HS1.2.2

EDI
Advances in river monitoring and modelling for a climate emergency: data-scarce environments, real-time approaches, inter-comparison of innovative and classical frameworks, uncertainties, harmonisation of methods and good practices

Water is our planet’s most vital resource, and the primary agent in some of the biggest hazards facing society and nature. Recent extreme heat and flood events are clear demonstrations of how our planet’s climate is changing, underlining the significance of water both as a threat and as an increasingly volatile resource.
The accurate and timely measurement of streamflow is therefore more critical than ever to enable the management of water for ecology, for people and industry, for flood risk management and for understanding changes to the hydrological regime. Despite this, effective monitoring networks remain scarce, under-resourced, and often under threat on a global scale. Even where they exist, observational networks are increasingly inadequate when faced with extreme conditions, and lack the precision and spatial coverage to fully represent crucial aspects of the hydrological cycle.

This session aims to tackle this problem by inviting presentations that demonstrate new and improved methods and approaches to streamflow monitoring, including:
1) Innovative methodologies for measuring/modelling/estimating river stream flows;
2) Real-time acquisition of hydrological variables;
3) Remote sensing and earth observation techniques for hydrological & morphological monitoring;
4) Measurement in extreme conditions associated with the changing climate;
5) Measurement of sudden-onset extreme flows associated with catastrophic events;
6) Strategies to quantify and describe hydro-morphological evolution of rivers;
7) New methods to cope with data-scarce environments;
8) Inter-comparison of innovative & classical models and approaches;
9) Evolution and refinement of existing methods;
10) Guidelines and standards for hydro-morphological streamflow monitoring;
11) Quantification of uncertainties;
12) Development of expert networks to advance methods.

Contributions are welcome with an emphasis on innovation, efficiency, operator safety, and meeting the growing challenges associated with the changing climate, and with natural and anthropogenically driven disasters such as dam failures and flash floods.

Additionally, presentations will be welcomed which explore options for greater collaboration in advancing riverflow methods and which link innovative research to operational monitoring.

Co-organized by GM5
Convener: Nick Everard | Co-conveners: Anette EltnerECSECS, Alexandre Hauet, Silvano F. Dal Sasso, Alonso Pizarro
Presentations
| Thu, 26 May, 15:10–18:23 (CEST)
 
Room 3.29/30

Presentations: Thu, 26 May | Room 3.29/30

Chairpersons: Nick Everard, Alexandre Hauet
15:10–15:15
15:15–15:22
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EGU22-2867
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ECS
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Presentation form not yet defined
Neeraj Sah, Wouter Buytaert, Jonathan D. Paul, Simon De Stercke, and Athanasios Paschalis

Long series of river discharge data are essential for developing improved river and water management strategies and for coping with water-related hazards such as floods. However, continuous direct measurement of river discharge is practically infeasible. Recently developed electromagnetic and ultrasonic methods can be used for automated (or direct) river discharge measurements; however, they are not widely used because they are expensive and are prone to damage during high flows.

At most gauging sites around the world, a rating curve is used to convert the measured stage into discharge. However, using rating curves is fraught with difficulties, including (a) hysteresis effect during unsteady flow, (b) extrapolation error during high flows, (c) need for regular updating due to change in hydraulic resistance and channel geometry. More recently, methods have been developed for dynamic river discharge estimation by solving governing equations of river flow i.e., shallow water equations (SWE). However, these methods (a) solve SWE in its conservative form, (b) are most suitable for prismatic channels with no lateral flow, (c) require one flow value, and (d) assume channel roughness or calibrate it by using observed stage data from two or three gauging locations. Although, stage data from two or three gauging locations are theoretically sufficient to calibrate channel roughness, in practice error margins are still high due to sub-optimal positioning of gauging stations, and coarse temporal resolution of existing measurement networks.

Therefore, motivated by a need to surmount the limitations in existing methods, we have developed a non-contact, robust, and cost-effective approach for dynamic river discharge estimation. We use an array of bespoke sensors to monitor the river stage at high resolutions and use these stage data to estimate river discharge. We present a methodology to calibrate a hydraulic model of a river reach by only using stage data from a network of such sensors. We use freely available HEC-RAS software as the solver for SWE. We have developed python scripts to control and automate HEC-RAS simulations and estimate river discharge dynamically.

How to cite: Sah, N., Buytaert, W., D. Paul, J., De Stercke, S., and Paschalis, A.: Non-contact, Low-cost Sensor Network for River Stage Monitoring and Dynamic Discharge Estimation , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2867, https://doi.org/10.5194/egusphere-egu22-2867, 2022.

15:22–15:29
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EGU22-13348
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Virtual presentation
Ida Westerberg, Valentin Mansanarez, Stephen Lyon, and Norris Lam

Climate change, together with other natural and anthropogenic drivers lead to changes in streamflow patterns that are now occurring with increasing frequency. At the same time traditional streamflow monitoring methods are time-consuming and costly so that it typically takes many years of significant field efforts to establish reliable streamflow data for a new location or for stations with major temporal changes to the stage—discharge relation. To provide timely and reliable streamflow data to tackle these changes to the hydrological regime and their impacts on society’s water management requires new cost-effective monitoring methods that can rapidly produce data with low uncertainty. Hydraulically modelled rating curves are a promising alternative to traditional power-law methods as they need much fewer calibration gaugings, but they are associated with additional uncertainty sources in the hydraulic knowledge and these need to be assessed.
We present the Rating curve Uncertainty estimation using Hydraulic Modelling (RUHM) framework which was developed to rapidly estimate rating curves and their uncertainty. The RUHM framework combines a one-dimensional hydraulic model with Bayesian inference to incorporate information from both hydraulic knowledge and the calibration gauging data. In this study we compare RUHM and the Bayesian power-law method BaRatin in application to a Swedish site using nine different gauging strategies associated with different costs. We compare results for the two methods in terms of accuracy, cost and time required for establishing rating curves. 
We found that rating curves with low uncertainty could be modelled with fewer gaugings for RUHM compared to BaRatin. As few as three gaugings were needed with RUHM if these gaugings covered low and medium flows, whereas high flow gaugings were not necessary. This makes the RUHM method both cost effective and time efficient as low and medium flows occur more frequently than high flows. When using all gaugings (i.e., a high-cost gauging strategy), the uncertainty for RUHM and BaRatin was similar. The results for this Swedish site show that hydraulic rating curve uncertainty estimation is a promising tool for quickly estimating rating curves and their uncertainties. Finally, we discuss the potential of using RUHM together with drone-derived data to make field efforts even more efficient.

How to cite: Westerberg, I., Mansanarez, V., Lyon, S., and Lam, N.: The RUHM framework for rapid rating curve uncertainty estimation: comparison to power-law methods and potential using drone-derived data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13348, https://doi.org/10.5194/egusphere-egu22-13348, 2022.

15:29–15:36
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EGU22-2218
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ECS
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Virtual presentation
Julia Zill, Christian Siebert, Tino Rödiger, Markus Weitere, and Ulf Mallast

The understanding of groundwater interactions with riverine systems is of utmost importance for ecosystem assessment and management. Diffuse groundwater born nutrients, such as N, P and C contribute significantly to an increase of algae growth in rivers and eventually in estuaries, leading to eutrophication with severe consequences for water quality and ecosystem health. Thus, knowledge of both location dynamics and temporal dynamics of diffuse groundwater discharge areas, as well as the discharging groundwater quantity are required.

Here we provide a multi-methodological approach to gain this information for a large river in Germany, i.e. the Elbe River. We applied complementary methods to a 450 km long stretch including: i) analysis of daily time series of hydraulic gradients between river- and groundwater levels, ii) a flux balance for river segments spanning between neighboring gauging stations, iii) inverse geochemical modeling of the river water composition for each segment, and iv) a Darcy approach as an additional tool based on the hydraulic conductivity of the upper aquifer. The results are manifold, including a spatiotemporal answer to the dynamics and orientation of groundwater interaction with the Elbe.

Groundwater inflow is variable but occurs along the entire river. Areas of high groundwater contribution are located in the upstream mountainous parts, where groundwater makes up to 11% of the total river flow. Further downstream, groundwater inflow decreases, while inversion of hydraulic gradients indicate an immense infiltration of river water into the river banks. Unexpectedly high input of groundwater-like fluids could be detected in the lowland, where geochemical modeling indicated a massive inflow of water in a magnitude of 10% of the total river flow. Given a missing surface and groundwater contribution, an unidentified but apparently large system of subsurface drainage ditches co-exists, which transports water to the Elbe River efficiently during and due to drought-related low flow conditions.

Gaining insight into such a large-scale setting with interfering surface water contributions, effluents of wastewater treatment plants, and diffuse groundwater in- and outflows was possible only by applying the combination of independent geochemical, hydraulic and balancing approaches. With a similar availability of river and well levels and the physical access to the latter, the presented multi-method approach may provide a blueprint for the assessment of other large river systems.

How to cite: Zill, J., Siebert, C., Rödiger, T., Weitere, M., and Mallast, U.: Revealing unexpected sources and quantities of groundwater discharge into major river systems during drought conditions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2218, https://doi.org/10.5194/egusphere-egu22-2218, 2022.

15:36–15:43
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EGU22-9619
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ECS
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Presentation form not yet defined
Christina Orieschnig, Gilles Belaud, Jean-Philippe Venot, and Sylvain Massuel

The annual monsoon inundations are vital in maintaining the fertility and productivity of the delta of the Mekong, Southeast Asia’s largest river. During the inundations, which traditionally last from July until November, nutrient-rich sediments are deposited on the floodplains, groundwater is recharged, and fish populations regenerate in the shallow waters. Consequently, local agriculture and fisheries are keyed to the timing of flood arrival and recession and reliant on overall flood duration. However, in recent years, the hydrological dynamics of the region have shifted. The Mekong’s hydrological regime has been impacted by shifts in land cover, the construction of hydropower infrastructure, and climate change. 

Yet the effects of these changes on the spatio-temporal patterns of inundations in the Mekong Delta remain largely unstudied, especially at local scales. Part of the reason for this is data sparsity: there is a lack of consistent long-term data on spatial inundation dynamics. No concerted in-situ monitoring efforts of flood extents existed until recently, while optical earth observation satellite missions such as Landsat often fail to provide data during the wet season due to cloud cover. Hydrological modelling approaches struggle with insufficiently precise elevation data - due to the flat topography of the Mekong Delta, even high-resolution Digital Elevation Models (DEMs) fail to capture small-scale dykes that determine whether large swaths of land become flooded. 

To cope with this data-scarce environment, we propose an innovative methodology harnessing recent satellite missions and long-term in-situ river water level measurements. This approach uses remote sensing data from the Sentinel-1 and 2 missions operated by the European Space Agency. Since 2017, these satellites provide optical and synthetic aperture radar (SAR) data at a spatial resolution of 10 m and a return frequency of 5-6 days. Furthermore, SAR provides data independent of cloud cover, which makes it particularly well-suited for operational flood monitoring purposes. After deriving inundation maps from available Sentinel images, we link these maps to water levels measured at a local hydrological station through a correlative approach to create a water-level flood link (WAFL). Using this link, we can describe the evolution of inundation patterns in the Mekong Delta since the 1990s. To quantify uncertainties, comparisons with historical inundation maps derived from available Landsat images,  and with a high- resolution DEM were carried out.  

The approach was tested in two study areas in the Cambodian Mekong Delta.  The results indicate that the accuracy of the WAFL for quantifying inundations on a per-pixel basis lies at 87%, reaching up to 93%. The spatio-temporal analysis shows that inundation incidence in the early wet season has declined by 21% since 1991 and that the average duration of inundations has decreased by 19 days. This illustrates that annual monsoon inundations have become an increasingly volatile resource, with significant impacts on agriculture, fisheries, and ecosystems. 

How to cite: Orieschnig, C., Belaud, G., Venot, J.-P., and Massuel, S.: Assessing long-term changes in annual monsoon inundations in the Mekong Delta (Cambodia): Testing an innovative approach linking remote sensing and in-situ measurements to overcome data scarcity , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9619, https://doi.org/10.5194/egusphere-egu22-9619, 2022.

15:43–15:50
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EGU22-10795
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On-site presentation
Gabriel Sentlinger

Non-contact and automated flow measurement in open channels is becoming more popular as techniques improve to measure surface velocity, reducing costs and risk to hydrographers.  However, these methods rely on estimates of bulk-to surface ratio estimates, as well as channel wetted area.  This study considers the accuracy and application of paired Up and Downstream Water Quality (WQ) measurements to estimate the Transit Time (TT) and average bulk velocity.  Combined with results from both the Automated Salt Dilution (AutoSalt) and Water Quality Mixing Model (WQMM) systems, we can calibrate the waterway for wetted area at a given water level, and hence estimate discharge from transit time velocity on a continuous basis using only temperature and conductivity insitu sensors.  This low-cost method can used to build or validate rating curves, measure peak and low flow events, and conduct cost-effective hydrological assessments over large regions for any size waterway,  to support climate change study and adaptation.  This method also has application to flood wave propagation and Initial Dilution Zone (IDZ) studies. Results from a large and a small waterway, along with uncertainty, is discussed.

How to cite: Sentlinger, G.: Water Quality Transit Time (WQTT) for Continuous Velocity/Discharge Measurement in Large and Small Waterways, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10795, https://doi.org/10.5194/egusphere-egu22-10795, 2022.

15:50–15:57
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EGU22-139
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ECS
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Virtual presentation
Gurpinder Singh and Rakesh Khosa

Streamflow measurement is essential in hydraulic engineering to develop and manage water resources and ensure they are managed correctly and adequately. Two primary parameters for discharge measurements in natural rivers, namely, the mean flow velocity and cross-sectional flow area at the measurement site, are requisites. The cross-sectional area of the section could be measured using river bathymetric surveys or by using advanced and modern methods such as Acoustic Doppler Current Profiler (ADCP). For mean velocity, numerous ways and tools are available depending on the fact, whether the measurements are taken from a distance (non-contact) or using a contact method (traditional approach). Nowadays, non-contact velocity measurement approaches are becoming more popular as they are less time-consuming and user‑friendly to deal with high flows and rough weather. In contrast, the entropy-based concepts (such as Shannon entropy, Tsallis entropy and Renyi entropy) are utilized to obtain the discharge from the non-contact measurements, which gives better results than the traditional approaches such as the velocity area method. Entropy-based velocity distribution depends on the crucial parameter called entropy parameter (a function of the mean and maximum velocity), which is linked to the channel characteristics such as channel roughness and bed slope. Due to a lack of concrete evidence regarding its variation with the channel characteristics, the entropy parameter was surmised as constant. In this study, the experimental velocity data was collected from a rectangular flume fitted with a mechanical apparatus to change the bed slope. The obtained velocity data was employed to comment on the actual variation of the Shannon entropy parameter for the one such channel characteristic, i.e., channel bed slope. The velocity data analysis depict only a slight variation in entropy parameter. In addition, the discharge error analysis provided a substantial justification for using a unique constant value of the entropy parameter for the whole cross-section can be utilized instead of individual values for each channel bed slope condition.

How to cite: Singh, G. and Khosa, R.: Effect of Channel Bed Slope on Shannon Entropy-Based Velocity Distribution in Open Channel Flow, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-139, https://doi.org/10.5194/egusphere-egu22-139, 2022.

15:57–16:04
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EGU22-9379
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On-site presentation
Mikael Lennermark and Alex Hauet

Since 2015, 13 agencies from all around the world (9 different countries in Europe, North and South America and Oceania) have been working together in an International Hydrometry Group, a loosely organized group of experts in instruments and methods for measuring discharge in rivers that meets virtually once a month to discuss scientific and technical issues relating to river flow measurements.

A main objective of the group is to lead the development and funding of an open-source software package, QRevInt, for postprocessing ADCP discharge measurements. The agencies participate in funding the software developer (Dave Mueller, Genesis HydroTech) or contribute scientific inputs. They define the annual development workplan and its funding, and monitor progress during monthly monitoring meetings.

In this presentation, the latest version of QRevInt is detailed and the main advantages of the software are explained, including processing of measurements from ADCP of different manufacturers (TRDI and SonTek) with the same calculation assumptions, objectification of the computation of unmeasured flow area (top, bottom, edges, invalid cells or ensembles), calculation of uncertainty and advanced graph options.

The workplan of the group for 2022 is presented, including the ongoing developments of QRevInt, and the new project for a software dedicated to mid- or mean-section ADCP measurement, QRevIntMS.

How to cite: Lennermark, M. and Hauet, A.: Developing a post-processing software for ADCP discharge measurement piloted by an international and inter-agency group: a unique, ambitious experience… and one that works!, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9379, https://doi.org/10.5194/egusphere-egu22-9379, 2022.

16:04–16:11
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EGU22-5037
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Presentation form not yet defined
Kristoffer Florvaag-Dybvik

Acoustic Doppler Current Profilers (ADCP) are used a lot all around the world to measure discharge in rivers. These instruments measure most of the vertical velocity profile in rivers, but due to technical and physical limitations they cannot measure all the way to the surface or all the way to the bottom. To calculate discharge, the instruments (or software) need to extrapolate data into the un-measured regions. Previously there was no good and available tools to aid the operators in selecting proper extrapolation. In 2010 USGS released the software Extrap, which plots relative velocity versus relative depth for ADCP measurements, and this tool made it way easier to determine the correct extrapolation of data. (Extrap is now a part of Qrev/QrevInt). Before the introduction of Extrap, 80-90% of the ADCP-measurements at NVE used the default power law extrapolation in the ADCP’s standard software (WinRiver at the time), and around 5% used constant at top and no-slip at the bottom. The first if these assumes a velocity profile that is very similar to the logarithmic velocity profile that comes from classical boundary layer theory. The latter one is much steeper (constant) close to the surface.

After starting to use Extrap regularly, 60% of the measurements use the constant/no-slip extrapolation, while 40 % uses the power law extrapolation. This impacts the reported discharge from the measurements by reducing the reported discharge by on average 4% for the measurements using constant/no-slip extrapolation, and data users must be aware, because these measurements eventually form the foundation for the long time, continuous data series for discharge in our archives.

How will a climate researcher react to a 4% decrease in annual run-off from Norway?

How to cite: Florvaag-Dybvik, K.: Climate change or just new software? The impact of Extrap software on ADCP discharge measurements in Norway, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5037, https://doi.org/10.5194/egusphere-egu22-5037, 2022.

16:11–16:18
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EGU22-4435
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ECS
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On-site presentation
Jana von Freyberg, Izabela Bujak, Andrea Rinaldo, and Ilja van Meerveld

Stream networks are important flow pathways along which water transports solutes, sediments and affects living communities. Field observations in headwater catchments have shown that the networks of actively flowing channels are not static, but rather expand and contract over time, depending on the intensity and timing of hydro-climatic forcing. Until now, however, flowing stream networks (FSNs) have been mapped only sporadically and environmental tracer data to explore the varying stream-landscape connectivity are lacking. Thus, little is known about how and why these networks change and what the implications are for streamflow, water quality and biodiversity. 

To gain detailed insights into the mechanistic links between FSNs and catchment hydrological processes, we investigated two 4-ha head watersheds in the Alptal valley in central Switzerland. We deployed a wireless sensor network in the field to obtain spatially distributed continuous data of flow occurrence. In addition, we conducted multiple mapping surveys using a self-developed mobile phone application. Our data show that the total flowing stream length increased rapidly by more than a factor of 3 during individual rainfall events. This suggests that different water stores become dynamically connected to the stream network and disconnect again during subsequent dry periods. We test this hypothesis by linking short-term changes in FSN length to variations in subsurface water storage and water chemistry. The results help to broaden our understanding of flow intermittency in pre-Alpine headwater catchments, and thus aids in developing effective strategies to protect ecosystems dependent on temporary flow conditions.

How to cite: von Freyberg, J., Bujak, I., Rinaldo, A., and van Meerveld, I.: Monitoring changes in temporary stream networks during rainfall events, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4435, https://doi.org/10.5194/egusphere-egu22-4435, 2022.

16:18–16:25
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EGU22-7517
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ECS
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Virtual presentation
Clemens Hiller, Lukas Walter, Kay Helfricht, Klemens Weisleitner, and Stefan Achleitner

High mountain environments have shown substantial geomorphological changes forced by rising temperatures in recent decades. As such, paraglacial transition zones in catchments with rapidly retreating glaciers and abundant sediments are key elements in high alpine river systems and promise to be revealing, yet challenging, areas of investigation for the quantification of current and future sediment transport. In this study, we explore the potential of semi-automatic image analysis to detect the extent of the inundation area and corresponding inundation frequency in a proglacial outwash plain (Jamtal valley, Austria) from terrestrial time-lapse imagery. We cumulated all available records of the inundated area from 2018-2020 and analysed the spatial and temporal patterns of flood flows. The approach presented here allows semi-automated monitoring of fundamental hydrological/hydraulic processes in an environment of scarce data. The pixel classification based on greyscale values from oblique hourly recordings returned plausible results of the spatial and temporal variability of surface runoff in the investigated glacier forefield. The image sets, processed in ImageJ, allowed geo-rectification to produce inundation frequency maps. Meteorological and discharge data from downstream measuring stations was consulted to interpret our findings. Runoff events and their intensity were quantified and attributed to either pronounced ablation, heavy precipitation, or a combination of both. We also detected an increasing degree of channel concentration within the observation period. The maximum inundation from one event alone took up 35% of the analysed area. About 10% of the observed area presented inundation in 60-70% of the analysed images. In contrast, 60-70% of the observed area was inundated in fewer than 10% of the analysed period. Despite some limitations in terms of image classification, prevailing weather conditions and illumination, the derived inundation frequency maps provide novel insights into the evolution of the proglacial channel network.

How to cite: Hiller, C., Walter, L., Helfricht, K., Weisleitner, K., and Achleitner, S.: Flood flow in a proglacial outwash plain - quantifying spatial extent and frequency of inundation from time-lapse imagery, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7517, https://doi.org/10.5194/egusphere-egu22-7517, 2022.

16:25–16:32
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EGU22-7229
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ECS
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On-site presentation
Rodolfo Roseto, Domenico Capolongo, and Pierfrancesco Dellino

A lot of different methods are used to estimate flood risk worldwide. The method that performs better depends on the catchment features and dimension, data time resolution and availability and uncertainty level required. Remote sensing approaches are more and more common, but because of the limited periods covered by time series derived by this new methodology, in-situ data integration is still required. A new methodology is proposed, based on a case-study of different reaches of Basento river, Basilicata (Southern Italy). Starting from hourly rainfall time series (covering not less than 20 years), for each pluviometric station taken into account into the catchment area, Intensity-Duration-Frequency (IDF) curves are computed (fitting a power law), in order to calculate the rainfall maximum at a certain percentile (typically 90° or 95° percentile are used) during the concentration time. Thiessen polygon method is used to divide the catchment area into smaller areas, each one corresponding to a pluviometric station, with the purpose of calculating weighted  rainfall values for each station area. A Digital Terrain Model is used to extract multiple cross sections of the river-bed, spanning different morphologies, from braided to meandering channels. For each cross section, starting from bankful level, it is possible to estimate diverse hydraulic parameters such as river stage, hydraulic radius, section’s surface area (using image analysis) and the mean velocity of the current, using the logarithmic law profile of the turbulent flow. Sediment size analysis is carried out as to estimate the river bed roughness for each cross section. The mean velocity value V can be used to estimate the concentration time t=L/V, where L is equal to the distance between the cross section and the hydraulically further point into the catchment area. The concentration time value t is used into the equation of the IDF curves, in order to link the corresponding rainfall height to the river stage reached at the cross section, eventually to estimate the rainfall value that, if exceeded, can cause flood. A FLO-2D model has been then used to run simulations with the aim to detect flood-prone areas, finding an overall good matching between the values of current mean velocity, discharge and river stage estimated in the cross sections.

How to cite: Roseto, R., Capolongo, D., and Dellino, P.: A new approach for flood risk estimation integrating remote sensing and in-situ data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7229, https://doi.org/10.5194/egusphere-egu22-7229, 2022.

Coffee break
Chairpersons: Nick Everard, Anette Eltner
17:00–17:03
17:03–17:10
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EGU22-3225
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ECS
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On-site presentation
Xabier Blanch, Franz Wagner, Ralf Hedel, Jens Grundmann, and Anette Eltner

The handling of natural disasters, especially heavy rainfall and corresponding floods, requires special demands on emergency services. The need to obtain a quick, efficient and real-time estimation of the water level is critical for monitoring a flood event. This is a challenging task and usually requires specially prepared river sections. In addition, in heavy flood events, some classical observation methods may be compromised.

With the technological advances derived from image-based observation methods and segmentation algorithms based on neural networks (NN), it is possible to generate real-time, low-cost monitoring systems. This new approach makes it possible to densify the observation network, improving flood warning and management. In addition, images can be obtained by remotely positioned cameras, preventing data loss during a major event.

The workflow we have developed for real-time monitoring consists of the integration of 3 different techniques. The first step consists of a topographic survey using Structure from Motion (SfM) strategies. In this stage, images of the area of interest are obtained using both terrestrial cameras and UAV images. The survey is completed by obtaining ground control point coordinates with multi-band GNSS equipment. The result is a 3D SfM model georeferenced to centimetre accuracy that allows us to reconstruct not only the river environment but also the riverbed.

The second step consists of segmenting the images obtained with a surveillance camera installed ad hoc to monitor the river. This segmentation is achieved with the use of convolutional neural networks (CNN). The aim is to automatically segment the time-lapse images obtained every 15 minutes. We have carried out this research by testing different CNN to choose the most suitable structure for river segmentation, adapted to each study area and at each time of the day (day and night).

The third step is based on the integration between the automatically segmented images and the 3D model acquired. The CNN-segmented river boundary is projected into the 3D SfM model to obtain a metric result of the water level based on the point of the 3D model closest to the image ray.

The possibility of automating the segmentation and reprojection in the 3D model will allow the generation of a robust centimetre-accurate workflow, capable of estimating the water level in near real time both day and night. This strategy represents the basis for a better understanding of river flooding and for the development of early warning systems.

How to cite: Blanch, X., Wagner, F., Hedel, R., Grundmann, J., and Eltner, A.: Towards automatic real-time water level estimation using surveillance cameras, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3225, https://doi.org/10.5194/egusphere-egu22-3225, 2022.

17:10–17:17
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EGU22-7071
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Virtual presentation
Davide Mancini, Matteo Roncoroni, Gilles Antoniazza, Boris Ouvry, and Stuart Nicholas Lane

The quantification of river bathymetry and its change through time is a primary challenge in fluvial geomorphology. Whilst there has been a very rapid development of methods for measuring exposed river morphology, inundated zones remain a problem. The development of cheap UAV platforms and SfM-MVS photogrammetry have been particularly important as these allow low cost, high resolution, and repeat surveys. Researches have now shown that provided that there is a signal of water depth then it is also possible to map inundated areas by adopting, for example, two media refraction correction if there is sufficient bed texture in the imagery. The main problem arises, however, when the water is so turbid that the river bed is not visible in imagery. This is the case for braided rivers in proglacial margins where high rates of glacial erosion create high suspended sediment concentrations and also morphodynamically active braided rivers. In this paper we test a new and simple hypothesis to predict water depth distribution based upon heuristic reasoning: that our experience of braided river environments allows us to make a series of qualitative statements about where water will be deeper and where it will be shallower; and that if we can quantify them, we can model the water depths associated with inundated zones.

The simplest statement is that water depth increases with distance away from the nearest river bank; and it is likely to do so more rapidly when the total wetted width is lower. A more rapid increase is also likely on the outer bank of curved sections; and conversely, a slower increase is likely on the inner bank. In a braided river, streamline convergence is likely to lead to deeper water; streamline divergence is likely to lead to shallow water. On this basis, we ought to be able to model water depths in a shallow braided river on the basis of: (1) distance from the nearest bank; (2) local channel width; (3) total inundated width (given a braided river is multi-channel); (4) local curvature magnitude and direction; and (5) planform streamline convergence/divergence. We measure these parameters for a shallow braided proglacial stream (Glacier d’Otemma, south-western Swiss Alps) with high suspended sediment concentrations. Over the summers of 2020 and 2021 we acquired high resolution UAV-based imagery, as well as spatially distributed GPS data of water depths. We used resultant ortho-imagery to extract these parameters and to calibrate predictive models of water-depth based upon multivariate statistical modelling. The independent validation data suggest that between 50% and 75% of the variance in water depths can be reconstructed and confidence in estimated depths are of the order of +/- 0.10m. Finally, we integrate these water depths and their uncertainty into elevation data derived using SfM-MVS photogrammetry for the exposed areas to produce digital elevation models with spatially dependent uncertainty. Comparison of these DEMs shows that they can be used to visualize quantitative geomorphological changes and that the associated uncertainties in volume of change estimates are sufficiently low to be used in sediment budget studies.

How to cite: Mancini, D., Roncoroni, M., Antoniazza, G., Ouvry, B., and Lane, S. N.: Heuristic measurement of river bathymetry in proglacial braided streams using SfM-MVS photogrammetry and statistical approaches, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7071, https://doi.org/10.5194/egusphere-egu22-7071, 2022.

17:17–17:24
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EGU22-3049
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ECS
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Presentation form not yet defined
Leonardo Zandonadi Moura, Rui Ferreira, and Rui Aleixo

Image-based monitoring of rivers is a growing field of research and is being popularized as a technical alternative for discharge, erosion and flood risk estimation applications. Surface velocimetry can also be a way to characterize the turbulence structure of shallow flows, making possible the remote determination of quantities of interest such as dissipation and integral length scales. To evaluate velocimetry methods and data processing workflows, a laboratory facility emulating a river reach was assembled at IST, and monitored using commercial grade cameras, in field-like conditions. In this work the results of estimates of turbulent dissipation and integral length scales using multiple methods are provided, along with a discussion on the differences among methods and possible applications of the derived data in hydrodynamic model parameter calibration and data assimilation. LSPIV and PTV display similar results with regard to velocity estimation and vortex detection. In the estimation of integral lengths, the longitudinal scales are most affected by limitations in the measurement setup, whereas for the dissipation and turbulent viscosity estimates, spectrum methods seem to be less reliable than simpler methods based on dimensional analysis and integral length scales.

How to cite: Zandonadi Moura, L., Ferreira, R., and Aleixo, R.: Turbulence Metrics from Surface Image Velocimetry, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3049, https://doi.org/10.5194/egusphere-egu22-3049, 2022.

17:24–17:31
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EGU22-13487
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Virtual presentation
Erika Johnson

A remote method of measuring surface and near-surface currents in wavy riverine environments
at high spatial and temporal resolution is presented. A two-dimensional power spectral density
technique (2D PSD), which is based on calculating the cross-spectrum between two images is
developed and compared with the established 3D PSD technique. In contrast to the 3D PSD
technique, the 2D PSD algorithm is capable of determining velocity time series and spectra,
thereby facilitating remote measurements of turbulence. Moreover, the 2D PSD algorithm can
accurately determine near-surface flows from fewer images. Results are presented from imagery
collected from an unmanned aerial vehicle and satellite imagery from a number of different
riverine locations.

How to cite: Johnson, E.: Measuring Instantaneous Velocity Fields Remotely using a Two-Dimensional Power Spectral Density Technique, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13487, https://doi.org/10.5194/egusphere-egu22-13487, 2022.

17:31–17:38
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EGU22-5967
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ECS
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Presentation form not yet defined
André Kutscher, Jens Grundmann, Anette Eltner, Xabier Blanch, and Ralf Hedel

The importance of optical measurement methods in hydrology is increasing in the last years. In contrast to conventional gauging techniques, they can be applied remotely, making the measurement safe for humans and equipment, even under difficult measurement conditions. One important hydrological parameter to measure is discharge. Deriving discharge with remote sensing can be done by applying particle tracking velocimetry (PTV) in combination with the velocity area method (VAM). VAM is a standardized and established method in hydrology. For reliable discharge results with the VAM, surface flow velocity measurements and thus trackable particles in the case of PTV usage are required across the entire width of the river cross section, which is not always the case in natural observation conditions. To fill these data gaps several statistical methods were investigated that incorporate information provided at different measurement times but with similar discharge conditions.

In this study, data were collected over longer time periods with different cameras at a gauging station of a medium scale river in Saxony, Germany. Stationary cameras recorded short videos, which are used to estimate the velocity distribution at the water surface using PTV incorporated in the FlowVelo tool (Eltner, 2020), and afterwards, to estimate the discharge using VAM. The obtained discharge time series from different cameras and camera positions were used to analyse the performance of different gap filling approaches. The results were compared to discharge and water level measurements of the official gauging station maintained by the federal measuring agency. They show, that the adjustment to the data of the reference measurements increases significantly by application of the gap filling methods. Next steps are to enhance the presented methods by using targeted data filtering and deep learning.

Keywords: velocity area method, particle tracking velocimetry, camera based discharge estimation

How to cite: Kutscher, A., Grundmann, J., Eltner, A., Blanch, X., and Hedel, R.: Determination of continuous discharge time series based on the optical Particle Tracking Velocity (PTV), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5967, https://doi.org/10.5194/egusphere-egu22-5967, 2022.

17:38–17:45
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EGU22-10797
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Virtual presentation
Alejandro Dussaillant, Nelson Sepúlveda, Felipe Aguilar, Johnny Valencia, Joel Ancan, Jaime Cotroneo, Rodrigo Herrera, Nikky Leiva, Carolina Peña, Alejandro Alfaro, Javier Fernández, and Antonio Muñoz

Debris flows and flash floods occur frequently in Chile due to geology, geomorphology and weather, costing human lives and impacting settlements, infrastructure and economic activities. One of the problems relates to the lack of adequate monitoring technology in remote areas with limited connectivity. We have developed a low cost system that processes acquired lidar and image data in-situ with a Raspberry Pi obtaining flow level and velocity and transmits near real time via satellite (or cellular network if available). The low implementation cost allows to replicate the system in the many hazardous sites, as well as advance towards early warning systems in locations with limited communication networks. The velocimetry method consists of two steps: first obtaining the images, and then a brightness filter and normalized cross correlation. To eliminate outliers a flow direction filter is used, and velocities are obtained by tracking of flow surface elements. Also the flow level is measured with a lidar also connected to the R-Pi. We will present both laboratory and field test results.

How to cite: Dussaillant, A., Sepúlveda, N., Aguilar, F., Valencia, J., Ancan, J., Cotroneo, J., Herrera, R., Leiva, N., Peña, C., Alfaro, A., Fernández, J., and Muñoz, A.: In-Situ, Near Real Time and Low Cost Image Velocimetry for Debris Flows and Flash Flood Monitoring in the Chilean Andes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10797, https://doi.org/10.5194/egusphere-egu22-10797, 2022.

17:45–17:52
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EGU22-4457
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ECS
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Virtual presentation
Guillaume Bodart, Jérôme Le Coz, Magali Jodeau, and Alexandre Hauet

The operator effect is a prominent error source in image-based velocimetry methods. Video sampling, ortho-rectification parameters, motion analysis parameters and filters can strongly impact velocity and discharge measurements. This has been reported in the literature (e.g. Detert, 2021) and highlighted by the Video Globe Challenge 2020, a video gauging intercomparison (Le Coz et al., 2021). The parameter choices made by the operator must be assisted to contain errors and to make image analysis methods accessible to non-specialists.

An investigation of the operator effect (or parameter effect) in various situations is proposed. The analysis focuses on the LSPIV measurements carried out during the Video Globe Challenge 2020. This contest involved around 15 participants with varying levels of experience, challenged over 8 videos. All the LSPIV measurements were replayed based on the data submitted by the participants. The objective was to identify the most sensitive parameter(s) for each video, based on an extensive analysis of the replayed velocity and discharge results.

The data retrieved were: video sampling rate, number of frames, ortho-rectification resolution, IA and SA sizes, correlation based and vector based filters, surface velocity coefficient (a.k.a. alpha) and transect interpolation parameters. To ensure valuable comparisons, grid points and video sequencing were fixed the same for all the participants. Replaying LSPIV measurements allowed to play with the parameters methodically and to quantify their impact on the measured discharge deviation from the reference.

Several lessons were learned from these analyses thanks to the variety of conditions offered by the 8 videos. A tendency to under-estimate the discharge in case of inappropriate parameters was observed. The influence of the video sampling rate has been noticed in many cases. It turns out to have more impact than the motion analysis parameters. The dataset was used to evaluate the benefit of automated parameters setting tools, e.g. ensemble correlation, automated time-interval, automated video sequencing.

 

Detert, M. (2021). How to avoid and correct biased riverine surface image velocimetry. Water Resources Research, 57, e2020WR027833. https://doi.org/10.1029/2020WR027833

 

Le Coz, J., Hauet, A., and Despax, A. (2021). The Video Globe Challenge 2020, a video streamgauging race during the Covid-19 lockdown, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2116, https://doi.org/10.5194/egusphere-egu21-2116, 2021

How to cite: Bodart, G., Le Coz, J., Jodeau, M., and Hauet, A.: Quantifying the operator effect in LSPIV image-based velocity and discharge measurements, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4457, https://doi.org/10.5194/egusphere-egu22-4457, 2022.

17:52–17:59
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EGU22-6198
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On-site presentation
Salvador Peña-Haro, Beat Lüthi, Rudolf Düster, Issa Hansen, Kai Vogel, Mohammed Gad, and Mohammed Magdy

Monitoring volumetric flow in arid and semi-arid regions is a major challenge due to their harsh and continuously changing environment (e.g. extreme temperature, severe sand storms). In these regions, hydrological events such as rainfall storms and flash flood events occur intermittently, time between major events may take years. Drainage water courses in these areas are often referred as Wadis, which are ephemeral drainage courses. Wadis are normally dry except after a rain event, often resulting in flash floods events with flood peak values occurring in the first few minutes of the event.

Monitoring the volumetric flow under these environments requires robust devices which record continuously at relatively short recording intervals, e.g. minutes or less, to be able to capture the steep ramp of the flood peak.

On April 2021 a DischargeKeeper, an image-based system for flow monitoring, with a PTZ camera was installed at the Wadi Naqab located in northern United Arab Emirates. The Wadi is approximately 50m wide and has been dry for most of the time. One event occurred at the beginning of January 2022, reaching a peak discharge of 78 m3/s just 15min after water started flowing. In this session we will show the system, its challenges and the results of the event.

How to cite: Peña-Haro, S., Lüthi, B., Düster, R., Hansen, I., Vogel, K., Gad, M., and Magdy, M.: Non-contact volumetric flow monitoring in a semi-arid regions’ Wadi, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6198, https://doi.org/10.5194/egusphere-egu22-6198, 2022.

17:59–18:06
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EGU22-11030
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ECS
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Virtual presentation
Nicholas Hutley, Daniel Wagenaar, Ryan Beecroft, Josh Soutar, Lee Pimble, Blake Edwards, Alistair Grinham, and Simon Albert

The gauging of open channel flows in waterways provides the foundation to monitor, understand and manage the water resources of our built and natural environment. Several methods are available for measuring the flow, with each of these methods having its own advantages and limitations. For a significant economic and environmental cost, hydraulic control structures can be built to measure the flow using analytical relationships with water height often by measuring the pressure head invasively in the water. Another common approach using the proxy measurement of water height without a hydraulic control structure is the expensive development and maintenance of a discharge rating table relating the measured water height to an estimated flow which has been manually measured at a previous time by acoustic instruments with technically proficient operators. Whilst these approaches are typically able to reasonably estimate flow within their measurement range, the safety risks in monitoring high flow events and the ongoing costs involved are prohibitive to increasing the spatial coverage of these approaches. As water resources become increasingly vulnerable to climate variability, modification of waterways, and increased extraction, there is a critical need to develop monitoring tools that can be flexible, cost-effective, and safe.

Much research has been undertaken into optical non-contact methods to estimate flow in waterways by measuring surface velocities without intrusive instruments or structures. However, to date, these surface velocimetry methods are limited to a narrow operational window of certain stream types and flow velocities due to inherent challenging optical variability in stream environments. A cost-effective stereographic camera-based stream gauging device has been developed for rapid stream gauging through the remote sensing of water height and stream velocities to estimate flows and employ the learning of an adaptive discharge rating envelope. The device includes embedded edge computing capabilities, local app connectivity for setup, and online cloud fleet management with a data dashboard for streamlined deployment and ongoing operational monitoring. Automated analysis is performed reconstructing the point cloud of the scene in front of the camera out to 40 m in order to estimate the water level without any instream equipment. An optical flow algorithm is passed over the short videos collected, generating an array of net motion in the scene which is projected out of the image plane onto the assumed water surface plane using the water level estimation combined with the accelerometer and the embedded intrinsic camera properties. The optically measured motions which are out of the plane of the waterway surface are then able to be automatically filtered and integrated into a water level indexed learning surface velocity distribution which generates an updating adaptive discharge rating envelope for the site. With over 100,000 videos recorded and analysed across 20 sites, the computer vision stream gauging approach has achieved discharge measurements within 15% RMSE of traditional acoustic gauging. This work evaluates this innovative approach across sites on the east coast of Australia and demonstrates the potential to improve the operational reliability and performance of surface velocimetry stream gauging.

How to cite: Hutley, N., Wagenaar, D., Beecroft, R., Soutar, J., Pimble, L., Edwards, B., Grinham, A., and Albert, S.: A stereo computer vision approach to automated stream gauging, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11030, https://doi.org/10.5194/egusphere-egu22-11030, 2022.

18:06–18:16
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EGU22-11627
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solicited
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Highlight
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Presentation form not yet defined
Hessel Winsemius, Frank Annor, Rick Hagenaars, Willem Luxemburg, Gijs Van den Munckhof, Paul Heeskens, and Nick Van de Giesen

OpenRiverCam is a fully open-source, user-friendly, low cost and sustainable web-software stack with API to establish and maintain river rating curves (relationships between geometry and river discharge) in small to medium sized streams based on Large Scale Particle Image Velocimetry (LSPIV). The software is co-designed with practitioners from The Netherlands (Waterboard Limburg and KNMI) and Tanzania (Wami - Ruvu Basin Authority and TMA) with the principle that organizations should be able to establish and maintain operational flow monitoring sites and networks at low costs. A user only requires to establish a temporary or permanent camera site; a simple field survey to measure river cross sections and several control points; and feeding operational videos into the dashboard of the software.

In July 2021, a severe flood event hit several Western European countries including parts of Germany, France, The Netherlands and Belgium. Also the Geul river, a tributary to the Meuse river was severely affected. One of our camera setups was operational at the Geul, near the village Hommerich during the event. The camera recorded 10 second videos every 15 minutes. Through the recordings of this single event, we were able to reconstruct flows and prepare a large number of rating points over a wide diversity of flow domains within a period of less than 12 hours. In this presentation we will share the results of our analysis, and validation against formal flow observations of the Waterboard Limburg. We plan to extend the software with improved pre-processing and allow use of less precise smart phone videos.

How to cite: Winsemius, H., Annor, F., Hagenaars, R., Luxemburg, W., Van den Munckhof, G., Heeskens, P., and Van de Giesen, N.: Capturing the Europe July 2021 flood event flows with an IP camera and OpenRiverCam, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11627, https://doi.org/10.5194/egusphere-egu22-11627, 2022.

18:16–18:23
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EGU22-10350
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ECS
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Virtual presentation
Seth Schweitzer and Edwin Cowen

The changing climate and corresponding increased variability in weather events globally have made clear the need for accurate measurements of streamflow, and the ability to respond quickly to conditions as they occur.

We present Infrared Quantitative Image Velocimetry (IR-QIV), a nearfield remote sensing method that uses infrared imagery of the surface of a river or other body of water to accurately calculate the surface flow field at high resolution in space (~10cm resolution) and time (>1Hz), accurately and continuously, over large areas (1,000s of m^2), for extended periods of time.

IR-QIV is similar to LSPIV (Large Scale Particle Image Velocimetry) and other image-based velocity measurement methods, however, it does not require any illumination or tracer particles since it uses thermal infrared images. IR-QIV has the advantages of being able to measure instantaneous velocity, in addition to mean velocity, and hence makes it possible to calculate metrics of turbulence, from which additional hydrodynamic properties of the flow can be found, including estimates of local bathymetry and bed stress, which allow estimation of discharge from a single, non-contact, measurement.

Since IR-QIV can be used to measure a wide range of flows, can operate day or night and in most weather conditions, and can continuously and robustly measure at high spatial resolution over large areas, it is particularly of use where high accuracy and resolution measurements are required, such as for fish management applications, near hydraulic structures or at other locations with complex hydrodynamics, or at locations where physical access to the water is restricted or dangerous. Because measurements can be set up relatively quickly and without requiring contact with the water, we expect IR-QIV to increasingly become an important tool in responding to changing environmental conditions.

IR-QIV was developed in a partnership between Cornell University, the California Department of Water Resources (DWR), and the US Geological Survey (USGS) for applications including monitoring flow and discharge, and high resolution hydrodynamic measurements near fish guidance structures and barriers. In this presentation we will present an overview of the method, and discuss its capabilities and applications, including considerations that are relevant for any image-based velocity measurement methods, regardless of the imaged wavelengths (thermal, or visible-light).

Figure 1. IR-QIV example: Velocity calculated by IR-QIV (black arrows), plotted over an infrared image of the water surface at Sutter Slough, Califiornia, USA, superimposed on an aerial image.  From: Schweitzer, S. A., & Cowen, E. A. (2021). Instantaneous river-wide water surface velocity field measurements at centimeter scales using infrared quantitative image velocimetryWater Resources Research57, e2020WR029279. https://doi.org/10.1029/2020WR029279

How to cite: Schweitzer, S. and Cowen, E.: Infrared Quantitative Image Velocimetry (IR-QIV): Instantaneous River-Wide Water Surface Velocity  Measurements at Centimeter Scales, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10350, https://doi.org/10.5194/egusphere-egu22-10350, 2022.