Problems in the water sector range from degradation and depletion of water resources as a result of the impacts of land based anthropogenic activities, to the impacts of natural hydrological disasters and floods, while inadequate availability of water is at the core of most water related disputes in arid and semi-arid areas at local, regional, national and international levels. In the past, finding practical solutions for these problems fell neatly within the traditional scope of water resources management, which hinged almost entirely on economic viability of engineering oriented endeavors. However, a new set of management challenges has arisen following the high priority nowadays given to equity in water allocation and the protection of the natural environment above other issues. These new challenges have created a need for devising and adopting suitable management approaches, especially that would take social considerations into account. One of the approaches that provides promise relative to the new directions in dealing with contemporary water issues is integrated water resources management (IWRM). One objective of this study was to critically review the definitions and the fundamental principles of IWRM with the view of determining its applicability in developing countries and highlighting difficulties that may be faced regarding the adoption and implementation of this integrated approach. Swaziland is atypical example ofa developing country that is engulfed by the diverse water resources issues highlighted above and is currently engaged in updating water management legislation. Hence, Swaziland's experiences were used to put in perspective the key points and barriers regarding the adoption and implementation of IWRM. The catchment, the recommended spatial unit of IWRM, poses the first practical barrier, as catchments often cross both political and administrative boundaries, thereby creating the need for many water management problems to be solved across catchments with international security issues, cultural issues, different levels of development and different hydroclimatic regimes. The successful implementation of IWRM depends on effective participation of stakeholders. Lack of information flow between stakeholders of different backgrounds limits informed participation. Therefore, it is necessary to develop tools such as decision support systems (DSSs) that will foster easier multilateral information flow and aid decision making. IWRM requires information which itself should be managed in an integrated manner and be readily accessible. This is not always the case in developing countries with shortage of funds for data collection, manipulation and storage as well as adequately trained and experienced staff With the shortage of sufficiently long and reliable hydrological data for water management, the alternative is to synthesize records through hydrological modelling. Another objective of this study was to evaluate and test the suitability of the ACRU modelling system, a daily time-step agrohydrological model, to simulate catchment level hydrological processes and land use impacts as part of the assessment studies which form an integral part of integrated water resources management. ACRU was set up for the Mbuluzi, a 2958 km2 catchment in Swaziland. The catchment was subdivided into 40 sub catchments, after which the model was used for assessing both the impacts of land use and management changes on runoff yields and available water resources by evaluating present and future sectoral water demands, determining whether river flow from Swaziland into Mozambique meets the quantitative requirements of the international agreement existing between the two countries, and evaluating sediment yield and its spatial and temporal variation as well as its response to potential changes in land management. The physical-conceptual structure of the model, its multi-level adeptness regarding input information requirements, coupled with in-built decision support systems and generic default values make ACRU a suitable modelling tool in developing countries, as it makes it possible to obtain reasonable simulations for a range of levels of input information. Together with the model's multi-purpose nature, the ability of simulating ''what if scenarios", which was utilised in this study, makes it useful in the generation of information for IWRM. Future research needs which were identified include finding means of encouraging effective communication between scientists, water managers and other stakeholders, who may be "lay people". There is a need to conduct research that will lead to equipping ACRU with sediment routing and deposition algorithms, as well as routines to account more explicitly for dam operating rules and ecological issues, which would render its output even more useful in IWRM than the model's present structure allows. / Thesis (M.Sc.)-University of Natal, Pietermaritzburg, 2001.

This thesis describes the development of a decision support tool to be used in the operation of Vanderkloof Dam on the Orange River so that the supply of water to the lower Orange River can be optimised. The decision support tool is based on a hydrodynamic model that was customised to incorporate real time data recorded at several points on the river. By incorporating these data into the model the simulated flows are corrected to the actual flow conditions recorded on the river, thereby generating a best estimate of flow conditions at any given time. This information is then used as the initial conditions for forecast simulations to assess whether the discharge volumes and schedules from the dam satisfy the water demands of downstream users, some of which are 1400km or up to 8 weeks away. The various components of the decision support system, their functionality and their interaction are described. The details regarding the development of these components include: • The hydraulic model of the Orange River downstream of Vanderkloof Dam. The population and calibration of the model are described. • The modification of the code of the hydrodynamic engine so that real time recorded stage and flow data can be incorporated into the model • The development of a graphical user interface to facilitate the exchange of data between the real time network of flow gauging stations on the Orange River and the hydraulic model • The investigation into the effect of including the real time data on the simulated flows • Testing the effectiveness of the decision support system. / Thesis (M.Sc.)-University of Natal, Durban, 2002.

The Klein Berg River catchment is intensely cultivated with orchards, vineyards and wheat, while also ensuring a water supply to the main urban center, Tulbagh, and the two conservation areas (Waterval and Groot Winterhoek). The primary objective of this thesis is to determine channel change over a long and short time period, and to relate these changes to landuse change within the catchment. <br /> <br /> Assessing stability of a selected reach within the catchment was done on a short term basis with the use of erosion pins and cross<br /> profiles, while aerial photographs of over 55 years (acquired during 1942, 1967, 1987 and 1997) which were analysed using Geographic Informations Systems. Rainfall and discharge data, which were available for a period of 49-years were statistically analysed and used to determine trends. Vegetation characteristics were assessed by means of transects within the study reach. The results over the short time period (18 months) indicate noticeable channel change in the form of erosion and deposition within the channel. Bank material composition and riparian invasive alien vegetation play an important role in bank stability. Sand was the dominant grain size of the bank material, and fluvial entrainment occurred during periods of high flow. Woody alien trees prevent the growth of protective ground vegetation, and thus the soil is prone to erosion. Undercutting was also observed with the invasive woody trees, resulting in treefall. Debris dams were also common in the channel and depending on their position in the channel, either cause or prevent bank erosion. Landuse change over the 55-year period illustrated its effects on channel stability. Shrublands within the catchment has been replaced with invasive alien vegetation along the riparian zone, while shrublands along the Obiekwa Mountains, were replaced with cultivated lands. The patterns (shape and size) of lateral and point bars within the study area changed significantly within the 55-year period, which indicates a change in the discharge and sediment dynamics within the catchment. The change in sediment dynamics may be due to agricultural activities and urbanization. The increased trend in rainfall, especially during the winter season within the catchment is also an important catchment control. The study has revealed the integrated nature of variables within the catchment. It is thus recommended that a holistic and integrated approach at a catchment scale is required in the assessment of channel change of a river.

Our understanding of subsurface flow depends on assumptions of how event characteristics and spatial scale affect the relationships between subsurface water velocity, discharge, water table dynamics, and runoff response. In this thesis, three chapters explore some of these patterns for a hillslope and small watershed in coastal British Columbia. In the first chapter, tracers were applied under natural and steady state conditions to determine the relationship between lateral tracer velocities and various hillslope and event characteristics; such as hillslope subsurface flow, rainfall intensity, water table level, hillslope length, and antecedent condition. The results showed that preferential flow made up a large percentage of the subsurface flow from the gauged hillslope. Flow velocities as measured by tracers were affected by slope length and boundary conditions. The flow velocity was most closely related to the rainfall intensity, and changes in flow velocity were large compared to the changes in the water table. In the second chapter, the preferential flow features that transmitted water during steady state were investigated by staining the soil with a food dye solution and excavating the soil. These data were used to explore the link between the topographical factors (slope and contributing area), the network of preferential features and soil properties. The contributing area appeared to be an indicator of the size of the preferential features and their connectivity. In the final manuscript chapter, water table level and stream discharge measurements were used to determine if areas within a watershed with runoff dominated by preferential flow could be grouped based on the observable physical information such as slope, contributing area, distance to stream, and vegetation. Preferential flow made the water table responses dynamic and thus, distinct zones could not be identified. Models of the water table – runoff were not able to predict the water table response for other sites with similar physical characteristics. Even though there was high variability in the results, the patterns and relationships revealed in this thesis conform to existing conceptual models of hillslope subsurface preferential flow. These patterns and relationships may be useful in developing or validating numerical models.

This study presents a new Lagrangian model for predicting dynamics and transport in rivers and shallow water flows. A hydrostatic model is developed for the prediction of rivers and floodplain flow and lateral interactions between them. The model is extended to the Boussinesq weakly non-linear, non-hydrostatic model for the simulation of solitary waves and undular bores. A model for advection-diffusion transport of tracers in open channel flow is also presented. The simulation results are compared against an analytical solution and published laboratory data, field data and theoretical results. It is demonstrated that the Lagrangian moving grid eliminates numerical diffusion and oscillations; the model is dynamically adaptive, providing higher resolution under the wave by compressing the parcels (grid). It also allows flow over dry beds and moving boundaries to be handled efficiently. The hydrostatic model results have shown that the model accurately simulates wave propagation and non-linear steepening until wave breaking. The model is successfully applied to simulate flow and lateral interactions in a compound channel and flood wave movement in a natural river. The non-hydrostatic model has successfully reproduced the general features of solitary waves such as the balance between non-linearity and wave dispersion and non-linear interactions of two solitary waves by phase-shift. Also, the model successfully reproduced undular bores (high frequency short waves) from a long wave and the predicted maximum height of the leading wave agreed very well with the published results. It is shown that the simple second order accurate Lagrangian scheme efficiently simulates dispersive waves without any numerical diffusion. Lagrangian modeling of advection-diffusion transport of Gaussian tracer distributions, top hat tracer distributions and steep fronts (step function) in steady, uniform flow has provided exact results and has shown that the scheme allows the use of a large time step without any numerical diffusion and oscillations, including for the advection of steep fronts. The scheme can handle large Courant numbers (results are presented for Cr = 0 to 20) and the entire range of grid Peclet numbers from zero to infinity. The model is successfully applied to tracer transport due to flow induced by simple waves, solitary waves and undular bores

Thesis (M.Sc. Hons.) -- University of Western Sydney, Hawkesbury, 1999. / Thesis submitted for the degree of Master of Science (Honours). Includes bibliographical references (leaves 112-117).

Thesis (M.S.) -- University of Montana, 2008. / Title from author supplied metadata. Description based on contents viewed on June 26, 2009. Includes bibliographical references.

Neste estudo, trata-se de avaliar fontes de precipitação baseadas em estimativas por satélite e técnicas de assimilação de dados para previsão de vazões por meio do modelo hidrológico distribuído MGB-IPH. A insuficiente representatividade espacial dos pluviômetros torna difícil a correta representação dos campos de precipitações. Por outro lado, as estimativas de satélite, embora forneçam uma descrição espacial mais consistente, são potencialmente menos acuradas. Sendo assim, procura-se utilizar métodos que combinem os dados de ambas as fontes para gerar um campo de precipitação mais consistente. Neste trabalho, implementaramse dois modelos de combinação pluviômetro-satélite, CHUVSAT e MERGEHQ, através de uma metodologia de interpolação. Por outro lado, as técnicas de assimilação de dados acoplados aos modelos de previsão hidrológica são também de interesse neste estudo, pois minimizam as incertezas associadas ao processo de calibração de parâmetros, às variáveis de estado e dados de entrada do modelo hidrológico. Para esse propósito, escolheu-se a bacia do rio Tocantins e implementou-se particularmente a técnica de assimilação de dados de tipo sequencial chamado na literatura de filtro de partículas, conjuntamente com o método de filtro Kalman por conjunto e o método de assimilação AsMGB atualmente acoplado ao modelo MGB-IPH. O estudo mostra que a precipitação combinada utilizada como dado de entrada na simulação hidrológica permitiu reproduzir adequadamente os hidrogramas observados para o período de calibração e validação. Já para o caso das vazões resultantes, durante a etapa de previsão, a precipitação combinada mostrou-se com melhor desempenho em termos estatísticos que os métodos sem combinar, sobretudo após 24 horas de antecedência. Finalmente, a técnica de assimilação de dados por filtro de partículas conseguiu absorver os erros da simulação melhorando as medidas de desempenho na etapa de previsão sendo superior ao modelo de previsão sem considerar assimilação. / The objective of this study is to evaluate precipitation sources based on satellite estimates and data assimilation techniques for prediction of flows by means of the distributed hydrological model MGB-IPH. The insufficient spatial availability of rain gauges makes difficult to represent precipitation fields appropriately. In contrast, satellite estimates, although providing a more consistent spatial description, are potentially less accurate. Thus, raingauge satellite merging methods that combine data from both sources to generate a more consistent precipitation field are used herein. For this purpose, two models namely CHUVSAT and MERGEHQ were implemented using an interpolation technique. On the other hand, data assimilation techniques coupled with hydrological forecasting models are also assessed in this study. The assimilation process minimizes the uncertainties associated with the parameter calibration procedure, variable state and hydrological input data. In this manner, the sequential data assimilation technique namely particle filter in conjunction with the Kalman filter method and the assimilation method AsMGB, which is currently coupled to the MGBIPH model, were implemented and applied to the Tocantis basin. The obtained results showed that the combined precipitation used as input data in the hydrological simulation allowed reproducing adequately the observed hydrograms for the periods of calibration and validation. In the case of the resulting flows during the forecast stage, the merging precipitation was shown to perform better in statistical terms than the uncombined methods, especially after 24 hours in advance. Finally, the data assimilation technique by particle filter was able to absorb all simulation errors, improving the performance measures in the forecasting stage, thus being superior to the forecasting model without considering assimilation.

Diversas atividades humanas são fortemente dependentes do clima e da sua variabilidade, especialmente aquelas relacionadas ao uso da água. A operação integrada de reservatórios com múltiplos usos requer uma série de decisões que definem quanta água deve ser alocada, ao longo do tempo para cada um dos usos, e quais os volumes dos reservatórios a serem mantidos. O conhecimento antecipado das condições climáticas resulta de vital importância para os operadores de reservatórios, pois o insumo dos reservatórios é a vazão dos rios, que por sua vez é dependente de condições atmosféricas e hidrológicas em diferentes escalas de tempo e espaço. A pesquisa trata sobre três importantes elementos de subsídio à tomada de decisão na operação de reservatórios baseada na previsão de variáveis hidrológicas: (a) as previsões de vazão de curto prazo; (b) as previsões de precipitação de longo prazo e (c) as medidas de desempenho das previsões. O reservatório de Furnas, localizado na bacia do Rio Grande, em Minas Gerais, foi selecionado como estudo de caso devido, principalmente, à disponibilidade de previsões quantitativas de chuva e pela importância desse reservatório na região analisada. A previsão de curto prazo de vazão com base na precipitação foi estimada com um modelo empírico (rede neural artificial) e a previsão de precipitação foi obtida pelo modelo regional ETA. Uma metodologia de treinamento e validação da rede neural artificial foi desenvolvida utilizando previsões perfeitas de chuva (considerando a chuva observada como previsão) e utilizando o maior número de dados disponíveis, favorecendo a representatividade dos resultados obtidos. A metodologia empírica alcançou os desempenhos obtidos com um modelo hidrológico conceitual, mostrando-se menos sensitiva aos erros na previsão quantitativa de precipitação nessa bacia. Os resultados obtidos mostraram que as previsões de vazão utilizando modelos empíricos e conceituais e incorporando previsões quantitativas de precipitação são melhores que a metodologia utilizada pelo ONS no local de estudo. A redução dos erros de previsão relativos à metodologia empregada pelo ONS foi em torno de 20% quando usadas previsões quantitativas de precipitação definidas pelo modelo regional ETA e superiores a 50% quando usadas previsões perfeitas de precipitação. Embora essas últimas previsões nunca possam ser obtidas na prática, os resultados sugerem o quanto o incremento do desempenho das previsões quantitativas de chuva melhoraria as previsões de vazão. A previsão de precipitação de longo prazo para a bacia analisada foi também estimada com um modelo empírico de redes neurais artificiais e utilizando índices climáticos como variáveis de entrada. Nesse sentido, foram estimadas previsões de precipitação acumulada no período mais chuvoso (DJF) utilizando índices climáticos associados a fenômenos climáticos, como o El Niño - Oscilação Sul e a Oscilação Decadal do Pacífico, e a modos de variabilidade climática, como a Oscilação do Atlântico Norte e o Modo Anular do Hemisfério Sul. Apesar das redes neurais artificiais terem sido aplicadas em diversos problemas relacionados a hidrometeorologia, a aplicação dessas técnicas na previsão de precipitação de longo prazo é ainda rara. Os resultados obtidos nesse trabalho mostraram que consideráveis reduções dos erros da previsão relativos ao uso apenas da média climatológica como previsão podem ser obtidos com a metodologia utilizada. Foram obtidas reduções dos erros de, no mínimo 50%, e chegando até um valor próximo a 75% nos diferentes testes efetuados no estudo de caso. Uma medida de desempenho da previsão foi desenvolvida baseada no uso de tabelas de contingência e levando em conta a utilidade da previsão. Essa medida de desempenho foi calculada com base nos resultados do uso das previsões por um modelo de operação de reservatório, e não apenas na comparação de vazões previstas e observadas. Nos testes realizados durante essa pesquisa, ficou evidente que não existe uma relação unívoca entre qualidade das previsões e utilidade das previsões. No entanto, em função de comportamentos particulares das previsões, tendências foram encontradas, como por exemplo nos modelos cuja previsão apresenta apenas defasagem. Nesses modelos, a utilidade das previsões tende a crescer na medida que a qualidade das mesmas aumenta. Por fim, uma das grandes virtudes da medida de desempenho desenvolvida nesse trabalho foi sua capacidade de distinguir o desempenho de modelos que apresentaram a mesma qualidade. / Several human activities are strongly dependent on climate and its variability, especially those related to water use. The operation of multi-purpose reservoirs systems defines how much water should be allocated and the reservoir storage volumes to be maintained, over time. Knowing in advance the weather conditions helps the decision making process, as the major inputs to reservoirs are the streamflows, which are dependent on atmospheric and hydrological conditions at different time-space scales. This research deals with three important aspects towards the decision making process of multi-purpose reservoir operation based on forecast of hydrological variables: (a) short-term streamflow forecast, (b) long-range precipitation forecast and (c) performance measures. The Furnas reservoir on the Rio Grande basin was selected as the case study, primarily because of the availability of quantitative precipitation forecasts from the Brazilian Center for Weather Prediction and Climate Studies and due to its importance in the Brazilian hydropower generation system. Short-term streamflow forecasts were estimated by an empirical model (artificial neural network – ANN) and incorporating forecast of rainfall. Quantitative precipitation forecasts (QPFs), defined by the ETA regional model, were used as inputs to the ANN models. A methodology for training and validating the ANN models was developed using perfect precipitation forecasts (i.e., using the observed precipitation as if it was a forecast) and considering the largest number of available samples, in order to increase the representativeness of the results. The empirical methodology achieved the performance obtained with a conceptual hydrological model and seemed to be less sensitive to precipitation forecast error relative to the conceptual hydrological model. Although limited to one reservoir, the results obtained show that streamflow forecasting using empirical and conceptual models and incorporating QPFs performs better than the methodology used by ONS. Reduction in the forecast errors relative to the ONS method was about 20% when using QPFs provided by ETA model, and greater than 50% when using the perfect precipitation forecast. Although the latter can never be achieved in practice, these results suggest that improving QPFs would lead to better forecasts of reservoir inflows. Long-range precipitation forecast was also estimated by an empirical model based on artificial neural networks and using climate indices as input variables. The output variable is the summer (DJF) precipitation over the Furnas watershed. It was estimated using climate indices related to climatic phenomena such as El Niño - Southern Oscillation and the Pacific Decadal Oscillation and modes of climate variability, such as the North Atlantic Oscillation and the Southern Annular Mode. Despite of ANN has been applied in several problems of hydrometeorological areas, the application of such technique for long-range precipitation forecast is still rare. The results obtained demonstrate how the methodology for seasonal precipitation forecast based on ANN can be particularly helpful, with the use of available time series of climate indices. Reductions in the forecast errors achieved by using only the climatological mean as forecast were considerable, being at least of 50% and reaching values close to 75% in several tests. A performance measure based on the use of contingency tables was developed taking into account the utility of the forecast. This performance measure was calculated based on the results of the use of the forecasts by a reservoir operation model, and not only by comparing streamflow observed and forecast. The performed tests show that there is no unequivocal relationship between quality and utility of the forecasts. However, when the forecast has a particular behavior, trends were found in the relationship between utility and quality of the forecast, such as models that generate streamflow forecast with lags in comparison to the observed values. In these models, the utility of the forecasts tends to enhance as the quality increases. Finally, the ability to distinguish the performance of forecast models having similar quality was one of the main merits of the performance measure developed in this research.

Diversas atividades humanas são fortemente dependentes do clima e da sua variabilidade, especialmente aquelas relacionadas ao uso da água. A operação integrada de reservatórios com múltiplos usos requer uma série de decisões que definem quanta água deve ser alocada, ao longo do tempo para cada um dos usos, e quais os volumes dos reservatórios a serem mantidos. O conhecimento antecipado das condições climáticas resulta de vital importância para os operadores de reservatórios, pois o insumo dos reservatórios é a vazão dos rios, que por sua vez é dependente de condições atmosféricas e hidrológicas em diferentes escalas de tempo e espaço. A pesquisa trata sobre três importantes elementos de subsídio à tomada de decisão na operação de reservatórios baseada na previsão de variáveis hidrológicas: (a) as previsões de vazão de curto prazo; (b) as previsões de precipitação de longo prazo e (c) as medidas de desempenho das previsões. O reservatório de Furnas, localizado na bacia do Rio Grande, em Minas Gerais, foi selecionado como estudo de caso devido, principalmente, à disponibilidade de previsões quantitativas de chuva e pela importância desse reservatório na região analisada. A previsão de curto prazo de vazão com base na precipitação foi estimada com um modelo empírico (rede neural artificial) e a previsão de precipitação foi obtida pelo modelo regional ETA. Uma metodologia de treinamento e validação da rede neural artificial foi desenvolvida utilizando previsões perfeitas de chuva (considerando a chuva observada como previsão) e utilizando o maior número de dados disponíveis, favorecendo a representatividade dos resultados obtidos. A metodologia empírica alcançou os desempenhos obtidos com um modelo hidrológico conceitual, mostrando-se menos sensitiva aos erros na previsão quantitativa de precipitação nessa bacia. Os resultados obtidos mostraram que as previsões de vazão utilizando modelos empíricos e conceituais e incorporando previsões quantitativas de precipitação são melhores que a metodologia utilizada pelo ONS no local de estudo. A redução dos erros de previsão relativos à metodologia empregada pelo ONS foi em torno de 20% quando usadas previsões quantitativas de precipitação definidas pelo modelo regional ETA e superiores a 50% quando usadas previsões perfeitas de precipitação. Embora essas últimas previsões nunca possam ser obtidas na prática, os resultados sugerem o quanto o incremento do desempenho das previsões quantitativas de chuva melhoraria as previsões de vazão. A previsão de precipitação de longo prazo para a bacia analisada foi também estimada com um modelo empírico de redes neurais artificiais e utilizando índices climáticos como variáveis de entrada. Nesse sentido, foram estimadas previsões de precipitação acumulada no período mais chuvoso (DJF) utilizando índices climáticos associados a fenômenos climáticos, como o El Niño - Oscilação Sul e a Oscilação Decadal do Pacífico, e a modos de variabilidade climática, como a Oscilação do Atlântico Norte e o Modo Anular do Hemisfério Sul. Apesar das redes neurais artificiais terem sido aplicadas em diversos problemas relacionados a hidrometeorologia, a aplicação dessas técnicas na previsão de precipitação de longo prazo é ainda rara. Os resultados obtidos nesse trabalho mostraram que consideráveis reduções dos erros da previsão relativos ao uso apenas da média climatológica como previsão podem ser obtidos com a metodologia utilizada. Foram obtidas reduções dos erros de, no mínimo 50%, e chegando até um valor próximo a 75% nos diferentes testes efetuados no estudo de caso. Uma medida de desempenho da previsão foi desenvolvida baseada no uso de tabelas de contingência e levando em conta a utilidade da previsão. Essa medida de desempenho foi calculada com base nos resultados do uso das previsões por um modelo de operação de reservatório, e não apenas na comparação de vazões previstas e observadas. Nos testes realizados durante essa pesquisa, ficou evidente que não existe uma relação unívoca entre qualidade das previsões e utilidade das previsões. No entanto, em função de comportamentos particulares das previsões, tendências foram encontradas, como por exemplo nos modelos cuja previsão apresenta apenas defasagem. Nesses modelos, a utilidade das previsões tende a crescer na medida que a qualidade das mesmas aumenta. Por fim, uma das grandes virtudes da medida de desempenho desenvolvida nesse trabalho foi sua capacidade de distinguir o desempenho de modelos que apresentaram a mesma qualidade. / Several human activities are strongly dependent on climate and its variability, especially those related to water use. The operation of multi-purpose reservoirs systems defines how much water should be allocated and the reservoir storage volumes to be maintained, over time. Knowing in advance the weather conditions helps the decision making process, as the major inputs to reservoirs are the streamflows, which are dependent on atmospheric and hydrological conditions at different time-space scales. This research deals with three important aspects towards the decision making process of multi-purpose reservoir operation based on forecast of hydrological variables: (a) short-term streamflow forecast, (b) long-range precipitation forecast and (c) performance measures. The Furnas reservoir on the Rio Grande basin was selected as the case study, primarily because of the availability of quantitative precipitation forecasts from the Brazilian Center for Weather Prediction and Climate Studies and due to its importance in the Brazilian hydropower generation system. Short-term streamflow forecasts were estimated by an empirical model (artificial neural network – ANN) and incorporating forecast of rainfall. Quantitative precipitation forecasts (QPFs), defined by the ETA regional model, were used as inputs to the ANN models. A methodology for training and validating the ANN models was developed using perfect precipitation forecasts (i.e., using the observed precipitation as if it was a forecast) and considering the largest number of available samples, in order to increase the representativeness of the results. The empirical methodology achieved the performance obtained with a conceptual hydrological model and seemed to be less sensitive to precipitation forecast error relative to the conceptual hydrological model. Although limited to one reservoir, the results obtained show that streamflow forecasting using empirical and conceptual models and incorporating QPFs performs better than the methodology used by ONS. Reduction in the forecast errors relative to the ONS method was about 20% when using QPFs provided by ETA model, and greater than 50% when using the perfect precipitation forecast. Although the latter can never be achieved in practice, these results suggest that improving QPFs would lead to better forecasts of reservoir inflows. Long-range precipitation forecast was also estimated by an empirical model based on artificial neural networks and using climate indices as input variables. The output variable is the summer (DJF) precipitation over the Furnas watershed. It was estimated using climate indices related to climatic phenomena such as El Niño - Southern Oscillation and the Pacific Decadal Oscillation and modes of climate variability, such as the North Atlantic Oscillation and the Southern Annular Mode. Despite of ANN has been applied in several problems of hydrometeorological areas, the application of such technique for long-range precipitation forecast is still rare. The results obtained demonstrate how the methodology for seasonal precipitation forecast based on ANN can be particularly helpful, with the use of available time series of climate indices. Reductions in the forecast errors achieved by using only the climatological mean as forecast were considerable, being at least of 50% and reaching values close to 75% in several tests. A performance measure based on the use of contingency tables was developed taking into account the utility of the forecast. This performance measure was calculated based on the results of the use of the forecasts by a reservoir operation model, and not only by comparing streamflow observed and forecast. The performed tests show that there is no unequivocal relationship between quality and utility of the forecasts. However, when the forecast has a particular behavior, trends were found in the relationship between utility and quality of the forecast, such as models that generate streamflow forecast with lags in comparison to the observed values. In these models, the utility of the forecasts tends to enhance as the quality increases. Finally, the ability to distinguish the performance of forecast models having similar quality was one of the main merits of the performance measure developed in this research.