Himalayan Re-gridded and Observational Experiment (HiROX): Part I – Development

The present work serves as a theoretical commentary over the Himalayan Re-gridded and Observational EXperiment (HiROX) and distributes the conceived framework grown under the two-phase experiment. HiROX is a scientific effort aimed at revising the existing precipitation knowledge over the Central Himalayas over the state of Uttarakhand, including the Upper Ganga Basin. The first phase of the study is experimenting with machine learning methods aimed at developing re-gridded precipitation datasets capable of incorporating selective precipitation features and distributing the developed machine learning framework to the research community. The model is designed for easy reproduction of re-gridded precipitation data and the ability to adapt to the available precipitation datasets subject to missing and/or modified inputs. The model is capable of ingesting gridded precipitation datasets at varying spatial resolutions, along with point data values, and can return gridded precipitation information at a spatial resolution of 5 × 5 km². The second objective of the companion paper under HiROX is the realization of the features of the developed model and exercising it in the development of two high-resolution precipitation datasets at daily timesteps, namely HiROX-1 (~50 yrs) and HiROX-2 (~20 yrs). The present paper limits the discourse to robust development, and the produced datasets are discussed and distributed in the joint paper – Himalayan Re-gridded and Observational Experiment (HiROX): Part II – Application.