Use of ACRU, a distributed hydrological model, to evaluate how errors from downscaled rainfall are propagated in simulated runoff in uMngeni catchment, South Africa. Issue 12 (10th September 2017)

• Record Type: Journal Article
• Authors: Kusangaya, Samuel; Warburton, Michele; Archer van Garderen, Emma
• Is Part Of: Hydrological sciences journal. Volume 62:Issue 12(2017)
• Page Start: 1995
• Page End: 2011
• Publication Date: 2017-09-10
• Subjects: error propagation -- downscaled climate models -- input data uncertainty -- rainfall -- runoff; Hydrology -- Periodicals; 551.4805
• DOI: 10.1080/02626667.2017.1349317
• Languages: English
• ISSNs: 0262-6667

Abstract

ABSTRACT: The hypothesis of no significant difference in errors between observed (historical) and downscaled global circulation model (GCM) rainfall and corresponding errors in simulated runoff was tested. The percentage difference in mean and standard deviation, normalized errors and normalized bias metrics were used. The ACRU hydrological model was used to simulate runoff. Results indicated that errors in rainfall lead to amplified errors in simulated runoff. A 10% error magnitude in mean rainfall was amplified three times in mean runoff. Rainfall variability was amplified by twice as much from rainfall to simulated runoff. These findings indicate that uncertainty in input downscaled rainfall is amplified in simulated runoff, hence the quality of input rainfall is a strong determining factor of the simulated runoff. Ultimately, there is a need for continuous improvement in the GCM downscaling process, particularly model process description, so as to minimize uncertainties due to GCM model description. EDITOR A. Castellarin;ASSOCIATE EDITOR S. Kanae