SBIR-STTR Award

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.

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project will result from improved financial resilience of hundreds of thousands of water-dependent businesses and municipalities currently threatened by hydrologic volatility and severely strained ecosystems. This SBIR research will enable the seamless integration of scientific and financial modeling for the water economy. The innovation lowers the costs and improves the performance of two climate risk mitigation investments: 1) Green Infrastructure (projects that emulate or protect nature in order to ensure clean water supply for commercial and public use); and 2) weather insurance contracts, which provide businesses and utilities with financial relief from droughts and floods that hamper their operations. With 50% of the global population projected to face water scarcity by 2050 (according to the UN), and $10B in economic value destroyed annually by floods, droughts, freezes in the US, these new approaches to risk mitigation are crucial to reducing water demand stresses through a free-market approach to water resource conservation. This Small Business Innovation Research (SBIR) Phase II project aims to eliminate technical barriers currently hindering seamless data and model integration for hydrology and finance. The Phase I project validated technical feasibility by demonstrating the utility of a semantic web technology to provide end-to-end modeling solutions for quantifying hydro-financial risk. Phase I established that the technology 1) greatly improves the interoperability between massive heterogeneous data sets and models for quantifying hydrologic-financial risk, and 2) enables data and models to be linked through a tamper-proof distributed network. The Phase II project builds on the technological foundation to deploy a production environment for running a suite of models encompassing ecosystem services, hydrology, and actuarial sciences. The project builds foundations for AI-enabled decision support tools. If successful, this research will enable significant reductions in the time and costs associated with modeling the financial value of investment in natural water infrastructure, generating comparisons between a wide range of water projects and financial structures seamlessly and without compromising scientific rigor. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.