Volume 19 Issue 2
May  2026
Turn off MathJax
Article Contents
Germano de Oliveira Mattosinho, Fabiana de Oliveira Ferreira, Geraldo de Freitas Maciel. 2026: Deterministic modeling and uncertainty quantification of wind waves in Ilha Solteira Reservoir, Brazil. Water Science and Engineering, 19(2): 291-301. doi: 10.1016/j.wse.2026.02.006
Citation: Germano de Oliveira Mattosinho, Fabiana de Oliveira Ferreira, Geraldo de Freitas Maciel. 2026: Deterministic modeling and uncertainty quantification of wind waves in Ilha Solteira Reservoir, Brazil. Water Science and Engineering, 19(2): 291-301. doi: 10.1016/j.wse.2026.02.006

Deterministic modeling and uncertainty quantification of wind waves in Ilha Solteira Reservoir, Brazil

doi: 10.1016/j.wse.2026.02.006
  • Received Date: 2024-12-20
  • Accepted Date: 2026-02-07
  • Available Online: 2026-05-30
  • Wind waves in reservoirs represent a key hydrodynamic process influencing shoreline stability, navigation safety, and the design of hydraulic infrastructure. Despite their practical relevance, wave prediction in inland waters remains subject to significant uncertainties, particularly related to wind forcing and empirical model parameters. This study integrated deterministic and probabilistic approaches for predicting wind waves in reservoirs. Using a deterministic approach, the Simulating Waves Nearshore (SWAN) model was applied to estimate wave height and period. Key variables analyzed included wind velocity, wind direction, the Joint North Sea Wave Project (JONSWAP) bottom friction coefficient, the whitecapping coefficient, and the depth-induced breaking index. Through a probabilistic approach, uncertainties were quantified using polynomial chaos expansion (PCE), and sensitivity analysis was performed via Sobol indices. This framework was applied to a case study of the Tietê—Paraná Waterway in the Ilha Solteira Reservoir, São Paulo, Brazil. Simulations using the Janssen formulation yielded the most accurate wave height estimates. Sensitivity analysis based on Sobol indices identified wind velocity and the whitecapping coefficient as the most influential factors governing wave behavior. This integrated approach enables the generation of contour maps for wave height and period, offering valuable insights for project planning. Thus, the combination of deterministic and probabilistic analyses enhances the understanding of wind wave dynamics in inland waters.

     

  • loading
  • [1]
    Alves, J.-H., Tolman, H. L., Roland, A., Abdolali, A., Ardhuin, F., Mann, G., Chawla, A., Smith, J. M., 2022. NOAA’s Great Lakes Wave Prediction System: A successful framework for accelerating the transition of innovations to operations. Bulletin of the American Meteorological Society 104(4), E837-E850. https://doi.org/10.1175/bams-d-22-0094.1.
    [2]
    Booij, N., Holthuijsen, L.H., Ris, R.C., 1996. The “Swan” wave model for shallow water. In: Proceedings of the 25th International Conference on Coastal Engineering. ASCE, Orlando, pp. 668-676. https://doi.org/10.1061/9780784402429.05.
    [3]
    Booij, N., Ris, R.C., Holthuijsen, L.H., 1999. A third-generation wave model for coastal regions: 1. Model description and validation. Journal of Geophysical Research: Oceans 104(C4), 7649-7666. https://doi.org/10.1029/98jc02622.
    [4]
    Coastal Engineering Research Center (CERC), 1984. Shore Protection Manual, Volume 1. U.S. Army Corps of Engineers, Washington D.C. https://repository.tudelft.nl/islandora/object/uuid%3A98791127-e7ae-40a1-b850-67d5757a1289.
    [5]
    Crestaux, T., Le Maitre, O., Martinez, J.M., 2009. Polynomial chaos expansion for sensitivity analysis. Reliability Engineering & System Safety 94(7), 1161-1172. https://doi.org/10.1016/j.ress.2008.10.008.
    [6]
    Gruijthuijsen, M.F.J., 1996. Validation of the Wave Prediction Model SWAN Using Field Data from Lake George, Australia. Master Thesis. Delft University of Technology, Delft. https://repository.tudelft.nl/islandora/object/uuid%3Aa8a1face-a0ef-4307-85cf-7931c92bb6eb.
    [7]
    Hernandez, F.B.T., 2010. Severe Weather Event Affects Power Generation at Ilha Solteira. UNESP - Hydraulics and Irrigation Area, Sao Paulo State University (UNESP). https://www2.feis.unesp.br/irrigacao/temmais_com_19out10.php.
    [8]
    Jin, K.-R., Ji, Z.-G., 2001. Calibration and verification of a spectral wind-wave model for Lake Okeechobee. Ocean Engineering 28(5), 571-584. https://doi.org/10.1016/s0029-8018(00)00009-3.
    [9]
    Lemke, N., Calliari, L.J., Fontoura, J.A.S., Aguiar, D.F., 2017. Wave directional measurement in Patos Lagoon, RS, Brazil. Brazilian Journal of Water Resources 22, e1. https://doi.org/10.1590/2318-0331.011716053.
    [10]
    Lemke, N., Calliari, L.J., Fontoura, J.A.S., Serpa, C.G., Silva, M., 2021. Morphodynamics of the Caraha Stream Mouth in a microtidal coastal lagoon (Patos Lagoon, Southern Brazil). Pesquisas Em Geociencias 48(3). https://doi.org/10.22456/1807-9806.111039.
    [11]
    Lemke, N., Fontoura, J.A.S., Calliari, L.J., Ferreira, N.M., 2018. Estimation of characteristic wave scenarios in the Sao Lourenco do Sul Bay, Patos Lagoon - RS, Brazil. Exatas & Engenharia 8(20), 25-42. https://doi.org/10.25242/885x82020181305.
    [12]
    Li, J., Zang, J., Liu, S., Jia, W., Chen, Q., 2019. Numerical investigation of wave propagation and transformation over a submerged reef. Coastal Engineering Journal 61(3), 363-379. https://doi.org/10.1080/21664250.2019.1609712.
    [13]
    Mao, M., van der Westhuysen, M., Xia, M., Schwab, D.J., Chawla, A., 2016. Modeling wind waves from deep to shallow waters in Lake Michigan using unstructured SWAN. Journal of Geophysical Research: Oceans 121(6), 3836-3865. https://doi.org/10.1002/2015jc011340.
    [14]
    Marelli, S., Luthen, N., Sudret, B., 2022. UQLAB User Manual Polynomial Chaos Expansions. Chair of Risk, Safety and Uncertainty Quantification, ETH, Zurich. https://www.uqlab.com/pce-user-manual.
    [15]
    Marinho, C., Neto, J.A., Nicolodi, J.L., Lemke, N., Fontoura, J.A.S., 2020. Wave regime characterization in the northern sector of Patos Lagoon, Rio Grande do Sul, Brazil. Ocean and Coastal Research 68, e20295. https://doi.org/10.1590/s2675-28242020068295.
    [16]
    Marques, M., Andrade, F.O., 2017. Automated computation of two-dimensional fetch fields: Case study of the Salto Caxias Reservoir in southern Brazil. Lake and Reservoir Management 33(1), 62-73. https://doi.org/10.1080/10402381.2016.1264514.
    [17]
    Mattosinho, G.O., Ferreira, F.O., Maciel, G.F., Vieira, A. S., Sao, Y.T., 2022. Meteorological-hydrodynamic model coupling for safe inland navigation of waterway stretches in dam reservoirs, using a scarce database. Brazilian Journal of Water Resources 27, e1. https://doi.org/10.1590/2318-0331.272220210107.
    [18]
    Mattosinho, G.O., Nishigima, M.B., Ferreira, F.O., Cunha, E.F., Maciel, G.F., 2023. Wave modeling in reservoirs: Innovations for optimizing the multiple uses of the Ilha Solteira Reservoir. Peer Review 5(13), 271-290. https://doi.org/10.53660/617.prw1715.
    [19]
    Moeini, M.H., Etemad-Shahidi, A., 2009. Wave parameter hindcasting in a lake using the SWAN model. Scientia Iranica 16(2), 156-164.
    [20]
    Nagel, J.B., Rieckermann, J., Sudret, B., 2020. Principal component analysis and sparse polynomial chaos expansions for global sensitivity analysis and model calibration: Application to urban drainage simulation. Reliability Engineering & System Safety 195, 106737. https://doi.org/10.1016/j.ress.2019.106737.
    [21]
    Nikishova, A., Kalyuzhnaya, A., Boukhanovsky, A., Hoekstra, A., 2017. Uncertainty quantification and sensitivity analysis applied to the wind wave model SWAN. Environmental Modelling & Software 95, 344-357. https://doi.org/10.1016/j.envsoft.2017.06.030.
    [22]
    Nispel, A., Ekwaro-Osire, S., Dias, J.P., Cunha, A., 2021. Uncertainty quantification for fatigue life of offshore wind turbine structure. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B: Mechanical Engineering 7(4), 040901. https://doi.org/10.1115/1.4051162.
    [23]
    Palar, P.S., Zuhal, L.R., Shimoyama, K., Tsuchiya, T., 2018. Global sensitivity analysis via multi-fidelity polynomial chaos expansion. Reliability Engineering & System Safety 170, 175-190. https://doi.org/10.1016/j.ress.2017.10.013.
    [24]
    Rogers, W.E., Babanin, A.V., Wang, D.W., 2012. Observation-consistent input and whitecapping dissipation in a model for wind-generated surface waves: Description and simple calculations. Journal of Atmospheric and Oceanic Technology 29(9), 1329-1346. https://doi.org/10.1175/jtech-d-11-00092.1.
    [25]
    Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., Tarantola, S., 2007. Global Sensitivity Analysis. The Primer. John Wiley & Sons, Chichester. https://doi.org/10.1002/9780470725184.
    [26]
    Sapiega, P., Zalewska, T., Struzik, P., 2023. Application of SWAN model for wave forecasting in the southern Baltic Sea supplemented with measurement and satellite data. Environmental Modelling & Software 163, 105624. https://doi.org/10.1016/j.envsoft.2023.105624.
    [27]
    Sobol, I.M., Shukman, B.V., 1993. Random and quasirandom sequences: Numerical estimates of uniformity of distribution. Mathematical and Computer Modelling 18(8), 39-45. https://doi.org/10.1016/0895-7177(93)90160-Z.
    [28]
    Soize, C., 2018. Uncertainty Quantification: An Accelerated Course with Advanced Applications in Computational Engineering. Springer, Berlin.
    [29]
    The American Society of Mechanical Engineers (ASME), 2008. Procedure for estimation and reporting of uncertainty due to discretization in CFD applications. Journal of Fluids Engineering 130(7), 078001. https://doi.org/10.1115/1.2960953.
    [30]
    The American Society of Mechanical Engineers (ASME), 2009. Verification & Validation in Computational Fluid Dynamics & Heat Transfer. ASME, New York. https://www.asme.org/codes-standards/find-codes-standards/v-v-20-standard-verification-validation-computational-fluid-dynamics-heat-transfer.
    [31]
    The SWAN team, 2020a. Scientific and Technical Documentation. Environmental Fluid Mechanics Section, Delft University of Technology, Delft.
    [32]
    The SWAN team, 2020b. User Manual SWAN Cycle III Version 41.31. Environmental Fluid Mechanics Section, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft.
    [33]
    Vieira, A.S., 2013. Numerical and Experimental Analyses, Applications, and Validations of the SWAN Model in Restricted and Offshore Areas. Ph.D. Dissertation. São Paulo State University, São Paulo (in Portuguese).
    [34]
    Willmott, C.J., Ackleson, S.G., Davis, R.E., Feddema, J.J., Klink, K.M., Legates, D.R., O’Donnell, J., Rowe, C.M., 1985. Statistics for the evaluation and comparison of models. Journal of Geophysical Research Oceans 90(C5), 8995. https://doi.org/10.1029/jc090ic05p08995.
    [35]
    Wu, Z., Jiang, C., Deng, B., Chen, J., Cao, Y., Li, L., 2018. Evaluation of numerical wave model for typhoon wave simulation in South China Sea. Water Science and Engineering 11(3), 229-235. https://doi.org/10.1016/j.wse.2018.09.001.
    [36]
    Zhang, W., Zhao, H., Chen, G., Yang, J., 2023. Assessing the performance of SWAN model for wave simulations in the Bay of Bengal. Ocean Engineering 285, 115295. https://doi.org/10.1016/j.oceaneng.2023.115295.
    [37]
    Zieger, S., Babanin, A.V., Rogers, W.E., Young, I.R., 2015. Observation-based source terms in the third-generation wave model WAVEWATCH. Ocean Modelling 96, 2-25. https://doi.org/10.1016/j.ocemod.2015.06.011.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(1)

    Article Metrics

    Article views (16) PDF downloads(0) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return