The use of geostationary satellite based rainfall estimation and rainfall-runoff modelling for regional flash flood assessment

• 資料種別: 博士論文
• タイトル: The use of geostationary satellite based rainfall estimation and rainfall-runoff modelling for regional flash flood assessment
• 著者・編者: Suseno, Dwi Prabowo Yuga
• 著者標目: Suseno, Dwi Prabowo Yuga
• 出版事項: Hokkaido University
• 出版年月日等: 2013-09-25
• 出版年（W3CDTF）: 2013-09-25
• 並列タイトル等: 静止衛星による観測データを用いた降雨推定手法と降雨流出モデルによる山地流域における突発的出水評価
• 寄与者: 山田, 朋人; 泉, 典洋; 清水, 康行; 早坂, 洋史
• 授与機関名: 北海道大学
• 授与年月日: 2013-09-25
• 授与年月日（W3CDTF）: 2013-09-25
• 報告番号: 甲第11130号
• 学位: 博士(工学)
• 博論授与番号: 甲第11130号
• 本文の言語コード: eng
• NDC: 500
• 対象利用者: 一般
• 一般注記: The availability of rainfall triggered hazard information such as flash flood is crucial in the flood disaster management and mitigation. However, providing that information is mainly hampered by the shortage of data because of the sparse, uneven or absence the hydrological or meteorological observation. Remote sensing techniques that make frequent observations with continuous spatial coverage provide useful information for detecting the hydrometeorological phenomena such as rainfall and floods. This study aims to develop and evaluate geostationary satellite based rainfall estimation by considering cloud types and atmospheric environmental conditions. Furthermore, the satellite rainfall estimation is coupled with rainfall-runoff model for regional flash flood assessment. First, a simple rainfall estimation method using geostationary satellite i.e. Multifunctional Transport Satellite (MTSAT) blended with Tropical Rainfall Measuring Mission (TRMM) 2A12 is performed for Java Island, Indonesia and its surrounding area. The blending process is conducted by developing statistical relationship between cloud top temperature from MTSAT 10.8μm channel (TIR1) which is collocated with rainfall rate (RR) acquired by TRMM 2A12. Inter comparison with TRMM Multi Precipitation Analysis (TMPA) data product is conducted. Temporal validation result shows that TMPA demonstrated better statistical performance than TIR1 and RR statistical relationship model. However for the spatial correlation, TIR1 and RR statistical relationship model shows reasonably better performance than TMPA. Second, the rainfall estimation method basically uses an assumption the lower cloud top temperature is associated with heavier rainfall, particularly for convective cloud type. To fulfill such assumption, the statistical relationship is developed mainly for cumulonimbus (Cb) cloud type. A new two-dimensional threshold diagram (2D-THR) has been developed based on maximum likelihood cloud classification results, which can readily be applied for MTSAT split window datasets. The study area is Japan and its surrounding area. By integrating the cloud type classification especially by separating Cb cloud type from other cloud types can improve the TIR1 and RR statistical relationship, which is indicated by increasing correlation coefficient and the gradient of regression line. Therefore, underestimating rainfall intensity can be avoided by applying rainfall rate and cloud top temperature relationship that uses Cb cloud type only rather than using all cloud types. A good agreement between estimated and measured storm rainfall also has been demonstrated when use this approach. The geostationary satellite based rainfall estimation then applied for characterizing the storm severity. The Hosking-Wallis Regional Frequency Analysis (HW-RFA) method is used to define the frequency distribution of long-term hourly maximum rainfall over Hokkaido Island. HW-RFA indicates that Generalized Normal/Log Normal three parameters (GNO/LN3) is suitable to describe the frequency distribution of long-term hourly maximum rainfall over Hokkaido Island. A return period map during heavy rainfall event is generated by using MTSAT based rainfall estimation, based on the GNO/LN3 distribution. A comparison with AMeDAS return period of the same rainfall even demonstrates that that the return period information shown by MTSAT rainfall is comparable with AMeDAS rainfall return period. For assessing the return period of an extreme event in the area that observed rainfall is lacking, the use of geostationary satellite based is proved useful to overcome such problem. Third, total Precipitable Water Vapor (PWV) as a product of Global Positioning System observation and atmospheric vertical instability were considered to represent the atmospheric environmental conditions during the development of TIR1 and RR statistical models. The results demonstrated that the models that considered the combination of total PWV and atmospheric vertical instability were relatively more sensitive to heavy rainfall than were the models that considered no atmospheric environmental conditions. Intercomparison results with the TRMM 3B42 rainfall estimation product confirmed that MTSAT-based rainfall estimations made by considering atmospheric environmental conditions were more accurate for estimating rainfall generated by Cb cloud. Lastly, a regional flash flood assessment is conducted based on two rainfall-runoff models: (i) empirical regression model approach and (ii) physical based approach using land surface model. The empirical model uses the multiple regression approach to draw a relationship between the flash flood severity and hydrological, morphometrical and meteorological conditions. Particularly for flash flood severity related to hydrological condition the statistical relationship is strongly determined by initial soil moisture condition. The resulted empirical models shows that flash flood severity as the function of morphometrical factors can provide flash flood potential information. Moreover, flash flood severity as the function of hydrological and meteorological factors demonstrate more dynamic pattern since they are related to rainfall intensity distribution. The physical based approach for flash flood assessment had been conducted by implementing river flow simulation the minimal advance treatments of surface interaction and runoff (MATSIRO). The result indicates that the river flow simulated by MTSAT downscaled with relatively sparse rainfall observation is comparable with the river flow simulation using more dense rain observation network.; (主査) 准教授 山田 朋人, 教授 泉 典洋, 教授 清水 康行, 特任准教授 早坂 洋史; 工学院（環境フィールド工学専攻）
• DOI: 10.14943/doctoral.k11130; https://doi.org/10.14943/doctoral.k11130
• 国立国会図書館永続的識別子: info:ndljp/pid/8948718; https://dl.ndl.go.jp/pid/8948718
• コレクション（共通）: 障害者向け資料
• コレクション（障害者向け資料：レベル1）: テキストデータ
• コレクション（個別）: 国立国会図書館デジタルコレクション > デジタル化資料 > 博士論文; https://dl.ndl.go.jp/collections/A00014
• 収集根拠: 博士論文（自動収集）
• 公開開始日（W3CDTF）: 2015-03-03
• 受理日（W3CDTF）: 2015-02-03T05:25:05+09:00
• 作成日（W3CDTF）: 2013
• 記録形式（IMT）: PDF
• オンライン閲覧公開範囲: 国立国会図書館内限定公開
• デジタル化資料送信: 図書館・個人送信対象外
• 遠隔複写可否（NDL）: 可
• 掲載誌（URI）: http://dx.doi.org/10.14943/doctoral.k11130; http://hdl.handle.net/2115/53880
• 参照（URI）: http://hdl.handle.net/2115/53878
• 連携機関・データベース: 国立国会図書館 : 国立国会図書館デジタルコレクション; https://dl.ndl.go.jp

• 要約等: The availability of rainfall triggered hazard information such as flash flood is crucial in the flood disaster management and mitigation. However, pro...