<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Recently water resources reanalysis (WRR) global streamflow products are emerging from high- resolution global models as a means to provide long and consistent global streamflow products for assessment of global challenge such as climate change. Like any other products, the newly developed global streamflow products have limitations accurately represent the dynamics of local streamflow hydrographs. There is a need to locally evaluate and apply correction factors for better representation and make use of the data. This research focuses on the evaluation and correction of the bias embedded in the global streamflow product (WRR, 0.25°) developed by WaterGAP3 hydrological model in the upper Blue Nile basin part of Ethiopia. Three spatiotemporal dynamical bias correction schemes (temporal-spatial variable, temporal-spatial constant and spatial variable) tested in twelve watersheds of the basin. The temporal-spatial variable dynamical bias correction scheme significantly improves the streamflow estimation. The Nash-Sutcliffe coefficient (NSCE) improves by 30% and bias decreases by 19% for the twelve streamflow gauging stations applying leave one out cross-validation approach in turn. Therefore, the temporal-spatial variable scheme is applicable and can use as one method for the bias correction to use the global data for local applications in the upper Blue Nile basin.</p></abstract>

Recently water resources reanalysis (WRR) global streamflow products are emerging from high- resolution global models as a means to provide long and consistent global streamflow products for assessment of global challenge such as climate change. Like any other products, the newly developed global streamflow products have limitations accurately represent the dynamics of local streamflow hydrographs. There is a need to locally evaluate and apply correction factors for better representation and make use of the data. This research focuses on the evaluation and correction of the bias embedded in the global streamflow product (WRR, 0.25°) developed by WaterGAP3 hydrological model in the upper Blue Nile basin part of Ethiopia. Three spatiotemporal dynamical bias correction schemes (temporal-spatial variable, temporal-spatial constant and spatial variable) tested in twelve watersheds of the basin. The temporal-spatial variable dynamical bias correction scheme significantly improves the streamflow estimation. The Nash-Sutcliffe coefficient (NSCE) improves by 30% and bias decreases by 19% for the twelve streamflow gauging stations applying leave one out cross-validation approach in turn. Therefore, the temporal-spatial variable scheme is applicable and can use as one method for the bias correction to use the global data for local applications in the upper Blue Nile basin.

Dynamical bias correction procedure to improve global gridded daily streamflow data for local application in the Upper Blue Nile basin

Journal of Hydrology and Hydromechanics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Recently water resources reanalysis (WRR) global streamflow products are emerging from high- resolution global models as a means to provide long and consistent...

Dynamical bias correction procedure to improve global gridded daily streamflow data for local application in the Upper Blue Nile basin

Data publikacji: 26 sty 2021

Zakres stron: 41 - 48

Otrzymano: 31 sty 2020

Przyjęty: 25 wrz 2020

DOI: https://doi.org/10.2478/johh-2020-0040

Słowa kluczowe
Blue Nile, WaterGAP3, Bias Correction, Water Resource Reanalysis

© 2021 Haileyesus Belay Lakew et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Dynamical bias correction procedure to improve global gridded daily streamflow data for local application in the Upper Blue Nile basin

Data publikacji: 26 sty 2021

Zakres stron: 41 - 48

Otrzymano: 31 sty 2020

Przyjęty: 25 wrz 2020

DOI: https://doi.org/10.2478/johh-2020-0040

Słowa kluczoweBlue Nile, WaterGAP3, Bias Correction, Water Resource Reanalysis

© 2021 Haileyesus Belay Lakew et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
Blue Nile, WaterGAP3, Bias Correction, Water Resource Reanalysis