Streamflow forecasting generally relies on coupled rainfall-runoff-routing models calibrated and executed with data estimated by monitoring protocols that do not fully capture the dynamics of unsteady flows. This limits the ability to accurately forecast flood crests and issue hazard warnings. Here we utilize directly measured datasets acquired for streamflow estimation to develop a data-driven forecasting algorithm that does not require conventional physically-based modeling. We test the potential of our algorithm using measurements acquired at an index-velocity gaging station on the Illinois River, USA, between 2014 and 2019. We find that the forecasting protocol is able to deliver short-term predictions of flood crest magnitude and arrival time. The algorithm produces better agreement with larger events and is more reliable for single-peak storms possibly due to the prominence of hysteretic behavior in such events. We conclude that flood hazard can be forecast using directly measured index-velocity and stage alone.
Recent advances in instruments are transforming our capabilities to better understand, monitor, and model river systems. The present paper illustrates such capabilities by providing new insights into unsteady flows captured with a Horizontal Acoustic Current Profiler (HADCP) integrated at an operational index-velocity gaging station. The illustrations demonstrate that the high-resolution stage and velocity measurements directly acquired during flood wave propagation reveal the intricate interplay among flow variables that are essential for better supporting judicious decision making for river management, flooding, sediment transport, and stream ecology. The paper confirms that the index-velocity method better captures the unsteady flow dynamics in comparison with the stage-discharge monitoring approach. At a time when the intensity and frequency of floods is continuously increasing, a better understanding of the critical features of flood waves during extreme events and the possibility of capturing more accurately their dynamics in real time is of special socio-economic significance.
<p><strong>Abstract</strong></p><p>Flood inundation and hazard maps have played various crucial roles in terms of municipal hazard planning, timely flood control countermeasure operation, economic levee design, and developing flood forecasting or nowcasting systems. Given that the riparian areas prone to flood conventionally imposed special cares to justify applications of recently available flood inundation or hazard assessment numerical models on top of digital elevation models of dense spatial resolution such as LiDAR irrespective of their high costs. However, laborious and time & cost-consuming processes were required to proficiently produce inundation and hazard maps, which includes preparation of geometric and hydrologic data as input for the targeted numerical model, executing the model and post-processing, and inundation and subsequent hazard mapping. For example in Korea, field surveyed geometric dataset are provided in CAD format and should have to be manually converted into cross-sectional information compatible with HEC-RAS as a numerical model, where such dataset are not managed in centralized and standardized database. Then, flood inundation and hazard maps are generated one by one based on flood stage heights simulated from the HEC-RAS, where additional tools such as HEC-GeoRAS or manual drawing against DEM are usually applied. In order to efficiently and cost-effectively provide a series of flood inundation and hazard maps automatically with minimum practitioner involvement, this study demonstrates a set of open-source based tools that automated flood and hazard mapping processes as follows: a) parse CAD files containing geometric surveys like cross-sections and store them into server-based Arc River database approachable through website; b) retrieve geometric information using RiverML from Arc River and implicitly make them compatible with HEC-RAS input format; c) execute the HEC-RAS with some designated boundary conditions and various flood discharge; d) parse HEC-RAS output in binary format and draw flood inundation and hazard map on a given DEM through a developed add-on in QGIS using Python. We found that the proposed entire autonomous processes substantially enhanced efficiency and accuracy for flood mapping. The spatial accuracy of flood inundation and hazard map after applying above processes were validated throughout comparison with an inundation trace map acquired from typhoon Nari, 2007, in Hancheon basin located in Jeju Island, Korea, where a series of inundation and hazard maps were comprehensively investigated to track the burst of flood in the extreme flood events.</p><p>&#160;</p><p><strong>Acknowledgment</strong></p><p>This work was supported by the US Geological Survey Cooperative Grant Agreement #G19AC00257 and by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (21AWMP- B121092-06).</p>
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.