Large-scale circulation patterns have a significant modulating influence on local hydro-meteorological variables, and consequently on water availability. An understanding of the influence of these patterns on the hydrological cycle, and the ability to timely predict their impacts, is crucial for water resources planning and management. This dissertation focusses on the influence of two major large-scale circulation patterns, the El Niño Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO), on the Rio Grande basin and the state of Texas, US. Both study areas are subject to a varying climate, and are extremely vulnerable to droughts, which can have devastating socio-economic impacts. The strength and spatial correlation structure of the climate indices on gauged precipitation was first established. Precipitation is not linearly related to water availability; therefore a land surface model (LSM), with land use land cover constant, was used to create naturalized flow, as it incorporates all necessary hydro-meteorological factors. As not all ENSO events are created equal, the influence of individual El Niño and La Niña events, classified using four different metrics, on water availability was examined. A general increase (decrease) in runoff during El Niños (La Niñas) was noted, but some individual events actually caused a decrease (increase) in water availability. Long duration El Niños have more influence on water availability than short duration high intensity events. Positive PDO enhances the effect of El Niño, and dampens the negative effect of La Niña, but when it is in its neutral or transition phase, La Niña tends to dominate climatic conditions and reduce water availability. LSM derived runoffs were converted into 3-month Standardized Runoff Indices (SRI 3) from which water deficit durations and severities were extracted. Conditional probability models of duration and severity were developed and compared with that based on observed precipitations. It was found that model derived information can be used in regions having limited ground observation data, or can be used in tandem with observation driven conditional probabilities for more efficient water resources planning and management. Finally a multidimensional model was developed, using copulas, to predict precipitation based on the phase of ENSO and PDO. A bivariate model, with ENSO and precipitation, was compared to a trivariate model, which incorporates PDO, and it was found that information on the state of PDO is important for efficient precipitation predictions.

Conselho Nacional de Desenvolvimento Científico e Tecnológico / In this study, simulated to net radiation and energy flux in a region with rotation of crops, for two distinct periods: Period 1 (01 / Feb / 2009 to 31 / Jan / 2010) and period 2 (14 / Dec / 2009 to 28 / Apr / 2010). In these simulations we used the NOAH LSM surface model. For the period 1 initially, without any calibration simulations were performed only with the input of the local weather conditions, an adjustment of an experiment controlfile file and spin up for the stabilization of the initial conditions. In these simulations, the results were very poor, indicating a need to test the sensitivity of the model especially because of the launch conditions of temperature and soil moisture. After these tests it was found that the initial predictions of impact can be considerable conditions for the two cases. It is noticed that the soil moisture changes generate greater impact in the model that temperature variations boot. As a result, proposed a calibration for the model. The calibration method was to make some simulations manually varying the parameters of soil and vegetation, or both, according to the deficiencies of the NOAH LSM. The tests were carried out until they could get a more optimized forecast for the period studied. The initial analysis of the local conditions of the experimental site was very important for calibration, as it allowed establishing previous parameters corresponding to values close to those parameters when calibrated. Simulation results after calibration applied satisfactorily exhibited liquid radiation and heat flows. So it can be said that the calibration is proposed representing characteristics of vegetation and soil correctly. Nevertheless, the corrections that the model still needs, especially in sensible and latent heat fluxes, may be associated with representation in heat distribution processes and water, or by the fact that the colder months had considerable regime rains. So when there is cloud cover, the model still has problems in representation. Results for long periods of data, as in this work, may lose some of representativeness due to the seasonality of the vegetation parameters, for which varied the parameters for periods culture and fallow. The most important contribution made in this work was a model fit for an agricultural ecosystem area and validate it for the future, it may be used as an initial boundary condition in numerical weather prediction models. The implementation variations in LAI and albedo parameter applied in the simulations of period 2 (soybean) improved the description of the heat flux and net radiation. / Neste estudo simulou-se a radiação líquida e os fluxos de energia para uma região com rotações de cultivos agrícolas, para dois períodos distintos: Período 1 (01/Fev/2009 até 31/Jan/2010) e período 2 (14/Dez/2009 até 28/Abr/2010). Nestas simulações utilizou-se o modelo de superfície NOAH LSM. Inicialmente, para o período 1, foram feitas simulações sem nenhuma calibração, apenas com a entrada das condições meteorológicas locais, um ajuste do arquivo controlfile e um experimento spin up para a estabilização das condições iniciais. Nestas simulações, os resultados foram muito insatisfatórios, indicando uma necessidade de testar a sensibilidade do modelo principalmente frente às condições de inicialização da temperatura e da umidade do solo. Após estes testes verificou-se que impactos das previsões às condições iniciais podem ser consideráveis para os dois casos. Percebe-se que as variações de umidade do solo geram maior impacto no modelo devido à temperatura do solo que é simulada. Na sequência, propôs-se uma calibração para o modelo. O método de calibração consistiu em fazer algumas simulações variando-se manualmente os parâmetros de solo e vegetação, ou ambos, de acordo com as deficiências do NOAH LSM. Assim, os testes foram realizados até que se conseguisse uma previsão mais otimizada para o período estudado. A análise inicial das condições locais do sítio experimental foi de suma importância para a calibração, pois ela possibilitou estabelecer parâmetros prévios que correspondem a valores próximos dos parâmetros quando calibrados. Os resultados das simulações, após a calibração aplicada, representaram satisfatoriamente a radiação líquida e os fluxos de calor. Portanto, pode-se dizer que a calibração proposta está representando as características de vegetação e de solo de forma correta. Apesar disso, as correções que o modelo ainda necessita, principalmente nos fluxos de calor sensível e latente, podem estar associadas a representação nos processos de distribuição do calor e da água, ou ainda pelo fato de que os meses mais frios tiveram um considerável regime de chuvas. Assim, quando há nebulosidade, o modelo ainda apresenta problemas na representação. Os resultados para períodos longos de dados, os quais foram considerados neste trabalho podem perder um pouco da representatividade em função da sazonalidade dos parâmetros de vegetação, motivo pelo qual variou-se os parâmetros para períodos com cultura e com pousios. A contribuição mais importante realizada neste trabalho foi um ajuste do modelo para uma região de ecossistema agrícola e a sua validação para que futuramente, possa ser utilizado como condição de contorno inicial em modelos de previsão numérica do Tempo. A implementação das variações diárias no parâmetro IAF e no albedo, aplicada nas simulações do período 2 na cultura de soja, melhorou a descrição dos fluxos de calor e da radiação líquida.

This work introduces diagnostic methods for land surface model (LSM) evaluation that enable developers to identify structural shortcomings in model parameterizations by evaluating model 'signatures' (characteristic temporal and spatial patterns of behavior) in feature, cost-function, and parameter spaces. The ensemble-based methods allow researchers to draw conclusions about hypotheses and model realism that are independent of parameter choice. I compare the performance and physical realism of three versions of Noah LSM (a benchmark standard version [STD], a dynamic-vegetation enhanced version [DV], and a groundwater-enabled one [GW]) in simulating high-frequency near-surface states and land-to-atmosphere fluxes in-situ and over a catchment at high-resolution in the U.S. Southern Great Plains, a transition zone between humid and arid climates. Only at more humid sites do the more conceptually realistic, hydrologically enhanced LSMs (DV and GW) ameliorate biases in the estimation of root-zone moisture change and evaporative fraction. Although the improved simulations support the hypothesis that groundwater and vegetation processes shape fluxes in transition zones, further assessment of the timing and partitioning of the energy and water cycles indicates improvements to the movement of water within the soil column are needed. Distributed STD and GW underestimate the contribution of baseflow and simulate too-flashy streamflow. This work challenges common practices and assumptions in LSM development and offers researchers more stringent model evaluation methods. I show that, because of equifinality, ad-hoc evaluation using single parameter sets provides insufficient information for choosing among competing parameterizations, for addressing hypotheses under uncertainty, or for guiding model development. Posterior distributions of physically meaningful parameters differ between models and sites, and relationships between parameters themselves change. 'Plug and play' of modules and partial calibration likely introduce error and should be re-examined. Even though LSMs are 'physically based,' model parameters are effective and scale-, site- and model-dependent. Parameters are not functions of soil or vegetation type alone: they likely depend in part on climate and cannot be assumed to be transferable between sites with similar physical characteristics. By helping bridge the gap between the model identification and model development, this research contributes to the continued improvement of our understanding and modeling of environmental processes. / text