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DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 童慶斌(Ching-Pin Tung) | |
dc.contributor.author | Chung-Yi Lin | en |
dc.contributor.author | 林宗毅 | zh_TW |
dc.date.accessioned | 2021-05-11T05:09:28Z | - |
dc.date.available | 2019-02-15 | |
dc.date.available | 2021-05-11T05:09:28Z | - |
dc.date.copyright | 2019-02-15 | |
dc.date.issued | 2019 | |
dc.date.submitted | 2019-02-12 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/handle/123456789/835 | - |
dc.description.abstract | 氣候變遷調適風險評估日益受到重視。然而,具體之跨領域及跨治理部門合作框架與量化調適及風險的評估工具尚未被完整提出。因此,本研究在保有物理性的基礎下建立氣候、水資源與糧食調適整合評估模式(Agriculture and Hydrology Integrated Assessment Model, AgriHydro),結合氣候智慧調適演算法(Climate Smart Adaptation Algorithm, CSAA),提供一套跨領域合作之標準流程與量化分析之框架與工具。AgriHydro包含四個主要的子模式,分別為(1)多測站氣象合成模式(MultiSiteWthGen)、(2)GWLF水文模式(Generalized Watershed Loading Function, GWLF)、(3)石門水庫供水系統之系統動力模式與(4)AquaCrop作物模式,分別用於氣候情境降尺度、流量模擬、水庫操作模擬與產量及田間需水量的產製,各子模式驗證結果良好。本研究以桃園為研究區,示範發展架構的操作流程。根據CSAA,第一步驟,使用風險模板(Risk Template)解構水資源缺水風險與糧食減產風險之組成因子,並依跨領域之連結因子,建構作物產量-計畫灌溉用水量-石門水庫於各標的給水量的回饋機制。在二、三步驟中,以AgriHydro分析未來變化趨勢,發現缺水指標(Shortage Index, SI)與減產率指標(Yield Reduction Ratio, YRR)於未來趨於嚴重,唯二期稻作YRR有改善之趨勢。第四步驟中,根據未來短期(2021年至2040年)糧食生產風險,制定轉作大豆之調適選項,並分析其在跨領域風險中的改善效用。結果發現轉作大豆於一期稻作能協同改善水資源及糧食生產風險;於二期作則為競爭關係。然而,相同SI改善程度下,二期作僅須轉作低於40%之一期作轉作面積。在考慮到農民偏好種植產量較高的一期稻作與政府希望區域供水穩定的情況下,建議於二期作實施大豆轉作。此結論呼應自民國104年二期大豆收穫面積在政府政策之推動下,漸增的趨勢。藉由桃園案例的演示,本研究發展的AgriHydro與CSAA聯合操作之跨領域氣候調適與風險評估框架,能有效量化風險,並支持跨領域決策。希望未來研究能納入決策過程與監測資訊的回饋機制,形成動態調適路徑,完整呈現CSAA。 | zh_TW |
dc.description.abstract | Climate adaptation and risk assessment have become a significant issue. However, a framework for interdisciplinary collaboration and quantitative adaptation assessment has not been well developed. Therefore, this study proposes a standard climate adaptation risk assessment framework. To demonstrate the proposed framework, the Agriculture and Hydrology Integrated Assessment Model (AgriHydro) is developed and operates with Climate Smart Adaptation Algorithm (CSAA) as a tool. AgriHydro consists of four sub-models, which are (1) Multi-Site Weather Generator (MultiSiteWthGen), (2) the hydrological model of Generalized Watershed Loading Function (GWLF), (3) a system dynamic model for the Shimen reservoir water distribution system and (4) AquaCrop crop model. The AgriHydro and CSAA is applied to Taoyaun area in Taiwan. In the first step of CSAA, Risk Template is adopted to factorize risk components among water and agriculture disciplines. In the second and third steps, future trend of risks is simulated by AgriHydro. During the fourth step, adaptation options of substituting soybean for rice are tested. Consequently, synergies and trade-offs between SI and YRR were quantitatively displayed. In the short-term future, substitution in 2nd growing period revealed 2.5 times more efficient in reducing Shortage Index (SI) than in 1st growing period while it slightly increased Yield Reduction Ratio (YRR). However, the yield reduction risk caused by climate change was lower than the difference of actual yield between 1st and 2nd growing periods. Therefore, according to the result, the study suggests altering rice to soybean in 2nd growing period. This conclusion is parallel to the current agriculture policy promoted by the government. Overall, the Taoyuan case study successfully indicates our proposed framework is valuable in interdisciplinary climate adaptation assessment. | en |
dc.description.provenance | Made available in DSpace on 2021-05-11T05:09:28Z (GMT). No. of bitstreams: 1 ntu-108-R06622001-1.pdf: 10858065 bytes, checksum: 3195de700bed750c58bf93acf2210222 (MD5) Previous issue date: 2019 | en |
dc.description.tableofcontents | 謝誌 I
摘要 III Abstract V 目錄 VII 圖目錄 XI 表目錄 XV 第一章、 緒論 1 1.1 研究動機 1 1.2 研究目的 3 1.3 論文架構 3 第二章、 文獻回顧 5 2.1 跨領域調適整合評估架構與模式 5 2.1.1 調適步驟與跨領域合作框架 6 2.1.2 模式整合類別與方法 7 2.2 水資源與糧食生產評估 9 2.3 氣象合成模式 11 2.4 作物模式 12 第三章、AgriHydro各模組使用模式之理論介紹 15 3.1 氣候智慧調適演算法與指標 15 3.1.1 氣候智慧調適演算法 15 3.1.2 風險指標 20 3.2 情境設定模組 20 3.2.1 GCMs挑選與GCMs資料降尺度 21 3.2.2 多測站氣象合成模式 23 3.2.3 社會經濟情境設定 27 3.3 水資源模組 28 3.3.1 GWLF流量模式 28 3.3.2 系統動力模式 32 3.4 作物模組 34 3.4.1 AquaCrop作物模式 34 3.4.2 水稻整田用水與湛水深不足之減產機制 37 第四章、 AgriHydro氣候變遷調適整合評估模式 39 4.1 回饋機制 39 4.1.1 評估模式中的回饋機制 40 4.1.2 決策流程上的回饋機制 41 4.2 AgriHydro整合架構 41 4.2.1 AgriHydro農業灌溉計畫用水量與實際供水量之回饋與更新機制 42 4.2.2 其他水源之實際供水比率變動假設說明 44 4.3 AgriHydro與氣候智慧調適演算法之聯合操作流程 46 第五章、 研究區域介紹與使用資料說明 51 5.1 研究區域介紹 51 5.1.1 研究區背景介紹 51 5.1.2 桃園糧食生產系統與AgriHydro農業分區 52 5.2 情境模組之資料使用 54 5.2.1 多測站氣象合成模式資料使用及處理 55 5.2.2 社經情境資料使用及處理 59 5.3 水資源模組之資料使用 60 5.3.1 GWLF模式之資料使用與參數設定 60 5.3.2 桃園水資源系統之使用資料 61 5.4 作物模組之資料使用 65 第六章、 研究區域AgriHydro子模式驗證與合理性說明 73 6.1 情境設定模組 73 6.1.1 多測站氣象合成模式驗證:空間統計特性驗證 73 6.1.2 多測站氣象合成模式驗證:單測站統計特性驗證 77 6.2 水資源模組 82 6.2.1 GWLF流量模式驗證 82 6.2.2 石門水庫系統動力模式驗證 86 6.3 作物模組 88 6.3.1 AquaCrop作物模式合理性說明:水稻 88 6.3.2 AquaCrop作物模式合理性說明:大豆 95 6.3.3 小結 97 第七章、 AgriHydro模擬結果與討論 99 7.1 未來風險變化趨勢 99 7.1.1 氣候變遷 99 7.1.2 水庫水源量與SI指標變化 105 7.1.3 水稻減產風險變化 112 7.1.1 小結 113 7.2 調適選項的制定與模擬的結果與討論 115 7.2.1 調適選項的制定 115 7.2.2 調適選項的模擬的結果與討論 118 7.3 小結 121 第八章、 結論與建議 123 8.1 結論 123 8.2 建議 126 參考文獻 127 附件一、民國107年石門水庫灌溉及給水計畫配水量(106年11月24日審定版) 133 附件二、臺灣桃園農田水利會民國106年灌溉計畫表 135 附件三、未來氣候情境平均值修正值變化趨勢 136 附件四、未來各分區氣候情境修正值 138 | |
dc.language.iso | zh-TW | |
dc.title | 發展氣候、水資源和糧食跨領域整合模式與結合氣候智慧調適演算法之應用-以桃園為例 | zh_TW |
dc.title | Development of Interdisciplinary AgriHydro Model and Application with Climate Smart Adaptation Algorithm - A Case Study in Taoyuan | en |
dc.date.schoolyear | 107-1 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 李明旭(Ming-Hsu Li),胡明哲(Ming-Che Hu),許少瑜(Shao-Yiu Hsu) | |
dc.subject.keyword | AgriHydro,氣候智慧調適演算法,多測站氣象合成模式,風險模板,跨領域, | zh_TW |
dc.subject.keyword | AgriHydro,Climate Smart Adaptation Algorithm,Multi-Site Weather Generator,Risk Template,Interdisciplinary, | en |
dc.relation.page | 145 | |
dc.identifier.doi | 10.6342/NTU201900432 | |
dc.rights.note | 同意授權(全球公開) | |
dc.date.accepted | 2019-02-12 | |
dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
dc.contributor.author-dept | 生物環境系統工程學研究所 | zh_TW |
顯示於系所單位: | 生物環境系統工程學系 |
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