4 results for Punyawardena, B. V. R.

  • Assessment of growing seasons characteristics in the Dry zone of Sri Lanka based on stochastic simulation of rainfall and soil water status

    Punyawardena, B. V. R.

    Thesis
    Lincoln University

    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.

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  • Stochastic simulation of solar radiation from sunshine duration in Sri Lanka

    Punyawardena, B. V. R.; Kulasiri, Don

    Book
    Lincoln University

    A stochastic model for generating daily sunshine hours and solar radiation that reaches the earth's surface in dry zone of Sri Lanka has been developed. Historical sunshine data which represent a Weibull probability density function and universal Angstrom type equations were used in the model giving daily and monthly sunshine and solar radiation. Simulated values clearly follows the astronomical phenomenon and local atmospheric conditions. The lowest solar radiation values (14 to 15 MJ m⁻² day⁻¹) were obtained during the major rainy season. The highest values (around 20 MJ m⁻² day⁻¹) were evident around the Vernal Equinox (March and April). Sri Lanka being small in size (65,000 km²) and small latitudinal extent (<4°), the model may form the basis for more comprehensive solar radiation generating models for other areas in Sri Lanka.

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  • Spatial interpolation of rainfall in the dry zone of Sri Lanka

    Punyawardena, B. V. R.; Kulasiri, Don

    Book
    Lincoln University

    One of the problems which often arises in climatology is either data at a given site is missing or the site is ungauged. In this study, a spatial interpolation model was developed to estimate the weekly rainfall of the Dry zone of Sri Lanka at ungauged sites assuming that the spatial continuity of rainfall at two neighbouring locations are exponentially correlated. Twenty years of weekly rainfall data from six stations located in the Dry zone was used in the study. To support the methodology, the results of the exponential model were compared with the other two methods of spatial interpolation techniques, namely, the local mean and the inverse distance methods. The results of the study indicate that the exponential correlation model is a promising candidate for estimating mean weekly rainfall of the Dry zone. However, the local mean and the inverse distance methods compare quite well along with the exponential model, indicating that more complex models have no particular advantage over simple models for estimating rainfall in the Dry zone of Sri Lanka. Nevertheless, the results point towards the relative importance of the exponential model as opposed to the other two models when the neighbouring locations do not have long series of historical records.

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  • On development and comparative study of two Markov models of rainfall in the dry zone of Sri Lanka

    Punyawardena, B. V. R.; Kulasiri, Don

    Book
    Lincoln University

    Being closer to the equator, the most important climatic element for agricultural production in Sri Lanka is rainfall which is erratic and highly unpredictable in nature, especially in the dry zone. This study attempts to model the weekly rainfall climatology of dry zone using Markov processes as the driving mechanism based on the 51 years of past data. The weekly occurrence of rainfall was modelled by two-state first and second order Markov chains while the amount of rainfall on a rainy week was approximated by taking random variates from the best fitted right skewed probability distribution out of Gamma, Weibull, Log-Normal and Exponential distributions. The parameters of the both models namely, elements of transition matrices, and scale and shape parameters of the desired distribution, were determined using weekly data. Both first and second Markov chains performed similarly in terms of modelling weekly rainfall occurrence and amount of rainfall if rain occurred. Use of second order Markov chain did not enhance the representativeness of the simulated data to the observed data in spite of being penalised for its large number of computations. Weekly rainfall data generated with the first-order Markov chain model preserve the statistical and seasonal characteristics that exist in the historical records.

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