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 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.
Highlights
Any rainfall-runoff modeling requires accurate rainfall data as model input
CMORPH reports smaller rainfall amounts than gauge observations from mid-June to mid-August 2003, but reports larger rainfall amounts towards the end of the rainy season of 2003
This pattern is not shown in 2004, where positive and negative biases in CMORPH show lower variation in time. These results indicate that the bias in the CMORPH product exhibits pronounced variability in space and time over the study area
Summary
Any rainfall-runoff modeling requires accurate rainfall data as model input. Accurate rainfall information in many world regions is hampered by limitations of ground-based observational networks. Alternative to in situ network data are satellite rainfall estimates (SREs), which potentially can be a viable alternative. SREs are known to suffer from sampling and estimation inaccuracies, which are manifested in the form of systematic (bias) and random errors [3,4,5,6,7]. Though a number of studies report on usage of SREs for runoff and soil moisture simulations [6,8,9], aspects of accuracy and representativeness of SREs for hydrologic modeling are not well investigated
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