Approved for public release; distribution is unlimited / This thesis presents a statistical analysis of the monthly rainfall for the Monterey Peninsula and the Carmel Valley in Central California. The analysis begins with the simple first-order autoregressive Markov model, which is found to be weak. Next, 2X2 contingency tables are used to identify predictors, one of which is found to be January rainfall. Finally, logistic analysis is used to quantify the predictive ability of January. This paper attempts to analyze rainfall time series in the statistical sense. No attempt is made to provide a physical explanation of the findings from the point of view of a meteorologist. / http://archive.org/details/statisticalanaly00davi / Captain, United States Army

The Lake Kariba catchment area in southern Africa has one of the most variable climates of any major river basin, with an extreme range of conditions across the catchment and through time. The study characterized rainfall variability across the Lake Kariba catchment area, followed by describing prediction models for seasonal rainfall totals over the catchment and for inflows into Lake Kariba. The thesis therefore improved our understanding of rainfall variations over central southern Africa and provided evidence on how seasonal forecasts can be applied in order to potentially improve decision making in dam management. The prediction of the seasons in which floods or droughts are most likely to occur involves studying the characteristics of rainfall and inflows within these extreme seasons. The study started off by analyzing monthly rainfall data through statistical analysis. To determine the predictability of seasonal rainfall totals over the Lake Kariba catchment area, this study used low-level atmospheric circulation of a fully coupled ocean-atmosphere general circulation model over southern Africa, statistically downscaled to seasonal rainfall totals over the catchment. The verification of hindcasts showed that rainfall over the catchment is predictable at extended lead-times. Seasonal climate forecasts need to be integrated into application models in order to help with decision-making processes. The use of hydro-meteorological models may be proven effective for reservoir operations since accurate and reliable prediction of reservoir inflows can provide balanced solution to the problems faced by dam or reservoir managers. In order to reliably predict reservoir inflows for decision-making, the study investigated the use of a combination of physical and empirical models to predict seasonal inflows into the Lake. Two predictions systems were considered. First, antecedent seasonal rainfall totals over the upper Zambezi catchment were used as predictors in a statistical model for estimating seasonal inflows into Lake Kariba. The second and more sophisticated method used predicted low-level atmospheric circulation of a coupled ocean-atmosphere general circulation model downscaled to the inflows. Inflow hindcasts performed best during the austral mid-summer season of DJF (seasonal onset of inflows) and the autumn season of MAM (main inflow season). / Thesis (PhD)--University of Pretoria, 2015. / gm2015 / Geography, Geoinformatics and Meteorology / PhD / Unrestricted

Orientador: Carmen Regina Marcati / Resumo: Avaliar a estrutura anatômica de plantas que crescem em diferentes ambientes é uma maneira de compreender como as plantas se adaptam às variações destes ambientes. Algumas destas adaptações influenciam no transporte de água e de fotoassimilados, na proteção dos tecidos internos, na força mecânica e na capacidade de armazenamento dos tecidos, que são funções associadas ao caule das plantas. Assim, neste trabalho, avaliamos a estrutura caulinar de duas espécies, Moquiniastrum polymorphum e Zanthoxylum rhoifolium que ocorrem simultaneamente em diferentes tipos vegetacionais: o cerrado sensu stricto, o cerradão, a floresta estacional semidecídua e a floresta ombrófila densa. Os três primeiros tipos vegetacionais têm um período de seca durante o ano, enquanto que na floresta ombrófila densa o regime pluviométrico é relativamente constante ao longo do ano. Os solos de cada local apresentam diferentes propriedades físicas e químicas e no cerrado sensu stricto o fogo é um fator ambiental que pode ocorrer naturalmente. Estes fatores podem influenciar a estrutura anatômica dos tecidos vegetais. Para a descrição anatômica coletamos amostras do caule (a 1,30 m do solo) contendo xilema secundário e casca, pelo método não destrutivo, de cinco indivíduos de cada tipo vegetacional, que foram processadas conforme técnicas usuais em anatomia da madeira. Para verificar as diferenças entre os tipos vegetacionais, nós comparamos as características anatômicas por meio de uma análise de variância. ... (Resumo completo, clicar acesso eletrônico abaixo) / Doutor

Water resources management in the Sahel region of West Africa is extremely difficult because of high inter-annual rainfall variability as well as a general reduction of water availability in the region. Observed changes in streamflow directly disturb key socioeconomic activities such as the agriculture sector, which constitutes one of the main survival pillars of the West African population. Seasonal rainfall forecasting is considered as one possible way to increase resilience to climate variability by providing information in advance about the amount of rainfall expected in each upcoming rainy season. Moreover, the availability of reliable information about streamflow magnitude a few months before a rainy season will immensely benefit water users who want to plan their activities. However, since the 90s, several studies have attempted to evaluate the predictability of Sahelian weather characteristics and develop seasonal rainfall and streamflow forecast models to help stakeholders take better decisions. Unfortunately, two decades later, forecasting is still difficult, and forecasts have a limited value for decision-making. It is believed that the low performance in seasonal forecasting is due to the limits of commonly used predictors and forecast approaches for this region. In this study, new seasonal forecasting approaches are developed and new predictors tested in an attempt to predict the seasonal rainfall over the Sirba watershed located in between Niger and Burkina Faso, in West Africa. Using combined statistical methods, a pool of 84 predictors with physical links with the West African monsoon and its dynamics were selected, with their optimal lag times. They were first reduced through screening using linear correlation with satellite rainfall over West Africa. Correlation analysis and principal component analysis were used to keep the most predictive principal components. Linear regression was used to get synthetic forecasts, and the model was assessed to rank the tested predictors. The three best predictors, air temperature (from Pacific Tropical North), sea level pressure (from Atlantic Tropical South) and relative humidity (from Mediterranean East) were retained and tested as inputs for seasonal rainfall forecasting models. In this thesis it has been chosen to depart from the stationarity and linearity assumptions used in most seasonal forecasting methods: 1. Two probabilistic non-stationary methods based on change point detection were developed and tested. Each method uses one of the three best predictors. Model M1 allows for changes in model parameters according to annual rainfall magnitude, while M2 allows for changes in model parameters with time. M1 and M2 were compared to the classical linear model with constant parameters (M3) and to the linear model with climatology (M4). The model allowing changes in the predictand-predictor relationship according to rainfall amplitude (M1) and using AirTemp as a predictor was the best model for seasonal rainfall forecasting in the study area. 2. Non-linear models including regression trees, feed-forward neural networks and non-linear principal component analysis were implemented and tested to forecast seasonal rainfall using the same predictors. Forecast performances were compared using coefficients of determination, Nash-Sutcliffe coefficients and hit rate scores. Non-linear principal component analysis was the best non-linear model (R2: 0.46; Nash: 0.45; HIT: 60.7), while the feed-forward neural networks and regression tree models performed poorly. All the developed rainfall forecasting methods were subsequently used to forecast seasonal annual mean streamflow and maximum monthly streamflow by introducing the rainfall forecasted in a SWAT model of the Sirba watershed, and the results are summarized as follows: 1. Non-stationary models: Models M1 and M2 were compared to models M3 and M4, and the results revealed that model M3 using RHUM as a predictor at a lag time of 8 months was the best method for seasonal annual mean streamflow forecasting, whereas model M1 using air temperature as a predictor at a lag time of 4 months was the best model to predict maximum monthly streamflow in the Sirba watershed. Moreover, the calibrated SWAT model achieved a NASH value of 0.83. 2. Non-linear models: The seasonal rainfall obtained from the non-linear principal component analysis model was disaggregated into daily rainfall using the method of fragment, and then fed into the SWAT hydrological model to produce streamflow. This forecast was fairly acceptable, with a Nash value of 0.58. The evaluation of the level of risk associated with each seasonal forecast was carried out using a simple risk measure: the probability of overtopping of the flood protection dykes in Niamey, Niger. A HEC-RAS hydrodynamic model of the Niger River around Niamey was developed for the 1980-2014 period, and a copula analysis was used to model the dependence structure of streamflows and predict the distribution of streamflow in Niamey given the predicted streamflow on the Sirba watershed. Finally, the probabilities of overtopping of the flood protection dykes were estimated for each year in the 1980-2014 period. The findings of this study can be used as a guideline to improve the performance of seasonal forecasting in the Sahel. This research clearly confirmed the possibility of rainfall and streamflow forecasting in the Sirba watershed at a seasonal time scale using potential predictors other than sea surface temperature.

Dans les pays du Sud ruraux et à faibles revenus, la vulnérabilité des zones agricoles pluviales, face à la variabilité pluviométrique, nécessite de trouver des solutions efficaces pour limiter les effets des aléas climatiques sur les récoltes. La prévision des caractéristiques des saisons des pluies quelque temps avant leur démarrage devrait aider à l’établissement de stratégies agricoles d’adaptation aux aléas pluviométriques. C’est à cet objectif que s’attache ce travail, appliqué à l’Afrique de l’Est (Kenya et nord de la Tanzanie), et articulé en 3 parties :- Définir et comprendre le comportement des descripteurs intra saisonniers (DIS) qui feront l’objet de l’étude de prévisibilité. Un travail spécifique a permis le développement d’une nouvelle approche méthodologique dans la manière de définir les démarrages (DSP) et fins (FSP) de saisons des pluies à l’échelle régionale. Cette approche basée sur une analyse multivariée, permet de s’affranchir des choix subjectifs de seuils pluviométriques imposés par les définitions communément utilisée en agro-climatologie. Une analyse de cohérence spatiale à l’échelle inter annuelle montre que, pour les deux saisons des pluies (long rains et short rains), le cumul saisonnier et le nombre de jours de pluie présentent une forte cohérence spatiale, tandis qu’elle est plus modérée pour le démarrage et fin des saisons et faible pour l’intensité quotidienne moyenne.- Analyser la prévisibilité des DIS aux 2 échelles spatiales régionale et locale en s’appuyant sur les simulations numériques du modèle climatique global ECHAM 4.5. Les précipitations quotidiennes simulées par le modèle, même après correction des biais, ne permettent pas d’appréhender correctement la variabilité interannuelle des DIS. Une spécification de la variabilité des DSP et FSP menée par le biais de modèles statistiques construits à partir d’indices climatiques observés, présuppose une prévisibilité modérée des deux descripteurs à l’échelle locale (régionale), et cela quelle que soit la saison. Le développement de modèles statistico-dynamiques à partir des champs de vents simulés par ECHAM 4.5, en mode forcé par les températures marines observées d’une part et prévues d’autre part, montre également des performances faibles localement et régionalement. - Explorer la manière dont la variabilité spatio-temporelle des paramètres climatiques et environnementaux module la variabilité des rendements de sorgho. Ces rendements sont simulés par le modèle agronomique SARRA-H à partir de données climatiques observées (1973-2001) dans 3 stations localisées à différentes altitudes le long des pentes orientales du Mt Kenya. Le cumul précipité et la durée de la saison expliquent une part importante de la variabilité des rendements. D’autres variables apparaissent comme jouant un rôle non négligeable ; le nombre de jours de pluies, l’intensité quotidienne moyenne ou encore certains DIS relatifs à l’organisation temporelle des pluies au sein d’une saison en font partie. L’influence des autres variables météorologiques est seulement visible pour les ‘long rains’ avec une covariation négative entre les rendements et les températures maximales ou, le rayonnement global. La date de semis semble jouer un rôle dans la modulation des rendements pour les stations de haute et moyenne altitudes, mais avec des différences notables entre les deux saisons des pluies. / In Southern countries with rural low income populations, the vulnerability of rainfed agriculture to rainfall variability requires effective solutions to mitigate the effects of climatic hazards on crops. Predicting the characteristics of rainy seasons some time before they start should help the establishment of agricultural adaptation strategies to rainfall hazards. This is the objective of the present study, focused on East Africa (Kenya and northern Tanzania), and divided in three parts:- Define and document intra-seasonal descriptors (ISD) that will be considered in the predictability study. A new methodological approach has been developed in order to define the onset date (ORS) and the cessation date (CRS) of the rainy seasons at the regional level. Based on a multivariate analysis, it eliminates the subjective choice of rainfall thresholds imposed by the definitions commonly used in agroclimatology. An analysis of spatial coherence at interannual time-scale shows that for the two rainy seasons ("long rains" and "short rains"), the seasonal amount and the number of rainy days have a high spatial coherence, while it is medium for the onset and cessation dates and low for the average daily rainfall intensity.- Analyze the predictability of the ISD at both regional and local scales based on numerical simulations from the global climate model ECHAM 4.5. Daily precipitation simulated by the model, even after bias correction, do not correctly capture the IDS interannual variability. A specification of the ORS and CRS variability using statistical models applied to observed climate indices, suggests quite a low predictability of the descriptors at the local (regional) scale, regardless of the season. The development of statistical-dynamical models from wind fields simulated by ECHAM 4.5, in experiments forced by either observed or predicted sea temperatures, also shows quite poor skills locally and regionally.- Explore how the space-time variability of climatic and environmental factors modulate the variations of sorghum yields. Crop yields are simulated by the agronomic model SARRA-H using observed climate data (1973-2001) at three stations located at different elevations along the eastern slopes of Mt Kenya. The seasonal rainfall accumulation and the duration of the season account for a large part of the yields variability. Other rainfall variables also play a significant role, among which the number of rainy days, the average daily intensity and some ISD related to the temporal organization of rainfall within the season. The influence of other meteorological variables is only found during the long rains, in the form of a negative correlation between yields and both maximum temperature and global radiation. Sowing dates seem to play a role in modulating yields for high and medium altitude stations, but with notable differences between the two rainy seasons.