Holistic versus monomeric strategies for hydrological modelling of human-modified hydrosystems

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.

[doc_id=1055]

[English]

The modelling of human-modified basins that are inadequately measured constitutes a challenge for hydrological science. Often, models for such systems are detailed and hydraulics-based for only one part of the system while for other parts oversimplified models or rough assumptions are used. This is typically a bottom-up approach, which seeks to exploit knowledge of hydrological processes at the micro-scale at some components of the system. Also, it is a monomeric approach in two ways: first, essential interactions among system components may be poorly represented or even omitted; second, differences in the level of detail of process representation can lead to uncontrolled errors. Additionally, the calibration procedure merely accounts for the reproduction of the observed responses using typical fitting criteria. The paper aims to raise some critical issues, regarding the entire modelling approach for such hydrosystems. For this, two alternative modelling strategies are examined that reflect two modelling approaches or philosophies: a dominant bottom-up approach, which is also monomeric and, very often, based on output information, and a top-down and holistic approach based on generalized information. Critical options are examined, which codify the differences between the two strategies: the representation of surface, groundwater and water management processes, the schematization and parameterization concepts and the parameter estimation methodology. The first strategy is based on stand-alone models for surface and groundwater processes and for water management, which are employed sequentially. For each model, a different (detailed or coarse) parameterization is used, which is dictated by the hydrosystem schematization. The second strategy involves model integration for all processes, parsimonious parameterization and hybrid manual-automatic parameter optimization based on multiple objectives. A test case is examined in a hydrosystem in Greece with high complexities, such as extended surface-groundwater interactions, ill-defined boundaries, sinks to the sea and anthropogenic intervention with unmeasured abstractions both from surface water and aquifers. Criteria for comparison are the physical consistency of parameters, the reproduction of runoff hydrographs at multiple sites within the studied basin, the likelihood of uncontrolled model outputs, the required amount of computational effort and the performance within a stochastic simulation setting. Our work allows for investigating the deterioration of model performance in cases where no balanced attention is paid to all components of human-modified hydrosystems and the related information. Also, sources of errors are identified and their combined effect are evaluated.

PDF Full text (1733 KB)

PDF Additional material:

See also: http://dx.doi.org/10.5194/hess-15-743-2011

Our works referenced by this work:

1. I. Nalbantis, and E. Rozos, A system for the simulation of the hydrological cycle in the Boeoticos Kephisos basin, Modernisation of the supervision and management of the water resource system of Athens, Report 10, 72 pages, Department of Water Resources, Hydraulic and Maritime Engineering – National Technical University of Athens, Athens, December 2000.
2. 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.
3. I. Nalbantis, E. Rozos, G. M. T. Tentes, A. Efstratiadis, and D. Koutsoyiannis, Integrating groundwater models within a decision support system, Proceedings of the 5th International Conference of European Water Resources Association: "Water Resources Management in the Era of Transition", edited by G. Tsakiris, Athens, 279–286, European Water Resources Association, 2002.
4. D. Koutsoyiannis, G. Karavokiros, A. Efstratiadis, N. Mamassis, A. Koukouvinos, and A. Christofides, A decision support system for the management of the water resource system of Athens, Physics and Chemistry of the Earth, 28 (14-15), 599–609, doi:10.1016/S1474-7065(03)00106-2, 2003.
5. A. Efstratiadis, D. Koutsoyiannis, and D. Xenos, Minimizing water cost in the water resource management of Athens, Urban Water Journal, 1 (1), 3–15, doi:10.1080/15730620410001732099, 2004.
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. D. Koutsoyiannis, Stochastic simulation of hydrosystems, Water Encyclopedia, Vol. 4, Surface and Agricultural Water, edited by J. H. Lehr and J. Keeley, 421–430, doi:10.1002/047147844X.sw913, Wiley, New York, 2005.
8. E. Rozos, and D. Koutsoyiannis, A multicell karstic aquifer model with alternative flow equations, Journal of Hydrology, 325 (1-4), 340–355, 2006.
9. D. Koutsoyiannis, A. Efstratiadis, and K. Georgakakos, Uncertainty assessment of future hydroclimatic predictions: A comparison of probabilistic and scenario-based approaches, Journal of Hydrometeorology, 8 (3), 261–281, doi:10.1175/JHM576.1, 2007.
10. 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.
11. D. Koutsoyiannis, C. Makropoulos, A. Langousis, S. Baki, A. Efstratiadis, A. Christofides, G. Karavokiros, and N. Mamassis, Climate, hydrology, energy, water: recognizing uncertainty and seeking sustainability, Hydrology and Earth System Sciences, 13, 247–257, doi:10.5194/hess-13-247-2009, 2009.
12. M. Kopsiafti, Investigation of parameterization strategies for the hydrogeological module of Hydrogeios software - Application to the Boeoticos Kephisos basin, Postgraduate Thesis, 133 pages, Department of Water Resources and Environmental Engineering – National Technical University of Athens, March 2009.
13. A. Efstratiadis, and N. Mamassis, Evaluating models or evaluating modelling practices? - Interactive comment on HESS Opinions “Crash tests for a standardized evaluation of hydrological models”, Hydrology and Earth System Sciences Discussions, 6, C1404–C1409, 2009.
14. 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.
15. A. Efstratiadis, I. Nalbantis, E. Rozos, and D. Koutsoyiannis, Accounting for water management issues within hydrological simulation: Alternative modelling options and a network optimization approach, European Geosciences Union General Assembly 2010, Geophysical Research Abstracts, Vol. 12, Vienna, 10085, doi:10.13140/RG.2.2.22189.69603, European Geosciences Union, 2010.
16. E. Rozos, and D. Koutsoyiannis, Error analysis of a multi-cell groundwater model, Journal of Hydrology, 392 (1-2), 22–30, 2010.
17. 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.

Our works that reference this work:

1. A. Efstratiadis, A. D. Koussis, S. Lykoudis, A. Koukouvinos, A. Christofides, G. Karavokiros, N. Kappos, N. Mamassis, and D. Koutsoyiannis, Hydrometeorological network for flood monitoring and modeling, Proceedings of First International Conference on Remote Sensing and Geoinformation of Environment, Paphos, Cyprus, 8795, 10-1–10-10, doi:10.1117/12.2028621, Society of Photo-Optical Instrumentation Engineers (SPIE), 2013.
2. 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.
3. D. Koutsoyiannis, Reconciling hydrology with engineering, Hydrology Research, 45 (1), 2–22, doi:10.2166/nh.2013.092, 2014.
4. S. Ceola, A. Montanari, and D. Koutsoyiannis, Toward a theoretical framework for integrated modeling of hydrological change, WIREs Water, 1 (5), 427–438, doi:10.1002/wat2.1038, 2014.
5. 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.
6. 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.
7. 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.
8. T. Vergou, A. Efstratiadis, and D. Dermatas, Water balance model for evaluation of landfill malfunction due to leakage, Proceedings of ISWA 2016 World Congress, Novi Sad, Ιnternational Solid Waste Association, 2016.
9. E. Savvidou, A. Efstratiadis, A. D. Koussis, A. Koukouvinos, and D. Skarlatos, A curve number approach to formulate hydrological response units within distributed hydrological modelling, Hydrology and Earth System Sciences Discussions, doi:10.5194/hess-2016-627, 2016.
10. 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.
11. E. Savvidou, A. Efstratiadis, A. D. Koussis, A. Koukouvinos, and D. Skarlatos, The curve number concept as a driver for delineating hydrological response units, Water, 10 (2), 194, doi:10.3390/w10020194, 2018.

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

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

1. Gharari, S., M. Hrachowitz, F. Fenicia, and H. H. G. Savenije, Hydrological landscape classification: investigating the performance of HAND based landscape classifications in a central European meso-scale catchment, Hydrology and Earth System Sciences, 15, 3275-3291, doi:10.5194/hess-15-3275-2011, doi:10.5194/hess-15-3275-2011, 2011.
2. #Gharari, S., M. Hrachowitz, F. Fenicia, and H. H. G Savenije, Moving beyond traditional model calibration or how to better identify realistic model parameters: sub-period calibration, Hydrology and Earth System Science Discussions,, 9, 1885-1918, doi:10.5194/hessd-9-1885-2012, 2012.
3. Flipo, N., C. Monteil, M. Poulin, C. de Fouquet, and M. Krimissa, Hybrid fitting of a hydrosystem model: Long term insight into the Beauce aquifer functioning (France), Water Recourses Research, 48, W05509, doi:10.1029/2011WR011092, 2012.
4. Wang, X., T. Liu and W. Yang, Development of a robust runoff-prediction model by fusing the rational equation and a modified SCS-CN method, Hydrological Sciences Journal, 57(6), 1118-1140, doi:10.1080/02626667.2012.701305, 2012.
5. Maneta, M. P., and W. W. Wallender, Pilot-point based multi-objective calibration in a surface–subsurface distributed hydrological model, Hydrological Sciences Journal, 58(2), 390-407, doi:10.1080/02626667.2012.754987, 2013.
6. Hrachowitz, M., H.H.G. Savenije, G. Blöschl, J.J. McDonnell, M. Sivapalan, J.W. Pomeroy, B. Arheimer, T. Blume, M.P. Clark, U. Ehret, F. Fenicia, J.E. Freer, A. Gelfan, H.V. Gupta, D.A. Hughes, R.W. Hut, A. Montanari, S. Pande, D. Tetzlaff, P.A. Troch, S. Uhlenbrook, T. Wagener, H.C. Winsemius, R.A. Woods, E. Zehe, and C. Cudennec, A decade of Predictions in Ungauged Basins (PUB) — a review, Hydrological Sciences Journal, 58(6), 1198-1255, 2013.
7. #Loukas, A., and L. Vasiliades, Review of applied methods for flood-frequency analysis in a changing environment in Greece, In: A review of applied methods in Europe for flood-frequency analysis in a changing environment, Floodfreq COST action ES0901: European procedures for flood frequency estimation (ed. by H. Madsen et al.), Centre for Ecology & Hydrology, Wallingford, UK, 2013.
8. Flipo, N., A. Mouhri, B. Labarthe, S. Biancamaria, A. Rivière and P. Weill, Continental hydrosystem modelling: the concept of nested stream–aquifer interfaces, Hydrology and Earth System Sciences, 18, 3121-3149, doi:10.5194/hess-18-3121-2014, 2014.
9. Ivkovic, K. M., B. F. W. Croke and R. A.Kelly, Overcoming the challenges of using a rainfall-runoff model to estimate the impacts of groundwater extraction on low flows in an ephemeral stream, Hydrology Research, 45(1), 58-72, doi:10.2166/nh.2013.204, 2014.
10. Mateo, C. M., N. Hanasaki, D. Komori, K. Tanaka, M. Kiguchi, A. Champathong, T. Sukhapunnaphan, D.Yamazaki, and T. Oki, Assessing the impacts of reservoir operation to floodplain inundation by combining hydrological, reservoir management, and hydrodynamic models, Water Resources Research, 50(9), 7245–7266, doi:10.1002/2013WR014845, 2014.
11. Gharari, S., M. Hrachowitz, F. Fenicia, H. Gao, and H. H. G. Savenije, Using expert knowledge to increase realism in environmental system models can dramatically reduce the need for calibration, Hydrology and Earth System Sciences, 18, 4839-4859, doi:10.5194/hess-18-4839-2014, 2015.
12. Thirel, G., V. Andréassian, C. Perrin, J.-N. Audouy, L. Berthet, P. Edwards, N. Folton, C. Furusho, A. Kuentz, J. Lerat, G. Lindström, E. Martin, T. Mathevet, R. Merz, J. Parajka, D. Ruelland, and J. Vaze, Hydrology under change: an evaluation protocol to investigate how hydrological models deal with changing catchments, Hydrological Sciences Journal, 60(7-8), 1184-1199, doi:10.1080/02626667.2014.9672482014, 2015.
13. Pryet, A., B. Labarthe, F. Saleh, M. Akopian and N. Flipo, Reporting of stream-aquifer flow distribution at the regional scale with a distributed process-based model, Water Resources Management, 10.1007/s11269-014-0832-7, 29(1), 139-159, 2015.
14. Donnelly, C., J. C. M. Andersson, and B. Arheimer, Using flow signatures and catchment similarities to evaluate the E-HYPE multi-basin model across Europe, Hydrological Sciences Journal, 61(2), 255-273, doi:10.1080/02626667.2015.1027710, 2016.
15. Bellin, A., B. Majone, O. Cainelli, D. Alberici, and F. Villa, A continuous coupled hydrological and water resources management model, Environmental Modelling and Software, 75, 176–192, doi:10.1016/j.envsoft.2015.10.013, 2016.
16. Ajmal, M., J.-H. Ahn, and , T.-W. Kim, Excess stormwater quantification in ungauged watersheds using an event-based modified NRCS model, Water Resources Management, 30(4), 1433-1448, doi:10.1007/s11269-016-1231-z, 2016.
17. Ma, L., C. He, H. Bian, and L. Sheng, MIKE SHE modeling of ecohydrological processes: Merits, applications, and challenges, Ecological Engineering, 96, 137–149, doi:10.1016/j.ecoleng.2016.01.008, 2016.
18. Tigkas, D., V. Christelis, and G. Tsakiris, Comparative study of evolutionary algorithms for the automatic calibration of the Medbasin-D conceptual hydrological model, Environmental Processes, 3(3), 629–644, doi:10.1007/s40710-016-0147-1, 2016.
19. Ercan, A., E. C. Dogrul, and T. N. Kadir, Investigation of the groundwater modelling component of the Integrated Water Flow Model (IWFM), Hydrological Sciences Journal, 61(16), 2834-2848, doi:10.1080/02626667.2016.1161765, 2016.
20. Balbarini, N., W. M. Boon, E. Nicolajsen, J. M. Nordbotten, P. L. Bjerg, and P. J. Binning, A 3-D numerical model of the influence of meanders on groundwater discharge to a gaining stream in an unconfined sandy aquifer, Journal of Hydrology, 552, 168-181, doi:10.1016/j.jhydrol.2017.06.042, 2017.
21. Antonetti, M., and M. Zappa, How can expert knowledge increase the realism of conceptual hydrological models? A case study in the Swiss Pre-Alps, Hydrology and Earth System Sciences, 22, 4425-4447, doi:10.5194/hess-2017-322, 2018.
22. Gunda, T., B. L. Turner, and V. C. Tidwell, The influential role of sociocultural feedbacks on community-managed irrigation system behaviors during times of water stress, Water Resources Research, 54(4), 2697-2714, doi:10.1002/2017WR021223, 2018.
23. van Tol, J.J., and S.A. Lorentz, Hydropedological interpretation of regional soil information to conceptualize groundwater-surface water interactions, Vadose Zone Journal, 17:170097, doi:10.2136/vzj2017.05.0097, 2018
24. Christelis, V., and A. G. Hughes, Metamodel-assisted analysis of an integrated model composition: an example using linked surface water – groundwater models, Environmental Modelling and Software, 107, 298-306, doi:10.1016/j.envsoft.2018.05.004, 2018.
25. Stefanidis, S., and D. Stathis, Effect of climate change on soil erosion in a mountainous Mediterranean catchment (Central Pindus, Greece), Water, 10(10), 1469, doi:10.3390/w10101469, 2018.

Tagged under: Groundwater, Hydrological models, Hydrosystems