Spatial and temporal variation of extremely abundant maxima of precipitation in Hungary during the period between 1951 and 2010 - Lakatos, L.; Sümeghy, Z.; Soltész, M.; Nyéki, J. (International Journal of Horticultural Science, 2012)
The study deals with the accumulated database of 16 meteorological stations in Hungary during a period of 60 years. The purpose was to reveal the spatial and temporal structure of the appearance of extreme values in the daily distribution of data concerning precipitation. We strived to answer the question whether the frequency of incidences of daily maxima did they change or not during the 60 year-long period in the main growing regions of the country. It is demonstrated on geographical maps how the size and frequency of precipitation episodes ensued, and what are the typical traits of changes in intensity as well as in frequency of happenings projected according to their spatial and temporal distribution. From the point of view of fruit and vegetable growing, it is of prime interest what kind of frequency and intensity of changes occurred in precipitation. The temporal distribution of extremities though did not seem to change signifi cantly in some areas, but the recognition of changes may help conspicuously the planning and the choice between alternatives of species and varieties as well as technologies of horticultural managements for the long run. Extremely intense rains during a short time may cause erosion and stagnant water, thus we have to know what are the odds of risk. The temporary distribution of changes helps us to judge upon the reality of anxieties, which are expected according to the existence of trends. Seasonal or monthly distribution is visualised by maps, what is expected and what is accidental as for a decision in planning. The spatial distribution of coeffi cients of variation help us to decide what is the local chance of extreme happenings at different parts of the country and what is its coeffi cient of uncertainty. The risk of any undertaking dependent on conditions of weather could be expressed numerically by a coeffi cient of risk.