12th International Precipitation Conference (IPC12)


Precipitation remains one of the most challenging processes to model and predict at the local, regional and global scales with significant implications for our ability to quantify water cycle dynamics, inform decision making, and predict hydro-geomorphic hazards in response to extremes. A key to these efforts is adequate observations across space and time scales to constrain and improve models, inform data assimilation efforts, and detect and attribute changes in large-scale dynamics and regional extremes. This special collection of papers is based on advances presented at the 12th International Precipitation Conference (IPC12) which brought together the international community to integrate research, discuss challenges and opportunities, and craft future directions. Innovative contributions in this special collection include advances on three main themes: (1) estimation of precipitation from multiple sensors; (2) water cycle dynamics and predictive modeling at local to global scales; and (3) hydrologic impacts of precipitation extremes and anticipated change. This collection also includes a meeting summary published in BAMS: 10.1175/BAMS-D-20-0014.1.

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 gratefully acknowledged.

Collection organizer:
Efi Foufoula-Georgiou, Department of Civil and Environmental Engineering, University of California, Irvine (UCI)

12th International Precipitation Conference (IPC12)

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Giuseppe Mascaro


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 (N τ ) and daily (N 24h) 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 N τ > 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 N τ ≤ 5; however, if N 24h ≥ 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.

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