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Incorporating spatial dependence in regional frequency analysis.

Zhuo Wang1, Jun Yan2, Xuebin Zhang3

  • 1Department of Statistics, University of Connecticut, Storrs Connecticut, USA.

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Summary
This summary is machine-generated.

This study introduces a spatial index flood model to improve regional frequency analysis (RFA) by accounting for intersite dependence. The new method offers more efficient parameter and return level estimation, especially when spatial dependence is accurately modeled.

Keywords:
extreme analysismax-stable process

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Area of Science:

  • Hydrology
  • Extreme Value Theory
  • Statistical Modeling

Background:

  • Regional frequency analysis (RFA) efficiency is often reduced by ignoring intersite dependence during parameter estimation.
  • Traditional methods do not adequately capture the spatial correlations inherent in hydrological data.

Purpose of the Study:

  • To propose and evaluate a spatial index flood model that incorporates intersite dependence using extreme-value copulas and max-stable processes.
  • To compare the efficiency of the proposed pairwise likelihood estimation method against traditional L-moment and independence likelihood methods.

Main Methods:

  • Development of a spatial index flood model using marginal generalized extreme value distributions and an extreme-value copula based on a max-stable process.
  • Parameter estimation via a pairwise likelihood approach utilizing bivariate marginal generalized extreme value distributions.
  • Simulation studies to assess estimator efficiency under various spatial dependence structures and levels.

Main Results:

  • The pairwise likelihood method demonstrated superior efficiency (in terms of mean squared error) when the max-stable dependence model was correctly specified.
  • The pairwise likelihood method remained competitive even with misspecified dependence within max-stable models.
  • Significant bias in shape parameter and return level estimation was observed with non-max-stable dependence models, particularly under strong dependence.
  • Application to Swiss precipitation data showed a notable reduction in standard errors for return level estimates compared to the L-moment method.

Conclusions:

  • The proposed spatial index flood model effectively addresses intersite dependence in RFA.
  • The pairwise likelihood estimation method offers significant efficiency gains for flood frequency analysis when spatial dependence is properly modeled.
  • The method provides more reliable return level estimates, crucial for hydrological risk assessment.