The tropical Andes are one of the regions where climate change has been most evident. This is consistent with the notion that tropical high-elevation mountains will be more affected by warming. One of the main impacts of this warming is the retreat of glaciers; a process that may affect the availability of water for human consumption, irrigation and power production. This study presents results related to the most important changes in climate that might be expected in tropical South America, but especially in the tropical Andes, at the end of the 21st century. Results are provided by the comparison of two Regional Climate Model simulations based on the Hadley Center Regional Climate Modeling System, PRECIS. A medium-high CO2 emission scenario simulation for the period 2071-2100 (A2) is compared to a base-line mean climate state simulation for the 1961-1990 period. In addition, some results using a low-medium CO2 emission scenario (B2) are also presented for comparison. Results show a clear warming trend over South America reaching up to 8º C in northeastern South America. In this same place the largest decrease in precipitation and cloud cover are found. Along the Andes warming reaches up to 7º C in Cordillera Blanca in the A2 scenario and precipitation presents a mixed pattern of increases and decreases across the Cordillera. Warming is expected to be larger at higher elevations and significant changes in temperature variability are expected along both slopes of the Andes based on the A2 scenario. In addition both scenarios (B2 and A2) show an amplification of free tropospheric warming at higher altitudes. Finally, pressure-longitude cross-sections of zonal winds and vertical velocities at the latitudes of the Altiplano and the Cordillera Blanca show weakened mid- and upper tropospheric easterlies and strengthened westerlies in the A2 scenario. This change in the atmospheric circulation is conducive to a decrease in precipitation in those areas, and consequently may negatively impact glacier mass balance. In summary the obtained results reveal that anthropogenic climate change, as predicted with the A2 scenario, may constitute a serious threat to the survival of many tropical glaciers along the Andes Cordillera.

Over the years, global climate modelling has advanced, aiming for realistic and precise models by increasing their complexity. An integral component of climate models, the physics parameterizations, are a major limitation, but are required due to limited computational power. Grid adaptivity is an avenue that is being explored to mitigate these challenges, but comes with its own difficulties. For example, the question of whether the physics should be ``scale-aware’’, by adjusting according to the resolution and the fact that parameterizations are optimized for specific grid ranges. To research these challenges, test cases that work in both the adaptive and non-adaptive cases are required. This thesis concentrates on physics parameterizations of Atmospheric Global Climate Models (AGCMs) presenting the current hierarchy of idealized physics parameterizations found in the literature. It focuses on and provides a comprehensive explanation of a simplified dry physics model for AGCMs, exploring where it is situated in the current hierarchy and its steady states in the uncoupled case. A coupling of the physics model to the adaptive dynamical core wavetrisk is explained and explored. This includes characterizing the results in the non-adaptive case for time convergence, grid convergence, and the effects of the soil, while also benchmarking the climatology of the coupling. The simplified dry physics model introduces another level of complexity in the current dry physics hierarchy and is stable in the coupled and uncoupled cases. A decreasing temperature trend with height is observed, however warmer surface temperatures and cooler upper atmosphere temperatures, than that of Earth, are produced in the steady states. Additionally a linear rate of convergence in space is noted and an improvement in parallel efficiency with resolution is required. Overall these results can be used as a benchmark for future coupling in the adaptive case. / Thesis / Master of Science (MSc)

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Climate change is impacting the intensity, duration, and frequency of climatic events. With climate change comes a multitude of adverse conditions, including extreme heat events, changes in disease patterns, and increased likelihood and frequency of natural disasters, including in places previously not exposed to such conditions. Human health has foundations in the environment; therefore, these adverse climatic conditions are directly linked to human health. Rural communities in Appalachia are likely to experience negative consequences of climate change more severely due to unique geomorphology and sociopolitical realities of the region. Non-governmental organizations (NGOs) throughout the Appalachian region are currently working to build resilience and prepare for potential adverse effects from climate change. To aid in this process, projections of future climate scenarios are needed to understand possible situations and adequately prepare. In partnership with Ohio University and West Virginia University, this study aims to characterize potential future climatic scenarios from publicly-available global climate models (GCMs) and prepare information to share with Appalachian communities. Climate model information for this analysis was obtained from NASA's Coupled Model Intercomparison Project (CMIP6). All code for data processing and analysis was prepared using the open-source R programming language to support reproducibility. To confirm that models can accurately simulate Appalachian climatic conditions, CMIP6 hindcast simulations for precipitation and maximum temperature were compared to observed weather records from NOAA. Climate models over and underestimated average precipitation values depending on location, while models consistently underestimated extreme precipitation values, simulated by total five-day precipitation. For temperature, climate models consistently underestimated average and extreme high temperature indicators. For Appalachian region projections, three towns of interest (one for each state involved in the study: Virginia, West Virginia, and Ohio) were selected based on current community resilience efforts. In these locations, mid-century (2040 – 2064) and end-of-century (2075 – 2099) projections for precipitation and temperature were summarized under a low emissions scenario and a high emissions scenario. Increases in precipitation and temperature were observed under average and extreme scenarios; these increases were noticeably more extreme under higher emissions scenarios. These trends are consistent with other studies and climate science consensus. When compared to hindcast values, observed average precipitation values were overestimated and underestimated, while observed extreme precipitation indices, average temperatures, and heat wave indices were underestimated by GCMs. Context with observed data is important to understanding model accuracy for the Appalachian region. GCMs are a useful tool to project potential future climate scenarios at specific locations in the Appalachian region, though model data is best used to communicate general trends rather than as inputs for other physical models. / Master of Science / Climate change is driving previously unseen changes in many aspects of the environment. Among these aspects, and of particular concern, are increased precipitation and increased high temperatures, which have direct negative outcomes on human health. Climate change can impact human health in a variety of ways, such as increasing instances of heat-related illnesses like heatstroke, changing insect-carried diseases patterns (i.e. Lyme disease, malaria), worsening preexisting conditions like asthma, and increasing the likelihood of natural disasters like flooding. Climate change also impacts mental health, especially increasing instances of anxiety and post-traumatic stress disorder from disasters. Rural communities like Appalachia are more likely to experience severe negative outcomes due to lack of resources, remote location, and economies historically based on resource extraction. Appalachia specifically also faces unique challenges with flooding, as many towns are situated in valleys with streams or rivers running through the center of town. To address and prepare for possible climate change outcomes, community-based planning is required to build resiliency. Throughout many areas, but specifically in Appalachia, many community-based organizations are already working to strengthen their communities by providing stable housing, addressing flooding, and preparing emergency response teams. To aid in these efforts, information about potential future climate is beneficial to these organizations to understand and prepare for potential conditions. This study aims to use publicly-available climate models to generate information about possible future climate conditions to be shared with community organizations. Additionally, this project's datasets and procedures are publicly available, so this analysis can be performed by communities anywhere in the world given they have adequate computing power. To check that models are a good indicator of previous climate conditions, and therefore would be useful for future projections, historic projected climate model outputs were compared to observed weather data. After confirming that the models used were fairly consistent with observed data, projected values for midcentury (2040 – 2064) and end-of-century (2075 – 2099) were gathered for Appalachian towns with interested community organizations. Projected values show increases in high temperatures and precipitation throughout the Appalachian region, including in short-term event scenarios, which is consistent with other climate science. Higher emissions scenarios result in greater increases in average and extreme temperature and precipitation values. Climate models can be a useful tool in understanding potential general climatic trends for a specific location and can support climate science communication.

A mesoscale evaluation is performed to determine the impacts of climate change on terrestrial hydrological components and the Net Irrigation Water Requirement (NIWR) throughout the Chesapeake Bay watershed in the mid-Atlantic region of the United States. The Noah-MP land surface model is calibrated and evaluated against the observed datasets of United States Geological Survey (USGS) streamflow gages, actual evapotranspiration from USGS Simplified Surface Energy Balance (SSEBop) Model and soil moisture from Soil Analysis Climate Network (SCAN). Six best performing Global Climate Models (GCM) based on Multivariate Adaptive Constructed Analogs (MACA) scheme are included for two future scenarios (RCP 4.5 and RCP 8.5), to assess the change in water balance components, change in NIWR for two dominant crops (corn and soybeans) and uncertainty in GCM projections. Using these long-term simulations, the flood inundation maps are developed for future scenarios along the Susquehanna River including the City of Harrisburg in Pennsylvania. The HEC-RAS 2D model is calibrated and evaluated against the high-water marks from major historical flood events and the stage-discharge relationship of the available USGS streamgages. Finally, the impacts of climate change are assessed on flood inundation depth and extent by comparing a 30-yr and 100-yr flood event based on the historical and future (scenario-based) peak discharge estimates at the USGS streamgages. Interestingly, flood inundation extent and severity predicted by the model along the Susquehanna River near Harrisburg is expected to rise in the future climate scenarios due to the greater frequency of extreme events increasing total precipitation. / Master of Science / Climate change is inevitable due to increased greenhouse gas emissions, with impacts varying in space and time significantly throughout the globe. The impacts are strongly driven by the change in precipitation and temperature which affect the control of the movement of water on the surface of the Earth. These changes in the water cycle require an understanding of hydrological components like streamflow, soil moisture, and evapotranspiration. Development of long-term climate models and computational hydrological models (based on mathematical equations and governed by laws of physics) has helped us in understanding this climate variability in space and time. This study performs a long-term simulation using the datasets from six different climate models to analyze the change in terrestrial hydrological components for the entire Chesapeake Bay watershed in the mid-Atlantic region of the United States. The simulations provide an understanding of the interplay between various land surface processes due to climate change and can help determine future water availability and consumption. To illustrate the usefulness of such long-term simulations, the crop water requirement is quantified for the dominant crops in Chesapeake Bay watershed to project water availability and support the development of mitigation strategies. Flood inundation maps are also developed for a section of Susquehanna River near the City of Harrisburg in south-central Pennsylvania using the streamflow from long-term simulations. The flood inundation depth and extent for major flood events such as Tropical Storm Agnes (1972) and Tropical Storm Lee (2011) are compared along the Susquehanna River, which can aid in managing flood operations, reduce the future flood damages and prioritize the mitigation efforts for endangered communities near the City of Harrisburg.

There is ample growth in scientific evidence about climate change. Since, hydrometeorological processes are sensitive to climate variability and changes, ascertaining the linkages and feedbacks between the climate and the hydrometeorological processes becomes critical for environmental quality, economic development, social well-being etc. As the river basin integrates some of the important systems like ecological and socio-economic systems, the knowledge of plausible implications of climate change on hydrometeorology of a river basin will not only increase the awareness of how the hydrological systems may change over the coming century, but also prepare us for adapting to the impacts of climate changes on water resources for sustainable management and development. In general, quantitative climate impact studies are based on several meteorological variables and possible future climate scenarios. Among the meteorological variables, sic “cardinal” variables are identified as the most commonly used in impact studies (IPCC, 2001). These are maximum and minimum temperatures, precipitation, solar radiation, relative humidity and wind speed. The climate scenarios refer to plausible future climates, which have been constructed for explicit use for investigating the potential consequences of anthropogenic climate alterations, in addition to the natural climate variability. Among the climate scenarios adapted in impact assessments, General circulation model(GCM) projections based on marker scenarios given in Intergovernmental Panel on Climate Change’s (IPCC’s) Special Report on Emissions Scenarios(SRES) have become the standard scenarios. The GCMs are run at coarse resolutions and therefore the output climate variables for the various scenarios of these models cannot be used directly for impact assessment on a local(river basin)scale. Hence in the past, several methodologies such as downscaling and disaggregation have been developed to transfer information of atmospheric variables from the GCM scale to that of surface meteorological variables at local scale. The most commonly used downscaling approaches are based on transfer functions to represent the statistical relationships between the large scale atmospheric variables(predictors) and the local surface variables(predictands). Recently Support vector machine (SVM) is proposed, and is theoretically proved to have advantages over other techniques in use such as transfer functions. The SVM implements the structural risk minimization principle, which guarantees the global optimum solution. Further, for SVMs, the learning algorithm automatically decides the model architecture. These advantages make SVM a plausible choice for use in downscaling hydrometeorological variables. The literature review on use of transfer function for downscaling revealed that though a diverse range of transfer functions has been adopted for downscaling, only a few studies have evaluated the sensitivity of such downscaling models. Further, no studies have so far been carried out in India for downscaling hydrometeorological variables to a river basin scale, nor there was any prior work aimed at downscaling CGCM3 simulations to these variables at river basin scale for various IPCC SRES emission scenarios. The research presented in the thesis is motivated to assess the impact of climate change on streamflow at river basin scale for the various IPCC SRES scenarios (A1B, A2, B1 and COMMIT), by integrating implications of climate change on all the six cardinal variables. The catchment of Malaprabha river (upstream of Malaprabha reservoir) in India is chosen as the study area to demonstrate the effectiveness of the developed models, as it is considered to be a climatically sensitive region, because though the river originates in a region having high rainfall it feeds arid and semi-arid regions downstream. The data of the National Centers for Environmental Prediction (NCEP), the third generation Canadian Global Climate Model (CGCM3) of the Canadian Center for Climate Modeling and Analysis (CCCma), observed hydrometeorological variables, Digital Elevation model (DEM), land use/land cover map, and soil map prepared based on PAN and LISS III merged, satellite images are considered for use in the developed models. The thesis is broadly divided into four parts. The first part comprises of general introduction, data, techniques and tools used. The second part describes the process of assessment of the implications of climate change on monthly values of each of the six cardinal variables in the study region using SVM downscaling models and k-nearest neighbor (k-NN) disaggregation technique. Further, the sensitivity of the SVM downscaling models to the choice of predictors, predictand, calibration period, season and location is evaluated. The third part describes the impact assessment of climate change on streamflow in the study region using the SWAT hydrologic model, and SVM downscaling models. The fourth part presents summary of the work presented in the thesis, conclusions draws, and the scope for future research. The development of SVM downscaling model begins with the selection of probable predictors (large scale atmospheric variables). For this purpose, the cross-correlations are computed between the probable predictor variables in NCEP and GCM data sets, and the probable predictor variables in NCEP data set and the predictand. A pool of potential predictors is then stratified (which is optional and variable dependant) based on season and or location by specifying threshold values for the computed cross-correlations. The data on potential predictors are first standardized for a baseline period to reduce systemic bias (if any) in the mean and variance of predictors in GCM data, relative to those of the same in NCEP reanalysis data. The standardized NCEP predictor variables are then processed using principal component analysis (PCA) to extract principal components (PCs) which are orthogonal and which preserve more than 98% of the variance originally present in them. A feature vector is formed for each month using the PCs. The feature vector forms the input to the SVM model, and the contemporaneous value of predictand is its output. Finally, the downscaling model is calibrated to capture the relationship between NCEP data on potential predictors (i.e feature vectors) and the predictand. Grid search procedure is used to find the optimum range for each of the parameters. Subsequently, the optimum values of parameters are obtained from the selected ranges, using the stochastic search technique of genetic algorithm. The SVM model is subsequently validated, and then used to obtain projections of predictand for simulations of CGCM3. Results show that precipitation, maximum and minimum temperature, relative humidity and cloud cover are projected to increase in future for A1B, A2 and B1 scenarios, whereas no trend is discerned with theCOMMIT. The projected increase in predictands is high for A2 scenario and is least for B1 scenario. The wind speed is not projected to change in future for the study region for all the aforementioned scenarios. The solar radiation is projected to decrease in future for A1B, A2 and B1 scenarios, whereas no trend is discerned with the COMMIT. To assess the monthly streamflow responses to climate change, two methodologies are considered in this study namely (i) downscaling and disaggregating the meteorological variables for use as inputs in SWAT and (ii) directly downscaling streamflow using SVM. SWAT is a physically based, distributed, continuous time hydrological model that operates on a daily time scale. The hydrometeorologic variables obtained using SVM downscaling models are disaggregated to daily scale by using k-nearest neighbor method developed in this study. The other inputs to SWAT are DEM, land use/land cover map, soil map, which are considered to be the same for the present and future scenarios. The SWAT model has projected an increase in future streamflows for A1B, A2 andB1 scenarios, whereas no trend is discerned with the COMMIT. The monthly projections of streamflow at river basin scale are also obtained using two SVM based downscaling models. The first SVM model (called one-stage SVM model) considered feature vectors prepared based on monthly values of large scale atmospheric variables as inputs, whereas the second SVM model (called two-stage SVM model) considered feature vectors prepared from the monthly projections of cardinal variables as inputs. The trend in streamflows projected using two-stage SVM model is found to be similar to that projected by SWAT for each of the scenarios considered. The streamflow is not projected to change for any of the scenarios considered with the one-stage SVM downscaling model. The relative performance of the SWAT and the two SVM downscaling models in simulating observed streamflows is evaluated. In general, all the three models are able to simulate the streamflows well. Nevertheless, the performance of SWAT model is better. Further, among the two SVM models, the performance of one-stage streamflow downscaling model is marginally better than that of the two-stage streamflow downscaling model.

Title: Spatiotemporal variability of global and regional climate models Author: RNDr. Lenka Crhová Department: Department of Atmospheric Physics Supervisor: RNDr. Eva Holtanová, Ph.D., Department of Atmospheric Physics Abstract: This thesis deals with variability of basic meteorological variables in global and regional climate models (GCMs and RCMs) outputs. Three different approaches were used in order to analyse climate models' ability to represent different aspects of variability of meteorological variables. The temporal variability with focus on its changes during a time and temporal scale components were studied. The relationship between air temperature and precipitation were employed in order to investigate the representation of spatiotemporal variability in climate models. Moreover, the influence of different characteristics of climate model simulations, such as the size of the RCM integration domain or differences between RCM and GCM simulations, were also considered. Two simulations of RCM ALADIN-Climate/CZ with different sizes of integration domain and their driving simulation of GCM ARPÉGE-Climat were used for analysis of the temporal changes in temperature mean and variability and selected simulations of RCMs and GCMs from the EURO-CORDEX and CMIP5 projects were employed for analyses of...

Rapid environmental changes linked to human-induced increases in atmospheric greenhouse gas concentrations have been observed on a global scale over recent decades. Given the relative certainty of continued change across many earth systems, the information output from climate models is an essential resource for adaptation planning. But in the face of many known modeling deficiencies, how confident can we be in model projections of future climate? It stands to reason that a realistic simulation of the present climate is at least a necessary (but likely not sufficient) requirement for a model’s ability to realistically simulate the climate of the future. Here, I present the results of three studies that evaluate the 20th century performance of global climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5). The first study examines precipitation, geopotential height, and wind fields from 21 CMIP5 models to determine how well the North American monsoon system (NAMS) is simulated. Models that best capture large-scale circulation patterns at low levels usually have realistic representations of the NAMS, but even the best models poorly represent monsoon retreat. Difficulty in reproducing monsoon retreat results from an inaccurate representation of gradients in low-level geopotential height across the larger region, which causes an unrealistic flux of low-level moisture from the tropics into the NAMS region that extends well into the post-monsoon season. The second study examines the presence and severity of spurious Gibbs-type numerical oscillations across the CMIP5 suite of climate models. The oscillations can appear as unrealistic spatial waves near discontinuities or sharp gradients in global model fields (e.g., orography) and have been a known problem for decades. Multiple methods of oscillation reduction exist; consequently, the oscillations are presumed small in modern climate models and hence are rarely addressed in recent literature. Here we quantify the oscillations in 13 variables from 48 global climate models along a Pacific ocean transect near the Andes. Results show that 48% of nonspectral models and 95% of spectral models have at least one variable with oscillation amplitude as large as, or greater than, atmospheric interannual variability. The third study is an in-depth assessment model simulations of 20th century monthly minimum and maximum surface air temperature over eight US regions, using mean state, trend, and variability bias metrics. Transparent model performance information is provided in the form of model rankings for each bias type. A wide range in model skill is at the regional scale, but no strong relationships are seen between any of the three bias types or between 20th century bias and 21st century projected change. Using our model rankings, two smaller ensembles of models with better performance over the southwestern U.S. are selected, but they result in negligible differences from the all-model ensemble in the average 21st century projected temperature change and model spread. In other words, models of varied quality (and complexity) are projecting very similar changes in temperature, implying that the models are simulating warming for different physical reasons. Despite this result, we suggest that models with smaller 20th century biases have a greater likelihood of being more physically realistic and therefore, more confidence can be placed in their 21st century projections as compared to projections from models that have demonstrably poor skill over the observational period. This type of analysis is essential for responsibly informing climate resilience efforts.

By using climate models based on the Earth’s climate, you can get information about how the climate on exoplanets can look like. ROCKE-3D is a general circulation model based on ModelE2, which is used for simulations of modern and prehistoric Earth’s climate. ROCKE-3D, on the other hand, is used to simulate terrestrial planets both in our solar system and around other stars. The orbital eccentricity affects a planet’s climate, if the eccentricity is high, the planet will be closer to its star certain parts of the year and further away from it for other parts. Because of this, it is interesting to study the eccentricity’s influence on the climate of exoplanets, especially since the boundaries of the habitable zone change. In this report, the climate of an Earth-like planet with varying orbital eccentricity has been investigated using ROCKE-3D. The results show that the annual average temperature increased if the eccentricity increased, even though it was expected to decrease because the planet was further away from its star for longer periods than it was closer. The reason for this was that the ocean dampened the surface temperature drop. The amount of snow and ice was also examined. As eccentricity increased, the ocean ice became thicker and snow accumulated in the northern hemisphere. This can be explained, even though the annual average temperature increased due to the warmer winters, by the fact that the temperature in the summer decreased so much that the snow and ocean ice did not melt away completely and started to accumulate for the years with higher eccentricities.

Mudanças climáticas podem afetar a distribuição espacial e temporal das variáveis hidrológicas, tendo como consequências alterações nos regimes de precipitação e vazão dos rios. Aumentos ou reduções no volume de escoamento de uma bacia hidrográfica podem, por exemplo, produzir danos aos ecossistemas, afetar a produção de alimentos, abastecimento de água, navegação e geração de energia. Atualmente buscam-se relações que permitam entender os processos de mudanças climáticas a fim avaliar os impactos e mitigá-los, assim como avaliar as incertezas inerentes ao processo de modelagem hidrológica de tais mudanças. Neste contexto este trabalho apresenta uma metodologia de quantificação e análise de incertezas para estudos de mudanças climáticas, tomando como estudo de caso a bacia hidrográfica do Rio Uruguai (BHRU) com área aproximada de 110,000 Km². Para tanto três fontes de incerteza foram analisadas e comparadas: o modelo hidrológico, técnicas de remoção de viés e modelos climáticos. O modelo hidrológico MGB-IPH foi avaliado quanto ao processo de parametrização, utilizando diferentes períodos de simulação para calibração: (i) Período de calibração 1 – MGB/P1: representando a série completa de observações de 1960-1990 com verificação no período de 1992-1999; (ii) Período de calibração 2 - MGB/P2: calibração em período seco e verificação de período de cheias (iii) Período de calibração 3 – MGB/P3: calibração em período característico de cheias e verificação de período de estiagem. Três diferentes técnicas de remoção de viés foram aplicadas para analisar o grau de incerteza que a escolha de um determinado método de correção pode agregar ao resultado final: (i) RV1 - Técnica de Mapeamento Quantil-Quantil; (ii) RV2 - Técnica de Escalonamento Linear e (iii) RV3 - Técnica Delta change. Os modelos climáticos globais (GMC’s) foram analisados quanto a sua estrutura, comparando projeções de cinco diferentes modelos: MPEH5 (ECHAM5/MPIOM), GFCM21 (GFDL-CM2.1), MRCGCM (MRI-CGCM2.3.2), HADCM3 (UKMO-HadCM3) e NCCCSM (CCSM3). Adicionalmente, também foram analisadas as projeções climáticas de cinco diferentes versões do modelo climático regional (RCM) ETA/CPTEC: CT20, CT40, LOW, MID e HIGH. Inicialmente os resultados das simulações provenientes de cada uma destas fontes foram comparados de maneira isolada e em seguida de maneira combinada. Portanto, a metodologia foi dividida em Etapa (1) e Etapa (2). A Etapa (1) teve por objetivo responder a seguinte pergunta: Qual dentre as fontes de incerteza selecionadas agrega maior variação ao resultado final? Ou seja, qual destas fontes propaga maior incerteza em termos de impactos de mudanças climáticas na BHRU? Os resultados obtidos por cada uma das fontes de incerteza foram comparados em termos de anomalias de vazões médias de longo período (QMLP), máximas e mínimas anuais. Na Etapa (2) foi realizada a análise total de incerteza, ou seja, a análise combinada dos resultados obtidos na Etapa (1). As anomalias de vazões foram apresentadas utilizando as curvas de distribuição acumulada (CDF’s) e a incerteza total expressa pela diferença entre os percentis 5% e 95%. Considerando os resultados obtidos para as vazões médias de longo período (QMLP), as fontes podem ser ordenadas de forma decrescente, em relção ao grau de incerteza que propagam: modelos climáticos globais > modelos climáticos regionais > técnicas de remoção de viés > modelo hidrológico. Para as vazões extremas os RCM’s apresentam as maiores variações de anomalias se comparadas às dos modelos hidrológicos e técnicas de remoção de viés, inclusive para ambos os extremos, máximos e mínimos. Esta variação se dá principalmente, pelos resultados de LOW e MID. Estas informações podem ajudar os gestores e tomadores de decisão no adequado gerenciamento e planejamento dos recursos hídricos sob condições de mudanças climáticas, assim como o entendimento da incerteza associada. / Climate change can affect the spatial and temporal distribution of hydrological variables, with the consequences of changes in precipitation regimes and river flows. Increase or decrease the flow of rivers, for example, can cause damage to ecosystems, affecting food production, water supply, navigation and power generation. Currently seeking to relationships that allow understand climate change processes in order to assess the impacts and mitigate them, and assess the uncertainties inherent in hydrologic modeling process of such climate change. This thesis aimed at the development of a methodology for quantification and analysis of uncertainties for climate change studies in hydrology , taking as a case study the basin of the Uruguay River (BHRU) with a drainage area near 110,000 km². For that three sources of uncertainty were analyzed and compared: the hydrologic model, bias removal techniques and climate models. The hydrological model MGB-IPH was evaluated for parameterization, using different simulation periods for calibration: (i) MGB /P1: full range with calibration period (1960-1990) and validate (1992-1999); (ii) MGB / P2: calibrated in the period of dry and validated in the flood season (iii) MGB/P3: calibrated in the period of floods and validated in the dry season. Three different bias correction methods were applied to analyze the degree of uncertainty that the choice of a particular method of correction can add to the final result: (i) RV1 - Quantil-Quantil Mapping; (ii) RV2 - Linear Scaling, and (iii) RV3 - Delta Change Technique. Global climate models (GMC's) were analyzed for their structure, comparing projections of five different models: MPEH5 (ECHAM5/MPI-OM), GFCM21 (GFDLCM2.1), MRCGCM (MRI-CGCM2.3.2), HADCM3 (UKMO-HadCM3) e NCCCSM (CCSM3). Additionaly, climate projections from five different versions of the regional climate model (RCM) ETA / CPTEC were also analyzed: CT20, CT40, LOW, MID e HIGH. Initially the simulation results from each of the sources of uncertainty were compared individually (single propagation) and then in a combined way (multi propagation). Therefore, the methodology was divided in Step (1) and Step (2). Step (1) aimed to answer: Which of the selected sources of uncertainty adds more variation to the final result? Which of these sources propagates greater uncertainty in terms of impacts of climate change on BHRU? The results for each of the sources of uncertainty were compared in terms of long-term mean flow (QMLP), maximum and minimum annual flow. In Step (2) total uncertainty analysis was performed, therefore the combined analysis of the results obtained in Step (1). The anomalies in discharge were presented using the cumulative distribution function (CDF's) and the total uncertainty expressed by the difference between the percentiles 5% and 95%. Throughout the application of the proposed methodology it was concluded that: (i) for the extremes (maximum and minimum) annual discharges the largest source of uncertainty are the projections of the RCM's, followed by the the bias removal technique and finally the hydrological model; (ii) for the QMLP the largest source of uncertainty are followed global climate models, then the regional climate models. This information can help managers and decision makers in the proper management and planning of water resources under climate change conditions, as well as the understanding of the associated uncertainty.