Have a personal or library account? Click to login
Factors controlling alterations in the performance of a runoff model in changing climate conditions Cover

Factors controlling alterations in the performance of a runoff model in changing climate conditions

Open Access
|Oct 2018

References

  1. Andréassian, V., Perrin, C., Michel, C., Usart-Sanchez, I., Lavarbe, J., 2001. Impact of imperfect knowledge on the efficiency and the parameters of watershed models. Journal of Hydrology, 205, 1–4, 206–223. http://dx.doi.org/10.1016/S0022-1694(01)00437-1.10.1016/S0022-1694(01)00437-1
  2. Ardia, D., Mullen, K.M., Peterson, B.G., Ulrich, J., 2015. DE-optim: Diferential evolution in R. Version 2.2-3.
  3. Bai, P., Liu, X., Liang, K., Liu, C., 2015. Comparison of performance of twelve monthly water balance models in different climatic catchments of China. Journal of Hydrology, 529, 1030–1040. DOI: 10.1016/j.jhydrol.2015.09.015.10.1016/j.jhydrol.2015.09.015
  4. Bergström, S., 1995. The HBV model. In: Sing, V.P. (Ed.): Computers Models of Watershed Hydrology. Water. Resour. Publ., pp. 443–476.
  5. Beven, K.J., 2005. Rainfall-runoff modelling: Introduction. In: Anderson, M.G. (Ed): Encyclopedia of Hydrological Sciences, Wiley, Chichester, pp. 1857–1868.10.1002/0470848944.hsa130
  6. Brath, A., Montanari, A., Toth, E., 2004. Analysis of the effects of different scenarios of historical data availability on the calibration of a spatially-distributed hydrological model. Journal of Hydrology, 291, 3–4, 232–253. http://dx.doi.org/10.1016/j.jhydrol.2003.12.044.10.1016/j.jhydrol.2003.12.044
  7. Breiman, L., Friedman, J., Stone, C.J., Olshen, R.A., 1984. Classification and Regression Trees. The Wadsworth and Brooks-Cole Statistics-Probability Series. Taylor & Francis, 368 p. ISBN: 0412048418, 9780412048418.
  8. Brigode, P., Oudin, L., Perrin, C., 2013. Hydrological model parameter instability: A source of additional uncertainty in estimating the hydrological impacts of climate change? Journal of Hydrology, 476, 410–425. http://dx.doi.org/10.1016/j.jhydrol.2012.11.012.10.1016/j.jhydrol.2012.11.012
  9. Ceola, S., Arheimer, B., Baratti, E., Blöschl, G., Capell, R., Castellarin, A., Freer, J., Han, D., Hrachowitz, M., Hundecha, Y., Hutton, C., Lindström, G., Montanari, A., Nijzink, R., Parajka, J., Toth, E., Viglione, A., and Wagener, T., 2015. Virtual laboratories: new opportunities for collaborative water science. Hydrol. Earth Syst. Sci., 19, 2101–2117. DOI: 10.5194/hess-19-2101-2015.10.5194/hess-19-2101-2015
  10. Chiew, F.H.S., Teng, J., Vaze, J., Post, D.A., Perraud, J.M., Kirono, D.G.C., Viney, N.R., 2009. Estimating climate change impact on runoff across southeast Australia: Method, results, and implications of the modeling method, Water Re-sour. Res., 45, W10414. DOI: 10.1029/2008WR007338.10.1029/2008WR007338
  11. Coron, L., Andréassian, V., Bourqui, M., Perrin, C., Hendrickx, F., 2011. Pathologies of hydrological model used in changing climatic conditions: a review. Hydro-climatology: Variability and change. In: Proceedings of IUGG2011 symposium J-H02, Melbourne, Australia.
  12. Coron, L., Andréassian, 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, W05552. DOI: 10.1029/2011WR011721.10.1029/2011WR011721
  13. Coron, L., Andréassan, V., Perrin, C., Bourqui, M., Hendrickx, F., 2014. On the lack of robustness of hydrologic models regarding water balance simulation: a diagnostic approach applied to three models of increasing complexity on 20 mountainous catchments. Hydrol. Earth Syst. Sci., 18, 727–746. DOI: 10.5194/hess-18-727-2014.10.5194/hess-18-727-2014
  14. Das, T., Bárdossy, A., Zehe, E., He, Y., 2008. Comparison of conceptual model performance using different representations of spatial variability. J. Hydrol., 356, 106–118.10.1016/j.jhydrol.2008.04.008
  15. Farkas, C., Kværnø, S.H., Engebretsen, A., Barneveld, R., Deelstra, J., 2016. Applying profile and catchment-based mathematical models for evaluating the run-off from a Nordic catchment. J. Hydrol. Hydromech., 64, 3, 218–225. DOI: 10.1515/johh-2016-0022.10.1515/johh-2016-0022
  16. Fenicia, F., Kavetski, D., Savenije, H.H.G., 2011. Elements of a flexible approach for conceptual hydrological modeling: 1. Motivation and theoretical development. Water Resour. Res., 47, W11510. DOI: 10.1029/2010wr010174.10.1029/2010wr010174
  17. Finger, D., Heinrich, G., Gobiet, A., Bauder, A., 2012. Projections of future water resources and their uncertainty in a glacierized catchment in the Swiss Alps and the subsequent effects on hydropower production during the 21st century. Water Resour. Res., 48, 02521. DOI: 10.1029/2011WR010733, 2012.10.1029/2011WR0107332012
  18. Fowler, K.J.A., Peel, M.C., Western, A.W., Zhang, L., Peterson, T.J., 2016. Simulating runoff under changing climate conditions: Revising an apparent deficiency of conceptual rainfall-runoff models. Water. Resour. Res., 52, 1820–1846. DOI: 10.1002/2015WR018068.10.1002/2015WR018068
  19. Gaál, L., Szolgay, J., Kohnová, S., Parajka, J., Merz, R., Viglione, A., Blöschl, G., 2012. Flood timescales: Understanding the interplay of climate and catchment processes through comparative hydrology. Water Resour. Res., 48, W04511. DOI: 10.1029/2011WR011509.10.1029/2011WR011509
  20. Iorgulescu, I., Beven, K.J., 2004. Nonparametric direct mapping of rainfall-runoff relationships: An alternative approach to data analysis and modeling? Water Resour. Res., 40, W08403. DOI: 10.1029/2004WR003094.10.1029/2004WR003094
  21. Klemeš, V., 1986. Dilettantism in hydrology: Transition or destiny? Water Resour. Res., 22, 9, 177–188.10.1029/WR022i09Sp0177S
  22. Kuentz, A., Arheimer, B., Hundecha, Y., Wagener, T., 2016. Understanding hydrologic variability across Europe through catchment classification. Hydrol. Earth Syst. Sci. Discuss., 21, 6, 1–28. DOI: 10.5194/hess-2016-428.10.5194/hess-2016-428
  23. Magand, C., Ducharne, A., Le Moine, N., Brigode, P., 2015. Parameter transferability under changing climate: case study with a land surface model in the Durance watershed, France. Hydrological Sciences Journal, 60, 7–8, 1408–1423. DOI: 10.1080/02626667.2014.993643.10.1080/02626667.2014.993643
  24. Merz, R., Blöschl, G., 2004. Regionalisation of catchment model parameters. Journal of Hydrology. 27, 95–123. DOI: 10.1002/hyp.6253.10.1002/hyp.6253
  25. Merz, R., Blöschl, G., Parajka, J., 2009. Scale effects in conceptual hydrological modelling. Water Resour. Res., 45, W09405. DOI: 10.1029/2009WR007872.10.1029/2009WR007872
  26. Merz, R., Parajka, J., Blöschl, G., 2011. Time stability of catchment model parameters: Implications for climate impact analyses. Water. Resour. Res., 47, 1015–1031. DOI: 10.1029/2010WR009505.10.1029/2010WR009505
  27. Nash, J.E. Sutcliffe, J.V., 1970. River flow forecasting through conceptual models part I-A discussion of principles, Journal of Hydrology, 10, 3, 282–290. DOI: 10.1016/0022-1694(70)90255-6.10.1016/0022-1694(70)90255-6
  28. Nester, T., Kirnbauer, R., Gutknecht, D., Blöschl, G., 2011. Climate and catchment controls on the performance of regional flood simulations. Journal of Hydrology, 340–356. http://dx.doi.org/10.1016/j.jhydrol.2011.03.028.10.1016/j.jhydrol.2011.03.028
  29. Nester, T., Komma, J., Blöschl, G., 2016. Real time forecasting in the Upper Danube basin. J. Hydrol. Hydromech., 64, 4, 404–414. DOI: 10.1515/johh-2016-0033.10.1515/johh-2016-0033
  30. Nijzink, R.C., Samaniego, L., Mai, J., Kumar, R., Thober, S., Zink, M., Schäfer, D., Savenije, H.H.G., Hrachowitz, M., 2016. The importance of topography-controlled sub-grid process heterogeneity and semi-quantitative prior constraints in distributed hydrological models. Hydrol. Earth Syst. Sci., 20, 1151–1176. DOI:10.5194/hess-20-1151-2016.10.5194/hess-20-1151-2016
  31. Osuch, M., Romanowicz, R.J., Booij, M.J., 2015. The influence of parametric uncertainty on the relationships between HBV model parameters and climatic characteristics. Hydrological Sciences Journal, 60, 7–8, 1299–1316. DOI: 10.1080/02626667.2014.967694.10.1080/02626667.2014.967694
  32. Oudin, L., Perrin, C., Mathevet, T., Andréassian, V., and Michel, C., 2006. Impact of biased and randomly corrupted inputs on the efficiency and the parameters of watershed models. J. Hydrol., 320, 1–2, 62–83. DOI: 10.1016/j.jhydrol.2005.07.016.10.1016/j.jhydrol.2005.07.016
  33. Parajka, J., Blöschl, G., 2008. The value of MODIS snow cover data in validating and calibrating conceptual hydrologic models. Journal of Hydrology, 358, 3–4, 240–258. https://doi.org/10.1016/j.jhydrol.2008.06.006.10.1016/j.jhydrol.2008.06.006
  34. Parajka, J., Merz, R., Blöschl, G., 2005. A comparison of regionalisation methods for catchment model parameters. Hydrol. Earth Syst. Sci., 9, 157–171. DOI: 10.5194/hess-9-157-2005.10.5194/hess-9-157-2005
  35. Parajka, J., Merz, R., Blöschl, G., 2007. Uncertainty and multiple calibration in regional water balance modelling case study in 320 Austrian catchments. Hydrol. Process, 21, 435–446. DOI: 10.1002/hyp.6253.10.1002/hyp.6253
  36. Pechlivanidis, I.G., Arheimer, B., 2015. Large-scale hydrological modelling by using modified PUB recommendations: the India-HYPE case. Hydrol. Earth Syst. Sci., 19, 4559–4579. DOI: 10.5194/hess-19-4559-2015.10.5194/hess-19-4559-2015
  37. Pebesma, E.J., 2001. Gstat User’s Manual. Dep. of Phys. Geogr., Utrecht Univ., Utrecht, The Netherlands.
  38. Perrin, C., Michel, C., Andréassian, V., 2001. Does a large number of parameters enhance model performance? Comparative assessment of common catchment model structures on 429 catchments. J. Hydrol., 242, 275–301. https://doi.org/10.1016/S0022-1694(00)00393-0.10.1016/S0022-1694(00)00393-0
  39. Perrin, C., Michel, C., Andréassian, V., 2003. Improvement of a parsimonious model for streamflow simulation. J. Hydrol., 279, 275–289. DOI: 10.1016/s0022-1694(03)00225-7. Perrin, C., Oudin, L., Andréassian, V., Rojas-Serna, C., Michel, C., Mathevet, T., 2007. Impact of limited streamflow data on the efficiency and the parameters of rainfall-runoff models. Hydrol. Sci. J., 52, 1, 131. http://dx.doi.org/10.1623/hysj.52.1.131.10.1016/s0022-1694(03)00225-7....-...2007.streamflowrainfall-runoffmodels.Hydrol.Sci.J.,52,1,131.http://dx.doi.org/10.1623/hysj.52.1.131
  40. Perrin, C., Andréassian, V., Rojas-Serna, C., Mathevet, T., Le Moine, N., 2008. Discrete parameterization of hydrological models: Evaluating the use of parameter sets libraries over 900 catchments. Water Resour. Res., 44, W08447. DOI: 10.1029/2007WR006579.10.1029/2007WR006579
  41. Poncelet, C., Merz, R., Parajka, J., Oudin, L., Andréassian, V., Perrin, C., 2017. Process-based interpretation of conceptual hydrological model performance using a multinational catchment set. Water Resource Research. DOI: 10.1002/2016WR019991.10.1002/2016WR019991
  42. R Development Core Team, 2011. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org/.
  43. Saft, M., Western, A.W., Zhang, L., Peel, M.C., Potter, N.J., 2015. The influence of multiyear drought on the annual rainfall-runoff relationship: An Australian perspective. Water Re-sour. Res., 51, 2444–2463. DOI: 10.1002/2014WR015348.10.1002/2014WR015348
  44. Saft, M., Peel, M.C., Western, A.W., Zhang, L., 2016. Predicting shifts in rainfall-runoff partitioning during multiyear drought: Roles of dry period and catchment characteristics. Water Resour. Res., 52. DOI: 10.1002/2016WR019525.10.1002/2016WR019525
  45. Schaefli, B. Gupta, H.V., 2007. Do Nash values have value? Hydrol. Process., 21, 2075–2080. DOI: 10.1002/hyp.6825. Seibert, J., 2003. Reliability of model predictions outside calibration conditions. Nordic Hydrology, 34, 477–492.10.1002/hyp.6825.J.2003..34477492
  46. Seibert, M., Merz, B., Apel, H., 2016. Seasonal forecasting of hydrological drought in the Limpopo basin: A comparison of statistical methods. Hydrol. Earth Syst. Sci. Discuss. DOI: 10.5194/hess-2016-4, 2016.10.5194/hess-2016-42016
  47. Seifert, D., Sonnenborg, T.O., Refsgaard, J.C., Højberg, A.L., Troldborg, L., 2012. Assessment of hydrological model predictive ability given multiple conceptual geological models. Water Resour. Res., 48, W06503. DOI: 10.1029/2011WR011149.10.1029/2011WR011149
  48. Seiler, G., Anctil, F., Perrin, C., 2012. Multimodel evaluation of twenty lumped hydrological models under contrasted climate conditions. Hydrol. Earth Syst. Sci., 16, 4, 1171–1189. http://dx.doi.org/10.5194/hess-16-1171-2012.10.5194/hess-16-1171-2012
  49. Sleziak, P., Szolgay, J., Hlavčová, K., Parajka, J., 2016a. The impact of the variability of precipitation and temperatures on the efficiency of a conceptual rainfall-runoff model. Slovak Journal of Civil Engineering, 24, 4, 1–7. DOI: 10.1515/sjce-2016-0016.10.1515/sjce-2016-0016
  50. Sleziak, P., Szolgay, J., Hlavčová, K., Parajka, J., 2016b. Assessment of the performance of a hydrological model in relation to selected climatic characteristics. In: Proc. 16th International Multidisciplinary Scientific GeoConference SGEM 2016, Book 3 Vol. 3, pp. 43–52. DOI: 10.5593/SGEM2016/HB33/S02.006.10.5593/SGEM2016/HB33/S02.006
  51. Stauer, J.J., Stensvold, K.A., Gregory, M.B., 2010. Determination of biologically significant hydrologic condition metrics in urbanizing watersheds: an empirical analysis over a range of environmental settings. Hydrobiologia, 654, 1, 27–55. DOI: 10.1007/s10750-010-0362-0.10.1007/s10750-010-0362-0
  52. Sun, W., Wang, Y., Wang, G., Cui, X., Yu, J., Zuo, D., Xu, Z., 2017. Physically based distributed hydrological model calibration based on a short period of streamflow data: case studies in four Chinese basins. Hydrol. Earth Syst. Sci., 21, 251–265. DOI: 10.5194/hess-21-251-2017.10.5194/hess-21-251-2017
  53. Therneau, T., Atkinson, B., Ripley, B., 2017. Recursive partitioning and regression trees. Version 4.1-11.
  54. van Esse, W.R., Perrin, C., Booij, M.J., Augustijn, D.C.M., Fenicia, F., Kavetski, D., Lobligeois, F., 2013. The influence of conceptual model structure on model performance: a comparative study from 273 French catchments. Hydrol. Earth Syst. Sci., 17, 4227–4239. DOI: 10.5194/hess-17-4227-2013.10.5194/hess-17-4227-2013
  55. van Griensven, A., Meixner, T., Grunwald, S., Bishop, T., Diluzio, M., Srinivasan, R., 2006. A global sensitivity analysis tool for the parameters of multi-variable catchment models. Journal of Hydrology, 324, 10–23.10.1016/j.jhydrol.2005.09.008
  56. Valent, P., Szolgay, J., 2012. Assessment of the uncertainties of a conceptual hydrologic model by using artificially generated flows. Slovak Journal of Civil Engineering, 20, 4, 35–43. DOI: https://doi.org/10.2478/v10189-012-0020-9.10.2478/v10189-012-0020-9
  57. Vaze, J., Post, D.A., Chiew, F.H.S., Perraud, J.M., Viney, N.R., Teng, J., 2010. Climate nonstationarity – Validity of calibrated rainfall-runoff models for use in climatic changes studies. J. Hydrol., 394, 3–4, 447–457. DOI: 10.1016/j.jhydrol.2010.09.018.10.1016/j.jhydrol.2010.09.018
  58. Viglione, A., Parajka, J., Rogger, M., Salinas, J.L., Laaha, G., Sivapalan, M., Blöschl, G., 2013. Comparative assessment of predictions in ungauged basins – Part 3: Runoff signatures in Austria. Hydrol. Earth Syst. Sci., 17, 2263–2279. DOI: 10.5194/hess-17-2263-2013.10.5194/hess-17-2263-2013
  59. Viglione, A., Parajka, J., 2014. TUWmodel: Lumped hydrological model for educational purposes. Version 0.1-4. https://cran.r-project.org/web/packages/TUWmodel/index.html.
  60. Viviroli, D., Zappa, M., Schwanbeck, J., Gurtz, J., Weingartner, R., 2009. Continuous simulation for flood estimation in un-gauged mesoscale catchments of Switzerland – Part I: Modelling framework and calibration results. Journal of Hydro-logy, 377, 191–207. https://doi.org/10.1016/j.jhydrol. 2009.08.023.10.1016/j.jhydrol.2009.08.023
  61. Wang-Erlandsson, L., Bastiaanssen, W.G.M., Gao, H., Jager-meyer, J., Senay, G.B., van Dijk, A.I.J.M., Guerschman, J.P., Keys, P.W., Gordon, L.J., Savenije, H.H.G., 2016. Global root zone sorage capacity from satellite-based evaporation. Hydrol. Earth Syst. Sci., 20, 1459–1481. www.hydrol-earth-syst-sci.net/20/1459/2016/.10.5194/hess-20-1459-2016
  62. Wilby, R.L., 2005. Uncertainty in water resource model parameters used for climate change impact assessment. Hydrol. Processes, 19, 16, 3201–3219.10.1002/hyp.5819
DOI: https://doi.org/10.2478/johh-2018-0031 | Journal eISSN: 1338-4333 | Journal ISSN: 0042-790X
Language: English
Page range: 381 - 392
Submitted on: Oct 6, 2017
|
Accepted on: Apr 23, 2018
|
Published on: Oct 29, 2018
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2018 Patrik Sleziak, Ján Szolgay, Kamila Hlavčová, Doris Duethmann, Juraj Parajka, Michal Danko, published by Slovak Academy of Sciences, Institute of Hydrology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.