TY - GEN
T1 - Statistical analysis of extreme rainfall events over Indiana, USA
AU - Kao, Shih Chieh
AU - Govindaraju, Rao S.
PY - 2007
Y1 - 2007
N2 - Analysis of extreme rainfall events is important for hydraulic and hydrologic studies, and has conventionally been performed by pre-specifying rainfall duration as a filter to abstract the information of annual maximum rainfall depths for further examination. However, this single-variate approach does not account for dependence between rainfall properties. To characterize extreme rainfall events, a multi-variate analysis is conducted in this study using hourly precipitation data from Indiana, USA. Samples of extreme rainfall events are chosen based on two different criteria: annual maximum volume, and annual maximum peak intensity. Rainfall properties, such as total depth, duration, and peak intensity are analyzed using copulas to describe the dependence structures between rainfall variables and to construct their joint distribution for extreme rainfall events. Results from the derived multivariate model are compared to those from conventional single-variate analysis by computing the corresponding conditional distributions. The proposed stochastic model for extreme rainfall is expected to provide better estimates of design rainfall.
AB - Analysis of extreme rainfall events is important for hydraulic and hydrologic studies, and has conventionally been performed by pre-specifying rainfall duration as a filter to abstract the information of annual maximum rainfall depths for further examination. However, this single-variate approach does not account for dependence between rainfall properties. To characterize extreme rainfall events, a multi-variate analysis is conducted in this study using hourly precipitation data from Indiana, USA. Samples of extreme rainfall events are chosen based on two different criteria: annual maximum volume, and annual maximum peak intensity. Rainfall properties, such as total depth, duration, and peak intensity are analyzed using copulas to describe the dependence structures between rainfall variables and to construct their joint distribution for extreme rainfall events. Results from the derived multivariate model are compared to those from conventional single-variate analysis by computing the corresponding conditional distributions. The proposed stochastic model for extreme rainfall is expected to provide better estimates of design rainfall.
KW - Copulas
KW - Extreme rainfall
KW - Joint-distribution
KW - Multivariate analysis
UR - http://www.scopus.com/inward/record.url?scp=85087224041&partnerID=8YFLogxK
U2 - 10.1061/40927(243)412
DO - 10.1061/40927(243)412
M3 - Conference contribution
AN - SCOPUS:85087224041
SN - 9780784409275
T3 - Restoring Our Natural Habitat - Proceedings of the 2007 World Environmental and Water Resources Congress
BT - Restoring Our Natural Habitat - Proceedings of the 2007 World Environmental and Water Resources Congress
PB - American Society of Civil Engineers (ASCE)
ER -