Scholarly record
USE OF COPERNICUS METEOROLOGICAL DATA FOR THE PURPOSES OF HYDROLOGICAL MODELING
Abstract
Floods are one of the most devastating hazardous events often causing destruction of infrastructure and loss of lives. In order to mitigate negative flood impact a reliable hydrological forecast is of great importance. Thus using trustworthy information on the meteorological elements that mostly contribute to the occurrence of floods - precipitation and temperatures, is essential. Although the accuracy of the global forecasting models like ECMWF, GFS etc. has improved a lot, still sometimes there are extreme meteorological events not well forecasted or even missed. Given these cases hydrological forecasts also show to be ineffective with not having well forecasted certain events neither having warnings issued. That is why it is important to have robust meteorological analysis and reanalysis data using any available information from stations, radars, satellites, etc. An assessment of the results of modelling the hydrological processes in a watershed using Copernicus- ERA5 climate reanalysis data is presented in this paper. MIKE 11-NAM hydrological module for rainfall-runoff modelling was applied over Fakiyska river basin in Bulgaria and two meteorological datasets (dry temperature at 2 m and precipitation) for the period 2017-2022 were used: one with observed data and the other one with Copernicus- ERA5 climate reanalysis. As a first iteration the hydrological model was applied using observed meteorological data and was further calibrated and validated towards measured discharge data. Next the hydrological processes in the watershed were simulated again using meteorological information for the same period from ERA5 climate reanalysis data. An analysis of the observed meteorological data and the ERA5 dataset was performed prior to comparing the results of the hydrological modelling with the ERA5 dataset to the results of the modelling with observed data. Finally the results of both the scenarios were analyzed for evaluating model performance using ERA5 dataset.
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References11
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