资源环境科技发展态势分析平台

The sheer size of many water systems challenges the ability of in situ sensor networks to resolve spatiotemporal variability of hydrologic processes. New sources of vastly distributed and mobile measurements are, however, emerging to potentially fill these observational gaps. This paper poses the question: How can nontraditional measurements, such as those made by volunteer ship captains, be used to improve hydrometeorological estimates across large surface water systems? We answer this question through the analysis of one of the largest such data sets: an unprecedented collection of one million unique measurements made by ships on the North American Great Lakes from 2006 to 2014. We introduce a flexible probabilistic framework, which can be used to integrate ship measurements, or other sets of irregular point measurements, into contiguous data sets. The performance of this framework is validated through the development of a new ship-based spatial data product of water temperature, air temperature, and wind speed across the Great Lakes. An analysis of the final data product suggests that the availability of measurements across the Great Lakes will continue to play a large role in the confidence with which these large surface water systems can be studied and modeled. We discuss how this general and flexible approach can be applied to similar data sets, and how it will be of use to those seeking to merge large collections of measurements with other sources of data, such as physical models or remotely sensed products.

Plain Language Summary Hydrologists, particularly in the Great Lakes Basin, have access to a great number of large databases of both modeled and measured hydrologic data. Yet the community currently only uses a select number of tools to try and merge these disparate datasets together to create more useful information. This paper presents one such method for merging noisy observed data with modeled data to generate improved estimates of wind speed, water surface temperature, and air temperature on the Great Lakes.