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Water engineering research : international journal of KWRA v.4 no.3, 2003년, pp.111 - 126   피인용횟수: 1

STOCHASTIC SIMULATION OF DAILY WEATHER VARIABLES

Lee, Ju-Young   (Civil Engineering, Texas A&M University, College StationCC0123236  ); Kelly brumbelow, Kelly-Brumbelow   (Civil Engineering, Texas A&M University, College StationCC0123236  );
  • 초록

    Meteorological data are often needed to evaluate the long-term effects of proposed hydrologic changes. The evaluation is frequently undertaken using deterministic mathematical models that require daily weather data as input including precipitation amount, maximum and minimum temperature, relative humidity, solar radiation and wind speed. Stochastic generation of the required weather data offers alternative to the use of observed weather records. The precipitation is modeled by a Markov Chain-exponential model. The other variables are generated by multivariate model with means and standard deviations of the variables conditioned on the wet or dry status of the day as determined by the precipitation model. Ultimately, the objective of this paper is to compare Richardson's model and the improved weather generation model in their ability to provide daily weather data for the crop model to study potential impacts of climate change on the irrigation needs and crop yield. However this paper does not refer to the improved weather generation model and the crop model. The new weather generation model improved will be introduced in the Journal of KWRA.


  • 주제어

    Richardson′s Model .   Markov Chain .   Crop model .   Wet day and Dry day.  

  • 참고문헌 (20)

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  • 이 논문을 인용한 문헌 (1)

    1. 2004. "" Water engineering research : international journal of KWRA, 5(1): 47~54     

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