Weather and climate may affect all major aspects of the electric power sector, including electricity generation, transmission and distribution systems, and end-user demand for power. On the demand side, warmer winter temperatures in cold regions may reduce the demand for energy because less space heating will be required. On the other hand, higher temperatures during summer months in warm regions will lead to more demand for electricity to run air-conditioners and refrigerators. In order to assess these effects, the existing POLES demand model has been improved for setting apart the demand affected from climatechange. This demand is then modified by taking into account heating degree days (HDD) and cooling degree days (CDD) provided by the IMAGE/TIMER model, in the framework of a world where the average temperature may increase of +3.7°C compared to pre- industrial ages (or a concentration level of 771 ppmv in 2100).
Chapter 10 describes the use of Data Envelopment Analysis (DEA). Often farm models like FSSIM are applied for average farm types, using average data on inputs and outputs for these farms. For most FSSIM applications, ‘simple survey’ data based on expert knowledge were used, which were collected in the SEAMLESS project (Van Ittersum et al., 2008) and were based on expert knowledge for a region, characterizing the inputs-output coefficients of the most common activities. DEA provides an approach that can capture data on inputs and outputs from actual and individual farms. By using these data, it can recover current technical relationships (the current production functions) and rank individual farms based on their capacity to convert inputs into outputs. Farms that are superior with respect of converting inputs into outputs form the production frontier, while other farms are enveloped by this frontier. Based on the technical relationships and without any behavioural assumption (e.g. profit or utility maximization), DEA can furthermore suggest realistic farm level adaptation strategies to these farms. These are strategies to adapt to current conditions, including climate, markets and policy, to improve farm performance. When the input-output relationships of future agricultural activities are defined, realistic adaptation strategies for 2050 can also be identified for future farms. DEA can be coupled to a bio-economic farm model like FSSIM, where behavioural assumptions can be made to identify optimal strategies of farmers. DEA is thus a substitute for ‘simple survey’ data that are averaged per farm type, and besides, can answer additional questions. The main difference between using FSSIM with expert knowledge from ‘simple survey’ data and with DEA is that when using expert knowledge more specific agricultural activities and adaptation strategies can be included (rotations linked to management), whereas DEA depends on data available for actual farms. With DEA the most efficient rotations or production methods (in terms of input-output relationships) result from the analysis, and only these are included as input-output relationships in FSSIM.
105 uncertainty in the modelling steps are, the more dangerous and biased the efforts could be, with the probability of failure which is proportional to the magnitude of disconnection between what is projected and what is likely to occur. According to the provided evidences, the altitudinal gradient will play a very important role in determining different pattern of species distribution in futureclimate condition in Italy. This parameter strongly influences the shape, structure and specific composition of forests worldwide with a direct effect on a series of important process, such as water availability, temperature and soil properties (Lin et al., 2018; Littell et al., 2008; Zhang et al., 2016). The tendency in altitudinal shift of different organism, both animal and plant, is often confirmed by many research papers (Chen et al., 2011; Lenoir et al., 2008; Rumpf et al., 2018; Vacchiano and Motta, 2015) with the altitudinal shift generally occurring at very lower speed than latitudinal (Sáenz-Romero et al., 2016). One of the main issues is if the velocity of colonisation of new areas is too low if compared to expected climatechange scenarios. In this case most of the studies are focussed on the upper elevational limit, sometimes also called as leading edge, while the lower elevational limit or rear edges is less investigated even if fundamental to plan adequate conservation scenarios for threatened species (Hampe and Petit, 2005; Rumpf et al., 2019, 2018). According to Lenoir et al. (2008) an average trend shift of 29 meters in upward sense for decade seems to be reliable a value for forest tree species in southern of France considering the variation in optimum climatic of species in two different periods that it 1905-1985 and 1986-2005. A confirm of this process with regarding Italian mountains can be found in Rogora et al. (2018) where a progressive thermophilization process of climate and a progressive natural introduction of typical species of lower altitudinal strip both for Alps and Apennine has been detected. According to our results, the altitudinal movement of the forested areas with the worst scenario (COSMO) seemed to be lower and around 18 meters per decade, demonstrating a possibility of Italian forest tree species to colonize new lands. In this sense the higher sensitivity to climatechange of pure broadleaf stands is one of the main results of our modelling efforts. This result confirms the recent literature where a general contraction of broadleaves species, especially those species that are adapted to cold and wet conditions was studied (Hanewinkel et al., 2012; Ruiz-Labourdette et al., 2012).
Abstract: Climatechange could pose a significant threat to the energy sector in various countries. The objective of this study is to analyze the long-term impact of changes in precipitation and water availability on hydroelectric production. To do so, the study focuses on three hydroelectric power plants in Southern Spain combining climatological, technical and economic data and projections. A physical model has been designed that reproduces the plants’ operations and incorporates various scenarios for the evolution of contributions to the basin. The results predict a 10 to 49% drop in production by the end of the century, depending on the plant and scenario. This decrease in production, in accordance with our economic and operational hypotheses, would significantly affect the operating margins of the facilities and, in certain scenarios, could reach an economically unsustainable level by the end of the century. An investment analysis has been carried out as well, showing that climatechange may jeopardize future investments in similar facilities.
Also, this study limited itself to one potential online music system, a purchase-for- download system as is currently common in Amazon’s MP3 service and the iTunes store. However, many more systems exist, such as subscription systems where the user pays a fee per month for access to a catalog of albums that can then be streamed at will. Clearly this is an entirely different system, and future work should elucidate the energy and emissions associated with streaming audio. Further, streaming video is also a relatively unexplored area and one that is growing extremely quickly in Internet traffic. Extending previous studies on video rental (i.e., (Sivaraman et al., 2007)) to include both downloadable digital video rentals as well as streaming video systems would also be illuminating for tech-savvy customers.
The climatic and hydrological systems are tightly related and any induced changes cause chained interactions. In an attempt to adequately manage water resources in Greece, a series of experiments were conducted with different GCMs in selected study areas to understand this interplay. This paper is an overview of the studies carried out in the Aliakmon, the Upper Acheloos, the Portaikos, and the Pinios basins, where the regional hydrological cycle was evaluated on river basin spatial scale to assess regional impacts and variability. The impacts of climatechange on the water resources are presented in a synthetic quantitative way, in order to draw general conclusions concerning the trends of the hydrological indicators. A good agreement was observed between the different climatic experiments, and the trends on the selected hydrological indicators demonstrate an increase in temperature and PET, reduction in the mean annual precipitation and runoff, and a shifting of the snowmelt period towards the winter, while the snowpack storage was proved to be a con- trolling factor. It is accentuated that relatively small decreases in the mean annual precipitation cause dramatic increase of reservoir risk levels of annual firm water supply and energy production. As a result, radical increases of reservoir storage volume are required to maintain firm water and energy yields at tolerable risk levels. The adaptive capacity of the country is not that high, and a series of serious actions need to be taken in order to mitigate the effects of climatechange and assess its impacts.
cooling of buildings due to future temperature changes, considering a multi-model approach allowing a consistent analysis of the linkage between climate, energy and macro-economic dynamics. A first specific objective concerns the analysis of energy and technology decisions resulting from these changes at global and regional levels, taking a systems perspective that ac- counts for impacts on the entire energy system and resulting substitution eﬀects. This extends the analysis of Isaac and van Vuuren ( 2009 ), which focused on demands, ignoring substitution eﬀects. A second specific objective is the assessment of possible feedbacks on the climate sys- tem of changes in emissions from the energy system. The increase of electricity generation for cooling, if not compensated by the decrease of energy use for heating, may be a source of addi- tional greenhouse gases, which could accelerate climatechange. Fuel details, needed to assess this feedback, were not analyzed in ( Mima and Criqui , 2009 ). A third objective is to study both direct and indirect macro-economic impacts, including possible rebound eﬀects resulting from a decrease of energy costs for households. There are only a few studies that investigate macro-economic eﬀects of changes in energy demand due to climatechange, particularly the rebound eﬀect of prices. Bosello et al. ( 2007 ) rely on econometric estimation of the relationship between average temperature and long-run demand for energy goods. Aaheim et al. ( 2009 )
derived from historical data (Immerzeel, 2008; Chowdhury and Ward, 2004; Mirza et al., 2003). Immerzeel (2008) used the multiple regression technique to predict streamflow at the Bahadurabad station (the outlet of Brahmaputra Basin) un- der future temperature and precipitation conditions based on a statistically downscaled GCM (global circulation model) output. However, since most hydrologic processes are non- linear, they cannot be predicted accurately by extrapolating empirically derived regression equations to the future projec- tions. The alternative for the assessment of climatechangeimpacts on basin-scale hydrology is via well-calibrated hy- drologic modeling, but this has rarely been conducted for the GBM basin due to the lack of observed data for model cal- ibration and validation. Ghosh and Dutta (2012) applied a macroscale distributed hydrologic model to study the change of future flood characteristics at the Brahmaputra Basin, but their study domain is only focused on the regions inside In- dia. Gain et al. (2011) estimated future trends of the low and high flows in the lower Brahmaputra Basin using outputs from a global hydrologic model (grid resolution: 0.5 ◦ ) forced by multiple GCM outputs. Instead of model calibration, the simulated future streamflow is weighted against observations to assess the climatechangeimpacts.
In Northern Iraq, Al-Adhaim River is one of the fife tributaries of Tigris River. Al-Adhaim is the source of surface water for Kirkuk city. This basin has been suffering from water scarcity and pollution due to its extreme dry weather . Up to date, water issues related to climatechange in the Al-Adhaim catchment have not been well ad- dressed within climatechange analyses and climate policy construction . Therefore, the main objective of this study has been to assess the potential future climatic changes on the water sources of Al-Adhaim, specifically blue and green waters. The computer-based hydrological model Soil and Water Assessment Tool (SWAT) has been used to explore the effects of climatic change on streamflow of the study area. The model was set at monthly scale using available spatial and temporal data and calibrated against measured streamflow. Climatechange scenarios were obtained from general circulation models.
socio-economic scenarios. Indicators of impact cover the water resources, river and coastal flooding, agriculture, natural environment and built environment sectors. Impacts are assessed under four SRES socio-economic and emissions scenarios, and the effects of uncertainty in the projected pattern of climatechange are incorporated by constructing climate scenarios from 21 global climate models. There is considerable uncertainty in projected regional impacts across the climatemodel scenarios, and coherent assessments of impacts across sectors and regions therefore must be based on each model pattern separately; using ensemble means, for example, reduces variability between sectors and indicators. An example narrative assessment is presented in the paper. Under this narrative approximately 1 billion people would be exposed to increased water resources stress, around 450 million people exposed to increased river flooding, and 1.3 million extra people would be flooded in coastal floods each year. Crop productivity would fall in most regions, and residential energy demands would be reduced in most regions because reduced heating demands would offset higher cooling demands. Most of the global impacts on water stress and flooding would be in Asia, but the proportional impacts in the Middle East North Africa region would be larger. By 2050 there are emerging differences in impact between different emissions and socio-economic scenarios even though the changes in temperature and sea level are similar, and these differences are greater in 2080. However, for all the indicators, the range in projected impacts between different climate models is considerably greater than the range between emissions and socio-economic scenarios.
Our study underlines the need for biophysical impact modelers to be particularly meticulous in their analysis when it involves handling a subset of climate models. A first point on which extra care should be taken is the treatment of outputs. Our findings indicate that in many cases, the ensemble mean of a set of climate projections might not be a good representative of the projected changes, as ensemble members’ values might span both positive and negative values. In these cases, the variability of the projections is lost to a close to zero value of the ensemble mean. Moreover, the spatiotemporal aggregation over large domains or at country level that is typically used to communicate changes should be critically used and discussed, as it might cause loss of the benefits of higher resolution simulations. A second point that requires attention when interpreting results on impacts concerns the time-slices employed, especially when they are based on levels of warming. The different timing of crossing a specific warming level between the ensemble members has a direct impact on the results. The radiative forcing is evolving with time (depending on the emission scenario) and is different for each member depending on the time of crossing the GWLs of the driving GCM. When comparing the different ensembles, the different timing of GWLs between them, due to the different models and/or different number of models in the ensemble, might impose an extra source of uncertainty on the results. A third point requiring extra care when dealing with impacts at the regional scale is the selection of available RCM simulations. For many RCM domains, there is generally an imbalance in the number of available downscaling simulations. For some regional climatechangeassessment programs, the majority of simulations are conducted from a single RCM and a limited number of simulations by different RCMs complement the ensemble of available futureclimate projections. The disparity in available simulations might cause a bias of the projected impacts towards a specific climatemodel.
Climatechangeimpacts within river systems include changes in runoff, river flow and groundwater storage. In addition to these quantitative aspects, some water quality parameters are also expected to change. With respect to biogeochemical water quality, most climatechangeimpacts can be attributed to changes in stream water temperature. The impact of climatechange on stream water temperature is highly dependent on the future evolution of air temperature as well as on other meteorological and physical parameters. The present study showed that projected air temperature increase of 2 ˚C - 5˚C was very consistent across the province of New Brunswick and that the water temperature would most likely increase in the range of 70% of air temperature increase. This information was based on a long-term data and modeling analysis at Little Southwest Miramichi River. These changes will have an impact on dissolved oxygen concentrations and instream biological activities. The present study showed that dissolved oxygen (DO) was the only parameter (from the available water quality data in New Brunswick) which was correlated to water temperature. Therefore, a T w -DO relationship was
The scope of the book is not only to explore the intertwined problems of climatechange and peak oil, but also to assess the chances of an effective response. It becomes clear in the first few pages that The Future is a unique contribution. Many authors have written about the scale of the problem and bemoaned the lack of action; fewer have adequately explained inaction, or honestly appraised the likely impacts of collective failure. In a refreshingly stark and honest beginning to the book, Friedrichs sets out his aim, to “explain our inability to adequately grasp and confront our predicament” (p. viii). This review assesses the work on two levels: firstly it is assessed in its own terms - how well does it meet its aim? Secondly, I offer a more general assessment of the book's worth, from the perspective of an optimistic environmental geographer eager for a 'post-carbon' future.
Abstract. As China becomes increasingly urbanised, flood- ing has become a regular occurrence in its major cities. As- sessing the effects of futureclimatechange on urban flood volumes is crucial to informing better management of such disasters given the severity of the devastating impacts of flooding (e.g. the 2016 flooding events across China). Al- though recent studies have investigated the impacts of futureclimatechange on urban flooding, the effects of both cli- mate change mitigation and adaptation have rarely been ac- counted for together in a consistent framework. In this study, we assess the benefits of mitigating climatechange by re- ducing greenhouse gas (GHG) emissions and locally adapt- ing to climatechange by modifying drainage systems to re- duce urban flooding under various climatechange scenarios through a case study conducted in northern China. The ur- ban drainage model – Storm Water Management Model – was used to simulate urban flood volumes using current and two adapted drainage systems (i.e. pipe enlargement and low- impact development, LID), driven by bias-corrected meteo- rological forcing from five general circulation models in the Coupled Model Intercomparison Project Phase 5 archive. Re- sults indicate that urban flood volume is projected to increase by 52 % over 2020–2040 compared to the volume in 1971– 2000 under the business-as-usual scenario (i.e. Representa- tive Concentration Pathway (RCP) 8.5). The magnitudes of urban flood volumes are found to increase nonlinearly with changes in precipitation intensity. On average, the projected flood volume under RCP 2.6 is 13 % less than that under
Tampa Bay Water and the Southwest Florida Water Man- agement District (SWFWMD) developed the Integrated Hy- drologic Model (IHM) simulation engine which integrates the EPA Hydrologic Simulation Program-Fortran (Bicknell et al., 2005) for surface water modeling with the U.S. Geo- logical Survey (USGS) MODFLOW96 (Harbaugh and Mc- Donald, 1996) for groundwater modeling. The IHM simu- lates the dynamic interaction of surface water and groundwa- ter systems within the INTB region, including all processes which affect flow and water levels in uplands, within the un- saturated soil, and within wetlands, rivers, and aquifers. In addition, the INTB model can account for variability in cli- mate and anthropogenic stresses such as land use change, groundwater pumping, and diversions to/from rivers, lakes, and wetlands.
of sorghum in West Africa, we assumed the same proportion of each cultivar in each site and average across cultivars. We also made the assumption that soil and management practices were the same in the 35 locations. Although local variations of soils and management can have a major effect on crop yields, this assumption is possible because of the relative uniformity of the soil (over 95% of soils in this region are sandy with low levels of organic matter, total nitrogen, and effective cation exchange capacity: see Bationo et al 2005) and management practices (little or no agricultural inputs, no irrigation, sowing after the ﬁ rst major rain event: see Marteau et al 2011). Simulations were performed without any irrigation since most crop systems are rainfed (93% of all agricultural land in Sub-Saharan Africa) and, to our knowledge, irrigation is never used for sorghum in West Africa. For validation purpose, following the work done by Sultan et al (2013), we scaled-up the crop yield simulations by simply averaging the crop yields of each of the 35 locations. Simulated crop yields were validated against Food and Agriculture Organization of the United Nations (FAO) annual data submitted by its member nations. We extracted national sorghum yields from the FAO on-line database (http://faostat. fao.org/) and computed an average of countries ’ national yield (Senegal, Mali, Burkina Faso, Niger, Guinea, Gambia, Guinea Bissau, Togo, Benin) over the 1961 – 1990 period, weighted by the national cultivated area for sorghum. Both mean and variability of simulated yields were validated against FAO observations. We assess model ﬁ delity from the correlation between observed and predicted crop yield time series, after removal of any linear trends. FAO sorghum yields in some countries show increasing yields over 1961 – 1990 (Burkina- Faso, Senegal), while others face decreasing values (Chad, Niger). Local climate ﬂ uctuations may play a role in these trends, but non-climatic factors are likely to be the dominant drivers (land-degradation, intensi ﬁ cation, intra or extra-national migrations, economic crisis). Because these potential non- climatic effects will not be simulated by any climate-driven crop model, we detrend observations and only analyze interannual variability. The linear trend equation for FAO data is yield = 1.3*year + 568.
of sorghum in West Africa, we assumed the same proportion of each cultivar in each site and average across cultivars. We also made the assumption that soil and management practices were the same in the 35 locations. Although local variations of soils and management can have a major effect on crop yields, this assumption is possible because of the relative uniformity of the soil (over 95% of soils in this region are sandy with low levels of organic matter, total nitrogen, and effective cation exchange capacity: see Bationo et al 2005 ) and management practices (little or no agricultural inputs, no irrigation, sowing after the ﬁrst major rain event: see Marteau et al 2011 ). Simulations were performed without any irrigation since most crop systems are rainfed (93% of all agricultural land in Sub-Saharan Africa) and, to our knowledge, irrigation is never used for sorghum in West Africa. For validation purpose, following the work done by Sultan et al ( 2013 ), we scaled-up the crop yield simulations by simply averaging the crop yields of each of the 35 locations. Simulated crop yields were validated against Food and Agriculture Organization of the United Nations (FAO) annual data submitted by its member nations. We extracted national sorghum yields from the FAO on-line database ( http://faostat. fao.org/ ) and computed an average of countries ’ national yield (Senegal, Mali, Burkina Faso, Niger, Guinea, Gambia, Guinea Bissau, Togo, Benin) over the 1961 –1990 period, weighted by the national cultivated area for sorghum. Both mean and variability of simulated yields were validated against FAO observations. We assess model ﬁdelity from the correlation between observed and predicted crop yield time series, after removal of any linear trends. FAO sorghum yields in some countries show increasing yields over 1961 –1990 (Burkina- Faso, Senegal), while others face decreasing values (Chad, Niger). Local climate ﬂuctuations may play a role in these trends, but non-climatic factors are likely to be the dominant drivers (land-degradation, intensi ﬁcation, intra or extra-national migrations, economic crisis). Because these potential non- climatic effects will not be simulated by any climate-driven crop model, we detrend observations and only analyze interannual variability. The linear trend equation for FAO data is yield = 1.3*year + 568.
Cross-sectional models measure farm performances across climatic zones (Mendelsohn et al. 1994, 1996; Mendelsohn 2000, Mendelsohn & Dinar 1999, 2003; Sanghi 1998; Sanghi et al. 1998). The Ricardian approach is the common cross-sectional method that has been used to measure the impact of climatechange on agriculture. The method was named after David Ricardo (1772–1823) because of his original observation that land rents would reflect the net productivity of farmland (Mendelsohn & Dinar 2003). The Ricardian approach has been applied in the United States (Mendelsohn et al. 1994, 1996) and in some developing countries – Brazil (Sanghi 1998), India (Sanghi et al. 1998; Kumar & Parikh 1998) and South Africa (Gbetibouo & Hassan 2005) – to examine the sensitivity of agriculture to changes in climate. The Ricardian approach regresses farm performance (land value or net income) on a set of environmental factors, traditional inputs (land and labor) and support systems (infrastructure) to measure the contribution of each factor to the outcome and detect the effects of long-term climatechange on farm values (Mendelsohn et al. 1994, 1996; Mendelsohn & Dinar 1999). In a well-functioning market system, the value of a parcel of land should reflect its potential profitability, implying that spatial variations in climate derive spatial variations in land uses and in turn land values (Polsky 2004). With this background, it should be possible to estimate a meaningful climate–land value relationship by specifying a multivariate regression model. The estimated coefficients for the climate variables would reflect the economic value of climate to agriculture, holding other factors constant.
According to Nzau (2013), Kenya is also experiencing the impacts of climatechange. It is costing Kenya 2.4% of its Gross Domestic Product (GDP). He further states that; climate variability and change is impacting many sectors of the economy and there is a need for diversification at both national and local level to reduce on reliance on rainfed agriculture as the main source of livelihood for the local communities across Kenya. The agricultural sector is one of the major economic activity in Kenya, which accounts for about 30% of the GDP and 60% foreign exchange earner. It also forms the main source of employment (ICPAC, 2006). Some of the other sectors of the economy that are climate depended include; livestock keeping, hydro-energy generation, transport, and tourism. 60% of socio- economic activities are dependent on climate (WRI et al., 2007). Climate extreme such as floods and droughts have a high influence on the social-economic activities of the country and thus also affect performance of the country’s economy (GOK, 2013). Thus, understanding climate variability and change and its future scenario at a local scale is very important for economic growth and formulation of adaptation strategies that will increase the resilient of the local community.
In this section we describe the derivation and validation of baseline (1 January 1995 to 1 January 2005) heating degree days (HDDs) and cooling degree days (CDDs), calculated on the PLASIM-ENTS grid and mapped onto the regions of the TIAM-WORLD model (TIMES integrated assessmentmodel; Loulou and Labriet, 2008). HDDs provide a measure that reflects heating energy demands, calculated relative to some baseline temperature. On a given day, the average tem- perature is calculated and subtracted from the baseline tem- perature. If the value is less than or equal to zero, then that day has zero HDDs (no heating requirements). If the value is positive, then that number represents the number of HDDs on that day. The sum of HDDs over a month provides an indica- tion of the total heating requirements for that month. CDDs are directly analogous, but integrate the temperature excess relative to a baseline and provide a measure of the cooling demands for that month. An evaluation of the modern-day distribution of HDDs and CDDs therefore provides a useful validation of the baseline climate simulations, reflecting both spatial and seasonal variability, and furthermore provides a validation of the transformation of the emulated outputs into degree-day data and of the population-weighted mapping of this degree-day data onto the regional level for impacts eval- uation. The validation of the emulator itself will be addressed in Sect. 6.