Your search found 3 records
1 Kim, Y. O.; Jeong, D. I.; Kim, H. S. 2001. Improving water supply outlook in Korea with ensemble streamflow prediction. Water International, 26(4):563-568.
Hydrology ; Forecasting ; Stream flow ; Water supply ; Rainfall-runoff relationships ; Models ; Performance / Korea Republic / Keum River
(Location: IWMI-HQ Call no: PER Record No: H029627)
2 Kim, Y. O.; Seo, Y. W.; Lee, D. R.; Yoo, C. 2005. Potential effects of global warming on a water resources system in Korea. Water International, 30(3):400-405.
Climate change ; Reservoirs ; Dams ; Simulation models ; Precipitation ; Stream flow / Korea / Geum River Basin
(Location: IWMI-HQ Call no: PER Record No: H038411)
3 Tegegne, G.; Park, D. K.; Kim, Y.-O.. 2017. Comparison of hydrological models for the assessment of water resources in a data-scarce region, the Upper Blue Nile River Basin. Journal of Hydrology: Regional Studies, 14:49-66. [doi: https://doi.org/10.1016/j.ejrh.2017.10.002]
Water resources ; Assessment ; Hydrology ; Rainfall-runoff relationships ; Models ; Performance indexes ; Calibration ; River basins ; Watersheds ; Stream flow ; Discharges ; Sensitivity analysis ; Uncertainty / Ethiopia / Upper Blue Nile River Basin / Lake Tana Basin / Gilgelabay Watershed / Gummera Watershed / Megech Watershed / Ribb Watershed
(Location: IWMI HQ Call no: e-copy only Record No: H048437)
http://www.sciencedirect.com/science/article/pii/S2214581816301409/pdfft?md5=91c08d466b85e555a7a7bc4b056c6245&pid=1-s2.0-S2214581816301409-main.pdf
https://vlibrary.iwmi.org/pdf/H048437.pdf
https://vlibrary.iwmi.org/pdf/H048437.pdf
(1.53 MB) (1.53 MB)
Study region: The Lake Tana Basin (15,114 km2) in Ethiopia, which is a source of the Blue Nile River Basin.
Study focus: We assessed daily streamflow predictions by applying two simple conceptual models and one complex model for four major gauged watersheds of the study area and compared these model’s capabilities in reproducing observed streamflow in the time and quantile domains.
New hydrological insights for the region: The multi-criteria based model comparison shows that the simple conceptual models performed best in smaller watersheds for reproducing observed streamflow in the time domain, whereas the complex model performed best for the largest watershed. For reproducing observed streamflow in the quantile domain, the simple conceptual models performed best for simulation of high, moist, mid-range, and dry-flows in the Gilgelabay watershed; of dry and low-flows in the Gummera and Megech watersheds; and of high flows in the Ribb watershed. For the remaining flow ranges of each watershed, the complex model performed better. This study also addressed the sensitivity of the complex model for the number of partitioned subbasins. In the largest watershed, the performance of the complex model improved when the number of partitioned subbasins was increased. This finding indicates that the distributed models are especially applicable for the complex watershed because of its physical heterogeneity. In general, integrating these three models may be suitable for water resources assessment.
Study focus: We assessed daily streamflow predictions by applying two simple conceptual models and one complex model for four major gauged watersheds of the study area and compared these model’s capabilities in reproducing observed streamflow in the time and quantile domains.
New hydrological insights for the region: The multi-criteria based model comparison shows that the simple conceptual models performed best in smaller watersheds for reproducing observed streamflow in the time domain, whereas the complex model performed best for the largest watershed. For reproducing observed streamflow in the quantile domain, the simple conceptual models performed best for simulation of high, moist, mid-range, and dry-flows in the Gilgelabay watershed; of dry and low-flows in the Gummera and Megech watersheds; and of high flows in the Ribb watershed. For the remaining flow ranges of each watershed, the complex model performed better. This study also addressed the sensitivity of the complex model for the number of partitioned subbasins. In the largest watershed, the performance of the complex model improved when the number of partitioned subbasins was increased. This finding indicates that the distributed models are especially applicable for the complex watershed because of its physical heterogeneity. In general, integrating these three models may be suitable for water resources assessment.
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