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Optimal selection of rainfall gauges for safe extreme events estimation using a geostatistical approach

  • A. M. Abdelkhalek
  • A. G. Awadallah
  • N. Awadallah
Original Paper

Abstract

This paper’s objective is to present a method for optimizing rain gauge network aiming to determine the optimal number of stations and their locations, to achieve an acceptable error in extreme rainfall estimation. The optimization is based on the comparison between the maximum daily rainfall depths at high return periods deduced using the entire rain gauges networks and that was deduced using an “optimal” number of rain gauges. A latin hypercube sampling (LHS) method is used to generate samples of stations. Each generated sample is analyzed to obtain the optimum set of gauges locations. The optimal number and locations of rain gauges are obtained in two cases: the first one using a regional frequency analysis technique and the second using an at-site frequency analysis technique. The methodology is applied to the existing rain gauges network of the Walnut Gulch Experimental Watershed (WGEW), AZ, USA. The results showed that, a lower number of rain gauges is required based on the regional frequency analysis technique compared to the at-site frequency analysis technique to achieve the same relative error at the high return periods. The study also suggests optimum locations for the rain gauges.

Keywords

Optimization Regional frequency analysis Latin hypercube sampling Arizona Return period Rain gauge Extreme rainfall 

References

  1. Adhikary SK, Yilmaz AG, Muttil N (2015) Optimal design of rain gauge network in the Middle Yarra River catchment, Australia. Hydrol Process 29(11):2582–2599CrossRefGoogle Scholar
  2. Akaike H (1973) Information theory and extension of the maximum Likelihood principle. In: Petrov BN, Csaki F (eds) 2nd international symposium on information theory. Akadémiai Kiado, Budapest, p 267 281Google Scholar
  3. Akaike H (1974) Markovian representation of stochastic processes and its application to the analysis of autoregressive moving average processes. Ann I Stat Math 26:363–387CrossRefGoogle Scholar
  4. Al-Abadi, A. M., & Al-aboodi, A. H. D. (2014). Optimum rain-gauges network design of some cities in Iraq. Journal of University of Babylon 22(4):938–950Google Scholar
  5. Ayyub BM, McCuen RH (2011) Probability, statistics, and reliability for engineers and scientists, 3rd edn. CRC press, Boca RatonGoogle Scholar
  6. Bellu, A., Sanches Fernandes, L.F., Cortes, R.M.V., & Pacheco, F.A.L. (2016). A framework model for the dimensioning and allocation of a detention basin system: the case of a flood-prone mountainous watershed. J Hydrol, v 533, p. 567–580Google Scholar
  7. Bogardi I, Bardossy A (1985) Multicriterion network design using geostatistics. Water Resour Res 21(2):199–208CrossRefGoogle Scholar
  8. Caffey, J. E. (1965) Inter-station correlations in annual precipitation and annual effective precipitation. Hydrology Paper 6, Colorado State Univ., Fort Collins, Colorado, USAGoogle Scholar
  9. Haggag, M., Ali, A., & Awadallah, A. (2016) Evaluation of rain gauge network in arid regions using geostatistical approach: case study in northern Oman. Arab J Geosci 9(9):552Google Scholar
  10. Heilman, P., Nichols, M. H., Goodrich, D. C., Miller, S. N., & Guertin, D. P. (2008). Geographic information systems database, Walnut Gulch Experimental Watershed, Arizona , United States. 44(November 2007):1–6Google Scholar
  11. Hughes JP, Lettenmaier DP (1981) Data requirements for kriging: estimation and network design. Water Resour Res 17(6):1641–1165CrossRefGoogle Scholar
  12. Khairul, M., Mohd, B., Yusof, F., Daud, Z. M., Yusop, Z., Afif, M. (2016). Optimal design of rain gauge network in Johor by using geostatistics and particle swarm optimization. 11(25):2422–2428Google Scholar
  13. Matheron G (1971) The theory of regionalized variables and its applications. Ecole Des Mines, FontainebleauGoogle Scholar
  14. McKay MD, Conover WJ, Beckman RJ (1979) A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21:239–245Google Scholar
  15. National Research Council (1988) Committee on techniques for estimating probabilities of extreme floods, “estimating probabilities of extreme floods, methods and recommended research”. National Academy Press, Washington, D.C.Google Scholar
  16. Pardo-Igúzquiza E (1998) Optimal selection of number and location of rainfall gauges for areal rainfall estimation using geostatistics and simulated annealing. J Hydrol 210(1–4):206–220CrossRefGoogle Scholar
  17. Putthividhya, A., & Tanaka, K. (2012) Optimal rain gauge network design and spatial precipitation mapping based on geostatistical analysis from colocated elevation and humidity data. International Journal of Environmental Science and Development 3(2):124Google Scholar
  18. Renard KG, Nichols MH, Woolhiser DA, Osborn HB (2008) A brief background on the U.S. Department of Agriculture Agricultural Research Service Walnut Gulch Experimental Watershed. Water Resour Res 44:W05S02.  https://doi.org/10.1029/2006WR005691 Google Scholar
  19. Rodriguez-Iturbe I, Mejia JM (1974) The design of rainfall networks in time and space. Water Resour Res 10(4):713–728CrossRefGoogle Scholar
  20. Rouhani S (1985) Variance reduction analysis. Water Resour Res 21(6):837–846CrossRefGoogle Scholar
  21. Sanches Fernandes LF, Terêncio DPS, Pacheco FAL (2015) Rainwater harvesting systems for low demanding applications. Sci Total Environ 529:91–100CrossRefGoogle Scholar
  22. Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6:461–464CrossRefGoogle Scholar
  23. Southwest Watershed Research Center (SWRC) (2007) Walnut gulch experimental watershed (WGEW) brochure. United States Department of Agriculture USDA, TucsonGoogle Scholar
  24. Stedinger JR, Vogel RM, Foufoula-Georgiou E, E. (1993) Frequency analysis of extreme events. In: Maidment DR (ed) Handbook of Hydrology. McGraw-Hill, New York, pp 18.1–18.66Google Scholar
  25. Terêncio DPS, Sanches Fernandes LF, Cortes RMV, Pacheco FAL (2017) Improved framework model to allocate optimal rainwater harvesting sites in small watersheds for agro-forestry uses. J Hydrol 550:318–330CrossRefGoogle Scholar
  26. Terêncio DPS, Sanches Fernandes LF, Cortes RMV, Moura JP, Pacheco FAL (2018) Rainwater harvesting in catchments for agro-forestry uses: a study focused on the balance between sustainability values and storage capacity. Sci Total Environ 613–614:1079–1092CrossRefGoogle Scholar
  27. U.S. Water Resources Council (1981) Guidelines for determining flood flow frequency, bulletin 17B. Hydrology Committee, Water Resources Research Council, WashingtonGoogle Scholar
  28. Vogel RM, Fennessey NM (1993) L moment diagrams should replace product moment diagrams. Water Resour Res 29:1745–1752CrossRefGoogle Scholar
  29. Wiltshire SE (1986a) Regional flood frequency analysis I: homogeneity statistics. Hydrol Sci J 31(3):321–333CrossRefGoogle Scholar
  30. Wiltshire SE (1986b) Identification of homogeneous regions for flood frequency analysis. J Hydraul 84:287–307CrossRefGoogle Scholar

Copyright information

© Saudi Society for Geosciences 2018

Authors and Affiliations

  • A. M. Abdelkhalek
    • 1
  • A. G. Awadallah
    • 1
  • N. Awadallah
    • 1
  1. 1.Civil Engineering Department, Faculty of EngineeringFayoum UniversityFayoumEgypt

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