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Rainfall Frequency (rainfall + frequency)
Selected AbstractsEffects of nutrient loading and extreme rainfall events on coastal tallgrass prairies: invasion intensity, vegetation responses, and carbon and nitrogen distributionGLOBAL CHANGE BIOLOGY, Issue 10 2007EVAN SIEMANN Abstract Soil fertility and precipitation are major factors regulating transitions from grasslands to forests. Biotic regulation may influence the effects of these abiotic drivers. In this study, we examined the effects of extreme rainfall events, anthropogenic nutrient loading and insect herbivory on the ability of Chinese tallow tree (Sapium sebiferum) to invade coastal prairie to determine how these factors may influence woody invasion of a grassland. We manipulated soil fertility (NPK addition) and simulated variation in frequency of extreme rainfall events in a three growing season, full factorial field experiment. Adding water to or pumping water out of plots simulated increased and decreased rainfall frequencies. We added Sapium seeds and seedlings to each plot and manipulated insect herbivory on transplanted Sapium seedlings with insecticide. We measured soil moisture, Sapium performance, vegetation mass, and carbon and nitrogen in vegetation and soils (0,10 cm deep, 10,20 cm deep). Fertilization increased Sapium invasion intensity by increasing seedling survival, height growth and biomass. Insect damage was low and insect suppression had little effect in all conditions. Recruitment of Sapium from seed was very low and independent of treatments. Vegetation mass was increased by fertilization in both rainfall treatments but not in the ambient moisture treatment. The amount of carbon and nitrogen in plants was increased by fertilization, especially in modified moisture plots. Soil carbon and nitrogen were independent of all treatments. These results suggest that coastal tallgrass prairies are more likely to be impacted by nutrient loading, in terms of invasion severity and nutrient cycling, than by changes in the frequency of extreme rainfall events. [source] Statistical downscaling model based on canonical correlation analysis for winter extreme precipitation events in the Emilia-Romagna regionINTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 4 2008A. Busuioc Abstract Optimum statistical downscaling models for three winter precipitation indices in the Emilia-Romagna region, especially related to extreme events, were investigated. For this purpose, the indices referring to the number of events exceeding the long-term 90 percentile of rainy days, simple daily intensity and maximum number of consecutive dry days were calculated as spatial averages over homogeneous sub-regions identified by the cluster analysis. The statistical downscaling model (SDM) based on the canonical correlation analysis (CCA) was used as downscaling procedure. The CCA was also used to understand the large-/regional-scale mechanisms controlling precipitation variability across the analysed area, especially with respect to extreme events. The dynamic (mean sea-level pressure-SLP) and thermodynamic (potential instability-,Q and specific humidity-SH) variables were considered as predictors (either individually or together). The large-scale SLP can be considered a good predictor for all sub-regions in the dry index case and for two sub-regions in the case of the other two indices, showing the importance of dynamical forcing in these cases. Potential instability is the best predictor for the highest mountain region in the case of heavy rainfall frequency, when it can be considered as a single predictor. The combination of dynamic and thermodynamic predictors improves the SDM's skill for all sub-regions in the dry index case and for some sub-regions in the simple daily intensity index case. The selected SDMs are stable in time only in terms of correlation coefficient for all sub-regions for which they are skilful and only for some sub-regions in terms of explained variance. The reasons are linked to the changes in the atmospheric circulation patterns influencing the local rainfall variability in Emilia-Romagna as well as the differences in temporal variability over some sub-regions and sub-intervals. It was concluded that the average skill over an ensemble of the most skilful and stable SDMs for each region/sub-interval gives more consistent results. Copyright © 2007 Royal Meteorological Society [source] Effects of weather variables on grain mould of sorghum in South AfricaPLANT PATHOLOGY, Issue 2 2006G. Tarekegn Effects of weather variables of mould development on sorghum grain were studied over three consecutive seasons in South Africa. Five sorghum hybrids planted at different dates ensured developing seeds were exposed to different weather conditions. Incidence of grain mould fungi was determined at harvest by incubating seeds on 2% malt extract agar. Averages of different weather variables (maximum and minimum temperatures, maximum relative humidity, total precipitation and frequency of precipitation) were determined for all permutations of weekly time intervals for a 2-month postflowering period to identify when these variables and pathogen incidence were significantly correlated. Significant correlations were used to develop models to quantify relationships between variables. Significant positive correlations were observed between the incidence of mould fungi and weather 4,6 weeks after flowering in the shorter season hybrid cv. Buster, and 5,8 weeks after flowering in the remaining hybrids. In most hybrids, correlations between the incidence of grain mould pathogens, including Alternaria alternata, Curvularia spp. (C. lunata and C. clavata), Fusarium spp. (F. proliferatum and F. graminearum), and Drechslera sorghicola, and average minimum temperature, total rainfall and frequency of rainfall were significant (P = 0·05). In four hybrids, models showing a linear relationship between the logarithm of pathogen incidence and minimum temperature, and in one hybrid, between pathogen incidence and rainfall frequency, were developed. Depending on the hybrid, models that used minimum temperature as predictor described 60,82% of variation in the incidence of pathogens. Frequency of rainfall explained 93% of the variation in pathogen incidence in one sorghum hybrid genotype. Evaluation of the models using an independent data set yielded average prediction errors near zero, indicating that the models were acceptable. [source] Post-summer heavy rainfall events in Southeast Brazil associated with South Atlantic Convergence ZoneATMOSPHERIC SCIENCE LETTERS, Issue 1 2010Kellen Carla Lima Abstract Heavy rainfall events (HREs) in the post-summer month of March in Southeast Brazil cause disasters such as floods, mudslides and landslides, mainly because the soil becomes saturated by February. Forty-five years of rainfall data show that heavy rainfall frequency increases again in the month of March. The composite anomaly fields of the atmospheric circulation during and before HREs associated with the formation of South Atlantic Convergence Zone show some special characteristics that may be used as a guide for early warning. The convergence of moisture flux in the troposphere over the region grows 40% during the 48 h before the HRE in March. Copyright © 2010 Royal Meteorological Society [source] | |||||||||||||