Water managers for the City of Phoenix face the need to make informed policy decisions regarding long-term impacts of climate change on the Salt-Verde River basin. To provide a scientifically informed basis for this, we estimate the evolution of important components of the basin-scale water balance through the end of the 21st century. Bias-corrected and spatially downscaled climate projections from the Phase-3 Coupled Model Intercomparison Project of the World Climate Research Programme were used to drive a spatially distributed variable infiltration capacity model of the hydrologic processes in the Salt-Verde basin. From the many Global Climate Model's participating in the IPCC fourth assessment, we selected a five-model ensemble, including three that best reproduce the historical climatology for our study region, plus two others to represent wetter and drier than model average conditions; the latter two were requested by City of Phoenix water managers to more fully represent the full range of GCM prediction uncertainty. For each GCM, data for three emission scenarios (A1B, A2, B1) was used to drive the hydrologic model into the future. The model projections indicate a statistically significant 25% decrease in streamflow by the end of the 21st century. Contrary to previous assessments, this is not caused primarily by changes in the P/E ratio, but is found to result mainly from decreased winter precipitation accompanied by significant (temperature driven) reductions in storage of snow. The results show clearly the manner in which water management in central Arizona is likely to be impacted by changes in regional climate.

An increased awareness about the global warming has created a demand for more information on how the climate is affected by different activities.This master thesis was initiated by Tricorona, a Swedish company that offers its customers analysis and calculation of their climate impact. Tricorona also supplies climate neutralisation with CERs, in accordance with the Kyoto protocol and controlled by the UN. This work demands updated emission factors for greenhouse gases. An emission factor gives information about the greenhouse gasintensity of a service or a product [kg CO2-eq./ functional unit].The purpose of this thesis is to examine how electricity, district heating, hotels, taxis, food and materials affect the climate and how emission factors for these areas can be calculated.This was done by reviewing and comparing different studies and by interviewing experts. The information was evaluated and recommendations on calculations and emission factors were made.The consumption of energy is the main source of greenhouse gas emissions for district heating, electricity, hotels, taxis and materials. For food production the biogenic greenhousegas emissions are also important, such as the emissions of carbon dioxide and nitrous oxide from land use and the methane emissions from ruminants.For climate impact assessment of electricity, district heating, hotels and taxis it is recommended that the calculations should be based on an average consumption of energy. All types of energy carriers should be included in the calculations and the emission factors used should be based on Life Cycle Assessments. Climate impact assessments based on energy consumption is a simplification that underestimates the real greenhouse gas emissions. The recommended emission factors are associated with some uncertainties that originate from the quality of the data used, the assumptions made and the system boundaries that were chosen.Despite that, the recommended emission factors can be considered representative since they are based on the best available data. For food and materials it is recommended that emissionfactors from Life Cycle Inventories should be used.

There is an urgency within the building sector to reduce its greenhouse gas emissions and mitigate climate change. An increased proportion of biobased building materials in construction is a potential measure to reduce these emissions. Life cycle assessment (LCA) has often been applied to compare the climate impact from biobased materials with that from e.g. mineral based materials, mostly favouring biobased materials. Contradicting results have however been reported due to differences in methodology, as there is not yet consensus regarding certain aspects. The aim of this thesis is to study the implications from non-traditional practices in climate impact assessment of timber buildings, and to discuss the shortcomings of current practices when assessing such products and comparing them with non-renewable alternatives. The traditional practices for climate impact assessment of biobased materials have been identified, and then applied to a case study of a building with different timber frame designs and an alternative building with a concrete frame. Then, non-traditional practices were explored by calculating climate impact results using alternative methods to handle certain methodological aspects, which have been found relevant for forest products in previous research such as the timing of emissions, biogenic emissions, carbon storage in the products, end-of-life substitution credits, soil carbon disturbances and change in albedo. These alternative practices and their implications were also studied for low-carbon buildings. The use of non-traditional practices can affect the climate impact assessment results of timber buildings, and to some extent the comparison with buildings with lower content of biobased building materials. This effect is especially evident for energy-efficient buildings. Current normal practices tend to account separately for forest-related carbon flows and aspects such as biogenic carbon emissions and sequestration or effects from carbon storage in the products, missing to capture the forest carbon cycle as a whole. Climate neutrality of wood-based construction materials seems like a valid assumption for studies which require methodological simplification, while other aspects such as end-of-life substitution credits, soil carbon disturbances or changes in albedo should be studied carefully due to their potentially high implications and the uncertainties around the methods used to account for them. If forest phenomena are to be included in LCA studies, a robust and complete model of the forest carbon cycle should be used. Another shortcoming is the lack of clear communication of the way some important aspects were handled. / <p>QC 20150310</p>

General Circulation Models (GCMs) are tools designed to simulate time series of climate variables globally, accounting for effects of greenhouse gases in the atmosphere. They attempt to represent the physical processes in the atmosphere, ocean, cryosphere and land surface. They are currently the most credible tools available for simulating the response of the global climate system to increasing greenhouse gas concentrations, and to provide estimates of climate variables (e.g. air temperature, precipitation, wind speed, pressure etc.) on a global scale. GCMs demonstrate a significant skill at the continental and hemispheric spatial scales and incorporate a large proportion of the complexity of the global system; they are, however, inherently unable to represent local subgrid-scale features and dynamics. The spatial scale on which a GCM can operate (e.g., 3.75° longitude x 3.75° latitude for Coupled Global Climate Model, CGCM2) is very coarse compared to that of a hydrologic process (e.g., precipitation in a region, streamflow in a river etc.) of interest in the climate change impact assessment studies. Moreover, accuracy of GCMs, in general, decreases from climate related variables, such as wind, temperature, humidity and air pressure to hydrologic variables such as precipitation, evapotranspiration, runoff and soil moisture, which are also simulated by GCMs. These limitations of the GCMs restrict the direct use of their output in hydrology. This thesis deals with developing statistical downscaling models to assess climate change impacts and methodologies to address GCM and scenario uncertainties in assessing climate change impacts on hydrology. Downscaling, in the context of hydrology, is a method to project the hydrologic variables (e.g., rainfall and streamflow) at a smaller scale based on large scale climatological variables (e.g., mean sea level pressure) simulated by a GCM. A statistical downscaling model is first developed in the thesis to predict the rainfall over Orissa meteorological subdivision from GCM output of large scale Mean Sea Level Pressure (MSLP). Gridded monthly MSLP data for the period 1948 to 2002, are obtained from the National Center for Environmental Prediction/ National Center for Atmospheric Research (NCEP/NCAR) reanalysis project for a region spanning 150 N -250 N in latitude and 800 E -900 E in longitude that encapsulates the study region. The downscaling model comprises of Principal Component Analysis (PCA), Fuzzy Clustering and Linear Regression. PCA is carried out to reduce the dimensionality of the larger scale MSLP and also to convert the correlated variables to uncorrelated variables. Fuzzy clustering is performed to derive the membership of the principal components in each of the clusters and the memberships obtained are used in regression to statistically relate MSLP and rainfall. The statistical relationship thus obtained is used to predict the rainfall from GCM output. The rainfall predicted with the GCM developed by CCSR/NIES with B2 scenario presents a decreasing trend for non-monsoon period, for the case study. Climate change impact assessment models developed based on downscaled GCM output are subjected to a range of uncertainties due to both ‘incomplete knowledge’ and ‘unknowable future scenario’ (New and Hulme, 2000). ‘Incomplete knowledge’ mainly arises from inadequate information and understanding about the underlying geophysical process of global change, leading to limitations in the accuracy of GCMs. This is also termed as GCM uncertainty. Uncertainty due to ‘unknowable future scenario’ is associated with the unpredictability in the forecast of socio-economic and human behavior resulting in future Green House Gas (GHG) emission scenarios, and can also be termed as scenario uncertainty. Downscaled outputs of a single GCM with a single climate change scenario represent a single trajectory among a number of realizations derived using various GCMs and scenarios. Such a single trajectory alone can not represent a future hydrologic scenario, and will not be useful in assessing hydrologic impacts due to climate change. Nonparametric methods are developed in the thesis to model GCM and scenario uncertainty for prediction of drought scenario with Orissa meteorological subdivision as a case study. Using the downscaling technique described in the previous paragraph, future rainfall scenarios are obtained for all available GCMs and scenarios. After correcting for bias, equiprobability transformation is used to convert the precipitation into Standardized Precipitation Index-12 (SPI-12), an annual drought indicator, based on which a drought may be classified as a severe drought, mild drought etc. Disagreements are observed between different predictions of SPI-12, resulting from different GCMs and scenarios. Assuming SPI-12 to be a random variable at every time step, nonparametric methods based on kernel density estimation and orthonormal series are used to determine the nonparametric probability density function (pdf) of SPI-12. Probabilities for different categories of drought are computed from the estimated pdf. It is observed that there is an increasing trend in the probability of extreme drought and a decreasing trend in the probability of near normal conditions, in the Orissa meteorological subdivision. The single valued Cumulative Distribution Functions (CDFs) obtained from nonparametric methods suffer from limitations due to the following: (a) simulations for all scenarios are not available for all the GCMs, thus leading to a possibility that incorporation of these missing climate experiments may result in a different CDF, (b) the method may simply overfit to a multimodal distribution from a relatively small sample of GCMs with a limited number of scenarios, and (c) the set of all scenarios may not fully compose the universal sample space, and thus, the precise single valued probability distribution may not be representative enough for applications. To overcome these limitations, an interval regression is performed to fit an imprecise normal distribution to the SPI-12 to provide a band of CDFs instead of a single valued CDF. Such a band of CDFs represents the incomplete nature of knowledge, thus reflecting the extent of what is ignored in the climate change impact assessment. From imprecise CDFs, the imprecise probabilities of different categories of drought are computed. These results also show an increasing trend of the bounds of the probability of extreme drought and decreasing trend of the bounds of the probability of near normal conditions, in the Orissa meteorological subdivision. Water resources planning requires the information about future streamflow scenarios in a river basin to combat hydrologic extremes resulting from climate change. It is therefore necessary to downscale GCM projections for streamflow prediction at river basin scales. A statistical downscaling model based on PCA, fuzzy clustering and Relevance Vector Machine (RVM) is developed to predict the monsoon streamflow of Mahanadi river at Hirakud reservoir, from GCM projections of large scale climatological data. Surface air temperature at 2m, Mean Sea Level Pressure (MSLP), geopotential height at a pressure level of 500 hecto Pascal (hPa) and surface specific humidity are considered as the predictors for modeling Mahanadi streamflow in monsoon season. PCA is used to reduce the dimensionality of the predictor dataset and also to convert the correlated variables to uncorrelated variables. Fuzzy clustering is carried out to derive the membership of the principal components in each of the clusters and the memberships thus obtained are used in RVM regression model. RVM involves fewer number of relevant vectors and the chance of overfitting is less than that of Support Vector Machine (SVM). Different kernel functions are used for comparison purpose and it is concluded that heavy tailed Radial Basis Function (RBF) performs best for streamflow prediction with GCM output for the case considered. The GCM CCSR/NIES with B2 scenario projects a decreasing trend in future monsoon streamflow of Mahanadi which is likely to be due to high surface warming. A possibilistic approach is developed next, for modeling GCM and scenario uncertainty in projection of monsoon streamflow of Mahanadi river. Three GCMs, Center for Climate System Research/ National Institute for Environmental Studies (CCSR/NIES), Hadley Climate Model 3 (HadCM3) and Coupled Global Climate Model 2 (CGCM2) with two scenarios A2 and B2 are used for the purpose. Possibilities are assigned to GCMs and scenarios based on their system performance measure in predicting the streamflow during years 1991-2005, when signals of climate forcing are visible. The possibilities are used as weights for deriving the possibilistic mean CDF for the three standard time slices, 2020s, 2050s and 2080s. It is observed that the value of streamflow at which the possibilistic mean CDF reaches the value of 1 reduces with time, which shows reduction in probability of occurrence of extreme high flow events in future and therefore there is likely to be a decreasing trend in the monthly peak flow. One possible reason for such a decreasing trend may be the significant increase in temperature due to climate warming. Simultaneous occurrence of reduction in Mahandai streamflow and increase in extreme drought in Orissa meteorological subdivision is likely to pose a challenge for water resources engineers in meeting water demands in future.