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

这项小企业创新研究（SBIR）第二阶段项目的更广泛影响/商业潜力将源于数十万依赖水的企业和市政当局的财务弹性的提高，这些企业和市政当局目前受到水文波动和严重紧张的生态系统的威胁。这项SBIR研究将使水经济的科学和金融模型无缝集成。这项创新降低了两项气候风险缓解投资的成本并提高了绩效：1)绿色基础设施（模仿或保护自然以确保为商业和公共用途提供清洁水的项目）；2)天气保险合同，为企业和公用事业公司提供经济救济，以应对阻碍其运营的干旱和洪水。预计到2050年，全球50%的人口将面临水资源短缺（根据联合国的数据），美国每年因洪水、干旱和冰冻造成的经济损失达100亿美元，这些缓解风险的新方法对于通过自由市场方法保护水资源来减少水需求压力至关重要。这个小企业创新研究（SBIR）二期项目旨在消除目前阻碍水文和金融数据和模型无缝集成的技术障碍。第一阶段项目通过展示语义网技术的实用性来验证技术可行性，为量化水力金融风险提供端到端建模解决方案。第一阶段确立了该技术：1)极大地提高了海量异构数据集和模型之间的互操作性，用于量化水文金融风险；2)使数据和模型能够通过防篡改的分布式网络连接起来。第二阶段项目建立在技术基础上，部署一个生产环境，用于运行一套包含生态系统服务、水文学和精算科学的模型。该项目为支持人工智能的决策支持工具奠定了基础。如果成功，这项研究将大大减少与自然水基础设施投资财务价值建模相关的时间和成本，在不影响科学严谨性的情况下，无缝地比较各种水项目和财务结构。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.