Collaborative Research: ABI Innovation: Improving high performance super computer aquatic ecosystem models with the integration of real-time citizen science data

合作研究：ABI Innovation：通过集成实时公民科学数据改进高性能超级计算机水生生态系统模型

• 批准号: 1661156
• 负责人: Darren Ficklin
• 金额: $ 38.49万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-15 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1661156&HistoricalAwards=false
• 关键词: Collaborative; Research; ABI; Innovation; Improving

This research is designed to engage public participation in data collection and the development of a stream discharge, stream temperature, and aquatic species habitat forecasting model. Through the use of citizen-based observations of stream height and stream temperature, this approach will demonstrate how citizen-derived observations can contribute to forecasts of stream discharge, stream temperature, and identification of freshwater fish habitat. Freshwater fishes have significant ecological, economic, and recreational importance across the United States. However, freshwater species are among the most endangered groups of organisms in North America, largely due to the impact of human activities. Accurate representations of freshwater species habitat are needed to develop approaches to balance the needs of society with the conservation of freshwater resources. This can be accomplished through the collection of observed data by government and research organizations or by computer modeling of habitat, whereby the quality of the model depends upon the availability of observed data. However, the amount of observed data for freshwater systems has been declining due to decreases in funding. The data that do exist are generally focused on large rivers that are important for urban communities (i.e., flooding, water supply), which are locations not always relevant to freshwater species whose habitat often occurs in smaller headwater streams. Local communities of recreational users and their mobile phones offer an opportunity to close this data-availability gap through citizen science. As regular users of shared resources, like streams and waterways, outdoor enthusiasts have valuable knowledge of specific locations. This knowledge is vastly underutilized by scientific communities. This project will harness information and data collected by members of local communities and develop an approach for data collection, storage, and integration with computer models that can predict streamflow, stream temperature, and freshwater species habitat, which can then aid in sustainable management of these resources. The Boyne River Basin in Michigan, USA will be used as a test location, but the techniques can be used in watersheds throughout the world. The goal of this research is to develop techniques that integrate citizen science hydrology and stream temperature data with eco-hydrological models. Specifically, this research is designed to fully couple citizen participation in the development of a real-time stream discharge, temperature, and aquatic species habitat forecasting model framework. The project will install CrowdHydrology (a citizen science network that collects hydrologic data) equipment throughout the Boyne River Basin. The local community can then text (via cellphone) stream level and stream temperature data to the CrowdHydrology platform. These citizen science data will then be transformed and input into an eco-hydrological model for near real-time simulations of streamflow, stream temperature, and aquatic species habitat. This approach will demonstrate how citizen-derived observations can contribute to the modeling of stream discharge, stream temperature, and aquatic species habitat. The model simulations and forecasts (one week ahead), including stream flows, temperatures, and habitat distributions, will be presented in tables and simple spatial plots available for download on the CrowdHydrology website (http://www.crowdhydrology.com)

本研究的目的是让公众参与数据收集和河流流量、河流温度和水生物种栖息地预测模型的发展。通过利用市民对河流高度和河流温度的观测，这种方法将展示市民的观测如何有助于预测河流流量、河流温度和识别淡水鱼栖息地。淡水鱼在美国各地具有重要的生态、经济和娱乐意义。然而，淡水物种是北美最濒危的生物群体之一，主要是由于人类活动的影响。为了制定平衡社会需要与淡水资源养护的方法，需要准确地表示淡水物种的栖息地。这可以通过政府和研究组织收集观测数据或通过生境的计算机模拟来实现，其中模型的质量取决于观测数据的可用性。然而，由于资金的减少，淡水系统的观测数据量一直在下降。现有的数据一般集中在对城市社区很重要的大河上（即洪水、供水），这些地点并不总是与淡水物种有关，淡水物种的栖息地往往出现在较小的源头溪流中。当地社区的娱乐用户和他们的移动电话提供了一个机会，通过公民科学来缩小这一数据可用性差距。作为共享资源（如溪流和水道）的常规用户，户外爱好者对特定地点有宝贵的了解。科学界对这些知识的利用远远不够。该项目将利用当地社区成员收集的信息和数据，开发一种数据收集、存储方法，并与计算机模型相结合，以预测河流流量、河流温度和淡水物种栖息地，从而有助于对这些资源进行可持续管理。美国密歇根州的博因河流域将被用作试验地点，但这些技术可以在世界各地的流域使用。本研究的目标是开发将公民科学水文和河流温度数据与生态水文模型相结合的技术。具体而言，本研究旨在充分结合公民参与开发实时流量，温度和水生物种栖息地预测模型框架。该项目将在整个博因河流域安装CrowdHydrology（一个收集水文数据的公民科学网络）设备。然后，当地社区可以（通过手机）将水流水位和水流温度数据发送到CrowdHydrology平台。然后，这些公民科学数据将被转换并输入到生态水文模型中，用于近实时模拟河流流量、河流温度和水生物种栖息地。这种方法将展示公民的观测如何有助于建立河流流量、河流温度和水生物种栖息地的模型。模型模拟和预测（提前一周），包括河流流量、温度和栖息地分布，将以表格和简单的空间图的形式呈现，可在CrowdHydrology网站（http://www.crowdhydrology.com）下载。

项目成果

Growing Pains of Crowdsourced Stream Stage Monitoring Using Mobile Phones: The Development of CrowdHydrology

使用手机进行众包河流阶段监测的成长烦恼：CrowdHydrology 的发展

• DOI: 10.3389/feart.2019.00128
• 发表时间: 2019
• 期刊: Frontiers in Earth Science
• 影响因子: 2.9
• 作者: Lowry, Christopher S.;Fienen, Michael N.;Hall, Damon M.;Stepenuck, Kristine F.
• 通讯作者: Stepenuck, Kristine F.

Mechanisms for engaging social systems in freshwater science research

让社会系统参与淡水科学研究的机制

• DOI: 10.1086/713039
• 发表时间: 2021
• 期刊: Freshwater Science
• 影响因子: 1.8
• 作者: Hall, Damon M.;Gilbertz, Susan J.;Anderson, Matthew B.;Avellaneda, Pedro M.;Ficklin, Darren L.;Knouft, Jason H.;Lowry, Christopher S.
• 通讯作者: Lowry, Christopher S.

Improving Hydrological Models With the Assimilation of Crowdsourced Data

• DOI: 10.1029/2019wr026325
• 发表时间: 2020-05
• 期刊: Water Resources Research
• 影响因子: 5.4
• 作者: P. Avellaneda;D. Ficklin;C. Lowry;J. Knouft;D. M. Hall