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- Author or Editor: Giuseppe Mascaro x
- Journal of Applied Meteorology and Climatology x
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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 (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.
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 (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.
Abstract
In this study a temporal statistical downscaling scheme of rainfall is calibrated using observations from 2007 to 2010 at eight sites located along a 14-km topographic transect of 784 m in elevation in northwest Mexico. For this purpose, the rainfall statistical properties over a wide range of temporal scales (3 months–1 min) for the summer (July–September) and winter (November–March) seasons are first analyzed. Rainfall accumulation is found not to be significantly correlated with elevation in either season, and a strong diurnal cycle is found to be present only in summer, peaking in the late afternoon. Winter rainfall events are highly correlated between individual stations across the transect even at short aggregation times (<30 min), and summer storms are more localized in space and time. Spectral and scale invariance analyses showed the presence of three (two) scaling regimes in summer (winter), which are associated with typical meteorological phenomena of the corresponding time scales (frontal systems and relatively isolated convective systems). These analyses formed the basis for calibrating a temporal downscaling model to disaggregate daily precipitation to hourly resolution in the summer season, based on scale invariance and multifractal theory. In this downscaling scheme, a modulation function was used to reproduce the time heterogeneity introduced by the diurnal cycle. The model showed adequate performances in reproducing the small-scale observed precipitation variability. Results of this work are useful for the interpretation of storm-generation mechanisms in the region, and for creating hourly rainfall time series from daily rainfall data, obtained from observations or simulated by climate, meteorological, or other statistical models.
Abstract
In this study a temporal statistical downscaling scheme of rainfall is calibrated using observations from 2007 to 2010 at eight sites located along a 14-km topographic transect of 784 m in elevation in northwest Mexico. For this purpose, the rainfall statistical properties over a wide range of temporal scales (3 months–1 min) for the summer (July–September) and winter (November–March) seasons are first analyzed. Rainfall accumulation is found not to be significantly correlated with elevation in either season, and a strong diurnal cycle is found to be present only in summer, peaking in the late afternoon. Winter rainfall events are highly correlated between individual stations across the transect even at short aggregation times (<30 min), and summer storms are more localized in space and time. Spectral and scale invariance analyses showed the presence of three (two) scaling regimes in summer (winter), which are associated with typical meteorological phenomena of the corresponding time scales (frontal systems and relatively isolated convective systems). These analyses formed the basis for calibrating a temporal downscaling model to disaggregate daily precipitation to hourly resolution in the summer season, based on scale invariance and multifractal theory. In this downscaling scheme, a modulation function was used to reproduce the time heterogeneity introduced by the diurnal cycle. The model showed adequate performances in reproducing the small-scale observed precipitation variability. Results of this work are useful for the interpretation of storm-generation mechanisms in the region, and for creating hourly rainfall time series from daily rainfall data, obtained from observations or simulated by climate, meteorological, or other statistical models.
