Articles | Volume 24, issue 12
https://doi.org/10.5194/hess-24-5859-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-24-5859-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Dec 2020
Research article |
| 08 Dec 2020
| 08 Dec 2020
A framework for seasonal variations of hydrological model parameters: impact on model results and response to dynamic catchment characteristics
Tian Lan
School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China
Department of Geosciences, University of Oslo, P.O. Box 1047 Blindern,
0316 Oslo, Norway
Kairong Lin
CORRESPONDING AUTHOR
School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China
Guangdong Key Laboratory of Oceanic Civil Engineering, Sun Yat-sen
University, Guangzhou, 510275, China
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519000, China
Chong-Yu Xu
Department of Geosciences, University of Oslo, P.O. Box 1047 Blindern,
0316 Oslo, Norway
Zhiyong Liu
Guangdong Key Laboratory of Oceanic Civil Engineering, Sun Yat-sen
University, Guangzhou, 510275, China
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519000, China
Huayang Cai
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519000, China
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Overtide is a shallow water tidal component and its interaction with astronomical tides induces tidal wave deformation, which is an important process that controls sediment transport. We use a numerical tidal model to examine overtide changes in estuaries under varying river discharges and find spatially nonlinear changes and the threshold of an intermediate river that benefits maximal overtide generation. The findings inform management of sediment transport and flooding risk in estuaries.
Cited articles
Arora, S. and Singh, S.: The firefly optimization algorithm: convergence
analysis and parameter selection, Int. J. Comput. Appl., 69, 48–52, https://doi.org/10.5120/11826-7528, 2013.
Arsenault, R., Poulin, A., Côté, P., and Brissette, F.: Comparison
of Stochastic Optimization Algorithms in Hydrological Model Calibration, J.
Hydrol. Eng., 19, 1374–1384, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000938, 2014.
Bárdossy, A.: Calibration of hydrological model parameters for ungauged catchments, Hydrol. Earth Syst. Sci., 11, 703–710, https://doi.org/10.5194/hess-11-703-2007, 2007.
Beven, K. and Freer, J.: A dynamic TOPMODEL, Hydrol. Process., 15,
1993–2011, https://doi.org/10.1002/hyp.252, 2001.
Beven, K. J. and Kirkby, M. J.: A physically based, variable contributing
area model of basin hydrology, Un modèle à base physique de zone
d'appel variable de l'hydrologie du bassin versant, Hydrol. Sci.
B., 24, 43–69, https://doi.org/10.1080/02626667909491834, 1979.
Beven, K. J., Kirkby, M. J., Schofield, N., and Tagg, A. F.: Testing a
physically-based flood forecasting model (TOPMODEL) for three UK
catchments, J. Hydrol., 69, 119–143, https://doi.org/10.1016/0022-1694(84)90159-8, 1984.
Cheng, L., Yaeger, M., Viglione, A., Coopersmith, E., Ye, S., and Sivapalan, M.: Exploring the physical controls of regional patterns of flow duration curves – Part 1: Insights from statistical analyses, Hydrol. Earth Syst. Sci., 16, 4435–4446, https://doi.org/10.5194/hess-16-4435-2012, 2012.
CMDC: Home page, available at: https://data.cma.cn/en/?r=data/online&t=6, last access: December 2020.
Dakhlaoui, H., Ruelland, D., Tramblay, Y., and Bargaoui, Z.: Evaluating the
robustness of conceptual rainfall-runoff models under climate variability in
northern Tunisia, J. Hydrol., 550, 201–217, https://doi.org/10.1016/j.jhydrol.2017.04.032,
2017.
Dawkins, R.: Climbing mount improbable, WW Norton & Company, New York, USA, 1997.
Derrac, J., García, S., Hui, S., Suganthan, P. N., and Herrera, F.:
Analyzing convergence performance of evolutionary algorithms: A statistical
approach, Inform. Sciences, 289, 41–58,
https://doi.org/10.1016/j.ins.2014.06.009, 2014.
de Vos, N. J., Rientjes, T. H. M., and Gupta, H. V.: Diagnostic evaluation
of conceptual rainfall-runoff models using temporal clustering, Hydrol.
Process., 24, 2840–2850, https://doi.org/10.1002/hyp.7698, 2010.
Duan, Q., Sorooshian, S., and Gupta, V.: Effective and efficient global
optimization for conceptual rainfall-runoff models, Water Resour.
Res., 28, 1015–1031, https://doi.org/10.1029/91WR02985, 1992.
Duan, Q. Y., Gupta, V. K., and Sorooshian, S.: Shuffled Complex Evolution
Approach for Effective and Efficient Global Minimization, J.
Optimiz. Theory App., 76, 501–521, https://doi.org/10.1007/Bf00939380,
1993.
Duan, Q., Sorooshian, S., and Gupta, V. K.: Optimal use of the SCE-UA global
optimization method for calibrating watershed models, J. Hydrol.,
158, 265–284, 1994.
Fang, J., Song, Y., Liu, H., and Piao, S.: Vegetation-climate relationship
and its application in the division of vegetation zone in China, Acta
Bot. Sin., 44, 1105–1122, 2002.
Firat, M. and Güngör, M.: Hydrological time-series modelling using
an adaptive neuro-fuzzy inference system, Hydrol. Process., 22, 2122–2132, https://doi.org/10.1002/hyp.6812, 2008.
Fowler, K., Coxon, G., Freer, J., Peel, M., Wagener, T., Western, A., Woods,
R., and Zhang, L.: Simulating Runoff Under Changing Climatic Conditions: A
Framework for Model Improvement, Water Resour. Res., 54, 9812–9832,
https://doi.org/10.1029/2018wr023989, 2018.
Gharari, S., Hrachowitz, M., Fenicia, F., and Savenije, H. H. G.: An approach to identify time consistent model parameters: sub-period calibration, Hydrol. Earth Syst. Sci., 17, 149–161, https://doi.org/10.5194/hess-17-149-2013, 2013.
Gibbs, M. S., Maier, H. R., and Dandy, G. C.: Applying fitness landscape measures to water distribution optimization problems, in: Hydroinformatics, World Scientific, 795–802, 2004.
Gomez, J.: Stochastic global optimization algorithms: A systematic formal
approach, Inform. Sciences, 472, 53–76,
https://doi.org/10.1016/j.ins.2018.09.021, 2019.
Gupta, H. V., Sorooshian, S., and Yapo, P. O.: Toward improved calibration
of hydrologic models: Multiple and noncommensurable measures of information,
Water Resour. Res., 34, 751–763, https://doi.org/10.1029/97wr03495, 1998.
Harik, G. R., Lobo, F. G., and Goldberg, D. E.: The compact genetic
algorithm, IEEE T. Evolut. Comput., 3, 287–297, https://doi.org/10.1109/4235.797971, 1999.
Heinrich, J. and Weiskopf, D.: Parallel Coordinates for Multidimensional
Data Visualization: Basic Concepts, Comput. Sci. Eng.,
17, 70–76, https://doi.org/10.1109/mcse.2015.55, 2015.
Hintze, J. L. and Nelson, R. D.: Violin plots: a box plot-density trace
synergism, Am. Stat., 52, 181–184,
https://doi.org/10.2307/2685478, 1998.
Ho, M., Lall, U., Sun, X., and Cook, E. R.: Multiscale temporal variability
and regional patterns in 555 years of conterminous US streamflow, Water Resour. Res., 53, 3047–3066, https://doi.org/10.1002/2016wr019632, 2017.
Janetzko, H., Stein, M., Sacha, D., and Schreck, T.: Enhancing parallel
coordinates: Statistical visualizations for analyzing soccer data,
Electronic Imaging, 2016, 1–8, 2016.
Johansson, J. and Forsell, C.: Evaluation of Parallel Coordinates:
Overview, Categorization and Guidelines for Future Research, IEEE Trans. Vis.
Comput. Graph., 22, 579–588, https://doi.org/10.1109/TVCG.2015.2466992, 2016.
Jones, T. and Forrest, S.: Fitness distance correlation as a measure of
problem difficulty for genetic algorithms, Santa Fe Institute, Working Paper, available at: https://www.researchgate.net/publication/216300862 (last access: December 2020), 1995.
Kallel, F., Ophir, J., Magee, K., and Krouskop, T.: Elastographic Imaging of
Low-Contrast Elastic Modulus Distributions in Tissue, Ultrasound Med.
Biol., 24, 409–425, https://doi.org/10.1016/S0301-5629(97)00287-1,
1998.
Kauffman, S. A.: The origins of order: Self-organization and selection in
evolution, Oxford University Press, California, Santa Clara County, USA, 1993.
Kim, K. B. and Han, D.: Exploration of sub-annual calibration schemes of
hydrological models, Hydrol. Res., 48, 1014–1031,
https://doi.org/10.2166/nh.2016.296, 2017.
Klemeš, V.: Operational testing of hydrological simulation models,
Hydrol. Sci. J., 31, 13–24, 1986.
Klotz, D., Herrnegger, M., and Schulz, K.: Symbolic Regression for the
Estimation of Transfer Functions of Hydrological Models, Water Resour.
Res., 53, 9402–9423, https://doi.org/10.1002/2017wr021253, 2017.
Lan, T., Lin, K. R., Liu, Z. Y., He, Y. H., Xu, C. Y., Zhang, H. B., and
Chen, X. H.: A Clustering Preprocessing Framework for the Subannual
Calibration of a Hydrological Model Considering Climate-Land Surface
Variations, Water Resour. Res., 54, 10034–10052, https://doi.org/10.1029/2018wr023160,
2018.
Lan, T., Lin, K., Xu, C.-Y., Tan, X., and Chen, X.: Dynamics of hydrological-model parameters: mechanisms, problems and solutions, Hydrol. Earth Syst. Sci., 24, 1347–1366, https://doi.org/10.5194/hess-24-1347-2020, 2020.
Lin, K. R., Zhang, Q., and Chen, X. H.: An evaluation of impacts of DEM
resolution and parameter correlation on TOPMODEL modeling uncertainty, J.
Hydrol., 394, 370–383, https://doi.org/10.1016/j.jhydrol.2010.09.012, 2010.
Maier, H. R., Kapelan, Z., Kasprzyk, J., Kollat, J., Matott, L. S., Cunha,
M. C., Dandy, G. C., Gibbs, M. S., Keedwell, E., Marchi, A., Ostfeld, A.,
Savic, D., Solomatine, D. P., Vrugt, J. A., Zecchin, A. C., Minsker, B. S.,
Barbour, E. J., Kuczera, G., Pasha, F., Castelletti, A., Giuliani, M., and
Reed, P. M.: Evolutionary algorithms and other metaheuristics in water
resources: Current status, research challenges and future directions,
Environ. Modell. Softw., 62, 271–299, https://doi.org/10.1016/j.envsoft.2014.09.013, 2014.
Manfreda, S., Mita, L., Dal Sasso, S. F., Samela, C., and Mancusi, L.:
Exploiting the use of physical information for the calibration of a lumped
hydrological model, Hydrol. Process., 32, 1420–1433, https://doi.org/10.1002/hyp.11501, 2018.
Mitchell, M.: An introduction to genetic algorithms, Oxford University Press,
Oxford, England, 1998.
Motavita, D. F., Chow, R., Guthke, A., and Nowak, W.: The comprehensive
differential split-sample test: A stress-test for hydrological model
robustness under climate variability, J. Hydrol., 573, 501–515,
https://doi.org/10.1016/j.jhydrol.2019.03.054, 2019.
NASA: ASTER Global Digital Elevation Map Announcement, available at: https://asterweb.jpl.nasa.gov/gdem.asp, last access: December 2020.
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual
models part I – A discussion of principles, J. Hydrol., 10,
282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970.
Nijzink, R. C., Samaniego, L., Mai, J., Kumar, R., Thober, S., Zink, M., Schäfer, D., Savenije, H. H. G., and Hrachowitz, M.: 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, https://doi.org/10.5194/hess-20-1151-2016, 2016.
Paruolo, P., Saisana, M., and Saltelli, A.: Ratings and rankings: voodoo or
science?, J. Roy. Stat. Soc. A Stat., 176, 609–634, https://doi.org/10.1111/j.1467-985X.2012.01059.x, 2013.
Pathiraja, S., Marshall, L., Sharma, A., and Moradkhani, H.: Hydrologic
modeling in dynamic catchments: A data assimilation approach, Water
Resour. Res., 52, 3350–3372, https://doi.org/10.1002/2015wr017192, 2016.
Pathiraja, S., Anghileri, D., Burlando, P., Sharma, A., Marshall, L., and Moradkhani, H.: Time-varying parameter models for catchments with land use change: the importance of model structure, Hydrol. Earth Syst. Sci., 22, 2903–2919, https://doi.org/10.5194/hess-22-2903-2018, 2018.
Pfannerstill, M., Guse, B., and Fohrer, N.: Smart low flow signature metrics
for an improved overall performance evaluation of hydrological models, J.
Hydrol., 510, 447–458, https://doi.org/10.1016/j.jhydrol.2013.12.044, 2014.
Piel, F. B., Patil, A. P., Howes, R. E., Nyangiri, O. A., Gething, P. W.,
Williams, T. N., Weatherall, D. J., and Hay, S. I.: Global distribution of
the sickle cell gene and geographical confirmation of the malaria
hypothesis, Nat. Commun., 1, 104, https://doi.org/10.1038/ncomms1104, 2010.
Piotrowski, A. P., Napiorkowski, M. J., Napiorkowski, J. J., and Rowinski,
P. M.: Swarm Intelligence and Evolutionary Algorithms: Performance versus
speed, Inform. Sciences, 384, 34–85,
https://doi.org/10.1016/j.ins.2016.12.028, 2017.
Pokhrel, P., Yilmaz, K. K., and Gupta, H. V.: Multiple-criteria calibration
of a distributed watershed model using spatial regularization and response
signatures, J. Hydrol., 418, 49–60, https://doi.org/10.1016/j.jhydrol.2008.12.004, 2012.
Rahnamay Naeini, M., Yang, T., Sadegh, M., Agha Kouchak, A., Hsu, K.-L.,
Sorooshian, S., Duan, Q., and Lei, X.: Shuffled Complex-Self Adaptive Hybrid
EvoLution (SC-SAHEL) optimization framework, Environ. Modell. Softw., 104,
215–235, https://doi.org/10.1016/j.envsoft.2018.03.019, 2018.
Sorooshian, S., Duan, Q., and Gupta, V. K.: Calibration of rainfall-runoff
models: Application of global optimization to the Sacramento Soil Moisture
Accounting Model, Water Resour. Res., 29, 1185–1194,
https://doi.org/10.1029/92wr02617, 1993.
Vrotsou, K., Forsell, C., and Cooper, M.: 2D and 3D Representations for
Feature Recognition in Time Geographical Diary Data, Inform.
Visual., 9, 263–276, https://doi.org/10.1057/ivs.2009.30, 2010.
Vrugt, J. A., Diks, C. G. H., Gupta, H. V., Bouten, W., and Verstraten, J.
M.: Improved treatment of uncertainty in hydrologic modeling: Combining the
strengths of global optimization and data assimilation, Water Resour. Res., 41, W01017, https://doi.org/10.1029/2004wr003059, 2005.
Vrugt, J. A. and Beven, K. J.: Embracing equifinality with efficiency:
Limits of Acceptability sampling using the DREAM (LOA) algorithm, J.
Hydrol., 559, 954–971, https://doi.org/10.1016/j.jhydrol.2018.02.026, 2018.
Wagener, T., Boyle, D. P., Lees, M. J., Wheater, H. S., Gupta, H. V., and Sorooshian, S.: A framework for development and application of hydrological models, Hydrol. Earth Syst. Sci., 5, 13–26, https://doi.org/10.5194/hess-5-13-2001, 2001.
Wang, S., Huang, G. H., Baetz, B. W., Cai, X. M., Ancell, B. C., and Fan, Y.
R.: Examining dynamic interactions among experimental factors influencing
hydrologic data assimilation with the ensemble Kalman filter, J. Hydrol., 554,
743–757, https://doi.org/10.1016/j.jhydrol.2017.09.052, 2017.
Wang, S., Ancell, B., Huang, G., and Baetz, B.: Improving Robustness of
Hydrologic Ensemble Predictions Through Probabilistic Pre-and
Post-Processing in Sequential Data Assimilation, Water Resour. Res.,
54, 2129–2151, 2018.
Weinberger, E.: Correlated and Uncorrelated Fitness Landscapes and How to
Tell the Difference, Biol. Cybern., 63, 325–336, https://doi.org/10.1007/Bf00202749, 1990.
Weise, T.: Global optimization algorithms-theory and application,
Self-published, 2009.
Westra, S., Thyer, M., Leonard, M., Kavetski, D., and Lambert, M.: A
strategy for diagnosing and interpreting hydrological model nonstationarity,
Water Resour. Res., 50, 5090–5113, 2014.
Wright, S.: The roles of mutation, inbreeding, crossbreeding, and selection
in evolution, 1932.
Xiong, M., Liu, P., Cheng, L., Deng, C., Gui, Z., Zhang, X., and Liu, Y.:
Identifying time-varying hydrological model parameters to improve simulation
efficiency by the ensemble Kalman filter: A joint assimilation of streamflow
and actual evapotranspiration, J. Hydrol., 568, 758–768,
https://doi.org/10.1016/j.jhydrol.2018.11.038, 2019.
Yadav, M., Wagener, T., and Gupta, H.: Regionalization of constraints on
expected watershed response behavior for improved predictions in ungauged
basins, Adv. Water Resour., 30, 1756–1774,
https://doi.org/10.1016/j.advwatres.2007.01.005, 2007.
Yao, Z. and Wu, L.: 3D-Parallel Coordinates: Visualization for time varying
multidimensional data, in: 7th IEEE International Conference on Software
Engineering and Service Science (ICSESS), 26–28 August 2016, Beijing, China, 655–658, 2016.
Yapo, P. O., Gupta, H. V., and Sorooshian, S.: Automatic calibration of
conceptual rainfall-runoff models: Sensitivity to calibration data, J.
Hydrol., 181, 23–48, https://doi.org/10.1016/0022-1694(95)02918-4, 1996.
Yokoo, Y. and Sivapalan, M.: Towards reconstruction of the flow duration curve: development of a conceptual framework with a physical basis, Hydrol. Earth Syst. Sci., 15, 2805–2819, https://doi.org/10.5194/hess-15-2805-2011, 2011.
Zhang, X., Srinivasan, R., Zhao, K., and Liew, M. V.: Evaluation of global
optimization algorithms for parameter calibration of a computationally
intensive hydrologic model, Hydrol. Process., 23, 430–441,
https://doi.org/10.1002/hyp.7152, 2009.
Zhang, Y., Jia, S., Huang, H., Qiu, J., and Zhou, C.: A novel algorithm for
the precise calculation of the maximal information coefficient, Sci. Rep., 4,
6662, https://doi.org/10.1038/srep06662, 2014.
