RAPID: Acquisition of Critical Data for the Validation of Watershed Response Models in Eastern North Carolina

在过去的几年里，有许多飓风登陆或严重影响了美国东/东南海岸。这类影响陆地水循环的飓风的数据，如洪水-不同陆地表面类型的积水，往往很差。该项目将收集三种不同类型的观测数据--公民绘制洪水延伸图、使用无人驾驶飞行器对洪水泛滥地区进行成像以及北卡罗来纳州东部这些地区的地表水和地下水水质样本。这些数据将在农业、城市、郊区和屏障岛屿等不同土地使用类型中收集。这些数据将用于以后的研究，以了解飓风后创纪录的降水量的流动和存储的行为，并将此信息传递给社会。洪水淹没模型面临的问题是缺乏数据的校准和验证。洪水模型是分布式的，但传统的水文学通常只收集集总数据-在集水区和出口的河道中的几个点的流量。这不足以描述流域的空间响应。通过使用公民测绘、无人驾驶飞行器和水质样本收集佛罗伦萨飓风后的洪水淹没数据，PI和他的团队将能够生成一个数据集，用于回答洪水期间流域水文学的重要科学问题。这些问题包括：我们的流域洪水水文模型有多准确;景观从洪水中恢复的响应时间是多少？不同的土地利用类型之间是否存在差异？最后，我们能否改变土地利用管理以最大限度地减少洪水的影响？本RAPID项目收集的数据可充分回答这些问题。水质数据的收集有助于解决最后一个问题，即最大限度地减少洪水影响的土地管理战略。该奖项反映了NSF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。

There have been many hurricanes over the past few years that have made landfall or severely impacted the east/southeast coast of United States. Data from such hurricanes that impacts the terrestrial water cycle such as flooding - standing water on different land surface types is often poor. The project will collect three different kinds of observations - citizens mapping the flood extend, use of unmanned aerial vehicles to image flooded areas and the water quality samples for surface and groundwater in these areas in eastern North Carolina. These data will be collected over different land use types such as agriculture, urban, suburban and barrier islands. These data will be used in later studies to understand the behavior of flow and storage in the aftermath of record setting precipitation following a hurricane and to relay this information to the society.Flood inundation models face a problem of lack of data for calibration and validation. Models for flooding are distributed and yet traditional hydrology often collects only lumped data - discharge at a few points in the stream channel in the catchment and at the outlet. This is not sufficient to characterize the spatial response of a watershed. By collection of flood inundation data after Hurricane Florence of standing water using citizen mapping, use of unmanned aerial vehicles and water quality samples, the PI and his team will be able to generate a data set that can be used later for answering important science questions on water shed hydrology during floods. These questions include - how accurate are our flood hydrology models for a watershed; what is the response time for landscapes to recover from floods and does this vary among different land use type and lastly can we change the land use management to minimize the impact of floods. These questions can be adequately answered by the data collected in this RAPID project. The collection of water quality data feeds to the last question as to the land management strategies for minimizing impact of floods.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.