Novel Data-Driven Approach for Development of Synthetic Hyetograph

Simulation based flood risk or rainfall-runoff studies require multiple extreme rainfall sequences as input, for which synthetic hyetographs are generally employed. Temporal distribution for synthetic hyetograph can be generated by deterministic approaches (geometric shapes, standard temporal distribution) or stochastic approaches/weather generators. The pertinent limitations are the missing inherent randomness of rainfall, limited numbers (former), and/or complexity in implementation (latter). Addressing these limitations, a novel data-driven nonparametric approach is proposed in this article for extraction of a huge number of temporal patterns of rainfall from observations. When compared to stochastic approaches, generation of synthetic hyetographs is easier in the proposed approach. Synthetic hyetographs generated in this approach represented the observed storms better than other deterministic approaches from literature.