Development of downscaling technique of global hydrological predicted data and reduction of uncertanty of prediction

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The main objectives of this project are as follows;(1) Development of prediction technique by which one can predict meteorological and hydrological data in ungauged or poorly-gauged basins (PUB).(2) Development of method for reduction of various kind of uncertainty in terms of prediction.As for (1), we tried to downscale relatively (spatially / temporally) coarse data to more fine data. 'Regional flood frequency analysis' is appropriate for the downscaling. Moreover, application of scaling is also effective. These techniques are included in so-cold 'statistical analyses'. We tried to use MM5 (meso-scale meteorological program) and found that combination of statistical analysis and meteorological program is more effective (unpublished). Moreover, we developed new method by which goodness-of-fit is evaluated when IDF curve is obtained, namely, when one evaluate which stochastic distribution is appropriate for hydrological data.As for (2), we constructed new observation station in Kanto district. However, we have not finished analysis of these data at the stage.Moreover, we obtained a lot of results on scaling by which one can downscale spatially coarse data. Especially, we constructed new rainfall-runoff model and obtained new result on scaling by using the model. Scaling is potential appropriate method for PUB.