The purpose of this study is to forecast rainfall and river water levels for the Kahayan River in Indonesia and to provide the forecast information to mitigate peat fires. First, rainfall was predicted by using the rainfall dataset provided by the Global Precipitation Climatology Project (GPCP) and the Nearest-Neighbor Method (NNM). Then, water level prediction was conducted by inputting the predicted rainfall values into a hydrological model. We incorporated Sea Surface Temperature (SST) into the NNM to treat El Niño event and the Indian Ocean Dipole mode (IOD) as major factors influencing rainfall in Indonesia. As a result, the forecast accuracy of water level reductions that can lead to large-scale peat fires was improved. It was demonstrated that long-term water level forecasts with 1 to 3 month lead times were able to be done with reasonable accuracy.

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 Accurate forecasting of reservoir inflow is essential for effective reservoir management. In this study, artificial neural network (ANN)-based models and forecasting methods based on historical inflow analogues were used to forecast the monthly reservoir inflows of Sirikit Dam in the Nan River Basin of Thailand. Incorporating sea surface temperatures and ocean indices in the ANN model significantly improved the forecasting result. The wavelet decomposition of inputs before they were fed into the ANN model also improved the forecasting result. The variation analogue forecast produced the best result among the forecasting methods investigated, based on historical analogues. It was also superior to other forecasting methods when forecasting extreme inflow values.

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 This study focuses on evaluating uncertainties in river discharge projection in the Indochina Peninsula using nine ensemble experiments, which are combination of three different cloud convection schemes and three different sea surface temperatures (SSTs) change patterns. The nine river flow ensemble data were obtained using the outputs in the future climate (2075-2099) of the 60-km-mesh MRI-AGCM 3.2H under the high concentration RCP8.5 scenario. To clarify which factor of the use of different SST pattern changes or different cloud convection schemes contributes to the projected uncertainty on river discharge, the two-way analysis of variance (ANOVA) without interaction between multiple cloud convection schemes and multiple SST patterns was applied to the nine ensemble projections. The results reveal that the uncertainty in the future river discharge projection in the Indochina Peninsula derived mainly from the differences in the convection schemes.

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 To investigate the impacts of climate change and mitigation policies on future hydropower generation, we analyzed the global theoretical hydropower potential (THP) data calculated by the global hydrological model H08 and the hydropower generation (HG) data calculated by the Asia-Pacific Integrated Model/Computable General Equilibrium (AIM/CGE) model. The results implied that climate change would moderately increase the global total THP and socioeconomic change would substantially increase global total HG. Stringent mitigation policy would further increase HG. The increase rate of THP is much smaller than that of HG. There are large variations of increase or decrease in THP across regions, climate change scenarios and periods. The quantified influence demonstrated the significance of coupling H08 and AIM/CGE.

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 An assessment of hydrologic response (basin hydrology for snowmelt runoff, flood and drought) to future climate change in the Tone river basin was carried out using distributed biosphere hydrological model with energy balance based multilayer snow physics (WEB-DHM-S) and with integrated dam operation modules. Bias corrected output of the numerical weather prediction modeling with the incremental dynamical downscaling and analysis system (INDDAS) technique was used for long-term hydrologic simulation (1981-2009 and 2081-2109). Although variation in winter precipitation was found insignificant in future, summer precipitation was found remarkably increased. Hydrologic simulation results showed that snow melts at faster rate in future due to rise in temperature causing a considerable shift of snowmelt runoff from May to April at snow dominated sub basins. Top 30 flood analysis revealed that future flood risk at Fujiwara dam is reduced by the shift of seasonality of snowmelt runoff however, the flood at Yattajima point showed an increasing trend. Likeliness of drought in future will be reduced as future low flow will increase; however yearly analysis of the water level, dam inflow and outflow revealed that severe drought will prevail in future which follows the trend of occurrence of drought in the past.

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 A largest-class flood caused by a typhoon under a climate change condition at the Yodo River basin is examined by using rainfall data developed by a physically-based course ensemble typhoon experiment for the Isewan Typhoon in 1959 and a distributed rainfall-runoff model including flood regulation with dam reservoir operation. Simulated flood discharge at the Hirakata station under a pseudo global warming condition is analyzed, and it is found that the peak flood discharge at the Typhoon 18 in 2013 is larger than the largest peak discharge estimated by the Isewan Typhoon course ensemble simulation under a pseudo global warming condition. Magnitudes of floods with different return periods of rainfall patterns for the Isewan Typhoon and the Typhoon 18 in 2013 are also examined for evaluating the effect of flood control by dam reservoirs in the basin.

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 In previous studies, reanalysis data has been used instead of observation data for bias correction of GCM outputs in observation data scarece basin. However, reanalysis data does not match the real weather characteristics (e.g. precipitation, temperature) in a local scale since it is targeted at a global scale. Therefore, this study aims to develop a bias correction method for re-analysis data based on observation data. As a result, it is revealed that time-series pattern of precipitation in a reanalysis data can be corrected using three relations of monthly precipitation in reanalysis data and 1) number of precipitation events in each month, 2) average and 3) standard deviation of 6 hourly precipitation in observation data. Furthermore, it is suggested that water resources will decrease in this study site in the future by runoff analysis using GCM outputs with bias correction based on reanalysis data.

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 In a flood-control planning in Japan, a high-water level is estimated from selected target rainfall events. To draw up adaptive flood control measures for future climate change, information about possible maximum rainfall are indispensable. Climatological or statistical information about precipitation are not enough for that purpose. In this study, a method to evaluate future variations of a particular extreme weather event is developed by combining ensemble simulation and pseudo global warming method. Perturbations for ensemble members are generated from simulation results by Lagged Average Forecasting method. The perturbations are scaled up/down and added to the original conditions, and ensemble members are prepared. Ensemble technique enables to examine whether differences between the present and future simulations are caused by global warming or chaotic behaviors from perturbation. Probability density curves from ensemble results give clearer view of future variations of a weather event.

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 Future variations of precipitation around the Hokuriku region are investigated with dynamic downscaling results. Reanalysis data is used as initial and boundary conditions for the reproductive downscaling of the present climate. For future climate, five climate projection are used and the pseudo global warming technique is applied. Seasonal progression of precipitation in the reproductive downscaling result agrees well with AMeDAS observation. In present climate, peak of monthly precipitation is July in Hokuriku region. However, the peak shifts to June in future climate. Increasing frequency of strong precipitation (larger than 30 mm/day) in June and decreasing precipitation in July cause the shift of the precipitation. In other regions, such variation is not recognized. In addition, the maximum duration of precipitation becomes longer and total precipitation increases in future climate.

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 The incremental dynamical downscaling and analysis system (InDDAS) which has been developed from the pseudo-global-warming method by appending partial functions was applied for a probabilistic regional scale climate change projection with the target regions of Kanto and Japan Alps. In InDDAS, the most reliable future state was projected by a regional climate model (RCM) simulation with an ensemble mean among the climatological increments of multiple general circulation model (GCM) simulations. In addition, the uncertainty of the future projections is estimated by RCM simulations with the multi-modal statistical increments calculated by the singular vector decomposition of the multiple GCMs. An increase of rainfall with the change ratio of 7-16 % was projected in Kanto region, where the most reliable value was 10 %. The change ratios of the vicinity quantiles of extreme rainfall was projected to be larger than that of rainfall and was almost the same as the value explained by the Clausius—Clapeyron effect.

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 Water security is a concern in the Kathmandu Valley since long era due to the rapidly increased population and urbanization resulting in scarcity and degraded quality of water. Like other sectors, the devastating “Gorkha Earthquake 2015(GE2015)” has certainly impacted water sector. This article aims to assess impacts of GE2015 by analyzing situation of portable water supply, demand and water availability in the area before and after the event and recommend possible development options for sustainable water allocation. SWAT model was applied to estimate the water availability in the surface water source and GDACS recent satellite data analysis was used to estimate the extent of damage from the earthquake. Results showed almost 40% reduction in the amount of water supply after the earthquake affecting additional 0.15 and 0.24 million people under water deficit condition. Harnessing of only 48% and 65% of available surface water during dry and wet seasons, respectively, reveals the potentiality of developing additional water supply projects. However, even with optimal development of surface sources is inadequate to meet the valley's water demand and therefore projects on conjunctive use of surface and groundwater resources, with due care of potential areas for groundwater storage, may be required as a short-term solution to ensure water supply security in the post-GE2015.

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 Monitoring body temperature is useful for evaluation of the risk of urban thermal environment. This study aims to develop a compact skin temperature and a core body temperature instrument. They consist of compact and energy-efficient data logger (30 x 20 x 15 mm in size, 6.5 g in weight) and specific thermistors. The skin temperature instrument uses a compact thermistor with small heat capacity and that of core body temperature is an earplug-type thermistor which measures auditory temperature representing brain blood. The accuracy of the skin temperature is 0.3 °C with better response time and less influence of solar radiation than conventional cutaneous instrument. The accuracy of the body core instrument is 0.2 °C. These instruments enable the 24-hours continuous measurement under the subject's daily life and thus contributing the precaution of heat stroke and the evaluation of thermal comfort in urban districts.

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 To clarify the mechanism of the recent record-breaking high temperature at Ekawasaki AMeDAS, we conducted statistical analyses using 35-years AMeDAS data since 1988 and the field observation around Ekawasaki in 2015. From the statistical analyses, the following results were obtained; (1) the overlaying the warm air mass around Shimanto region contributed to high temperature there; (2) daily maximum temperature increased with the number of the consecutive preceding sunny days; (3) the southeasterly and northwesterly wind possibly contributed to higher temperature at Ekawaski; (4) though Ekawazaki was not the hottest area in Japan climatologically, the record breaking event occurred due to the combination of the several factors mentioned above. Also, the field observation showed the sea breeze from the Pacific Ocean (southeasterly wind) can cool Ekawasaki.

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 We proposed a new height parameter which explicitly and uniquely specifies the potential range of surface layer regardless of daytime mixed layer, nocturnal stable layer, or indoor free stream topped on it. This parameter is defined as the level where the momentum flux becomes zero by extrapolating the momentum flux profile within the surface layer to the top. We conducted multi-point Doppler lidar observations in Tokyo to test this parameter. Two Doppler lidars were used to measure the vertical distribution of the momentum flux at two locations to test the new parameter proposed in this study. The other one is used to measure the height of the atmospheric boundary layer for comparison. When the mixed-layer is well developed, the height of surface layer reached about 15% of the mixed layer height, which is consistent with the conventional estimates. It is also observed that the diurnal variations of the height of surface layer and mixed layer are negatively correlated.

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 We propose a new framework for the evaluation of urban thermal environment not using any meteorological sensors but only human sensory organs; collecting the big data on citizens' perceptions on the microclimate they are exposed and analysing them statistically to obtain spatially-rich information on urban thermal environment. To clarify the effectiveness of the proposed framework, we investigated the accuracy of our sensory organ and thermal receptor by correlating the subjects' thermal/comfort sensations and perceptions for each meteorological variables to actually measured ones. Our thermal/comfort sensation are highly correlated to the thermal stress index, SET*. In addition, our perceptions for wind speed and solar radiation were also reasonably correlated with wind speed and mean shortwave radiation measured by convectional meteorological sensors.

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 Recent studies have proven the need for realistic urban representation in weather models. However, cities modeled using distributed urban parameters with updated urban parameterization are still few and limited to highly developed countries. Furthermore, real building datasets used to estimate the urban parameters are unavailable or lacking in other cities. This study was conducted to address both issues by expanding the advance weather model methodology to another megacity and utilizing global datasets to readily estimate a distribution of urban and other surface parameters. Sensitivity of the models to either real or global-estimated urban parameters was conducted; and the urban effect to a synoptic circulation over Istanbul was investigated. The simulation results for a typical summer day over Istanbul suggest that global datasets can be used as alternative to real building data for estimating distributed urban parameters.

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 Weather models coupled with urban parameterizations require robust and realistic urban parameter inputs such as actual building distribution. Acquiring real building parameters often require huge amount of time and money. In this study, we introduce a simplified but precise approximation of urban parameters through a readily-available, high-resolution global dataset. Regression equations were derived from the spatial relationship of global 1-km population dataset adjusted by nightlight distribution to real urban parameters in Japan and Istanbul, Turkey. These equations can readily estimate urban parameters globally. Derived global urban parameters were incorporated into a weather model to investigate urban heat island in neighboring megacities in South and Southeast Asia. UHI phenomena of 5 mega cities depended a great deal on location and climate zone of each city.

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 The classification of clouds and its spatial and temporal variation is important to understand the concept of local climate. Thus, this study was carried out to explore the potential for using the statistical analysis (standard deviation) of time series of one hour averaged pyranometer observation to determine the types of clouds in the Matsuyama plain. Instruments were installed at 6 observation points covering the entire study area. Clear sky rate was considered as an index to identify the reduction in the solar radiation due to presence of cloud. The development of high raised cloud such as cirrostratus and cumulus were observed with small and large time standard deviation respectively and clear sky rate between 0.4 - 0.6. Also, the formation of cloud were observed to be high in the urban area and the inland area. This was further verified using the whole sky images taken by the camera installed at Ehime University. The results would be beneficial for the study of the rainfall pattern in different part of Matsuyama plain and to understand the effect of urbanization on the local climate.

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 The impact of parameterization scheme usage to precipitation was investigated in WRF model for simulating 24-km seasonal precipitation over Japan. Budget profile analysis was used to diagnose unresolved heating/drying processes controlled primarily by cumulus (CU) parameterizations, and how coupling of CU scheme with boundary layer (BL), microphysics (MP) and radiation (RAD) schemes modulate the unresolved processes that consequently impacts simulated precipitation. Results showed hothumid JJA precipitation is sensitive to CU scheme as shown by the large variation in PCC and SDR scores of different schemes validated against APHRODITE data. And, distinct structure of heating/drying profiles were seen from each CU scheme. The CU scheme coupling with BL scheme showed larger modulation on heating/drying profiles and larger PCC-SDR variation as compared to coupling with MP-RAD schemes. This provides a systematic evaluation of scheme usage and its combinations in simulating precipitation.

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 Global water and energy cycles occur with various interactions among atmosphere, ocean and land. Approximately 10 percent of the water exchanged between atmosphere and oceans transports through rivers and 40 percent of land precipitation flows into oceans from rivers, which suggest the major role of rivers in the global water cycle. In this study, we assessed the impact of inundation processes on various climatological aspects using different spatial resolutions. We implemented the inundation processes described in CaMa-Flood, a river inundation model, into a climate model MIROC5. By changing the spatial resolution of CaMa-Flood, we assessed the impact of higher resolution river inundation processes on the coupled model. The reproducibility of river discharge showed an overall improvement. Inland floodplains showed an increase due to the representation of complex topographical features, which also improved the model estimates with increased evaporation and decreased 2m air temperature.

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 From 16 to 18 December 2014, a storm surge induced by an explosive cyclone struck the city of Nemuro causing immense damage due to flooding. This explosive cyclone was rapidly intensified along the east coast of Tohoku region and remained stationary near the Nemuro Bay. The central pressure reached 946hPa and significant sea level rise was observed. It is expected that explosive cyclones, which are strengthened with latitude, cannot be ignored in flood prevention in northern Japan region.
 In this study, the path of explosive cyclone and central pressure in the past decades are analyzed and compared with those of typhoons which was considered as meteorological external force in flood prevention law. Furthermore, we discuss the synoptic meteorological fields in which persistent explosive cyclones occurred in northern Japan.

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 The time evolution of soil moisture from initial conditions given as a lower boundary condition in dynamical seasonal prediction was investigated with the Japan Meteorological Agency/Meteorological Research Institute Atmospheric General Circulation model. Both dynamical seasonal predictions from the initial conditions and historical simulations were performed and their differences in soil moisture in the second layer of a land-surface submodel were compared. The differences become smaller as an elapsed time increase in most regions except for the northern hemispheric mid- and high latitudes and northern Africa for the initial start of November, and Northern Africa to Pakistan, Eurasian inlands, and coastal land areas along the Antarctic Ocean for the initial start of May. A first-order Markov model for time evolution of soil moisture was introduced and examined with three simplified sensitivity numerical tests.

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 Rainfall-runoff phenomenon is more or less stochastic. From its stochastic viewpoint, runoff models are described by stochastic differential equations. We propose differential equations whose solutions estimate the first and second order central moments of discharge under the condition that the rainfall input, the storage coefficient and the initial condition of a storage function runoff model are treated as random variables respectively. The validity of these proposed differential equations was cross-checked by a simulation. The results in this study can be applicable to problems on the river engineering, such as the estimation of the design flood.

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 An inundation inside the levee analytical method that considered the base-flow of the drainage canal was considered for the suburban area where the mountainous district and the field were contained, and the drainage-canal flowed. The base-flow is presumed from the unsteady flow calculation using the water-levels data of the drainage-canal observed at low-water, and applied to an inundation inside the levee model. The analytical method of the present study was applied to inundation inside the levee in Nishisuga region in Tokushima city that had been generated by Typhoon No. 11 in 2014. The following results were achieved. (1)The water-levels in drainage-canal could be reproduced when it was flooded. (2)The runoff-coefficient presumed by the analytical modeling and the runoff-coefficient calculated from material concerning the land-uses were roughly corresponding. (3)The ratio of the base-flow at the flood peak was estimated to be about 50%. The importance of the evaluation of the base-flow was suggested.

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 This study aims to analyze the influence of observed river channel conditions on inundation simulation in terms of discharge within a river channel and inundation area and depth over a floodplain. The target area is the Lower Mekong River Basin, which suffered from severe floods in 2000 and 2011. First, measurement using an acoustic Doppler current profiler is conducted to understand the longitudinal distribution of cross sectional shapes and roughness change during a flood in a river channel. Based on the observation results, inundation simulation with the Rainfall-Runoff-Inundation model is conducted. The calculation results show that the reproducibility of discharge improves by taking the longitudinal distribution of the width and depth of the river reach into consideration, and that the water depth over the floodplain has a high sensitivity to both cross sectional shapes and roughness change during the flood.

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 In a relatively small urban river such as the Togagawa River in Kobe, a torrential rain causes a sudden increase of water depth and in the worst case a drowning accident such as occurred in 2008. In order to prevent such a disaster, both structural and non-structural countermeasures are required. However, in either case accurate estimation model of runoff process has to be established preferably based on actual discharge measurements. However, since such a river basin is small yielding an abrupt change of water flow, it is quite difficult to conduct a direct flow measurement. Therefore, we tried to utilize river monitoring cameras installed along the river for non-intrusively measure flow discharge by an imaging technique. At the same time, the runoff model was refined to include rainwater sewage channels. It was made clear that the developed system is capable to reproduce stage-discharge hydrographs with a reasonable accuracy.

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 Recent advances of regional ensemble weather predictions have increasingly improved predictability of rainfall forecasts, which would be useful for streamflow forecasts. We have developed an ensemble flood forecasting system, composed of an ensemble Kalman filter and regional model WRF (Weather Research and Forecasting) in atmospheric part, and Rainfall-Runoff-Inundation (RRI) model in hydrological part. We examined feasibility of the flood forecasting system on the Niigata-Fukushima torrential rainfall event in July 2011. The ensemble flood forecasting system showed improvements compared to deterministic forecasts in average accuracies and predictability of peak flow. However, the forecasting system failed to predict the largest flow peak in this event, because it could not predict the third of the main three rainfall peaks. It could improve the accuracy of JMA forecasts. However, at the same time, it depended on JMA forecasts accuracy. This study showed the advantages and limits of this ensemble flood forecasting system.

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 This study aims at improving the accuracy of a currently used flood runoff model by taking into account the soil water storage (SWS) before flooding and continuously changing the effective rainfall input into the model. In this study, SWS was estimated by using a "long-term hydrological assessment model considering snow process" (LoHAS) first. Then, the runoff rate, which is changing hour to hour, was determined by using the estimated SWS as the initial value of the runoff. Next, the estimated effective rainfall was input into the storage function model. Compared to the accuracy of the current flood runoff model, the accuracy of the new model in terms of the reproduced hydrograph was improved. The accuracy in reproducing the second peak of a two-peak flood and in reproducing flood runoff caused by snowmelt and rainfall was particularly improved.

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 This paper presents the parameter identification of a distributed runoff model applied to the Abe River basin where three water level stations exist. The errors between the observed and calculated discharges at the water level stations are minimized simultaneously by multi-objective optimization algorithms. Comparing the performance of five implementations of the optimization algorithms, two implementations of a genetic algorithm NSGA2 of the statistical software R are better than the others. It turned out to be difficult to represent the whole basin by the parameters optimized to a single water level station. However, it is possible to calculate the discharges which are near the observed ones by selecting the pareto optimal solutions under the limited errors of three water level stations.

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 The hydrological parameters of a flood forecasting model are normally calibrated based on an entire hydrograph of past flood events with an error assessment function such as mean square error and relative error. However the specific parts of a hydrograph, peak discharge and the rising part, are particularly important for flood forecasting in the sense that underestimation may lead to a more dangerous situation due to delay in flood prevention and evacuation activities. The purpose of this study is to develop an error assessment method for calibration appropriate for flood forecasting. The PWRI distributed hydrological model is applied to fifteen past floods in the Gokase River basin with 10,001 patterns of parameter sets determined by the Latin Hypercube Sampling. The cases with non-underestimation in the peak discharge and the rising part of hydrograph are analyzed as the appropriate cases for flood forecasting. Furthermore, the applicability of the appropriate parameter set for flood forecasting is validated by applying it to another flood event.

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 This paper describes a problem and a method to solve a problem on the particle filer which is applied for the water level prediction. A water level prediction model at the Yodo River has been constructed using the particle filter with the state variables such as the upstream and lateral discharge. We found the prediction model needs improvement from the results of the water level prediction by two floods, Typhoon No. 18 (2013) and Typhoon No. 11 (2014). Not only the upstream and lateral discharges, we newly added the Manning's coefficient to state variables, and then we found the improvement of the accuracy of the estimation of the upstream and lateral discharge. Moreover, we found that an increase of the number of state variables does not assure the improvement of the accuracy of the estimation of the boundary discharges even if the number of particle is increased. In a real-time water level prediction, it is important to consider the balance of the setting of state variable and the number of particle.

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 The real-time river stage prediction model is developed, using the artificial neural network model which is trained by the deep learning method. The model is composed of 4 layer feed-forward network. As a network training method, stochastic gradient descent method based on the back propagation method was applied. As a pre-training method, the denoising autoencoder was applied. The developed model is applied to the one catchment of the OOYODO River, one of the first-grade river in Japan. Input of the model is hourly change of water level and hourly rainfall, output data is water level of HIWATASHI. To clarify the suitable configuration of the model, case study was done. The prediction result is compared with the other prediction models, consequently the developed model showed the best performance.

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 In recent years, flood damage in urban areas due to localized heavy rain has been a serious problem. In order to save life from the flood damage, rainfall forecast is important even if 10-minute lead time. To improve the rainfall forecast system, an index is necessary to detect the hazardous storm.
 High Z_DR area which is extending above the freezing level, i.e. a Z_DR column, is associated with strong updraft. We investigated a relation between the Z_DR column and storm development using X-band polarimetric radar observations. It is known that Z_DR is susceptible from rainfall attenuation especially in X-band radar. On the other hand, K_DP is not affected from rainfall attenuation. Therefore, we also studied the effectiveness of K_DP column. We found that Z_DR column (K_DP column) were formed about 15 min (10 min) prior to the onset of heavy rainfall on the ground based on the cell tracking algorithm.

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 In July 2008, 5 people died by flash flood at Toga River in Kobe. This tragic disaster was brought by suddenly generated and developed isolated cumulonimbus cloud. This type of rainfall is called “Guerilla-heavy rainfall” in Japanese media reports. In this event, the water level was suddenly rose in just 10 minutes, so it's quite important to predict the risk of heavy rainfall even within a few minutes. Nakakita et al. developed the risk prediction system using vertical vorticity information inside the baby cell aloft. Baby cell is the first echo observed by radars aloft. However, it is unclear why baby cells having large vorticity grow to heavy rainfall. Thus, we tried to verify the mechanism of generating and developing process of the cells. First, we found not only positive but also negative vorticities in the baby cells, and found vertical vortex tubes inside the cells. Next, we analyzed a high Z_DR column in one event, and discovered vorticity associated with convective updraft.

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 To prevent localized torrential rainfall disasters called "Guerrilla-heavy-rainfall" disasters, a new approach to detect the convection genesis earlier is suggested. The meteorological model based on large-eddy simulation which can describe urban effects such as roughness of buildings and anthropogenic heat, is developed. Since our developed model also have a cloud microphysical process, a seamless integration through the urban canopy layer to above the boundary layer of atmosphere can be realized. The three kind of simulations, cumulus cloud at boundary layer over sea, supercell, and thermal plume over urban area, were carried out using the model, and the vortex tube can be seen at some of thermal plumes.

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 With the increase of extreme climate events due to the global climate change, urban areas have been becoming more prone to flooding. The advancement of numerical prediction method for heavy rainfall is a key technology for early warning. The objectives of this study are twofold. One is to estimate the accuracy of WRF (Weather and Research Forecasting) model through its application to the heavy rains in Hiroshima City in August 2014. The other is to discuss the evolution of rainfall system that caused the heavy rains based on the numerical results. It is shown that high-resolution downscaled simulation with WRF can reasonably reproduce the spatio-temporal distributions of the heavy rains. The evolution of the rainfall system and hence the high precipitation intensity zone are found to be sensitive to high moisture transport through the Bungo channel to Hiroshima city.

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 Under the frequent floods caused by localized concentrated heavy rainfall, it becomes considerably important to achieve detailed rainfall, especially localized concentrated heavy rainfall in a small urban watershed. X-band MP-radar, providing space-time distribution rainfall data, is expected to be made effective use of its advanced detailed data, while few studies on the precision evaluation of 1-minute X-band MP radar data were conducted. This paper discusses results on the precision evaluation of X-band MP radar data by comparison to 1-minte ground observation rainfall data, installed in high density at the upper basin of Kanda river in Tokyo. The results showed that 1-minute data of X-band MP radar realized detailed space and time scale rainfall distribution, though it was hardly met by 10-minutes data, and sufficiently described localized concentrated heavy rainfall in a small urban watershed.

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 Japan Meteorological Agency announces emergency warning of heavy rainfall when 48-hour rainfall amount is expected to exceed the 50-year value at more than fifty 5km-grids. Spatial distribution of heavy rainfall is important to judge the emergency warning. In this study, by using the GSMaP data from 2001 to 2009, 50-year value of 48-hour rainfall amount is calculated all over Japan and the surrounding ocean. As the number of available GSMaP data is limited and GSMaP may be affected by surface snow cover, the calculation method needs to be modified. 48-hour rainfall amount exceeded to its 50-year value on prefecture scale at two heavy rainfall events which are said to be corresponding to the emergency warning. It is important to check the spatial scale of grids with high return period rather than the absolute value of return period. This method is applied for Kanto and Tohoku heavy rainfall in September 2015.

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 GPM Core Observatory satellite was launched in February 2014. GPM Core Observatory satellite is equipped with the Dual-frequency Precipitation Radar (DPR), the DPR consists of a Ku-band precipitation radar (KuPR) and a Ka-band precipitation radar (KaPR). KaPR is useful for detecting weak rain and snowfall, and KaPR is important for high estimation precision of DPR. However, KaPR is newly installed radar, so performance is not well known. Therefore, we focus on the rain rate estimation-range of KaPR and compare the rain rate estimates by KaPR and KuPR. In addition, we improve the rain rate retrieval algorithm of KaPR and evaluate the rain rate estimates of test products.

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 Though the radar-raingauge analyzed precipitation data is useful to analyze the temporal and spatial characteristics of precipitation, less attention has been paid on the impact of land-use on precipitation in study of the urban climate. This study focuses on the availability and the potential of the radar-raingauge analyzed precipitation data for the evaluation of local and regional rainfall. Thus, for this purpose two indices, “Rainfall intensity index“ and “Rainfall skewed index” has been introduced. We observed that the 7- year total precipitation shows the urban effect especially in warm season. These indices are promising for understanding the characteristics of precipitation events, though they hardly detect the urban effect on precipitation.

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 The present study discusses the relationship among rainfall, geology characteristics and debris flow occurrence using effective rainfall and rainfall index R', which expressed rainfall history by one value that combines long-term effective rainfall R_w and short-term effective rainfall r_w. Three debris flow disasters in Osumi district, Nagiso-town and Hiroshima-city are chosen for investigation. We found examples in which the occurrence time of the debris flow was different, even when the disaster spots are in the same rain gauge station area, because R' is only determined by rainfall, which is the inducing factor of debris flow occurrence. We also scrutinized the geology of the disaster source points and found that many debris flows occurred in the distribution of volcanic sediment, the sedimentary rock of the accretionary complex, and granite.

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 The present study describes estimation of charge and electric field based on the precipitation particle distribution measurement in thundercloud. An X band multi parameter (MP-X) radar and a multi LF sensor network (BOLT) as lightning detectors were used. Polarization parameters obtained from MP-X radar were used to perform the precipitation particle distinction. Distinguished particle distribution from the polarization parameters including graupel gives particle size distribution, and electric charge amount in the collision of graupel and ice crystals. By solving the Poisson equation with charge amount obtained, the electric potential and the electric field have been calculated.
 In fact, the present study shows possibility to predict lightning by using MP-X radar through calculating charge and electric field in real time.

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 The utilization of groundwater resources becomes important in recent years. In addition, not only the quantity but also balance of groundwater is important for disaster prevention and river management. However, groundwater recharge volume is not clear and especially during flood it was difficult to measure it due to the transmission of flood wave. Recharge volume under flood condition can be estimated correctly by using ADCP and unsteady flow analysis. In the Nagara River, It clarified that about 20 % of river discharge decreases as groundwater recharge. The recharge rate is proportional to difference between river and groundwater level. Therefore, monitored river water level is able to derive recharge volume by estimated groundwater level.

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 Laboratory column experiment and numerical simulation were performed to understand soil water transport by plant water uptake and formation mechanism of salt accumulation. After illuminance level raised, temperature and volumetric water content (VWC) in the column were also raised, then water level of Marriott tank was lowered. The rise of VWC may be related to upward movement of solute with water. The results of adsorbed ion distribution showed variability. It seems results of repeated ions adsorption transported by plant water usage. Observed Sodium distributions were agreed well with the root distribution. The dense part of root had higher water absorption rate. According to the numerical results, cation exchange reaction and plant water uptake by root distribution should be considered in the modeling to understand qualitative distribution of chemical components.

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 In order to better understand heat and mass transfer in drainage bridge road equipped with a hydronic road heating system (HRHS), a series of experiments were conducted using a test piece of a bridge road with the HRHS, a drainage pavement, and a rainfall simulator. Main experimental outputs are the hydraulic conductivity of the porous pavement, percolation velocity, vertical temperature profile in a bridge road and aggregate temperature associated with the heat injection from the HRHS and precipitation. It is concluded that (i) the proposed experimental method and Dupuit's approximations can be a good combination to identify the hydraulic conductivity and percolation performance, (ii) percolation velocity is more than 100 times larger than rainfall intensity, (iii) thermal interactions between aggregate, percolation fluid and void air should be considered to evaluate the performance of the HRHS as an anti-icer or melting equipment.

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 The aim of this study was to propose a stochastic methodology using backward particle tracking to estimate time-related capture zones of a pumping well. Lattice cells with a certain length were introduced to assign binary values corresponding to particle pathlines within a certain travel time. Based on the particle travel histories in geostatistically generated hydraulic conductivity fields, ensemble of the lattice cell values provided the probability distributions of time-related capture zones. The methodology was investigated for a two-dimensional geometry with single and multiple wells, in homogeneous and heterogeneous aquifers under steady state flow conditions. The probability of each lattice cell was applied to the calculation of a quantitative measure expressing the uncertainty relevant to the probability distributions of the capture zones, demonstrating the effect of the heterogeneity on the uncertainty of capture zones.

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 In recent years, the pollution of the groundwater is a serious problem. In the case of groundwater contamination, the behavior of the polluter neighborhood is handled as an advection-dispersion phenomenon in the unsaturated vertical penetration field. And it is important to predict the transport of the pollutant in such an unsaturated field. The advection-dispersion analysis is used for the prediction method. Macroscopic dispersivity is one of the important values when conducting the advection-dispersion analysis. But the method that determine the macroscopic dispersivity in the unsaturated zone is not established. In this study, conducting the advection-dispersion analysis at such an unsaturated infiltration field, the property of macroscopic dispersivity which is obtained from numerical analysis is investigated.

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 This paper examined the effects of the mesh size, Δz_cal, of discretized convection-dispersion equation and of sampling thickness of a soil layer, Δz_samp, on the crystallized salt mass per unit area, M_salt, commencement time of crystallization, t_cryst, besides the vertical (z) profile of salt concentration, C, in the vicinity of the soil surface. A metal cylinder sampling method was developed to measure C of a thin layer with Δz_samp ≈ 0.2mm. It was seen that the Δz_samp should be less than 0.2mm to measure C(z) precisely. Otherwise, the value of C of the top soil layer did not reach the saturated concentration, although salt crystallization occurred on the soil surface. A numerical calculation could reproduce observed C(z) and M_salt for Δz_cal = 0.2mm. However, when Δz_cal > 0.2mm is used for calculating salt transfer, differences between calculated and measured M_salt and t_cryst appear significantly, although the shape of calculated C(z) is apparently similar to that of measured C(z).

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 Recently, many big floods and soil disasters have occurred in Japan due to heavy rainfall events. Also, we can see a hazard due to woody debris in the events. In the present paper, we propose a concept of “drift woods generation potential.” The potential is defined by empirical formula with data set of geography and woods. It can express the maximum possible generation of woody debris at a given point in a river. We attempt to adapt this assessment to all bridges along the Kagetsu River, Oita. From the results, it was confirmed that this concept was able to express the actual woody debris accumulation on a few bridges which were affected by them in the Northern Kyushu Rainfall Event 2012. In addition, a change of the risk due to reformation of a bridge can be evaluated by this method.

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 Woody debris flow causes various damage at the time of a flood. One of the causes of river channel blockade, dropped bridge and a fishing net damage is the woody debris flow. Based on the results of past research, it is considered that the woody debris behavior is correlated to the bars. In this study, we investigated detail relationship of development in the bars and the woody debris behavior such as their generation, re-movement and accumulation. For tracking the woody debris behavior, we tagged numbered tape to each piece of woody debris found along two selected rivers where the development situation of their bars was different. As a result, we confirmed the great correlation of the numbers of the woody debris per unit distance and the area of the bar at the rivers where bars develop. Thus, after a small scale rain event, the accumulated woody debris re-moved and then it re-accumulated again in a bar around the water's edge with the high submergence frequency. Therefore, we conclude that a bar has the effect which catches woody debris.

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