Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2017WR022498 |
Using Sensor Data to Dynamically Map Large-Scale Models to Site-Scale Forecasts: A Case Study Using the National Water Model | |
Fries, Kevin J.; Kerkez, Branko | |
2018-08-01 | |
发表期刊 | WATER RESOURCES RESEARCH
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ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2018 |
卷号 | 54期号:8页码:5636-5653 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
英文摘要 | There has been an explosive growth in the ability to model large water systems. While these models are effective at routing water across massive scales, they do not yet forecast the street-level information desired by local decision makers. Simultaneously, the increasing affordability of sensors has made it possible for even small communities to measure the state of their watersheds. However, these real-time measurements are often not attached to a predictive model, thus making them less useful for applications like flood warnings. In this paper, we ask the question: how can highly localized forecasts be generated by fusing site-scale sensor measurements with outputs from large-scale models? Rather than altering the larger physical model, our approach uses the outputs of the unmodified model as the inputs to a dynamical system. To evaluate the approach, a case study is carried out across the U.S. state of Iowa using publicly available measurements from over 180 water level sensors and outputs from the National Water Model. The approach performs well across a third of the studied sites, as quantified by a high normalized root mean squared error. A performance classification is carried out based on Principal Component Analysis and Random Forests. We discuss how these results will enable stakeholders with local measurements to quickly benefit from large-scale models without needing to run or modify the models themselves. The results are also placed into a broader sensor-placement context to provide guidance on how investments into local measurements can be made to maximize predictive benefits. |
英文关键词 | Transfer Function Flood Forecasting Data Driven Modeling |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000445451800027 |
WOS关键词 | DISCHARGE ESTIMATION ; RATING CURVE ; SYSTEM-IDENTIFICATION ; RIVER ; UNCERTAINTY ; PARAMETERS ; COST |
WOS类目 | Environmental Sciences ; Limnology ; Water Resources |
WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/21887 |
专题 | 资源环境科学 |
作者单位 | Univ Michigan, Dept Civil & Environm Engn, Ann Arbor, MI 48109 USA |
推荐引用方式 GB/T 7714 | Fries, Kevin J.,Kerkez, Branko. Using Sensor Data to Dynamically Map Large-Scale Models to Site-Scale Forecasts: A Case Study Using the National Water Model[J]. WATER RESOURCES RESEARCH,2018,54(8):5636-5653. |
APA | Fries, Kevin J.,&Kerkez, Branko.(2018).Using Sensor Data to Dynamically Map Large-Scale Models to Site-Scale Forecasts: A Case Study Using the National Water Model.WATER RESOURCES RESEARCH,54(8),5636-5653. |
MLA | Fries, Kevin J.,et al."Using Sensor Data to Dynamically Map Large-Scale Models to Site-Scale Forecasts: A Case Study Using the National Water Model".WATER RESOURCES RESEARCH 54.8(2018):5636-5653. |
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