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Gérémy Panthou, Alain Mailhot, Edward Laurence, and Guillaume Talbot

Abstract

Recent studies have examined the relationship between the intensity of extreme rainfall and temperature. Two main reasons justify this interest. First, the moisture-holding capacity of the atmosphere is governed by the Clausius–Clapeyron (CC) equation. Second, the temperature dependence of extreme-intensity rainfalls should follow a similar relationship assuming relative humidity remains constant and extreme rainfalls are driven by the actual water content of the atmosphere. The relationship between extreme rainfall intensity and air temperature (P extrT a) was assessed by analyzing maximum daily rainfall intensities for durations ranging from 5 min to 12 h for more than 100 meteorological stations across Canada. Different factors that could influence this relationship have been analyzed. It appears that the duration and the climatic region have a strong influence on this relationship. For short durations, the P extrT a relationship is close to the CC scaling for coastal regions while a super-CC scaling followed by an upper limit is observed for inland regions. As the duration increases, the slope of the relationship P extrT a decreases for all regions. The shape of the P extrT a curve is not sensitive to the percentile or season. Complementary analyses have been carried out to understand the departures from the expected Clausius–Clapeyron scaling. The relationship between dewpoint temperature and extreme rainfall intensity shows that the relative humidity is a limiting factor for inland regions, but not for coastal regions. Using hourly rainfall series, an event-based analysis is proposed in order to understand other deviations (super-CC, sub-CC, and monotonic decrease). The analyses suggest that the observed scaling is primarily due to the rainfall event dynamic.

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Catherine Wilcox, Claire Aly, Théo Vischel, Gérémy Panthou, Juliette Blanchet, Guillaume Quantin, and Thierry Lebel

Abstract

Stochastic rainfall generators aim to reproduce the main statistical features of rainfall at small spatial and temporal scales. The simulated synthetic rainfall series are recognized as suitable for use with impact analysis in water, agricultural, and ecological management. Convection-driven precipitation, dominant in certain regions of the world such as the intertropical belt regions, presents properties that require specific consideration when modeling: (i) strong rainfall intermittency, (ii) high variability of intensities within storms, (iii) strong spatiotemporal correlation of intensities, and (iv) marked seasonality of storm properties. In this article, improvements for an existing stochastic generator of rainfall fields that models convective storms are presented. Notable novelties include (i) the ability to model precipitation event timing, (ii) an improved temporal disaggregation scheme representing the rainfall distribution at subevent scales, and (iii) using covariates to reflect seasonal changes in precipitation occurrence and marginal distribution parameters. Extreme values are explicitly considered in the distribution of storm event intensities. The simulator is calibrated and validated using 28 years of 5-min precipitation data from the 30-rain-gauge AMMA-CATCH network in the Sahelian region of southwest Niger. Both large propagative systems and smaller local convective precipitation are generated. Results show that simulator improvements coherently represent the local climatology. The simulator can generate scenarios for impact studies with accurate representation of convective precipitation characteristics.

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