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  • 學位論文

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

Development of Interdisciplinary AgriHydro Model and Application with Climate Smart Adaptation Algorithm - A Case Study in Taoyuan

指導教授 : 童慶斌
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摘要


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。

並列摘要


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.

參考文獻


1. Amiri, E., Rezaei, M., Rezaei, E. E., & Bannayan, M. (2014). Evaluation of Ceres-Rice, Aquacrop and Oryza2000 models in simulation of rice yield response to different irrigation and nitrogen management strategies. Journal of plant nutrition, 37(11), 1749-1769.
2. Auffhammer, M., Ramanathan, V., & Vincent, J. R. (2012). Climate change, the monsoon, and rice yield in India. Climatic change, 111(2), 411-424.
3. Bellone, E., Hughes, J. P., & Guttorp, P. (2000). A hidden Markov model for downscaling synoptic atmospheric patterns to precipitation amounts. Climate Research, 15(1), 1-12.
4. Bithell, M., & Brasington, J. (2009). Coupling agent-based models of subsistence farming with individual-based forest models and dynamic models of water distribution. Environmental Modelling & Software, 24(2), 173-190.
5. Bouman, B. (2001). ORYZA2000: modeling lowland rice (Vol. 1): IRRI.

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