Hail observations and hailstorm characteristics in Europe: A review

Punge, Heinz Jürgen; Kunz, Michael

doi:10.1016/j.atmosres.2016.02.012

Cited by 195 publications

(167 citation statements)

References 138 publications

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“…We used the erosive rainfall event thresholds defined by Renard et al (1997), which were modified by Meusburger et al (2012). The unit rainfall energy (e r ) (MJ ha −1 mm −1 ) for each time interval is expressed as the intensity of rainfall (i r ) (mm h −1 ) during that time interval.…”

Section: Rainfall Erosivity (R-factor) Calculationmentioning

confidence: 99%

Regionalization of monthly rainfall erosivity patterns in Switzerland

Schmidt

Alewell

Panagos

et al. 2016

Hydrol. Earth Syst. Sci.

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Abstract. One major controlling factor of water erosion is rainfall erosivity, which is quantified as the product of total storm energy and a maximum 30 min intensity (I 30 ). Rainfall erosivity is often expressed as R-factor in soil erosion risk models like the Universal Soil Loss Equation (USLE) and its revised version (RUSLE). As rainfall erosivity is closely correlated with rainfall amount and intensity, the rainfall erosivity of Switzerland can be expected to have a regional characteristic and seasonal dynamic throughout the year. This intra-annual variability was mapped by a monthly modeling approach to assess simultaneously spatial and monthly patterns of rainfall erosivity. So far only national seasonal means and regional annual means exist for Switzerland. We used a network of 87 precipitation gauging stations with a 10 min temporal resolution to calculate long-term monthly mean Rfactors. Stepwise generalized linear regression (GLM) and leave-one-out cross-validation (LOOCV) were used to select spatial covariates which explain the spatial and temporal patterns of the R-factor for each month across Switzerland. The monthly R-factor is mapped by summarizing the predicted R-factor of the regression equation and the corresponding residues of the regression, which are interpolated by ordinary kriging (regression-kriging). As spatial covariates, a variety of precipitation indicator data has been included such as snow depths, a combination product of hourly precipitation measurements and radar observations (CombiPrecip), daily Alpine precipitation (EURO4M-APGD), and monthly precipitation sums (RhiresM). Topographic parameters (elevation, slope) were also significant explanatory variables for single months. The comparison of the 12 monthly rainfall erosivity maps showed a distinct seasonality with the highest rainfall erosivity in summer (June, July, and August) influenced by intense rainfall events. Winter months have the lowest rainfall erosivity. A proportion of 62 % of the total annual rainfall erosivity is identified within four months only (JuneSeptember). The highest erosion risk can be expected in July, where not only rainfall erosivity but also erosivity density is high. In addition to the intra-annual temporal regime, a spatial variability of this seasonality was detectable between different regions of Switzerland. The assessment of the dynamic behavior of the R-factor is valuable for the identification of susceptible seasons and regions.

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Section: Rainfall Erosivity (R-factor) Calculationmentioning

confidence: 99%

Regionalization of monthly rainfall erosivity patterns in Switzerland

Schmidt

Alewell

Panagos

et al. 2016

Hydrol. Earth Syst. Sci.

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“…Hail occurrence statistics are based mainly on radar-or satellite-derived hail proxies [see Punge and Kunz (2016) for an overview] and few hailpad networks.…”

Section: Local Probabilities and Long-term Statistics Of Hailmentioning

confidence: 99%

Challenges and Recent Advances in Hail Research

Martius

Hering

Kunz

et al. 2018

Bulletin of the American Meteorological Society

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“…The City of Belgrade and the rest of the territory of Serbia are not equipped with any instrumental means for the objective measurement of the effects of adverse weather conditions, although some other countries apply such measurements (Karlsruhe [10]). Furthermore, according to the annual reports of the Hydro-Meteorological Service of Serbia (RHMZ), in the city of Belgrade, there are 30-60 hail days per year when rocket-based anti-hail protection is applied [11].…”

Section: Introductionmentioning

confidence: 99%

Anti-Hail Protection—Assessment of Financial Effects on the Territory of Belgrade

Vukelić¹,

Cvetković

Gržetić

et al. 2018

Sustainability

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Abstract:The aim of this work is to assess damage to the City of Belgrade caused by the unfavorable weather condition of hail due to the absence of anti-hail defense of the city, and to argumentatively point out the necessity of introducing new technical-technological systems for preventing the effects of adverse weather conditions. The results are based on the direct correlation-analyses of two real unfavorable weather events and the estimated financial damage caused by these events. The overall calculation also takes into account financial investments (new financial model) necessary to establish two essentially different systems for anti-hail protection. The damage caused by the hail on the territory of the City of Belgrade and the financial investment in anti-hail protection to reduce it are empirically established for the first time. It is shown how the damage could be transformed to profit, as the financial investment in anti-hail protection is lower than the damage that hail can cause.

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Hail observations and hailstorm characteristics in Europe: A review

Cited by 195 publications

References 138 publications

Regionalization of monthly rainfall erosivity patterns in Switzerland

Regionalization of monthly rainfall erosivity patterns in Switzerland

Challenges and Recent Advances in Hail Research

Anti-Hail Protection—Assessment of Financial Effects on the Territory of Belgrade

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