Estimation of the return level of daily rainfall commonly employs the "annual maximum series" (AMS) method. However, the sample size of annual maxima is usually very small. Moreover, if the sample includes extremely large annual maxima values, then these few extreme data affect the accurate estimation of the return level and return period. The meta-statistical approach proposed in recent years enables more robust estimates than the AMS method because estimation is done with a large sample. For this study, we evaluated the accuracy of quantile estimation of observed rainfall data and rainfall data simulated using the Compound Poisson Model by application of the Meta statistical Extreme Value (MEV) distribution and the Simplified Meta statistical Extreme Value (SMEV) distribution. Additionally, we estimated the relation between the fitness of the probability distribution to daily rainfall and the accuracy of quantile estimation by adopting multiple daily rainfall probability distributions. Results indicated that the different sample sizes of daily rainfall in both distributions affect the accuracy of quantile estimates at high non-exceedance probabilities because of the sample size of daily rainfall used for parameter estimation. That sample size affects the number of extreme value samples. Furthermore, the relation between the sample dispersion, depending on the sample size of daily rainfall and the goodness of fit of daily probability distributions, affected the quantile estimation accuracy.

　Since the beginning of the Anthropocene epoch, as a result of human activities strongly affecting the global environment, numerous water-related difficulties such as floods, droughts, and conflicts have emerged worldwide. To understand and predict these effects and difficulties, "socio-hydrology" was established as an empirical discipline to elucidate the dynamics of human–water interaction in an integrated manner. About a decade has passed since this new discipline was established. Although the overall picture and framework of socio-hydrology is gradually emerging, the challenges and possibilities of interdisciplinary water research are becoming apparent through collaboration among hydrologists and humanities and social scientists in the field of socio-hydrology. This study reviews developments of socio-hydrology to date and the challenges and possibilities for interdisciplinary water research identified in this field. The findings provide a perspective for the promotion of interdisciplinary water research in Japan, pointing out challenges of interdisciplinary water research including a "lack of common language," "differences in scientific philosophies among disciplines," and "scale issues of time, space, and organization.” To address these issues, we proposed "value systems," "governance," "culture," and "history" as research frames for advancing interdisciplinary water research in Japan and Asia.