臺灣年平均降雨量相當豐沛，但受限於時空分佈及地狹人稠等因素影響，降雨量時空分布不均且每人每年可分配降雨量甚少，再加上近年氣候變遷的影響，使得熱浪和乾旱的發生頻率正逐漸增加，因此對氣候變遷下未來乾旱變化趨勢進行分析已刻不容緩。本研究旨在以單變數及雙變數方法探討臺灣地區氣候變遷對乾旱事件的影響，本文使用臺灣氣候變遷推估與資訊平台計畫(Taiwan Climate Change Projection and Information Platform, TCCIP)所產製的AR6統計降尺度日雨量資料，為避免模式數過少對分析結果產生不確定性，本文採用24個全球氣候模式的不同情境雨量來評估，各氣候模式包含四組暖化情境(SSP1-2.6、SSP2-4.5、SSP3-7.0、SSP5-8.5)，雨量資料時程上則包含歷史基期(1995~2014)、近未來(2021~2040)、中未來(2041~2060)、中遠未來(2061~2080)及遠未來(2081~2100)。本研究將各氣候模式雨量資料轉換為標準化降水指數(Standardized Precipitation Index, SPI)以定義乾旱事件，並由乾旱事件擷取三個乾旱特性(頻率、延時及嚴重程度)取系集平均後進行網格、時段、分區分析，再以關聯結構(copula)建立延時與嚴重程度雙變數機率分佈，設定三種乾旱事件強度以計算聯集重現期與交集重現期。經以各暖化情境的基期資料為基準，三個特性變化率之絕對值(變化程度)由大到小依序為頻率、嚴重程度、延時，而世紀中至世紀末之大部分乾旱特性由低(輕微)至高(嚴重)的情境排序為SSP1-2.6、SSP2-4.5、SSP5-8.5及SSP3-7.0，高濃度排放情境(SSP3-7.0、SSP5-8.5)可能發生劇烈乾旱事件且強度相似。臺灣水資源分區的分析結果顯示臺灣北部、東部受情境變化影響較中部、南部為大。

The frequency of drought increases due to recent climate change impacts. Analyzing future trends of drought characteristics under climate change is therefore urgent. This study aims to investigate the impacts of climate change on drought events in Taiwan using both univariate and bivariate methods. This study uses AR6 statistical downscaling daily precipitation data that is produced by Taiwan Climate Change Projection and Information Platform (TCCIP). A total of 24 global climate models are used in this study to explore drought characteristics under climate change. The precipitation data of each global climate model consists of 4 emission scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5), and the time period of precipitation data is divided into 5 segments (baseline (1995~2014), near future (2021~2040), medium future (2041~2060), medium far future (2061~2080), and far future (2081~2100)). First step in this study is to transform daily rainfall into monthly rainfall, and transform it into the Standardized Precipitation Index (SPI) to define the drought events. Three drought event characteristics (frequency, duration , and severity) are analyzed spatially, temporally and regionally using ensemble averages across grids and periods. Copula-based method is employed to establish bivariate probability distributions for drought duration and severity. Three intensities of drought events are considered in this study to compute two types of return periods (union and intersection). Compared to the baseline data for various emission scenarios, the relative changes of 3 drought characteristics from large to small is frequency, severity, and duration. Three drought characteristics range from low (mild) to high (severe) are SSP1-2.6, SSP2-4.5, SSP5-8.5, and SSP3-7.0 for the period from middle to end of the century. The results of return period indicate that severe drought events may occur under high-concentration emission scenarios (SSP3-7.0 and SSP5-8.5) with similar intensity. In addition, the northern and eastern regions in Taiwan are more affected by climate change.

1. Asif Khan, M., Faisal, M., Hashmi, M. Z., Nazeer, A., Ali, Z. & Hussain, I. (2021). Modeling drought duration and severity using two-dimensional copula. J. Atmos. Sol. Terr. Phys. 214, 105530.
2. Bento, V. A., Gouveia, C. M., DaCamara, C. C. & Trigo, I. F. (2018).A climatological assessment of drought impact on vegetation health index. Agric For Meteorol 259. 286–95
3. Bong, C.H.J., & Richard, J. (2020). Drought and climate change assessment using standardized precipitation index (Spi) for sarawak river basin. J Water Clim Chang. 11(4):956–965.
4. Chen, C. A., Hsu, H. H., & Liang, H. C. (2021).Evaluation and comparison of CMIP6 and CMIP5 model performance in simulating theseasonal extreme precipitation in the Western North Pacific and EastAsia.Weather Clim. Extremes31 (Mar): 100303.
5. Cherubini, U., Luciano, E., & Vecchiato, W. (2004). Copula Methods in Finance. John Wiley & Sons.
6. Genest, C., & McKay, J. (1986). The joy of copulas, bivariate distributions with uniform marginals. American Statistician 40, 280–283.
7. Guttman, N. B. (1999). Accepting the standardized precipitation index: a calculation algorithm. J. Am. Water Resour. 35, 311–322.
8. Haan, C. T. (1977). Statistical methods in hydrology, Iowa State University Press, Ames, Iowa.
9. Hayes, M. J., Svoboda, M. D., Wilhite, D. A., & Vanyarkho, O. V. (1999). Monitoring the 1996 drought using the Standardized Precipitation Index, B. Am. Meteorol. Soc., 80, 429–438.
10. Huang, W. R., Liu, P. Y., Lee, S. Y., & Wu, C. H. (2022). Changes in early summer precipitation characteristics over South China and Taiwan: CESM2-LE and CMIP6 multi-model simulations and projections. Journal of Geophysical Research: Atmospheres, 127(17), e2022JD037181.
11. Intergovernmental Panel on Climate Change (2023). Summary for Policymakers. In: Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, H. Lee and J. Romero (eds.)].IPCC, Geneva, Switzerland, pp. 1-34, doi: 10.59327/IPCC/AR6-9789291691647.001
12. Joe, H. (1997). Multivariate models and dependence concepts. Chapman and Hall, London
13. Kumar, V., & Bala, S. (2022). Best fit probability distribution analysis of precipitation and potential evapotranspiration of India’s highly dense population state-Bihar. MAUSAM.; 73(1): 139-150.
14. Ling, C. H. (1965) Representation of associative functions. Publ Math Debrecen. 12, 189-212.
15. McKee, T. B., Doesken, N. J., & Kleist, J. (1993). The relationship of drought frequency and duration to time scales. Proc. 8th Conf. Appl. Clim. 17, 179-183.
16. Mondal, S. K., Huang, J., Wang, Y., Su, B., Zhai, J., Tao, H., Wang, G., Fischer, T., Wen, S., & Jiang, T. (2021). Doubling of the population exposed to drought over south Asia: CMIP6 multi-model-based analysis. The Science of the Total Environment, 771(14), 145186.
17. Nabaei, S., Sharafati, A., Yaseen, Z. M. & Shahid, S. (2019). Copula based assessment of meteorological drought characteristics: Regional investigation of Iran. Agric. For. Meteorol. 276, 107611.
18. Nelsen, R. (2006). An introduction to copulas. Springer Verlag.
19. Nooni, I. K., Hagan, D. F. T., Ullah, W., Lu, J., Li, S., Prempeh, N. A., Gnitou, G. T., & Sian, K. T. C. L. K. (2022). Projections of Drought Characteristics Based on the CNRM-CM6 Model over Africa. Agriculture. 12(4), 1–19.
20. O’Neill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J.-F.,Lowe, J., Meehl, G. A., Moss, R., Riahi, K. , & Sanderson, B. M. (2016). The scenario model intercomparison project (ScenarioMIP) for CMIP6. Geosci. Model. Dev. 9, 3461–3482.
21. Šebenik, U.; Brilly, M., & Šraj, M. (2017). Drought analysis using the standardized precipitation index (SPI). Acta Geogr. Slov., 57, 31–49.
22. She, D., & Xia, J. (2018). Copulas-based drought characteristics analysis and risk assessment across the Loess Plateau of China. Water Resour. Manage. 32 (2), 547–564.
23. Shiau, J. T. (2006). Fitting drought duration and severity with two-dimensional copulas. Water Resources Management 20:795–815.
24. Shiau, J. T., & Modarres, R. (2009). Copula-based drought severity-duration-frequency analysis in Iran, Meteorol. Appl., 16(4), 481–489, doi:10.1002/met.145.
25. Shiau, J. T., & Shen, H. W. (2001). Recurrence analysis of hydrologic droughts of differing severity. J Water Resour Plan Manage ASCE, 127(1), 30–40.
26. Shin, Y., Shin, Y., Hong, J., Kim, M. K., Byun, Y. H., Boo, K. O.,Chung, I. U., Park, D. S., & Park, J. S. (2021). Future projections and uncertainty assessment of precipitation extremes in the Korean Peninsula from the CMIP6 Ensemble with a Statistical Framework. Atmosphere. 12:97
27. Sklar, A. (1959). Fonctions de r´epartition `a n dimensions e leurs marges. Publications del’Institut de Statistique de l’Univiversit´e de Paris 8, 229–231.
28. Spinoni, J., Naumann, G., Carrao, H., Barbosa, P., & Vogt, J. (2014). World drought frequency, duration, and severity for 1951–2010. International Journal of Climatology, 34(8), 2792–2804.
29. Stagge, J. H., Tallaksen, L. M., Gudmundsson, L., Van Loon, A. F., & Stahl, K. (2015). Candidate distributions for climatological drought indices (SPI and SPEI), Int. J. Climatol., 35, 4027–4040.
30. Sung, H. M., Kim, J., Shim, S., Seo, J. b., Kwon, S. H, Sun, M. A, Moon, H., Lee, J. H, Lim, Y. J., Boo, K. O., Kim, Y., Lee, J., Lee, J., Kim, J. S., Marzin, C., & Byun, Y. H. (2021). Climate change projection in the twenty-frst century simulated by NIMS-KMA CMIP6 model based on new GHGs concentration pathways. Asia-Pac J Atmos Sci 57:851–862.
31. Teng, T. Y., Liu, T. M., Tung, Y. S., & Cheng, K. S. (2021). Converting climate change gridded daily rainfall to station daily rainfall-a case study at Zengwen reservoir. Water 2021, 13(11), 1516
32. Thom, H. C. S. (1966). Some methods of climatological analysis. Secretariat of the World Meteorological Organization.
33. Tirivarombo, S., Osupile, D., & Eliasson, P. (2018). Drought monitoring and analysis: Standardised Precipitation Evapotranspiration Index (SPEI) and Standardised Precipitation Index (SPI). Phys. Chem. Earth, Parts A/B/C. 106, 1–10.
34. Vishwakarma, A., Choudhary, M. K., & Chauhan, M. S. (2020). Applicability of SPI and RDI for forthcoming drought events: a non-parametric trend and one way ANOVA approach. J Water Clim Change 11(S1):18–28.
35. World Economic Forum (2024). Global Risks Report 2024. World Economic Forum Global Risks Perception Survey 2023-2024.
36. Wu, C., Yeh, P. J. F., Chen, Y. Y., Lv, W., Hu, B. X., & Huang, G. (2021). Copula-based risk evaluation of global meteorological drought in the 21st century based on CMIP5 multi-model ensemble projections. Journal of Hydrology, 598, 126265.
37. Xu, K., Yang, D., Xu, X., & Lei, H. (2015). Copula based drought frequency analysis considering the spatio-temporal variability in Southwest China. Journal of Hydrology, 527, 630–640.
38. Yilmaz, B. (2019). Analysis of hydrological drought trends in the Gap Region (Southeastern Turkey) by Mann-Kendall test and Innovative Sen Method. Appl Ecol Environ Res 17:3325–3342.
39. Zhai, L., & Feng, Q. (2009). Spatial and temporal pattern of precipitation and drought in Gansu Province, Northwest China. Nat. Hazards 49, 1–24.
40. Zhang, L., & Singh, V. P. (2007a). Bivariate rainfall frequency distributions using Archimedean copulas. Journal of Hydrology, 332(1–2), 93–109.
41. Zhang, L., & Singh, V. P. (2007b). Gumbel–Hougaard copula for trivariate rainfall frequency analysis. Journal of Hydrology, 409–419.
42. Zhang, T.; Su, X.; Zhang, G.; Wu, H.; Wang, G., & Chu, J. (2022). Evaluation of the impacts of human activities on propagation from meteorological drought to hydrological drought in the Weihe River Basin, China. Sci. Total Environ. 819, 153030.
43. Zou, L., & Zhou, T. (2022). Mean and extreme precipitation changes over China under SSP scenarios: Results from high-resolution dynamical downscaling for CORDEX East Asia. Climate Dynamics, 58, 1015–1031.
44. 李振綱(2008)。探討股票市場與債券市場的關聯結構－動態Copula模型。碩士論文。國立交通大學。
45. 林修立、童裕翔、王俊寓、林士堯(2022)。AR6統計降尺度雨量資料生產履歷（1.0版）。臺灣氣候變遷推估資訊與調適知識平台。
46. 國家科學及技術委員會(2023)。情境說明：SSP共享社會經濟路徑Shared Socioeconomic Pathway簡介。台灣氣候變遷推估資訊與調適知識平台。
47. 國家科學及技術委員會(2023)。臺灣氣候變遷關鍵指標圖集：AR6統計降尺度版。台灣氣候變遷推估資訊與調適知識平台。
48. 陳昭安、李明營、劉子明、許晃雄、羅資婷、陳永明、童裕翔、吳芊瑩、洪浩哲、鄭兆尊、林思穎(2023)。2023臺灣氣候變遷分析系列報告：2020-2021極端乾旱事件與未來推估，國家災害防救科技中心。
49. 經濟部水利署(2022)。中華民國一一一年臺灣水文年報。