Microgrid can provide stable, clean, and sustainable electricity supply for remote places since it can operate on renewable energy sources and work isolated from the utility grid. This thesis evaluates the life cycle greenhouse gas (GHG) emissions of the microgrid system which is located at the ‘North-five Islands’ of Changshan archipelago in China. The existing electricity generation technologies of the microgrid system are wind turbine, PV system and diesel generators with the capacity of 2 MW, 300 kW and 2046 kW, respectively. The total demand of electricity (362.2 GWh) will be supplied by the wind turbine, PV system and diesel generators with 32.03%, 2.36% and 65.62%, respectively, if the microgrid system is required to supply the electricity demand for the ‘North-five Islands’ area alone under the islanded mode during 20 years lifespan. The thesis uses the Life Cycle Assessment (LCA) to evaluate the life cycle GHG emissions of the microgrid system. The life cycle stages of this study include: raw material extraction, manufacturing, transportation and operation. In order to assess the environmental benefits of the microgrid system, three electricity supply options – ‘microgrid electricity supply option’, ‘grid extension electricity supply option’, and ‘conventional fossil diesel generators electricity supply option’ are designed to evaluate the life cycle GHG emissions for supplying 20 years electricity demand (362.2 GWh) of the ‘Northfive Islands’. The results show that the life cycle GHG emissions of the ‘microgrid electricity supply option’ are 223.19 million kgCO2eq. Compared to the ‘grid extension electricity supply option’ and ‘conventional fossil diesel generators electricity supply option’, the net savings of the GHG emissions are 70.56 and 112.18 million kgCO2eq, respectively. It mainly results from the differences of the electricity supply methods of the three electricity supply options. For the ‘microgrid electricity supply option’ itself, the operation stage takes the most responsibility of the life cycle GHG emissions with 97.6%. The raw material extraction, manufacturing and transportation stages account for 1.93%, 0.44% and 0.026%, respectively. For the system components of the microgrid system, the wind turbine, PV system, diesel generators, energy storage system, and cables account for 0.34%, 0.18%, 97.75%, 0.60%, and 1.12%, respectively, of the microgrid system’s life cycle GHG emissions. The thesis conducts the sensitivity analysis of diesel burn rate efficiency (L/kWh) of the microgrid system’s diesel generators due to a large quantity (60.84 million L) of diesel consumption by the diesel generators during 20 years operation time. According to the results of the sensitivity analysis, the diesel burn rate efficiency can directly impact the diesel consumption of the diesel generators, and consequently has a significant impact on the life cycle GHG emissions of the ‘North-five Islands’ microgrid system. Since the diesel burn rate efficiency represents the amount of diesel consumption, this results highlight the significance of any factors that affect the diesel consumption (e.g. quantity of diesel, temperature, altitude, etc.), in affecting the life cycle GHG emissions of the ‘North-five Islands’ microgrid system. In addition, the thesis performers the sensitivity analysis of renewable energy (wind and solar energy in specific) fraction of the studied microgrid system because of the huge potential of available renewable energy (63.65 MW of wind turbines) nearby the microgrid system. The results of the sensitivity analysis show that the life cycle GHG emissions of the microgrid system decrease linearly with the increase of wind and solar energy fraction. Particularly, the life cycle GHG emissions of the microgrid system decrease 1.46% (3.26 million kgCO2eq) and 1.37% (3.05 million kgCO2eq) with an increase of 1% in wind and solar energy, respectively.

A quay is a structure which is constructed along the shoreline for docking purposes. When the quay is constructed using reinforced concrete, damage over time happens under the combined action of chemical and mechanical corrosion of the varied materials. The quay columns situated in the seawater are commonly corroded due to the chloride ingress and frost attack. These deteriorations significantly reduce the service life of the quay column. Reinforced concrete materials are traditionally used to repair the eroded quay structures in the present circumstances but have significant environmental impacts. As a sustainable alternative, a calcareous electrochemical precipitation, induced mineral deposition (IMD) after sea water electrolysis, has been suggested for refurbishing underwater structures. This thesis aims to evaluate the carbon footprint associated with the IMD approach from a life cycle perspective. The critical hotspots and the trade-offs of this approach are identified and compared with the concrete-based refurbishment. It is found that the primary contributors to the carbon emissions are the upstream processes involving the production of IMD materials. Similarly, a significant amount of carbon emissions occurs from the use of electricity in the downstream processes. However, IMD approach outperforms the concrete refurbishment on an overall climate change impact. The results from the sensitivity analysis also demonstrate that use of less carbon- intensive anode materials and the complete transition of grid electricity in the use phase into renewable energy resources would environmentally benefit the IMD approach. Therefore, for the refurbishment of marine quay columns, the IMD approach with asserted recommendations would be a sustainable alternative to the current practices. / En kaj är en betongkonstruktion som är byggd längs strandlinjen för dockningsändamål. När kajen är konstruerad med hjälp av armerad betong, händer skador med tiden under kombineradverkan av kemisk och mekanisk korrosion av de varierade materialen. Kajkolonner som ligger i havsvattnet kan frätas sönder (eroderas) genom inträngning från kloridsalter och frostattacker. Dessa frätskador minskar avsevärt kajkolonnens livslängd. Material av armerad betong används traditionellt för att reparera de eroderade kajstrukturerna, men har en betydande miljöpåverkan. Som ett hållbart alternativ har en kalkhaltig elektrokemisk nederbörd, ”inducerad mineral nedfall” (IMD) efter havet vatten elektrolys, föreslagits för renovering av kajkolonnens undervattensstruktur. Denna avhandling syftar till att utvärdera de koldioxidavtryck som är förknippade med IMD-ansatsen ur ett livscykelperspektiv. De kritiska ”hotspots” och kompromisser för detta tillvägagångssätt identifieras och jämförs med den traditionella betongbaserade renoveringen. Det konstateras att de främsta bidragsgivarna till koldioxidutsläppär de uppströmsprocesser som omfattar produktion av IMD-material. På samma sätt uppstår en betydande del av koldioxidutsläpp påverkan genom användning av el i de efterföljande processerna. IMD-metoden överträffar dock den traditionella renoveringen när det gäller den övergripande inverkan på klimatförändringarna. Resultaten från känslighetsanalysen visar också att användning av mindre kolintensiva anodmaterial och fullständig övergång av förnybar el i användningsfasen skulle gynna IMD-metoden utifrån ett miljöperspektiv. För renovering av betongkolonner för marina kajer skulle därför IMD-metoden med rekommendationer från denna studie vara ett hållbart alternativ till den nuvarande praxisen.

Transitional land uses produced through urbanisation continue to change the landscape and fragment ecological structures including green spaces across Europe (Nilsson et al., 2013). Green spaces offer significant benefits to humans, contributing to wellbeing and life satisfaction (Taylor, 2002). The understanding of how these unique green spaces spaces function and provide benefits to humans, and how landscape change in peri-urban contexts affects their performance, is important. The scope of this research is to contribute to an understanding of landscape fragmentation within some of Europe's polycentric urban regions, their peri-urban green spaces, and the associated impacts upon human quality of life. Two urban regional case studies, Paisley near Glasgow, Scotland, and Vantaa, near Helsinki, Finland were analysed and compared. The results indicate that humans interacting with more physically or ecologically fragmented peri-urban green spaces have higher self-reported life satisfaction levels. Though no statistically significant characteristics were apparent between life satisfaction and fragmented green space characteristics, this research was able to identify those specific structural attributes and physical characteristics of interstitial peri-urban green spaces within a polycentric region in a fragmented state that contribute to the physical, social, and psychological aspects of human wellbeing. The statistically significant eco-spatial characteristics of polycentric peri-urban interstitial green spaces that are reported to impact human wellbeing are the size, proximity, maintenance and management, and the level of greenness within its vegetation composition and setting. Overall, a spatially diverse, fragmented, peri-urban landscape whose green spaces are extensively sized, naturalistically shaped with horizontal vegetation and normal sized edges, most often parks or woodlands or forests which are integrated and physically connected to another green space which is moderately clean and somewhat safe as well as being located close to or adjacent to a heavy-trafficked road provide the most human wellbeing benefits.

<p>Economics is about the management of scare resources. In agricultural production, water stress and excess heat are the main constraints. The three essays of this dissertation try to improve our understandings of how climate and water resources interact with agricultural markets, and how global changes in agricultural markets may affect water resources. I construct empirical and simulation models to explain the interplay between agriculture and water. These models integrate economic theories with environmental sciences to analyze the hydroclimatic and economic information at different geospatial scales in a changing climate. </p> <p>In the first essay, I illustrate how irrigation, as a potential adaptation channel, can reduce the volatility of crop yields and year-on-year variations caused by the projected heat stress. This work includes estimation of yield response to climate variation for irrigated and rainfed crops; and global projections of change in the mean and the variation of crop yields. I use my estimated response function to project future yield variations using NASA NEX-GDDP climate data. I show that the impact of heat stress on rainfed corn is around twice as big as irrigated practices. </p> <p>In the second essay, I establish a framework for estimating the value of soil moisture for rainfed production. This framework is an extension of Schlenker and Roberts (2009) model enabled by the detailed soil moisture information available from the Water Balance Model (WBM). An important contribution is the introduction of a cumulative yield production function considering the daily interaction of heat and soil moisture. I use this framework to investigate the impacts of soil moisture on corn yields in the United States. However, this framework can be used for the valuation of other ecosystem services at daily basis.</p> <p>In the third essay, I have constructed a model that explains how the global market economy interacts with local land and water resources. This helps us to broaden the scope of global to local analysis of systems sustainability. I have employed SIMPLE-G-W (a Simplified International Model of agricultural Prices, Land use, and the Environment- Gridded Water version) to explain the reallocation across regions. The model is based on a cost minimization behavior for irrigation technology choice for around 75,000 grid cells in the United States constrained by water rights, water availability, and quasi-irreversibility of groundwater supply. This model is used to examine the vulnerability of US land and water resources from global changes.</p>

During the 21st century, climatic change and non-climatic stressors are likely to impact forests leading to large-scale forest and biodiversity loss, and diminished ecological benefits. Assessing the vulnerability of forests and addressing the sources of vulnerability is an important risk management strategy. The overall goal of this research work is to develop methodological approaches at different scales and apply them to assess the vulnerability of forests in India for developing strategies for forest adaptation. Indicator-based methodological approaches have been developed for vulnerability assessment at local, landscape and national scales under current climate scenario, and at national scale under future climate scenario. Under current climate scenario, the concept of inherent vulnerability of forests has emerged by treating vulnerability as a characteristic internal property of a forest ecosystem independent of exposure. This approach to assess vulnerability is consistent with the framework presented in the latest report of Intergovernmental Panel on Climate Change (IPCC AR5 2014). Assessment of vulnerability under future climate scenario is presented only at national scale due to challenges associated with model-based climate projections and impact assessment at finer scales. The framework to assess inherent vulnerability of forests at local scale involves selection of vulnerability indicators and pair wise comparison method (PCM) to assign the indicator weights. The methodology is applied in the field to a 300-ha moist deciduous case study forest (Aduvalli Protected Forest, Chikmagalur district) in the Western Ghats area, where a vulnerability index value of 0.248 is estimated. Results of the study indicate that two indicators - ‘preponderance of invasive species’ and ‘forest dependence of community’ - are the major drivers of inherent vulnerability at present. The methodology developed to assess the inherent vulnerability at landscape scale involves use of vulnerability indicators, the pair wise comparison method, and geographic information system (GIS) tools. Using the methodology, assessment of inherent vulnerability of Western Ghats Karnataka (WGK) landscape forests is carried out. Four vulnerability indicators namely, biological richness, disturbance index, canopy cover and slope having weights 0.552, 0.266, 0.123 and 0.059, respectively are used. The study shows that forests at one-third of the grid points in the landscape have high and very high inherent vulnerability, and natural forests are inherently less vulnerable than plantation forests. The methodology used for assessment of forest inherent vulnerability at the national scale was same as used at landscape scale. 40% of forest grid points in India are assessed with high and very high inherent vulnerability. Except in pockets, the forests in the three biodiversity hotspots in India i.e., the Western Ghats in peninsular India, northeastern India, and the northern Himalayan region are assessed to have low to medium inherent vulnerability. Vulnerability of forests under future climate scenario at national scale is estimated by combining the results of assessment of climate change impact and inherent vulnerability. In the present study, ensemble climatology from five CMIP5 (Coupled Model Intercomparison Project phase 5) climate models for RCP (Representative Concentration Pathways) 4.5 and 8.5 in short (2030s) and long term (2080s) is used as input to IBIS (Integrated Biosphere Simulator) dynamic vegetation model. Forest grid points projected to experience vegetation-shift to a new plant functional type (PFT) under future climate are categorized under ‘extremely high’ vulnerability category. Such forest grid points in India are 22 and 23% in the short term under RCP4.5 and 8.5 respectively, and these percentages increase to 31 and 37% in the long term. IBIS simulated vegetation projections are also compared with LPJ (Lund-Potsdam-Jena) simulated projections. Both the vegetation models agree that forests at about one-third of the grid points could be impacted by future climate but the spatial distribution of impacted grid points differs between the models. Vulnerability assessment is a powerful tool for building long-term resilience in the forest sector in the context of projected climate change. From this study, three forest scenarios emerge in India for developing adaptation strategies namely: (a) less disturbed primary forests; (b) degraded and fragmented primary forests; and (c) secondary (plantation) forests. Minimizing anthropogenic disturbance and conserving biodiversity are critical to reduce forest vulnerability of less disturbed primary forests. For disturbed forests and plantations, adaptive management aimed at forest restoration is necessary to build resilience. Mainstreaming forest adaptation in India through Forest Working Plans and realignment of the forestry programs is necessary to manage the risk to forests under climate change.

abstract: Energy consumption in buildings, accounting for 41% of 2010 primary energy consumption in the United States (US), is particularly vulnerable to climate change due to the direct relationship between space heating/cooling and temperature. Past studies have assessed the impact of climate change on long-term mean and/or peak energy demands. However, these studies usually neglected spatial variations in the “balance point” temperature, population distribution effects, air-conditioner (AC) saturation, and the extremes at smaller spatiotemporal scales, making the implications of local-scale vulnerability incomplete. Here I develop empirical relationships between building energy consumption and temperature to explore the impact of climate change on long-term mean and extremes of energy demand, and test the sensitivity of these impacts to various factors. I find increases in summertime electricity demand exceeding 50% and decreases in wintertime non-electric energy demand of more than 40% in some states by the end of the century. The occurrence of the most extreme (appearing once-per-56-years) electricity demand increases more than 2600 fold, while the occurrence of the once per year extreme events increases more than 70 fold by the end of this century. If the changes in population and AC saturation are also accounted for, the impact of climate change on building energy demand will be exacerbated. Using the individual building energy simulation approach, I also estimate the impact of climate change to different building types at over 900 US locations. Large increases in building energy consumption are found in the summer, especially during the daytime (e.g., >100% increase for warehouses, 5-6 pm). Large variation of impact is also found within climate zones, suggesting a potential bias when estimating climate-zone scale changes with a small number of representative locations. As a result of climate change, the building energy expenditures increase in some states (as much as $3 billion/year) while in others, costs decline (as much as $1.4 billion/year). Integrated across the contiguous US, these variations result in a net savings of roughly $4.7 billion/year. However, this must be weighed against the cost (exceeding $19 billion) of adding electricity generation capacity in order to maintain the electricity grid’s reliability in summer. / Dissertation/Thesis / Doctoral Dissertation Environmental Social Science 2016

Research leading to this thesis aims to assess the policy relevant mitigation potential of Indian forests as well as aims to assess the impact of climate change on carbon stocks, vegetation boundary shifts, Net Primary Productivity (NPP) and the mitigation potential of Indian forests. To project the impact of climate change we chose a dynamic global vegetation model ‘Integrated Biosphere Simulator’ (IBIS V.2.6b4). We selected A2 and B2 scenarios for projecting the impacts. Mitigation potential was assessed using the ‘Generalized Comprehensive Mitigation Assessment Process’ (GCOMAP) model. We assess the mitigation potential of Indian forests in the light of India’s long-term policy objective of bringing 33% of its total geographical area under forest cover. We analyzed the mitigation potential of this policy objective under two scenarios: the first comprising of rapid afforestation scenario with the target to achieving the goal by 2020 and the second a moderate afforestation scenario in which this goal is achieved by 2030. We estimate that afforestation could offset about 9% of India’s average national emissions over the 2010-2030 period, while about 6.7% could be mitigated under the moderate afforestation scenario over the same period. We analyze the impact of climate change on the four key attributes of Indian forests, i.e. impact on vegetation distribution, impact on forest productivity (NPP), impact on soil carbon (SOC) and impact on biomass carbon. IBIS simulations suggest that approximately 39% and 34% of forest grids are projected to experience change in vegetation type under A2 and B2 climate scenarios, respectively over the period 2070¬2100. Simulations further indicate that NPP is projected to increase by an average of 66% under the A2 scenario and 49% under the B2 scenario. The increase is higher in the northeastern part of India. However, in the central and western Indian forests NPP remains stable or increases only moderately, and in some places even decreases. Our assessment of the impact of climate change on Soil Organic Carbon (SOC) suggests a trend similar to NPP distribution, which is to be expected as increased NPP is the primary driver of higher litter input to the soil. However, the quantum of increase in this case is lower, around 37% and 30%, for the A2 and B2 scenario respectively (averaged over India). The biomass carbon is also projected to increase all over India on the lines similar to NPP gains. However, projected gains in biomass, NPP and SOC should be viewed with caution as IBIS tends to simulate a fairly strong CO2 fertilization effect that may not necessarily be realized under conditions of nutrient and water limitations and under conditions of increased pest and fire outbreaks. Further we analyzed the impact of climate change on the mitigation potential of Indian forests by linking impact assessment models to mitigation potential assessment model GCOMAP. Two impact assessment models BIOME4 and IBIS are used for simulating the impact of climate change. IBIS is a dynamic vegetation model while BIOME4 is an equilibrium model. Our assessment suggests that with the BIOME4 simulations the cumulative mitigation potential increases by up to 21% under the A2 scenario over the period 2008 to 2108, whereas, under the B2 scenario the mitigation potential increases only by 14% over the same period. However cumulative mitigation potential estimates obtained from the IBIS simulations suggest much smaller gains, where mitigation potential increases by only 6% and 5% over the period 2008 to 2108, under A2 and B2 scenarios respectively. To enable effective policy analysis and to build a synergy between the mitigation and adaptation efforts in the Indian forest sector, a vulnerability index for the forested grids is constructed. The vulnerability index is based on the premise that forests in India are already subjected to multiple stresses including over extraction, insect outbreaks, live¬stock grazing, forest fires and other anthropogenic pressures -with climate change being an additional stress. The forest vulnerability index suggests that nearly 39% of the forest grids in India are projected to be vulnerable to the impacts of climate change under the A2 scenario, while 34% of the forest grids are projected to be vulnerable under the B2 scenario. The vulnerability index suggests that forests in the central part of India, a significant part of the western Himalayan forests and northern and central parts of the Western Ghats are particularly vulnerable to the impacts of climate change. Forests in the northeastern part of India are seemingly resilient to the impacts of climate change. It also suggests that given the high deforestation rate in northeast, this region be prioritized for reducing deforestation and forest degradation (REDD) projects under the United Nations Framework Convention on Climate Change (UNFCCC) mechanisms.

<p>This dissertation examines the impact of the expansion of renewable technology at both national and local level, through distinct essays. At the national level, the first paper analyzes the effects of economic and distributional impacts of climate mitigation policy, in the context of a developing country, to understand the interactions between the energy system and the macroeconomic environment. In the case of the local level, the second paper uses synthetic control method, to estimate the effect at the county level of utility scale wind in the development indicators for two counties in the U.S. </p> <p>The first paper assesses the economic and distributional impacts of Nicaragua’s commitments to limit future greenhouse gas emissions in the context of the Paris Agreement, known as the Nationally Determined Contributions (NDCs). The analysis relies on two distinct models. The first is a top-down approach based on a single-country computable general equilibrium (CGE) model, known as the Mitigation, Adaptation and New Technologies Applied General Equilibrium (MANAGE) Model. The second is a bottom-up approach based on the Open-Source energy Modeling System (OSeMOSYS), which is technology rich energy model. The combined model is calibrated to an updated social accounting matrix for Nicaragua, which disaggregates households into 20 representative types: 10 rural and 10 urban households. For the household disaggregation we have used information from the 2014 Living Standards Measurement Study (LSMS) for Nicaragua. Our analysis focuses on the distributional impacts of meeting the NDCs as well as additional scenarios—in a dynamic framework as the MANAGE model is a (recursive) dynamic model. The results show that a carbon tax has greatest potential for reduction in emissions, with modest impact in macro variables. An expansion of the renewable sources in the electricity matrix also leads to significant reduction in emissions. Only a carbon tax achieves a reduction in emissions consistent with keeping global warming below 2°C. Nicaragua’s NDC alone would not achieve the target and mitigation instruments are needed. An expansion of generation from renewable sources, does not lead to a scenario consistent with a 2°C pathway. </p> <p>The second paper measures the impact of wind generation on county level outcomes through the use of the Synthetic Control Method (SCM). SCM avoids the pitfalls of other methods such as input-output models and project level case studies that do not provide county level estimates. We find that the local per capita income effect of utility wind scale is 6 percent (translate into an increase of $1,511 in per capita income for 2019) for Benton County and 8 percent for White county in Indiana (an increase of $2,100 in per capita income for 2019). The per capita income effect measures the average impact, which includes the gains in rents from capital, land, and labor from wind power in these counties. Moreover, we find that most of the rents from wind power accrue to the owners of capital and labor. Even assuming the lowest projections of electricity prices and the highest reasonable cost we still find a 10 percent minimum rate of return to capital for both Benton and White counties’ wind power generators. Furthermore, we find that there are excess rents that could be taxed and redistributed at the county, state, or federal level without disincentivizing investment in wind power.</p>

General Circulation Models (GCMs), which are mathematical models based on principles of fluid dynamics, thermodynamics and radiative transfer, are the most reliable tools available for projecting climate change. However, the spatial scale on which typical GCMs operate is very coarse as compared to that of a hydrologic process and hence, the output from a GCM cannot be directly used in hydrologic models. Statistical Downscaling (SD) derives a statistical or empirical relationship between the variables simulated by the GCM (predictors) and a point-scale meteorological series (predictand). In this work, a new downscaling model called CRF-downscaling model, is developed where the conditional distribution of the hydrologic predictand sequence, given atmospheric predictor variables, is represented as a conditional random field (CRF) to downscale the predictand in a probabilistic framework. Features defined in the downscaling model capture information about various factors influencing precipitation such as circulation patterns, temperature and pressure gradients and specific humidity levels. Uncertainty in prediction is addressed by projecting future cumulative distribution functions (CDFs) for a number of most likely precipitation sequences. Direct classification of dry/wet days as well as precipitation amount is achieved within a single modeling framework, and changes in the non-parametric distribution of precipitation and dry and wet spell lengths are projected. Application of the method is demonstrated with the case study of downscaling to daily precipitation in the Mahanadi basin in Orissa, with the A1B scenario of the MIROC3.2 GCM from the Center for Climate System Research (CCSR), Japan. An uncertainty modeling framework is presented in this work, which combines GCM, scenario and downscaling uncertainty using the Dempster-Shafer (D-S) evidence theory for representing and combining uncertainty. The methodology for combining uncertainties is applied to projections of hydrologic drought in terms of monsoon standardized streamflow index (SSFI-4) from streamflow projections for the Mahanadi river at Hirakud. The results from the work indicate an increasing probability of extreme, severe and moderate drought and decreasing probability of normal to wet conditions, as a result of a decrease in monsoon streamflow in the Mahanadi river due to climate change. In most studies to date, the nature of the downscaling relationship is assumed stationary, or remaining unchanged in a future climate. In this work, an uncertainty modeling framework is presented in which, in addition to GCM and scenario uncertainty, uncertainty in the downscaling relationship itself is explored by linking downscaling with changes in frequencies of modes of natural variability. Downscaling relationships are derived for each natural variability cluster and used for projections of hydrologic drought. Each projection is weighted with the future projected frequency of occurrence of that cluster, called ‘cluster-linking’, and scaled by the GCM performance with respect to the associated cluster for the present period, called ‘frequency scaling’. The uncertainty modeling framework is applied to a case study of projections of hydrologic drought or SSFI-4 classifications, using projected streamflows for the Mahanadi river at Hirakud. It is shown that a stationary downscaling relationship will either over- or under-predict downscaled hydrologic variable values and associated uncertainty. Results from the work show improved agreement between GCM predictions at the regional scale, which are validated for the 20th century, implying that frequency scaling and cluster-linking may indeed be a valid method for constraining uncertainty. To assess the impact of climate change on reservoir performance, in this study, a range of integrated hydrologic scenarios are projected for the future. The hydrologic scenarios incorporate increased irrigation demands; rule curves dictated by increased need for flood storage and downscaled projections of streamflow from an ensemble of GCMs and emission scenarios. The impact of climate change on multipurpose reservoir performance is quantified, using annual hydropower and RRV criteria, under GCM and scenario uncertainty. The ‘business-as-usual’ case using Standard Operating Policy (SOP) is studied initially for quantifying impacts. Adaptive Stochastic Dynamic Programming (SDP) policies are subsequently derived for the range of future hydrologic scenarios, with the objective of maximizing reliabilities with respect to multiple reservoir purposes of hydropower, irrigation and flood control. It is shown that the hydrologic impact of climate change is likely to result in decreases in performance criteria and annual hydropower generation for Hirakud reservoir. Adaptive policies show that a marginal reduction in irrigation and flood control reliability can achieve increased hydropower reliability in future. Hence, reservoir rules for flood control may have to be revised in the future.

Submitted by Cássia Santos (cassia.bcufg@gmail.com) on 2016-05-31T09:35:51Z No. of bitstreams: 2 Tese - Frederico Augusto Martins Valtuille Faleiro - 2016.pdf: 7096330 bytes, checksum: 04cfce04ef128c5bd6e99ce18bb7f650 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2016-05-31T10:52:51Z (GMT) No. of bitstreams: 2 Tese - Frederico Augusto Martins Valtuille Faleiro - 2016.pdf: 7096330 bytes, checksum: 04cfce04ef128c5bd6e99ce18bb7f650 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Made available in DSpace on 2016-05-31T10:52:51Z (GMT). No. of bitstreams: 2 Tese - Frederico Augusto Martins Valtuille Faleiro - 2016.pdf: 7096330 bytes, checksum: 04cfce04ef128c5bd6e99ce18bb7f650 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2016-03-07 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / The climate changes are one of the major threats to the biodiversity and it is expected to increase its impact along the 21st century. The climate change affect all levels of the biodiversity from individuals to biomes, reducing the ecosystem services. Despite of this, the prediction of climate change impacts on biodiversity is still a challenge. Overcoming these issues depends on improvements in different aspects of science that support predictions of climate change impact on biodiversity. The common practice to predict the climate change impact consists in formulate ecological niche models based in the current climate and project the changes based in the future climate predicted by the climate models. However, there are some recognized limitations both in the formulation of the ecological niche model and in the use of predictions from the climate models that need to be analyzed. Here, in the first chapter we review the science behind the climate models in order to reduce the knowledge gap between the scientific community that formulate the climate models and the community that use the predictions of these models. We showed that there is not consensus about evaluate the climate models, obtain regional models with higher spatial resolution and define consensual models. However, we gave some guidelines for use the predictions of the climate models. In the second chapter, we tested if the predictions of correlative ecological niche models fitted with presence-absence match the predictions of models fitted with abundance data on the metrics of climate change impact on orchid bees in the Atlantic Forest. We found that the presence-absence models were a partial proxy of change in abundance when the output of the models was continuous, but the same was not true when the predictions were converted to binary. The orchid bees in general will decrease the abundance in the future, but will retain a good amount of suitable sites in the future and the distance to gained climatic suitable areas can be very close, despite of great variation. The change in the species richness and turnover will be mainly in the western and some regions of southern of the Atlantic Forest. In the third chapter, we discussed the drawbacks in using the estimations of realized niche instead the fundamental niche, such as overpredicting the effect of climate change on species’ extinction risk. We proposed a framework based on phylogenetic comparative and missing data methods to predict the dimensions of the fundamental niche of species with missing data. Moreover, we explore sources of uncertainty in predictions of fundamental niche and highlight future directions to overcome current limitations of phylogenetic comparative and missing data methods to improve predictions. We conclude that it is possible to make better use of the current knowledge about species’ fundamental niche with phylogenetic information and auxiliary traits to predict the fundamental niche of poorly-studied species. In the fourth chapter, we used the framework of the chapter three to test the performance of two recent phylogenetic modeling methods to predict the thermal niche of mammals. We showed that PhyloPars had better performance than Phylogenetic Eigenvector Maps in predict the thermal niche. Moreover, the error and bias had similar phylogenetic pattern for both margins of the thermal niche while they had differences in the geographic pattern. The variance in the performance was explained by taxonomic differences and not by methodological aspects. Finally, our models better predicted the upper margin than the lower margin of the thermal niche. This is a good news for predicting the effect of climate change on species without physiological data. We hope our finds can be used to improve the predictions of climate change effect on the biodiversity in future studies and support the political decisions on minimizing the effects of climate change on biodiversity. / As mudanças climáticas são uma das principais ameaças à biodiversidade e é esperado que aumente seu impacto ao longo do século XXI. As mudanças climáticas afetam todos os níveis de biodiversidade, de indivíduos à biomas, reduzindo os serviços ecossistêmicos. Apesar disso, as predições dos impactos das mudanças climáticas na biodiversidade é ainda um desafio. A superação dessas questões depende de melhorias em diferentes aspectos da ciência que dá suporte para predizer o impacto das mudanças climáticas na biodiversidade. A prática comum para predizer o impacto das mudanças climáticas consiste em formular modelos de nicho ecológico baseado no clima atual e projetar as mudanças baseadas no clima futuro predito pelos modelos climáticos. No entanto, existem algumas limitações reconhecidas na formulação do modelo de nicho ecológico e no uso das predições dos modelos climáticos que precisam ser analisadas. Aqui, no primeiro capítulo nós revisamos a ciência por detrás dos modelos climáticos com o intuito de reduzir a lacuna de conhecimentos entre a comunidade científica que formula os modelos climáticos e a comunidade que usa as predições dos modelos. Nós mostramos que não existe consenso sobre avaliar os modelos climáticos, obter modelos regionais com maior resolução espacial e definir modelos consensuais. No entanto, nós damos algumas orientações para usar as predições dos modelos climáticos. No segundo capítulo, nós testamos se as predições dos modelos correlativos de nicho ecológicos ajustados com presença-ausência são congruentes com aqueles ajustados com dados de abundância nas medidas de impacto das mudanças climáticas em abelhas de orquídeas da Mata Atlântica. Nós encontramos que os modelos com presença-ausência foram substitutos parciais das mudanças na abundância quando o resultado dos modelos foi contínuo (adequabilidade), mas o mesmo não ocorreu quando as predições foram convertidas para binárias. As espécies de abelhas, de modo geral, irão diminuir em abundância no futuro, mas reterão uma boa quantidade de locais adequados no futuro e a distância para áreas climáticas adequadas ganhadas podem estar bem próximo, apesar da grande variação. A mudança na riqueza e na substituição de espécies ocorrerá principalmente no Oeste e algumas regiões no sul da Mata Atlântica. No terceiro capítulo, nós discutimos as desvantagens no uso de estimativas do nicho realizado ao invés do nicho fundamental, como superestimar o efeito das mudanças climáticas no risco de extinção das espécies. Nós propomos um esquema geral baseado em métodos filogenéticos comparativos e métodos de dados faltantes para predizer as dimensões do nicho fundamental das espécies com dados faltantes. Além disso, nós exploramos as fontes de incerteza nas predições do nicho fundamental e destacamos direções futuras para superar as limitações atuais dos métodos comparativos filogenéticas e métodos de dados faltantes para melhorar as predições. Nós concluímos que é possível fazer melhor uso do conhecimento atual sobre o nicho fundamental das espécies com informação filogenética e caracteres auxiliares para predizer o nicho fundamental de espécies pouco estudadas. No quarto capítulo, nós usamos o esquema geral do capítulo três para testar a performance de dois novos métodos de modelagem filogenética para predizer o nicho térmico dos mamíferos. Nós mostramos que o “PhyloPars” teve uma melhor performance que o “Phylogenetic Eigenvector Maps” em predizer o nicho térmico. Além disso, o erro e o viés tiveram um padrão filogenético similar para ambas as margens do nicho térmico, enquanto eles apresentaram diferentes padrões espaciais. A variância na performance foi explicada pelas diferenças taxonômicas e não pelas diferenças em aspectos metodológicos. Finalmente, nossos modelos melhor predizem a margem superior do que a margem inferior do nicho térmico. Essa é uma boa notícia para predizer o efeito das mudanças climáticas em espécies sem dados fisiológicos. Nós esperamos que nossos resultados possam ser usados para melhorar as predições do efeito das mudanças climáticas na biodiversidade em estudos futuros e dar suporte para decisões políticas para minimização dos efeitos das mudanças climáticas na biodiversidade.