Evaluation of Global Water Resources Reanalysis Runoff Products for Local Water Resources Applications: Case Study-Upper Blue Nile Basin of Ethiopia

  • Haileyesus Belay LakewEmail author
  • Semu Ayalew Moges
  • Emmanouil N. Anagnostou
  • Efthymios I. Nikolopoulos
  • Dereje Hailu Asfaw


The increasing availability of global observation datasets, both from in situ and remote sensors, and advancements in earth system models and data assimilation algorithms have generated a number of water resources reanalysis products that are available at global scale and high spatial and temporal resolutions. These products hold great potential for water resources applications, but their levels of uncertainty need to be evaluated at local scale. In this work, we evaluate the runoff product from two multi-model global water resources reanalyses (WRRs), available at 0.5° (WRR1) and 0.25° (WRR2) grid resolutions, which were produced within the framework of a European Union project (eartH2Observe) in the upper Blue Nile basin. Analysis indicates that the recently released WRR2 UniK product exhibits consistently better performance statistics than the earlier coarser-resolution WRR1 and the rest of the WRR2 products at all ranges of temporal and spatial scale evaluated. Streamflow simulations based on gauged rainfall forcing and the locally set hydrological model CREST outperforms all the other products, including UniK. Global hydrological products can be a data source for various water resources planning and management applications in data-scarce areas of Africa. This study cautions against using available global hydrological products without prior uncertainty evaluation.


Blue Nile eartH2Observe Water resource reanalysis Error characterization 



This work is supported by the EU-funded eartH2Observe (ENVE.2013.6.3-3) project. The authors would like to thank the Ethiopian Ministry of Water, Irrigation, and Electricity for the updated observed river runoff data. This study is coordinated by Addis Ababa University, School of Civil and Environmental Engineering.

Compliance with Ethical Standards

Conflicts of Interest

The authors declare no conflict of interest.


  1. Adler RF et al (2003) The Version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979–present). J Hydrometeorol 4(6):1147–1167CrossRefGoogle Scholar
  2. Alcamo J, Henrichs T, Rösch T (2000) Global modeling and scenario analysis. In: Rijsberman F (ed) World water scenarios – analyses. Earthscan Publications, LondonGoogle Scholar
  3. Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration - Guidelines for computing crop water requirements - FAO Irrigation and drainage paper 56, FAO Irrigation and drainage paper 56. Food and Agriculture Organization of the United Nations, Rome. ISBN 92-5-104219-5Google Scholar
  4. Balsamo G, Beljaars A, Scipal K, Viterbo P, van den Hurk B, Hirschi M, Betts AK (2009) A revised hydrology for the ECMWF model: verification from field site to terrestrial water storage and impact in the integrated forecast system. J Hydrometeorol 10(3):623–643CrossRefGoogle Scholar
  5. Beck H, van Dijk A, Levizzani V, Schellekens J, Miralles D, Martens B, Roo A (2017a) MSWEP: 3-hourly 0.25° global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data, 589–615 ppGoogle Scholar
  6. Beck H, van Dijk AIJM, de Roo A, Dutra E, Fink G, Orth R, Schellekens J (2017b) Global evaluation of runoff from 10 state-of-the-art hydrological models. Hydrol Earth Syst Sci 21(6):2881–2903CrossRefGoogle Scholar
  7. Burek P, Roo A, van der Knijff J (2013) LISFLOOD - distributed water balance and flood simulation model - revised user manualGoogle Scholar
  8. Dantec-Nédélec S et al (2017) Testing the capability of ORCHIDEE land surface model to simulate Arctic ecosystems: sensitivity analysis and site-level model calibration. Journal of Advances in Modeling Earth Systems 9(2):1212–1230CrossRefGoogle Scholar
  9. Doell P, Alcamo J, Henrichs T, Kaspar F, Lehner B, Rösch T, Siebert S (2001) The global integrated water model WaterGAP 2.1Google Scholar
  10. Donaldson RJ, Dyer RM, Krauss M (1975) An objective evaluator of techniques for predicting severe weather events. Preprints, Ninth Conf. on Severe Local Storms, Amer. Meteor. Soc., Norman, OK, pp 321–326Google Scholar
  11. Doswell C, Davies-Jones R, Keller DL (1990) On summary measures of skill in rare event forecasting based on contingency tablesGoogle Scholar
  12. Duan Q, Sorooshian S, Gupta V (1992) Effective and efficient global optimization for conceptual rainfall-runoff models. Water Resour Res 28Google Scholar
  13. Ducoudré NI, Laval K, Perrier A (1993) SECHIBA, a new set of parameterizations of the hydrologic exchanges at the land-atmosphere Interface within the LMD atmospheric general circulation model. J Clim 6(2):248–273CrossRefGoogle Scholar
  14. El-Sadek A, Bleiweiss M, Shukla M, Guldan S, Fernald A (2011) Alternative climate data sources for distributed hydrological modelling on a daily time step. Hydrol Process 25(10):1542–1557CrossRefGoogle Scholar
  15. Elshamy ME, Seierstad IA, Sorteberg A (2009) Impacts of climate change on Blue Nile flows using bias-corrected GCM scenarios. Hydrol Earth Syst Sci 13(5):551–565CrossRefGoogle Scholar
  16. Haberlandt U, Kite GW (1998) Estimation of daily space–time precipitation series for macroscale hydrological modelling. Hydrol Process 12(9):1419–1432CrossRefGoogle Scholar
  17. Hwang S, Graham WD, Adams A, Geurink J (2013) Assessment of the utility of dynamically-downscaled regional reanalysis data to predict streamflow in west Central Florida using an integrated hydrologic model. Reg Environ Chang 13(S1):69–80CrossRefGoogle Scholar
  18. Kanamaru H, Kanamitsu M (2007) Fifty-seven-year California reanalysis downscaling at 10 km (CaRD10). Part II: comparison with north American regional reanalysis. J Clim 20(22):5572–5592CrossRefGoogle Scholar
  19. Krinner G, Viovy N, de Noblet-Ducoudré N, Ogée J, Polcher J, Friedlingstein P, Ciais P, Sitch S, Prentice IC (2005) A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system. Glob Biogeochem Cycles 19(1)Google Scholar
  20. Lakew HB, Moges SA, Asfaw DH (2017) Hydrological evaluation of satellite and reanalysis precipitation products in the upper Blue Nile Basin: a case study of Gilgel Abbay. Hydrology 4(3):39CrossRefGoogle Scholar
  21. Le Moigne P (2009) SURFEX scientific documentation, 211 ppGoogle Scholar
  22. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I — a discussion of principles. J Hydrol 10(3):282–290CrossRefGoogle Scholar
  23. Peña-Arancibia J, van Dijk A, Stenson M, Viney NR (2011) Opportunities to evaluate a landscape hydrological model (AWRA-L) using global data setsGoogle Scholar
  24. Petrescu AMR, van Beek LPH, van Huissteden J, Prigent C, Sachs T, Corradi CAR, Parmentier FJW, Dolman AJ (2010) Modeling regional to global CH4 emissions of boreal and arctic wetlands. Glob Biogeochem Cycles 24(4):GB4009CrossRefGoogle Scholar
  25. Schellekens J et al (2017) A global water resources ensemble of hydrological models: the eartH2Observe Tier-1 dataset. Earth Syst Sci Data 9(2):389–413CrossRefGoogle Scholar
  26. Shen X, Hong Y (2014) CREST Coupled Routing and Excess STorage CREST user manual v2.1. University of Oklahoma (OU) HyDROS Lab. Accessed 14 Nov 2017
  27. Sutcliffe JV, Parks YP (1999) The hydrology of the Nile, xi + 179 pp. IAHS Press, WallingfordGoogle Scholar
  28. Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res: Atmos 106(D7):7183–7192CrossRefGoogle Scholar
  29. van den Hurk B, Viterbo P (2003) The Torne-Kalix PILPS 2(e) experiment as a test bed for modifications to the ECMWF land surface scheme. Glob Planet Chang 38(1):165–173CrossRefGoogle Scholar
  30. van Dijk A, Renzullo L (2011) The role of satellite observation in Australian water resources monitoring, 10–15 ppGoogle Scholar
  31. Wang J et al (2011) The coupled routing and excess storage (CREST) distributed hydrological model. Hydrol Sci J 56(1):84–98CrossRefGoogle Scholar
  32. Weedon GP, Gianpaolo B, Nicolas B, Sandra G, J BM, Pedro V (2014) The WFDEI meteorological forcing data set: WATCH forcing data methodology applied to ERA-interim reanalysis data. Water Resour Res 50(9):7505–7514CrossRefGoogle Scholar
  33. Wilk MB, Gnanadesikan R (1968) Probability plotting methods for the analysis for the analysis of data. Biometrika 55(1):1–17Google Scholar
  34. World Bank (2018) In: World Bank edited by W. Bank. Accessed 14 Nov 2017

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  • Haileyesus Belay Lakew
    • 1
    Email author
  • Semu Ayalew Moges
    • 1
    • 2
  • Emmanouil N. Anagnostou
    • 2
  • Efthymios I. Nikolopoulos
    • 2
  • Dereje Hailu Asfaw
    • 1
  1. 1.School of Civil and Environmental EngineeringAddis Ababa University, Institute of TechnologyAddis AbabaEthiopia
  2. 2.Civil and Environmental EngineeringUniversity of ConnecticutStorrsUSA

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