Regional frequency analysis of extreme rainfall in Belgium based on radar estimates

Goudenhoofdt, Edouard; Delobbe, Laurent; Willems, Patrick

doi:10.5194/hess-21-5385-2017

Cited by 44 publications

(33 citation statements)

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“…Future developments may be found in the employment of different types of data, such as satellite‐ and radar‐based estimates and reanalysis products (e.g., Gabriele & Chiaravalloti, 2013; Goudenhoofdt, Delobbe, & Willems, 2017; Hosseinzadehtalaei, Tabari, & Willems, 2018; Sun & Barros, 2010), which may lead to a better knowledge and observability of the rainfall extremes.…”

Section: Discussionmentioning

confidence: 99%

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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Section: Discussionmentioning

confidence: 99%

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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“…Thus, so far very few studies have looked at the systematic discrepancies between radar and gauges in times of heavy rain. Using a 12-year archive of 1×1 km and 5-min radar rainfall estimates for Belgium between 2005-2016, Goudenhoofdt et al (2017) found that hourly radar extremes around Brussels tend to be 30-70% lower than those observed in gauge data. In the Netherlands, Overeem et al (2009b) compiled a 10-year climatology of radar-based extreme rainfall estimates to derive intensity-duration-frequency curves (Overeem et al, 2009a) and areal extremes (Overeem et al, 2010) for time scales of 15 min to 24 h. The authors concluded that radar data may be suitable to estimate local and regional extreme rainfall statistics, provided that they are carefully quality controlled and bias corrected.…”

Section: Introductionmentioning

confidence: 92%

“…In addition, each sensor has its own measurement uncertainty and limitations in times of heavy rain. For example, gauges are known to underestimate rainfall rates in conditions of high winds (e.g., Sieck et al, 2007;Goudenhoofdt et al, 2017;Pollock et al, 2018) which is common during thunderstorms while radar is known to suffer from signal attenuation, non-uniform beam filling, clutter, hail contamination and overshooting (Krajewski et al, 2010;Villarini and Krajewski, 2010;Berne and Krajewski, 2013). The main goal here is not to make a statement about which measurement is closer to the truth but to quantify the average discrepancies between the gauge and radar measurements as a function of the event, time scale, intensity and radar product.…”

Section: Performance Metricsmentioning

confidence: 99%

The accuracy of weather radar in heavy rain: a comparative study for Denmark, the Netherlands, Finland and Sweden

Schleiss¹,

Olsson²,

Berg³

et al. 2019

Preprint

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Abstract. Weather radar has become an invaluable tool for monitoring rainfall and studying its link to hydrological response. However, when it comes to accurately measuring small-scale rainfall extremes responsible for urban flooding, many challenges remain. The most important of them is that radar tends to underestimate rainfall compared to gauges. The hope is that by moving to higher resolution and making use of dual-polarization, these mismatches can be reduced. Each country has developed its own strategy for addressing this issue. But since there is no common benchmark, improvements are hard to quantify objectively. This study sheds new light on current performances by conducting a multinational assessment of radar's ability to capture heavy rain events at scales of 5 min up to 2 hours. The work is performed within the context of the joint experiment framework of project MUFFIN (Multiscale Urban Flood Forecasting), which aims at better understanding the link between rainfall and urban pluvial flooding across scales. In total, 6 different radar products in Denmark, the Netherlands, Finland and Sweden were considered. The top 50 events for each country were used to quantify the overall agreement between radar and gauges and the errors affecting the peaks. Results show that the overall agreement between radar and gauges in heavy rain is fair, with multiplicative biases in the order of 1.41–1.66 (i.e., radar underestimates by 29–39.8 %) and correlation coefficients of 0.71–0.83 across countries. However, the bias increases with intensity, reaching 45.9 %–66.2 % during the peaks. Only part of the bias (i.e., roughly 13 %–30 % depending on the radar product) can be explained by differences in measurement areas between gauges and radar. Radar products with higher spatial and temporal resolutions agreed better with the gauges, highlighting the importance of high-resolution radar for urban hydrology. However, for capturing peak intensity and reducing the bias during the most intense part of a storm, the ability to combine measurements from multiple overlapping radars to help mitigate attenuation seemed to play a more important role than resolution. The use of dual-polarization and phase information (e.g., Kdp) in the experimental Finnish OSAPOL product also seemed to provide a slight advantage in heavy rain. But improvements were hard to quantify and similarly good results were achieved in the Netherlands by applying a simple Z–R relation together with a mean field bias-correction.

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“…In this study, solid Earth and ocean loading effects were removed by computing tidal parameter sets using the ETERNA package (Wenzel, 1996) on the gravity time series extending from 1 June 2004 to 3 January 2015 (3825.75 record days). The tidal potential is the Hartmann-Wenzel (Hartmann and Wenzel, 1995) catalog with 7761 waves. The adjusted tidal parameters make it possible to compute a tidal signal, which includes both the solid Earth tide and ocean loading effects.…”

Section: Gravimeter Datamentioning

confidence: 99%

Exploring the use of underground gravity monitoring to evaluate radar estimates of heavy rainfall

Delobbe

Watlet

Wilfert

et al. 2019

Hydrol. Earth Syst. Sci.

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Abstract. The radar-based estimation of intense precipitation produced by convective storms is a challenging task and the verification through comparison with gauges is questionable due to the very high spatial variability of such types of precipitation. In this study, we explore the potential benefit of using a superconducting gravimeter as a new source of in situ observations for the evaluation of radar-based precipitation estimates. The superconducting gravimeter used in this study is installed in Membach (BE), 48 m underneath the surface, at 85 km distance from a C-band weather radar located in Wideumont (BE). The 15-year observation record 2003–2017 is available for both gravimeter and radar with 1 and 5 min time steps, respectively. Water mass increase at ground due to precipitation results in a decrease in underground measured gravity. The gravimeter integrates soil water in a radius of about 400 m around the instrument. This allows capture of rainfall at a larger spatial scale than traditional rain gauges. The precision of the gravimeter is a few tenths of nm s−2, 1 nm s−2 corresponding to 2.6 mm of water. The comparison of reflectivity and gravity time series shows that short-duration intense rainfall events produce a rapid decrease in the underground measured gravity. A remarkable correspondence between radar and gravimeter time series is found. The precipitation amounts derived from gravity measurements and from radar observations are further compared for 505 rainfall events. A correlation coefficient of 0.58, a mean bias (radar–gravimeter)/gravimeter of 0.24 and a mean absolute difference (MAD) of 3.19 mm are obtained. A better agreement is reached when applying a hail correction by truncating reflectivity values to a given threshold. No bias, a correlation coefficient of 0.64 and a MAD of 2.3 mm are reached using a 48 dBZ threshold. The added value of underground gravity measurements as a verification dataset is discussed. The two main benefits are the spatial scale at which precipitation is captured and the interesting property that gravity measurements are directly influenced by water mass at ground no matter the type of precipitation: hail or rain.

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Regional frequency analysis of extreme rainfall in Belgium based on radar estimates

Cited by 44 publications

References 50 publications

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

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

The accuracy of weather radar in heavy rain: a comparative study for Denmark, the Netherlands, Finland and Sweden

Exploring the use of underground gravity monitoring to evaluate radar estimates of heavy rainfall

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