Skip to main content
Login
  1. Home
  2. The Field Guide to Mixing Social and Biophysical Methods in Environmental Research
  3. 31. Hydrological modelling
Open Book Publishers

Hydrological modelling

  • Lieke Melsen(author)
Chapter of: The Field Guide to Mixing Social and Biophysical Methods in Environmental Research(pp. 493–502)
  • Export Metadata
  • Metadata
  • Locations
  • Contributors
  • References

Export Metadata

Metadata
Title Hydrological modelling
ContributorLieke Melsen(author)
DOIhttps://doi.org/10.11647/obp.0418.31
Landing pagehttps://www.openbookpublishers.com/books/10.11647/obp.0418/chapters/10.11647/obp.0418.31
Licensehttps://creativecommons.org/licenses/by-nc/4.0/
CopyrightLieke Melsen;
PublisherOpen Book Publishers
Published on2025-02-25
Long abstract

Numerical hydrological models can be useful tools to explore elements of the hydrological cycle. While there is a wide range of model types available, they are all inherently subject to uncertainty.

Page rangepp. 493–502
Print length10 pages
LanguageEnglish (Original)
Locations
Landing PageFull text URLPlatform
PDFhttps://www.openbookpublishers.com/books/10.11647/obp.0418/chapters/10.11647/obp.0418.31Landing pagehttps://books.openbookpublishers.com/10.11647/obp.0418.31.pdfFull text URL
HTMLhttps://www.openbookpublishers.com/books/10.11647/obp.0418/chapters/10.11647/obp.0418.31Landing pagehttps://books.openbookpublishers.com/10.11647/obp.0418/ch31.xhtmlFull text URLPublisher Website
Contributors

Lieke Melsen

(author)
Associate Professor Computational Hydrology at Wageningen University & Research
https://orcid.org/0000-0003-0062-1301
References
  1. For the basics of (rainfall-runoff) modelling: Beven, K.J. 2012. ‘Rainfall-runoff modelling, the primer’, in Down to Basics: Runoff Processes and the Modelling Process (John Wiley and Sons). https://doi.org/10.1002/9781119951001
  2. Addor, N. and L. Melsen. 2019. ‘Legacy, rather than adequacy, drives the selection of hydrological models’, Water Resources Research, 55. https://doi.org/10.1029/2018wr022958 
  3. Andréassian, V., C. Perrin, L. Berthet, N. Le Moine, J. Lerat, C. Loumagne, L. Oudin, T. Mathevet, M. Ramos, and A. Valéry. 2009. ‘HESS opinions: Crash tests for a standardized evaluation of hydrological models’, Hydrology and Earth System Sciences, 13, pp. 1757–1764. https://doi.org/10.5194/hess-13-1757-2009 
  4. Arnell, N., D. van Vuuren, and M. Isaac. 2011. ‘The implications of climate policy for the impacts of climate change on global water resources’, Global Environmental Change, 21.2, pp. 592–603. https://doi.org/10.1016/j.gloenvcha.2011.01.015 
  5. Babel, L., D. Vinck, and D. Karssenberg. 2019. ‘Decision-making in model construction: Unveiling habits’, Environmental Modelling & Software, 120.  https://doi.org/10.1016/j.envsoft.2019.07.015 
  6. Bennett, N., B. Croke, G. Guariso, J. Guillaume, S. Hamilton, A. Jakeman, S. Marsili-Libelli, L. Newham, J. Norton, C. Perrin, S. Pierce, B. Robson, R. Seppelt, A. Voinov, B. Fath, and V. Andréassian. 2013. ‘Characterising performance of environmental models’, Environmental Modelling & Software, 40, pp. 1–20. https://doi.org/10.1016/j.envsoft.2012.09.011 
  7. Best, M., G. Abramowitz, H. Johnson, A. Pitman, G. Balsamo, A. Boone, M. Cuntz, B. Decharme, P. Dirmeyer, J. Dong, M. Ek, Z. Guo, V. Haverd, B. van den Hurk, G. Nearing, B. Pak, C. Peters-Lidard, J. Santanello Jr., L. Stevens, and N. Vuichard. 2015. ‘The plumbing of land surface models: Benchmarking model performance’, Journal of Hydrometeorology, 16, pp. 1425–1442. https://doi.org/10.1175/jhm-d-14-0158.1 
  8. Beven, K.J. 2012. ‘Rainfall-runoff modelling, the primer’, in Down to Basics: Runoff Processes and the Modelling Process, by K.J. Beven (John Wiley and Sons). https://doi.org/10.1002/9781119951001 
  9. Clark, M. and D. Kavetski. 2010. ‘Ancient numerical daemons of conceptual hydrological modeling: 1. Fidelity and efficiency of time stepping schemes’, Water Resources Research, 46. https://doi.org/10.1029/2009wr008894 
  10. Clark, M., H. McMillan, D. Collins, D. Kavetski, and R. Woods. 2011. ‘Hydro- logical field data from a modeller’s perspective: Part 2: Process-based evaluation of model hypotheses’, Hydrological Processes, 25, pp. 523–543.  https://doi.org/10.1002/hyp.7902 
  11. Clark, M., B. Nijssen, J. Lundquist, D. Kavetski, D. Rupp, R. Woods, J. Freer, E. Gutmann, A. Wood, L.D. Brekke, J. Arnold, D. Gochis, and R. Rasmussen. 2015. ‘A unified approach for process-based hydrologic modeling: 1. Modeling concept’, Water Resources Research, 51, pp. 2498–2514. https://doi.org/10.1002/2015wr017198 
  12. Clark, M. P., A.G. Slater, D.E. Rupp, R.A. Woods, J.A. Vrugt, H.V. Gupta, T. Wagener, and L.E. Hay. 2008. ‘Framework for Understanding Structural Errors (FUSE): a modular framework to diagnose differences between hydrological models’, Water Resources Research, 44.  https://doi.org/10.1029/2007wr006735 
  13. Comber, A., P. Fisher, and R. Wadsworth. 2005. ‘What is land cover?’, Environment and Planning B: Planning and Design, 32, pp. 199–209. https://doi.org/10.1068/b31135 
  14. Dooge, J. 1986. Reflections in Hydrology: Science and Practice (American Geophysical Union).
  15. Gharari, S., H.V. Gupta, M.P. Clark, M. Hrachowitz, F. Fenicia, P. Matgen, and H.H.G. Savenije. 2021. ‘Understanding the information content in the hierarchy of model development decisions: Learning from data’, Water Resources Research, 57.6.  https://doi.org/10.1029/2020wr027948 
  16. Gupta, H.V., M.P. Clark, J.A.V.G. Abramowitz, and M. Ye. 2012. ‘Towards a comprehensive assessment of model structural adequacy’, Water Resources Research, 48. https://doi.org/10.1029/2011wr011044 
  17. Hämäläinen, R. 2015. ‘Behavioural issues in environmental modelling—the missing perspective’, Environmental Modelling & Software, 73, pp. 244–253. https://doi.org/10.1016/j.envsoft.2015.08.019 
  18. Hämäläinen, R. and T. Lahtinen. 2016. ‘Path dependence in operational research—how the modeling process can influence the results’, Operations Research Perspectives, 3, pp. 14–20. https://doi.org/10.1016/j.orp.2016.03.001 
  19. Hamilton, S., B. Fu, J. Guillaume, S. Pierce, and F. Zare. 2019. ‘A framework for characterising and evaluating the effectiveness of environmental modelling’, Environ. Modell. Softw., 118, pp. 83–98. https://doi.org/10.1016/j.envsoft.2019.04.008 
  20. Höge, M., T. Wöhling, and W. Nowak. 2018. ‘A primer for model selection: The decisive role of model complexity’, Water Resources Research, 54.3, pp. 1688–1715. https://doi.org/10.1002/2017wr021902 
  21. Horton, P., B. Schaefli, and M. Kauzlaric. 2022. ‘Why do we have so many different hydrological models? a review based on the case of Switzerland’, WIREs Water, 9.1. https://doi.org/10.1002/wat2.1574 
  22. Jansen, K. F., A.J. Teuling, J.R. Craig, M. Dal Molin, W.J.M. Knoben, J. Parajka, M. Vis, and L.A. Melsen. 2021. ‘Mimicry of a conceptual hydrological model (HBV): What’s in a name?’, Water Resources Research, 57.5. https://doi.org/10.1029/2020wr029143 
  23. Kavetski, D. and M. Clark. 2010. ‘Ancient numerical daemons of conceptual hydrological modeling: 2. Impact of time stepping schemes on model analysis and prediction’, Water Resources Research, 46.  https://doi.org/10.1029/2009wr008896 
  24. Knoben, W. J. M., J.E. Freer, K.J.A. Fowler, M.C. Peel, and R.A. Woods. 2019. ‘Modular assessment of rainfall–runoff models toolbox (MARRMoT) v1.2: an open-source, extendable framework providing implementations of 46 conceptual hydrologic models as continuous state-space formulations’, Geoscientific Model Development, 12.6, pp. 2463–2480. https://doi.org/10.5194/gmd-12-2463-2019 
  25. Kratzert, F., D. Klotz, C. Brenner, K. Schulz, and M. Herrnegger. 2018. ‘Rainfall–runoff modelling using long short-term memory (LSTM) networks’, Hydrology and Earth System Sciences, 22.11, pp. 6005–6022. https://doi.org/10.5194/hess-22-6005-2018 
  26. Krueger, T., T. Page, K. Hubacek, L. Smith, and K. Hiscock. 2012. ‘The role of expert opinion in environmental modelling’, Environmental Modelling & Software, 36, pp. 4–18. https://doi.org/10.1016/j.envsoft.2012.01.011 
  27. La Follette, P.T., A.J. Teuling, N. Addor, M. Clark, K. Jansen, and L.A. Melsen. 2021. ‘Numerical daemons of hydrological models are summoned by extreme precipitation’, Hydrology and Earth System Sciences, 25.10, pp. 5425–5446. https://doi.org/10.5194/hess-25-5425-2021 
  28. Lane, S.N. 2011. ‘Making mathematical models perform in geographical space(s),’ in The Sage Handbook of Geographical Knowledge, ed. by J.A. Agnew and D.N. Livingston (Sage Publications).
  29. Lane, S.N. 2014. ‘Acting, predicting and intervening in a socio-hydrological world’, Hydrology and Earth System Sciences, 18.3, pp. 927–952.  https://doi.org/10.5194/hess-18-927-2014 
  30. Lim, T., P. Glynn, G. Bitterman, J. Guillaume, J. Little, and D. Webster. 2023. ‘Recognizing political influences in participatory socio-ecological systems modeling’, Socio-Environmental Systems Modelling, 5. https://doi.org/10.18174/sesmo.18509 
  31. Liu, Y. and H.V. Gupta. 2007. ‘Uncertainty in hydrologic modeling: Towards an integrated data assimilation framework’, Water Resources Research, 43. https://doi.org/10.1029/2006wr005756 
  32. Melsen, L. 2022. ‘It takes a village to run a model: the social practices of hydrological modelling’, Water Resources Research, 58.2. https://doi.org/10.1029/2021wr030600 
  33. Melsen, L. 2023. ‘The modeling toolkit: how recruitment strategies for modeling positions influence model progress’, Frontiers in Water, 5. https://doi.org/10.3389/frwa.2023.1149590 
  34. Melsen, L., J. Vos, and R. Boelens. 2018. ‘What is the role of the model in socio-hydrology? discussion of ‘prediction in a socio-hydrological world’, Hydrological Sciences Journal, 63, pp. 1435–1443.  https://doi.org/10.1080/02626667.2018.1499025 
  35. Nearing, G. S., F. Kratzert, A.K. Sampson, C.S. Pelissier, D. Klotz, J.M. Frame, C. Prieto, and H.V.Gupta. 2021. ‘What role does hydrological science play in the age of machine learning?’, Water Resources Research, 57.3.  https://doi.org/10.1029/2020wr028091 
  36. Odoni, N. and S. Lane. 2010. ‘Knowledge-theoretic models in hydrology’, Progress in Physical Geography: Earth and Environment, 34, pp. 151–171. https://doi.org/10.1177/0309133309359893 
  37. Oreskes, N., K. Shrader-Frechette, and K. Belitz. 1994. ‘Verification, validation, and confirmation of numerical models in the Earth Sciences’, Science, 263.5147, pp. 641–646. https://doi.org/10.1126/science.263.5147.641 
  38. Packett, E., N. Grigg, J. Wu, S. Cuddy, P. Wallbrink, and A. Jakeman. 2020. ‘Mainstreaming gender into water management modelling processes’, Environmental Modelling & Software, 127.  https://doi.org/10.1016/j.envsoft.2020.104683 
  39. Pianosi, F., K. Beven, J. Freer, J. Hall, J. Rougier, D. Stephanson, and T. Wagener. 2016. ‘Sensitivity analysis of environmental models: A systematic review with practical workflow’, Environmental Modelling & Software, 79, pp. 214–232. https://doi.org/10.1016/j.envsoft.2016.02.008 
  40. Puy, A., R. Sheikholeslami, H. Gupta, J. Hall, B. Lankford, S. Lo Piano, J. Meier, F. Pappenberger, A. Porporato, G. Vico, and A. Saltelli. 2022. ‘The delusive accuracy of global irrigation water withdrawal estimates’, Nature Communications, 13.3183. https://doi.org/10.1038/s41467-022-30731-8 
  41. Razavi, S., D.M. Hannah, A. Elshorbagy, S. Kumar, L. Marshall, D.P. Solomatine, A. Dezfuli, M. Sadegh, and J. Famiglietti. 2022. ‘Coevolution of machine learning and process-based modelling to revolutionize earth and environmental sciences: A perspective’, Hydrological Processes, 36.6. https://doi.org/10.1002/hyp.14596 
  42. Saltelli, A., L. Benini, S. Funtowicz, M. Giampietro, M. Kaiser, E. Reinert, and J.P. van der Sluijs. 2020. ‘The technique is never neutral. How methodological choices condition the generation of narratives for sustainability’, Environmental Science and Policy, 106, pp. 87–98. https://doi.org/10.1016/j.envsci.2020.01.008 
  43. Sanz, D., Vos, J., Rambags, F., Hoogesteger, J., Cassiraga, E., & Gómez-Alday, J. J. (2018). The social construction and consequences of groundwater modelling: insight from the Mancha Oriental aquifer, Spain. International Journal of Water Resources Development, 35(5), 808–829. https://doi.org/10.1080/07900627.2018.1495619
  44. Savenije, H. 2009. ‘HESS opinions: The art of hydrology’, Hydrology and Earth System Sciences, 13, pp. 157–161. https://doi.org/10.5194/hess-13-157-2009 
  45. Walker, W., P. Harremoës, J. Rotmans, J. van der Sluijs, M. van Asselt, P. Janssen, and M. Krayer von Krauss. 2003. ‘Defining uncertainty: A conceptual basis for uncertainty management in model-based decision support’, Integrated Assessment, 4.1, pp. 5–17. https://doi.org/10.1076/iaij.4.1.5.16466 
  46. Weiler, M. and K. Beven. 2015. ‘Do we need a Community Hydrological Model?’, Water Resources Research, 51, pp. 7777–7784.  https://doi.org/10.1002/2014wr016731 

Export Metadata

UK registered social enterprise and Community Interest Company (CIC).

Company registration 14549556

Metadata

  • By book
  • By publisher
  • GraphQL API
  • Export API

Resources

  • Downloads
  • Videos
  • Merch
  • Presentations
  • Service status

Contact

  • Email
  • Bluesky
  • Mastodon
  • Github

Copyright © 2026 Thoth Open Metadata. Except where otherwise noted, content on this site is licensed under a Creative Commons Attribution 4.0 International license.