Quantifying water security using hyperresolution hydrological modelling on top of an Open Data Cube (ODC)

For many areas across the globe physically-based hydrological models have a fundamental role helping devise a comprehensive and robust plan for future climate change adaption and preparedness informing water management and flood initiatives. Now that the advances in satellite and sensor technology coupled with the development of cloud computing have enable the advancement of hydrology as a data-intensive science, there is a considerable impetus and interest in future research and approaches in the use of these emerging technologies to develop new insights that contribute to fundamental aspects of the hydrological sciences. Whilst increasing volumes of Earth Observation (EO) data couple with advances in cloud computing have enable the enhancement of hydrological modelling, one of the remaining challenges is ensuring a seamless data pipeline to the final hydrological prediction. As a result, this poses a significant set of questions in the use of EO data for hydrology. The current research is situated at the junction of three areas: hydrological physical modelling, satellite EO data and the implementation of the Earth Observation Data Cube (EODC) paradigma. This presentation will outline the development and use of a open source modelling workflow integrating analysis ready data (ARD) through the implementation of the Open Data Cube (ODC) data exploitation architecture with a physically-based, spatially-distributed hydrological model (SHETRAN), as glimpse into the relevance of EO data cube solutions in lowering the technology and EO data barriers. Thus, enabling users to harnes existent open source EO datasets and software at minimum cost and effort with the objective to enable a more open and reproducible hydrological science.