The purpose of this paper is to demonstrate that a finite state cellular automata model is suitable for modeling rainfall in the space-time plane. The time-series properties of the simulated series are matched with historical rainfall data gathered from Whenuapai, NZ. The spatial scale of the model cells in related to land-area by optimizing the cross-correlation between sites at lag 0 relative...

rainfall and flow rate are of the most important climatic and hydrologic parameters whose investigation and determination of their behavior are of great importance in water resources management. one of the most suitable methods available for assessment of hydro-climatological conditions in the watershed is trend analysis which is used to examine the changes of a variable during time. current st...

The modelling of rainfall and other hydrological data has been the point of much discussion in past research. Hurst (1951) heuristically detected the presence of long range dependence (or long memory) in the well-known series of annual minima of the Nile River. These processes are so called because they display a high degree of association between observations which are distant in time. Followi...

Using time series of Normalized Difference Vegetation Index (NDVI) and rainfall data, we investigated historical vegetation productivity trends from 1982 to 2011 over the Bani River Basin in Mali. Statistical agreements between long-term trends in vegetation productivty, corresponding rainfall and rate of land cover change from Landsat time-series imagery was used to discern climate versus huma...

This study compares the accuracy of the short-term rainfall forecasts obtained with time-series analysis techniques, using past rainfall depths as the only input information. The techniques proposed here are linear stochastic auto-regressive movingaverage (ARMA) models, artificial neural networks (ANN) and the non-parametric nearest-neighbours method. The rainfall forecasts obtained using the c...

Drought is one of the most serious problems arising for human societies and ecosystems from climatevariability. This phenomenon causes billions of dollars in global damages annually and affects morepeople than any other form of natural disasters.In this study, the trends of rainfall and meteorological drought time series were investigated at tenstations located in the east of Iran for the perio...

Considerable rainfall occurs in India during the post-monsoon period from October to December, particularly over north-eastern, eastern and southern regions, and this is of great significance in agricultural and allied sectors. For the first time, we have attempted to provide detailed information on the variability and predictability of the post-monsoon rainfall (PMR) of the country. Details on...

[1] Rainfall variability occurs over a wide range of time scales owing to processes initiated by cloud microphysics and sustained by atmospheric circulation. A central topic in rainfall research is to determine whether rainfall variability at a given scale is caused by dynamics acting at some other scales. Random multiplicative cascades (RMCs) are standard approaches for describing rainfall var...

Time series cross-correlation analysis is appropriate when measuring relationships between two different time series. Using this approach, the authors quantified the relationship between the time series air temperature (AT), soil temperature (ST), rainfall, relative humidity (RH) and Diaprepes abbreviatus (L.) (Coleoptera: Curculionidae) root weevil across a period of 30 months, and examined ho...