Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / A savana de Roraima apresenta um mosaico de fitofisionomias com distintas estruturas e composições florísticas que podem estar associadas a diferentes fatores edáficos (fertilidade, textura e inundação sazonal do solo). O objetivo deste trabalho foi verificar se fatores edáficos determinam composição, riqueza, cobertura de espécies e grupos taxonômicos (famílias) de plantas em áreas da savana de Roraima. O estudo foi realizado em 20 parcelas permanentes situadas no Campus Cauamé (UFRR) e no Campo Experimental Água Boa (Embrapa Roraima). Foi realizado um inventário florístico (composição e riqueza) e utilizado a cobertura (%) de indivíduos e espécies como variável descritora de habitats tomando como referência três categorias de inundação (bem, mal e imperfeitamente drenado). Técnicas multivariadas foram utilizadas para determinar padrões de ocorrência e agrupamento de plantas. Foram encontradas 130 espécies classificadas em 34 famílias botânicas. As famílias Cyperaceae, Poaceae e Fabaceae foram às de maior riqueza de espécies nas parcelas amostradas. Por meio de regressão linear constatou-se que o estrato herbáceo possui relação negativa com inundação sazonal. Verificou-se que cobertura vegetal (%) dos grupos taxonômicos nas categorias de inundação foi dominado pelo estrato herbáceo. No entanto, nas parcelas com solos imperfeitamente drenados (72,3 %) e mal drenados (79,5 %) foi verificada uma maior cobertura média de herbáceas em relação à categoria de solos bem drenados (50,5 %). As famílias de maior cobertura (%) em todas categorias foram Poaceae e Cyperaceae. P. carinatum (Poaceae) foi a espécie comum de maior cobertura presente nos habitats distintos por drenagem. Assim, conclui-se que fatores edáficos, em especial a drenagem, determinam distinções florísticas e estruturais nas áreas de savana estudadas em Roraima. / The Savanna of Roraima presents a mosaic of vegetation types with distinct structures and floristic composition that may be associated with different edaphic factors (fertility, texture and seasonal flooded soils). The objective of this study was to determine whether soil factors determine composition, richness and coverage of species and taxonomic groups (botany families) of plants in the savanna areas of Roraima. The study was conducted in 20 permanent plots located in the Campus Cauamé (UFRR) and the Campo Experimental Água Boa (Embrapa Roraima). A floristic inventory (composition and richness) was carried out and cover (%) of individuals and species was used as descriptor variable of habitats taking account three categories of flooded soils (well-, poor- and imperfectly drained). Multivariate techniques were used to determine patterns of occurrence and clustering. It was found 130 species classified into 34 plant families. The Cyperaceae, Poaceae and Fabaceae families were the most species richness in the plots. By linear regression it was found that the herbaceous layer has negative relation to seasonal flooding. The vegetation cover (%) of taxonomic groups in the flood category was dominated by herbaceous. However, in the plots with imperfectly drained soils (72.3%) and poorly drained (79.5%) was observed a higher average coverage of herbaceous related to well-drained soils (50.5%). The largest families coverage (%) in all categories were Poaceae and Cyperaceae. P. carinatum (Poaceae) was the most common kind of coverage present in different habitats for drainage. Thus, it is concluded that soil factors, particularly drainage, determine floristic and structural distinctions in the areas of savannah studied in Roraima.

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.