Big Data and Model Supported Environmental Decision Making under Uncertainty

包括气候变化在内的当今环境问题正以指数方式变得更加复杂。因此，在日常和战略环境管理中都涉及到大量的信息和数据。基于数据，环境建模和评估工具在理解这些复杂的环境问题和帮助重要的决策过程方面发挥了重要作用。然而，由于数据可用性和数据管理问题，数据和最先进模型之间的严重差距极大地限制了建模工具的适用性。例如，申请人在过去十年中提出了以下环境模型，并报道了现场应用：石油污染土壤和地下水的多相多组分化学命运和运输模型，加拿大近海水域污染物扩散的耦合近场和远场数值模型，以及应用于加利福尼亚州的gis支持的空间多源空气质量模型。对于上述模型开发和应用程序，数据处理花费的时间和精力是前者的一半或更多。

Today’s environmental problems including climate changes are getting exponentially more complex. Accordingly, huge volumes of information and data are involved in both daily and strategic environmental management. Based on data, environmental modeling and assessment tools have been playing an essential role in understanding those complex environmental problems and in helping vital decision-making processes. However, the critical gap between the data and the state of the art models has significantly limited the applicability of modeling tools due to data availability and data management issues. For example, the applicant has put forward the following environmental models with reported field applications in the past decade: a multiphase multicomponent chemical fate and transport model for the petroleum-contaminated soil and groundwater, a coupled near- and far- field numerical model for modeling contaminant's dispersion in Canadian offshore water, and a GIS-supported spatial multi-source air quality model with application to California. Data processing costs half or more of the time and efforts for the above-mentioned model developments and applications.