Environmental Engineering Research

  • 제21권3호
  • /
  • Pages.226-232
  • /
  • 2016
  • /
  • 1226-1025(pISSN)
  • /
  • 2005-968X(eISSN)
대한환경공학회 (Korean Society of Environmental Engineers)
권호 표지
DOI QR코드

DOI QR Code

A mathematical spatial interpolation method for the estimation of convective rainfall distribution over small watersheds

  • Zhang, Shengtang (College of Earth Science and Engineering, Shandong University of Science and Technology) ;
  • Zhang, Jingzhou (College of Earth Science and Engineering, Shandong University of Science and Technology) ;
  • Liu, Yin (College of Mining and Safety Engineering, Shandong University of Science and Technology) ;
  • Liu, Yuanchen (College of Earth Science and Engineering, Shandong University of Science and Technology)
  • 투고 : 2015.12.16
  • 심사 : 2016.03.24
  • 발행 : 2016.09.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Rainfall is one of crucial factors that impact on our environment. Rainfall data is important in water resources management, flood forecasting, and designing hydraulic structures. However, it is not available in some rural watersheds without rain gauges. Thus, effective ways of interpolating the available records are needed. Despite many widely used spatial interpolation methods, few studies have investigated rainfall center characteristics. Based on the theory that the spatial distribution of convective rainfall event has a definite center with maximum rainfall, we present a mathematical interpolation method to estimate convective rainfall distribution and indicate the rainfall center location and the center rainfall volume. We apply the method to estimate three convective rainfall events in Santa Catalina Island where reliable hydrological data is available. A cross-validation technique is used to evaluate the method. The result shows that the method will suffer from high relative error in two situations: 1) when estimating the minimum rainfall and 2) when estimating an external site. For all other situations, the method's performance is reasonable and acceptable. Since the method is based on a continuous function, it can provide distributed rainfall data for distributed hydrological model sand indicate statistical characteristics of given areas via mathematical calculation.

키워드

참고문헌

  1. Manohar A, Singh P, Goel NK, Singh RD. Spatial distribution and seasonal variability of rainfall in a mountainous basin in the Himalayan region. Water Resour. Manag. 2006;20:489-508. https://doi.org/10.1007/s11269-006-8773-4
  2. Gan TY, Dlamini EM, Biftu GF. Effects of model complexity and structure, data quality and objective functions on hydrologic modeling. J. Hydrol. 1997;192:81-103. https://doi.org/10.1016/S0022-1694(96)03114-9
  3. Francesca B, Daniela C, Fedele G, Elena S. Spatial reconstruction of rainfall fields from rain gauge and radar data. Stoch. Env. Res. Risk A. 2014;28:1235-1245. https://doi.org/10.1007/s00477-013-0812-0
  4. Yeboah GA, Geoffrey P. Interpolation of daily rainfall networks using simulated radar fields for realistic hydrological modelling of spatial rain field ensembles. J. Hydrol. 2014;519:777-791. https://doi.org/10.1016/j.jhydrol.2014.08.006
  5. Shah SMS, O'Connell PE, Hosking JRM. Modelling the effects of spatial variability in rainfall on catchment response. 2. Experiments with distributed and lumped models. J. Hydrol. 1996;175:89-111. https://doi.org/10.1016/S0022-1694(96)80007-2
  6. Segond ML, Wheater HS, Onof C. The significance of spatial rainfall representation for flood runoff estimation: A numerical evaluation based on the Lee catchment, UK. J. Hydrol. 2007;347:116-131. https://doi.org/10.1016/j.jhydrol.2007.09.040
  7. Bell VA, Moore RJ. The sensitivity of catchment runoff models to rainfall data at different spatial scales. Hydrol. Earth Syst. Sc. 2000;4:653-667. https://doi.org/10.5194/hess-4-653-2000
  8. Goovaerts P. Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. J. Hydrol. 2000;228:113-129. https://doi.org/10.1016/S0022-1694(00)00144-X
  9. Mair A, Fares A. Comparison of rainfall interpolation methods in a mountainous region of a tropical island. J. Hydrol. Eng. 2011;16:371-383. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000330
  10. Hesbon O, Yang JX, Liu WW, Han DW. Influence of rain gauge density on interpolation method selection. J. Hydrol. Eng. 2014;19:1-8. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000773
  11. Buytaert W, Celleri R, Willems P, Bievre DB, Wyseure G. Spatial and temporal rainfall variability in mountainous areas: A case study from the south Ecuadorian Andes. J. Hydrol. 2006;329: 413-421. https://doi.org/10.1016/j.jhydrol.2006.02.031
  12. Campling P, Gobin A, Feyen J. Temporal and spatial rainfall analysis across a humid tropical catchment. Hydrol. Process 2001;15:359-375. https://doi.org/10.1002/hyp.98
  13. Drogue G, Humbert J, Deraisme J, Mahr N, Freslonc N. A statistical topographic model using an omni-directional parameterization of the relief for mapping orographic rainfall. Int. J. Climatol. 2002;22:599-613. https://doi.org/10.1002/joc.671
  14. Kong YF, Tong WW. Spatial exploration and interpolation of the surface precipitation data. Geogr. Res. 2008;27:1097-1108.
  15. Gotway CA, Ferguson RB, Hergert GW, Peterson TA. Comparison of Kriging and inverse-distance methods for mapping soil parameters. Soil Sci. Soc. Am. J. 1996;60:1237-1247. https://doi.org/10.2136/sssaj1996.03615995006000040040x
  16. Wagner PD, Fiener P, Wilken F, Kumar S, Schneider K. Comparison and evaluation of spatial interpolation schemes for daily rainfall in data scarce regions. J. Hydrol. 2012;464-465: 388-400. https://doi.org/10.1016/j.jhydrol.2012.07.026
  17. Webster R, Oliver M. Geostatistics for environmental scientists. New York: Wiley; 2000. p. 45-60.
  18. Hrachowitz M, Weiler M. Uncertainty of precipitation estimates caused by sparse gauging networks in a small, mountainous watershed. J. Hydrol. Eng. 2011;16:460-471. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000331
  19. Bacchi B, Ranzi R, Borga M. Statistical characterization of spatial patterns of rainfall cells in extratropical cyclones. J. Geophys. Res. 1996;101:26277-26286. https://doi.org/10.1029/96JD01381
  20. De Lannoy GJM, Verhoest NEC, De Troch FP. Characteristics of rainstorms over a temperate region derived from multiple time series of weather radar images. J. Hydrol. 2005;307:126-144. https://doi.org/10.1016/j.jhydrol.2004.10.007
  21. Goudenhoofdt E, Delobbe L. Statistical characteristics of convective storms in Belgium derived from volumetric weather radar observations. J. Appl. Meteor. Climatol. 2013; 52:918-934. https://doi.org/10.1175/JAMC-D-12-079.1
  22. Linsley RK, Franzini JB. Water Resources Engineering. New York: McGraw-Hill; 1979. p. 11-12
  23. Wood ET, Sivalapan M, Beven K, Band L. Effects of spatial variability and scale with implications to hydrologic modeling. J. Hydrol.1988;102:29-47. https://doi.org/10.1016/0022-1694(88)90090-X
  24. Abo-Monasar, Al-Zahrani. Estimation of rainfall distribution for the southwestern region of saudi arabia. Hydrolog. Sci. J. 2014;59:420-431. https://doi.org/10.1080/02626667.2013.872788
  25. Hattermann FF, Wattenbach M, Krysanova V, Wechsung F. Runoff simulations on the macroscale with the ecohydrological model SWIM in the Elbe catchment-validation and uncertainty analysis. Hydrol. Process. 2005;19:693-714. https://doi.org/10.1002/hyp.5625
  26. Lloyd CD. Assessing the effect of integrating elevation data into the estimation of monthly precipitation in Great Britain. J. Hydrol. 2005;308:128-150. https://doi.org/10.1016/j.jhydrol.2004.10.026