Physically-based distributed hydrological modelling, Rainfall-Runoff-Inundation (RRI) model is used to evaluate runoff accuracy by using six satellite based rainfall products such as GPM, GSMaP, TRMM 3B42V7, CMORPH, and PERSIANN. These products input to drive the model on the Nan River basin, Thailand that is the watershed of 13,000 km 2 . The performance of the precipitation products, rainfall depth and runoff, was evaluated from storm event on 2014 by using statistical approach, Volume bias, Peak bias, RMSE, Correlation, and Mean bias, to compare with observation data. Overall of the satellite based products, the CMORPH and GPM performed the best that was provided by the statistical values, comparing with average observed rainfall data. For the runoff estimated from GPM closed to the observed data and was better than other five products, satellite and rain gauge, to provide the high correlation and small RMSE value. This study presents the uncertainty of satellites that have a potential for runoff estimation to apply for water resources management.

Digital Elevation Model (DEM) is used to represent the terrain of the earth. A free provided DEMs are the 10 m DEM produced by the Geographical Survey Institute of Japan (GSI-DEM), Advanced Space Borne Thermal Emission and Reflection Radiometer-Global DEM, Shuttle Radar Topography Mission, Global Multiresolution Terrain Elevation Data 2010, Hydrological data and maps based on Shuttle Elevation Derivatives at multiple Scales, and Global 30 Arc-Second Elevation that are actually used in scientific studies. DEMs have made a high accuracy to assess an error using an observation elevation point. The DEMs in this study at an original spatial resolution of the Shikoku Island, Japan were collected that were evaluated and corrected by using the referent elevation points observed by global position system. The evaluation and correction method of the DEMs were based on the statistical measures and linear transformation algorithm respectively. The results reveal that the GSI-DEM has higher accuracy than the five DEMs, and these DEMs have gotten more accuracy after corrected by the transform's parameters. This approach will be used to recommend for a new DEM in a future, and it can be applied for making a high accuracy DEM to model the earth's terrain.

An innovative dilatancy polishing pad of which characteristics are controlled with processing conditions is proposed to establish high-efficiency, high-quality polishing of hard-to-machine materials for next-generation high-power devices. To make the best use of the property of the dilatancy pad, a highly durable polishing machine which enables high-pressure, high-speed, and immersed polishing was developed. Dilatancy properties were evaluated for various viscoelastic materials to select appropriate materials for a pad. The selected viscoelastic material was mixed with a special filler and abrasive particles, and integrated into a conventional polishing pad to form a dilatancy pad. Application of the dilatancy pad to polishing of SiC realized a smart polishing which achieves both high efficiency and high quality in any processing conditions. In addition, it was demonstrated that the processing conditions could be selected for the purpose of each polishing step, i.e. mid- to high-speed conditions for high-efficiency polishing and low-speed condition for high-quality finishing. A newly developed highly durable polishing machine is capable of achieving wide range of processing conditions. To avoid overheating under high load conditions, the machine can polish a work piece in slurry fluid. The material removal rate using the dilatancy pad showed superlinear dependence on the rotation speed, which outperforms the conventional polishing following the Preston's law. This innovative process can significantly reduce the polishing time of hard-to-machine materials for next-generation semiconductor devices.

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In hydrological processes, rainfall is one of the important components of water supply for human life. We considered how well the statistical distribution simulates rainfall intensity. We propose an asymmetric statistical probability distribution joined by zeroinflated to fit the daily continuous record of rainfall data in Thailand. The candidate statistical probabilities are General Pareto, Exponential, Beta, Gamma, Generalize extreme value, Extreme Value, Normal, Lognormal, Weibull and Rayleigh distribution, to fit the daily data from 123 rain gauges in Thailand. The statistical distributions estimated on the statistical coefficient, using the maximum likelihood estimation (MLE) method and resulted in a cumulative density function (CDF). The CDF compared to the CDF of observed data that estimated, using Kaplan-Meier algorithm. The comparisons were evaluated by Goodness of fit (GOF) in 3 null hypothesis tests (Kolmogorov-Smirnov, Anderson-Darling and Chi-Square test). The best fit distribution was identified by minimum residual (R) index and maximum correlation (Cor) index based on difference value between the estimated and observed data. The Weibull distribution matched to the 118 rain gauges while 5 rain gauges were best fitted by the Gamma distribution.