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Abstract. Estimating future short-duration extreme precipitation in mountainous regions is fundamental for risk management. High-resolution convection-permitting models (CPMs) represent the state of the art for these projections, as they resolve convective processes that are key to short-duration extremes. Recent observational studies reported a decrease in the intensity of extreme hourly precipitation with elevation. This “reverse orographic effect” could be related to processes which are subgrid even for CPMs. To quantify the reliability of future projections of extreme short-duration precipitation in mountainous regions, it is thus crucial to understand to what extent CPMs can reproduce this effect. Due to the computational demands however, CPM simulations are still too short for analyzing extremes using conventional methods. We use a non-asymptotic statistical approach (Simplified Metastatistical Extreme Value: SMEV) for the analysis of extremes from short time periods, such as the ones of CPM simulations. We analyze an ERA-Interim-driven Consortium for Small-Scale Modeling (COSMO-crCLIM, convection-resolving Climate Modelling) simulation (2000–2009; 2.2 km resolution), and we use hourly precipitation from 174 rain gauges in an orographically complex area in northeastern Italy as a benchmark. We investigate the ability of the model to simulate the orographic effect on short-duration precipitation extremes, as compared to observational data. We focus on extremes as high as the 20-year return levels. While overall good agreement is reported at daily and hourly duration, the CPM tends to increasingly overestimate hourly extremes with increasing elevation, implying that the reverse orographic effect is not fully captured. These findings suggest that CPM bias-correction approaches should account for orography. SMEV's capability of estimating reliable rare extremes from short periods promises further applications on short-time-period CPM projections and model ensembles.

Resazurin (Raz) is a phenoxazine dye that can be reduced irreversibly to the daughter compound resorufin (Rru) by aerobic respiration. Previous hydrologic studies using the Raz‐Rru reactive tracer system to quantify water‐sediment interactions and metabolic activity have reported that dilution‐corrected masses of Raz and Rru recovered are smaller than the mass of Raz injected. This lack of mass balance closure has been reported as a nonideality of this tracer system and, to date, it is still unclear what drives incomplete recovery. We used controlled laboratory experiments varying the initial concentrations of Raz, the duration of the experiments, and the type of microbial communities present to quantify mass balances of Raz and Rru under conditions that removed other suspected causes of incomplete recovery in field experiments, i.e., sorption to sediments and photodecay. We used the summation of Raz and Rru concentrations over time to assess mass recovery and variability and found mass recoveries in the range of 85.6–110.4%, with a maximum standard deviation of 7.5%. In three of the four experiments, no strong temporal trend in mass recovery is present. In an experiment with Bacillus subtilis bacteria, lower recovery and evidence of a temporal trend in recovery only occurred after 13 hr past the complete transformation of Raz (i.e., beyond the duration of most field experiments). These results suggest that the lack of mass recovery in field studies is likely associated with physical or chemical mechanisms rather than biological interactions with the Raz‐Rru tracer system.

Understanding past changes in precipitation extremes could help us predict their future dynamics. We present a novel approach for analyzing trends in extremes and attributing them to changes in the local precipitation regime. The approach relies on the separation between intensity and occurrence of storms. We examine the relevant case of the Eastern Italian Alps, where significant trends in extreme precipitation were reported. The model is able to reproduce the observed trends at all durations between 15 min and 24 hr, and allows us to quantify trends in extreme return levels. Despite the significant increase in storm occurrence and typical intensity, the observed trends can be only explained considering changes in the tail heaviness of the intensity distribution, that is the proportion between heavy and mild events. Our results suggest that the observed changes are caused by an increased proportion of summer convective storms.