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大氣科學

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篇名 台灣區域土壤含水率觀測網之建置與資料分析
卷期 43:2
並列篇名 Establish Soil Moisture Observing Network in Taiwan and Data Analysis
作者 江昭輝簡光佑莊秉潔黨美齡李育棋洪景山郭珮萱蔡徵霖
頁次 133-150
關鍵字 土壤含水率地表模式GLDASHRLDAS降雨量Soil moistureSurface Land modelRain
出刊日期 201506

中文摘要

土壤含水率與土溫為影響大氣與地表間能量交互作用的重要參數之一。相對於台灣地區氣象測站之密集度,目前台灣地區土壤含水率觀測資料在時間及空間上的分布仍相當缺乏,本研究團隊在國科會計畫經費補助並與中央氣象局合作下,已於氣象局台中、高雄、恆春、日月潭、嘉義、阿里山與永康等氣象站及中興大學北東眼山測站設置土壤含水率觀測網,今年度完成氣象局北部(台北氣象檢校中心及新竹氣象站)與東部(宜蘭、花蓮與台東等氣象站)之土壤含水率觀測網建置。 初步結果顯示,土壤含水率與降雨量呈現良好的正相關,當降雨量上升時含水率會有相應之反饋。本研究分析重點放在於降雨結束後,表層土壤含水率隨時間衰退的情形。在定性分析方面,針對不同土壤含水量的背景值,探討其消退曲線的表現。定量分析方面,以固定時距觀察單一測站在不同降雨事件的土壤含水量消散的趨勢。在時間分佈上,而土壤含水率變化主要是受到季節降雨特性所影響,1~5月各測站土壤含水率相對較低,平均約10%左右;5月開始的梅雨季與相繼而來的颱風季節帶來的降雨使得各測站土壤含水率普遍偏高,約20~30%左右;冬季11~12月各測站土壤含水率偏低,平均約10%左右。地表模式GLDAS及HRLDAS反演2014年4、5月份不同深度之平均土壤含水率分佈情形。GLDAS呈現南部地區高,中部及北部地區低的情形;HRLDAS則顯示各個深度之平均土壤含水率均在30%以上。

英文摘要

Soil moisture and soil temperature are the important parameters in the energy interaction of the land surface and the atmosphere. In Taiwan, the temporal and spatial distributions of soil moisture are still lacking. Thus, under the NSC’s funding, we have cooperated with Taiwan Central Weather Bureau (TWCWB) and conducted several soil moisture observed experiments in 7 TWCWB’s observation stations (Taichung, Kaohsiung, Hengchun, Sun Moon Lake, Chiayi, Alishan, and Tainan stations) and in 1 forest station (Huisun Forest Station). In this year, more experiments are conducted in Taipei, Hsinchu, Ilan, Hualien and Taitung Stations. The results showed a positive correlation between the rainfall and the soil moisture. Soil moisture increased after rain, and gradually decreased until the next rainfall. The study focused on surface soil moisture over time drop down analysis at the end of a rainfall event. Soil moisture background values were focused by qualitative and quantitative analysis. Furthermore, the soil moisture was about 10% low from January to May, increased to 20% -30% in rain and typhoon season (June to September), and then gradually decreased to 10% in November and December. In addition, two surface models, ‘GLDAS’ and ‘HRLDAS’, were also used to retrieve the soil moisture in Apr and May 2014. The soil moisture of GLDAS was high in a south area and low in the middle and north area. HRLDAS showed that soil moisture was higher than 30% at each depth.

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