Stochastic multi-site generation of daily rainfall occurrence in south Florida

Tae woong Kim, Hosung Ahn, Gunhui Chung, Chulsang Yoo

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)


This paper presents a stochastic model to generate daily rainfall occurrences at multiple gauging stations in south Florida. The model developed in this study is a space-time model that takes into account the spatial as well as temporal dependences of daily rainfall occurrence based on a chain-dependent process. In the model, a Markovian method was used to represent the temporal dependence of daily rainfall occurrence and a direct acyclic graph (DAG) method was introduced to encode the spatial dependence of daily rainfall occurrences among gauging stations. The DAG method provides an optimal sequence of generation by maximizing the spatial dependence index of daily rainfall occurrences over the region. The proposed space-time model shows more promising performance in generating rainfall occurrences in time and space than the conventional Markov type model. The space-time model well represents the temporal as well as the spatial dependence of daily rainfall occurrences, which can reduce the complexity in the generation of daily rainfall amounts.

Original languageEnglish
Pages (from-to)705-717
Number of pages13
JournalStochastic Environmental Research and Risk Assessment
Issue number6
Publication statusPublished - 2008


  • Daily rainfall
  • Markov process
  • Occurrence
  • Space-time model

ASJC Scopus subject areas

  • Environmental Engineering
  • Environmental Chemistry
  • Safety, Risk, Reliability and Quality
  • Water Science and Technology
  • Environmental Science(all)


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