IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v31y2017i6d10.1007_s11269-017-1614-9.html
   My bibliography  Save this article

Non-Stationary Rainfall Intensity-Duration-Frequency Relationship: a Comparison between Annual Maximum and Partial Duration Series

Author

Listed:
  • V. Agilan

    (National Institute of Technology Warangal)

  • N. V. Umamahesh

    (National Institute of Technology Warangal)

Abstract

The rainfall Intensity-Duration-Frequency (IDF) relationship is the primary input for storm water management and other engineering design applications across the world and it is developed by fitting an appropriate theoretical probability distribution to annual maximum (AM) series or partial duration series (PDS) of rainfall. The existing IDF relationship developing methods consider the extreme rainfall series as a stationary series. There exist few studies that compared AM and PDS datasets for developing rainfall IDF relationship in a stationary condition. However, during the last few decades, the intensity and frequency of extreme rainfall events are increasing due to global climate change and creating a non-stationary component in the extreme rainfall series. Therefore, the rainfall IDF relationship developed with the stationary assumption is no longer tenable in a changing climate. Hence, it is inevitable to develop non-stationary rainfall IDF relationship and to understand the differences in non-stationary rainfall IDF relationships derived using AM and PDS datasets. Consequently, the objectives of this study are: (1) to develop non-stationary rainfall IDF relationships using both AM and PDS datasets; (2) to compare them in terms of return level estimation. In particular, the non-linear trend in different durations’ PDS and AM datasets of Hyderabad city (India) rainfall is modeled using Multi-objective Genetic Algorithm (MGA) generated Time based covariate. In this study, the PDS datasets are modeled by the Generalized Pareto Distribution (GPD) while the AM datasets are modeled by the Generalized Extreme Value Distribution (GEVD). The time-varying component is introduced in the scale parameter of the GPD and the location parameter of the GEVD by linking the MGA generated covariate. In addition, the complexity of each non-stationary model is identified using the corrected Akaike Information Criteria (AICc) and the statistical significance of trend parameter in the non-stationary models is estimated using the Likelihood Ratio (LR) test. Upon detecting significant superiority of non-stationary models, the return levels of extreme rainfall event for 2-, 5-, 10- and 25-year return periods are calculated using non-stationary models. From the results, it is observed that the non-stationary return levels estimated with PDS datasets are higher than those estimated with AM datasets for short durations and smaller return periods while the non-stationary return levels estimated with AM datasets are higher than those estimated with PDS datasets for long durations and higher return periods.

Suggested Citation

  • V. Agilan & N. V. Umamahesh, 2017. "Non-Stationary Rainfall Intensity-Duration-Frequency Relationship: a Comparison between Annual Maximum and Partial Duration Series," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(6), pages 1825-1841, April.
    • Handle: RePEc:spr:waterr:v:31:y:2017:i:6:d:10.1007_s11269-017-1614-9
    DOI: 10.1007/s11269-017-1614-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-017-1614-9
    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/s11269-017-1614-9?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. L. Vasiliades & P. Galiatsatou & A. Loukas, 2015. "Nonstationary Frequency Analysis of Annual Maximum Rainfall Using Climate Covariates," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(2), pages 339-358, January.
    2. Linyin Cheng & Amir AghaKouchak & Eric Gilleland & Richard Katz, 2014. "Non-stationary extreme value analysis in a changing climate," Climatic Change, Springer, vol. 127(2), pages 353-369, November.
    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. Antonino Cancelliere, 2017. "Non Stationary Analysis of Extreme Events," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(10), pages 3097-3110, August.
    2. Ioannis M. Kourtis & Ioannis Nalbantis & George Tsakiris & Basil Ε. Psiloglou & Vassilios A. Tsihrintzis, 2023. "Updating IDF Curves Under Climate Change: Impact on Rainfall-Induced Runoff in Urban Basins," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(6), pages 2403-2428, May.

    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. Lazhar Belkhiri & Tae-Jeong Kim, 2021. "Individual Influence of Climate Variability Indices on Annual Maximum Precipitation Across the Global Scale," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(9), pages 2987-3003, July.
    2. Yixuan Wang & Jianzhu Li & Ping Feng & Rong Hu, 2015. "A Time-Dependent Drought Index for Non-Stationary Precipitation Series," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(15), pages 5631-5647, December.
    3. Peng Jiang & Zhongbo Yu & Mahesh R. Gautam & Kumud Acharya, 2016. "The Spatiotemporal Characteristics of Extreme Precipitation Events in the Western United States," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(13), pages 4807-4821, October.
    4. Hongxiang Yan & Hamid Moradkhani, 2016. "Toward more robust extreme flood prediction by Bayesian hierarchical and multimodeling," 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. 81(1), pages 203-225, March.
    5. Robert L. Ceres & Chris E. Forest & Klaus Keller, 2017. "Understanding the detectability of potential changes to the 100-year peak storm surge," Climatic Change, Springer, vol. 145(1), pages 221-235, November.
    6. Mojtaba Sadegh & Amir AghaKouchak & Alejandro Flores & Iman Mallakpour & Mohammad Reza Nikoo, 2019. "A Multi-Model Nonstationary Rainfall-Runoff Modeling Framework: Analysis and Toolbox," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(9), pages 3011-3024, July.
    7. Jittima Singvejsakul & Chukiat Chaiboonsri & Songsak Sriboonchitta, 2021. "The Optimization of Bayesian Extreme Value: Empirical Evidence for the Agricultural Commodities in the US," Economies, MDPI, vol. 9(1), pages 1-10, March.
    8. Lihua Xiong & Tao Du & Chong-Yu Xu & Shenglian Guo & Cong Jiang & Christopher Gippel, 2015. "Non-Stationary Annual Maximum Flood Frequency Analysis Using the Norming Constants Method to Consider Non-Stationarity in the Annual Daily Flow Series," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(10), pages 3615-3633, August.
    9. Jianzhu Li & Yuming Lei & Senming Tan & Colin D. Bell & Bernard A. Engel & Yixuan Wang, 2018. "Nonstationary Flood Frequency Analysis for Annual Flood Peak and Volume Series in Both Univariate and Bivariate Domain," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(13), pages 4239-4252, October.
    10. Uddameri, Venkatesh & Ghaseminejad, Ali & Hernandez, E. Annette, 2020. "A tiered stochastic framework for assessing crop yield loss risks due to water scarcity under different uncertainty levels," Agricultural Water Management, Elsevier, vol. 238(C).
    11. Ting Wei & Songbai Song, 2019. "Utilization of the Copula-Based Composite Likelihood Approach to Improve Design Precipitation Estimates Accuracy," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(15), pages 5089-5106, December.
    12. Christian L. E. Franzke & Marcin Czupryna, 2020. "Probabilistic assessment and projections of US weather and climate risks and economic damages," Climatic Change, Springer, vol. 158(3), pages 503-515, February.
    13. Linhan Yang & Jianzhu Li & Aiqing Kang & Shuai Li & Ping Feng, 2020. "The Effect of Nonstationarity in Rainfall on Urban Flooding Based on Coupling SWMM and MIKE21," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(4), pages 1535-1551, March.
    14. M. Karamouz & F. Fooladi Mahani, 2021. "DEM Uncertainty Based Coastal Flood Inundation Modeling Considering Water Quality Impacts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(10), pages 3083-3103, August.
    15. Hongxiang Yan & Hamid Moradkhani, 2016. "Toward more robust extreme flood prediction by Bayesian hierarchical and multimodeling," 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. 81(1), pages 203-225, March.
    16. Ali Razmi & Saeed Golian & Zahra Zahmatkesh, 2017. "Non-Stationary Frequency Analysis of Extreme Water Level: Application of Annual Maximum Series and Peak-over Threshold Approaches," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(7), pages 2065-2083, May.
    17. Goyal, Manish Kumar & Gupta, Anil Kumar & Jha, Srinidhi & Rakkasagi, Shivukumar & Jain, Vijay, 2022. "Climate change impact on precipitation extremes over Indian cities: Non-stationary analysis," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    18. Mohammad Karamouz & Helia Farzaneh & Mehri Dolatshahi, 2020. "Margin of Safety Based Flood Reliability Evaluation of Wastewater Treatment Plants: Part 1 – Basic Concepts and Statistical Settings," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(2), pages 579-594, January.
    19. Chen, Haoling & Zhao, Tongtiegang, 2020. "Modeling power loss during blackouts in China using non-stationary generalized extreme value distribution," Energy, Elsevier, vol. 195(C).
    20. Yiming Hu & Zhongmin Liang & Vijay P. Singh & Xuebin Zhang & Jun Wang & Binquan Li & Huimin Wang, 2018. "Concept of Equivalent Reliability for Estimating the Design Flood under Non-stationary Conditions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(3), pages 997-1011, February.

    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:6:d:10.1007_s11269-017-1614-9. 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.