Μια δεκαετία προσεγγίσεων πολυκριτηριακής βαθμονόμησης στην υδρολογική μοντελοποίηση: Επισκόπηση

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.

[Μια δεκαετία προσεγγίσεων πολυκριτηριακής βαθμονόμησης στην υδρολογική μοντελοποίηση: Επισκόπηση]

[doc_id=924]

[Αγγλικά]

Μια δεκαετία μετά τις πρώτες δημοσιεύσεις στην πολυκριτηριακή βαθμονόμηση υδρολογικών μοντέλων, συνοψίζουμε την εμπειρία που έχει ως τώρα αποκτηθεί, υπογραμμίζοντας τις κύριες προοπτικές που παρέχονται από τέτοιες προσεγγίσεις με σκοπό τη βελτίωση του προσδιορισμού των παραμέτρων. Μετά την επισκόπηση των θεμελιωδών αρχών της θεωρίας διανυσματικής βελτιστοποίησης, συνδέουμε την προσέγγιση της πολυκριτηριακής βαθμονόμησης με τις έννοιες της αβεβαιότητας και της ισοδυναμίας (equifinality). Συγκεκριμένα, το πολυκριτηριακό πλαίσιο επιτρέπει την αναγνώριση και το χειρισμό σφαλμάτων και αβεβαιοτήτων, και τον εντοπισμό πρόσφορων και καλά προσαρμόσιμων (behavioural) λύσεων, με αποδεκτές αντισταθμίσεις. Ειδικά σε μοντέλα με σύνθετη παραμετροποίηση η πολυκριτηριακή προσέγγιση καθίσταται αναγκαία για τη βελτίωση του προσδιορισμού των παραμέτρων και την επαύξηση της πληροφορίας που περιέχεται στη βαθμονόμηση, τόσο με τη μορφή μετρήσεων για πολλαπλές αποκρίσεις όσο και εμπειρικών μέτρων («χαλαρή» πληροφορία), στα οποία αντικατοπτρίζεται η υδρολογική εμπειρία. Με βάση τη βιβλιογραφική επισκόπηση, παρέχουμε ακόμη εναλλακτικές τεχνικές για την αντιμετώπιση αντικρουόμενων και μη συμμετρούμενων (non-commeasurable) κριτηρίων, καθώς και υβριδικές στρατηγικές για την αξιοποίηση της πληροφορίας που αποκτάται, στην κατεύθυνση του προσδιορισμού υποσχόμενων συμβιβαστικών λύσεων που εξασφαλίζουν συνεπείς και αξιόπιστες βαθμονομήσεις.

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Εργασίες μας στις οποίες αναφέρεται αυτή η εργασία:

1. 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.
2. 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.
3. A. Efstratiadis, and D. Koutsoyiannis, Fitting hydrological models on multiple responses using the multiobjective evolutionary annealing simplex approach, Practical hydroinformatics: Computational intelligence and technological developments in water applications, edited by R.J. Abrahart, L. M. See, and D. P. Solomatine, 259–273, doi:10.1007/978-3-540-79881-1_19, Springer, 2008.
4. A. Efstratiadis, and D. Koutsoyiannis, On the practical use of multiobjective optimisation in hydrological model calibration, European Geosciences Union General Assembly 2009, Geophysical Research Abstracts, Vol. 11, Vienna, 2326, doi:10.13140/RG.2.2.10445.64480, European Geosciences Union, 2009.

Εργασίες μας που αναφέρονται σ' αυτή την εργασία:

1. 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.
2. J. A. P. Pollacco, B. P. Mohanty, and A. Efstratiadis, Weighted objective function selector algorithm for parameter estimation of SVAT models with remote sensing data, Water Resources Research, 49 (10), 6959–6978, doi:10.1002/wrcr.20554, 2013.
3. 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.
4. I. Tsoukalas, and C. Makropoulos, Multiobjective optimisation on a budget: Exploring surrogate modelling for robust multi-reservoir rules generation under hydrological uncertainty, Environmental Modelling and Software, 69, 396–413, doi:10.1016/j.envsoft.2014.09.023, 2015.
5. I. Tsoukalas, and C. Makropoulos, A surrogate based optimization approach for the development of uncertainty-aware reservoir operational rules: the case of Nestos hydrosystem, Water Resources Management, 29 (13), 4719–4734, doi:10.1007/s11269-015-1086-8, 2015.
6. 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.
7. 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.
8. A. Tegos, N. Malamos, A. Efstratiadis, I. Tsoukalas, A. Karanasios, and D. Koutsoyiannis, Parametric modelling of potential evapotranspiration: a global survey, Water, 9 (10), 795, doi:10.3390/w9100795, 2017.
9. 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.
10. K. Risva, D. Nikolopoulos, A. Efstratiadis, and I. Nalbantis, A framework for dry period low flow forecasting in Mediterranean streams, Water Resources Management, 32 (15), 4911–1432, doi:10.1007/s11269-018-2060-z, 2018.
11. G. Papacharalampous, H. Tyralis, A. Langousis, A. W. Jayawardena, B. Sivakumar, N. Mamassis, A. Montanari, and D. Koutsoyiannis, Probabilistic hydrological post-processing at scale: Why and how to apply machine-learning quantile regression algorithms, Water, doi:10.3390/w11102126, 2019.
12. G. Papacharalampous, D. Koutsoyiannis, and A. Montanari, Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: Methodology development and investigation using toy models, Advances in Water Resources, 136, 103471, doi:10.1016/j.advwatres.2019.103471, 2020.
13. G. Papacharalampous, H. Tyralis, D. Koutsoyiannis, and A. Montanari, Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: A large-sample experiment at monthly timescale, Advances in Water Resources, 136, 103470, doi:10.1016/j.advwatres.2019.103470, 2020.
14. R. Ioannidis, N. Mamassis, A. Efstratiadis, and D. Koutsoyiannis, Reversing visibility analysis: Towards an accelerated a priori assessment of landscape impacts of renewable energy projects, Renewable and Sustainable Energy Reviews, 161, 112389, doi:10.1016/j.rser.2022.112389, 2022.
15. A. Efstratiadis, and G.-K. Sakki, Revisiting the management of water-energy systems under the umbrella of resilience optimization, e-Proceedings of the 5th EWaS International Conference, Naples, 596–603, 2022.
16. A. Efstratiadis, and G.-K. Sakki, Revisiting the management of water–energy systems under the umbrella of resilience optimization, Environmental Sciences Proceedings, 21 (1), 72, doi:10.3390/environsciproc2022021072, 2022.
17. S. Tsattalios, I. Tsoukalas, P. Dimas, P. Kossieris, A. Efstratiadis, and C. Makropoulos, Advancing surrogate-based optimization of time-expensive environmental problems through adaptive multi-model search, Environmental Modelling and Software, 162, 105639, doi:10.1016/j.envsoft.2023.105639, 2023.
18. 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.
19. E. Boucoyiannis, P. Kossieris, V. Bellos, A. Efstratiadis, and C. Makropoulos, A grey-box approach in the optimization of regulation structures used in urban-water conveyance systems, Urban Water Journal, 21 (4), 483–497, doi:10.1080/1573062X.2024.2312510, 2024.
20. G.-K. Sakki, A. Castelletti, C. Makropoulos, and A. Efstratiadis, Unwrapping the triptych of climatic, social and energy-market uncertainties in the operation of multipurpose hydropower reservoirs, Journal of Hydrology, 2024, (υπό έκδοση).

Άλλες εργασίες που αναφέρονται σ' αυτή την εργασία: Δείτε τις στο Google Scholar ή στο ResearchGate

Άλλες εργασίες που αναφέρονται σ' αυτή την εργασία (αυτός ο κατάλογος μπορεί να μην είναι ενημερωμένος):

1. Booij, M. J., and M. S. Krol, Balance between calibration objectives in a conceptual hydrological model, Hydrological Sciences Journal, 55(6), 1017-1032, 2010.
2. Moussa, R., When monstrosity can be beautiful while normality can be ugly: assessing the performance of event-based flood models, Hydrological Sciences Journal, 55(6), 1074-1084, 2010.
3. Moussu, F., L. Oudin, V. Plagnes, A. Mangin, and H. Bendjoudi, A multi-objective calibration framework for rainfall-discharge models applied to karst systems, Journal of Hydrology, 400(3-4), 364-376, 2011.
4. Guinot, V., B. Cappelaere, C. Delenne, and D. Ruelland, Towards improved criteria for hydrological model calibration: Theoretical analysis of distance- and weak form-based functions, Journal of Hydrology, 401(1-2), 1-13, 2011.
5. Peel, M. C., and G. Blöschl, Hydrological modelling in a changing world, Progress in Physical Geography, 35 (2), 249-261, 2011.
6. Ford, D. E., and M. C. Kennedy, Assessment of uncertainty in functional–structural plant models, Annals of Botany, 108 (6), 1043-1053, 2011.
7. #Shinma, T. A., and L. F. R. Reis, Multiobjective automatic calibration of the storm water management model (SWMM) using non-dominated sorting genetic algorithm II (NSGA-II), Proceedings of the 2011 World Environmental and Water Resources Congress: Bearing Knowledge for Sustainability, 598-607, 2011.
8. Mediero, L., L. Garrote and F. J. Martín-Carrasco, Probabilistic calibration of a distributed hydrological model for flood forecasting, Hydrological Sciences Journal, 56(7), 1129–1149, 2011.
9. Kennedy, M. C., and E. D. Ford, Using multicriteria analysis of simulation models to understand complex biological systems, BioScience, 61(12), 994–1004, 2011.
10. #Van Hoey, S., P. Seuntjens, J. van der Kwast, J.-L. de Kok, G. Engelen, and I. Nopens, Flexible framework for diagnosing alternative model structures through sensitivity and uncertainty analysis, In: Chan, F., D. Marinova, and R. S. Anderssen (eds.), MODSIM2011, 19th International Congress on Modelling and Simulation, Modelling and Simulation Society of Australia and New Zealand, December 2011, pp. 3924-3930, ISBN: 978-0-9872143-1-7, 2011.
11. Reed, P. M., and J. B. Kollat, Save now, pay later? Multi-period many-objective groundwater monitoring design given systematic model errors and uncertainty, Advances in Water Resources, 35, 55-68, 2012.
12. Pushpalatha, R., C. Perrin, N. Le Moine, and V. Andréassian, A review of efficiency criteria suitable for evaluating low–flow simulations, Journal of Hydrology, 420-421, 171-182, 2012.
13. Ruelland, D., S. Ardoin-Bardin, L. Collet, and P. Roucou, Simulating future trends in hydrological regime of a large Sudano-Sahelian catchment under climate change, Journal of Hydrology, 424-425, 207-216, 2012.
14. Andréassian, V., N. Le Moine, C. Perrin, M.-H. Ramos, L. Oudin, T. Mathevet, J. Lerat, and L. Berthet, All that glitters is not gold: the case of calibrating hydrological models, Hydrological Processes, 26(14), 2206-2210, 2012.
15. Kollat, J. B., P. M. Reed, and T. Wagener, When are multiobjective calibration trade-offs in hydrologic models meaningful?, Water Resources Research, 48, W03520, 2012.
16. Dumedah, G., A. A. Berg, and M. Wineberg, Evaluating autoselection methods used for choosing solutions from Pareto-optimal set: Does nondominance persist from calibration to validation phase? Journal of Hydrologic Engineering, 17(1), 150-159, 2012.
17. Hill, M. C., D. Kavetski, M. Clark, M. Ye, and D. Lu, Uncertainty quantification 2012: Uncertainty quantification for environmental models, Society for Industrial and Applied Mathematics News, 45(9), 2012.
18. Rye, C. J., I. Willis, N. S. Arnold, and J. Kohler, On the need for automated multi-objective optimization and uncertainty estimation of glacier mass balance models, Journal of Geophysical Research, 117, F02005, doi: 10.1029/2011JF002184, 2012.
19. Rothfuss, Y., I. Braud, N. Le Moine, P. Biron, J.-L. Durand, M. Vauclin, and T. Bariac, Factors controlling the isotopic partitioning between soil evaporation and plant transpiration: assessment using a multi-objective calibration of SiSPAT-Isotope under controlled conditions, Journal of Hydrology, 442-443, 161-179, 2012.
20. Peng, W., R. V. Mayorga, and S. Imran, A rapid fuzzy optimisation approach to multiple sources water blending problem in water distribution systems, Urban Water Journal, 9(3), 177-187, 2012.
21. 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.
22. Pollacco, J. A. P., and B. P. Mohanty, Uncertainties of water fluxes in SVAT models: inverting surface soil moisture and evapotranspiration retrieved from remote sensing, Vadose Zone Journal, 11(3), vzj2011.0167, 2012.
23. Muleta, M. K., Model performance sensitivity to objective function during automated calibrations, Journal of Hydrologic Engineering, 17(6), 756-767, 2012.
24. Dumedah, G., Formulation of the evolutionary-based data assimilation and its implementation in hydrological forecasting, Water Resources Management, 26(13), 3853-3870, 2012.
25. Reichert, P., and N. Schuwirth, Linking statistical bias description to multiobjective model calibration, Water Resources Research, 48, W09543, doi:10.1029/2011WR011391, 2012.
26. Price, K., S. T. Purucker, S. R. Kraemer, and J. Babendreier, Tradeoffs among watershed model calibration targets for parameter estimation, Water Resources Research, 48, W10542, doi:10.1029/2012WR012005, 2012.
27. Krauße, T., J. Cullmann, P. Saile, and G. H. Schmitz, Robust multi-objective calibration strategies – possibilities for improving flood forecasting, Hydrology and Earth System Sciences, 16, 3579-3606, 2012.
28. Koskela, J. J., B. Croke, H. Koivusalo, A. Jakeman, and T. Kokkonen, Bayesian inference of uncertainties in precipitation-streamflow modeling in a snow affected catchment, Water Resources Research, 48, W11513, doi: 10.1029/2011WR011773, 2012.
29. Jarvis, N., and M. Larsbo, MACRO (V5.2): Model use, calibration, and validation, Transactions of the ASABE, 55(4), 1413-1423, 2012.
30. Hallema, D. W., R. Moussa, P. Andrieux, and M. Voltz, Parameterisation and multi-criteria calibration of a distributed storm flow model applied to a Mediterranean agricultural catchment, Hydrological Processes, 27(10), 1379-1398, 2013.
31. Gharari, S., M. Hrachowitz, F. Fenicia and H. H. G. Savenije, An approach to identify time consistent model parameters: sub-period calibration, Hydrology and Earth System Sciences, 17, 149-161, 10.5194/hess-17-149-2013, 2013.
32. Kasprzyk, J. R, S. Nataraj, P. M. Reed, and R. J. Lempert, Many objective robust decision making for complex environmental systems undergoing change, Environmental Modelling & Software, 42, 55-71, 2013.
33. Reed, P. M., D. Hadka, J. D. Herman, J. R. Kasprzyk, and J. B. Kollat, Evolutionary multiobjective optimization in water resources: the past, present, and future, Advances in Water Resources, 51, 438-456, 2013.
34. Spaaks, J. H. and W. Bouten, Resolving structural errors in a spatially distributed hydrologic model using ensemble Kalman filter state updates, Hydrology and Earth System Sciences, 17, 3455–3472, 2013.
35. Wöhling, T., L. Samaniego, and R. Kumar, Evaluating multiple performance criteria to calibrate the distributed hydrological model of the upper Neckar catchment, Environmental Earth Sciences, 69(2), 453-468, 2013.
36. Ghimire, S. R., and J. M. Johnston, Impacts of domestic and agricultural rainwater harvesting systems on watershed hydrology: A case study in the Albemarle-Pamlico river basins (USA), Ecohydrology & Hydrobiology, 13(2), 159-171, 2013.
37. Hartmann, A., T. Wagener, A. Rimmer, J. Lange, H. Brielmann, and M. Weiler, Testing the realism of model structures to identify karst system processes using water quality and quantity signatures, Water Resources Research, 49(6), 3345-3358, 2013.
38. Hill, M. C., C. C. Faunt, W. R. Belcher, D. S. Sweetkind, C. R. Tiedeman and D. Kavetski, Knowledge, transparency, and refutability in groundwater models, an example from the Death Valley regional groundwater flow system, Physics and Chemistry of the Earth, 64, 105-116, 2013.
39. Muñoz, E., J. L. Arumí and D. Rivera, Watersheds are not static: Implications of climate variability and hydrologic dynamics in modeling [Las cuencas no son estacionarias: implicancias de la variabilidad climática y dinámicas hidrológicas en la modelación, Bosque, 34 (1), 7-11, 2013.
40. 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.
41. Xu, C., H. Chen, and S. Guo, Hydrological modeling in a changing environment: issues and challenges, Journal of Water Resources Research, 2, 85-95, 2013.
42. Ramin, M., and G. B. Arhonditsis, Bayesian calibration of mathematical models: Optimization of model structure and examination of the role of process error covariance, Ecological Informatics, 18, 107-116, 2013.
43. Dumedah, G., and P. Coulibaly, Evaluating forecasting performance for data assimilation methods: the Ensemble Kalman Filter, the Particle Filter, and the Evolutionary-based assimilation Advances in Water Resources, 60, 47-63, 2013.
44. Wöhling, T., S. Gayler, E. Priesack, J. Ingwersen, H.-D. Wizemann, P. Högy, M. Cuntz, S. Attinger, V. Wulfmeyer, and T. Streck, Multiresponse, multiobjective calibration as a diagnostic tool to compare accuracy and structural limitations of five coupled soil-plant models and CLM3.5, Water Resources Research, 49(12), 8200–8221, 2013.
45. Romanowicz, R., M. Osuch and M. Grabowiecka, On the choice of calibration periods and objective functions: A practical guide to model parameter identification, Acta Geophysica, 61(6), 1477-1503, 10.2478/s11600-013-0157-6, 2013.
46. Rientjes, T.H.M., L.P. Muthuwatta, M.G. Bos, M.J. Booij, and H.A. Bhatti, Multi-variable calibration of a semi-distributed hydrological model using streamflow data and satellite-based evapotranspiration, Journal of Hydrology, 505, 276-290, 2013.
47. Guerrero, J. L., I. K. Westerberg, S. Halldin, L.-C. Lundin, and C.-Y. Xu, Exploring the hydrological robustness of model-parameter values with alpha shapes, Water Resources Research, 49 (10), 6700-6715, 2013.
48. Hsie, M., S. W. Yan and N. F. Pan, Improvement of rainfall-runoff simulations using the Runoff-Scale Weighting Method, Journal of Hydrologic Engineering, 19(7), 1330-1339, 10.1061/(ASCE)HE.1943-5584.0000921, 2014.
49. Gharari, S., M. Shafiei, M. Hrachowitz, F. Fenicia, H. V. Gupta, and H. H. G. Savenije, A constraint-based search algorithm for parameter identification of environmental models, Hydrology and Earth System Sciences, 18, 4861-4870, doi:10.5194/hess-18-4861-2014, 2014.
50. Shinma, T. A., and L. F. A. Reis, Incorporating multi-event and multi-site data in the calibration of SWMM, Procedia Engineering, 70, 75-84, 2014.
51. Coron, L., V. Andréassian, C. Perrin, M. Bourqui, and F. Hendrickx, On the lack of robustness of hydrologic models regarding water balance simulation – a diagnostic approach on 20 mountainous catchments using three models of increasing complexity, Hydrology and Earth System Sciences, 18, 727-746, 2014.
52. Dumedah, G., and J. P. Walker, Evaluation of model parameter convergence when using data assimilation for soil moisture estimation, Journal of Hydrometeorology, 15(1), 359-375, 2014.
53. Black, D. C., P. J. Wallbrink, and P. W. Jordan, Towards best practice implementation and application of models for analysis of water resources management scenarios, Environmental Modelling and Software, 52, 136-148, 2014.
54. Loukas, A., and L. Vasiliades, Streamflow simulation methods for ungauged and poorly gauged watersheds, Natural Hazards and Earth System Sciences, 14, 1641-1661, doi:10.5194/nhess-14-1641-2014, 2014.
55. Brauer, C. C., P. J. J. F. Torfs, A. J. Teuling, and R. Uijlenhoet, The Wageningen Lowland Runoff Simulator (WALRUS): application to the Hupsel Brook catchment and Cabauw polder, Hydrology and Earth System Sciences , 18, 4007-4028, 10.5194/hess-18-4007-2014, 2014.
56. Kloss, S., N. Schütze, and U. Schmidhalter, Evaluation of very high soil-water tension threshold values in sensor-based deficit irrigation systems, Journal of Irrigation and Drainage Engineering, 140 (9), 10.1061/(ASCE)IR.1943-4774.0000722, 2014.
57. Brauer, C. C., A. J. Teuling, P. J. J. F. Torfs, and R. Uijlenhoet, The Wageningen Lowland Runoff Simulator (WALRUS): a lumped rainfall–runoff model for catchments with shallow groundwater, Geoscientific Model Development, 7, 2313-2332, doi:10.5194/gmd-7-2313-2014, 2014.
58. #Hörmann, G., N. Fohrer, and W. Kluge, Modelle zum Wasserhaushalt, Handbuch der Umweltwissenschaften, 2014.
59. Zeff, H. B., J. R. Kasprzyk, J. D. Herman, P. M. Reed, and G. W. Characklis, Navigating financial and supply reliability tradeoffs in regional drought management portfolios, Water Resources Research, 50(6), 4906–4923, 2014.
60. Minville, M., D. Cartier, C. Guay, L.-A. Leclaire, C. Audet, S. Le Digabel, and J. Merleau, Improving process representation in conceptual hydrological model calibration using climate simulations, Water Resources Research, 50(6), 5044–5073, 2014.
61. Gao, W., F. Zhou, Y.-J. Dong, H.-C. Guo, J.-T. Peng, P. Xu, and , L. Zhao, PEST-based multi-objective automatic calibration of hydrologic parameters for HSPF model, Journal of Natural Resources, 29(5), 855-867, 2014.
62. #Houle, E., and J. Kasprzyk, Investigating parameter sensitivity for management in snow-driven watersheds, Proceedings of 7th International Congress on Environmental Modelling and Software, Daniel P. Ames, Nigel W.T. Quinn and Andrea E. Rizzoli (eds.), San Diego, CA, USA, 2014.
63. #Kasprzyk, J., J. Kollat, and C. Danilo, Balancing conflicting management objectives using interactive, three-dimensional visual analytics, Proceedings of 7th International Congress on Environmental Modelling and Software, Daniel P. Ames, Nigel W.T. Quinn and Andrea E. Rizzoli (eds.), San Diego, CA, USA, 2014.
64. Reynoso-Meza, G., J. Sanchis, X. Blasco, and S. García-Nieto, Physical programming for preference driven evolutionary multi-objective optimisation, Applied Soft Computing, 24, 341-362, 2014
65. Zhang, Y. Y., Q. X. Shao, A. Z. Ye and H. T. Xing, An integrated water system model considering hydrological and biogeochemical processes at basin scale: model construction and application, Hydrol. Earth Syst. Sci. Discuss., 11, 9219-9279, 10.5194/hessd-11-9219-2014, 2014.
66. Mayr, E., M. Juen, C. Mayer, R. Usubaliev and W. Hagg, Modeling runoff from the Inylchek glaciers and filling of ice‐dammed Lake Merzbacher, Central Tian Shan, Geografiska Annaler: Series A, Physical Geography, 96(4), 609–625, 10.1111/geoa.12061, 2014.
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