TY - JOUR
T1 - Comparison of local, regional, and scaling models for rainfall intensity–duration–frequency analysis
AU - Mascaro, Giuseppe
N1 - Funding Information:
Acknowledgments. The author thanks Dr. Juliette Blanchet, two anonymous reviewers, and the peer-review editor for their constructive comments that helped to improve the quality of the paper. This work has been supported by the National Science Foundation (NSF) Award ‘‘SCC: Community-Based Automated Information for Urban Flooding’’ (Award 1831475). The support by NSF (Grant EAR-1928724) and NASA (Grant 80NSSC19K0726) to organize the 12th International Precipitation Conference (IPC12), Irvine, California, June 2019, and produce the IPC12 special collection of papers is also gratefully acknowledged. The author thanks Stephen D. Waters from the Flood Control District of Maricopa County for providing the rainfall data of the network. FCDMC data are also available online (https:// www.fcd.maricopa.gov/625/Rainfall-Data).
Funding Information:
The author thanks Dr. Juliette Blanchet, two anonymous reviewers, and the peer-review editor for their constructive comments that helped to improve the quality of the paper. This work has been supported by the National Science Foundation (NSF) Award ??SCC: Community-Based Automated Information for Urban Flooding?? (Award 1831475). The support by NSF (Grant EAR-1928724) and NASA (Grant 80NSSC19K0726) to organize the 12th International Precipitation Conference (IPC12), Irvine, California, June 2019, and produce the IPC12 special collection of papers is also gratefully acknowledged. The author thanks Stephen D. Waters from the Flood Control District of Maricopa County for providing the rainfall data of the network. FCDMC data are also available online (https:// www.fcd.maricopa.gov/625/Rainfall-Data).
Publisher Copyright:
© 2020 American Meteorological Society.
PY - 2020/9
Y1 - 2020/9
N2 - Intensity–duration–frequency (IDF) analyses of rainfall extremes provide critical information to mitigate, manage, and adapt to urban flooding. The accuracy and uncertainty of IDF analyses depend on the availability of historical rainfall records, which are more accessible at daily resolution and, quite often, are very sparse in developing countries. In this work, we quantify performances of different IDF models as a function of the number of available high-resolution (Nt) and daily (N24h) rain gauges. For this aim, we apply a cross-validation framework that is based on Monte Carlo bootstrapping experiments on records of 223 high-resolution gauges in central Arizona. We test five IDF models based on (two) local, (one) regional, and (two) scaling frequency analyses of annual rainfall maxima from 30-min to 24-h durations with the generalized extreme value (GEV) distribution. All models exhibit similar performances in simulating observed quantiles associated with return periods up to 30 years. When Nt > 10, local and regional models have the best accuracy; bias correcting the GEV shape parameter for record length is recommended to estimate quantiles for large return periods. The uncertainty of all models, evaluated via Monte Carlo experiments, is very large when Nt ≤ 5; however, if N24h ≥ 10 additional daily gauges are available, the uncertainty is greatly reduced and accuracy is increased by applying simple scaling models, which infer estimates on subdaily rainfall statistics from information at daily scale. For all models, performances depend on the ability to capture the elevation control on their parameters. Although our work is site specific, its results provide insights to conduct future IDF analyses, especially in regions with sparse data.
AB - Intensity–duration–frequency (IDF) analyses of rainfall extremes provide critical information to mitigate, manage, and adapt to urban flooding. The accuracy and uncertainty of IDF analyses depend on the availability of historical rainfall records, which are more accessible at daily resolution and, quite often, are very sparse in developing countries. In this work, we quantify performances of different IDF models as a function of the number of available high-resolution (Nt) and daily (N24h) rain gauges. For this aim, we apply a cross-validation framework that is based on Monte Carlo bootstrapping experiments on records of 223 high-resolution gauges in central Arizona. We test five IDF models based on (two) local, (one) regional, and (two) scaling frequency analyses of annual rainfall maxima from 30-min to 24-h durations with the generalized extreme value (GEV) distribution. All models exhibit similar performances in simulating observed quantiles associated with return periods up to 30 years. When Nt > 10, local and regional models have the best accuracy; bias correcting the GEV shape parameter for record length is recommended to estimate quantiles for large return periods. The uncertainty of all models, evaluated via Monte Carlo experiments, is very large when Nt ≤ 5; however, if N24h ≥ 10 additional daily gauges are available, the uncertainty is greatly reduced and accuracy is increased by applying simple scaling models, which infer estimates on subdaily rainfall statistics from information at daily scale. For all models, performances depend on the ability to capture the elevation control on their parameters. Although our work is site specific, its results provide insights to conduct future IDF analyses, especially in regions with sparse data.
KW - Extreme events
KW - Hydrology
KW - Precipitation
KW - Statistics
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UR - http://www.scopus.com/inward/citedby.url?scp=85091959771&partnerID=8YFLogxK
U2 - 10.1175/JAMC-D-20-0094.1
DO - 10.1175/JAMC-D-20-0094.1
M3 - Article
AN - SCOPUS:85091959771
SN - 1558-8424
VL - 59
SP - 1519
EP - 1536
JO - Journal of Applied Meteorology and Climatology
JF - Journal of Applied Meteorology and Climatology
IS - 9
ER -