O Estado do Tocantins está localizado entre o domínio do Cerrado e da Floresta Amazônica, o que confere ao mesmo uma diversidade climática importante, especialmente no tocante a ocorrência de chuvas. O conhecimento das chuvas intensas permite o planejamento adequado diante da atuação da precipitação na erosão do solo, inundações em áreas rurais e urbanas, obras hidráulicas, dentre outros. Neste contexto, o objetivo deste estudo foi o mapeamento das chuvas intensas no estado do Tocantins, estruturado em três capítulos com objetivos específicos: (1) com base em 10 estações pluviográficas, determinar constantes de desagregação de chuvas intensas para o Estado do Tocantins; (2) promover o mapeamento de chuvas intensas com durações de 5, 10, 20, 30, 40, 50, 60, 120, 180, 240, 360, 720 e 1440 minutos, associadas com as recorrências de 5, 10, 20, 30, 50, 100, 500 e 1000 anos; e (3) mapear a precipitação máxima provável (PMP) para as durações de 10, 20, 30, 40, 50, 60, 120, 180, 240, 360, 720 e 1440 minutos. Para a modelagem da frequência das chuvas intensas de diferentes durações foi empregada a distribuição de probabilidades de Gumbel para 10 estações pluviográficas. Para o mapeamento das chuvas intensas foram aplicadas séries históricas de 95 postos pluviométricos pertencentes à rede hidrometeorológica da Agência Nacional de Água (ANA), disponibilizadas pelo site Hidroweb, localizados no Tocantins e proximidades, considerando o período de 1983 a 2013, aplicando a geoestatística e avaliando os modelos de semivariograma esférico, exponencial e gaussiano. Para o mapeamento da PMP foi adotado o interpolador inverso do quadrado da distância, tendo sido a sua qualidade avaliada pelo procedimento de validação cruzada, a partir do cálculo da tendência (bias) e do erro médio percentual absoluto (EMPA). Na desagregação das chuvas intensas para o Estado do Tocantins, foram obtidas as seguintes constantes: h10min/h30min = 0,46, h20min/h30min = 0,76, h30min/h1h = 0,68, h40min/h1h = 0,83, h50min/h1h = 0,92, h1h/h24h = 0,61, h2h/h24h = 0,72, h3h/h24h = 0,78, h4h/h24h = 0,82, h6h/h24h = 0,86, h12h/h24h = 0,93. Para o mapeamento das chuvas intensas, o modelo que apresentou o menor erro médio obtido por validação cruzada foi aplicado ao processo de mapeamento por krigagem ordinária, tendo sido observado bom desempenho do modelo esférico para precipitação máxima diária anual e do gaussiano para chuvas desagregadas e associadas a um tempo de retorno. As regiões do Bico do Papagaio (extremo norte), Ilha do Bananal (extremo sudoeste) e noroeste, sob ocorrência de clima Amazônico, respondem pelos valores críticos de chuvas intensas no Estado do Tocantins. Para a maior duração de PMP avaliada (24h), encontraram-se lâminas variando de 410,8 a 768,2 mm, enquanto que, para a menor duração avaliada (10’) as lâminas variaram de 62,5 a 104,6 mm, com padrão de distribuição espacial semelhante às chuvas intensas mapeadas. / The State of Tocantins is located between the area of the Cerrado and the Amazon rainforest, which gives the same an important climatic diversity, especially with regard to rainfall. Knowledge of heavy rainfall allows proper planning considering the action of rainfall on soil erosion, floods in rural and urban areas, waterworks, among others. In this context, the objective of this study was the heavy rainfall mapping in the State of Tocantins, divided into three chapters with specific objectives: (1) based on 10 pluviograph stations, to determine disaggregation constants of heavy rainfall for the State of Tocantins; (2) promote heavy rainfall mapping at durations of 5, 10, 20, 30, 40, 50, 60, 120, 180, 240, 360, 720 to 1440 minutes associated with recurrences of 5, 10, 20, 30, 50, 100, 500 and 1000 years; and (3) to map the probable maximum precipitation for the state of Tocantins based on Hershfield methodology for durations of 10, 20, 30, 40, 50, 60, 120, 180, 240, 360, 720 and 1440 minutes. For modeling the frequency of intense rainfalls of different durations, was used the Gumbel distribution of probabilities for 10 pluviograph stations. For the mapping of heavy rainfall were applied historical series of 95 rain gauge stations belonging to the hydrometeorological network of the National Water Agency (NWA), provided by Hidroweb site, located on the Tocantins and nearby, considering the period 1983-2013, applying geostatistics and evaluating models of semivariogram spherical, exponential and gaussian. For the PMP mapping, was adopted the inverse-square-distance interpolator (ISD), being their quality assessed by cross-validation procedure from the calculation of the trend (bias) and the mean absolute percentage error. In the heavy rainfall disaggregation for the State of Tocantins, the constants were obtained: h10min/h30min = 0.46, h20min/h30min = 0.76, h30min/h1h = 0.68, h40min/h1h = 0.83, h50min/h1h = 0.92, h1h/h24h = 0.61, h2h/h24h = 0.72, h3h/h24h = 0.78, h4h/h24h = 0.82, h6h/h24h = 0.86, h12h/h24h = 0.93. For the heavy rainfall mapping, the model had the lowest average error obtained by cross-validation was applied to mapping by ordinary kriging process, having been observed good performance of the spherical model for maximum annual daily rainfall and gaussian to disaggregate and associated rains a return time. The regions of the Bico do Papagaio (north end), Bananal Island (extreme southwest) and northwest, under occurrence of Amazonian climate account for the critical values of heavy rainfall in the State of Tocantins. For longer duration evaluated PMP (24h), met blades ranging from 410.8 - 768.2 mm, while for the lowest measured duration (10') slides ranged from 62.5 - 104.6 mm with spatial distribution pattern similar to heavy rains mapped.

Uncertainties with a modeling framework consisting of a weather generator, two precipitation disaggregation models and the hydrological HBV model was assessed with respect to hydrometeorological extremes in Tyrol, Austria. Extreme precipitation events are expected to increase in intensity and frequency in the Alps during a warmer climate. The Alpine regions may be particularly vulnerable to such changes in climate where many floods in Europe occurred during recent years and caused major damage and loss of life. Weather generators typically provide time series at daily resolution. Different disaggregation methods have therefore been proposed and successfully tested to increase temporal resolution in precipitation. This is essential since flood peaks may be maintained for as little as minutes. Here, the non-parametric method of fragments was tested and compared with the multiplicative microcanonical cascade model with uniform splitting on the reproduction of precipitation extremes. It is also demonstrated that the method of fragments model can be transformed to disaggregate temperature with slight changes in the model structure. Preliminary test results show that the simulation of discharge peaks can be improved by disaggregating temperature in comparison with using daily averages as input in the HBV model.  Test results show that precipitation extremes were simulated within confidence bounds for Kelchsauer and Gurglbach when using historical observations as input. These two catchments had longer records of data available in comparison with Ruetz where the majority of simulated precipitation extremes were found outside confidence ranges. This indicates that the model is data driven. Synthetic data series were constructed with the weather generator from historical data and disaggregated with the two disaggregation models. The differences between the models were bigger for Ruetz where less observed data was available. The method of fragments simulates extremes with the closest resemblance to extremes. This is also true for the reproduction of wet spells and simulated variance. To account for parameter uncertainty in the HBV model, it is highly motivated to simulate discharge with different but suitable parameter sets to account for equifinality. However, the large amount of data produced when disaggregating the weather generated time series transcended the data capacity of the HBV model and made it crash. Other uncertainties related to the framework are the use of theoretical probability distributions in the weather generator and the dependence of high-resolution data for the disaggregation model. Despite these uncertainties, the framework is closer to a physical understanding of the causes of floods than the uncertain frequency analysis method. The framework is also applicable to land-use and climate change studies.