GEOGRAPHIC INFORMATION MODEL OF PRECIPITATION INTENSITY
DOI:
https://doi.org/10.33042/2522-1809-2024-3-184-139-146Keywords:
modelling, precipitation intensity, geodatabase, geoprocessing, seasonality, software modules, SAGA GISAbstract
The paper analyses the technological features of building a geoinformation model to study the precipitation intensity in Ukraine. Analysis of natural phenomena requires promptness in obtaining and updating initial data. For this reason, today, geoinformation technologies most often use remote sensing data, mainly in raster format. We collected the raw data on precipitation intensity from the Global Precipitation Climatology Centre website of the German Meteorological Service (DWD). It is monthly data for 2022 in raster format with a raster size of 1 degree.
For the organisation and analysis of geographical information, we chose the most suitable new software product, SAGA GIS, designed for geoscientific analysis automation. The advantages of this system are the availability of a complete set of geophysical analysis methods, the spatial algorithms implementation, and open-source code.
After data loading, we geoprocessed the data, in particular, using the SAGA software module ‘Set Coordinate Reference System’ and selected the WGS84 coordinate system for the required layers. To prepare the data for analytical studies, we limited them to the administrative boundaries of Ukraine and transformed the WGS84 coordinate system into a UTM 36N projection. Using SAGA analytical tools, we grouped the data by season. Then, we determined the distribution of precipitation intensity over the territory for each season, identifying areas with the highest and lowest precipitation intensity and a part of the territory with the highest annual precipitation.
The results of this work may have practical significance for various sectors of the economy and decision-making on adaptation to climate change. They can be relevant both for scientific research and practical application in fields related to environmental assessment.
By supplementing the model with data for other subsequent or previous periods, it will be possible to determine trends in precipitation intensity by regions of Ukraine or physiographic zones of its territory.
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