SBIR Phase I: Hydro-financial modeling architecture for the automated optimization of low basis risk indices

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is the improved financial resilience of the numerous industries that are reliant on stable water conditions. The innovation will enable the transfer and pooling of the financial risks posed by drought and flood in an increasingly efficient market. In the US, over $250B of industrial activity depends on stable freshwater systems across the energy, agricultural, and utility markets. Floods, droughts, freezes, and other conditions destroy roughly $10B in economic value annually in the US alone - value which cannot be recovered until it is accurately quantified and mapped to clear and measurable indicators. This SBIR research will enable the generation of accurate risk indices that bring clarity to the opaque and complex financial impacts of volatile water conditions. This will improve the accessibility, cost, and effectiveness of index-based insurance contracts, which provide businesses with crucial financial relief from the droughts and floods that hamper their operations. Such contracts also create new investment opportunities with diversification benefits for investors. This Small Business Innovation Research (SBIR) Phase I project seeks to overcome the technical challenges of seamlessly and scalably 1) combining and analyzing massive heterogeneous datasets relevant to hydrologic-financial risks, 2) managing a diverse set of models that cover hydrology, industrial operations, markets, and actuarial sciences, and 3) optimizing the configuration of data and models to generate accurate and precise risk indices. This project will construct and test a unified semantic data model for hydrologic-financial risk and deploy optimizations to maximize accuracy of the indices for a specific industrial use case. The architecture allows for the automated provisioning of disparate datasets and the deployment of best-in-class modeling and optimization tools to generate detailed risk indices. The research will create new vocabulary, when necessary, to bridge the gaps between existing ontologies in hydrology, industrial asset operations, actuarial analysis, and financial market conditions. If successful, the research will enable a dramatic reduction in both the time and cost required to produce hydrologic-financial risk analyses and the instances of errors in those analyses.