Gauge based precipitation estimation and associated model and product uncertainties

Journal of Hydrology

Volumn 444-445, Issue , 2012, Pages 100-112

  Shao, Quanxi ^a   Lerat, Julien ^a   Brink, Heron ^a   Tomkins, Kerrie ^a   Yang, Ang ^a   Peeters, Luk ^a   Li, Ming ^a   Zhang, Lu ^a   Podger, Geoff ^a   Renzullo, Luigi J ^a  

^a CSIRO   (Australia)

Author keywords

Bootstrap; Nonparametric kernel smoothing; Rainfall product; Uncertainty estimation; Upscaling

Indexed keywords

AREAL PRECIPITATION; BOOTSTRAP; BOOTSTRAP METHOD; BOOTSTRAPPING METHOD; DIRECT USE; EASTERN AUSTRALIA; GRID SIZE; KERNEL SMOOTHING; MURRAY-DARLING BASIN; PARAMETER UNCERTAINTY; PRECIPITATION ESTIMATION; PRECIPITATION PRODUCTS; RAINFALL ESTIMATIONS; RAINFALL MODELS; SMOOTHING TECHNIQUES; SPATIAL SCALE; UNCERTAINTY ESTIMATION; UPSCALING; WATERRESOURCE MANAGEMENT;

ESTIMATION; GAGES; SOIL MOISTURE; WATER MANAGEMENT;

UNCERTAINTY ANALYSIS;

BOOTSTRAPPING; PRECIPITATION (CLIMATOLOGY); PRECIPITATION ASSESSMENT; RAINFALL; RAINGAUGE; SMOOTHING; UNCERTAINTY ANALYSIS; UPSCALING; WATER RESOURCE;

AUSTRALIA; MURRAY-DARLING BASIN;

EID: 84861221338     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2012.04.009     Document Type: Article

References (35)

1. Bailey W.A. Bootstrapping for order statistics sans random numbers (operational bootstrapping). Exploring the Limits of Bootstrap 1992, 309-318. Wiley, New York. R. LePage, L. Billard (Eds.).
2. Beesley, C., Frost, A., Zajaczkowski, J., 2009. A comparison of the BAWAP and SILO spatially interpolated daily rainfall datasets. In: 18th World IMACS/MODSIM Congress, Cairns, Australia, 13-17. <>. http://www.mssanz.org.au/modsim09/I13/beesley.pdf.
3. Chiew FHS, Vaze J, Viney NR, Jordan PW, Perraud J-M, Zhang L, Teng J, Young WJ, Penaarancibia J, Morden RA, Freebairn A, Austin J, Hill PI, Wiesenfeld CR, Murphy R. 2008. Rainfall-runoff modelling across the Murray-Darling Basin, A report to the Australian Government from the CSIRO Murray-Darling Basin Sustainable Yields Project Water for a Healthy Country Flagship. CSIRO, 70 pp.
4. Chu C.K., Deng W.S. An interpolation method for adapting to sparse design in multivariate nonparametric regression. J. Statist. Plann. Infer. 2003, 116:91-111.
5. CSIRO, 2008. Water availability in the Murray-Darling Basin. A report to the Australian Government from the CSIRO Murray-Darling Basin Sustainable Yields Project. CSIRO, Australia, 67 pp.
6. Delicado P., del Río M. Weighted nonparametric regression. Commun. Statist.-Theory Methods 1997, 26(12):2983-2998.
7. Di Marzio M., Taylor C.C. Boosting kernel density estimates: a bias reduction technique?. Biometrika 2004, 91(1):226-233.
8. Efron B. Bootstrap methods: another look at the jackknife. Ann. Statist. 1979, 7:1-26.
9. Efron B. The Jackknife, the Bootstrap, and Other Resampling Plans 1982, Society for Industrial Mathematics, Philadelphia, Pennsylvania.
10. Efron B., Tibshirani R.J. An Introduction to the Bootstrap 1993, Chapman & Hall, New York.
11. Gasser T., Müller H.G., Mammitzsch V. Kernels for nonparametric curve estimation. J. Royal Statis. Society Series B 1985, 47:238-252.
12. Guttman I. Statistical Tolerance Regions 1970, Hafner Publishing, Darien, CT.
13. Hall P., Turlach B.A. Interpolation methods for adapting to sparse design in nonparametric regression (with discussion). J. Am. Statist. Assoc. 1997, 92:466-476.
14. Hapuarachchi H.A.P., Takeuchi K., Fukami K., Inomana H., Zhou M. Importance of rainfall measurements for the flood forecasting of the Mekong River Basin 2007, Research Report, Public Works Research Institute, Japan.
15. Hwang C.R., Ken M.L. Double smoothing estimation of the multivariate regression function in nonparametric regression. Commun. Statist. - Theory Methods 2002, 31:419-434.
16. Jeffery, S.J., 2006. Error analysis for the interpolation of monthly rainfall used in the generation of SILO rainfall datasets. Technical Report, the Queensland Department of Natural Resources. <>. http://www.nrw.qld.gov.au/silo/pdf/interpUpdate.pdf.
17. Jeffrey S.J., Cater J.O., Moodie K.B., Beswich A.R. Using spatial interpolation to construct a comprehensive archive of Australian climate data. Environ. Modell. Softw. 2001, 16:300-309.
18. Jones M.C. Do not weight for heteroscedasticity in nonparametric regression. Aust. J. Statist. 1993, 35(1):89-92.
19. Jones, D.A., Wang, W., Fawcett, R., 2007. Climate data for the Australian Water Availability Project, Final Milestone Report, Bureau of Meteorology, Australia.
20. Kummerow C., Simpson J., Thiele O., Barnes W., Chang A.T.C., Stocker E., Adler R.F., Hou A., Kakar R., Wentz F., Ashcroft P., Kozu T., Hong Y., Okamoto K., Iguchi T., Kuroiwa H., Im E., Haddad Z., Huffman G., Ferrier B., Olson W.S., Zipser E., Smith E.A., Wilheit T.T., North G., Krishnamurti T., Nakamura K. The status of the Tropical Rainfall Measuring Mission (TRMM) after two years in orbit. J. Appl. Meteorology 2000, 39:1965-1982.
21. Larson L.W., Peck E.L. Accuracy of precipitation measurements for hydrologic modeling. Water Resour. Res. 1974, 10:857-863.
22. Li M., Shao Q. An improved statistical approach to merge satellite rainfall estimates and raingauge data. J. Hydrol. 2010, 385:51-64. 10.1016/j.jhydrol.2010.01.023.
23. Li Z.L., Shao Q.X., Xu Z.X., Cai X.T. Analysis of parameter uncertainty in semi-distributed hydrological models using bootstrap method: A case study of SWAT model applied to Yingluoxia watershed in northwest China. J. Hydrol. 2010, 385:76-83.
24. Li, Z.L., Shao, Q.X., Xu, Z.X., Xu, C.-Y., 2012. Uncertainty issues of a conceptual hydrological model applied in a semi-arid watershed in northwest China. Hydrol. Process. in press. http://dx.doi.org/10.1002/hyp.9258002/hyp.9258.
25. McMillan H., Jackson B., Clark M., Kavetski D., Woods R. Rainfall uncertainty in hydrological modelling: An evaluation of multiplicative error models. J. Hydrol. 2011, 400:83-94.
26. Okabe A., Boots B., Sugihara K., Chiu S.N. Spatial Tessellations. Concept and Applications of Voronoi Diagrams 2000, Wiley, England.
27. Peters-Lidard C.D., Mocko D.M., Garcia M., Santanello J.A., Tischler M.A., Moran M.S., Wu Y. Role of precipitation uncertainty in the estimation of hydrologic soil properties using remotely sensed soil moisture in a semiarid environment. Water Resour. Res. 2008, 44:W05S18. 10.1029/2007WR005884.
28. Renzullo, L.J., Chappell, A., Raupach, T., Dyce, P., Ming, L., Shao, Q. 2011. An assessment of statistically blended satellite-gauge precipitation data for daily rainfall analysis in Australia. In: Proceeding of the 34th International Symposium of Remote Sensing of Environment. Sydney, Australia. 10-15 April 2011, pp. 4.
29. Selle B., Hannah M. A bootstrap approach to assess parameter uncertainty in simple catchment models. Environ. Modell. Softw. 2010, 25:919-926.
30. Simonoff J.S. Smoothing Methods in Statistics 1996, Springer, New York.
31. Stine R.A. Bootstrap prediction intervals for regression. J. Am. Statist. Assoc. 1985, 80(392):1026-1031.
32. Thiessen A.H. Precipitation average for large areas. Mon. Weather Rev. 1911, 39:1082-1084.
33. Vaze J., Post D., Chiew F., Perraud J., Viney N., Teng J. Climate nonstationarity-validity of calibrated rainfall-runoff models for use in climate change studies. J. Hydrol. 2010, 394(3-4):447-457.
34. Vrugt J.A., ter Braak C.J.F., Clark M.P., Hyman J.M., Robinson B.A. Treatment of input uncertainty in hydrologic modeling: doing hydrology backward with Markov chain Monte Carlo simulation. Water Resour. Res. 2008, 44:W00B09. 10.1029/2007WR006720.
35. Wood S.J., Jones D.A., Moore R.J. Accuracy of rainfall measurement for scales of hydrological interest. Hydrol. Earth Syst. Sci. 2000, 4(4):531-543.