Stochastic weather generators are useful for producing daily sequences that reproduce climatic statistics aggregated to, e.g., a monthly time scale, for use with biological simulation models. This paper describes a stochastic weather generator that disaggregates monthly rainfall by adjusting input parameters or by constraining output to match target rainfall totals, and demonstrates its use wit...

in this paper, we present an application of evolved neural networks using a real coded genetic algorithm for simulations of monthly groundwater levels in a coastal aquifer located in the shabestar plain, iran. after initializing the model with groundwater elevations observed at a given time, the developed hybrid genetic algorithm-back propagation (ga-bp) should be able to reproduce groundwater ...

abstract. the purpose of this study was to investigate vegetation dynamics and range conditions considering the climatic conditions and soil properties in haftooman, khoor and biabanak deserts, iran. for this purpose, after determining vegetation types and the associated species, the type of rangeland utilization, grazing season, the livestock type, the other relevant information, vegetation da...

a prediction of maximum monthly rainfall is indispensable for management of agriculture and for management of water resources. previous studies have demonstrated that see surface temperatures affect rainfall in their surrounding regions. in this study the relationship between sea surface temperatures of persian gulf and that of red sea, and maximum monthly rainfalls recorded at illam meteorolog...

In this article, we study conditional heteroskedasticity in a market index on the Bombay Stock Exchange, from April 1979 to March 1995. We nd strong evidence of heteroskedasticity in daily, weekly and monthly returns. The conditional variance of all three data series seem best approximated by a garch(1,1) model. The garch parameter estimates at all data frequencies exhibit strong persistence in...

This paper presents user-friendly SAS macro and SAS programs to check the three parts of a time series: Trend, Cyclical patterns, and Stationarity. The SAS macro KENDALL performs the trend analysis. Seasonal Kendall Trend analysis, including summery statistics; overall Tau and the P-value of the test for trend; monthly Tau values, the corresponding P-values for each month; the Seasonal Kendall ...