The area chosen for this study is the Central Elburz of Iran surrounding the southern end of the Caspian Sea. It includes an important rainfall dependent agricultural area and merges into the semi arid zone with a well defined boundary. From social and agricultural points of view rainfall is by far the most important climatic factor in many tropical and subtropical countries. An important aspect in the development of agriculture in the Central Elburz is the determination of the seasonal rainfall patterns. This thesis should be considered as a contribution to the study of the seasonal rainfall regime of Iran, with direct value to agriculture in the region. The purpose of this thesis is to examine the pattern of rainfall especially in relation to the growing season both in time and spatially. Also the thesis describes the variation in the availability of moisture for growth throughout this region. The thesis examines annual, monthly and seasonal rainfall over the region. Multivariate analysis has demonstrated that the study area can be divided into three rainfall regions. Before considering any analysis which could allow prediction on probability of future rainfall amounts, it is important to consider whether or not there on trends or fluctuations. The result of this analysis is that, few stations show positive trends, others negative trends. Rainfall variability has been quantified by the coefficient of variation index and analysed in a similar way to that of rainfall distribution. The results for most stations shown a relationship between variability indices and rainfall amounts. In relation to the study of variability, an assessment of rainfall probability and reliability has been considered with reference to rainfall critical for crops. Probability has a particular value in its application to agriculture. In this study 90% probability is examined and this is related to the economy of the agriculture units in the Central Elburz. Also 80 and 75 percent probability are presented. These spatial patterns of probability thus provide maps of agricultural potential. Rainfall records are analysed to provide estimates, percentage points of rainfall totals, variation in the start, and end of the rainy season and or the probability of dry spells within the rainy season. The estimation of potential evapotranspiration which are discussed and explained in this study are based on the Blaney- Criddle and Pan evaporation methods. The water balance approach provides the most rational method of analysing seasonal pattern since it considers characteristics of the dominant crops in this area, wheat and citrus, and it examines effectiveness of rainfall for these crops. Blaney- Criddle and Pan evaporation have been used to estimate potential evaporation for wheat and citrus and compared to actual evapotranspiration to give potential water deficit. The adoption of probability analysis of potential water deficit provides a valuable description of water availability for crops in this region of variable rainfall.

To investigate seasonal patterns of precipitation, statistical analysis was performed on a dataset of daily rainfall observed at 63 south Louisiana stations from 1836 to 2002. Each station record was examined for data quality and continuity with special attention to time periods surrounding station relocation or equipment exchange. Mean Areal Precipitation (MAP) sheets were compiled for every month from 1836 to 2002 to document the daily rainfall across south Louisiana and neighboring portions of southern and coastal Mississippi. Using these MAP sheets, missing data was examined to see if a reasonable value could be substituted to extend the continuity of a station's rainfall record. Once these data quality and continuity checks were completed, a series of statistical tests were conducted to determine an accurate scheme to form station groups. To group stations together, each station was required to have a normal distribution of monthly average rainfall, a statistically equivalent variance, and a statistically equivalent mean when compared with other stations in the group. As a result of the Shapiro-Wilk Test, the F-Test, and the Student T-test, eight station groups were formed. To define seasonal rainfall patterns across south Louisiana, statistical tests were conducted for a 12 month period and six and three month intervals. For the six month intervals, group rainfall averages and pooled variances were calculated for each interval beginning with January-July and ending with December-May. For the three month intervals, group rainfall averages and pooled variances were calculated for January-March and concluded with December-February. To test the hypothesis of a statistically significant difference in mean rainfall between the eight groups for a 12, six, and three month period, the Student T-test was conducted. For an annual basis, there is a statistically significant difference in average rainfall at a five percent level of significance between all of the groups except the Southshore (S.S) group when compared to the SW1 group. For six and three month intervals, statistically significant differences exist between the eight groups especially during winter and segments of the Hurricane season from June to November.

京都大学 / 新制・課程博士 / 博士(工学) / 甲第24892号 / 工博第5172号 / 新制||工||1988(附属図書館) / 京都大学大学院工学研究科社会基盤工学専攻 / (主査)教授 渦岡 良介, 教授 安原 英明, 教授 肥後 陽介 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DGAM

Thesis (M. Sc. (Statistics)) -- University of Limpopo, 2021 / The effects of ozone depletion on climate change has, in recent years, become a reality, impacting on changes in rainfall patterns and severity of extreme floods or extreme droughts. The majority of people across the entire African continent live in semi-arid and drought-prone areas. Extreme droughts are prevalent in Somalia and eastern Africa, while life-threatening floods are common in Mozambique and some parts of other SADC (Southern African Development Community) countries. Research has cautioned that climate change in South Africa might lead to increased temperatures and reduced amounts of rainfall, thereby altering their timing and putting more pressure on the country’s scarce water resources, with implications for agriculture, employment and food security. The average annual rainfall for South Africa is about 464mm, falling far below the average annual global rainfall of 860mm. The Limpopo Province, which is one of the nine provinces in South Africa, and of interest to this study, is predominantly agrarian, basically relying on availability of water, with rainfall being the major source for water supply. It is, therefore, pertinent that the rainfall pattern in the province be monitored effectively to ascertain the rainy period for farming activities and other uses. Modelling and forecasting rainfall have been studied for a long time worldwide. However, from time to time, researchers are always looking for new models that can predict rainfall more accurately in the midst of climate change and capture the underlying dynamics such as seasonality and the trend, attributed to rainfall. This study employed Exponetial Smoothing (ETS) State Space and Seasonal Autoregressive Integrated Moving Average (SARIMA) models and compared their forecasting ability using root mean square error (RMSE). Both models were used to capture the sporadic behaviour of rainfall. These two models have been widely applied to climatic data by many scholars and adjudged to perform creditably well. In an attempt to find a suitable prediction model for monthly rainfall patterns in Limpopo Province, data ranging from January 1900 to December 2015, for seven weather stations: Macuville Agriculture, Mara Agriculture, Marnits, Groendraal, Letaba, Pietersburg Hospital and Nebo, were analysed. The results showed that the two models were adequate in predicting rainfall patterns for the different stations in the Limpopo Province. / National Research Foundation (NRF)