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Hydrological modelling using remote sensing techniques in Bulgaria
Author(s): Georgy Koshinchanov; Snezhanka Balabanova
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Paper Abstract

The availability of detailed well distributed in space information on precipitation is of essential importance for the hydrological modeling and forecasting. Conventional measurements of precipitation are in a limited number of points represented by synoptic, climatic and precipitation stations. This information is not sufficient for correct spatial distribution of precipitation. The distribution of the ground stations are quite irregular and thus distances between stations could be quite big, sometimes more than 35 km. On the other hand, precipitation has high variability in space. One of the possibilities to achieve high density gridded precipitation is remote sensing techniques such as products derived from satellites, radars etc. One of the aims HSAF project is to produce different products for precipitation, snow and soil moisture in order to facilitate the hydrological modelling. Bulgaria, in particular NIMH, is a partner in HSAF project since 2009 and the obligations are to validate and hydrovalidate these products. This paper will present simulations with MIKE11 and Artificial Neural Networks (ANN) over two different basins in Bulgaria using precipitation data from the HSAF's H05 product. Analyses will be made using data for the period June 2016 - May 2017. Conclusions will be made on the behaviour of the precipitation product over the two basins and will be discussed the possibility to use this product.

Paper Details

Date Published: 27 June 2019
PDF: 9 pages
Proc. SPIE 11174, Seventh International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2019), 111740Z (27 June 2019); doi: 10.1117/12.2533155
Show Author Affiliations
Georgy Koshinchanov, National Institute of Meteorology and Hydrology (Bulgaria)
Snezhanka Balabanova, National Institute of Meteorology and Hydrology (Bulgaria)


Published in SPIE Proceedings Vol. 11174:
Seventh International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2019)
Kyriacos Themistocleous; Giorgos Papadavid; Silas Michaelides; Vincent Ambrosia; Diofantos G. Hadjimitsis, Editor(s)

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