Articles | Volume 16, issue 14
https://doi.org/10.5194/gmd-16-4315-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-4315-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
28 Jul 2023
Development and technical paper |
| 28 Jul 2023
| 28 Jul 2023
Recalibration of a three-dimensional water quality model with a newly developed autocalibration toolkit (EFDC-ACT v1.0.0): how much improvement will be achieved with a wider hydrological variability?
State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300350, China
Tianyu Fu
State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300350, China
Cited articles
Adrian, R., O'Reilly, C. M., Zagarese, H., Baines, S. B., Hessen, D. O.,
Keller, W., Livingstone, D. M., Sommaruga, R., Straile, D., Van Donk, E.,
Weyhenmeyer, G. A., and Winder, M.: Lakes as sentinels of climate change,
Limnol. Oceanogr., 54, 2283–2297,
https://doi.org/10.4319/lo.2009.54.6_part_2.2283, 2009.
Alexandrov, G. A., Ames, D., Bellocchi, G., Bruen, M., Crout, N.,
Erechtchoukova, M., Hildebrandt, A., Hoffman, F., Jackisch, C., Khaiter, P.,
Mannina, G., Matsunaga, T., Purucker, S. T., Rivington, M., and Samaniego,
L.: Technical assessment and evaluation of environmental models and
software: Letter to the Editor, Environ. Modell. Softw., 26, 328–336,
https://doi.org/10.1016/j.envsoft.2010.08.004, 2011.
Arhonditsis, G. and Brett, M.: Evaluation of the current state of
mechanistic aquatic biogeochemical modeling, Mar. Ecol. Prog. Ser., 271,
13–26, https://doi.org/10.3354/meps271013, 2004.
Arhonditsis, G. B., Qian, S. S., Stow, C. A., Lamon, E. C., and Reckhow, K.
H.: Eutrophication risk assessment using Bayesian calibration of
process-based models: Application to a mesotrophic lake, Ecol. Model., 208,
215–229, https://doi.org/10.1016/j.ecolmodel.2007.05.020, 2007.
Arifin, R. R., James, S. C., de Alwis Pitts, D. A., Hamlet, A. F., Sharma,
A., and Fernando, H. J. S.: Simulating the thermal behavior in Lake Ontario
using EFDC, J. Gt. Lakes Res., 42, 511–523,
https://doi.org/10.1016/j.jglr.2016.03.011, 2016.
Basijokaite, R. and Kelleher, C.: Time-Varying Sensitivity Analysis Reveals
Relationships Between Watershed Climate and Variations in Annual Parameter
Importance in Regions With Strong Interannual Variability, Water Resour.
Res., 57, e2020WR028544, https://doi.org/10.1029/2020WR028544, 2021.
Bennett, N. D., Croke, B. F. W., Guariso, G., Guillaume, J. H. A., Hamilton,
S. H., Jakeman, A. J., Marsili-Libelli, S., Newham, L. T. H., Norton, J. P.,
Perrin, C., Pierce, S. A., Robson, B., Seppelt, R., Voinov, A. A., Fath, B.
D., and Andreassian, V.: Characterising performance of environmental models,
Environ. Modell. Softw., 40, 1–20,
https://doi.org/10.1016/j.envsoft.2012.09.011, 2013.
Bowie, G. L., Mills, W. B., Porcella, D. B., Campbell, C. L., Pagenkopf, J.
R., Rupp, G. L., Johnson, K. M., Chan, P. W. H., and Gherini, S. A.: Rates, Constants, and Kinetics Formulations in Surface Water Quality Modeling, U.S. Environmental Protection Agency, Washington, D.C., EPA/600/3-85/040, https://cfpub.epa.gov/si/si_public_record_report.cfm?Lab=ORD&dirEntryId=34685 (last access: 27 July 2023), 1985.
Cerco, C. F. and Cole, T.: Three-Dimensional Eutrophication Model of
Chesapeake Bay, Volume 1: Main Report, US Army Corps of Engineers Waterways
Experiment Station, https://www.chesapeakebay.net/what/publications/three-dimensional-eutrophication-model-of-chesapeake-bay (last access: 27 July 2023), 1994.
Cerco, C. F. and Noel, M. R.: Incremental Improvements in Chesapeake Bay
Environmental Model Package, J. Environ. Eng., 131, 745–754,
https://doi.org/10.1061/(ASCE)0733-9372(2005)131:5(745), 2005.
Cerco, C. F., Noel, M. R., and Linker, L.: Managing for Water Clarity in
Chesapeake Bay, J. Environ. Eng., 130, 631–642,
https://doi.org/10.1061/(ASCE)0733-9372(2004)130:6(631), 2004.
Chen, Y. Y., Zhang, C., Gao, X. P., and Wang, L. Y.: Long-term variations of
water quality in a reservoir in China, Water Sci. Technol., 65, 1454–1460,
https://doi.org/10.2166/wst.2012.034, 2012.
Delpla, I., Jung, A.-V., Baures, E., Clement, M., and Thomas, O.: Impacts of
climate change on surface water quality in relation to drinking water
production, Environ. Int., 35, 1225–1233,
https://doi.org/10.1016/j.envint.2009.07.001, 2009.
Efstratiadis, A. and Koutsoyiannis, D.: Fitting Hydrological Models on
Multiple Responses Using the Multiobjective Evolutionary Annealing-Simplex
Approach, in: Practical Hydroinformatics, vol. 68, edited by: Abrahart, R.
J., See, L. M., and Solomatine, D. P., Springer Berlin Heidelberg, Berlin,
Heidelberg, 259–273,
https://doi.org/10.1007/978-3-540-79881-1_19, 2008.
Franks, P. J. S.: Planktonic ecosystem models: perplexing parameterizations
and a failure to fail, J. Plankton Res., 31, 1299–1306,
https://doi.org/10.1093/plankt/fbp069, 2009.
Fu, B., Merritt, W. S., Croke, B. F. W., Weber, T. R., and Jakeman, A. J.: A
review of catchment-scale water quality and erosion models and a synthesis
of future prospects, Environ. Modell. Softw., 114, 75–97,
https://doi.org/10.1016/j.envsoft.2018.12.008, 2019.
Furey, P. C., Nordin, R. N., and Mazumder, A.: Water Level Drawdown Affects
Physical and Biogeochemical Properties of Littoral Sediments of a Reservoir
and a Natural Lake, Lake Reserv. Manage., 20, 280–295,
https://doi.org/10.1080/07438140409354158, 2004.
Grant, L., Vanderkelen, I., Gudmundsson, L., Tan, Z., Perroud, M.,
Stepanenko, V. M., Debolskiy, A. V., Droppers, B., Janssen, A. B. G.,
Woolway, R. I., Choulga, M., Balsamo, G., Kirillin, G., Schewe, J., Zhao,
F., del Valle, I. V., Golub, M., Pierson, D., Marcé, R., Seneviratne, S.
I., and Thiery, W.: Attribution of global lake systems change to
anthropogenic forcing, Nat. Geosci., 14, 849–854,
https://doi.org/10.1038/s41561-021-00833-x, 2021.
Gupta, H. V., Sorooshian, S., and Yapo, P. O.: Status of Automatic
Calibration for Hydrologic Models: Comparison with Multilevel Expert
Calibration, J. Hydrol. Eng., 4, 135–143,
https://doi.org/10.1061/(ASCE)1084-0699(1999)4:2(135), 1999.
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition
of the mean squared error and NSE performance criteria: Implications for
improving hydrological modelling, J. Hydrol., 377, 80–91,
https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009.
Hamrick, J. M.: A Three-Dimensional Environmental Fluid Dynamics Computer
Code: Theoretical and computational aspects, Virginia Institute of Marine
Science, College of William and Mary, https://doi.org/10.21220/V5TT6C, 1992.
Hipsey, M. R., Gal, G., Arhonditsis, G. B., Carey, C. C., Elliott, J. A.,
Frassl, M. A., Janse, J. H., de Mora, L., and Robson, B. J.: A system of
metrics for the assessment and improvement of aquatic ecosystem models,
Environ. Modell. Softw., 128, 104697,
https://doi.org/10.1016/j.envsoft.2020.104697, 2020.
Huang, Y.: Multi-objective calibration of a reservoir water quality model in
aggregation and non-dominated sorting approaches, J. Hydrol., 510, 280–292,
https://doi.org/10.1016/j.jhydrol.2013.12.036, 2014.
James, R. T.: Recalibration of the Lake Okeechobee Water Quality Model
(LOWQM) to extreme hydro-meteorological events, Ecol. Model., 325, 71–83,
https://doi.org/10.1016/j.ecolmodel.2016.01.007, 2016.
Janssen, P. H. M. and Heuberger, P. S. C.: Calibration of process-oriented
models, Ecol. Model., 83, 55–66,
https://doi.org/10.1016/0304-3800(95)00084-9, 1995.
Ji, Z.-G.: Hydrodynamics and water quality: modeling rivers, lakes, and
estuaries, 2nd Edn., John Wiley and Sons, Inc, Hoboken, NJ,
https://doi.org/10.1002/9781119371946, 2017.
Ji, Z.-G., Morton, M. R., and Hamrick, J. M.: Wetting and Drying Simulation
of Estuarine Processes, Estuar. Coast. Shelf S., 53, 683–700,
https://doi.org/10.1006/ecss.2001.0818, 2001.
Jia, H., Xu, T., Liang, S., Zhao, P., and Xu, C.: Bayesian framework of
parameter sensitivity, uncertainty, and identifiability analysis in complex
water quality models, Environ. Modell. Softw., 104, 13–26,
https://doi.org/10.1016/j.envsoft.2018.03.001, 2018.
Jiang, L., Li, Y., Zhao, X., Tillotson, M. R., Wang, W., Zhang, S., Sarpong,
L., Asmaa, Q., and Pan, B.: Parameter uncertainty and sensitivity analysis
of water quality model in Lake Taihu, China, Ecol. Model., 375, 1–12,
https://doi.org/10.1016/j.ecolmodel.2018.02.014, 2018.
Jørgensen, S. E. and Fath, B. D.: Fundamentals of ecological modelling:
applications in environmental management and research, 4th Edn., Elsevier,
Amsterdam, Boston, 399 pp., ISBN 978 0 444 53567 2, 2011.
Kim, J., Seo, D., Jang, M., and Kim, J.: Augmentation of limited input data
using an artificial neural network method to improve the accuracy of water
quality modeling in a large lake, J. Hydrol., 602, 126817,
https://doi.org/10.1016/j.jhydrol.2021.126817, 2021.
Knoben, W. J. M., Freer, J. E., and Woods, R. A.: Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores, Hydrol. Earth Syst. Sci., 23, 4323–4331, https://doi.org/10.5194/hess-23-4323-2019, 2019.
Kong, X., Ghaffar, S., Determann, M., Friese, K., Jomaa, S., Mi, C.,
Shatwell, T., Rinke, K., and Rode, M.: Reservoir water quality deterioration
due to deforestation emphasizes the indirect effects of global change, Water
Res., 221, 118721, https://doi.org/10.1016/j.watres.2022.118721, 2022.
Krolová, M., Čížková, H., Hejzlar, J., and
Poláková, S.: Response of littoral macrophytes to water level
fluctuations in a storage reservoir, Knowl. Manag. Aquat. Ecosyst., 408, 21 pp.,
https://doi.org/10.1051/kmae/2013042, 2013.
Krueger, T., Page, T., Hubacek, K., Smith, L., and Hiscock, K.: The role of
expert opinion in environmental modelling, Environ. Modell. Softw., 36,
4–18, https://doi.org/10.1016/j.envsoft.2012.01.011, 2012.
Li, X., Li, Y., and Li, G.: A scientometric review of the research on the
impacts of climate change on water quality during 1998–2018, Environ. Sci.
Pollut. Res., 27, 14322–14341, https://doi.org/10.1007/s11356-020-08176-7,
2020.
Lu, J., Bunn, S. E., and Burford, M. A.: Nutrient release and uptake by
littoral macrophytes during water level fluctuations, Sci. Total Environ.,
622–623, 29–40, https://doi.org/10.1016/j.scitotenv.2017.11.199, 2018.
Lung, W.-S. and Nice, A. J.: Eutrophication Model for the Patuxent Estuary:
Advances in Predictive Capabilities, J. Environ. Eng., 133, 917–930,
https://doi.org/10.1061/(ASCE)0733-9372(2007)133:9(917), 2007.
Luo, L., Hamilton, D., Lan, J., McBride, C., and Trolle, D.: Autocalibration of a one-dimensional hydrodynamic-ecological model (DYRESM 4.0-CAEDYM 3.1) using a Monte Carlo approach: simulations of hypoxic events in a polymictic lake, Geosci. Model Dev., 11, 903–913, https://doi.org/10.5194/gmd-11-903-2018, 2018.
McDonald, C. P. and Urban, N. R.: Using a model selection criterion to
identify appropriate complexity in aquatic biogeochemical models, Ecol.
Model., 221, 428-432, https://doi.org/10.1016/j.ecolmodel.2009.10.021, 2010.
Monteil, C., Zaoui, F., Le Moine, N., and Hendrickx, F.: Multi-objective calibration by combination of stochastic and gradient-like parameter generation rules – the caRamel algorithm, Hydrol. Earth Syst. Sci., 24, 3189–3209, https://doi.org/10.5194/hess-24-3189-2020, 2020.
Mooij, W. M., Trolle, D., Jeppesen, E., Arhonditsis, G., Belolipetsky, P.
V., Chitamwebwa, D. B. R., Degermendzhy, A. G., DeAngelis, D. L., De
Senerpont Domis, L. N., Downing, A. S., Elliott, J. A., Fragoso, C. R.,
Gaedke, U., Genova, S. N., Gulati, R. D., Håkanson, L., Hamilton, D. P.,
Hipsey, M. R., `t Hoen, J., Hülsmann, S., Los, F. H., Makler-Pick, V.,
Petzoldt, T., Prokopkin, I. G., Rinke, K., Schep, S. A., Tominaga, K., Van
Dam, A. A., Van Nes, E. H., Wells, S. A., and Janse, J. H.: Challenges and
opportunities for integrating lake ecosystem modelling approaches, Aquat.
Ecol., 44, 633–667, https://doi.org/10.1007/s10452-010-9339-3, 2010.
Mooij, W. M., van Wijk, D., Beusen, A. H., Brederveld, R. J., Chang, M.,
Cobben, M. M., DeAngelis, D. L., Downing, A. S., Green, P., Gsell, A. S.,
Huttunen, I., Janse, J. H., Janssen, A. B., Hengeveld, G. M., Kong, X.,
Kramer, L., Kuiper, J. J., Langan, S. J., Nolet, B. A., Nuijten, R. J.,
Strokal, M., Troost, T. A., van Dam, A. A., and Teurlincx, S.: Modeling
water quality in the Anthropocene: directions for the next-generation
aquatic ecosystem models, Curr. Opin. Env. Sust., 36, 85–95,
https://doi.org/10.1016/j.cosust.2018.10.012, 2019.
Moriasi, D. N., Arnold, J. G., Liew, M. W. V., Bingner, R. L., Harmel, R.
D., and Veith, T. L.: Model evaluation guidelines for systematic
quantification of accuracy in watershed simulations, T.
ASABE, 50, 885–900, https://doi.org/10.13031/2013.23153, 2007.
Nielsen, A., Trolle, D., Bjerring, R., Søndergaard, M., Olesen, J. E.,
Janse, J. H., Mooij, W. M., and Jeppesen, E.: Effects of climate and
nutrient load on the water quality of shallow lakes assessed through
ensemble runs by PCLake, Ecol. Appl., 24, 1926–1944,
https://doi.org/10.1890/13-0790.1, 2014.
Ostfeld, A. and Salomons, S.: A hybrid genetic—instance based learning
algorithm for CE-QUAL-W2 calibration, J. Hydrol., 310, 122–142,
https://doi.org/10.1016/j.jhydrol.2004.12.004, 2005.
Paudel, R.: Does increased model complexity improve description of
phosphorus dynamics in a large treatment wetland?, Ecol. Eng., 42, 283–294,
https://doi.org/10.1016/j.ecoleng.2012.02.014, 2012.
Reed, P. and Devireddy, V.: Groundwater monitoring design: a case study
combining epsilon dominance archiving and automatic parameterization for the
NSGA-II, in: Advances in Natural Computation, vol. 1, World Scientific,
79–100, https://doi.org/10.1142/9789812567796_0004, 2004.
Ren, Z., Qu, X., Zhang, M., Yu, Y., and Peng, W.: Distinct Bacterial
Communities in Wet and Dry Seasons During a Seasonal Water Level Fluctuation
in the Largest Freshwater Lake (Poyang Lake) in China, Front. Microbiol.,
10, 1167, https://doi.org/10.3389/fmicb.2019.01167, 2019.
Rigosi, A., Marcé, R., Escot, C., and Rueda, F. J.: A calibration
strategy for dynamic succession models including several phytoplankton
groups, Environ. Modell. Softw., 26, 697–710,
https://doi.org/10.1016/j.envsoft.2011.01.007, 2011.
Robson, B. J.: State of the art in modelling of phosphorus in aquatic
systems: Review, criticisms and commentary, Environ. Modell. Softw., 61,
339–359, https://doi.org/10.1016/j.envsoft.2014.01.012, 2014.
Robson, B. J., Arhonditsis, G. B., Baird, M. E., Brebion, J., Edwards, K.
F., Geoffroy, L., Hébert, M.-P., van Dongen-Vogels, V., Jones, E. M.,
Kruk, C., Mongin, M., Shimoda, Y., Skerratt, J. H., Trevathan-Tackett, S.
M., Wild-Allen, K., Kong, X., and Steven, A.: Towards evidence-based
parameter values and priors for aquatic ecosystem modelling, Environ. Modell.
Softw., 100, 74–81, https://doi.org/10.1016/j.envsoft.2017.11.018, 2018.
Salk, K. R., Venkiteswaran, J. J., Couture, R., Higgins, S. N., Paterson, M.
J., and Schiff, S. L.: Warming combined with experimental eutrophication
intensifies lake phytoplankton blooms, Limnol. Oceanogr., 67, 147–158,
https://doi.org/10.1002/lno.11982, 2022.
Schnedler-Meyer, N. A., Andersen, T. K., Hu, F. R. S., Bolding, K., Nielsen, A., and Trolle, D.: Water Ecosystems Tool (WET) 1.0 – a new generation of flexible aquatic ecosystem model, Geosci. Model Dev., 15, 3861–3878, https://doi.org/10.5194/gmd-15-3861-2022, 2022.
Shimoda, Y. and Arhonditsis, G. B.: Phytoplankton functional type modelling:
Running before we can walk? A critical evaluation of the current state of
knowledge, Ecol. Model., 320, 29–43,
https://doi.org/10.1016/j.ecolmodel.2015.08.029, 2016.
Sinha, E., Michalak, A. M., and Balaji, V.: Eutrophication will increase
during the 21st century as a result of precipitation changes, Science, 357,
405–408, https://doi.org/10.1126/science.aan2409, 2017.
Soares, L. M. V. and Calijuri, M. D. C.: Deterministic modelling of
freshwater lakes and reservoirs: Current trends and recent progress,
Environ. Modell. Softw., 144, 105143,
https://doi.org/10.1016/j.envsoft.2021.105143, 2021.
Špoljar, M., Zhang, C., Dražina, T., Zhao, G., Lajtner, J., and
Radonić, G.: Development of submerged macrophyte and epiphyton in a
flow-through system: Assessment and modelling predictions in interconnected
reservoirs, Ecol. Indic., 75, 145–154,
https://doi.org/10.1016/j.ecolind.2016.12.038, 2017.
Vaze, J., Post, D. A., Chiew, F. H. S., Perraud, J.-M., Viney, N. R., and
Teng, J.: Climate non-stationarity – Validity of calibrated
rainfall–runoff models for use in climate change studies, J. Hydrol., 394,
447–457, https://doi.org/10.1016/j.jhydrol.2010.09.018, 2010.
Williamson, C. E., Saros, J. E., and Schindler, D. W.: Sentinels of Change,
Science, 323, 887–888, https://doi.org/10.1126/science.1169443, 2009.
Wood, D. M., Houck, M. H., and Bell, J. M.: Automated Calibration and Use of
Stream-Quality Simulation Model, J. Environ. Eng., 116, 236–249,
https://doi.org/10.1061/(ASCE)0733-9372(1990)116:2(236), 1990.
Wu, G. and Xu, Z.: Prediction of algal blooming using EFDC model: Case study
in the Daoxiang Lake, Ecol. Model., 222, 1245–1252,
https://doi.org/10.1016/j.ecolmodel.2010.12.021, 2011.
Xia, W. and Shoemaker, C.: GOPS: efficient RBF surrogate global optimization
algorithm with high dimensions and many parallel processors including
application to multimodal water quality PDE model calibration, Optim. Eng.,
22, 2741–2777, https://doi.org/10.1007/s11081-020-09556-1, 2021.
Xia, W. and Shoemaker, C. A.: A Repetitive Parameterization and Optimization
Strategy for the Calibration of Complex and Computationally Expensive
Process-Based Models With Application to a 3D Water Quality Model of a
Tropical Reservoir, Water Resour. Res., 58, e2021WR031054,
https://doi.org/10.1029/2021WR031054, 2022.
Xia, W., Akhtar, T., and Shoemaker, C. A.: A novel objective function DYNO for automatic multivariable calibration of 3D lake models, Hydrol. Earth Syst. Sci., 26, 3651–3671, https://doi.org/10.5194/hess-26-3651-2022, 2022.
Xu, Y., Xie, R., Wang, Y., and Sha, J.: Spatio-temporal variations of water
quality in Yuqiao Reservoir Basin, North China, Front. Environ. Sci. Eng.,
9, 649–664, https://doi.org/10.1007/s11783-014-0702-9, 2015.
Yi, X., Zou, R., and Guo, H.: Global sensitivity analysis of a
three-dimensional nutrients-algae dynamic model for a large shallow lake,
Ecol. Model., 327, 74–84, https://doi.org/10.1016/j.ecolmodel.2016.01.005,
2016.
Yu, R. and Zhang, C.: Early warning of water quality degradation: A
copula-based Bayesian network model for highly efficient water quality risk
assessment, J. Environ. Manage., 292, 112749,
https://doi.org/10.1016/j.jenvman.2021.112749, 2021.
Zhang, C. and Fu, T.: Environmental Fluid Dynamics Code Automatic Calibration Toolkit (1.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.7438143, 2022.
Zhang, C. and Fu, T.: Observed data, Zenodo [data set], https://doi.org/10.5281/zenodo.8083303, 2023.
Zhang, C., Gao, X., Wang, L., and Chen, Y.: Analysis of agricultural
pollution by flood flow impact on water quality in a reservoir using a
three-dimensional water quality model, J. Hydroinform., 15, 1061–1072,
https://doi.org/10.2166/hydro.2012.131, 2013.
Zhang, C., Gao, X., Wang, L., and Chen, X.: Modelling the role of epiphyton
and water level for submerged macrophyte development with a modified
submerged aquatic vegetation model in a shallow reservoir in China, Ecol.
Eng., 81, 123–132, https://doi.org/10.1016/j.ecoleng.2015.04.048, 2015.
Zhang, C., Liu, H., Gao, X., and Zhang, H.: Modeling nutrients, oxygen and
critical phosphorus loading in a shallow reservoir in China with a coupled
water quality – Macrophytes model, Ecol. Indic., 66, 212–219,
https://doi.org/10.1016/j.ecolind.2016.01.053, 2016.
Zhang, C., Huang, Y., Špoljar, M., Zhang, W., and Kuczyńska-Kippen,
N.: Epiphyton dependency of macrophyte biomass in shallow reservoirs and
implications for water transparency, Aquat. Bot., 150, 46–52,
https://doi.org/10.1016/j.aquabot.2018.07.001, 2018a.
Zhang, C., Brett, M. T., Brattebo, S. K., and Welch, E. B.: How Well Does
the Mechanistic Water Quality Model CE-QUAL-W2 Represent Biogeochemical
Responses to Climatic and Hydrologic Forcing?, Water Resour. Res., 54,
6609–6624, https://doi.org/10.1029/2018WR022580, 2018b.
Zhang, C., Huang, Y., Javed, A., and Arhonditsis, G. B.: An ensemble
modeling framework to study the effects of climate change on the trophic
state of shallow reservoirs, Sci. Total Environ., 697, 134078,
https://doi.org/10.1016/j.scitotenv.2019.134078, 2019.
Zhang, C., Yan, Q., Kuczyńska-Kippen, N., and Gao, X.: An Ensemble
Kalman Filter approach to assess the effects of hydrological variability,
water diversion, and meteorological forcing on the total phosphorus
concentration in a shallow reservoir, Sci. Total Environ., 724, 138215,
https://doi.org/10.1016/j.scitotenv.2020.138215, 2020.
Zhao, G., Gao, X., Zhang, C., and Sang, G.: The effects of turbulence on
phytoplankton and implications for energy transfer with an integrated water
quality-ecosystem model in a shallow lake, J. Environ. Manage., 256, 109954,
https://doi.org/10.1016/j.jenvman.2019.109954, 2020.
Short summary
A new automatic calibration toolkit was developed and implemented into the recalibration of a 3-D water quality model, with observations in a wider range of hydrological variability. Compared to the model calibrated with the original strategy, the recalibrated model performed significantly better in modeled total phosphorus, chlorophyll a, and dissolved oxygen. Our work indicates that hydrological variability in the calibration periods has a non-negligible impact on the water quality models.
A new automatic calibration toolkit was developed and implemented into the recalibration of a...
