|Renschler, C - UNIVERSITY AT BUFFALO|
|Vining, R - USDA-NRCS|
Submitted to: Hydrological Processes
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: December 1, 2006
Publication Date: N/A
Interpretive Summary: In order to accurately predict how much soil erosion may occur due to natural rain storm events, climate generation prediction models are necessary, which use observed weather information and statistical relationships to create years of simulated daily weather. Random number generators are often used in these climate prediction models, and this paper describes problems with how well these generators perform. A solution to the problem is also demonstrated, in which a quality control approach is used to test numbers from the random number generators, and reject ones which greatly depart from the target statistical distribution. This research impacts developers and users of weather prediction models that are dependent upon random number generators. It also impacts scientists, conservationists, agency personnel, farmers, and others who may use the results of these weather predictions in wind or water erosion prediction models.
Technical Abstract: For decades stochastic modelers have used computerized random number generators to produce random numeric sequences fitting a specified statistical distribution. Unfortunately, none of the random number generators we tested satisfactorily produced the target distribution. The result is generated distributions whose mean even diverges from the mean used to generate them, regardless of the length of run. Non-uniform distributions from short sequences of random numbers are a major problem in stochastic climate generation because truly uniform distributions are required to produce the intended climate parameter distributions. In order to ensure generation of a representative climate with the stochastic weather generator CLIGEN within a 30 year run, we tested the climate output resulting from various random number generators. The resulting distributions of climate parameters showed significant departures from the target distributions in all cases. We traced this failure back to the uniform random number generators themselves. This paper proposes a quality control approach to select only those numbers that conform to the expected distribution being retained for subsequent use. The approach is based on goodness of fit analysis applied to the generated random numbers. Normally-distributed deviates are further tested with confidence interval tests on their means and standard deviations. The positive effect of the new approach on the generated climate characteristics and subsequent deterministic process-based hydrology and soil erosion modeling are illustrated for four climatologically diverse sites.