Charles “Chuck” Meyer passed away on 4 February 2007.
Version of Record online: 24 SEP 2007
Copyright © 2007 John Wiley & Sons, Ltd.
Volume 22, Issue 8, pages 1069–1079, 15 April 2008
How to Cite
Meyer, C. R., Renschler, C. S. and Vining, R. C. (2008), Implementing quality control on a random number stream to improve a stochastic weather generator. Hydrol. Process., 22: 1069–1079. doi: 10.1002/hyp.6668
The contributions of Charles R. Meyer and Roel C. Vining to this article were prepared as part of their official duties as United States Federal Government employees.
The method and source code used in this research is publicly available on the CLIGEN website of the National Soil Erosion Research Laboratory (http://topsoil.nserl.purdue.edu/nserlweb/weppmain/cligen/).
- Issue online: 25 MAR 2008
- Version of Record online: 24 SEP 2007
- Manuscript Accepted: 15 DEC 2006
- Manuscript Received: 10 APR 2006
- US Department of Agriculture–Agricultural Research Service, National Soil Erosion Research Laboratory.
- stochastic model;
- quality control;
- random number;
- weather generator;
- soil erosion;
- deterministic model
For decades, stochastic modellers 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 random numbers generated. 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 climate characteristics generated and the subsequent deterministic process-based hydrology and soil erosion modelling are illustrated for four climatologically diverse sites. Copyright © 2007 John Wiley & Sons, Ltd.