| Popis: |
Study region: Metropolitan stations in India with distinct climatic conditions, namely, Delhi, Chennai, Kolkata, and Mumbai were selected for this study. Study focus: Rainfall disaggregation models were studied based on four models, namely Neyman-Scott Rectangular pulse (NSRP) process, Microcanonical Multiplicative Random Cascade (MMRC) process and its variant MMRC-K, and one Deep-Learning based process (ANN-K), using metrics like dry periods, event rainfall volumes, extreme rainfall characteristics, etc. New hydrological insights for the region: The study successfully established individual rainfall volume, event rainfall volume, and event durations are all within 10% of each other for the four stations. Delhi due to its continental climate showed a higher percentage of dry periods and longer dry periods, which were most successfully modelled by MMRC and MMRC-K. Kolkata and Mumbai stations displayed a higher number of extremely intense and cloudburst types of rainfall, which were modeled effectively by the Deep-Learning Model. Chennai has a different rainfall pattern due to returning monsoon which was also captured by the models. Generally, for extreme rainfall parameters, the ANN-K model performs significantly better, successfully reproducing the characteristics at all quantiles, especially with rainfall above 100 mm/hour intensity or cloud bursts, while 50% of the models overestimated these. NSRP on the other hand performs reasonably well for most considered parameters, without being exceptional at any of them. MMRC and MMRC-K most accurately modeled the dry period parameters. |
