Hydrological modelling of temporally-varying catchments: Facets of change and the value of information

A. Efstratiadis, I. Nalbantis, and D. Koutsoyiannis, Hydrological modelling of temporally-varying catchments: Facets of change and the value of information, Hydrological Sciences Journal, 60 (7-8), 1438–1461, doi:10.1080/02626667.2014.982123, 2015.



River basins are by definition temporally varying systems: changes are apparent at every temporal scale, in terms of changing meteorological inputs and catchment characteristics, respectively due to inherently uncertain natural processes and anthropogenic interventions. In an operational context, the ultimate goal of hydrological modelling is predicting responses of the basin under conditions that are similar or different from those observed in the past. Since water management studies require that anthropogenic effects are considered known and a long hypothetical period is simulated, the combined use of stochastic models, for generating the inputs, and deterministic models that also represent the human interventions in modified basins, is found to be a powerful approach for providing realistic and statistically consistent simulations (in terms of product moments and correlations, at multiple time scales, and long-term persistence). The proposed framework is investigated on the Ferson Creek basin (USA) that exhibits significantly growing urbanization during the last 30 years. Alternative deterministic modelling options include a lumped water balance model with one time-varying parameter and a semi-distributed scheme based on the concept of hydrological response units. Model inputs and errors are respectively represented through linear and non-linear stochastic models. The resulting nonlinear stochastic framework maximizes the exploitation of the existing information, by taking advantage of the calibration protocol used in this issue.

Full text is only available to the NTUA network due to copyright restrictions

PDF Additional material:

See also: http://dx.doi.org/10.1080/02626667.2014.982123

Our works referenced by this work:

1. D. Koutsoyiannis, Optimal decomposition of covariance matrices for multivariate stochastic models in hydrology, Water Resources Research, 35 (4), 1219–1229, doi:10.1029/1998WR900093, 1999.
2. D. Koutsoyiannis, A generalized mathematical framework for stochastic simulation and forecast of hydrologic time series, Water Resources Research, 36 (6), 1519–1533, doi:10.1029/2000WR900044, 2000.
3. D. Koutsoyiannis, Coupling stochastic models of different time scales, Water Resources Research, 37 (2), 379–391, doi:10.1029/2000WR900200, 2001.
4. A. Efstratiadis, and D. Koutsoyiannis, An evolutionary annealing-simplex algorithm for global optimisation of water resource systems, Proceedings of the Fifth International Conference on Hydroinformatics, Cardiff, UK, 1423–1428, doi:10.13140/RG.2.1.1038.6162, International Water Association, 2002.
5. D. Koutsoyiannis, C. Onof, and H. S. Wheater, Multivariate rainfall disaggregation at a fine timescale, Water Resources Research, 39 (7), 1173, doi:10.1029/2002WR001600, 2003.
6. E. Rozos, A. Efstratiadis, I. Nalbantis, and D. Koutsoyiannis, Calibration of a semi-distributed model for conjunctive simulation of surface and groundwater flows, Hydrological Sciences Journal, 49 (5), 819–842, doi:10.1623/hysj.49.5.819.55130, 2004.
7. A. Efstratiadis, I. Nalbantis, A. Koukouvinos, E. Rozos, and D. Koutsoyiannis, HYDROGEIOS: A semi-distributed GIS-based hydrological model for modified river basins, Hydrology and Earth System Sciences, 12, 989–1006, doi:10.5194/hess-12-989-2008, 2008.
8. A. Efstratiadis, and D. Koutsoyiannis, One decade of multiobjective calibration approaches in hydrological modelling: a review, Hydrological Sciences Journal, 55 (1), 58–78, doi:10.1080/02626660903526292, 2010.
9. D. Koutsoyiannis, Hurst-Kolmogorov dynamics and uncertainty, Journal of the American Water Resources Association, 47 (3), 481–495, doi:10.1111/j.1752-1688.2011.00543.x, 2011.
10. I. Nalbantis, A. Efstratiadis, E. Rozos, M. Kopsiafti, and D. Koutsoyiannis, Holistic versus monomeric strategies for hydrological modelling of human-modified hydrosystems, Hydrology and Earth System Sciences, 15, 743–758, doi:10.5194/hess-15-743-2011, 2011.
11. A. Efstratiadis, New insights on model evaluation inspired by the stochastic simulation paradigm, European Geosciences Union General Assembly 2011, Geophysical Research Abstracts, Vol. 13, Vienna, 1852, European Geosciences Union, 2011.
12. A. Montanari, and D. Koutsoyiannis, A blueprint for process-based modeling of uncertain hydrological systems, Water Resources Research, 48, W09555, doi:10.1029/2011WR011412, 2012.
13. D. Koutsoyiannis, Hydrology and Change, Hydrological Sciences Journal, 58 (6), 1177–1197, doi:10.1080/02626667.2013.804626, 2013.
14. A. Montanari, G. Young, H. H. G. Savenije, D. Hughes, T. Wagener, L. L. Ren, D. Koutsoyiannis, C. Cudennec, E. Toth, S. Grimaldi, G. Blöschl, M. Sivapalan, K. Beven, H. Gupta, M. Hipsey, B. Schaefli, B. Arheimer, E. Boegh, S. J. Schymanski, G. Di Baldassarre, B. Yu, P. Hubert, Y. Huang, A. Schumann, D. Post, V. Srinivasan, C. Harman, S. Thompson, M. Rogger, A. Viglione, H. McMillan, G. Characklis, Z. Pang, and V. Belyaev, “Panta Rhei – Everything Flows”, Change in Hydrology and Society – The IAHS Scientific Decade 2013-2022, Hydrological Sciences Journal, 58 (6), 1256–1275, doi:10.1080/02626667.2013.809088, 2013.
15. D. Koutsoyiannis, Entropy: from thermodynamics to hydrology, Entropy, 16 (3), 1287–1314, doi:10.3390/e16031287, 2014.
16. A. Efstratiadis, Y. Dialynas, S. Kozanis, and D. Koutsoyiannis, A multivariate stochastic model for the generation of synthetic time series at multiple time scales reproducing long-term persistence, Environmental Modelling and Software, 62, 139–152, doi:10.1016/j.envsoft.2014.08.017, 2014.
17. A. Sikorska, A. Montanari, and D. Koutsoyiannis, Estimating the uncertainty of hydrological predictions through data-driven resampling techniques, Journal of Hydrologic Engineering (ASCE), 20 (1), doi:10.1061/(ASCE)HE.1943-5584.0000926, 2015.

Our works that reference this work:

1. A. Efstratiadis, Y. Dialynas, S. Kozanis, and D. Koutsoyiannis, A multivariate stochastic model for the generation of synthetic time series at multiple time scales reproducing long-term persistence, Environmental Modelling and Software, 62, 139–152, doi:10.1016/j.envsoft.2014.08.017, 2014.
2. I. Tsoukalas, P. Kossieris, A. Efstratiadis, and C. Makropoulos, Surrogate-enhanced evolutionary annealing simplex algorithm for effective and efficient optimization of water resources problems on a budget, Environmental Modelling and Software, 77, 122–142, doi:10.1016/j.envsoft.2015.12.008, 2016.
3. K. Papoulakos, G. Pollakis, Y. Moustakis, A. Markopoulos, T. Iliopoulou, P. Dimitriadis, D. Koutsoyiannis, and A. Efstratiadis, Simulation of water-energy fluxes through small-scale reservoir systems under limited data availability, Energy Procedia, 125, 405–414, doi:10.1016/j.egypro.2017.08.078, 2017.
4. P. Kossieris, C. Makropoulos, C. Onof, and D. Koutsoyiannis, A rainfall disaggregation scheme for sub-hourly time scales: Coupling a Bartlett-Lewis based model with adjusting procedures, Journal of Hydrology, 556, 980–992, doi:10.1016/j.jhydrol.2016.07.015, 2018.
5. G.-K. Sakki, I. Tsoukalas, and A. Efstratiadis, A reverse engineering approach across small hydropower plants: a hidden treasure of hydrological data?, Hydrological Sciences Journal, 67 (1), 94–106, doi:10.1080/02626667.2021.2000992, 2022.
6. K.-K. Drakaki, G.-K. Sakki, I. Tsoukalas, P. Kossieris, and A. Efstratiadis, Day-ahead energy production in small hydropower plants: uncertainty-aware forecasts through effective coupling of knowledge and data, Advances in Geosciences, 56, 155–162, doi:10.5194/adgeo-56-155-2022, 2022.
7. A. Efstratiadis, I. Tsoukalas, and P. Kossieris, Improving hydrological model identifiability by driving calibration with stochastic inputs, Advances in Hydroinformatics: Machine Learning and Optimization for Water Resources, edited by G. A. Corzo Perez and D. P. Solomatine, doi:10.1002/9781119639268.ch2, American Geophysical Union, 2024.

Works that cite this document: View on Google Scholar or ResearchGate

Other works that reference this work (this list might be obsolete):

1. Thirel, G., V. Andréassian, and C. Perrin, On the need to test hydrological models under changing conditions, Hydrological Sciences Journal, 60(7-8), 1165-1173, doi:10.1080/02626667.2015.1050027, 2015.
2. Biao, I. E., S. Gaba, A. E. Alamou, and A. Afouda, Influence of the uncertainties related to the random component of rainfall inflow in the Ouémé River Basin (Benin, West Africa), International Journal of Current Engineering and Technology, 5(3), 1618-1629, 2015.
3. #Christelis, V., and A. Mantoglou, Pumping optimization of coastal aquifers using radial basis function metamodels, Proceedings of 9th World Congress EWRA “Water Resources Management in a Changing World: Challenges and Opportunities”, Istanbul, 2015.
4. Christelis, V., and A. Mantoglou, Coastal aquifer management based on the joint use of density-dependent and sharp interface models, Water Resources Management, 30(2), 861-876, doi:10.1007/s11269-015-1195-4, 2016.
5. McMillan, H., A. Montanari, C. Cudennec, H. Savenjie, H. Kreibich, T. Krüger, J. Liu, A. Meija, A. van Loon, H. Aksoy, G. Di Baldassarre, Y. Huang, D. Mazvimavi, M. Rogger, S. Bellie, T. Bibikova, A. Castellarin, Y. Chen, D. Finger, A. Gelfan, D. Hannah, A. Hoekstra, H. Li, S. Maskey, T. Mathevet, A. Mijic, A. Pedrozo Acuña, M. J. Polo, V. Rosales, P. Smith, A. Viglione, V. Srinivasan, E. Toth, R. van Nooyen, and J. Xia, Panta Rhei 2013-2015: Global perspectives on hydrology, society and change, Hydrological Sciences Journal, 61(7), 1174-1191, doi:10.1080/02626667.2016.1159308, 2016.
6. Biao, I. E., A. E. Alamou, and A. Afouda, Improving rainfall–runoff modelling through the control of uncertainties under increasing climate variability in the Ouémé River basin (Benin, West Africa), Hydrological Sciences Journal, 61(16), 2902-2915, doi:10.1080/02626667.2016.1164315, 2016.
7. Pathiraja, S., L. Marshall, A. Sharma, and H. Moradkhani, Detecting non-stationary hydrologic model parameters in a paired catchment system using data assimilation, Advances in Water Resources, 94, 103–119, doi:10.1016/j.advwatres.2016.04.021, 2016.
8. Christelis, V., and A. Mantoglou, Pumping optimization of coastal aquifers assisted by adaptive metamodelling methods and radial basis functions, Water Resources Management, 30(15), 5845–5859, doi:10.1007/s11269-016-1337-3, 2016.
9. Seibert, J., and I. van Meerveld, Hydrological change modeling: Challenges and opportunities, Hydrological Processes, 30(26), 4966–4971, doi:10.1002/hyp.10999, 2016.
10. Ceola, S., A. Montanari, T. Krueger, F. Dyer, H. Kreibich, I. Westerberg, G. Carr, C. Cudennec, A. Elshorbagy, H. Savenije, P. van der Zaag, D. Rosbjerg, H. Aksoy, F. Viola, G. Petrucci, K. MacLeod, B. Croke, D. Ganora, L. Hermans, M. J. Polo, Z. Xu, M. Borga, J. Helmschrot, E. Toth, R., A. Castellarin, A. Hurford, M. Brilly, A. Viglione, G. Blöschl, M. Sivapalan, A. Domeneghetti, A. Marinelli, and G. Di Baldassarre, Adaptation of water resources systems to changing society and environment: a statement by the International Association of Hydrological Sciences, Hydrological Sciences Journal, 61(16), 2803-2817, doi:10.1080/02626667.2016.1230674, 2016.
11. #Christelis, V., V. Bellos, and G. Tsakiris, Employing surrogate modelling for the calibration of a 2D flood simulation model, Sustainable Hydraulics in the Era of Global Change: Proceedings of the 4th IAHR Europe Congress (Liege, Belgium, 27-29 July 2016), A. S. Erpicum, M. Pirotton, B. Dewals, P. Archambeau (editors), CRC Press, 2016.
12. Nauditt, A., C. Birkel, C. Soulsby, and L. Ribbe, Conceptual modelling to assess the influence of hydroclimatic variability on runoff processes in data scarce semi-arid Andean catchments, Hydrological Sciences Journal, 62(4), 515-532, doi:10.1080/02626667.2016.1240870, 2017.
13. Sophocleous C., and I. Nalbantis, Effect of land use change on flood extent in the inflow stream of lake Paralimni, Cyprus, European Water, 60, 147-153, 2017.
14. Tegos, M., I. Nalbantis, and A. Tegos, Environmental flow assessment through integrated approaches, European Water, 60, 167-173, 2017.
15. Pathiraja, S., D. Anghileri, P. Burlando, A. Sharma, L. Marshall, and H. Moradkhani, Insights on the impact of systematic model errors on data assimilation performance in changing catchments, Advances in Water Resources, 113, 202-222, doi:10.1016/j.advwatres.2017.12.006, 2018.
16. Salas, J. D., J. Obeysekera, and R. M. Vogel, Techniques for assessing water infrastructure for nonstationary extreme events: a review, Hydrological Sciences Journal, 63(3), 325-352, doi:10.1080/02626667.2018.1426858, 2018.
17. Pathiraja, S., D. Anghileri, P. Burlando, A. Sharma, L. Marshall, and H. Moradkhani, Time varying parameter models for catchments with land use change: the importance of model structure, Hydrology and Earth System Sciences, 22, 2903-2919, doi:10.5194/hess-2017-382, 2018.
18. Varouchakis, E. A., K. Yetilmezsoy, and G. P. Karatzas, A decision-making framework for sustainable management of groundwater resources under uncertainty: combination of Bayesian risk approach and statistical tools, Water Policy, 21(3), 602-622, doi:10.2166/wp.2019.128, 2019.
19. Sadegh, M., A. AghaKouchak, A. Flores, I. Mallakpour, and M. R. Nikoo, A multi-model nonstationary rainfall-runoff modeling framework: analysis and toolbox, Water Resources Management, 33(9), 3011-3024, doi:10.1007/s11269-019-02283-y, 2019.
20. Zhao, B., J. Mao, Q. Dai, D. Han, H. Daiand, and G. Rong, Exploration on hydrological model calibration by considering the hydro-meteorological variability, Hydrology Research, 51(1), 30-46, doi:10.2166/nh.2019.047, 2020.
21. Nicolle, P., V. Andréassian, P. Royer-Gaspard, C. Perrin, G. Thirel, L. Coron, and L. Santos, Technical Note – RAT: a Robustness Assessment Test for calibrated and uncalibrated hydrological models, Hydrology and Earth System Sciences, 25, 5013–5027, doi:10.5194/hess-25-5013-2021, 2021.
22. Li, H., Q. Xu, Y. He, X. Fan, H. Yang, and S. Li, Temporal detection of sharp landslide deformation with ensemble-based LSTM-RNNs and Hurst exponent, Geomatics, Natural Hazards and Risk, 12(1), 3089-3113, doi:10.1080/19475705.2021.1994474, 2021.
23. Louloudis, G., E. Louloudis, C. Roumpos, E. Mertiri, G. Kasfikis, and K. Chatzopoulos, Forecasting development of mine pit lake water surface levels based on time series analysis and neural networks, Mine Water and the Environment, 41, 458–474, doi:10.1007/s10230-021-00844-5, 2022.
24. Saadi, M., and C. Furusho-Percot, Which range of streamflow data is most informative in the calibration of an hourly hydrological model? Hydrological Sciences Journal, doi:10.1080/02626667.2023.2277835, 2023.
25. Ejaz, F., A. Guthke, T. Wöhling, and W. Nowak, Comprehensive uncertainty analysis for surface water and groundwater projections under climate change based on a lumped geo-hydrological model, Journal of Hydrology, 626(B), 130323, doi:10.1016/j.jhydrol.2023.130323, 2023.

Tagged under: Determinism vs. stochasticity, Hydrological models, Stochastics, Uncertainty