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River Flow Monitoring: Needs, Advances and Challenges

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A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydraulics and Hydrodynamics".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 22294

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Special Issue Editors

Dr. Salvador Peña-Haro

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Guest Editor

Photrack AG: Flow Measurements, Ankerstrasse 16a, 8004 Zürich, Switzerland
Interests: river flow monitoring; image-based environmental monitoring; water resources management; water resources modelling

Dr. Beat Lüthi

E-Mail Website
Guest Editor

Photrack AG: Flow Measurements, Ankerstrasse 16a, 8004 Zürich, Switzerland
Interests: river flow monitoring; image based experimental methods; hydromechanics; turbulence

Special Issue Information

Dear Colleagues,

We are pleased to announce a call for contributions on topics related to 'River flow monitoring: needs, advances and challenges', for submission to a Special Issue of the journal Water. River flow monitoring is of paramount importance. In order to plan water resources it is needed to have timely, accurate data with an appropriate temporal and spatial resolution; however, data are not always available.

This Special Issue seeks to publish articles that demonstrate needs, advances and challenges on river flow monitoring, and those that explain the need of data and how to collect it. We especially encourage papers that present novel techniques that improve the quality of data but also simplify installation and maintenance, as well as devices that are proven to work under extreme events.

Of particular interest are research articles and commentaries related to river flow monitoring and the following aspects:

Current monitoring technologies: improvements and challenges;
How data is used in water resources management and planning, from both modelling and institutional perspectives.
Comparisons between different monitoring technologies and that stress their advantages and disadvantages.
New technologies for river monitoring.
How to bring new technologies into operation.
Artificial intelligence (AI) in river monitoring and alternative methods.
Research articles that report technical advances are also welcomed.

Dr. Salvador Peña-Haro
Dr. Beat Lüthi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

river flow monitoring
data acquisition
artificial intelligence
accuracy
data needs
crowd sourcing
image-based
ADCP
tracer tests
radar

Published Papers (7 papers)

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Research

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16 pages, 24103 KiB

Open AccessArticle

Monitoring Discharge in Vegetated Floodplains: A Case Study of the Piave River

by Verónica Herrera Gómez, Giovanni Ravazzani, Marco Mancini, Niccolò Marchi, Emanuele Lingua and Michele Ferri

Water 2023, 15(19), 3470; https://doi.org/10.3390/w15193470 - 30 Sep 2023

Viewed by 1721

Abstract

The accurate assessment of discharge in vegetated floodplains during floods is a persistent challenge in river engineering due to the difficulty of acquiring hydraulic data, the variability in vegetation roughness, and the limitations of on-site vegetation characterization. This study introduces a novel approach that combines the continuous slope-area method with LiDAR-derived vegetation data and water depths measured with piezoresistive sensors to evaluate floodplain discharges while considering variations in roughness coefficients induced by arboreal vegetation. We apply this approach to a specific reach of the Piave River in Italy using data collected during the December 2020 flood event. The study demonstrates the capability of the employed measurement system to record extreme floods and emphasizes the importance of including vegetation roughness variations in floodplain discharge calculations. The proposed approach has the potential to be applied in similar scenarios, providing valuable insights for floodplain discharge estimation in vegetated areas. Full article

(This article belongs to the Special Issue River Flow Monitoring: Needs, Advances and Challenges)

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18 pages, 6447 KiB

Open AccessArticle

A Robust Regime Shift Change Detection Algorithm for Water-Flow Dynamics

by Hatef Dastour, Anil Gupta, Gopal Achari and Quazi K. Hassan

Water 2023, 15(8), 1571; https://doi.org/10.3390/w15081571 - 17 Apr 2023

Viewed by 3736

Abstract

Stream and river monitoring have an influential role in agriculture, the fishing industry, land surveillance, the oil and gas industry, etc. Recognizing sudden changes in the behavior of streamflow could also provide tremendous insight for decision-making and administration purposes. The primary purpose of this study is to offer a new robust Regime Shift Change Detection (RSCD) algorithm which can identify periods and regime changes without any assumptions regarding the length of these periods. A regime shift algorithm using two different refined method approaches is proposed in this article. The RSCD with Relative Difference (RSCD-RD) and RSCD with Growth Rate (RSCD-GR) are the two main specializations of this regime shift algorithm. We compared these two specializations on train and test datasets and commented on the advantages and each specialization. RSCD-GR and RSCD-RD were equally effective in detecting regime changes when thresholds were pinpointed for each station and season. However, RSCD-RD outperformed RSCD-GR when general thresholds were used for cold and warm months. A strength of RSCD-GR is the ability to investigate newly observed data separately, while RSCD-RD may require re-investigation of historical data in some cases. A regime change was detected in the monthly streamflow data of the Athabasca River at Athabasca (07BE001) in May 2007, while no such change was observed in the monthly streamflow data of the Athabasca River below Fort McMurray (07DA001). The discrepancy could be attributed to factors such as the clarity of the river water from Saskatchewan or the utilization of industrial water. Additional investigation might be required to determine the underlying causes. Full article

(This article belongs to the Special Issue River Flow Monitoring: Needs, Advances and Challenges)

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11 pages, 1541 KiB

Open AccessArticle

Integration of Distributed Streamflow Measurement Metadata for Improved Water Resource Decision-Making

by Kendra E. Kaiser, Kyle Blasch and Steven Schmitz

Water 2023, 15(4), 679; https://doi.org/10.3390/w15040679 - 9 Feb 2023

Cited by 1 | Viewed by 1758

Abstract

Streamflow data are critical for monitoring and managing water resources, yet there are significant spatial gaps in our federal monitoring networks with biases toward large perennial rivers. In some cases, streamflow monitoring exists in these spatial gaps, but information about these monitoring locations is challenging to obtain. Here, we present a streamflow catalog for the United States Pacific Northwest that includes current and historical streamflow monitoring location information obtained from 32 organizations (other than the U.S. Geological Survey), which includes 2661 continuous streamflow gaging locations (22% are currently active) and 30,557 discrete streamflow measurements. A stakeholder advisory board with representatives from organizations that operate streamflow monitoring networks identified metadata requirements and provided feedback on the Streamflow Data Catalog user interface. Engagement with the water resources community through this effort highlighted challenges that water professionals face in collecting and managing streamflow data so that data are findable, accessible, interoperable, and reusable (FAIR). Over 60% of the streamflow monitoring locations in the Streamflow Data Catalog are not available online and are thus not findable through web search engines. Providing organizations technical assistance with standard measurement procedures, metadata collection, and web accessibility could substantially increase the availability and utility of streamflow information to water resources communities. Full article

(This article belongs to the Special Issue River Flow Monitoring: Needs, Advances and Challenges)

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23 pages, 13253 KiB

Open AccessArticle

Uncertainty Analysis for Image-Based Streamflow Measurement: The Influence of Ground Control Points

by Wen-Cheng Liu, Wei-Che Huang and Chih-Chieh Young

Water 2023, 15(1), 123; https://doi.org/10.3390/w15010123 - 29 Dec 2022

Cited by 3 | Viewed by 2603

Abstract

Large-scale particle image velocimetry (LSPIV) provides a cost-effective, rapid, and secure monitoring tool for streamflow measurements. However, surveys of ground control points (GCPs) might affect the camera parameters through the solution of collinearity equations and then impose uncertainty on the measurement results. In this paper, we explore and present an uncertainty analysis for image-based streamflow measurements with the main focus on the ground control points. The study area was Yufeng Creek, which is upstream of the Shimen Reservoir in Northern Taiwan. A monitoring system with dual cameras was set up on the platform of a gauge station to measure the surface velocity. To evaluate the feasibility and accuracy of image-based LSPIV, a comparison with the conventional measurement using a flow meter was conducted. Furthermore, the degree of uncertainty in LSPIV streamflow measurements influenced by the ground control points was quantified using Monte Carlo simulation (MCS). Different operations (with survey times from one to nine) and standard errors (30 mm, 10 mm, and 3 mm) during GCP measurements were considered. Overall, the impacts in the case of single GCP measurement are apparent, i.e., a shifted and wider confidence interval. This uncertainty can be alleviated if the coordinates of the control points are measured and averaged with three repetitions. In terms of the standard errors, the degrees of uncertainty (i.e., normalized confidence intervals) in the streamflow measurement were 20.7%, 12.8%, and 10.7%. Given a smaller SE in GCPs, less uncertain estimations of the river surface velocity and streamflow from LSPIV could be obtained. Full article

(This article belongs to the Special Issue River Flow Monitoring: Needs, Advances and Challenges)

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14 pages, 3338 KiB

Open AccessArticle

Surface Water Extent Mapping in Denmark: Comparing Airborne Thermal Imagery and Satellite Earth Observation

by Peter Bauer-Gottwein, Henrik Grosen, Daniel Druce, Christian Tottrup, Heidi E. Johansen and Roland Löwe

Water 2022, 14(22), 3742; https://doi.org/10.3390/w14223742 - 17 Nov 2022

Viewed by 1638

Abstract

Mapping and prediction of inundated areas are increasingly important for climate change adaptation and emergency preparedness. Flood forecasting tools and flood risk models have to be compared to observe flooding patterns for training, calibration, validation, and benchmarking. At the regional to continental scales, satellite earth observation (EO) is the established method for surface water extent (SWE) mapping, and several operational global-scale data products are available. However, the spatial resolution of satellite-derived SWE maps remains a limiting factor, especially in low-lying areas with complex hydrography, such as Denmark. We collected thermal imagery using an unmanned airborne system (UAS) for three areas in Denmark shortly after major flooding events. We combined the thermal imagery with an airborne lidar-derived high-resolution digital surface model of the country to retrieve high-resolution (40 cm) SWE maps. The resulting SWE maps were compared with low-resolution SWE maps derived from satellite earth observation and with potential flooded areas derived from the high-resolution digital elevation model. We conclude that UASs have significant potential for SWE mapping at intermediate scales (up to a few square kilometers), can bridge the scale gap between ground observations and satellite EO, and can be used to benchmark and validate SWE mapping products derived from satellite EO as well as models predicting inundation. Full article

(This article belongs to the Special Issue River Flow Monitoring: Needs, Advances and Challenges)

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Review

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21 pages, 4621 KiB

Open AccessReview

Surface Velocity to Depth-Averaged Velocity—A Review of Methods to Estimate Alpha and Remaining Challenges

by Hamish Biggs, Graeme Smart, Martin Doyle, Niklas Eickelberg, Jochen Aberle, Mark Randall and Martin Detert

Water 2023, 15(21), 3711; https://doi.org/10.3390/w15213711 - 24 Oct 2023

Cited by 2 | Viewed by 1656

Abstract

The accuracy of discharge measurements derived from surface velocities are highly dependent on the accuracy of conversions from surface velocity

u_{s}

to depth-averaged velocity

U

. This conversion factor is typically known as the ‘velocity coefficient’, ‘velocity index’, ‘calibration factor’, ‘alpha coefficient’, [...] Read more.

The accuracy of discharge measurements derived from surface velocities are highly dependent on the accuracy of conversions from surface velocity

u_{s}

to depth-averaged velocity

U

. This conversion factor is typically known as the ‘velocity coefficient’, ‘velocity index’, ‘calibration factor’, ‘alpha coefficient’, or simply ‘alpha’, where

α = {U / u}_{s}

. At some field sites detailed in situ measurements can be made to calculate alpha, while in other situations (such as rapid response flood measurements) alpha must be estimated. This paper provides a review of existing methods for estimating alpha and presents a workflow for selecting the appropriate method, based on available data. Approaches to estimating alpha include: reference discharge and surface velocimetry measurements; extrapolated ADCP velocity profiles; log law profiles; power law profiles; site characteristics; and default assumed values. Additional methods for estimating alpha that require further development or validation are also described. This paper then summarises methods for accounting for spatial and temporal heterogeneity in alpha, such as ‘stage to alpha rating curves’, ‘site alpha vs. local alpha’, and ‘the divided channel method’. Remaining challenges for the accurate estimation of alpha are discussed, as well as future directions that will help to address these challenges. Although significant work remains to improve the estimation of alpha (notably to address surface wind effects and velocity dip), the methods covered in this paper could provide a substantial accuracy improvement over selecting the ‘default value’ of 0.857 for alpha for every discharge measurement. Full article

(This article belongs to the Special Issue River Flow Monitoring: Needs, Advances and Challenges)

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17 pages, 2916 KiB

Open AccessReview

Discharge Estimation over Piano Key Weirs: A Review of Recent Developments

by Raj Kumar Bhukya, Manish Pandey, Manousos Valyrakis and Panagiotis Michalis

Water 2022, 14(19), 3029; https://doi.org/10.3390/w14193029 - 26 Sep 2022

Cited by 9 | Viewed by 3424

Abstract

The piano key (PK) weir has advanced over the labyrinth weir to increase the discharge capacity. Piano key weirs exhibit nonlinear flow behavior and are easy to place on the existing spillway or newly constructed dam with less base area. Various investigators are given equations to calculate the discharge coefficient for free and submerged flow conditions. The study focuses on reviewing the impacts of the PK weir geometry on the weir flow discharge coefficient, including weir length and height, upstream and downstream key widths, and apex overhangs. In this study, all possible aspects of PK weirs were briefly reviewed. From sensitivity analysis, it is observed that the discharge coefficient of the PK weir is more sensitive for the L/W dimensionless ratio followed by the B/P ratio. L is total length of the weir crest, W is width of the weir, B is total width of PK weir and P is height of the weir. This review paper is intended to serve as an accessible resource for hydraulic structures researchers and hydraulic engineering professionals alike interested in the hydraulics of PK weirs. Full article

(This article belongs to the Special Issue River Flow Monitoring: Needs, Advances and Challenges)

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