318 research outputs found

    Impacts of climate change on Chinese agriculture: an adaptation framework and case study for Ningxia

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    The impact of year-to-year changes in the weather on the seasonal dynamics of lakes

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    The methods currently used to monitor and model lakes were developed when weather conditions were very different to what they are today. Most are based on samples collected at weekly or fortnightly intervals and cannot quantify the effects of short-term, more extreme, variations in the weather. In this article, the author presents some examples to show the importance of developing new monitoring methods using case studies from a number of lakes in the English Lake District. The impact of year-to-year changes and short-term changes on the dynamics of of lakes are highlighted

    MCDM์„ ํ™œ์šฉํ•œ ๋ฌผ๊ด€๋ฆฌ ๋ถ€๋ฌธ ๊ธฐํ›„๋ณ€ํ™” ์ ์‘์ •์ฑ… ์šฐ์„ ์ˆœ์œ„ ์„ ์ •

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๋†์—…์ƒ๋ช…๊ณผํ•™๋Œ€ํ•™ ์ƒํƒœ์กฐ๊ฒฝยท์ง€์—ญ์‹œ์Šคํ…œ๊ณตํ•™๋ถ€(์ƒํƒœ์กฐ๊ฒฝํ•™), 2020. 8. ์ด๋™๊ทผ.When establishing the climate adaptation planning, policy priority should be set for each sector based on the results of the synthesized analysis of climate change impact or vulnerability. No consensus on the uncertainty of climate change, and different interests make difficulties in selecting priorities. Decision-making methodologies used for climate change adaptation should be flexible as priorities vary greatly depending on stakeholder composition or adaptation options changes. Meanwhile, multi-criteria decision-making (MCDM), is used to evaluate objects with various aspects, distinguishes between characteristics of options through conflicting indicators. TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), one of the MCDM methods, evaluates the closeness of a hypothetical optimal alternative. By using this method, it is possible to reflect the personal characteristics of the respondents as much as possible, have less problems of ranking reversal, and have the advantage of judging the difference/similarity between alternatives, which can be a useful evaluation method for climate adaptation planning. In this study, expert group including municipalities and civic organizations were formed as a governance, and trustworthy adaptation policy priorities were derived via the evaluation results of the governance. A total of 65 experts participated in the questionnaire, and specifically, the governments and local government officials participating in the decision-making process, academic researchers who derive and interpret scientific results, and general citizens participated in the decision-making process. Most of the survey participants consisted of experts with over 10 years of experience in climate change adaptation management. Since different priority results can be generated for each group using TOPSIS, the method provides flexible priority, not one best priority. This method will allow decision-makers to expand their choices not only at the national level but also at the local level by adjusting the settings to suit the region. Priority results were presented for the 21 adaptation options derived for the water management sector, and the results are interpreted as relative closeness values. This study confirmed that selecting priorities in the adaptation requires a prioritizing method that can function flexibly according to the needs of decision makers. It also suggested how assessment indicators should be constructed appropriate for climate change adaptation and evaluation of adaptation options. From within-sector adaptation to external effects of climate change, indicators have been constructed to reflect how urgent it is in terms of policy feasibility. As a result of the survey, the priority of drought strategies such as Industrial, agricultural water demand management, Groundwater resource management, and Expansion of sewage reuse was high in the water management sector, followed by flood and water ecosystem strategies such as Build flood safety system at development stage and Water safety plan. While the results produced are only an example, the reliability and validity of the process can be improved by referring to these results in the decision-making process. It can be helpful in the planning process in that uncertain information can be assessed with limited resources, and the consistency of the process can be provided, and it can be used as a more useful way to link weighting methods with scientific data, such as impact assessment results in the future.๊ธฐํ›„๋ณ€ํ™” ์ ์‘๊ณ„ํš์˜ ์ˆ˜๋ฆฝ ์‹œ์—๋Š” ๊ธฐํ›„๋ณ€ํ™” ์˜ํ–ฅ ๋˜๋Š” ์ทจ์•ฝ์„ฑ์— ๋Œ€ํ•œ ์ข…ํ•ฉ ๋ถ„์„ ๊ฒฐ๊ณผ์— ๋”ฐ๋ผ ๊ฐ ๋ถ€๋ฌธ์˜ ์ •์ฑ… ์šฐ์„ ์ˆœ์œ„๋ฅผ ์„ค์ •ํ•ด์•ผ ํ•œ๋‹ค. ๊ธฐํ›„๋ณ€ํ™”์— ๋Œ€ํ•œ ๋ถˆํ™•์‹ค์„ฑ, ๊ทธ๋ฆฌ๊ณ  ์„œ๋กœ ๋‹ค๋ฅธ ์ดํ•ด๊ด€๊ณ„๋กœ ์ธํ•˜์—ฌ ์šฐ์„ ์ˆœ์œ„๋ฅผ ๊ฒฐ์ •ํ•˜๋Š” ๊ฒƒ์€ ์‰ฝ์ง€ ์•Š์€ ์ž‘์—…์ด๋‹ค. ๋”ํ•˜์—ฌ ๊ธฐํ›„๋ณ€ํ™” ์ ์‘์— ์‚ฌ์šฉ๋˜๊ธฐ ์œ„ํ•œ ์˜์‚ฌ๊ฒฐ์ • ๋ฐฉ์‹์€ ์ดํ•ด๊ด€๊ณ„์ž์˜ ๊ตฌ์„ฑ ๋ณ€ํ™” ํ˜น์€ ์ •์ฑ… ๋ณ€๊ฒฝ์— ์œ ์—ฐํ•˜๊ฒŒ ๋Œ€์ฒ˜ํ•  ์ˆ˜ ์žˆ์–ด์•ผ ํ•œ๋‹ค. ํ•œํŽธ, ๋‹ค๊ธฐ์ค€ ์˜์‚ฌ๊ฒฐ์ • ๋ฐฉ๋ฒ•๋ก (Multi-criteria decision-making; MCDM)์€ ์—ฌ๋Ÿฌ ์ธก๋ฉด์—์„œ ๋Œ€์ƒ์„ ํ‰๊ฐ€ํ•˜๊ณ  ์„œ๋กœ ๋‹ค๋ฅธ ์„ฑ๊ฒฉ์˜ ์ง€ํ‘œ๋ฅผ ํ†ตํ•ด์„œ ํ‰๊ฐ€ ๋Œ€์ƒ์„ ๊ตฌ๋ณ„ํ•˜๋Š” ๋ฐ์— ์‚ฌ์šฉ๋œ๋‹ค. MCDM์˜ ํ•˜๋‚˜์ธ ์ด์ƒ ํ•ด(่งฃ) ์œ ์‚ฌ์„ฑ ์„ ํ˜ธ ๊ธฐ๋ฒ•(Technique for Order Preference by Similarity to Ideal Solution; TOPSIS)์€ ๊ฐ€์ƒ์˜ ์ตœ์  ๋Œ€์•ˆ๊ณผ์˜ ๊ทผ์ ‘๋„๋ฅผ ํ‰๊ฐ€ํ•œ๋‹ค. ์ด๋Š” ๊ฐœ๋ณ„ ์‘๋‹ต์ž์˜ ๊ฐœ์ธ ํŠน์„ฑ์ด ๋ฐ˜์˜๋˜๋ฉด์„œ๋„ ์ˆœ์œ„ ๋ฐ˜์ „ ๋ฌธ์ œ๋ฅผ ํ”ผํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ๋Œ€์•ˆ ๊ฐ„์˜ ์ฐจ์ด์™€ ์œ ์‚ฌ์„ฑ์„ ํŒ๋‹จํ•  ์ˆ˜ ์žˆ์–ด ๊ธฐํ›„ ์ ์‘ ๋ถ„์•ผ์—์„œ ์œ ์šฉํ•˜๊ฒŒ ์‚ฌ์šฉ๋  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ง€๋ฐฉ์ž์น˜๋‹จ์ฒด์™€ ์‹œ๋ฏผ๋‹จ์ฒด๋ฅผ ํฌํ•จํ•œ ์ „๋ฌธ๊ฐ€ ์ง‘๋‹จ์ด ๊ฑฐ๋ฒ„๋„Œ์Šค๋ฅผ ๊ตฌ์„ฑํ•˜์˜€์œผ๋ฉฐ, ์ด๋“ค์˜ ์„ค๋ฌธ ์‘๋‹ต์„ ํ†ตํ•ด ์‹ ๋ขฐํ•  ์ˆ˜ ์žˆ๋Š” ์ ์‘์ •์ฑ… ์šฐ์„ ์ˆœ์œ„๋ฅผ ๋„์ถœํ•˜์˜€๋‹ค. TOPSIS๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ๊ฑฐ๋ฒ„๋„Œ์Šค์˜ ๊ฐ ๊ทธ๋ฃน์—์„œ ์„œ๋กœ ๋‹ค๋ฅธ ์šฐ์„ ์ˆœ์œ„ ๊ฒฐ๊ณผ๊ฐ€ ์ƒ์„ฑ๋˜๋ฉฐ, ์ด ๋ฐฉ๋ฒ•์€ ํ•˜๋‚˜์˜ ์ตœ์„  ํ•ด(่งฃ)๋ฅผ ์ œ๊ณตํ•˜๋Š” ๊ฒƒ์ด ์•„๋‹ˆ๋ผ ์—ฌ๋Ÿฌ ๋Œ€์•ˆ์˜ ์šฐ์„ ์ˆœ์œ„ ์˜ต์…˜์„ ์ œ์‹œํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ์˜์‚ฌ๊ฒฐ์ •์ž๋Š” ์ง€์—ญ ์กฐ๊ฑด์— ๋งž๋Š” ์„ค์ •์ด ๊ฐ€๋Šฅํ•˜๋ฉฐ, ๊ตญ๊ฐ€ ์ˆ˜์ค€์—์„œ์˜ ์šฐ์„ ์ˆœ์œ„๋งŒ ์•„๋‹ˆ๋ผ ์ง€์—ญ ์ˆ˜์ค€์—์„œ๋„ ์„ ํƒ ๊ฐ€๋Šฅํ•œ ์˜์—ญ์œผ๋กœ ํ™•์žฅ๋  ์ˆ˜ ์žˆ๋‹ค. ๋ฌผ๊ด€๋ฆฌ ๋ถ€๋ฌธ์— ๋Œ€ํ•ด์„œ ๋„์ถœ๋œ 21๊ฐœ์˜ ์ ์‘ ์˜ต์…˜์— ๋Œ€ํ•ด์„œ ์šฐ์„ ์ˆœ์œ„๋ฅผ ๋„์ถœํ•˜์˜€์œผ๋ฉฐ, ๊ฒฐ๊ณผ๊ฐ’์€ ์ƒ๋Œ€์ ์ธ closeness ๊ฐ’์œผ๋กœ ๋„์ถœ๋œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ, ์ ์‘ ๋ถ„์•ผ์—์„œ ์šฐ์„ ์ˆœ์œ„๋ฅผ ์„ ์ •ํ•˜๋Š” ๋ฐ์—๋Š” ์˜์‚ฌ๊ฒฐ์ •์ž์˜ ์š”๊ตฌ์— ๋”ฐ๋ผ ํƒ„๋ ฅ์ ์œผ๋กœ ๊ธฐ๋Šฅํ•  ์ˆ˜ ์žˆ๋Š” ์šฐ์„ ์ˆœ์œ„ ์„ ์ • ๋ฐฉ๋ฒ•์ด ํ•„์š”ํ•˜๋‹ค๋Š” ์ ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๊ธฐํ›„๋ณ€ํ™” ์ ์‘๊ณผ ์ ์‘์˜ต์…˜ ํ‰๊ฐ€์— ์ ํ•ฉํ•œ ํ‰๊ฐ€์ง€ํ‘œ๋ฅผ ์–ด๋–ป๊ฒŒ ๊ตฌ์„ฑํ•ด์•ผ ํ•˜๋Š”์ง€๋ฅผ ์ œ์‹œํ•˜์˜€๋‹ค. ์ž‘๊ฒŒ๋Š” ๋ถ€๋ฌธ ๋‚ด ์ ์‘์—์„œ๋ถ€ํ„ฐ ๊ธฐํ›„๋ณ€ํ™” ์™ธ์ ์ธ ํšจ๊ณผ๊นŒ์ง€ ๊ณ ๋ คํ•ด์•ผ ํ•˜๋ฉฐ, ์ •์ฑ…ํƒ€๋‹น์„ฑ ์ธก๋ฉด์—์„œ ์–ผ๋งˆ๋‚˜ ์‹œ๊ธ‰ํ•œ์ง€๋„ ๋ฐ˜์˜ํ•  ์ˆ˜ ์žˆ๋Š” ์ง€ํ‘œ๋ฅผ ๊ตฌ์„ฑํ•˜์˜€๋‹ค. ์„ค๋ฌธ์ง€ ์กฐ์‚ฌ์— ๋”ฐ๋ฅธ ์šฐ์„ ์ˆœ์œ„ ๋„์ถœ ๊ฒฐ๊ณผ ๋ฌผ๊ด€๋ฆฌ ๋ถ€๋ฌธ์—์„œ๋Š” ์‚ฐ์—…/๋†์—… ์ˆ˜์ž์› ์ˆ˜์š” ๊ด€๋ฆฌ, ์ง€ํ•˜์ˆ˜์ž์›๊ด€๋ฆฌ, ํ•˜์ˆ˜ ์žฌ์ด์šฉ ํ™•๋Œ€ ๋“ฑ ๊ฐ€๋ญ„์ •์ฑ…์˜ ์šฐ์„ ์ˆœ์œ„๊ฐ€ ๋†’๊ฒŒ ๋‚˜ํƒ€๋‚ฌ์œผ๋ฉฐ, ์นจ์ˆ˜์•ˆ์ „ ํ™•๋ณด ์ฒด๊ณ„ ๊ตฌ์ถ•, ๋ฌผ ์•ˆ์ „ ๊ณ„ํš ๋“ฑ์˜ ํ™์ˆ˜์™€ ์ˆ˜์ƒํƒœ๊ณ„ ์ •์ฑ…์ด ๊ทธ ๋’ค๋ฅผ ๋”ฐ๋ž๋‹ค. ๋„์ถœ๋œ ๊ฒฐ๊ณผ๋Š” ํ•˜๋‚˜์˜ ์˜ˆ์‹œ์ผ ๋ฟ์ด์ง€๋งŒ, ์˜์‚ฌ๊ฒฐ์ • ๊ณผ์ •์—์„œ ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋ฅผ ์ฐธ๊ณ ํ•˜์—ฌ ๊ณผ์ •์˜ ์‹ ๋ขฐ์„ฑ๊ณผ ํƒ€๋‹น์„ฑ์„ ํ–ฅ์ƒ์‹œํ‚ฌ ์ˆ˜ ์žˆ๋‹ค. ์ž์›์ด ์ œํ•œ๋˜์–ด ์žˆ๋Š” ์ƒํƒœ์—์„œ ๋ถˆํ™•์‹คํ•œ ์ •๋ณด๋ฅผ ํ‰๊ฐ€ํ•  ์ˆ˜ ์žˆ๊ณ , ๊ทธ ๊ณผ์ •์˜ ์ •ํ•ฉ์„ฑ์„ ์ œ๊ณตํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ์ ์—์„œ ๊ณ„ํš ๊ณผ์ •์—์„œ ๋„์›€์ด ๋  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ํ–ฅํ›„ ๊ฐ€์ค‘์น˜ ๋ถ€์—ฌ ๋ฐฉ์‹ ๋“ฑ์„ ์˜ํ–ฅํ‰๊ฐ€ ๊ฒฐ๊ณผ ๋“ฑ ๊ณผํ•™์  ๋ฐ์ดํ„ฐ์™€ ์—ฐ๊ณ„ํ•œ๋‹ค๋ฉด ๋”์šฑ ์œ ์šฉํ•œ ๋ฐฉ๋ฒ•์œผ๋กœ ํ™œ์šฉ๋  ์ˆ˜ ์žˆ๋‹ค.Chapter 1. Introduction ๏ผ‘ 1.1. Study Background ๏ผ‘ 1.2. Purpose of Research ๏ผ“ Chapter 2. Literature Review ๏ผ• Chapter 3. Methodology ๏ผ™ 3.1. Constructing Evaluation Criteria and a List of Adaptation Options in the Sector ๏ผ™ 3.2. Obtaining Stakeholder Opinion and Conducting the Policy Evaluation Questionnaire ๏ผ‘๏ผ• 3.3. Choosing a Method to Synthesize Responses Determining the Final Priority ๏ผ‘๏ผ— Chapter 4. Results ๏ผ’๏ผ‘ 4.1. List of Adaptation Options in the Water Management Sector ๏ผ’๏ผ‘ 4.2. Adaptation Options Priority Result in Water Management Sector ๏ผ’๏ผ’ Chapter 5. Discussion ๏ผ’๏ผ˜ 5.1. Prioritization method suitable for climate change adaptation governance ๏ผ’๏ผ˜ 5.2. Proper criteria for evaluating adaptation options priority ๏ผ“๏ผ 5.3. Discussion on the results of prioritization and key priority options ๏ผ“๏ผ‘ Chapter 6. Conclusion ๏ผ“๏ผ“ Bibliography ๏ผ“๏ผ– Abstract in Korean ๏ผ”๏ผ• Appendix ๏ผ”๏ผ˜Maste

    Delivering organisational adaptation through legislative mechanisms: Evidence from the Adaptation Reporting Power (Climate Change Act 2008)

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    There is increasing recognition that organisations, particularly in key infrastructure sectors, are potentially vulnerable to climate change and extreme weather events, and require organisational responses to ensure they are resilient and adaptive. However, detailed evidence of how adaptation is facilitated, implemented and reported, particularly through legislative mechanisms is lacking. The United Kingdom Climate Change Act (2008), introduced the Adaptation Reporting Power, enabling the Government to direct so-called reporting authorities to report their climate change risks and adaptation plans. We describe the authors' unique role and experience supporting the Department for Environment, Food and Rural Affairs (Defra) during the Adaptation Reporting Power's first round. An evaluation framework, used to review the adaptation reports, is presented alongside evidence on how the process provides new insights into adaptation activities and triggered organisational change in 78% of reporting authorities, including the embedding of climate risk and adaptation issues. The role of legislative mechanisms and risk-based approaches in driving and delivering adaptation is discussed alongside future research needs, including the development of organisational maturity models to determine resilient and well adapting organisations. The Adaptation Reporting Power process provides a basis for similar initiatives in other countries, although a clear engagement strategy to ensure buy-in to the process and research on its long-term legacy, including the potential merits of voluntary approaches, is required

    The environmental impact of climate change adaptation on land use and water quality

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    Encouraging adaptation is an essential aspect of the policy response to climate change1. Adaptation seeks to reduce the harmful consequences and harness any beneficial opportunities arising from the changing climate. However, given that human activities are the main cause of environmental transformations worldwide2, it follows that adaptation itself also has the potential to generate further pressures, creating new threats for both local and global ecosystems. From this perspective, policies designed to encourage adaptation may conflict with regulation aimed at preserving or enhancing environmental quality. This aspect of adaptation has received relatively little consideration in either policy design or academic debate. To highlight this issue, we analyse the trade-offs between two fundamental ecosystem services that will be impacted by climate change: provisioning services derived from agriculture and regulating services in the form of freshwater quality. Results indicate that climate adaptation in the farming sector will generate fundamental changes in river water quality. In some areas, policies that encourage adaptation are expected to be in conflict with existing regulations aimed at improving freshwater ecosystems. These findings illustrate the importance of anticipating the wider impacts of human adaptation to climate change when designing environmental policies

    Irrigation demand modelling using the UKCP09 weather generator: lessons learned

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    The determination of irrigation demand is typically based on crop modelling using a long historic record of local daily weather data. However, there are rarely adequate weather station records near to given sites; often any local records cover a limited number of years, are incomplete, costly or are of poor quality. This paper examines whether version 1 of the UKCP09 weather generator can provide a simpler and effective method of calculating irrigation demand with sufficient accuracy for regulatory and design purposes. The irrigation demands at seven sites distributed around England were modelled using the UKCP09 baseline climatology and compared with results modelled using daily observed weather records. For the design dry year used for irrigation planning, the weather generator replicated the observed conditions with reasonable accuracy. The weather generator was however less successful at replicating extreme dry years. These results are encouraging but also provide a note of caution for the use of these generated datasets for studying current irrigation demand and by implication for modelling future needs under climate change. The study also demonstrated a simple sub-sampling approach for reducing the processing demands if using the dataset in more complex models, though this would not remove any underlying error
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