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Gradual global climate change poses new challenges to the mathematical sciences, which are related to forecasting of meteorological conditions, preparing the infrastructure for possible rains, storms, droughts, and other climatic disasters. One of the most common approaches is synthetic regression-probability models, which use the spatio-temporal probability density functions of precipitation level. This approach is applied to the statistics of precipitation in the Kharkiv region, which shows the tendency to a gradual increase in air temperature, high indices of basic water stress, indices of drought and riverside flood threats. Open data on temperature distributions and precipitation were processed using various probability statistics. It is shown that the lognormal distribution most accurately describes the measurement data and allows making more accurate prognoses. Estimates of drought and flood probabilities in Kharkiv region under different scenarios of climate change dynamics have been carried out. The results of the study can be used for management of water resources on urban territories at global climate warming.

Probabilistic models of water resources management on urbanized areas