IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v31y2017i3d10.1007_s11269-016-1557-6.html
   My bibliography  Save this article

Impact of Distribution Type in Bayes Probability Flood Forecasting

Author

Listed:
  • Wei Li

    (Huazhong University of Science and Technology
    Hubei Key Laboratory of Digital Valley Science and Technology)

  • Jianzhong Zhou

    (Huazhong University of Science and Technology
    Hubei Key Laboratory of Digital Valley Science and Technology)

  • Huaiwei Sun

    (Huazhong University of Science and Technology
    Hubei Key Laboratory of Digital Valley Science and Technology)

  • Kuaile Feng

    (Huazhong University of Science and Technology
    Hubei Key Laboratory of Digital Valley Science and Technology)

  • Hairong Zhang

    (Huazhong University of Science and Technology
    Hubei Key Laboratory of Digital Valley Science and Technology)

  • Muhammad Tayyab

    (Huazhong University of Science and Technology
    Hubei Key Laboratory of Digital Valley Science and Technology)

Abstract

Bayesian forecasting system (BFS) is widely applied to the hydrological forecast. Hydrological forecast processor (HUP), a key part of the Bayesian probability prediction, is conducted at the assumption that the rainfall is certain, which can simultaneously quantify the uncertainty of hydrological model and parameter. In the HUP, the runoff is usually assumed to obey Logweibull distribution or Normal distribution. However, Distribution type of the runoff is not certain at different areas, and there are few distribution types of HUP in existence. So common distribution types are needed to develop the HUP to provide an effective forecast result. In this paper, Nonparametric kernel density estimation, Pearson III and Empirical distribution were introduced as the prior distribution of HUP to eliminate the parameter uncertainty of probability density function. Also, the five distributions were compared in this study to get the diversity of distribution types and search the best appropriate distribution type. The 52 floods during 2004a-2014a of ZheXi basin are employed to calibrate and validate the different distribution types of BFS. The results show that the LogWeibull and Empirical Bayesian probabilistic model has the best performance on average results compared with the other four distribution models. Meanwhile the other distribution types proposed in this study have the similar ability on interval width and the containing rate of probability forecasting results. This demonstrates that more new distributions are required to make BFS more robust.

Suggested Citation

  • Wei Li & Jianzhong Zhou & Huaiwei Sun & Kuaile Feng & Hairong Zhang & Muhammad Tayyab, 2017. "Impact of Distribution Type in Bayes Probability Flood Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(3), pages 961-977, February.
    • Handle: RePEc:spr:waterr:v:31:y:2017:i:3:d:10.1007_s11269-016-1557-6
    DOI: 10.1007/s11269-016-1557-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-016-1557-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11269-016-1557-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. C.-Y. Xu & V. P. Singh, 2004. "Review on Regional Water Resources Assessment Models under Stationary and Changing Climate," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 18(6), pages 591-612, December.
    2. Xuesong Zhang & Kaiguang Zhao, 2012. "Bayesian Neural Networks for Uncertainty Analysis of Hydrologic Modeling: A Comparison of Two Schemes," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(8), pages 2365-2382, June.
    3. Zhangjun Liu & Shenglian Guo & Honggang Zhang & Dedi Liu & Guang Yang, 2016. "Comparative Study of Three Updating Procedures for Real-Time Flood Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(7), pages 2111-2126, May.
    4. Saeed Golian & Bahram Saghafian & Reza Maknoon, 2010. "Derivation of Probabilistic Thresholds of Spatially Distributed Rainfall for Flood Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(13), pages 3547-3559, October.
    5. Xi Chen & Tao Yang & Xiaoyan Wang & Chong-Yu Xu & Zhongbo Yu, 2013. "Uncertainty Intercomparison of Different Hydrological Models in Simulating Extreme Flows," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(5), pages 1393-1409, March.
    6. Shahab Araghinejad, 2011. "An Approach for Probabilistic Hydrological Drought Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(1), pages 191-200, January.
    7. Hong Li & Chong-Yu Xu & Stein Beldring & Lena Merete Tallaksen & Sharad K. Jain, 2016. "Water Resources Under Climate Change in Himalayan Basins," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(2), pages 843-859, January.
    8. Hong Li & Chong-Yu Xu & Stein Beldring & Lena Tallaksen & Sharad Jain, 2016. "Water Resources Under Climate Change in Himalayan Basins," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(2), pages 843-859, January.
    9. Si-Hui Dong, 2008. "Genetic Algorithm Based Parameter Estimation of Nash Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 22(4), pages 525-533, April.
    10. Richard Arsenault & Marco Latraverse & Thierry Duchesne, 2016. "An Efficient Method to Correct Under-Dispersion in Ensemble Streamflow Prediction of Inflow Volumes for Reservoir Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(12), pages 4363-4380, September.
    11. Hairong Zhang & Jianzhong Zhou & Lei Ye & Xiaofan Zeng & Yufan Chen, 2015. "Lower Upper Bound Estimation Method Considering Symmetry for Construction of Prediction Intervals in Flood Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(15), pages 5505-5519, December.
    12. Pao-Shan Yu & Tao-Chang Yang & Chen-Min Kuo & Yi-Tai Wang, 2014. "A Stochastic Approach for Seasonal Water-Shortage Probability Forecasting Based on Seasonal Weather Outlook," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(12), pages 3905-3920, September.
    13. Ye Tian & Yue-Ping Xu & Xu-Jie Zhang, 2013. "Assessment of Climate Change Impacts on River High Flows through Comparative Use of GR4J, HBV and Xinanjiang Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(8), pages 2871-2888, June.
    14. Salman Sharifazari & Shahab Araghinejad, 2015. "Development of a Nonparametric Model for Multivariate Hydrological Monthly Series Simulation Considering Climate Change Impacts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(14), pages 5309-5322, November.
    15. Andres Ticlavilca & Mac McKee, 2011. "Multivariate Bayesian Regression Approach to Forecast Releases from a System of Multiple Reservoirs," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(2), pages 523-543, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wentong Hu & Wenquan Gu & Donghao Miao & Dongguo Shao, 2022. "Research on the Ecological Flow and Water Replenishment Thresholds for Diversion Rivers Based on the MC-LOR Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(14), pages 5353-5369, November.
    2. Muhammad Shafeeq ul Rehman Khan & Zamir Hussain & Ishfaq Ahmad, 2021. "Effects of L-Moments, Maximum Likelihood and Maximum Product of Spacing Estimation Methods in Using Pearson Type-3 Distribution for Modeling Extreme Values," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(5), pages 1415-1431, March.
    3. Tian Peng & Chu Zhang & Jianzhong Zhou & Xin Xia & Xiaoming Xue, 2019. "Multi-Objective Optimization for Flood Interval Prediction Based on Orthogonal Chaotic NSGA-II and Kernel Extreme Learning Machine," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(14), pages 4731-4748, November.
    4. Jianzhong Zhou & Kuaile Feng & Yi Liu & Chao Zhou & Feifei He & Guangbiao Liu & Zhongzheng He, 2020. "A Hydrologic Uncertainty Processor Using Linear Derivation in the Normal Quantile Transform Space," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(11), pages 3649-3665, September.
    5. Kuaile Feng & Jianzhong Zhou & Yi Liu & Chengwei Lu & Zhongzheng He, 2019. "Hydrological Uncertainty Processor (HUP) with Estimation of the Marginal Distribution by a Gaussian Mixture Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(9), pages 2975-2990, July.
    6. Zhangjun Liu & Jingwen Zhang & Tianfu Wen & Jingqing Cheng, 2022. "Uncertainty Quantification of Rainfall-runoff Simulations Using the Copula-based Bayesian Processor: Impacts of Seasonality, Copula Selection and Correlation Coefficient," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(13), pages 4981-4993, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wei Li & Jianzhong Zhou & Lu Chen & Kuaile Feng & Hairong Zhang & Changqing Meng & Na Sun, 2019. "Upper and Lower Bound Interval Forecasting Methodology Based on Ideal Boundary and Multiple Linear Regression Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(3), pages 1203-1215, February.
    2. Yuyin Liang & Shuguang Liu & Yiping Guo & Hong Hua, 2017. "L-Moment-Based Regional Frequency Analysis of Annual Extreme Precipitation and its Uncertainty Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(12), pages 3899-3919, September.
    3. Sarmad Dashti Latif & Ali Najah Ahmed, 2023. "A review of deep learning and machine learning techniques for hydrological inflow forecasting," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(11), pages 12189-12216, November.
    4. Shashidhar Kumar Jha & Ajeet Kumar Negi & Juha Mikael Alatalo & Vignesh Prabhu & Mani Bhushan Jha & Hemant Kumar, 2022. "Forest Degradation Index: A Tool for Forest Vulnerability Assessment in Indian Western Himalaya," Sustainability, MDPI, vol. 14(23), pages 1-29, November.
    5. Jiazheng Lu & Jun Guo & Li Yang & Xunjian Xu, 2017. "Research of reservoir watershed fine zoning and flood forecasting method," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 89(3), pages 1291-1306, December.
    6. Simmons, Aaron T. & Perovic, David J. & Roth, Guy, 2022. "Making waves – Are water scarcity footprints of irrigated agricultural commodities suitable to inform consumer decisions?," Agricultural Water Management, Elsevier, vol. 268(C).
    7. Chandra Lal Pandey, 2021. "Managing urban water security: challenges and prospects in Nepal," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(1), pages 241-257, January.
    8. Vahid Nourani & Nardin Jabbarian Paknezhad & Hitoshi Tanaka, 2021. "Prediction Interval Estimation Methods for Artificial Neural Network (ANN)-Based Modeling of the Hydro-Climatic Processes, a Review," Sustainability, MDPI, vol. 13(4), pages 1-18, February.
    9. Pao-Shan Yu & Tao-Chang Yang & Chen-Min Kuo & Yi-Tai Wang, 2014. "A Stochastic Approach for Seasonal Water-Shortage Probability Forecasting Based on Seasonal Weather Outlook," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(12), pages 3905-3920, September.
    10. Hairong Zhang & Jianzhong Zhou & Lei Ye & Xiaofan Zeng & Yufan Chen, 2015. "Lower Upper Bound Estimation Method Considering Symmetry for Construction of Prediction Intervals in Flood Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(15), pages 5505-5519, December.
    11. Hadush Meresa & Yongqiang Zhang, 2021. "Contrasting Uncertainties in Estimating Floods and Low Flow Extremes," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(6), pages 1775-1795, April.
    12. Adam P. Piotrowski & Jaroslaw J. Napiorkowski & Marzena Osuch, 2019. "Relationship Between Calibration Time and Final Performance of Conceptual Rainfall-Runoff Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(1), pages 19-37, January.
    13. Sheng Sheng & Hua Chen & Fu-Qiang Guo & Jie Chen & Chong-Yu Xu & Sheng-lian Guo, 2020. "Transferability of a Conceptual Hydrological Model across Different Temporal Scales and Basin Sizes," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(9), pages 2953-2968, July.
    14. Xiaqing Feng & Guangxin Zhang & Xiongrui Yin, 2011. "Hydrological Responses to Climate Change in Nenjiang River Basin, Northeastern China," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(2), pages 677-689, January.
    15. Lu Zhuo & Dawei Han & Qiang Dai & Tanvir Islam & Prashant Srivastava, 2015. "Appraisal of NLDAS-2 Multi-Model Simulated Soil Moistures for Hydrological Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(10), pages 3503-3517, August.
    16. Rakotoarimanana Zy Harifidy & Rakotoarimanana Zy Misa Harivelo & Ishidaira Hiroshi & Magome Jun & Souma Kazuyoshi, 2022. "A Systematic Review of Water Resources Assessment at a Large River Basin Scale: Case of the Major River Basins in Madagascar," Sustainability, MDPI, vol. 14(19), pages 1-15, September.
    17. Isabel Kaufmann Almeida & Aleska Kaufmann Almeida & Jorge Luiz Steffen & Teodorico Alves Sobrinho, 2016. "Model for Estimating the Time of Concentration in Watersheds," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(12), pages 4083-4096, September.
    18. Menghao Wang & Shanhu Jiang & Liliang Ren & Chong-Yu Xu & Linyong Wei & Hao Cui & Fei Yuan & Yi Liu & Xiaoli Yang, 2022. "The Development of a Nonstationary Standardised Streamflow Index Using Climate and Reservoir Indices as Covariates," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(4), pages 1377-1392, March.
    19. Sandra Mourato & Madalena Moreira & João Corte-Real, 2015. "Water Resources Impact Assessment Under Climate Change Scenarios in Mediterranean Watersheds," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(7), pages 2377-2391, May.
    20. Ali Assani & Raphaëlle Landry & Jonathan Daigle & Alain Chalifour, 2011. "Reservoirs Effects on the Interannual Variability of Winter and Spring Streamflow in the St-Maurice River Watershed (Quebec, Canada)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(14), pages 3661-3675, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:waterr:v:31:y:2017:i:3:d:10.1007_s11269-016-1557-6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.