There is a growing recognition within the hydrologic modeling community that the results from real-time hydrologic modeling will need to be analyzed and presented via standardized web/cloud-based tools in a manner that facilitates expeditious interpretation of what could be considered unwieldy large scientific data-sets. Furthermore, there is also growing recognition that best-in-class weather forecast data can potentially add significant value to the resultant hydrologic simulation results.

To support on-going research into real-time hydrologic modeling at Aquanty, the post-doc will develop a pilot application for HGS real-time modeling at a spatial scale relevant to groundwater and surface water management professionals (i.e. >1000 km2) who have interests across agriculture, urban, and industrial water issues. However, there are still technical challenges that must be overcome before real time fully-integrated hydrologic modeling can become operational at a scale large enough to attract significant end user commercial interest. In the project herein, data assimilation methodology will be developed in order to facilitate using high resolution, spatially distributed weather forecast data for multiple time frames (i.e. 1 d, 3d, 7d, 10d), as the principle driver for watershed scale (~4000 km2) fully integrated real-time hydrologic modeling. Data analytics and visualization methodology will also be developed so that large (>1 TB) model output data-sets can be readily interpreted via a cloud hosted dashboard platform. The outcome from the effort will be in the form of a pilot demonstration of a cloud based hydrologic forecasting system.

Industry Partner(s):Aquanty Inc.

Academic Institution:University of Waterloo

Academic Researcher: Ed Sudicky

Focus Areas: Water

Platforms: Cloud