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Estimation of extreme quantiles at ungaged sites based on region-of-influence and weighting approaches to regional frequency analysis of maximum 24-h rainfall

  • Farshad Fathian
  • Zohreh DehghanEmail author
  • Seyed Saeid Eslamian
Original Paper
  • 28 Downloads

Abstract

Lack of adequate and reliable data for estimating the extreme values of hydrological variables at ungaged sites has always been one of the issues facing hydrologists in designing and planning water resource projects. Regionalizing the considered hydrological variable, finding an acceptable relationship for estimating its extreme values at ungaged sites using given data of other stations, and applying their available attributes are the solutions for the mentioned issue. In this study, historical data of maximum 24-h rainfall (M24-hR) covering the statistical period of 30 years (1979–2008) were collected and used from 63 rainfall gaging stations situated at Lake Urmia basin, northwestern Iran. Afterwards, using the method of region-of-influence (ROI) regionalization, the study area was regionalized through the geographic attributes of the stations (including latitude, longitude, elevation above mean sea level, and distance to the center of Lake Urmia). Then, all possible situations were considered for providing an appropriate regression relationship to estimate the extreme quantiles of M24-hR at ungaged sites by defining various scenarios of weighting to the geographic attributes and rainfall quantiles. The results showed that among different defined weighting scenarios, weighting to both stations and attributes in the at-site situation had an effective impact on forming an appropriate regression relationship for the estimation of extreme quantiles at ungaged sites. However, in the regional situation, a scenario considering no weight for both stations and attributes resulted in the most acceptable estimation of the quantiles with the lowest error (MSE = 1.29 mm). Further, the study showed that in most scenarios, the extreme quantiles estimated by means of regional regression relationships at ungaged sites (MSE = 1.29~1.75 mm) resulted in lower errors than the at-site ones (MSE = 1.35~7.64 mm).

Keywords

Regionalization Extreme quantiles Ungaged sites Regression relationship Weighting procedure Lake Urmia basin 

Notes

Acknowledgments

The authors acknowledge the Water Resources Management Company of Iran, the Meteorological Organization of Iran, and Isfahan University of Technology for providing data. The authors also appreciate the two anonymous reviewers for their helpful comments, which improved the quality of the paper.

References

  1. Acreman MC (1987) Regional flood frequency analysis in the UK. Recent Research New Ideas, Institute of Hydrology, Wallingford, UKGoogle Scholar
  2. Acreman MC, Wiltshire SE (1987) Identification of regions for regional flood frequency analysis. Am Geophys Union 68(44):1262Google Scholar
  3. Bharath R, Srinivas VV (2015) Regionalization of extreme rainfall in India. J Climatol 35:1142–1156CrossRefGoogle Scholar
  4. Burn DH (1990a) An appraisal of the region of influence approach to flood frequency analysis. Hydrol Sci J 35(2):149–165CrossRefGoogle Scholar
  5. Burn DH (1990b) Evaluation of regional flood frequency analysis with a region of influence approach. J Water Resour Res 26(10):2257–2265CrossRefGoogle Scholar
  6. Cassalho F, Beskow S, Rogério de Mello C, Oliveira LF, Sanchotene de Aguiar M (2019) Evaluation of flood timing and regularity over hydrological regionalization in Southern Brazil. J Hydrol Eng 24(8):05019022CrossRefGoogle Scholar
  7. Chokmani K, Ouarda TBMJ (2004) Physiographical space based kriging for regional flood frequency estimation at ungauged sites. Water Resour Res 40:W12514CrossRefGoogle Scholar
  8. Chow VTD, Maidment DR, Mays LW (1998) Applied hydrology. McGraw-HillGoogle Scholar
  9. Darand M, Mansouri Daneshvar MR (2014) Regionalization of precipitation regimes in Iran using principal component analysis and hierarchical clustering analysis. J Environ Processes 1(4):517–532CrossRefGoogle Scholar
  10. Das S (2019) Extreme rainfall estimation at ungauged sites: comparison between region-of-influence approach of regional analysis and spatial interpolation technique. Int J Climatol 39(1):407–423CrossRefGoogle Scholar
  11. Debbarma N, Choudhury P, Roy P (2019) Identification of homogeneous rainfall regions using a genetic algorithm involving multi-criteria decision making techniques. Water Supply 19(5):1491–1499CrossRefGoogle Scholar
  12. Dehghan Z, Eslamian SS, Fathian F, Modarres R (2019) Regional frequency analysis with development of region-of-influence approach for maximum 24-h rainfall (case study: Urmia Lake Basin, Iran). Theor Appl Climatol 136(3-4):1483–1494CrossRefGoogle Scholar
  13. Dehghan Z, Eslamian SS, Modarres R (2018) Spatial clustering of maximum 24-h rainfall over Urmia Lake Basin by new weighting approaches. Int J Climatol 38(5):2298–2313CrossRefGoogle Scholar
  14. Fathian F, Dehghan Z (2019) Using hybrid weighting-clustering approach for regional frequency analysis of maximum 24-hr rainfall based on climatic, geographical, and statistical attributes. Int J Climatol 39(11):4413–4428CrossRefGoogle Scholar
  15. Fazel N, Berndtsson R, Bertacchi Uvo C, Madani K, Klove B (2018) Regionalization of precipitation characteristics in Iran’s lake Urmia basin. Theor Appl Climatol 132(1-2):363–373CrossRefGoogle Scholar
  16. Gaál L, Kohnová S, Szolgay J. 2010. Revisiting regional flood frequency analysis in Slovakia: the region-of-influence method vs. traditional regional approaches. In EGU General Assembly Conference Abstracts, Vol. 12, p. 13693.Google Scholar
  17. Gaal L, Kysel YJ, Szolgay J (2008) Region-of-influence approach to a frequency analysis of heavy precipitation in Slovakia. J Hydrol Earth Syst Sci 12:825–839CrossRefGoogle Scholar
  18. Gado TA, Van Nguyen VT (2015) Comparison of homogenous region delineation approaches for regional flood frequency analysis at ungauged sites. J Hydrol Eng 21(3):1–10Google Scholar
  19. Hosking JR, Wallis JR (1997) Regional frequency analysis an approach based on L-moments. Cambridge University Press, Cambridge, 224 ppCrossRefGoogle Scholar
  20. Hosking JR, Wallis JR, Wood EF (1985) Estimation of the generalized extreme-value distribution by the method of probability-weighted moments. Technometrics Taylor and Francis 27(3):251–226Google Scholar
  21. Kysely J, Gaal L, Romana B, Eva P (2011) Climate change scenarios of precipitation extremes in Central Europe from ENSEMBLES regional climat models. Theor Appl Climatol 104:529–542CrossRefGoogle Scholar
  22. Shu C, Ouarda TBMJ (2007) Flood frequency analysis at ungauged sites using artificial neural networks in canonical correlation analysis physiographic space. J Water Resour Res 43(7)Google Scholar
  23. Szolgay J, Parajka J, Kohnová S, Hlavčová K (2009) Comparison of mapping approaches of design annual maximum daily precipitation. Atmos Res 92(3):289–307CrossRefGoogle Scholar
  24. Zrinji Z, Burn DH (1994) Flood frequency analysis for ungauged sites using a region of influence approach. J Hydrol 153(1–4):1–21CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2019

Authors and Affiliations

  • Farshad Fathian
    • 1
  • Zohreh Dehghan
    • 2
    Email author
  • Seyed Saeid Eslamian
    • 2
  1. 1.Department of Water Science and Engineering, Faculty of AgricultureVali-e-Asr University of RafsanjanRafsanjanIran
  2. 2.Department of Water Engineering, Faculty of AgricultureIsfahan University of TechnologyIsfahanIran

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