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Evaluation of Runoff Simulation Using the Global BROOK90-R Model for Three Sub-Basins in Türkiye

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  • Muhammet Cafer Ulker

    (Department of Civil Engineering, Konya Technical University, 42250 Konya, Türkiye)

  • Meral Buyukyildiz

    (Department of Civil Engineering, Konya Technical University, 42250 Konya, Türkiye)

Abstract

The use of physically based hydrological models in the observation of hydrological processes has some disadvantages as well as advantages. One of these disadvantages is the large supply of data pertaining to the study area that is required for the model to be run. However, the ability to run the Global BROOK90 R (GB90-R) model for any location and period has made a significant contribution to the science of hydrology. In this study, the GB90-R model was established in three different basins (Çarşamba, Karasu, and Körkün) in Türkiye with different drainage areas and climates, and the flow forecasting performance was comprehensively evaluated. In addition, the evaporation, ground moisture, and snowmelt outputs obtained were examined comparatively. According to the results, Karasu Basin, with the smallest drainage area, was the basin with the highest model success in flow estimation with NSE = 0.670, while Körkün Basin, with the largest drainage area, was the basin with the lowest model success with NSE = 0.337. It is thought that the increase in the drainage area is one of the important factors reducing the success of the model.

Suggested Citation

  • Muhammet Cafer Ulker & Meral Buyukyildiz, 2023. "Evaluation of Runoff Simulation Using the Global BROOK90-R Model for Three Sub-Basins in Türkiye," Sustainability, MDPI, vol. 15(6), pages 1-19, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:5103-:d:1096396
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    References listed on IDEAS

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    1. Arunima Sarkar Basu & Laurence William Gill & Francesco Pilla & Bidroha Basu, 2022. "Assessment of Variations in Runoff Due to Landcover Changes Using the SWAT Model in an Urban River in Dublin, Ireland," Sustainability, MDPI, vol. 14(1), pages 1-22, January.
    2. Tomislav Hengl & Jorge Mendes de Jesus & Gerard B M Heuvelink & Maria Ruiperez Gonzalez & Milan Kilibarda & Aleksandar Blagotić & Wei Shangguan & Marvin N Wright & Xiaoyuan Geng & Bernhard Bauer-Marsc, 2017. "SoilGrids250m: Global gridded soil information based on machine learning," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-40, February.
    3. Arunima Sarkar Basu & Laurence William Gill & Francesco Pilla & Bidroha Basu, 2022. "Assessment of Climate Change Impact on the Annual Maximum Flood in an Urban River in Dublin, Ireland," Sustainability, MDPI, vol. 14(8), pages 1-23, April.
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