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

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

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

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
Volume22
Issue number6
DOIs
Publication statusPublished - 2008 Aug 29

Fingerprint

Rainfall
Rain
rainfall
Space-time Models
Spatial Dependence
Gaging
Stochastic models
Graph in graph theory
Stochastic Model
Model
Dependent
method

Keywords

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

ASJC Scopus subject areas

  • Environmental Engineering
  • Environmental Science(all)
  • Environmental Chemistry
  • Water Science and Technology
  • Statistics and Probability
  • Civil and Structural Engineering

Cite this

Stochastic multi-site generation of daily rainfall occurrence in south Florida. / Kim, Tae woong; Ahn, Hosung; Chung, Gunhui; Yoo, Chulsang.

In: Stochastic Environmental Research and Risk Assessment, Vol. 22, No. 6, 29.08.2008, p. 705-717.

Research output: Contribution to journalArticle

@article{fb409e30326145b99393cfe0f6ee96e5,
title = "Stochastic multi-site generation of daily rainfall occurrence in south Florida",
abstract = "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.",
keywords = "Daily rainfall, Markov process, Occurrence, Space-time model",
author = "Kim, {Tae woong} and Hosung Ahn and Gunhui Chung and Chulsang Yoo",
year = "2008",
month = "8",
day = "29",
doi = "10.1007/s00477-007-0180-8",
language = "English",
volume = "22",
pages = "705--717",
journal = "Stochastic Environmental Research and Risk Assessment",
issn = "1436-3240",
publisher = "Springer New York",
number = "6",

}

TY - JOUR

T1 - Stochastic multi-site generation of daily rainfall occurrence in south Florida

AU - Kim, Tae woong

AU - Ahn, Hosung

AU - Chung, Gunhui

AU - Yoo, Chulsang

PY - 2008/8/29

Y1 - 2008/8/29

N2 - 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.

AB - 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.

KW - Daily rainfall

KW - Markov process

KW - Occurrence

KW - Space-time model

UR - http://www.scopus.com/inward/record.url?scp=50149102178&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=50149102178&partnerID=8YFLogxK

U2 - 10.1007/s00477-007-0180-8

DO - 10.1007/s00477-007-0180-8

M3 - Article

AN - SCOPUS:50149102178

VL - 22

SP - 705

EP - 717

JO - Stochastic Environmental Research and Risk Assessment

JF - Stochastic Environmental Research and Risk Assessment

SN - 1436-3240

IS - 6

ER -