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

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

doi:10.1007/s00477-007-0180-8

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

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

School of Civil, Environmental and Architectural Engineering

Research output: Contribution to journal › Article › peer-review

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 language	English
Pages (from-to)	705-717
Number of pages	13
Journal	Stochastic Environmental Research and Risk Assessment
Volume	22
Issue number	6
DOIs	https://doi.org/10.1007/s00477-007-0180-8
Publication status	Published - 2008

Keywords

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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10.1007/s00477-007-0180-8

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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",

note = "Funding Information: This study was supported by the grants from the Everglades Research Fellowship (ERF) Program at University of Florida and the 2003 Core Construction Technology Development (CCTD) Project (03-SANHAKYOUN-C01-01). The ERF Program was funded by the Everglades National Park USA and the CCTD Project was funded through The Urban Flood Disaster Management Research Center in KICTTEP of MOCT Korea. However, the views expressed in this article do not necessarily represent the views of both agencies. The authors would like to thank Dr. Upmanu Lall at Columbia University and anonymous reviewers for their thoughtful reviewing of the manuscript and constructive comments.",

year = "2008",

doi = "10.1007/s00477-007-0180-8",

language = "English",

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AU - Kim, Tae woong

AU - Ahn, Hosung

AU - Chung, Gunhui

AU - Yoo, Chulsang

N1 - Funding Information: This study was supported by the grants from the Everglades Research Fellowship (ERF) Program at University of Florida and the 2003 Core Construction Technology Development (CCTD) Project (03-SANHAKYOUN-C01-01). The ERF Program was funded by the Everglades National Park USA and the CCTD Project was funded through The Urban Flood Disaster Management Research Center in KICTTEP of MOCT Korea. However, the views expressed in this article do not necessarily represent the views of both agencies. The authors would like to thank Dr. Upmanu Lall at Columbia University and anonymous reviewers for their thoughtful reviewing of the manuscript and constructive comments.

PY - 2008

Y1 - 2008

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.

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KW - Occurrence

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

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Keywords

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