Civil and Environmental Engineering and Grantham Institute for Climate Change, Imperial College London, Skempton Building, SW7 2AZ London, UK.
Environ Sci Technol. 2012 Feb 21;46(4):1971-6. doi: 10.1021/es2031278. Epub 2012 Feb 7.
Data availability in environmental sciences is growing rapidly. Conventional monitoring systems are collecting data at increasing spatial and temporal resolutions; satellites provide a constant stream of global observations, and citizen scientist generate local data with electronic gadgets and cheap devices. There is a need to process this stream of heterogeneous data into useful information, both for science and for decision-making. Advances in networking and computer technologies increasingly enable accessing, combining, processing, and visualizing these data. This Feature reflects upon the role of environmental models in this process. We consider models as the primary tool for data processing, pattern identification, and scenario analysis. As such, they are an essential element of science-based decision-making. The new technologies analyzed here have the potential to turn the typical top-down flow of information from scientists to users into a much more direct, interactive approach. This may accelerate the dissemination of environmental information to a larger community of users. It may also facilitate harvesting feedback, and evaluating simulations and predictions from different perspectives. However, the evolution poses challenges, not only to model development but also to the communication of model results and their assumptions, shortcomings, and errors.
数据在环境科学领域的可用性正在迅速增长。传统的监测系统正在以越来越高的时空分辨率收集数据;卫星提供源源不断的全球观测数据,而公民科学家则利用电子小工具和廉价设备生成本地数据。我们需要将这些异质数据流处理成有用的信息,无论是用于科学研究还是决策制定。网络和计算机技术的进步使我们能够越来越多地访问、组合、处理和可视化这些数据。本文探讨了环境模型在这一过程中的作用。我们认为模型是数据处理、模式识别和情景分析的主要工具。因此,它们是基于科学的决策制定的重要组成部分。这里分析的新技术有可能将信息从科学家到用户的典型自上而下的流动转变为更加直接、互动的方式。这可能会加速环境信息向更广泛的用户群体传播。它还可以促进从不同角度收集反馈,并评估模拟和预测。然而,这种演变不仅对模型开发提出了挑战,还对模型结果及其假设、局限性和错误的沟通提出了挑战。