Comparison of hydrological models for the assessment of water resources in a data-scarce region, the Upper Blue Nile River Basin

•Hydrological model comparison in a data-scarce region.•We demonstrate the sensitivity of the complex model for the number of partitioned subbasins.•The simple conceptual models perform comparably to the more complex model.•The combination of the three model outputs with the artificial neural network produce the minimum root mean square error. The Lake Tana Basin (15,114km2) in Ethiopia, which is a source of the Blue Nile River Basin. 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. 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.