Exploring the Possibility of Establishing Mass Movement Warning System for Taiwan Using the Soil Water Index
土壤雨量指數(soil water index; SWI)可表現目前受當下及前期降雨影響的概估土壤含水量，是日本氣象廳做為全國坡地災害警戒參考的重要指標。近年來更有許多研究指出，坡地災害的發生與否和當時的SWI是否為同一點位SWI歷史排序之前位有關。因此，前人研究提出一量化之方法，計算某一點位的SWI與過去十年的最大值之比值(即為normalized soil water index; NSWI)，可作為坡地災害預測的重要指標。本研究利用2006–2017年間發生的坡地災害(n = 344)，分析臺灣誘發坡地災害發生之降雨事件期間的SWI與NSWI變化。結果顯示，坡地災害發生時的SWI介於2.90至700.93之間，平均為308.42；而NSWI則介於0.01至1.87之間，平均為0.84。平均來說坡地災害發生在NSWI最大值出現後的3.42小時之內。我們進一步將NSWI區分成0–0.2、0.2–0.4、0.4–0.6、0.6–0.8、0.8–1.0、及>1.0等六個範圍，依此對全臺灣的坡地災害發生的可能性，定義不同的警戒等級。本研究並藉由臺灣氣候變遷推估資訊平臺計畫(Taiwan Climate Change Projection Information and Adaptation Knowledge Platform; TCCIP)所開發的多資料多模式整合系統(Multi-data and Multi-model Integrated System; MMIS)串接過去的時雨量資料，建置可查詢SWI與NSWI在歷史降雨事件中的變化情形，以及在某特定時間下的數值之介面。未來若能進一步串接即時雨量，甚至未來數小時的預估雨量，將可提供臺灣在坡地災害預警系統上的一重大突破。
The soil water index (SWI) represents the estimated soil moisture content affected by current and antecedent rainfall, and is an important indicator used by the Japan Meteorological Agency (JMA) as a reference to issue nationwide mass movement warnings. In recent years, many studies had pointed out that the occurrence of mass movements is highly related to whether the value of SWI is within the top historical ranking (recorded at the same location). According to this finding, some studies hadproposed a quantitative method, which is to calculate the ratio of the SWI at a particular location to the maximum value recorded in the past decade (i.e. normalized soil water index; NSWI). This NSWI can serve as an important indicator for predicting mass movement occurrence. In this study, 344 mass movements occurred during 2006–2017 were analyzed. Changes in SWI and NSWI during numerous rainfall events were calculated. The result shows that the SWI at the time of mass movements was between 2.90 and 700.93, with an average of 308.42; while the NSWI was between 0.01 and 1.87, with an average of 0.84. On average, mass movements occurred within 3.42 hours after the maximum NSWI had been reached. We also defined different warning levels based on six ranges of NSWI: 0–0.2, 0.2–0.4, 0.4–0.6, 0.6–0.8, 0.8–1.0, and > 1.0. These levels would represent different possibilities of mass movement occurrence. This study also utilized the Multi-data and Multi-model Integrated System (MMIS), which was developed by the Taiwan Climate Change Projection Information and Adaptation Knowledge Platform (TCCIP), to link together the historical rainfall data and to establish an interface. This allows us to easily explore the changes in SWI and NSWI during historical rainfall events or at a specific time of interest. In the future, if we could further integrate this system with real-time rainfall data and the predicted rainfall amount for the upcoming time, it would provide a major breakthrough in the mass movement warning system in Taiwan.