Climate Change Impacts

In conditions of sedimentation surplus, mangroves colonise seaward either into bays especially offshore of river mouths or over reef flats. Panapitukkul et al. [49] demonstrated mangrove progradation of c. 38 m a -1 at Pak Phanang Bay in SE Thailand with high rates of river delivery of sediment. Such mangrove areas will be more resilient during rising sea-level. Mangroves have a unique feedback to climate change impacts in that they are able to promote sedimentation, and so protect the coastline from inundation owing their ability to foster high rates of sediment accretion. Other types of forest cover are unable to do this, mainly because they lack the sediment supply provided by tidal waters in estuarine situations.

Despite the aforementioned measures and mitigation strategies, the effects of climate change on water re- sources in Greece are already obvious, and demonstrated indicatively by different researchers that studied the phenomenon [20-23]. The hydrological cycle is affected by the climate warming and the increased temperature causes changes in the precipitation and evaporation pat- terns, and the sea level. Those changes cause a chained reaction affecting the surface runoff, the soil moisture and the groundwater regime. Thus, future water re- sources management is at risk as the sustainability of the key aspects of water supply, agricultural water use, res- ervoir failure, energy production, soil drainage, flood protection, and water quality becomes vulnerable and uncertain. To combat these problems, approaches to es- timate and assess the regional hydrological impacts of climate change have been attempted for the Greek terri- tory in an effort to draw key conclusions and adequately plan for the future. The Laboratory of Hydrology and Water Resources Management of the National Technical University of Athens (NTUA) has conducted various case studies through EU funded research programs, mainly in central and northern Greece, where the major river systems are located, the results of which will be presented and discussed in the following sections. These studies, aiming in assessing the climate change impacts on selected hydrological indicators, and integrating vari- ous climate change experiments in hydrological models’ simulations, follow in general the subsequent methodol- ogy: 1) Quantitative estimation of climate change using

This study provides an overview of present (existing) global hydropower generation and its future prospects with respect to climate change. The focus of this work is global (all countries) i.e., low resolution (less detail), although for clarity’s sake, some large countries like Australia, Brazil, Canada, China, India and USA had to be subdivided into provinces or states. Assessment of climate change impacts on hydropower can be done at various levels of detail with different methods. On a global scale, low resolution analysis is acceptable as detailed modeling may be costly and tedious. While recognizing the fact that climate change impacts hydropower in different ways—volume of flow, timings of flow, etc., the analysis has been confined to changes in mean flows (volume of flow). In addition, there is no estimate of the future hydropower development as doing so would require more detailed data (national development plans or trends) for each state and country. The study aims to answer questions related to national, regional and global hydropower generation and the expected increases or decreases in the same due to future changes in climate and water availability, and the extent of such changes. In order to answer the above, GIS analysis has been utilized to understand and visualize regional scenarios of hydropower generation. The analysis makes no attempt to analyze the impact of climate change on electricity demand, as it focuses on the side of generation. The GIS has been used here as a tool to merge and analyze different databases in order to gain insights into the anticipated changes. The database included data on world countries hydropower capacities, generation, global water resources, global runoff, dams, hydropower plants, etc. Table 2 shows regional hydropower statistics and of special interest is the installed capacity and hydropower generation in 2009. The table highlights the technically feasible, annual average potential, and feasible increase. The capacity factor of a power plant is the ratio of the actual output of a power plant over a period of time and its output if it had operated at full nameplate capacity the entire time. The lowest capacity factor is in Europe and clearly shows that hydropower in Europe is used mainly for peaking purposes than in the other regions [3].

Robust assessments of climate change impacts are important for assessing the scale of adaptation required, and for estimating the implications of climate mitigation pathways (Collins, 2007; IPCC, 2014). A comprehensive understanding of uncertainties in projected impacts is a key element of making robust assess- ments (Challinor et al., 2013; Katz et al., 2013). Uncertainties arise from a range of sources in climate projections (model struc- tural differences, initial conditions, scenarios, parameters and resolution/bias-correction), climate impact models (CIMs) and observations (e.g., Challinor et al., 2009a,b; Hawkins and Sutton, 2009; Osborne et al., 2013). Multi-member model ensembles (Collins et al., 2010, for example) and model intercomparison projects (MIPs) are used to assess uncertainties in future cli- mate and climate impacts. These studies include the Coupled MIP (CMIP—Taylor et al., 2012), Water MIP (WaterMIP— Haddeland et al., 2011), the Agricultural MIP (AgMIP— Rosenzweig et al., 2013), and the Inter-Sectoral Impacts MIP (ISI-MIP—Warszawski et al., 2013), which contributed to the IPCC reports (IPCC, 2013, 2014).

In order to assess the rates of change, it is required to understand the relationships that exist between climate change and its impacted entities. One common technique is to apply Causal Networks. Causal Networks are a dia- grammatic representation of relationships demonstrating the causality in relationships. There are a number of ways of performing a causal network analysis. It can be as simple as constructing a form of Directed Graphs or as complex as performing systems dynamics and data min- ing tasks. However, since this paper will require not only the understanding of the impacts from climate change, but how the impacts are interrelated, where the more related they are the more probable that the rates of change will not be constant. Meanwhile, there is limited resource in terms of time and data availability. Thus, for this paper, a Cause and Effect diagram will be conducted. A Cause and Effect Diagram is a type of Directed Graphs where elements are textually stated and with their rela- tionships represented using arrows, often without quanti- tative data [7]. The Cause and Effect diagram does not give a lot of information but it does present a general idea of the problems, which would allow a first stance perception of an issue – in this case climate change im- pacts.

For proposed and existing hydropower projects, project man- agers need to pull together a meaningful body of knowledge about the hydroclimate system, employing all of the tech- niques described in the preceding section. In doing so, it may be necessary to evaluate and potentially deploy addi- tional observational systems and, if appropriate, keep them running over the lifespan of the project for decision support. At the current time, researchers are most confident about his- torical trends in climate in the Far North that are derived from long-term, consistently instrumented stations or remote sensing records (Curran et al., 2012). In general, these obser- vations are limited to air temperature, ground temperature, snow-covered area, and river discharge at just a few stations, or they have limited temporal coverage. From the subset of these records that cover at least 40 years or more, meaning- ful statistics can be generated about the historic patterns of variability and change; 60–80 years of data are even better. The more that observational networks expand and improve to accurately measure ET, precipitation, groundwater, and wa- ter storage over long periods of time and in many locations, the more confident we will be in our knowledge of water re- source availability.

While it is known that specific models are better at predicting specific parameters in certain regions, choosing a single model is not advisable due to the high level of uncertainty in modelling climate change. Rather an ‘ensemble’ of models is acknowledged as the best way of addressing the uncertainty inherent in making decisions influenced by future climate (Ziervogel et al. 2008; Frame et al. 2007). Ensemble forecasts of alternatives can inform decisions, but climate modelling also contains the ‘what if’ of the changing parameter value of greenhouse gases (GHG) that will be emitted into the atmosphere over time. This is where the IPCC has developed various emissions scenarios based on different driving forces of future emissions. The SRES (Special Report on Emissions Scenarios) scenarios were those used by the IPCC until the fourth Assessment report and these four narratives (A1, A2, B1 and B2) cover different demographic and technological futures, i.e. a fossil fuel intensive future (A1F1 scenario) versus a predominantly non-fossil fuel future (A1T) (See IPCC 2000). For the Fifth Assessment report of the IPCC, new scenarios, the Representative Concentration Pathways (RCPs) were developed. Each pathway represents a set of internally consistent socioeconomic assumptions that result in four levels of radiative forcing: RCP 8.5, RCP6, RCP4.5 and RCP2.6 (See Moss et al. 2008). Hence, RCP 8.5 (the pathway with the highest (8.5) radiative forcing) shows a world with little to no mitigation and an increase in fossil fuels whereas RCP4.5 assumes continued global development that shifts towards service industries, but does not aim to reduce GHG emissions and is similar to SRES scenario B1 (See Table 1). When referring to ‘low mitigation futures,’ modelers are usually referring to an RCP8.5 or RCP6 world or SRES A1F1 and A2.

Climate change scenarios for Viet Nam were developed using the coupled method (MAGICC/SCENGEN 5.3) as well as the statistical downscaling method, and are based on emission scenarios in IPCC’s Fourth Assessment Report. Scenarios used are the medium-high emission (A2 1 ), low emission (B1 2 ) and medium-medium emission scenario (B2 3 ).

In the past decade, much attention in the climate change researches has been focused on the potential impacts on temperature and precipitation. Recently, a growing number of studies have looked at potential impacts on renewable energy resources, and on wind power (Sailor et al., 2008). In particular, it was found that wind power potential throughout Finland might in- crease by 2–10% under conditions of climate change Using GCM output from the Hadley (Venäläinen et al., 2004). Pryor (Pryor et al., 2005a) has found that annual wind power potential over Northern Europe under the IPCC A2 and B2 scenarios was highly dependent on the boundary conditions used in Rossby Centre coupled Regional Climate Model (RCAO). Using empirical downscaling of five GCMs for 46 stations over Northern Europe, it’s shown a slight decrease in mean wind speeds under a 2080–2100 climate projection (Pryor et al., 2005b). Breslow & Sailor explored climate change impli- cations for wind power in California and Texas using neural network-based downscaling (Breslow & Sailor, 2002). In the recent research using statistically down- scaling tools (Sailor et al., 2008), the summertime wind speeds in the Northwest U.S. may decrease by 5–10% which suggests a 40% reduction in summertime gener- ation potential. RCM was also used in Brazil to find out that the wind power potential in Brazil would not be jeopardized in the future due to possible new climate conditions (Breslow & Sailor, 2002). However, within all these researches, uncertainty remains a bottle- neck. The models, methods and scenarios used are so sophisticated that the massive uncertainty is im- measurable, which leads to a relatively low credibility of the findings. There is also a lack of studies on the substantial impacts of changing wind speed on the actual wind power production.

Figure 2 focuses on all points in the database, thereby mix- ing projections across different time horizons. To evaluate if the predicted impacts may be more pronounced by the end of the 21st century because of rising greenhouse gas concentra- tions, we split the database into two parts based on the time horizon studied: early 21st (2020–2060) and late 21st century (2061–2100). Figure 3 shows that the selected future time period does not strongly influence the overall sign of future runoff evolution: the median for the early 21st century is 0 % and the median for the late 21st century is +2 %, but the dif- ference is not significant. It is however important to note that both panels (early and late) do not include the same rivers, models or scenarios. The increase in interquartile range as time proceeds nevertheless suggests that signals of change become more pronounced with increasing greenhouse gas levels. This is also confirmed when evaluating the projections of individual studies that consider different time horizons. In 68 % of the cases, the impact of climate is more evident in the late 21st century, especially when the emission scenario

endangered and endemic species; preventing habitat fragmentation. The biodiversity can be conserved by management programmes including ecosystem conservation and restoration. The forest need to be conserved with practices of reforestation and afforestation as they have 80% of the total carbon stored in terrestrial vegetation. The indigenous knowledge can also be used to prevent climate change or adapt to it (2). Strategies by the United Framework Convention on Climate Change (UNFCCC) focuses on cutting down greenhouse gas emissions to prevent climate change. Kyoto protocol has brought into existence joint implementation, emission trading and Clean Development Mechanism (CDM) to reduce greenhouse gas emission. Like all other countries National Action Plan on Climate Change of India was released in Delhi in 2009 and involves eight missions on solar mission, enhanced energy efficiency, sustainable habitat, water mission, sustaining Himalayan ecosystem, Green India through massive tree plantation, sustainable agriculture and strategic knowledge for climate change by establishing a knowledge platform on climate change (22). Successful implementation of all these plans would surely help reduce global warming and conserve biodiversity.

The climate change scenarios simulated in this paper point to a reversal of the current pattern of internal migration in Brazil. The severe climate-induced effects on some Northeast states would cause a new pressure for migrants to leave those regions. Model results presented here point to the MarPiaui sub-region ( Maranhao a nd Pia ui state s) as t he most a dversely eff ected i n ter ms of a gricultural production, increasing emigration from that region. Perhaps more surprisingly, the same would happen to Mato Grosso do Sul state, in the Center-west region. The Southeast and South regions would be the recipient regions, a movement that reverses current flows.

because of the higher river flows and is expected to decrease for future scenarios due to the sea level rise. 384[r]

This article is review and the aims of Climate change impacts on agricultural systems. The experiment 1 was conducted by Mary and Majule (2009). The study was carried out in Manyoni District in Singida Region, Tanzania. The district lies between 6°7°S and 34°35°E covering an area of 28,620 km2 that is about 58% of the entire area of Singida Region. Manyoni District was selected for the following reasons; first, it falls within the semi-arid areas of Tanzania where there are frequent food shortages due to uncertainty of rainfall. According to URT (2005) the 2000/01 household surveys, the district fell within regions with worst assessment of food poverty. In the district, 55% of its populations are living below the food poverty line. Average per capita earning of residents is estimated at 170 US dollars (URT, 2005). (Mary and Majule 2009). Second, the area provides an opportunity to study impacts associated with CC & V on crop and livestock and third it is within the project area on “Strengthening Local Agricultural Innovation Systems to adapt to climate change in Tanzania and Malawi Project”. The climate of Manyoni District is basically of an inland equatorial type modified by the effects of altitude and distance from the Equator. The district forms part of the semi-arid central zone of Tanzania experiencing low rainfall and short rainy seasons which are often erratic with fairly widespread drought of one year in four (Mary and Majule, 2009). Manyoni District has a unimodal rainfall regime, which is concentrated in a period of six months from November to April. The long-term mean annual rainfall is 624 mm with a standard deviation of 179 mm and a coefficient of variation of 28.7%. The long- term mean number of rainy days is 49 with a standard

In this paper, observed climate change impacts in the country were collated and tabulated to provide the baseline information on the prevalent climate ha- zards associated with the impacts. Available climate and socio-economic da- tasets for the country were then subjected to the GeoClim software analyses in order to generate the spatial patterns of exposure, sensitivity and adaptive ca- pacity parameters. Composite layers of these parameters were overlayed to ge- nerate the vulnerability map. Finally, effectiveness of the country’s existing poli- cies and capacities in addressing the vulnerabilities has been evaluated. Results have revealed that the entire country is vulnerable. However, the Northern parts as well as the Southern tip of the coastal strip are the most vulnerable. Flood and drought hazards result in the greatest impacts to the Kenyan socie- ty. Significant gaps and weaknesses have been observed in the existing policies and capacities which render them inadequate to effectively address the vulne- rability. It is concluded that the country urgently requires a raft of measures to address the current and future vulnerabilities presented by climate change.

in regional climate responses are assessed by Betts et al. (2018), examining the ranges of outcomes in the impact to inform risk assessments. They reported that the projections for weather extremes indices and biophysical impacts quantities support that the magnitude of temperature changeis in general large for 2°C global warming than 1.5°C. They concluded that the hot extremes become even hotter, with the increase being more intense than seen in CMIP5 (Coupled Model Inter-comparison Project Phase 5) projections and precipitation-related extremes show high geographical variation with some increases and some decreases in both extremes (floods and droughts). Therefore, it is important to understand the climate change impacts on hydrological variables (like precipitation, stream flow), and assess the uncertainties arising due to various factors. In India, climate change impact assessment on hydrology (Gosain et al ., 2006) and quantification of uncertainties at each stage of the modelling, using variety of methods (Ghosh and Mujumdar, 2009; Ghosh et al., 2010; Ghosh and Katkar, 2012; Sengupta and Rajeevan, 2013; Kannan et al., 2014) have been carried out. Several novel methods have been developed, including from the second author, for quantifying the uncertainties and assessing the climate change. All such works from the author are summarized and conclusions drawn from the developed approaches for a better management of water resources are consolidated.

study. It is hard to predict how this omission would have affected the interpretation of my results as members of Archaeorhizomycetes have been reported from different soil types, and their specific ecological niches are still uncertain (Rosling et al., 2011). Additionally, in the development of the LSU primers, it was necessary to create two sets of primers to capture all important groups of fungi in peat samples (i.e. one set to capture Ascomycota and another set to recover all other fungi). The consequence of this, which became pronounced in chapter four and five, was that I was not able to comprehensively describe the climate change impacts on fungal communities in their totality. Although it was possible to combine analyses, doing so would increase the computation time and the probability of exaggerating the fungal diversity discovered (Gobet et al., 2010). Therefore, I decided to analyze the datasets for each fungal group separately to get a more accurate estimate of fungal diversity in peat samples. As such I could not see the whole community changing at once, which prevented getting a full assessment of the dynamics of fungal community under experimental climate change conditions.

The mortality functions used in this study will be established in order to explicitly comprehend the relationships between climatic factors and macroeconomic conditions and mortality. Three characteristics are addressed in relation to these mortality functions. The first one is that the impact of both the climate and macroeconomic factors on mortality could be examined at the same time as is shown in Model I. The second one is that seasonal dummy variables and cross-multiplication with temperature are taken into consideration to examine how temperature in different seasons will affect the mortality that is shown in Model II. The third one is that summer and regional dummy variables, as well as terms multiplied by temperature, are simultaneously considered in the mortality function to investigate whether mortality rates are higher in European countries in summer than in non-European countries as shown in Model III. Each dependent and independent variable is transformed into logarithmic form to better express the non-linearity of mortality and climate and the macroeconomic conditions. The mortality functions could be specified as follows:

Abstract. Hydropower is an important renewable energy source in China, but it is sensitive to climate change, be- cause the changing climate may alter hydrological conditions (e.g., river flow and reservoir storage). Future changes and associated uncertainties in China’s gross hydropower poten- tial (GHP) and developed hydropower potential (DHP) are projected using simulations from eight global hydrological models (GHMs), including a large-scale reservoir regulation model, forced by five general circulation models (GCMs) with climate data under two representative concentration pathways (RCP2.6 and RCP8.5). Results show that the es- timation of the present GHP of China is comparable to other studies; overall, the annual GHP is projected to change by − 1.7 to 2 % in the near future (2020–2050) and increase by 3 to 6 % in the late 21st century (2070–2099). The annual DHP is projected to change by − 2.2 to − 5.4 % (0.7–1.7 % of the total installed hydropower capacity (IHC)) and − 1.3 to − 4 % (0.4–1.3 % of total IHC) for 2020–2050 and 2070–2099, re- spectively. Regional variations emerge: GHP will increase in northern China but decrease in southern China – mostly in south central China and eastern China – where numerous reservoirs and large IHCs currently are located. The area with the highest GHP in southwest China will have more GHP, while DHP will reduce in the regions with high IHC (e.g., Sichuan and Hubei) in the future. The largest decrease in DHP (in %) will occur in autumn or winter, when streamflow is relatively low and water use is competitive. Large ranges in hydropower estimates across GHMs and GCMs highlight the necessity of using multimodel assessments under climate

Methods used to assess the impacts of LU and climate on streamflow can be broadly classified into four categories: (i) experimental paired catchment approach, (ii) statistical techniques such as Mann–Kendall test, (iii) empirical or con- ceptual models and (iv) distributed physically based hydro- logic models. Among these techniques, the paired catchment approach is most difficult but often considered as the best ap- proach for smaller catchments. However, applicability of the paired catchment approach over large catchments may not be possible (Lørup et al., 1998) since it requires years of con- tinuous monitoring to gather sufficient data for the analysis. Statistical trend detection tests have been proved to be very useful in qualitatively determining the presence of a signif- icant trend in the time series along with direction and rate of change (Zhang et al., 2008; Li et al., 2009). But these techniques cannot be used for quantifying the change and attributing it to a particular cause due to a lack of a physical mechanism (Li et al., 2009). Empirical or conceptual mod- els are simple hydrologic models that require only a few pa- rameters to simulate a catchment. However, a major draw- back with these models is that the parameters may not be di- rectly related to the physical conditions of the catchment, and thus may lack the ability to correctly represent a catchment. Therefore, one is left with the option of using distributed physically based hydrologic models, which are by far the most appealing tools to carry out impact assessment stud- ies (Ott and Uhlenbrook, 2004; Mango et al., 2011; Wang et al., 2012). These models operate within a distributed frame- work to take physical and meteorological conditions of the basin into account (Refsgaard and Knudsen, 1996). Physi- cally distributed models include both fully distributed and semi-distributed models. Owing to their extensive parame- terization, fully distributed models are difficult to employ at a large catchment scale which make comparatively less data- intensive semi-distributed models a practical alternative. This paper presents a simple hydrologic modeling-based approach to isolate the impacts of land use and climate on stream- flow. For this purpose, a physically based macroscale vari- able infiltration capacity (VIC) hydrologic model (Liang et al., 1994) has been employed for the analysis.