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: 1661324
• 负责人: Christopher Lowry
• 金额: $ 12.53万
• 依托单位: SUNY at Buffalo
• 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=1661324&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)

这项研究的目的是让公众参与数据收集和开发的流流量，流温度和水生物种栖息地预测模型。通过使用基于公民的观测流的高度和流的温度，这种方法将展示如何公民派生的观测可以有助于预测流流量，流的温度，并确定淡水鱼的栖息地。淡水鱼在美国具有重要的生态、经济和娱乐价值。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。需要准确地说明淡水物种的生境，以制定办法，平衡社会需求和淡水资源的养护。这可以通过政府和研究组织收集观测数据或通过计算机模拟生境来实现，其中模型的质量取决于观测数据的可用性。然而，由于资金减少，淡水系统的观测数据量一直在下降。现有的数据通常集中在对城市社区很重要的大型河流上（即，洪水、供水），这些地点并不总是与淡水物种有关，因为淡水物种的栖息地往往在较小的源头河流中。娱乐用户和他们的移动的电话的当地社区提供了一个机会，通过公民科学来缩小这一数据可用性差距。作为溪流和水道等共享资源的常规用户，户外爱好者对特定位置有着宝贵的知识。科学界对这些知识的利用远远不足。该项目将利用当地社区成员收集的信息和数据，并制定一种数据收集、存储和与计算机模型结合的方法，这些模型可以预测径流、水温和淡水物种的栖息地，从而有助于这些资源的可持续管理。美国密歇根州的博因河流域将被用作试验地点，但这些技术可用于世界各地的流域。本研究的目标是开发技术，将公民科学水文和河流温度数据与生态水文模型相结合。具体而言，本研究的目的是充分耦合公民参与的实时流流量，温度和水生物种栖息地预测模型框架的发展。该项目将在整个博因河流域安装CrowdHydrology（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）将水位和水温数据发送到CrowdHydrology平台。然后，这些公民科学数据将被转换并输入到一个生态水文模型中，用于近实时模拟径流，河流温度和水生物种栖息地。这种方法将展示如何公民派生的意见，可以有助于流流量，流温度和水生物种栖息地的建模。模型模拟和预测（提前一周），包括溪流、温度和栖息地分布，将以表格和简单空间图的形式呈现，可在CrowdHydrology网站（http：//www.example.com）www.crowdhydrology.com

项目成果

Is Citizen Science Dead?

公民科学死了吗？

• DOI: 10.1021/acs.est.0c07873
• 发表时间: 2021
• 期刊: Environmental Science & Technology
• 影响因子: 11.4
• 作者: Lowry, Christopher S.;Stepenuck, Kristine F.
• 通讯作者: Stepenuck, Kristine F.

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.

Exploring the Use of Decision Tree Methodology in Hydrology Using Crowdsourced Data

利用众包数据探索决策树方法在水文学中的应用

• DOI: 10.1111/1752-1688.12882
• 发表时间: 2020
• 期刊: Journal of the American Water Resources Association
• 影响因子: 2.4
• 作者: Wu, D.: Del

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