Rainfall and crop water demand are two major agro-climatic variables that determine the crop production in the Dry zone of Sri Lanka. The lack of long series of historical data of these variables often hinders the proper understanding of the agricultural potential of the region. The large random variability displayed by such variables means that they are best simulated by appropriate stochastic models and can be used to replace the existing short series of data. The main objectives of this thesis are to characterise the major growing seasons of the Dry zone, Yala and Maha, using extended temporal variability of rainfall and crop water demand through the stochastic simulation and to predict the characteristics of upcoming seasons using the simulated and historical data.
The rainfall process was modelled using three Markovian models: the first-order discrete time Markov model, the second-order discrete time Markov model and the continuous time Markov model. Out of them, the first-order discrete time Markov model is the preferred model on the basis of its statistical performance and the practical ease. The crop water use was estimated using a single-layer water balance model which accounts evapotranspiration as a stochastic element.
A weekly system model was developed that incorporated the first-order Markov rainfall model and the soil water balance model. It characterises the two major growing seasons of the Dry zone using five agro-climatic indices: mean rainfall, dependable rainfall (DRF), moisture availability index (MAI), ratio of actual to potential evapotranspiration (AET/PET) and crop water satisfaction index (CWSI).
The simulated mean onset of the Yala and Maha seasons were the standard weeks 13 and 40, respectively. The mean end of the Yala season was the standard week 20 whereas the mean end of the Maha season could occur any time after the standard week 5 and it varied depending on the index used. The simulation also revealed that though the Maha season is ceased by late January, the soil moisture remains well above the 50% of available soil moisture during the inter-season dry month, February. According to the simulation, at least one out of every ten years the Yala season could experience a complete crop failure and the possibility of occurrence of such a catastrophic event during the Maha season is negligible. The onset time of the seasonal rains as a predictor of the seasonal characteristics of Yala or Maha season was not clearly evident in this simulation study though such links have been apparent in other monsoonal areas of the tropic. Nevertheless, cursory examination of observed rainfall data and the appearance of EI Nino conditions in the Pacific Ocean points towards a possible trend of seasonal rainfall in the Dry zone.
A special case of spatial interpolation of rainfall data was examined assuming that the spatial continuity of two neighbouring locations are exponentially correlated. It was shown that the exponential spatial interpolation model is a good candidate to estimate the mean parameters of weekly rainfall in the Dry zone
