臺灣地區降雨型態分布不均，尤其以臺灣西南部更為嚴重，近年受全球氣候變遷影響，豐枯水年降雨差異逐年上升，極端降雨事件頻繁發生。在此情形下，地下水作為地表水供應不足時的補充被廣泛開發。本研究選擇臺灣南區的三大地下水區:嘉南平原、屏東平原以及花東縱谷平原中南段作為研究區域，針對2000年至2018年期間19年的地下水位站之水文歷線，以及1999年至2018年期間的雨量觀測站水文歷線，進行標準化以及相關性分析，找出地下水與雨量兩者間之相互關係，利用地下水對降雨的延遲特性，以期能對於全區地下水枯旱狀態的時間分布特性做出分析整理，作為未來地下水管理的研究參考。
本研究首先利用Mann-Kendall檢定法評估研究區域內地下水測站水位變動趨勢，結果顯示，在嘉南平原方面，鹽水溪流域以北多呈現下降趨勢，以南則多呈現上升趨勢。屏東平原方面，上游多呈現下降趨勢，而下游則多呈現上升趨勢。最後在花東平原方面，研究區域內的地下水測站水位變動趨勢並未有太多的差異。
接著將區域內地下水測站的地下水位累積值轉換成標準化SGI指標；對區域內雨量測站進行不同時間尺度的累積值的標準化SPI轉換。其結果顯示，在嘉南平原方面，南北兩區的SGI與SPI枯旱訊號差異並不大，但南區在研究期間早期(2002至2005年)有強烈的枯旱訊號發生，這導致南區多數地下水測站呈現上升的趨勢。在屏東平原方面，枯旱訊號分別在上游的晚期(2011至2015年)以及下游的早期(2002至2004年)發生。在花東平原方面，SGI與SPI枯旱訊號發生年份相近，並且較強烈的枯旱訊號發生在研究期間的中晚期(2014至2016年)。
根據Mann-Kendall檢定法以及標準化SGI與SPI指標的結果分別對三個地下水區進行分區後，找出各分區中SGI值與SPI值相對強度之中位數作為代表並對彼此進行相關性分析，分析結果顯示SGI180與SGI360中，以SGI180作為後續迴歸分析的主體較佳。並且相關性分析找出的最佳相關係數的SPI尺度及延遲日數，在嘉南平原方面，北區為SPI230延遲日數30日的曲線，南區為SPI230延遲日數35日的曲線；屏東平原方面，上游為SPI210延遲55日的曲線，下游為SPI260延遲日數30日的曲線；花東平原方面則為SPI240延遲日數35日的曲線。以這些SPI曲線與原SGI曲線進行迴歸分析，並對SGI最低點日期進行預測，結果顯示嘉南以及屏東兩地下水區的誤差較小，在花東方面，受到實際SGI曲線波動較大的影響，誤差結果並不理想。
而若考量臺灣豐水與枯水期頻率以半年各一次的特性，針對SGI180與SPI180近行分析，並根據相關係數分析結果，選擇SPI180中最佳延遲日數的曲線與SGI180進行迴歸分析，嘉南平原北區與南區最佳延遲日數皆為60日，屏東平原上游與下游皆為70日，花東平原則為60日。其預測結果與上一組比較，整體的預測誤差較大。雖然SGI180與SPI180有使用上的便利性，本研究顯示在預測上仍然需要考慮相關係數最高的SPI尺度，預測的結果將會更佳。

In recent years, due to global climate change, extreme rainfall events have occurred frequently. The rainfall pattern in Taiwan has been unevenly distributed since ancient times, especially in southwestern Taiwan. Under this condition, groundwater is widely developed as a supplement water resource when surface water supply is insufficient. In this study, three groundwater areas, the Jianan Plain, the Pingtung Plain and the central and southern section of the Huatung Longitudinal Valley Plain, were selected in southern Taiwan. The standardization index and correlation analysis were used to find out the relationship between the hydrological data of groundwater level stations from 2000 to 2018 and the hydrological data of rainfall observation stations from 1999 to 2018. Statistic methods were performed. First, the Mann-Kendall test was used to evaluate the trend of water level changes of groundwater stations in study areas. Then, the hydrological data from groundwater measuring station and rainfall measuring station in three areas were converted into Standardized Groundwater Index (SGI) and Standardized Precipitation Index (SPI), respectively. The correlation analysis between SGI and SPI under various time scales was performed to find out the best correlation coefficients, and then obtained delay time. Thus, a regression equation between the SPI and SGI180/SGI 360 was also provided for each area. Finally, this regression equation with shift the delay time can be used to predict SGI values which were compared the error with the actual SGI data. Based on Mann-Kendall test and correlation analysis of the SPI and SGI data, the results showed that the three groundwater areas can be divided into five partitions. The results also showed that the prediction errors of SGI in Jianan Plain and Pingtung Plain are relatively small, and that in the Huatung Longitudinal Valley Plain is relatively large.

Amirataee, B., Montaseri, M. and Sanikhani, H., 2016, “The analysis of trend variations of reference evapotranspiration via eliminating the significance effect of all autocorrelation coefficients”, Theoretical and Applied Climatology, 126, pp.131-139.
Bloomfield, J.P. and Marchant, B.P., 2013, “Analysis of groundwater drought building on the standardised precipitation index approach”, Hydrology and Earth System Sciences, 17, pp.4769-4787.
Brunner, M.I., Liechti, K. and Zappa, M., 2019, “Extremeness of recent drought events in Switzerland: dependenceon variable and return period choice”, Natural Hazards and Earth System Sciences Discussions, 19, pp.2311-2323.
Burke, E.J., Brown, S.J. and Christidis, N., 2006, “Modeling the recent evolution of global drought and projections for the twenty-first century with the hadley centre climate model”, Journal of Hydrometeorology, 7, pp.1113–1125.
Calow, R., Robins, N., Macdonald, A. and Nicol, A., 1999, “Planning for groundwater　drought in Africa”, In: Proceedings of the International Conference on Integrated　Drought Management: Lessons for Sub-Saharan Africa, IHP-V, Technical Documents in Hydrology, 35, pp.255-270.
Chang, T.J. and Teoh, C.B., 1995, “Use of the kriging method for studying characteristics of ground water droughts”, J. Am. Water Resou. Assoc., 31, pp.1001–1007.
Chen, H., Guo, S., Xu, C.Y. and Singh, V.P., 2007, “Historical temporal trends of hydro-climatic variables and runoff response to climate variability and their relevance in water resource management in the Hanjiang basin”, J Hydrol , 344, pp.171–184.
Collins, M., Osborn, T.J., Tett, S.F.B., Briffa, K.R. and Schweingruber, F.H., 2002, “A comparison of the variability of a climate model with paleotemperature estimates from a network of tree-ring densities”, Journal of Climate, 15, pp.1497–1515.
Dai, A., Trenberth, K.E. and Qian, T., 2004, “A global data set of Palmer drought severity index for 1870–2002: relationship with soil moisture and effects of surface warming”, Journal of Hydrometeorology, 5, pp.1117–1130.
Dai, A., 2011, “Drought under global warming: a review”, Wiley Interdisciplinary Reviews: Climate Change 2011, 2, pp.45-65.
Department for Department of Economic, & Statistical Division, 2020, Population and vital statistics report, New York: United Nations Publications.
Eltahir, E.A.B. and Yeh, P.J.F., 1999, “On the asymmetric response of aquifer water level to floods and droughts in Illinois”, Water Resour. Res., 35(4), pp.1199-1217.
Fontes, F., Gorst, A., and Palmer, C., 2017, Does choice of drought index influence estimates of drought-induced cereal losses in India?, London: Grantham Research Institute on Climate Change and the Environment.
Gibbs, W.J., 1975, “Drought, its definition, delineation and effects”, Drought: Lectures Presented at the 26th Session of the WMO Executive Committee, Special Environmental Report No. 5, WMO, Geneva, pp. 3–30.
Gocic, M., Trajkovic, S., Milanovic, M., 2020, “Precipitation and Drought Analysis in Serbia for the Period 1946–2017”, In: Negm, A., Romanescu, G., Zelenakova, M. (eds.), Water Resources Management in Balkan Countries, Springer Water, Springer, Cham.
Guttman, N.B., 1999, “Accepting the standardized precipitation index: a calculation algorithm”, Journal of the American Water Resources Association, 35 (2), pp.311–322.
Hegerl, G.C., Karl, T.R., Allen, M., Bindoff, N.L., Gillett, N., Karoly, D., ...Zwiers, F., 2006, “Climate change detection and attribution: beyond mean temperature signals”, Journal of Climate, 19, pp.5058–5077.
Hulme, M., Barrow, E.M., Arnell, N.W., Harrison, P.A., Johns, T.C. and Downing, T.E., 1999, “Relative impacts of human-induced climate change and natural climate variability”, Nature, 397, pp.688–691.
IPCC , 2007, Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge: Cambridge University Press.
Jansen, E., Overpeck, J., Briffa, K.R., Duplessy, J-C., Joos, F., MassonDelmotte, V., Olago, D., …Zhang, D., 2007, Palaeoclimate. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.)], Cambridge, United Kingdom: Cambridge University Press and NY, USA: New York.
Kemdall, M.G., 1975, “Rank correlation methods”, Charles Griffin, London.
Kiem, A.S., Johnson, F., Westra, S., Dijk, A.V., Evans, J.P., O’Donnell, A., …Mehrotra, R., 2016, “Natural hazards in Australia: droughts”, Climatic Change, 139, pp.37-54.
King, D.A., 2004, “Climate change science:adapt, mitigate, or ignore?”, Science, 303, pp.176–177.
Lee Rodgers, J., and Nicewander, W. A., 1988, “Thirteen ways to look at the correlation coefficient”, The American Statistician, 42(1), pp.59-66.
Lloyd-Hughes, B. and Saunders, M.A., 2002, “A drought climatology for Europe”, Int. J. Climatol., 22, pp. 1571–1592.
Mann, H.B., 1945, “Non-parametric test against trend”, Econometrica, 13, pp.245-259.
McKee, T.B., Doesken, N.J. and Kleist, J., 1993, “The relationship of drought frequency and duration time scales”, 8th Conference on Applied Climatology, American Meteorological Society, Anaheim, California, 17–22 January, pp.179-184.
Milly, P.C.D., Wetherald, R.T., Dunne, K.A. and Delworth, T.L., 2002, “Increasing risk of great floods in a changing climate”, Nature, 415, pp.514–517.
Mishra, A.K. and Singh, V.P., 2010, “ A review of drought concepts”, Journal of Hydrology, 391, pp.202-216.
Nalbantis, I., 2008, “Evaluation of a Hydrological Drought Index”, European Water, 23(24), pp.67-77.
Palmer, W.C., 1965, “Meteorologic Drought”, US Department of Commerce, Weather Bureau, Research Paper No.45, pp.58.
Palmer, W.C., 1968, “Keeping track of crop moisture conditions, nationwide: the new crop moisture index”, Weatherwise, 21, pp.156-161.
Pittock, A.B., 1999, “Climate change: the question of significance”, Nature, 397, pp.657–658.
Shafer, B.A. and Dezman, L.E., 1982, “Development of a Surface Water Supply Index (SWSI) to Assess the Severity of Drought Conditions in Snowpack Runoff Areas”, In: Preprints, Western SnowConf., Reno, NV, Colorado State University, pp.164-175.
Sheffield, J. and Wood, E.F., 2008, “Projected changes in drought occurrence under future global warming from multi-model, multi-scenario, IPCC AR4 simulations”, Climate Dynamics, 13, pp.79-105.
Tallaksen, L.M. and van Lanen, H.A.J., 2004, “Hydrological drought Processes and estimation methods for streamflow and groundwater”, the Netherlands: Elsevier, Developments in Water Sciences 48.
van Lanen, H.A.J. and Peters, E., 2000, “Definition, effects and assessment of groundwater droughts”, In: Vogt, J.V., Somma, F. (Eds.), Drought and Drought Mitigation in Europe, Kluwer Academic Publishers, Dordrecht, pp.49-61.
Vicente-Serrano, S.M., Begueria, S. and Lopez-Moreno, J.I., 2010, “A multi-scalar drought index sensitive to global warming: the Standardized Precipitation Evapotranspiration Index”, Journal of Climate, 23, pp.1696–1718.
Wilhite, D.A. and Glantz, M.H., 1985, “Understanding the drought phenomenon: the role of definitions”, Water International, 10, pp.111-120.
Wilhite, D.A., 2000, Drought: A Global Assessment (Vols. 1), New York: Routledge.
Wu, H., Hayes, M.J., Wilhite, D.A. and Svoboda, M.D., 2005, “The effect of the length of record on the standardized precipitation index calculation”, International Journal of Climatology, 25, pp.505–520.
Yevjevich, V., 1967, An Objective Approach to Definitions and Investigations of Continental Hydrologic Drought, Hydrology Paper No. 23, Fort Collins, Colo: Colorado State University.
Zargar, A., Sadiq, R., Naser, B., and Khan, F.I., 2011, “A review of drought indices”, Environmental Reviews, 19(NA), pp.333-349.
中興工程顧問社，2016，｢地下水水文地質與補注模式研究-105 年度地下水主要補注區補充地質調查｣，經濟部中央地質調查所，初稿。
何春蓀，1986，臺灣地質概論(增訂第二版)，經濟部中央地質調查所，共 163 頁。
武冠群，2019，｢以標準化地下水位指數法評估屏東平原地下水水位枯旱特徵之研究｣，國立成功大學資源工程學系碩士論文。
林朝棨，1957，臺灣地形，臺灣省通志稿，卷 1，共 424 頁。
陳文山，2016，臺灣地質概論，中華民國地質學會，共 179 頁。
陳鴻圖，2009，嘉南平原水利事業的變遷，臺南縣政府，共239頁。
莊家棋，2018，｢應用標準化地下水位指數法評估濁水溪沖積扇地下水水位枯旱狀況之研究｣，國立成功大學資源工程學系碩士論文。
楊偉甫，2010，｢台灣地區水資源利用現況與未來發展問題｣，用水合理化與新生水水源開發論壇，台灣水環境再生協會。
經濟部，2008，｢花蓮台東地區井下地質｣，經濟部中央地質調查所。
經濟部，2014，｢地下水補注地質敏感區劃定計畫書-G0002屏東平原｣。
經濟部，2016，｢地下水補注地質敏感區劃定計畫書-G0006嘉南平原｣。
經濟部水利署，2016a，「臺灣地區乾旱時期地下水備援用水評估系統建置」，經濟部水利署。
經濟部水利署，2016b，「高屏與嘉南集水區地下水開發區位及其可開發量評估(1/2)」，經濟部水利署。
經濟部水利署，2017，「高屏與嘉南集水區地下水開發區位及其可開發量評估(2/2)」，經濟部水利署。
經濟部水利署，2019，｢中華民國108年臺灣地區水文年報｣，經濟部水利署。
葉信富、張家富、李哲瑋、李振誥，2016a，以標準化地下水與降雨指數法評估高屏溪流域之乾旱特性，中華水土保持學報，第47卷，第1期，45-52。
葉信富、葉振峰、李振誥，2016b，以Mann-Kendall及Theil-Sen檢定法評估臺灣地區長期河川流量時空趨勢變化，中華水土保持學報，第47卷，第2期，73-83。