Regional frequency analysis of precipitation using large-scale atmospheric variables

Effective estimates of the magnitude and frequency of precipitation are necessary for hydrological designs. However, often the available data at target site are inadequate to arrive at reliable estimates. Practicing hydrometeorologists overcome this impediment by pooling information at target site with that from other locations depicting similar characteristics of precipitation. To facilitate pooling of information, hydrometeorologists use regionalization approaches for partitioning sites in the study region into groups having similar precipitation characteristics. The conventional approaches to regionalization are based on statistics computed from observed precipitation, rather than attributes affecting hydrometeorology in a region. Therefore independent validation of the delineated regions for homogeneity in precipitation was not possible. To address this issue, a new approach is proposed. Large-scale atmospheric variables affecting the precipitation in study region and location attributes are suggested as features for regionalization by K-means cluster analysis. This allows independent validation of the identified regions for homogeneity using statistics computed from the observed precipitation. The summer monsoon rainfall ( SMR) regions that are currently in use by India Meteorological Department ( IMD) are shown to be heterogeneous. Subsequently the effectiveness of the proposed approach to regionalization is illustrated through application to India for delineating new SMR regions. Frequency distributions are identified to fit rainfall in the regions using L-moment-based goodness-of-fit test. Error in rainfall quantile estimates for the new regions is found to be significantly less than that estimated for the IMD SMR regions. The results show that the proposed approach to regional frequency analysis of precipitation is promising.