Stochastic Hydrology and Hydraulics

, Volume 11, Issue 6, pp 523–547 | Cite as

Evaluation of kernel density estimation methods for daily precipitation resampling

  • Balaji Rajagopalan
  • Upmanu Lall
  • David G. Tarboton


Kernel density estimators are useful building blocks for empirical statistical modeling of precipitation and other hydroclimatic variables. Data driven estimates of the marginal probability density function of these variables (which may have discrete or continuous arguments) provide a useful basis for Monte Carlo resampling and are also useful for posing and testing hypotheses (e.g bimodality) as to the frequency distributions of the variable. In this paper, some issues related to the selection and design of univariate kernel density estimators are reviewed. Some strategies for bandwidth and kernel selection are discussed in an applied context and recommendations for parameter selection are offered. This paper complements the nonparametric wet/dry spell resampling methodology presented in Lall et al. (1996).


Precipitation Probability Density Function Density Estimation Daily Precipitation Kernel Density 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag 1997

Authors and Affiliations

  • Balaji Rajagopalan
    • 1
  • Upmanu Lall
    • 2
  • David G. Tarboton
    • 2
  1. 1.Lamont-Doherty Earth Observatory of Columbia UniversityPalisades
  2. 2.Dept. of Civil & Environmental Engineering, Utah Water Res. Lab.Utah State UniversityLoganUSA

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