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A new methodology for estimating rainfall aggressiveness risk based on daily rainfall records for multi-decennial periods

Leoncio García-Barrón, Julia Morales, Arturo Sousa



Highlights.
A new methodology is proposed to estimate the rainfall aggressiveness risk.
This methodology is based on daily rainfall records.
A new synthesis parameter based on Modified Fournier and Oliver's indices is used.
It is calibrated with respect to the erosivity R factor for a simultaneity period.
The SW of Iberian Peninsula has been selected to test its regional application.

Abstract. The temporal irregularity of rainfall, characteristic of a Mediterranean climate, corresponds to the irregularity of the environmental effects on soil. We used aggressiveness as an indicator to quantify the potential environmental impact of rainfall. However, quantifying rainfall aggressiveness is conditioned by the lack of sub-hourly frequency records on which intensity models are based. On the other hand, volume models are characterized by a lack of precision in the treatment of heavy rainfall events because they are based on monthly series. Therefore, in this study, we propose a new methodology for estimating rainfall aggressiveness risk. A new synthesis parameter based on reformulation using daily data of the Modified Fournier and Oliver's Precipitation Concentration indices is defined. The weighting of both indices for calculating the aggressiveness risk is established by multiple regression with respect to the local erosion R factor estimated in the last decades. We concluded that the proposed methodology overcomes the previously mentioned limitations of the traditional intensity and volume models and provides accurate information; therefore, it is appropriate for determining potential rainfall impact over long time periods. Specifically, we applied this methodology to the daily rainfall time series from the San Fernando Observatory (1870–2010) in southwest Europe. An interannual aggressiveness risk series was generated, which allowed analysis of its evolution and determination of the temporal variability. The results imply that environmental management can use data from long-term historical series as a reference for decision making.


Science of The Total Environment, Vol. 615 (2018), pp. 564-571 
Key: INRMM:14448832

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