第6次結合モデル相互比較プロジェクトCMIP6の陸域過程に着目した新しいプロジェクトとして，LS3MIP(The Land Surface, Snow and Soil moisuture Model Intercomparison Project)があり，2018年5月の時点で各陸域モデルグループがモデル実験の準備を行っている．本研究では，LS3MIPの概要およびLS3MIPの必須実験の一つである20世紀陸域シミュレーションの実験設定について説明する．加えて，20世紀陸域シミュレーションのテスト実験を陸面過程モデルMATSIROを用いて行い，その初期解析結果からみられるMATSIROが計算する流出量，積雪被覆率，土壌水分，蒸発散量の20世紀の変化傾向とその要因について報告する．トレンド解析の結果，北半球の大半の地域で水収支は増加傾向にあったが，ヒマラヤ山脈の周辺では減少傾向にあり，これは同地域で特徴的な降水量の減少傾向によるものと考えられた．
気候復元により長期間にわたって気候変動の解析が可能になる．将来の気候予測のためにも過去の気候変動を捉えることは重要である．本研究ではOkazaki and Yoshimura (2017)の手法に基づき，アイスコア，サンゴ殻，樹木年輪セルロースの酸素同位体比データを用い，データ同化により過去千年の気候場を算出した．プロキシの同位体比の同化により他の気候要素が拘束されることを確認した．気候場の年々変動の再現にはプロキシデータの数量による影響がある．ただ，プロキシデータ量が多い程，解析値に与える影響が大きいわけではなく，用いるプロキシデータが全体の約20％程度でも全てのプロキシデータを用いた場合と同様に地上気温などの年々変動を再現できる可能性が示唆された．ただ，プロキシによる解析値への影響は時空間的に異なる．特に，中部太平洋熱帯域のサンゴ殻による影響が大きいことが分かった．
マイクロ波放射計GMIとAMSR2から，地表水指標Normalized Differential Frequency Index(NDFI)を算出し，0.1°格子・日単位での全球地表水マップを2013～2017年の5年間分作成した．作成にあたり，NDFIの日内変動やセンサ間バイアスについての補正を行った．作成した地表水マップについて，グローバルデータを用いた基礎的な検証を行った．日本付近で，NDFIの5年平均と，Global Surface Waterによる冠水率の空間分布には，高い正の相関がみられた．NDFIと，Yesterday’s Earth at EORCによる氾濫面積率の月変動は，大河川の河道沿いなどに正の相関を示す地域が広くみられた．日単位のNDFIが突然上昇する現象は，乾燥地域を中心にみられる．それらの地域の多くでは，NDFIと先行降雨指数の日変動が，正の相関を示していることから，外水よりも内水の増加を表しているとみられる．
Parameter uncertainty analysis of rainfall-runoff models is very important especially in urban watersheds due to the high flood risk in these areas. Among the different methods available for uncertainty analysis, bootstrap method gained popularity in view of its flexibility. Hence, this study aims to conduct the parameter uncertainty analysis of the urban storage function (USF) model, a storage function model specifically developed for the urban watersheds, using the model-based bootstrap method. We successfully evaluated the uncertainty of USF model parameters and the results exhibited that the 95% confidence interval of all parameters is wide compared with the search range during parameter estimation except for two parameters. Moreover, the parameters with the highest and least uncertainties were identified. Further, model simulation efficiency using the estimated parameters was found to be high with a Nash-Sutcliffe Efficiency value of 97%. Lastly, the effect of parameter uncertainty on model simulation uncertainty was analysed and found that the SCE-UA method along with the model-based bootstrap method can predict, on an average, 68% of observed data within the simulation uncertainty range of USF model.
Data assimilation techniques are becoming popular in estimating hydraulic variables in ungauged basins with the recent advancements in the satellite technology. The Local Ensemble Transformation Kalman Filter (LETKF), which limits the assimilation domain by a “local patch”, is an efficient method for a global-scale data assimilation, but the optimization of the size and weighting function of the local patch is still challenging especially for river hydrodynamic models. Here we propose a method to estimate a reasonable local patch parameters, by fitting a Gaussian semi-variogram to the transformed Water Surface Elevation (WSE) data and defining the autocorrelation length for each river pixel. WSE simulated by CaMa-Flood hydrodynamic model was de-trended, seasonality removed and standardized to make the data suitable for semi-variogram analysis. A case study over the Amazon mainstem suggested that the auto-correlation lengths for upstream and downstream of Obidos GRDC location were derived respectively as 1886.69 km and 688.66 km. The semi-variogram analysis indicated that the river pixels of entire mainstream of the Amazon are correlated together. The estimated auto-correlation length and weighing function could be useful to determine the optimum parameters of the LETKF local patch.
基盤地図情報 標高データと国土数値情報 水域データを用い，日本全域1秒(約30m)解像度で各ピクセルにおける地表水流下方向を表す表面流向データを整備した．標高データに含まれる誤差などのために広域での表面流向データ開発は困難と考えられていたが，最急勾配法で算定した表面流向を必要に応じて逆転させるアルゴリズムの開発によって効率的な表面流向計算を実現した．入力データの高精度化と計算手法の改良により，開発した日本域表面流向データは既存のHydroSHEDSなどと比較して正確かつ詳細な河道ネットワークを表現することを確かめた．また，表面流向に加えて上流集水面積・水文補正標高・河道幅などの付加的データも，変数間の整合性が取れるように整備した．開発した表面流向データは Web で公開予定であり，水工学に限らず多様な地球科学分野への応用が期待できる．
In this study, we provide a contribution toward monitoring the unsteady behavior of discharge-stage hysteresis in a mountainous river. In order to monitor the unsteady patterns of river discharge hysteresis estimated by means of a novel fluvial acoustic tomography system (FATS), we therefore observed the corresponding river water surface slopes hysteresis during different rainfall events. We found that the temporal variations in water surface slope-stage during different scales of rainfall events resulted in different patterns of hysteresis loops which can be classified into three main categorizes: (i) no hysteresis, (ii) clockwise hysteresis, and (iii) counterclockwise hysteresis. In the case of low intensity rainfall events, no hysteresis behavior was detected. Meanwhile, for medium and high intensity rainfall events resulted in either clockwise or counter clockwise (WS-WL) hysteresis patterns. Finally, for medium and high intense hydrological events, water surface slope become almost constant after passing a certain level.
Mount Sakurajima is one of the most active volcanoes in the world, where its frequent eruption causes the occurrence of debris flows every year. This paper discusses the fundamental aspect of spatial and temporal rainfall variability in Mt. Sakurajima by using X-band polarimetric (multi parameter) RAdar Information Network (XRAIN) related to debris flow occurrence. The analysis used XRAIN data from May 2015 to April 2016. The Meiyu-baiu rainfall phenomenon strongly affects the seasonal variability of rainfall in Mt. Sakurajima, which increases the precipitation in June to July. There are the possibilities of the beam blockage and the Vertical Profile Reflectivity problems to underestimate the XRAIN rainfall in Arimura and northern area of Sakurajima for a total of more than 1000 mm depth annually. However, there is no debris flow event reported in northern part during 2015 despite thicker accumulated volcanic ash, which may indicate that this area indeed receives less rain throughout a year. Most of the debris flows happen because of more than 9 hours long rainfall events, which validates the importance of rainfall on debris flow occurrence in Sakurajima.
Radar-based Quantitative Precipitation Estimation (QPE) is primarily stated as techniques representing the best-fit rain rate measured at the ground. The current QPE method relies on either CAPPI maximum value or lowest radar tilt value. In fact, some discrepancies due to lag time and spatial variance remain found which cause the errors systematically propagated over a period. Furthermore, uncertainty factors on a void interspace due to the existence of a gap between available radar beams and ground have caused misleading in determining the actual rain rate. The real-time QPE model in this study is intended to improve the nowcasting rainfall predictor system. The multivariate projection model is used to predict the actual rain rate through the entanglement of physically precipitation factors such as wind shear, relative humidity, evaporation rate, and vertical moisture flux obtained from atmospheric sounding data. The vertical profiles of rainfall at various CAPPIs are collected and used as the response variable by the use of physical factors as a predictor variable to obtain the parameter coefficient value. This value interprets the signature of rainfall at the observed CAPPIs which then could be used for actual rainfall projection at the lower altitudes by real-time. Finally, the validation is taken through radar-gauge cross-correlation representing the actual rain rate. The model is performing well when it has a high correlation, least bias, and zero lag time.
The objective of this study is to verify the capability of Rapid Scan Observation (RSO) of Himawari-8 data for a deeper understanding of the development of cumulus cloud, which can generate Guerilla-heavy rainfall (GHR). The first approach in this verification is utilizing the Rapid Development Cumulus Area (RDCA) index, which is generated by RSO, for estimating cumulus life stage. The estimation of cumulus life stage will be approached by comparing between RDCA index and cumulus life stage estimated by radar. Based on the first trial of the comparison, we found the possibility of RDCA index to estimate the cumulus life stage, as we discovered that there is a good correlation between RDCA index and cumulus life stage in our study case. In the second approach, we compare the radar-estimated hydrometeor type with cloud top conditions retrieved from RSO in the earliest stage of rain. The three types of brightness temperature difference (BTD) using band no. 11, 13 and 15 of RSO data are used to know the cloud condition by distinguishing water and ice phases in a cloud in the baby-rain stage. In this first trial of one case analysis, we found a potential of utilization of RSO to estimate cloud phase in the early stage of cumulus cloud since there is a good spatial correlation between them.
A study of the variability of precipitation in space and time has been carried in southwestern Bangladesh. Quality controlled homogenous daily precipitation data of 9 stations for the period of 1981-2010 are used for the study. A total of 9 indices and monthly mean are examined. Mann−Kendall test in conjunction with Theil−Sen’s slope method has been used to reveal the significance of trends and quantify their magnitudes. A significant increasing trend in consecutive dry days is observed. An increasing trend of annual maximum consecutive 5-day precipitation is also observed. A projected trend in near future (2020-2050) is analysed using three general circulation models under two representative concentration pathway scenarios. In coming days, it is anticipated to have wetter monsoon and drier winter.