Introduction. The need to simulate hydrological processes is caused by, among other factors, the complexity of hydrological systems and data insufficiency due to the unavailability or a small number of instrumental observations. Recently, the reanalysis of the climate data supplied by the world’s leading meteorological centres has been used quite successfully in the regions that suffer from the deficit of instrumental information. This paper assesses the applicability of climate reanalysis data to rainfall runoff (“rainfall runoff”) modelling in the poorly studied river basin in Eritrea.
Materials and methods. Climate Forecast System Reanalysis (CFSR) data generated by the National Centre for Environmental Prediction (USA) were used. Besides, high-resolution topographic information, generated by the SRTM international research project, was also applied to set the drainage area boundaries and to simulate the river network using such tools as MIKE and GIS. In addition, calibration and validation (evaluation) of the hydrological model (simulation quality) were performed using the Nash-Sutcliffe efficiency criterion, the determination coefficient, and the root mean square error of volumetric and peak flow rates.
Results. The results suggest that a considerable overestimation of precipitation in the reanalysis data set, which in turn has a significant effect on other variables such as potential evapotranspiration, leads to a significant discrepancy between water balance values which are simulated and registered by the hydrographs.
Conclusions. The applicability of Climate Forecast System Reanalysis (CFSR) data to river flow modelling in arid and semi-arid regions such as Eritrea is questionable. The incompatibility of spatial and temporal variations of initial variables (e.g. precipitation), derived from reanalysis data sets and instrumental observations, is undoubtedly the main reason for errors. Thus, the application of reanalysis data sets and development of hydrological models for the region under study requires further intensive research aimed at identifying most effective mechanisms designated for the harmonization of differences between reanalysis data and field observations. In the course of further research, CFSR information is to be converted into more realistic data; climate reanalysis indicators, provided by other sources and designated for different time scales in the context of the “rainfall runoff” model are to be assessed, and the efficiency of other software systems is to be compared with MIKE 11-NAM.
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