Effect of Bias Correction of Satellite-Rainfall Estimates on Runoff Simulations at the Source of the Upper Blue Nile

Abstract

Abstract:Results of numerous evaluation studies indicated that satellite-rainfall products are contaminated with significant systematic and random errors. Therefore, such products may require refinement and correction before being used for hydrologic applications. In the present study, we explore a rainfall-runoff modeling application using the Climate Prediction Center-MORPHing (CMORPH) satellite rainfall product. The study area is the Gilgel Abbay catchment situated at the source basin of the Upper Blue Nile basin in Ethiopia, Eastern Africa. Rain gauge networks in such area are typically sparse. We examine different bias correction schemes applied locally to the CMORPH product. These schemes vary in the degree to which spatial and temporal variability in the CMORPH bias fields are accounted for. Three schemes are tested: space and time-invariant, time-variant and spatially invariant, and space and time variant. Bias-corrected CMORPH products were used to calibrate and drive the Hydrologiska Byråns Vattenbalansavdelning (HBV) rainfall-runoff model. Applying the space and time-fixed bias correction scheme resulted in slight improvement of the CMORPH-driven runoff simulations, but in some instances OPEN ACCESSRemote Sens. 2014, 6 6689 caused deterioration. Accounting for temporal variation in the bias reduced the rainfall bias by up to 50%. Additional improvements were observed when both the spatial and temporal variability in the bias was accounted for. The rainfall bias was found to have a pronounced effect on model calibration. The calibrated model parameters changed significantly when using rainfall input from gauges alone, uncorrected, and bias-corrected CMORPH estimates. Changes of up to 81% were obtained for model parameters controlling the stream flow volume.

Applications of Satellite-Based Rainfall Estimates in Flood Inundation Modeling—A Case Study in Mundeni Aru River Basin, Sri Lanka

“…For ungauged areas, however, use of multisource SREs would be another possibility of the bias correction, and monthly-and seasonal-basis scale factors can also be employed for the bias adjustment. There have been several studies to apply and evaluate some advanced methods which enable bias correction of SREs, even in sparsely gauged areas [37,38]. At the same time, dependence of SREs on topography, season, and other factors are being studied for bias correction [39,40].…”

Impact of Climate Change on Streamflow Hydrology in Headwater Catchments of the Upper Blue Nile Basin, Ethiopia

“…The consistent under prediction could be easily bias corrected with a linear bias correction [26]. While maximum temperature did not show a consistent bias, in this case, bias could be corrected with histogram matching, gamma or power transformation functions among others [48,[58][59][60].…”

Using gridded rainfall products in simulating streamflow in a tropical catchment – A case study of the Srepok River Catchment, Vietnam

“…Among the two approaches, Artan et al (2007) and Zeweldi et al (2011) indicated that performance of a hydrological model when the model was calibrated using gridded rainfall data is better than this when it was calibrated using rain gauges data. However, calibration could result in parameter values that are unrealistic as the model attempt to compensate for the large errors in rainfall input (Habib et al, 2014). Thus, the first approach was used in this study.…”