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Infrastructure are at the center of three trends: accelerating human activities, increasing uncertainty in social, technological, and climatological factors, and increasing complexity of the systems themselves and environments in which they operate. Resilience theory can help infrastructure managers navigate increasing complexity. Engineering framings of resilience will need to evolve beyond robustness to consider adaptation and transformation, and the ability to handle surprise. Agility and flexibility in both physical assets and governance will need to be emphasized, and sensemaking capabilities will need to be reoriented. Transforming infrastructure is necessary to ensuring that core systems keep pace with a changing world.

 

Pluvial flooding in urban regions is a natural hazard that has been rarely investigated. Here, we evaluate the utility of three radar (Stage IV, MRMS, and GCMRMS) quantitative precipitation estimates (QPEs) and the SWMM hydrologic-hydraulic model to simulate pluvial flooding during the North American Monsoon in Phoenix. We focus on an urban catchment of 2.38 km2 and, for four storms, we simulate a set of flooding metrics using the original QPEs and an ensemble of 100 QPEs characterizing radar uncertainty through a statistical error model. We find that Stage IV QPEs are the most accurate, while MRMS QPEs are positively biased and their utility to simulate flooding increases with the gage correction done for GCMRMS. For all radar products, simulated flood metrics have lower uncertainty than QPEs as a result of rainfall-runoff transformation. By relying on extensive precipitation and basin datasets, this work provides useful insights for urban flood predictions. 

Abstract 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 (Nτ) 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 Nτ > 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 Nτ ≤ 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.