds adopted for fitting the GPD, it is expected to reflect the on-site climatic signature and thus appears particularly suitable for hydrological applications and regional analyses. A second objective is to develop and test the Multiple Threshold Method (MTM) to infer the parameters of interest by using exceedances over a wide range of thresholds applying again the concept of parameters threshold-invariance. We show the ability of the MTM in fitting historical daily rainfall time series recorded with different resolutions and with a significative percentage of heavily quantized data. Finally, we prove the supremacy of the MTM fit against the standard single threshold fit, often adopted for partial duration series, by evaluating and comparing the performances on Monte Carlo samples drawn by GPDs with different shape and scale parameters and different discretizations.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/ed7389f3558db516735a8fd5817bdf68b237807b

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In Atmospheric Science

  1. Andreas Langousis
  2. Michelangelo Puliga
  3. Antonios Mamalakis
  4. R. Deidda
  5. Daniele Veneziano
  6. Vito Iacobellis
  7. P. Furcolo
  8. Marco Marani

In Global and Planetary Change

  1. R. Deidda
  2. Andreas Langousis
  3. Antonios Mamalakis
  4. Michelangelo Puliga
  5. Daniele Veneziano
  6. Jun Yan
  7. Xuebin Zhang
  8. Brian Bader

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