Statistical identification of orographic effects in the regional analysis of extreme rainfall

Furcolo, Pierluigi; Pelosi, Anna; Rossi, Federico

doi:10.1002/hyp.10719

Cited by 30 publications

(35 citation statements)

References 26 publications

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“…The study in [14] provides orographic entities, hierarchically organized in nested orographic classes. Based on the results of a recent study [7], we have considered groups (4th order) and systems (3rd order) as meaningful objects for the regionalization study here presented (Figure 3). Table 1 provides the extent of the selected orographic objects within the study area compared to its total extension.…”

Section: Description Of the Study Areamentioning

confidence: 99%

“…Figure 3. Location of the study area with the rain gauges (triangles), the "anomalous" rain gauges (squares) (Section 2.3.2 and [7]) and the orographic barriers (solid grey) [14].…”

Section: Rainfall Datamentioning

confidence: 99%

“…In a recent work, we identified areas with enhanced spatial variability of the mean of the annual daily rainfall maxima through an innovative statistical procedure [7]. The procedure demonstrates how linear geostatistics is not able to capture the spatial variability of the regionalized variable in areas with a strong orographic forcing.…”

Section: Identification Of Spatial Outliers In Extreme Rainfallmentioning

confidence: 99%

“…Such statistical parameters of the annual maxima are affected by a multitude of events, with different characteristics. Physically based, meteorological models, although continuously improving, are storm-based models, therefore they are not suitable for the scope: the influence of orography needs to be embedded into the regionalization model of the parameters of the probability distribution of the rainfall extremes [7].…”

Section: Introductionmentioning

confidence: 99%

“…This approach to regional modeling of extreme rainfall can be improved by introducing an objective description and identification of orographic features [7,14]. This allows dividing the region into "topographically" homogeneous areas, for which different spatial rainfall variability is considered.…”

Section: Introductionmentioning

confidence: 99%

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An Amplification Model for the Regional Estimation of Extreme Rainfall within Orographic Areas in Campania Region (Italy)

Pelosi

Furcolo

2015

Water

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Orography strongly interacts with the atmospheric circulation, especially during frontal events, generating an enhanced spatial variability of the rainfall field. Regional models of extreme rainfall have to deal with these circumstances in order to provide good spatial estimation of the regionalized variable. We present a model for the regional estimation of the mean of the probability distribution of the annual daily rainfall maxima in a region (Campania, Southern Italy) with complex orography. In a recent work, we found that areas with enhanced variability of extreme rainfall, in the same region, correspond to a particular set of orographic objects, which had been classified through an automatic, GIS-based geomorphological procedure. Here, we propose an approach that considers the same orographic objects as building blocks for a regional model that is able to capture the amplification of extreme rainfall caused by orography. The regional model is then the product of a basic stationary random spatial process and an amplification factor, whose values are related to the topographic features of the orographic objects. This approach represents a step towards the improvement of the predictive ability of regional models of extreme rainfall within orographically complex areas.

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Section: Description Of the Study Areamentioning

confidence: 99%

“…Figure 3. Location of the study area with the rain gauges (triangles), the "anomalous" rain gauges (squares) (Section 2.3.2 and [7]) and the orographic barriers (solid grey) [14].…”

Section: Rainfall Datamentioning

confidence: 99%

Section: Identification Of Spatial Outliers In Extreme Rainfallmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An Amplification Model for the Regional Estimation of Extreme Rainfall within Orographic Areas in Campania Region (Italy)

Pelosi

Furcolo

2015

Water

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A parametric approach for simultaneous bias correction and high‐resolution downscaling of climate model rainfall

Mamalakis

Langousis

Deidda

et al. 2017

Water Resources Research

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Distribution mapping has been identified as the most efficient approach to bias‐correct climate model rainfall, while reproducing its statistics at spatial and temporal resolutions suitable to run hydrologic models. Yet its implementation based on empirical distributions derived from control samples (referred to as nonparametric distribution mapping) makes the method's performance sensitive to sample length variations, the presence of outliers, the spatial resolution of climate model results, and may lead to biases, especially in extreme rainfall estimation. To address these shortcomings, we propose a methodology for simultaneous bias correction and high‐resolution downscaling of climate model rainfall products that uses: (a) a two‐component theoretical distribution model (i.e., a generalized Pareto (GP) model for rainfall intensities above a specified threshold u*, and an exponential model for lower rainrates), and (b) proper interpolation of the corresponding distribution parameters on a user‐defined high‐resolution grid, using kriging for uncertain data. We assess the performance of the suggested parametric approach relative to the nonparametric one, using daily raingauge measurements from a dense network in the island of Sardinia (Italy), and rainfall data from four GCM/RCM model chains of the ENSEMBLES project. The obtained results shed light on the competitive advantages of the parametric approach, which is proved more accurate and considerably less sensitive to the characteristics of the calibration period, independent of the GCM/RCM combination used. This is especially the case for extreme rainfall estimation, where the GP assumption allows for more accurate and robust estimates, also beyond the range of the available data.

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The characterization of Extraordinary Extreme Events (EEEs) for the assessment of design rainfall depths with high return periods

Pelosi

Furcolo

Rossi

et al. 2020

Hydrological Processes

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The occurrence of rainfall Extraordinary Extreme Events (EEEs) in Mediterranean areas causes serious concerns to the engineers involved in the design of flood and landslide risk mitigation plans as well as of strategic hydraulic engineering structures, such as dams. These extraordinary maxima are characterized by very low frequencies and spatial extent scales that are smaller than those of ordinary maxima, and are usually identified as outliers by classical regional frequency analysis. Extreme Value mixture models, such as the Two-Component Extreme Value distribution, have been introduced in regional frequency analysis in order to overcome this problem. Nevertheless, the rainfall maxima series available for carrying out these regional analyses present coarse spatial spacing and small temporal extent, when compared with EEEs spatial structures and frequencies. Thus, regional statistical analyses with mixture models tend to overestimate EEEs return periods as well. This study presents a new operational statistical approach to properly retrieve the EEEs frequency from the available database and thus to avoid dramatic underestimations of the rainfall depth at very high return periods. The proposed approach implies the analysis of the EEEs at a given support scale in order to assess (a) the percentage of EEEs among the annual maxima and (b) the conditional distribution of the annual maxima given the occurrence of an EEE. The descriptive properties of the proposed procedure have been tested in Italy, by comparing the performances of the proposed procedure in predicting rainfall depths at very high return periods (i.e., larger than 100 years) with those provided by the TCEV-based regional analysis currently adopted as national reference approach. K E Y W O R D Sdaily annual maxima, design rainfall depths, extraordinary extremes, rainfall extremes, rainfalltriggered hazards, regional frequency analysis

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Statistical identification of orographic effects in the regional analysis of extreme rainfall

Cited by 30 publications

References 26 publications

An Amplification Model for the Regional Estimation of Extreme Rainfall within Orographic Areas in Campania Region (Italy)

An Amplification Model for the Regional Estimation of Extreme Rainfall within Orographic Areas in Campania Region (Italy)

A parametric approach for simultaneous bias correction and high‐resolution downscaling of climate model rainfall

The characterization of Extraordinary Extreme Events (EEEs) for the assessment of design rainfall depths with high return periods

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