Statistical downscaling of precipitation using inclusive multiple modelling (IMM) at two levels

Abstract

Topical research on hydrological behaviour of climate change in terms of downscaling of monthly precipitation is investigated in this paper by formulating an inclusive multiple modelling (IMM) strategy. IMM strategies manage multiple models at two levels and the paper uses statistical downscaling model, Sugeno fuzzy logic and support vector machine at Level 1 and feeds their outputs to a neuro-fuzzy model at Level 2. In the downscaling stage, large-scale NCEP (National Centres for Environmental Prediction)/NCAR (National Centre for Atmospheric Research) data are used for a station with local data record from 1961 to 2005 for training and testing Level 1 models. The results are found to be ‘fit-for-purpose’, but the variations between them signify some room for improvements. The model at Level 2 combines outputs of those at Level 1 and produces Level 2 results, which improve compared with those at the Level 1 models in terms of dispersion of residual errors. In this way, IMM provides a more defensible modelling strategy for application in the projection stage. The comparison between observed and projected precipitation indicates that precipitation will be likely to reduce compared with observed precipitation in cold seasons (October–February), but the projected precipitation will be likely to increase slightly in wet seasons (April and May). HIGHLIGHTS Inclusive multiple modelling (IMM) is formulated for the downscaling of precipitation.; IMM manages multiple regression and artificial intelligence models at two levels.; Performances of IMM are improved over Level 1 models in the downscaling stage.; IMM provides a more defensible strategy for application in the projection stage.;

Keywords

• artificial intelligence models
• downscaling
• inclusive multiple modelling (imm)
• precipitation
• projection