Non-intrusive technologies for the in-situ measurement of river morphological features are increasingly popular in the scientific and practice communities due to their efficient and productive data acquisition. While the measurement of suspended load with optical and acoustic technologies is currently an active area of research, the measurement of bedform dynamics has not experienced similar progress. We have successfully demonstrated through laboratory experiments that, by combining acoustic mapping with image velocimetry concepts, we can characterize the planar dynamics of the bedform migration. The technique, labeled Acoustic Mapping Velocimetry (AMV), is currently transferred to field conditions using multiple-beam echo-sounders (MBES) for producing acoustic maps. During this transfer, new questions emerged because, in field conditions, many of the morphologic features targeted by AMV measurements are not a priori known. Moreover, the image velocimetry processing can be approached with several alternatives, each of them characterized by strength and limitations. This paper assembles guidelines for establishing optimal parameters for the acquisition of the acoustic maps based on analytical considerations, and for selecting essential features of the processing for image velocimetry. We test these guidelines using MBES data acquired in the Mississippi River.

Characterizing morphological features in shallow streams such as dunes and ripples is vital to studies on fluvial geomorphology and in-stream habitat assessments of stream ecology. The paper aimed to examine the feasibility of a conventional hyperspectral method called linear optimal band ratio analysis for capturing the detailed morphologies in shallow small streams, allowing the identification of ripples and dunes. The present study involved a dedicated field experiment at the Gam stream, which is a tributary of the Nakdong River, South Korea. An unmanned aerial vehicle based hyperspectral image was obtained with a spatial resolution of <10 cm and developed an optimal depth-band ratio rating by densely scattered in situ bathymetry measurements with a portable Real Time Kinematic Global Positioning System. The derived hyperspectral bathymetric map with a 7 cm spatial resolution successfully captured the detailed bed morphology, where dunes with sizes of 1.5 m were clearly identifiable. The correlation with depth measured by RTK-GPS was found to be 0.956, with Root Mean Square Error of 0.033 meter. The research confirmed that the conventional linear OBRA used for low-altitude UAV-based hyperspectral images can capture morphological features in shallow streams with a high spatial resolution.

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. AcknowledgmentThis 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).

Non-intrusive technologies for the in-situ measurement of river hydro-morphological features are increasingly popular in the scientific and practice communities due to their efficient and productive data acquisition. Our research team have successfully demonstrated through laboratory experiments and field measurements that, by combining acoustic mapping with image velocimetry concepts, we can characterize the planar dynamics of the bedform migration and eventually rates of bedload transport. The technique, labeled Acoustic Mapping Velocimetry (AMV), is currently transferred to field conditions using multiple-beam echo-sounders (MBES) and Acoustic-Doppler Current Profilers (ADCP) for producing acoustic maps and tracking the bedform dynamics.A constant preoccupation of the research team during this transfer has been the validation of the AMV in field conditions. Such validation requires the use of identical input data and the availability of a similar capability measurement system in terms of measurement output, spatial and temporal coverage for the measurement. Fortunately, there is a similar system for estimation of bedload transport labeled Integrated Section Surface Difference Over Time (ISSDOT).  The latter method has been developed and extensively tested by a research group of US Corps of Engineers. While the data inputs (acoustic maps) and the underlying principle (i.e., dune tracking) are the same as for AMV, ISSDOT is based on purely geometrical estimation of the bedload transports rates. The present paper described a comparison between AMV and ISSDOT applied to a set of repeated maps acquired in the Mississippi River. In the absence of a third measurement alternative to be used as benchmark, the paper draws inferences from the comparisons of the two instruments.