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

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An Erratum to this article was published on 06 January 2017

Abstract

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.

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References

  • Biondi D, Freni G, Iacobellis V, Mascaro G, Montanari A (2012) Validation of hydrological models: conceptual basis, methodological approaches and a proposal for a code of practice. Phys Chem Earth 42-44:70–76

    Article  Google Scholar 

  • Coles S (2001) An introduction to statistical modeling of extreme values. Springer-Verlag, London, UK

    Book  Google Scholar 

  • Cornelissen T, Diekkruger B, Giertz S (2013) A comparison of hydrological models for assessing the impact of land use and climate change on discharge in a tropical catchment. J Hydrol 498:221–236

    Article  Google Scholar 

  • Coron L, Andreassian V, Perrin C, Lerat J, Vaze J, Bourqui M, Hendrickx F (2012) Crash testing hydrological models in contrasted climate conditions: An experiment on 216 Australian catchments. Water Resour Res 48.

  • Criss RE, Winston WE (2008) Do Nash values have value? Discussion and alternate proposals. Hydrol Process 22:2723–2725

    Article  Google Scholar 

  • Crochemore L, Perrin C, Andreassian V, Ehret U, Seibert SP, Grimaldi S, Gupta H, Paturel J (2015) Comparing expert judgement and numerical criteria for hydrograph evaluation. Hydrol Sci J 60:402–423

    Article  Google Scholar 

  • Dankers R, Arnell NW, Clark DB, Falloon PD, Fekete BM, Gosling SN, Heinke J, Kim H, Masaki Y, Satoh Y, Stacke T, Wada Y, Wisser D (2014) First look at changes in flood hazard in the inter-sectoral impact model intercomparison project ensemble. P Natl Acad Sci USA 111:3257–3261

    Article  Google Scholar 

  • Davie JCS, Falloon PD, Kahana R, Dankers R, Betts R, Portmann FT, Wisser D, Clark DB, Ito A, Masaki Y, Nishina K, Fekete B, Tessler Z, Wada Y, Liu X, Tang Q, Hagemann S, Stacke T, Pavlick R, Schaphoff S, Gosling SN, Franssen W, Arnell N (2013) Comparing projections of future changes in runoff from hydrological and biome models in ISI-MIP. Earth Syst Dynam 4:359–374

    Article  Google Scholar 

  • Dawson CW, Abrahart RJ, See LM (2007) HydroTest: a web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts. Environ Model Softw 22:1034–1052

    Article  Google Scholar 

  • Gao C, Yao MT, Wang YJ, Zhai JQ, Buda S, Fischer T, Zeng XF, Wang WP (2015) Hydrological model comparison and assessment: criteria from catchment scales and temporal resolution. Hydrol Sci J

  • Gudmundsson L, Tallaksen LM, Stahl K, Clark DB, Dumont E, Hagemann S, Bertrand N, Gerten D, Heinke J, Hanasaki N, Voss F, Koirala S (2012a) Comparing large-scale hydrological model simulations to observed runoff percentiles in Europe. J Hydrometeorol 13:604–620

    Article  Google Scholar 

  • Gudmundsson L, Wagener T, Tallaksen LM, Engeland K (2012b) Evaluation of nine large-scale hydrological models with respect to the seasonal runoff climatology in Europe. Water Resour Res 48

  • Gupta HV, Kling H, Yilmaz KK, Martinez GF (2009) Decomposition of the mean squared error and NSE performance criteria: implications for improving hydrological modelling. J Hydrol 377:80–91

    Article  Google Scholar 

  • Huang SC, Krysanova V, Hattermann FF (2013) Projection of low flow conditions in Germany under climate change by combining three RCMs and a regional hydrological model. Acta Geophys 61:151–193

    Article  Google Scholar 

  • Huang SC, Krysanova V, Hattermann FF (2014) Does bias correction increase reliability of flood projections under climate change? A case study of large rivers in Germany. Int J Climatol 34:3780–3800

    Article  Google Scholar 

  • Jiang T, Chen YQD, Xu CYY, Chen XH, Chen X, Singh VP (2007) Comparison of hydrological impacts of climate change simulated by six hydrological models in the Dongjiang Basin, South China. J Hydrol 336:316–333

    Article  Google Scholar 

  • Kay AL, Rudd AC, Davies HN, Kendon EJ, Jones RG (2015) Use of very high resolution climate model data for hydrological modelling: baseline performance and future flood changes. Clim Chang 133:193–208

    Article  Google Scholar 

  • Kling H, Fuchs M, Paulin M (2012) Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios. J Hydrol 424:264–277

    Article  Google Scholar 

  • Ludwig R, May I, Turcotte R, Vescovi L, Braun M, Cyr JF, Fortin LG, Chaumont D, Biner S, Chartier I, Caya D, Mauser W (2009) The role of hydrological model complexity and uncertainty in climate change impact assessment. Adv Geosci 21:63–71

    Article  Google Scholar 

  • Moriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD, Veith TL (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. T Asabe 50:885–900

    Article  Google Scholar 

  • Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I -a discussion of principles. J Hydrol 10:282–290

    Article  Google Scholar 

  • Poulin A, Brissette F, Leconte R, Arsenault R, Malo JS (2011) Uncertainty of hydrological modelling in climate change impact studies in a Canadian, snow-dominated river basin. J Hydrol 409:626–636

    Article  Google Scholar 

  • Prudhomme C, Parry S, Hannaford J, Clark DB, Hagemann S, Voss F (2011) How well do large-scale models reproduce regional hydrological extremes in Europe? J Hydrometeorol 12:1181–1204

    Article  Google Scholar 

  • Prudhomme C, Giuntoli I, Robinson EL, Clark DB, Arnell NW, Dankers R, Fekete BM, Franssen W, Gerten D, Gosling SN, Hagemann S, Hannah DM, Kim H, Masaki Y, Satoh Y, Stacke T, Wada Y, Wisser D (2014) Hydrological droughts in the twenty-first century, hotspots and uncertainties from a global multimodel ensemble experiment. P Natl Acad Sci USA 111:3262–3267

    Article  Google Scholar 

  • Pushpalatha R, Perrin C, Le Moine N, Andreassian V (2012) A review of efficiency criteria suitable for evaluating low-flow simulations. J Hydrol 420:171–182

    Article  Google Scholar 

  • Ritter A, Munoz-Carpena R (2013) Performance evaluation of hydrological models: statistical significance for reducing subjectivity in goodness-of-fit assessments. J Hydrol 480:33–45

    Article  Google Scholar 

  • Schaefli B, Gupta HV (2007) Do Nash values have value? Hydrol Process 21:2075–2080

    Article  Google Scholar 

  • Staudinger M, Stahl K, Seibert J, Clark MP, Tallaksen LM (2011) Comparison of hydrological model structures based on recession and low flow simulations. Hydrol Earth Syst Sci 15:3447–3459

    Article  Google Scholar 

  • Strauch M, Kumar R, Eisner S, Mulligan M, Reinhardt J, Santini W, Vetter T, Friesen J (2016) Adjustment of global precipitation data for enhanced hydrologic modeling of tropical Andean watersheds. Clim Chang. doi:10.1007/s10584-016-1706-1

    Google Scholar 

  • Van Loon AF, Van Huijgevoort MHJ, Van Lanen HAJ (2012) Evaluation of drought propagation in an ensemble mean of large-scale hydrological models. Hydrol Earth Syst Sci 16:4057–4078

    Article  Google Scholar 

  • Vansteenkiste T, Tavakoli M, Van Steenbergen N, De Smedt F, Batelaan O, Pereira F, Willems P (2014) Intercomparison of five lumped and distributed models for catchment runoff and extreme flow simulation. J Hydrol 511:335–349

    Article  Google Scholar 

  • Velazquez JA, Schmid J, Ricard S, Muerth MJ, St-Denis BG, Minville M, Chaumont D, Caya D, Ludwig R, Turcotte R (2013) An ensemble approach to assess hydrological models’ contribution to uncertainties in the analysis of climate change impact on water resources. Hydrol Earth Syst Sci 17:565–578

    Article  Google Scholar 

  • Vetter T, Huang S, Aich V, Yang T, Wang X, Krysanova V, Hattermann F (2015) Multi-model climate impact assessment and intercomparison for three large-scale river basins on three continents. Earth Syst Dynam 6:17–43

    Article  Google Scholar 

  • Weedon GP, Gomes S, Viterbo P, Shuttleworth WJ, Blyth E, Osterle H, Adam JC, Bellouin N, Boucher O, Best M (2011) Creation of the WATCH forcing data and its use to assess global and regional reference crop evaporation over land during the twentieth century. J Hydrometeorol 12:823–848

    Article  Google Scholar 

  • Yilmaz KK, Gupta HV, Wagener T (2008) A process-based diagnostic approach to model evaluation: Application to the NWS distributed hydrologic model. Water Resour Res 44

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Acknowledgments

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).

Author information

Authors and Affiliations

  1. Norwegian Water Resources and Energy Directorate (NVE), Oslo, Norway

    Shaochun Huang

  2. UFZ-Helmholtz Centre for Environmental Research, Leipzig, Germany

    Rohini Kumar & Michael Strauch

  3. Center for Environmental Systems Research, University of Kassel, Kassel, Germany

    Martina Flörke

  4. State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Center for Global Change and Water Cycle, Hohai University, Nanjing, China

    Tao Yang

  5. Swedish Meteorological and Hydrological Institute, Norrköping, Sweden

    Yeshewatesfa Hundecha

  6. Justus-Liebig-University Gießen, Gießen, Germany

    Philipp Kraft

  7. College of Territorial Resources and Tourism, Anhui Normal University, Wuhu, China

    Chao Gao

  8. Water Problems Institute, Russian Academy of Sciences, Moscow, Russian Federation

    Alexander Gelfan

  9. Potsdam Institute for Climate Impact Research (PIK), Potsdam, Germany

    Stefan Liersch, Anastasia Lobanova, Julia Reinhardt & Valentina Krysanova

  10. Centre for Carbon, Water and Food (FAE), The University of Sydney, Sydney, NSW, Australia

    Floris van Ogtrop

  11. Institute of Water Resources Management, Leibniz University of Hannover, Hannover, Germany

    Uwe Haberlandt

Authors
  1. Shaochun Huang
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  2. Rohini Kumar
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  3. Martina Flörke
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  4. Tao Yang
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  5. Yeshewatesfa Hundecha
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  6. Philipp Kraft
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  7. Chao Gao
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  8. Alexander Gelfan
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  9. Stefan Liersch
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  10. Anastasia Lobanova
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  11. Michael Strauch
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  12. Floris van Ogtrop
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  13. Julia Reinhardt
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  14. Uwe Haberlandt
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  15. Valentina Krysanova
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Corresponding author

Correspondence to Shaochun Huang.

Additional information

This article is part of a Special Issue on "Hydrological Model Intercomparison for Climate Impact Assessment" edited by Valentina Krysanova and Fred Hattermann.

An erratum to this article is available at http://dx.doi.org/10.1007/s10584-016-1895-7.

Electronic supplementary material

ESM 1

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

High Resolution Image (EPS 2905 kb)

ESM 2

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

High Resolution Image (EPS 8531 kb)

ESM 3

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

High Resolution Image (EPS 1118 kb)

ESM 4

Table A list of all statistic results for each basin (XLSX 27 kb)

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Huang, S., Kumar, R., Flörke, M. et al. Evaluation of an ensemble of regional hydrological models in 12 large-scale river basins worldwide. Climatic Change 141, 381–397 (2017). https://doi.org/10.1007/s10584-016-1841-8

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  • DOI: https://doi.org/10.1007/s10584-016-1841-8

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