Climatic Change

, Volume 141, Issue 3, pp 381–397 | Cite as

Evaluation of an ensemble of regional hydrological models in 12 large-scale river basins worldwide

  • Shaochun Huang
  • Rohini Kumar
  • Martina Flörke
  • Tao Yang
  • Yeshewatesfa Hundecha
  • Philipp Kraft
  • Chao Gao
  • Alexander Gelfan
  • Stefan Liersch
  • Anastasia Lobanova
  • Michael Strauch
  • Floris van Ogtrop
  • Julia Reinhardt
  • Uwe Haberlandt
  • Valentina Krysanova


In regional climate impact studies, good performance of regional models under present/historical climate conditions is a prerequisite for reliable future projections. This study aims to investigate the overall performance of 9 hydrological models for 12 large-scale river basins worldwide driven by the reanalysis climate data from the Water and Global Change (WATCH) project. The results serve as the basis of the application of regional hydrological models for climate impact assessment within the second phase of the Inter-Sectoral Impact Model Intercomparison project (ISI-MIP2). The simulated discharges by each individual hydrological model, as well as the ensemble mean and median series were compared against the observed discharges for the period 1971–2001. In addition to a visual comparison, 12 statistical criteria were selected to assess the fidelity of model simulations for monthly hydrograph, seasonal dynamics, flow duration curves, extreme floods and low flows. The results show that most regional hydrological models reproduce monthly discharge and seasonal dynamics successfully in all basins except the Darling in Australia. The moderate flow and high flows (0.02–0.1 flow exceedance probabilities) are also captured satisfactory in many cases according to the performance ratings defined in this study. In contrast, the simulation of low flow is problematic for most basins. Overall, the ensemble discharge statistics exhibited good agreement with the observed ones except for extremes in particular basins that need further scrutiny to improve representation of hydrological processes. The performances of both the conceptual and process-based models are comparable in all basins.


Hydrological Model Generalize Extreme Value Generalize Pareto Distribution Monthly Discharge Volumetric Efficiency 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors would like to thank all project partners who contributed to this study in the ISI-MIP2 project. The Chinese partner was funded by National Natural Science Foundation of China (41571018).

Supplementary material

10584_2016_1841_Fig4_ESM.gif (288 kb)

Fig. A The comparison of FDC high-segment (0–0.1 flow exceedance probabilities) at ten gauges. (GIF 287 kb)

10584_2016_1841_MOESM1_ESM.eps (2.8 mb)
High Resolution Image (EPS 2905 kb)
10584_2016_1841_Fig5_ESM.gif (341 kb)

Fig. B The comparison of FDC low-segment (0.7–1 flow exceedance probabilities) at ten gauges. (GIF 341 kb)

10584_2016_1841_MOESM2_ESM.eps (8.3 mb)
High Resolution Image (EPS 8531 kb)
10584_2016_1841_Fig6_ESM.gif (260 kb)

Fig. C Comparison of model performance between the conceptual and process-based models. (GIF 260 kb)

10584_2016_1841_MOESM3_ESM.eps (1.1 mb)
High Resolution Image (EPS 1118 kb)
10584_2016_1841_MOESM4_ESM.xlsx (27 kb)
ESM 4 Table A list of all statistic results for each basin (XLSX 27 kb)


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Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Shaochun Huang
    • 1
  • Rohini Kumar
    • 2
  • Martina Flörke
    • 3
  • Tao Yang
    • 4
  • Yeshewatesfa Hundecha
    • 5
  • Philipp Kraft
    • 6
  • Chao Gao
    • 7
  • Alexander Gelfan
    • 8
  • Stefan Liersch
    • 9
  • Anastasia Lobanova
    • 9
  • Michael Strauch
    • 2
  • Floris van Ogtrop
    • 10
  • Julia Reinhardt
    • 9
  • Uwe Haberlandt
    • 11
  • Valentina Krysanova
    • 9
  1. 1.Norwegian Water Resources and Energy Directorate (NVE)OsloNorway
  2. 2.UFZ-Helmholtz Centre for Environmental ResearchLeipzigGermany
  3. 3.Center for Environmental Systems ResearchUniversity of KasselKasselGermany
  4. 4.State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Center for Global Change and Water CycleHohai UniversityNanjingChina
  5. 5.Swedish Meteorological and Hydrological InstituteNorrköpingSweden
  6. 6.Justus-Liebig-University GießenGießenGermany
  7. 7.College of Territorial Resources and TourismAnhui Normal UniversityWuhuChina
  8. 8.Water Problems InstituteRussian Academy of SciencesMoscowRussian Federation
  9. 9.Potsdam Institute for Climate Impact Research (PIK)PotsdamGermany
  10. 10.Centre for Carbon, Water and Food (FAE)The University of SydneySydneyAustralia
  11. 11.Institute of Water Resources ManagementLeibniz University of HannoverHannoverGermany

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