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Performance of bias-correction schemes for CMORPH rainfall estimates in the Zambezi River Basin

Author

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  • Gumindoga, W.
  • Rientjes, T. H. M.
  • Haile, Alemseged Tamiru
  • Makurira, H.
  • Reggiani, P.

Abstract

Satellite rainfall estimates (SREs) are prone to bias as they are indirect derivatives of the visible, infrared, and/or microwave cloud properties, and hence SREs need correction. We evaluate the influence of elevation and distance from large-scale open water bodies on bias for Climate Prediction Center-MORPHing (CMORPH) rainfall estimates in the Zambezi basin. The effectiveness of five linear/non-linear and time–space-variant/-invariant bias-correction schemes was evaluated for daily rainfall estimates and climatic seasonality. The schemes used are spatio-temporal bias (STB), elevation zone bias (EZ), power transform (PT), distribution transformation (DT), and quantile mapping based on an empirical distribution (QME). We used daily time series (1998–2013) from 60 gauge stations and CMORPH SREs for the Zambezi basin. To evaluate the effectiveness of the bias-correction schemes spatial and temporal crossvalidation was applied based on eight stations and on the 1998–1999 CMORPH time series, respectively. For correction, STB and EZ schemes proved to be more effective in removing bias. STB improved the correlation coefficient and Nash–Sutcliffe efficiency by 50 % and 53 %, respectively, and reduced the root mean squared difference and relative bias by 25 % and 33 %, respectively. Paired t tests showed that there is no significant difference (p- q) plots. The spatial cross-validation approach revealed that most bias-correction schemes removed bias by >28 %. The temporal cross-validation approach showed effectiveness of the bias-correction schemes. Taylor diagrams show that station elevation has an influence on CMORPH performance. Effects of distance >10 km from large-scale open water bodies are minimal, whereas effects at shorter distances are indicated but are not conclusive for a lack of rain gauges. Findings of this study show the importance of applying bias correction to SREs.

Suggested Citation

  • Gumindoga, W. & Rientjes, T. H. M. & Haile, Alemseged Tamiru & Makurira, H. & Reggiani, P., 2019. "Performance of bias-correction schemes for CMORPH rainfall estimates in the Zambezi River Basin," Papers published in Journals (Open Access), International Water Management Institute, pages 23(7):2915-.
    • Handle: RePEc:iwt:jounls:h049387
    DOI: 10.5194/hess-23-2915-2019
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    References listed on IDEAS

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    1. C. Schlosser & Kenneth Strzepek, 2015. "Regional climate change of the greater Zambezi River Basin: a hybrid assessment," Climatic Change, Springer, vol. 130(1), pages 9-19, May.
    2. Daniel Fylstra & Leon Lasdon & John Watson & Allan Waren, 1998. "Design and Use of the Microsoft Excel Solver," Interfaces, INFORMS, vol. 28(5), pages 29-55, October.
    3. Matthias Themeßl & Andreas Gobiet & Georg Heinrich, 2012. "Empirical-statistical downscaling and error correction of regional climate models and its impact on the climate change signal," Climatic Change, Springer, vol. 112(2), pages 449-468, May.
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    1. Anik Juli Dwi Astuti & Sofie Annys & Mekete Dessie & Jan Nyssen & Stefaan Dondeyne, 2022. "To What Extent Is Hydrologic Connectivity Taken into Account in Catchment Studies in the Lake Tana Basin, Ethiopia? A Review," Land, MDPI, vol. 11(12), pages 1-18, November.
    2. Omondi, Calisto Kennedy & Rientjes, Tom H.M. & Booij, Martijn J. & Nelson, Andrew D., 2021. "Satellite rainfall bias assessment for crop growth simulation – A case study of maize growth in Kenya," Agricultural Water Management, Elsevier, vol. 258(C).

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