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

Lotic Labs, Inc., Hingham MA

Investigators

Abstract

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.