I-Corps: Geospatial Trend Detection for Hydro-power and Critical Infrastructure Design

I-Corps项目的更广泛影响/商业潜力是开发趋势检测软件工具，以预测与水有关的气候变化风险。气候变化风险主要通过水表现出来，导致极端事件（干旱和洪水）以及水系统时间序列的系统性变化。有了强大的趋势检测工具，在计算大型和昂贵的基础设施项目（如水坝）的年损失变化率和净经济回报时，可能会考虑气候变化的影响。所提出的技术可以识别具有逐年变化的水文气候（或其他）变量的变化平均条件，但平均而言可能偏离历史平均行为，称为平均值的非平稳性。可能受益于这项技术的基础设施的一些例子包括供水或废水管网、水坝（水力发电）和气候对关键基础设施（电网、核设施和矿场）的气候极端回归期（如干旱和洪水）的工程设计估计。此外，这项技术可能会促进气候适应型基础设施设计标准的快速采用。这个I-Corps项目是基于稳健趋势检测技术的发展，或“非平稳性检测”。提出的技术结合了几十年来水文气候科学和计量经济学独立文献的结果，在检测非平稳趋势方面实现了逐步改进。除了地球科学，这些发展在其他应用科学领域也具有重要的基础意义，包括计量经济学、生物计量学和心理计量学。这些领域中的许多变量随着时间的变化行为（趋势行为）被监视，但它们的变化通常也在时间上强烈相关（可预测的振荡），这通常会混淆检测趋势行为的能力。拟议中的技术有助于消除这种噪音。这样，非平稳性检测的假阴性和假阳性分别减少了30%和400%。通过利用这些信息，可以最大限度地减少对气候适应型基础设施的不当支出，并在最需要的地方最大化支出，从而优化运营和资本规划成本。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 I-Corps project is the development of a trend detection software tool to predict climate change risks related to water. Climate change risks manifest primarily through water, causing extremes (droughts and floods) and systematic shifts in the water systems’ time series. With robust trend detection tools, it may be possible to factor in climate change impacts when calculating the annual rate of change of loss and net economic returns from building large and expensive infrastructure projects, like dams. The proposed technology may identify the changing mean conditions of hydro-climatic (or other) variables that have a year-to-year variability, but on average could be shifting away from the historical mean behavior, called non-stationarity of mean. Some examples of infrastructure that may benefit from this technology include engineering design estimations of hydro-climate extreme return periods (e.g., droughts and floods) for pipe networks for water supply or wastewater, dams (hydro-power generation), and climate-impact on critical infrastructure (power grids, nuclear and mining sites). In addition, this technology may promote rapid adoption of climate resilient infrastructure design standards.This I-Corps project is based on the development of technology for robust trend detection, or “non-stationarity detection.” The proposed technology combines the results from several decades of independent literature in hydro-climate sciences and econometrics to achieve a step-change improvement in detecting non-stationary trends. In addition to geosciences, these developments are of fundamental importance in other applied science fields, including econometrics, biometrics, and psychometrics. Numerous variables in these domains are monitored for their changing behavior over time (trending behavior), but often their changes are also strongly correlated in time (predictably oscillation), which typically confounds the ability to detect a trending behavior. The proposed technology helps cut through this noise. In doing so, false negatives and false positives on non-stationarity detection were reduced by up to 30% and 400%, respectively. By using this information, operational and capital planning costs may be optimized by minimizing misplaced spending for climate resilient infrastructure, and maximizing spending where it is needed most.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.