Abstract
Understanding the spatial variability of precipitation extremes and their relationship to the El Niño Southern Oscillation (ENSO) phenomenon is a valuable task since precipitation extremes are often related to natural disasters such as flooding. This study aims to evaluate and provide insight into the relationship between ENSO and the parameters of the extreme value distributions over South America for precipitation data between 1978-2004. The relationship between these values, as described by the global and local Moran I statistics, has been analysed against the Southern Oscillation Index (SOI), which is generally used as a measure of the intensity of ENSO at a given time. Through analysis, we are the first to show increased spatial dependence of the parameters of the extreme value distributions between regions during strong El Niño periods.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - IEEE International Conference on Data Mining, ICDM |
| Pages | 685-690 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 2007 |
| Externally published | Yes |
| Event | 17th IEEE International Conference on Data Mining Workshops, ICDM Workshops 2007 - Omaha, NE Duration: 28 Oct 2007 → 31 Oct 2007 |
Other
| Other | 17th IEEE International Conference on Data Mining Workshops, ICDM Workshops 2007 |
|---|---|
| City | Omaha, NE |
| Period | 28/10/07 → 31/10/07 |
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ASJC Scopus subject areas
- Engineering(all)
Cite this
Spatio-temporal analysis of the relationship between South American precipitation extremes and the El Niño Southern Oscillation. / Wu, Elizabeth; Chawla, Sanjay.
Proceedings - IEEE International Conference on Data Mining, ICDM. 2007. p. 685-690 4476742.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Spatio-temporal analysis of the relationship between South American precipitation extremes and the El Niño Southern Oscillation
AU - Wu, Elizabeth
AU - Chawla, Sanjay
PY - 2007
Y1 - 2007
N2 - Understanding the spatial variability of precipitation extremes and their relationship to the El Niño Southern Oscillation (ENSO) phenomenon is a valuable task since precipitation extremes are often related to natural disasters such as flooding. This study aims to evaluate and provide insight into the relationship between ENSO and the parameters of the extreme value distributions over South America for precipitation data between 1978-2004. The relationship between these values, as described by the global and local Moran I statistics, has been analysed against the Southern Oscillation Index (SOI), which is generally used as a measure of the intensity of ENSO at a given time. Through analysis, we are the first to show increased spatial dependence of the parameters of the extreme value distributions between regions during strong El Niño periods.
AB - Understanding the spatial variability of precipitation extremes and their relationship to the El Niño Southern Oscillation (ENSO) phenomenon is a valuable task since precipitation extremes are often related to natural disasters such as flooding. This study aims to evaluate and provide insight into the relationship between ENSO and the parameters of the extreme value distributions over South America for precipitation data between 1978-2004. The relationship between these values, as described by the global and local Moran I statistics, has been analysed against the Southern Oscillation Index (SOI), which is generally used as a measure of the intensity of ENSO at a given time. Through analysis, we are the first to show increased spatial dependence of the parameters of the extreme value distributions between regions during strong El Niño periods.
UR - http://www.scopus.com/inward/record.url?scp=49549092667&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=49549092667&partnerID=8YFLogxK
U2 - 10.1109/ICDMW.2007.102
DO - 10.1109/ICDMW.2007.102
M3 - Conference contribution
SN - 0769530192
SN - 9780769530192
SP - 685
EP - 690
BT - Proceedings - IEEE International Conference on Data Mining, ICDM
ER -