This work evaluates two numerical warning indicators of severe weather. These indicators, the MKI and RDI indices, were developed within the framework of the EU- funded FLASH project which studies flash flood events in the Mediterranean Basin. The MKI (Modified K-Index) is a modification of the K-Index, which expresses probability of lightning activity, and the RDI (Rain Dynamical Index) is the integrated upward moisture flux. The indices were tested on 59 episodes which occurred during nine rainstorms in Israel, Greece, Spain, Italy, and Cyprus. The data for calculation of the indices included rain cell identification derived from microwave radiometer imagery of polar orbiting NOAA satellites, rain RADAR data, and lightning activity from the international ZEUS detection system. Atmospheric data with 0.5? 9 0.5? spatial resolution and 6-h time res- olution were used for the calculation and the display of the two indices. The indices were tested by calculating the spatially correlating locations with high index values and actual locations of intense rain and intense lightning. The RDI detected both event types: rain and lightning, with a statistically significant success rate and a low rate of false results. The MKI was successful in indicating intense lightning activity, but the rate of correct indi- cations was not statistically significant and there was a high rate of false indications. The results suggest that the RDI computed with output of weather prediction models is a potentially good predictor of torrential rain and therefore can predict flash floods caused by such rain in the Mediterranean region.

The typical short generation length of insects makes their population dynamics highly sensitive not only to mean annual temperatures but also to their intra-annual variations. To consider the combined effect of both thermal factors under global warming, we propose a modeling framework that links general circulation models (GCMs) with a stochastic weather generator and population dynamics models to predict species population responses to inter- and intra-annual temperature changes. This framework was utilized to explore future changes in populations of Bemisia tabaci, an invasive insect pest-species that affects multiple agricultural systems in the Mediterranean region. We considered three locations representing different pest status and climatic conditions: Montpellier (France), Seville (Spain), and Beit-Jamal (Israel). We produced ensembles of local daily temperature realizations representing current and future (mid-21st century) climatic conditions under two emission scenarios for the three locations. Our simulations predicted a significant increase in the average number of annual generations and in population size, and a significant lengthening of the growing season in all three locations. A negative effect was found only in Seville for the summer season, where future temperatures lead to a reduction in population size. High variability in population size was observed between years with similar annual mean temperatures, suggesting a strong effect of intra-annual temperature variation. Critical periods were from late spring to late summer in Montpellier and from late winter to early summer in Seville and Beit-Jamal. Although our analysis suggested that earlier seasonal activity does not necessarily lead to increased populations load unless an additional generation is produced, it is highly likely that the insect will become a significant pest of open-fields at Mediterranean latitudes above 40° during the next 50 years. Our simulations also implied that current predictions based on mean temperature anomalies are relatively conservative and it is better to apply stochastic tools to resolve complex responses to climate change while taking natural variability into account. In summary, we propose a modeling framework capable of determining distinct intra-annual temperature patterns leading to large or small population sizes, for pest risk assessment and management planning of both natural and agricultural ecosystems.