We present the development of the River Platform for Monitoring Erosion (RIPLE) designed for monitoring at high temporal
frequency (
Sediment transport has an impact on the ecological status of water bodies, the morphological dynamics of the river, the stability of river banks and structures, and many human activities such as energy production and drinking water supply (Renwick et al., 2005; Lee and Foster, 2013). Two modes of transport are classically considered: bedload and suspended load (Julien, 1995). Bedload consists of coarse particles transported by sliding, rolling or saltation on the bottom of the river. Suspended load refers to the transport of fine particles through the turbulent field within the water column. In this study, suspended load applies for particles finer than 2 mm. The river sediment material is generally a mixture of coarse and fine particles that can interact together in relation to hydrology (the succession of floods and low flows), vegetation and human activities (material extraction, riverbed modification, presence of dams and weirs) (Corenblit et al., 2007). The transfer of coarse and fine particles downstream can lead to progressive siltation of structures, such as hydroelectric power plants (Morris and Fan, 1998; Walling and Fang, 2003), or to degradation of river quality by clogging the bottom or by direct attack on the respiratory organs of fish species (Owens et al., 2005). Alternatively, high-energy rivers with insufficient supply of sediment load can lead to erosion of the main channel (Frings et al., 2014). Suspended sediments are also a privileged vector for the transport of nutrients (C, N, P) or contaminants (pesticides, metals, organic products, microorganisms). Finally, the increase in turbidity leads to significant costs for water treatment when it is used as drinking water (Sobieszczyk, 2007). Overall, there is a need for systems that are able to monitor water and solid discharges in rivers, and these systems should take into account both fine and coarse sediments.
High-frequency monitoring of water and solid discharges is also a key element for the scientific community interested in the functioning of the critical zone (earth's near surface) in response to global changes (climatic change, land use and land cover changes). The understanding of the critical zone requires a holistic approach involving the monitoring of a combination of variables such as hydrological, chemical and biological variables (Brantley et al., 2016; Gaillardet et al., 2018) in different compartments of the catchment, i.e. soil, groundwater, river, surface–atmosphere interface and vegetation. One of the main issues for the people working on the critical zone is the determination of mass balance of water and associated matter and energy balances. The fluxes exported through the river system are of primary importance as the river collects a large part of the surface and subsurface flows within the catchment. There has been a recent attempt by the French critical zone community (Gaillardet et al., 2018) to develop a list of variables to be monitored continuously in each of the compartments of the critical zone. For rivers, this list includes variables such as flow discharge, electrical conductivity, temperature, turbidity, suspended sediment concentration (SSC), and chemical and isotopic composition of the water.
Estimates of suspended sediment flux are usually obtained by multiplying the
water discharge by the SSC, typically expressed in grams per litre of the mixture of
water and suspended sediment. High-frequency SSC monitoring is required for
reliable estimates of the suspended sediment flux. Nevertheless, a reliable
and easy method to obtain a direct, continuous SSC measurement is not
currently available. Alternatively, a proxy of the SSC, which can be easily
monitored continuously and related to SSC, is employed. The most commonly
used proxy to date is turbidity (Gray and Gartner, 2009; Rasmussen et al.,
2009; Navratil et al., 2011). In turbulent rivers, it is assumed that the
SSC is relatively homogeneous within the cross section. Therefore, a point
measurement of turbidity from the river bank is acceptable. This is easily
the case in rivers where the slope of the bed is greater than 0.1 % (Mano,
2008; Navratil et al., 2011) and for silt-sized particles or finer. However,
this assumption is questionable for sand-sized particles (Camenen et al.,
2019), and there is no reference method for sand-sized particles to date. The
turbidity–SSC rating curve is established from direct measurements of SSC
that are performed through automatic water sampling. Samples collected at
regular intervals or when thresholds are exceeded (e.g. water level,
turbidity) are manually retrieved during site visits and brought to the
laboratory. They are then analysed to measure the SSC using the filtration
method (for low SSC, typically < 2 g L
High-frequency bedload sampling is required for reliable estimates of the bedload flux since bedload transport is a very dynamic process and can even be discontinuous through the occurrence of intermittent pulses (Aigner et al., 2017). However, bedload samplings are very challenging to perform continuously over long periods. Alternatively, hydraulic-based equations are mostly used to compute bedload sediment budget (e.g. Recking et al., 2012), but their reliability is sometimes questionable (Gray and Simões, 2008). In the last decades, several proxies were used to get a continuous monitoring of bedload processes with surrogate methods (Gray et al., 2010). Among these methods, three of them do not need the installation of heavy, dedicated structures in the stream flow: (i) acoustic Doppler current profiler (aDcp), (ii) passive acoustic monitoring, and (iii) seismic monitoring. The aDcp can be used to measure an apparent bedload velocity which is related to bedload fluxes (Rennie et al., 2002, 2017). However, the deployment of an aDcp operating from the surface is not appropriate in the case of rivers with steep slope (typically > 1 %), because the presence of waves at the surface of the water hinders the measurement, and floating objects transported during floods may damage the device. Passive acoustic and seismic monitoring record the noises that are naturally generated by bedload transport when impacting the riverbed (Thorne and Foden, 1988; Tsai et al., 2012). Passive acoustic monitoring is achieved with hydrophones that sense the acoustic waves propagating under water. The acoustic power of bedload sounds has been related to bedload fluxes by using site-specific calibration curves in gravel-bed rivers (Marineau et al., 2016; Geay et al., 2017a). Seismic monitoring is achieved with geophones or seismometers which record the seismic waves propagating in the soil layer (surface waves). Similarly, the power of bedload seismic noises has been related to bedload fluxes in laboratory experiments (Gimbert et al., 2018) and partially in field experiments (Roth et al., 2016). Acoustic and seismic monitoring provide continuous proxies that are related to bedload fluxes, but these proxies are also dependent on additional bedload parameters (i.e. grain size distribution, bedload kinematics) and on processes related to wave propagation (Geay et al., 2017b). To date, whatever the indirect method used, and calibration efforts with direct bedload samplings are needed to elaborate rating curves and to finally provide a continuous monitoring of bedload fluxes.
Knowledge of water discharge is essential whether it is for estimating suspended sediment fluxes, dissolved matter fluxes, or nutrient or contaminant fluxes associated with fine particles. The monitoring of water discharge is not easy, especially in mountainous rivers where flow discharge can vary by several orders of magnitude in a few hours (Borga et al., 2014) and solid discharges can demonstrate a hysteretic and transitory behaviour, more impulsive than the flow itself, resulting in significant changes in the morphology of the river. Therefore, monitoring discharge deserves a special attention. The conventional monitoring of flow discharge involves the measurement of a primary variable such as water depth or water level. A calibration curve, the so-called stage–discharge rating curve, is usually established based on information such as gauging (punctual discharge measurements performed using different techniques such as slug injection of a tracer in solution, impeller or electromagnetic current meter, aDcp, handheld surface velocity radar gun) or hydraulic modelling to convert the water level time series into flow discharge time series (World Meteorological Organization, 2010; Tomkins, 2012). This work typically involves substantial human and financial effort. It is indeed very demanding to mobilize a technical work force to carry out gauging. Moreover, the mobilization of a work force during floods is not without risk for the operators. It is also not so frequent to be present in the field during the biggest hydrological events. Finally, when a morphogenic flood occurs, the changes of the river geometry implies that we should start over the calibration curve. Thus, new methods are required to provide more direct access to the water discharge (without using the stage–discharge rating curve) and therefore reduce the field work related to gauging and bathymetry surveys. These methods involve the use of other variables in addition to the water level.
The first variable that has been added to hydrometric stations is velocity. During the last decade, the introduction of fixed flow velocity monitoring systems (transit time ultrasonic flowmeters, acoustic Doppler current profiler horizontal (H-aDcp) and vertical (V-aDcp), surface velocity radars) into hydrometric station equipment has gradually become more widespread (Costa et al., 2006; Levesque and Oberg, 2012; Nord et al., 2014; Thollet et al., 2017). These systems all provide a more or less local monitoring of flow velocity. More recently, systems using cameras and the large-scale particle imagery velocity (LSPIV) method have been developed and deployed in the field to estimate the surface velocity field (Hauet et al., 2008; Leduc et al., 2018; Le Coz et al., 2010; Muste et al., 2008; Stumpf et al., 2016). These systems provide field data of velocity on a stretch of river. Whether it is fixed flow velocity monitoring systems or LSPIV systems, there is no direct access to the mean channel velocity, which is the variable of interest to estimate directly the flow discharge by multiplying the mean channel velocity by the cross-sectional flow area. Thus the development of innovative methods is necessary to estimate the mean channel velocity from surrogate monitoring. The first attempts concern the index velocity method, but this approach is still largely based on the use of gauging (Levesque and Oberg, 2012; Morlock et al., 2002). Continuous monitoring of the cross-sectional flow area by a direct method is not possible to date because of the lack of dedicated technology. Alternatively, a relation between stage and channel area, called a “stage-area rating” is established based on topographic surveys, which are repeated typically once a year or after each morphogenic flood. Changes in the water-level–velocity relationship make it possible to detect changes in the bathymetry and the need to survey new bathymetry of the section (Thollet et al., 2017).
Therefore, being able to carry out high-frequency monitoring of water and solid discharges in any river and at any site of convenience is a high expectation for both operational and research applications. This should make it possible to extend the monitoring to isolated sites that have not yet been gauged, i.e. in environments that may be remote, difficult to access, under potentially extreme conditions (humidity, temperature, wind, radiation) or subject to destructive phenomena (cyclone, floods, vandalism, etc.). The availability of power, continuous data transmission and possible remote configuration remains a challenge in these environments. There are studies that have developed autonomous monitoring platforms in hostile environments to remain unattended for several months or years (Musko et al., 2009; Clauer et al., 2014; Peters et al., 2014; Morschhauser et al., 2017), but few or none for monitoring of water and solid (both fine and coarse particle) discharges in rivers (Mueller et al., 2013; Navratil et al., 2011; Comiti et al., 2014; Griffiths et al., 2014).
This study presents the development of a multi-instrumental platform called RIPLE (River Platform for Monitoring Erosion). The different variables measured relate to the flow discharge, water quality, fluxes of fine and coarse sediment, and optionally the properties of suspended fine sediments (settling velocity). A user interface is also presented that enables data visualization and remote configuration of the platform. A case study is described to validate the platform's operation.
The development of RIPLE is preferably aimed at mesoscale rivers, with
potentially high SSC values (peak values typically between 1 and 100 g L
The platform should allow for interaction between the different measurements (enabling triggering conditions to be activated and easily parameterized), data storage, remote data transfer, sending alarms, and remote and real-time management. In addition, the platform should be autonomous in energy and easily movable from one site to another. The different functions taken into account in the development of this platform are shown in the diagram of Fig. 1.
Diagram of the functions to be taken into account for the design of the RIPLE platform.
The sections presented in the following correspond to the sequential tasks that have been performed during the development of the platform: (1) the definition of the architecture and the choice of the acquisition and control system; (2) the development of an integrated solution (definition, development, test and formalization of protocols; power supply; sensor electronics; data storage; remote data transfer; enclosure and mechanics; and control software); (3) the development of a software interface allowing remote archiving and visualization of data and maintenance of the platform by sending new programs to modify the operation of the control system and the interaction between measurements; and (4) the deployment of the platform in the field for test and validation.
The instruments integrated into RIPLE are listed in Table 1. They are sorted according to the type of measurement they provide: hydrometry, water quality, suspended load and bedload. The name of the variable, the physical principle of the measurement, the name of the model, the name of the manufacturer and the integration status in RIPLE are given for each instrument. A detailed description of each instrument is given in Sect. 3.3. All instruments are produced and marketed by manufacturers with the exceptions of the optic fibre turbidimeter (the so-called “capteur marseillais”) for high turbidity and PASS (automatic water sampling), which are developed in public research laboratories and individually manufactured at the moment. The SCAF (a sediment settling velocity characterization device) has recently been marketed by the Aqualabo company. Two instruments, i.e. PASS (automatic water sampling) and SCAF, are now integrated into RIPLE but were not included in the case study presented in Sect. 6. Therefore, they appear as optional in Table 1.
Selection of instruments integrated into the RIPLE platform. The “ni” exponent in the column “Instrument name” means non-intrusive instruments.
The devices integrated into RIPLE are all controlled by the Campbell CR6 data logger (Fig. 2). This data logger was chosen because of its robustness in isolated sites and under difficult environmental conditions (humidity, temperature) and its flexibility in terms of ports (universal ports), e.g. Ethernet port, Secure Digital (SD) memory card slot and a large number of possible expansion modules (CDM-A108: analogue expansion module, SDM-SIO1A: RS-232, RS-485, RS-422 expansion modules), enabling users to control the large number of instruments integrated. This control system has a wireless connection option, allowing users to remotely control the system from the car or from a shelter during bad weather. The type of communication with each instrument is shown in Fig. 2. The other technical elements necessary for the operation of the platform are a solar panel, a solar regulator, a battery and a modem.
The RIPLE platform architecture.
The sampling period of the platform is 10 min, which is a good compromise between saving energy and obtaining a good description of water and sediment flux temporal variability in mesoscale rivers (Navratil et al., 2011). During the day, data are streamed every hour via a 3G/GPRS modem to the server located in the laboratory in Grenoble, and a digital control image is also transmitted. The modem is switched off at night to limit power consumption. For sites where a 3G connection would not be available, it is possible to switch to a 2.5G connection. The 2.5G connection is still acceptable for ASCII data, but it should be kept in mind that few images can be transmitted in this case. Alternatively, a satellite connection could be considered while taking into account the higher subscription cost.
RIPLE is powered by a combination of battery and photovoltaic panel to make it autonomous in energy, and it can be installed in a wide range of sites, even if there is no wired power grid. The power of the solar panel must be checked for each installation according to the sunshine on the site (latitude and elevation of the site, orientation and angle of the panel, presence of masks).
Power balance of instruments integrated into the RIPLE platform.
In Table 2, the details of the power balance is
displayed for the case study presented in Sect. 6. The total consumption
of the platform was estimated at 7758 mAh d
The different instruments integrated into the RIPLE platform are presented in Table 1 and Fig. 2. This section aims to provide more detailed information on each of the sensors and equipment.
The aerial control camera acts as a webcam, allowing users to remotely visualize
the instruments and the river. The camera can help to make a remote
diagnosis in case of malfunctioning of the platform. The camera is an AXIS
P1427-E. Its selection was based on various criteria such as image quality,
zoom and focus, power consumption, robustness to the environment, and
transfer of the images in real time. The maximal image resolution of the camera
is 5 MP (megapixel). Tests in the lab have shown that the image resolution
should be at least 3 MP to allow for details to be seen by zooming in the image.
The minimum-to-maximum focal lengths of the zoom lens are 2.8–9.8 mm. The
lens has a fixed aperture of
The triggering of the acquisition is programmed as follows: (i) the camera is started by the data logger only during daytime via the electrical relay (typically once an hour); (ii) the camera takes a picture at the end of a 2 min heating period; (iii) the camera sends the picture to an FTP address via the data logger and also locally records a copy of the image on its 64 GB SD memory card; and (iv) the camera is switched off via the relay.
Ideally, it would have been possible to zoom in and even orient the camera remotely. However, these specifications were not retained since they would have had a strong impact on the power consumption of the platform (motors within the camera and need to keep the modem on when driving the camera) and on the amount of data passing through the network (limited by the GPRS subscription). As a result, the camera maintains fixed position, focal length and focus.
It is necessary to regularly retrieve the images that are stored directly on the SD card of the control camera by connecting to the camera from a personal computer (PC) using the Ethernet link from the RIPLE cabinet during the field visits. This allows users to retrieve all the control images for subsequent archiving, because it is possible for some images to be poorly or not at all remotely transferred by the data logger due to the variable quality of the 3G connection.
This camera is dedicated to the large-scale particle image velocimetry (LSPIV) analysis, which is an optical technique for measuring surface velocity fields from image processing algorithms, and analysing the movement of natural tracers (leaves, floating branches, turbulent eddies) present on the water surface using a video of the river. A transect of surface velocity along the cross section of the river is extracted and converted to a transect of depth-averaged velocity over the vertical using a coefficient that relates the depth-averaged velocity to the surface velocity. Such a coefficient commonly ranges between 0.75 and 0.85 (Hauet et al., 2008; Le Coz et al., 2010; Welber et al., 2016), but it is preferable to define it from aDcp or impeller or electromagnetic current meter measurements in accordance with ISO 748 and USGS (U.S. Geological Survey) guidelines. Afterwards, knowing the cross section geometry, discharge is calculated by integrating depth-averaged velocities along the cross section with an accuracy of approximately 20 % (Welber et al., 2016).
The major advantages of this technique are the non-intrusive aspect (sensor
out of water) and the automation of the acquisition, which allows users to obtain
surface velocity fields without any human resources on site. There is no
risk for operators and no risk of missing the peaks of the floods, with the
exception of night-time and technical problems. In this study, the LSPIV
technique is not used to obtain continuous time series of discharge due to
the limitations of the method (see below) and our inability to monitor the
bathymetry of the section continuously. Alternatively, this technique
provides a set of “automatic” discharge measurements that makes the building of a stage–discharge relation easier and
faster. The main drawbacks of the
technique are as follows: (i) a manual selection of the video sequences is
necessary, because some videos are not usable (e.g. lack of brightness, sun
reflections, presence of dirt or water drops on the lens); (ii) the LSPIV
processing steps are relatively time consuming and require a specific
expertise. These steps include a possible correction (depending on the focal
length used) of image distortion, orthorectification of images
(transformation of the image from fixed objects whose exact GPS location is
known) in order to have the same scale at each point of the images,
calculation of surface velocity and flow discharge. These steps are
performed using the Fudaa-LSPIV software, a free software available online
(
The camera is an AXIS P1435-LE. Its selection was based on various criteria
such as image resolution, acquisition frequency, zoom and focus, power
consumption, robustness to the environment, possibility of recording on SD
card, and the presence of an integrated IR projector for tests at night-time. The
camera has an adjustable resolution from
The frame rate is up to 50 or 60 fps (frames per second) in all resolutions. A
minimum frame rate of 25 fps is used in this study. It is
important that the interval between two images be constant and accurate. The
consumption of the camera is 5 W, and it can be turned off while it is not
taking videos. The camera is connected to a PoE (Power over Ethernet)
injector (see Fig. 2). The PoE injector is
connected to the power supply. The data logger controls a relay that
supplies power to the PoE injector. In terms of protection against solids
and liquids, the enclosure of the camera complies with the Ingress
Protection IP66. The camera can operate from
The triggering of the acquisition is programmed as follows: (i) the camera is started by the data logger via the relay for 2 min at regular intervals (typically every 30 min) when triggering conditions of turbidity and water levels are exceeded, i.e. during flood, (ii) the camera takes a short video (10 s) of the river and records the acquisition directly on its 64 GB SD memory card, and (iii) the camera is switched off via the relay.
The video sequences stored in the LSPIV camera are retrieved regularly during field visits by connecting to the camera from a PC using the Ethernet link from the RIPLE cabinet. The LSPIV processing steps are executed back in the laboratory in Grenoble. It is important not to change the position, angle of view or focus of the camera in order to easily reproduce the LSPIV processing chain. Finally, it was chosen not to use the same camera for the control and for the LSPIV, even though this solution had initially been considered, because the installation constraints specific to each of them are generally very different.
The surface velocity radar model RG-30 from Sommer company was selected in this study. It was designed for non-contact measurement of the surface flow velocity of river and channels. The sensor is mounted above the river, usually installed on bridges or river banks using extension arms (Fig. 4). The radar sensor requires a low maintenance operation over many years.
The sensor emits a 24 GHz microwave beam (K-band) towards the water surface
inclined by an angle of 58
The enclosure of the RG-30 complies with the Ingress Protection IP68. It has
been designed to withstand exceptional floods with punctual immersion and
can operate from
Every 10 min, the measurement is ordered by the data logger using a Serial Data Interface at 1200 baud (SDI12) protocol. Every measurement is time averaged over 30 records obtained in a time interval of 30 s. In addition to the velocity, a quality value of the measurement is provided and recorded. The radar is put on standby by the data logger between each measurement to limit power consumption.
The water level radar CRUZOE manufactured by Paratronic was selected in this study. It is designed for non-contact measurement of water levels in rivers or channels. The sensor is mounted above the river, usually installed on bridges or river banks using extension arms (Fig. 4). Flow areas that can be hydraulically disturbed by bridge piers must be avoided. The sensor should preferably be placed in the same section as the staff gauge.
The system emits a short microwave impulse (24.125 GHz, K-band) towards the
water surface and captures the reflected signal. The radar sensor has an
opening angle of 12
The radar CRUZOE is easy to use, and it does not require any parameter setting. Indeed, the factory settings allow for its direct use in most cases. It is only necessary to convert the output value into an elevation on the staff gauge, which is the absolute elevation reference of the station. Readings of the staff gauge during field visits or by mean values of the images sent by the control camera allow users to check the validity of the measurement and detect any possible changes in the geometry of the riverbed.
The enclosure of the CRUZOE radar complies with the Ingress Protection IP68.
It has been designed to withstand exceptional floods with immersion for 100 d under 1 m and can operate from
This radar implements the JBUS protocol on an RS-485 link. MODBUS instructions of the data logger allow communication with this radar. Every 10 min, the measurement is ordered by the data logger. Each logged value is the average of three measuring cycles, each lasting 4 s and separated by 5 s. During the measurement cycles, the instrument makes 16 measurements per second. In addition, the standard deviation of water level, the ambient air temperature, and three quality indicators of the distance measurement are recorded. The radar is put on standby by the data logger between each measurement to limit power consumption.
The Campbell Scientific 547 (CS547) probe with the A547 interface was
selected for the measurement of electrical conductivity (EC) and temperature
of water. The EC sensor consists of three stainless-steel rings mounted in
an epoxy tube. Electrical resistance of the water in the tube is measured by excitation of
the centre electrode with positive and negative voltage. Temperature is
measured with a thermistor since the EC of a solution is highly dependent on
the water temperature. Indeed, as the temperature of a sample increases, the
viscosity of the sample decreases, resulting in increased ion mobility. As a
result, the observed conductivity of the sample also increases, even if the
ion concentration remains constant. To obtain comparable results, the
measured values must be reported at a uniform reference temperature
(generally 25
The CS547 probe is resistant to water and corrosion. It is also easy to clean.
The output signal is an analogical signal (4–20 mA). The range of measurement for EC
is from 0.005 to 7 mS cm
Every 10 min, the measurement is ordered by the data logger. Every measurement is time averaged over 30 records and obtained in a time interval of 30 s. In addition to the average EC and average temperature of water, the min and max values of EC, the standard deviation of EC, and the average value of raw EC (the value with no correction for temperature effect) are recorded.
Two types of automatic river water samplers were selected for this study:
the ISCO 3700 manufactured by Teledyne and (ii) the PASS (Huon et al.,
2017; M-Tropics, 2017) developed by IRD (Institut de Recherche pour le
Développement) for low-cost applications with an adaptable number and
volume of sampling bottles. As shown in Fig. 2, automatic water samplers
are useful both for measuring SSC and for all subsequent analyses of
dissolved and particulate phases, including major ions, nutrients,
contaminants, microorganisms and DNA. The ISCO 3700 portable sampler is commonly used in hydrological,
biogeochemical and suspended sediment studies. It can contain 24 wedge-shaped 1 L polypropylene bottles or 24 cylindrical 350 mL glass
bottles. The ISCO 3700 allows users to perform sequential or composite samples
based on time or physical conditions that come from other sensors (e.g.
water level, discharge or turbidity). In this study, only sequential samples
are taken and the sampling is triggered with external impulses coming from
the data logger. The ISCO 3700 features a patented liquid detector. It is
equipped with a peristaltic pump which delivers accurate and repeatable sample
volumes time after time. The system includes an automatic compensation for
changes in head heights and an automatic suction-line rinsing to eliminate
sample cross-contamination. The pump maintains a suction velocity of 0.66 m s In the case of exceeding turbidity and water level thresholds, the values of
which depend on the site and the season, the data logger will send an
impulse to the sampler which will initiate the sampling of a river water
sample. If both turbidity and water level thresholds continue to be exceeded,
and if a time interval is reached since the last sampling, the next sample
will be collected. The time interval between two samplings depends mainly on
the site and the season. The PASS sampler is a more flexible alternative than the ISCO in terms
of the number and shape of its containers. Any container in plastic, glass
or other material with a top opening can be used. This system is controlled
by the Campbell CR200 data logger that controls a pump and four stepper motors (two in
Two types of turbidimeters were selected for this study: (i) a standard
instrument manufactured by MJK (the SuSix sensor) for turbidity ranging from
0.001 to 9999 FNU/NTU (FNU: Formazin Nephelometric Unit, NTU: Nephelometric Turbidity Unit) (equivalent to suspended solids ranging from 0.001 to
400 g L The SuSix sensor uses a multi-beam pulsed-infrared-light system (wavelength of 860 nm). The beam-forming optics for multi-angle detection
combined with a progressive algorithm using neural logic constitute a
reliable high-quality measurement of turbidity in a single sensor according
to the manufacturer. The turbidity measurement complies with ISO standard 7027. The SuSix sensor is equipped with a wiper to remove mineral and
organic deposits from the optical cells. A SuSix converter without display
(10–30 V DC) on which certain parameters can be adjusted (unit, measuring
range) is needed between the SuSix sensor and the data logger. The standard
RS-485 connector is used for the serial communication between the sensor and the
converter with a proprietary protocol. An additional display unit for SuSix
is installed and connected exceptionally for the configuration of the system
and the sensor. During normal operation, the converter without display
allows for the most economical use possible in terms of power consumption. The
converter outputs a 4–20 mA signal to the data logger. The setting of the
4–20 mA output range depends on the turbidity range. In the case of a river
with high SSC, the following values are used: 4 mA The converter is started by the data logger via the relay every 10 min.
Every measurement lasts for 30 s and records 30 values. In addition to
the average turbidity, the min and max values of turbidity, the standard
deviation of turbidity, and raw value of turbidity in millivolts are recorded.
Finally, the converter is switched off via the relay. The wiper for cleaning
the sensor is activated when a 12 V pulse is received, generated once a day
by the data logger. The turbidimeter developed by IUSTI, the so-called capteur marseillais,
is a sensor made up of a bundle of optical fibres. This sensor which was
initially designed for the Draix-Bléone Observatory has shown great robustness,
operating on site without maintenance since July 1994. Four sensors and
their electronics are still in operation today. The operating principle of
the capteur marseillais is described in detail by Bergougnoux (1995),
Bergougnoux et al. (1998) and Bellino et al. (2001). The sensor head is made
of optical fibres with a diameter of 750 Every 10 min, the data logger starts up the power supply of the capteur marseillais via the relay. Every measurement lasts for 30 s and
records 30 values. The recorded measurements are the average and standard
deviation of the output voltage of each receiver fibre layer as well as
the average and standard deviation of the logarithm of the ratio between the
signals of the two receiver fibre layers.
Photos of the box that houses all the electronics of the platform
and the battery:
Photos illustrating the deployment of instruments in situ
The system characterizing aggregates and flocs (SCAF) is an optical settling
column composed of a series of 16 infrared emitters/receivers regularly
spaced every centimetre (Wendling et al., 2015). The device enables users to
measure the temporal evolution of the vertical profile of optical absorbance
during the quiescent settling of a suspension, immediately after its
sampling from the river. From the slopes of multiple iso-absorbance lines,
settling velocity distributions (SVDs) of suspended sediment can be
calculated, as well as an indicator of the propensity of particles to
flocculate (Wendling et al., 2015). The SCAF is able to operate for a wide
range of SSC (from 1 g L
Each unit instrument was designed to be incorporated into sequential
samplers. The SCAF was adapted to fit into typical 1 L wedge-shaped
polypropylene bottles used for ISCO 3700 automatic samplers. A round bottle
in glass (0.20 m high and 0.035 m in diameter) receiving the suspension
sampled from the river (170 mL) and the associated memory card is housed
inside the 1 L wedge-shaped polypropylene bottle. The optical system is
composed of 16 infrared (
The SCAF measurements begin after a delay of 20 s following the impulses emitted by the data logger to the ISCO. The delay corresponds to the time of purge and pumping of the 170 mL of the suspension by the ISCO sampler. Measurements are acquired every 100 ms, each value being the average of 10 measurements. The SCAF measurements typically last over 5 h as shown in Legout et al. (2018). Each SCAF unit is working separately, waiting for the various impulse sequences of the data logger.
The echo sounder Airmar SS510 was selected to be integrated into the RIPLE
platform. It allows users to perform a continuous measurement of the distance to
the riverbed at one point of the cross section. As a result, changes in the
geometry of the cross section related to erosion/deposition of sediment or
bedload transport can be detected at high temporal resolution, especially
during flood events when geomorphic processes may occur. As shown in Fig. 2, the echo sounder provides information on the bathymetry of the cross
section that is useful for both bedload and hydrometry. The Airmar SS510
sensor, featuring embedded micro-electronics, processes depth and
temperature signals inside the sensor and transmits data via two separate
communication protocols. The first is a bidirectional interface compliant
with the NMEA-0183 protocol, and the second is a transmit-only interface with
a proprietary protocol using RS-485, which is used in this study. The
acoustic frequency used by the sensor is 235 kHz. The power output from the
transmitter is 100 W. There are minimal side lobes for concentrated energy
on target. The beam width is 8
Every 10 min, the measurement is ordered by the data logger via a RS-422 serial communication. By default, the echo sounder returns the measured depth and water temperature every second. Every measurement is time averaged over 30 records. The recorded measurements are the average, min, max and standard deviation of depth as well as the average, min, max and standard deviation of water temperature.
Additionally, the RS-485 interface available on the echo sounder allows users to retrieve detailed information on each measurement made by the echo sounder. To do this, it is necessary to establish a connection from a PC to the echo sounder with a converter from FTDI (Future Technology Devices International) USB to RS-485 and open a 921 600 baud terminal to display these data.
The deployment of hydrophones in the watercourse allows for a continuous monitoring of the sounds naturally generated by bedload transport in the river (Marineau et al., 2016; Geay et al., 2017a). The hydrophone Colmar GP0190 interfaced with the SDA14 acoustic data recorder was selected in this study. The Colmar GP0190 is a pre-amplified omnidirectional hydrophone, for application up to 170 kHz (working band: 5–170 000 Hz). The hydrophone can work up to 1000 m depth. The body of the instrument is in stainless steel. The SDA14 acoustic data recorder was designed by the RTsys company for the acquisition of acoustic signals from passive or pre-amplified hydrophones. It integrates four analogue receivers, allowing for recording of four sound sources simultaneously. Its broadband analogue inputs allow for a frequency of over 500 kHz (from 3 Hz to over 500 kHz) with a dynamic range greater than 100 dB, guaranteeing an efficient signal-to-noise ratio. The embedded digital signal processor allows for high-speed acquisition, filtering, storage and preprocessing of the acoustic data. Its power consumption is between 600 mW to 2 W in active mode (i.e. during measurements) and less than 1 mW in sleep mode. The system is designed to operate in stand-alone mode or towed mode. The stand-alone mode is used in this study. Configuration of the SDA14 acoustic data recorder is possible by connecting a PC via Ethernet and using a web interface.
Architecture of the main program that controls the RIPLE platform.
The SDA14 is controlled by the data logger via an RS-232 link and a 0–5 V output. The triggering of the acquisition is programmed as follows: (i) the data logger sends a 5 V pulse to wake up the SDA14; (ii) the SDA14 starts and automatically launches an acquisition (manual mode): a record of 30 s at 156 kHz and with a 24-bit resolution is performed. For information, the parameters of duration, frequency and resolution of the acquisition can be modified in the data logger acquisition program; (iii) data are stored in files with a “.wav” format; (iv) a fast Fourier transform algorithm is operated by the SDA14 to compute the acoustic root-mean-square power in one-third octave bands; (v) the SDA14 transmits integrative spectrum data (RMS power in one-third octave bands, about 20 points of the spectrum) to the data logger via an RS-232 link, so useful information on the operation of the hydrophone can then be tele-transmitted to the manager of the platform (it would not be possible to tele-transmit the “.wav” files by GPRS or 3G as they are too large, i.e. 10 MB for 30 s of recording); and (vi) the data logger puts the SDA14 in standby via the RS-232 link.
Concerning step (iii) on data storage, the SDA14 is equipped with a 128 GB SD memory card and a 2 TB (terabyte) hard disc drive (HDD) (ext4 format). In order to limit the number of on/off times of the HDD (which would severely limit its lifetime), acquisitions are first recorded on an SD memory card and once it is full, the HDD is turned on to empty the SD card. This is called the “hybrid” mode. There is an RS-232 command returning the available memory in the SD card and in the HDD. The data logger will send an alert when the hard disc is almost full. The SDA14, the SD memory card and the HDD are located in the main electronics box of the platform. The transfer time from the 2 TB HDD to a PC would be far too long to be done in the field. A second HDD was therefore purchased to allow rotations to be done: when a disk is almost full (the state of the storage of the HDD is visible in the RIPLE interface), it is simply replaced by the second HDD previously emptied, and the transfer of the full HDD can be done in the laboratory.
The hydrophone is housed in a polyethylene tube next to the other immersed instruments (turbidimeter, conductivity and temperature probes), which are not switched on during hydrophone acquisitions.
Diagram describing the links between the RIPLE platform, the remote server and the user interface. The “Riple_IP.txt” file contains the current IP address (Internet Protocol, public and dynamic) of the RIPLE platform. The “Riple_DATA.txt” file contains the data from each instrument of the RIPLE platform. The “Riple_SAV.txt” file contains the data that enable users to remotely control the proper functioning of the RIPLE platform. “Config.ini” is the configuration file of the platform, which is generated when the user wants to change the configuration of the platform from a remote location using the RIPLE interface (Sect. 5). A backup of the old configuration is made in the “Config_old.ini” file. “imageControle_Date.jpg” is the image file that the data logger puts on the FTP server. A copy of the image is saved locally on the camera in order to make a reliable archiving of the images (in case of malfunction of the remote transmission for example).
The 2G/3G modem Erco&Gener GenPro 325e was selected in this study as it allows the data logger to upload data and images to the FTP server of the laboratory in Grenoble and also to send alert SMS (text) messages. 2G antennas are no longer maintained by access providers in France, so we opted for a modem that can use 3G to remotely transmit data. A SIM (subscriber identity module) card linked to an M2M (machine-to-machine) subscription is inserted into the modem to ensure its operation.
The solution of a private IP address was selected in this study. By making this choice, we accept the dependence on the LoggerNet software of Campbell Scientific, which makes it easy to manage the data logger and set up automatic collections of data. Having a private IP address avoids the possibility of being hacked by a “robot” circulating on the net, which could cause a very significant increase in expenses related to the GPRS subscription. In France, Internet service providers only provide dynamic IP addresses. The LoggerNet software was configured to establish a connection with a station whose address is dynamic by accepting the possibility of temporarily losing the connection because of a change in IP address.
All procedures for the data collection and data transfer to the remote FTP server are presented in Fig. 7. The modem queries the Internet service provider to obtain a private IP address. The data logger is then connected to the 2G/3G network. The data logger contacts the LoggerNet server via port 6786. The LoggerNet software running on the LoggerNet server recognizes the data logger with its PakBus number (225 for RIPLE), the connection between the data logger and the LoggerNet server is then established. It is then possible for the manager of the platform to communicate remotely with the data logger. The LoggerNet software can manage different stations; the distinction between different data loggers is made by a unique internal Pakbus address assigned to each station and not by IP address or domain name. In the case of a change of IP address, a connection failure occurs until the data logger automatically sends the next tag to the LoggerNet server and can communicate once again with it. The IP address is changed on average every 24 h and also when the modem starts up. It is unlikely that the change of IP address occurs within a few hours after the modem is started. As long as the connection is established, the data logger sends its data tables and control images to the FTP server of the laboratory using FTPClient instructions.
Diagram of the data collection and data transfer to the remote FTP server. (0) The data logger continuously retrieves data from the instruments integrated into RIPLE and stores the results in a table. (1) The data logger supplies power to the control camera for a few minutes, which automatically takes an image when it starts up. (2) The camera, configured as an FTP client, places the image on the data logger, which includes an FTP server. (3) The data logger supplies power to the GPRS modem. (4) The modem queries the Internet service provider to obtain a private IP address. (5) The data logger is then connected to the 2.5G, 3G or 4G network. (6) The data logger contacts the LoggerNet server via port 6786. (7) The LoggerNet software running on the LoggerNet server recognizes the data logger with its PakBus number and the connection is established. It is now possible for the manager of the platform to communicate remotely with the data logger. (8) The data logger sends its data tables to the FTP server of the laboratory (FTPClient instruction). (9) The data logger transmits the control image to the FTP server of the laboratory (FTPClient instruction). (10) The data logger switches off the modem after a few hours. (11) A web page created under Shiny (R package) is updated after each new data transmission.
The advantages of the selected solution are (i) the possibility to use a classic GPRS modem (low energy consumption and low cost), (ii) the management of data flow in case of transmission errors, and (iii) the possibility to have a two-way communication link.
Screenshot of the “Data visualization” menu of the user interface in the default mode. Four time series graphs are displayed from top to bottom: (1) water level and surface water velocity, (2) water level and turbidity, (3) water level and temperature, and (4) water level and conductivity.
The shortcomings are (i) the dependency to the LoggerNet package proposed by Cambpell Scientific and (ii) the likelihood to temporarily lose the connection if the IP address is changed.
The CR6 turns the modem on during the day and off at night to limit the power consumption of the platform. Therefore, no data or alerts can be transmitted during the night.
Screenshot of the “Data visualization” menu of the user
interface in the personalized mode. A graph can be created with two
variables on the first
The RIPLE platform is organized in several parts: (i) the control block, (ii) submerged instruments and (iii) non-intrusive instruments.
The control system (CR6, CDM-A108, 4 SDM-SIO1A), the power supply
(battery, solar regulator, four relays, two PoE injectors), the instrument
electronics boxes (SuSix turbidimeter interface, power supply of the
capteur marseillais, A547 interface of the conductivity probe, SDA14
card of the hydrophone) and the remote transmission module (modem, antenna)
are grouped in an electrical box (dimensions Three main submerged instruments (conductivity probe, SuSix
turbidimeter, hydrophone) are housed in polypropylene tubes, 3 m long and
0.10 m in diameter, fixed to the bank of the river, parallel to each other.
These tubes are clamped between metal profiles at three points (top, middle and
bottom of the tubes). The metal profiles are themselves anchored in the bank
of the river (bedrock or large blocks) using threaded rods. The three polypropylene tubes are perpendicular to the direction of the flow in the
river (see Fig. 4). The instruments are installed
within the tubes using PVC pieces of the inner diameter of the tubes that
are machined to allow the sensors to be inserted. These PVC pieces,
connected to the top of the pipes by 4 mm diameter threaded rods, prevent
the movement of the sensors inside the pipes, allow the sensors to be easily
removed without human intervention in the river and allow users to put the sensors
back to the same location. The lower end of the tubes is at a level low
enough to ensure that the instruments are submerged during low-water
periods. In addition, the capteur marseillais, which is composed of a waterproof
box including the LED source, photodiodes and a 1.5 m long sheath
with the optical fibres, is fixed to the outside of the polypropylene
tubes using cable ties. The dimensions of the box and the length of the
optical fibres mean that this instrument cannot be housed inside the
polypropylene tubes and the optical fibre heads cannot be lowered to the
lower end of the tubes. The measurement is made at a higher level in the
water column than the SuSix turbidimeter. The flexible plastic tube that
allows ISCO to collect water and suspended sediment from the river is also
attached to the polypropylene tubes using cable ties. The end of the
flexible tube is positioned very close to the SuSix turbidimeter, at the
same level in order to have a maximum correspondence between the two
measurements. The other submerged instrument that is not installed within
the polypropylene tubes is the echo sounder. This one has a specific support
that has been designed to be fixed to a vertical wall (bridge pier for
example) by adjusting the angle of orientation of the instrument with
respect to the vertical. Finally, a staff gauge is installed in the cross
section near the water level radar. The four non-intrusive instruments used for hydrometric purposes are
fixed on masts for both cameras (control, LSPIV) and on extendible mounting
brackets for both radars (water level, water surface velocity) as shown in
Fig. 4. The brackets are easily movable to
facilitate radar maintenance. These devices (masts or mounting brackets) are
attached to the structure of a bridge for example. The velocity radar should
preferably be placed in the centre of the cross section in the zone of
highest velocities. The cameras must be located on the banks at a level high
enough to see the full width of the river and part of the banks.
The data logger controlling program is written in the CRBasic programming language, which is the proprietary format of Campbell Scientific. As shown in Fig. 5, there is a main program that reads a configuration file, initializes the instruments and controls two families of sub-programs: those which are active every 10 min or every hour.
The sub-programs that are active every 10 min control all instruments except the control camera. When the water level and turbidity conditions are exceeded and the time since the last sample exceeds a certain interval set by the user, the first automatic sampler is launched. A SCAF measurement is also started, preferably using a second automatic sampler dedicated exclusively to SCAF measurements. Similarly, when a water level condition is exceeded and the time since the last video shot exceeds a certain interval set by the user, the LSPIV camera records a video sequence. The other sub-programs that are called at an hourly frequency are only active during daytime. This concerns the operation of the modem, image capture by the control camera, image and data transmission, and SMS message sending in case of alerts (low battery voltage, full ISCO sampler, etc.).
The RIPLE platform is interfaced with the rest of the world via the FTP server, which allows for exchanges between the interface (see Sect. 5) and the platform as shown in Fig. 6. The procedure for the operation of data storage and transfer can be illustrated by Fig. 7.
Concerning specifically the control camera, we use the data logger as an FTP server, on which the camera places an image every hour. The data logger then transmits this image to the FTP server of the laboratory in Grenoble. Since the LoggerNet software is not able to automatically collect files other than data tables, the data logger must therefore perform the PUSH command on the FTP of the laboratory server to retrieve the control images (the data logger can be both server and FTP client). The advantage of this method is that a traditional GPRS modem can be used. A drawback is that the images have to pass through the data logger storage memory, but this does not have much impact on performance and consumption of the control system.
A solution with a remote web server was chosen, i.e. it is the server in the lab that generates a web page from the collected data. The RIPLE user interface is developed in R using the Shiny package (JavaScript elements for web interfaces) and dygraphs (graphics). An executable file for this application has been generated to display the results on a dedicated web page to avoid having to install RStudio and to have access to the interface from any terminal equipped with an internet connection.
Screenshots of the “Images of control” menu of the user
interface at two dates:
By default, the interface starts on the “Data visualization” menu, in which
all the data transmitted by the RIPLE platform can be seen (the display may take
a little time due to the amount of data). It is possible to choose the type
of time series to display:
Fixed time series (default) are four graphs displaying the more common
data as shown in Fig. 8, the water level being
present in each of these graphs as a common reference. There is a first
graph with water level and surface water velocity, a second graph with water
level and turbidity, a third graph with water level and water temperature, and a
fourth graph with water level and water electrical conductivity. These
graphs give an overview of how the station works for the basic variables. Customized time series (optional) is a single graph on which it is
possible to add two curves on each All the data that can be displayed is read from the Riple_DATA.txt file (Fig. 6) that is located on the FTP
server in Grenoble, i.e. the file uploaded by the RIPLE platform. For each type of time series, it is possible to do the following. To modify the time window to be displayed, there are four options: the
last day, the last week, the last month or a manual selection of start and
end dates. To download the displayed data in an ASCII file in the same format as the
one originally produced by the CR6, it is possible to select either
specific variables or all variables as in the file uploaded by RIPLE.
Screenshots of the “Supervision” menu of the user interface. A
time series graph is displayed with minimum and maximum battery voltage on
the first
A second menu “Control images” allows users to remotely view the RIPLE platform by displaying the control images that are stored on the FTP server in the laboratory. For example, it allows users to visualize the hydraulic behaviour during floods and at low flows (see Fig. 10). Only fully transmitted images are accessible by default. It is still possible to consult all the control images later on, after having retrieved manually the control images during field visits. All control images are thus stored in an archive directory independent of the FTP server.
Screenshots of the “Configuration” menu of the user interface.
All the external variables of the program that can be modified are given
below.
“Numéros de tel pour alertes SMS”: list of phone numbers that receive
SMS alerts sent by the RIPLE platform.
“SEUIL_ALERT_PTEMP”: threshold on the
temperature of the data logger (in degree Celsius) above which an SMS alert
is sent.
“SEUIL_ALERTE_BATTERIE”: threshold on the
battery voltage (in volts) below which an SMS alert is sent.
“SEUIL_VENTILO_PTEMP”: threshold on the
temperature of the data logger (in degree Celsius) above which the fan is
switched on.
“SEUIL_VENTILO_BATTERIE”: minimum battery
voltage (in volts) to allow the fan to work.
“HEURE_MODEM_ON”: UTC time at which the
RIPLE modem turns on each day, allowing data and images to be transmitted
remotely.
“HEURE_MODEM_OFF”: UTC time at which the
RIPLE modem turns off to limit power consumption.
“INTERVALLE_SMS”: time interval (in hours) between two SMS
alerts.
“SEUIL_TURB_ACQ_LSPIV”:
turbidity threshold (in FNU) above which the LSPIV digital camera takes a
video.
“SEUIL_H_ACQ_LSPIV”: water
level threshold (in millimetres) above which the LSPIV digital camera takes a video.
“INTERVALLE_LSPIV”: time interval between 2 consecutive
LSPIV video acquisitions (in minutes). Must be a multiple of the scan time (10 min in this study).
“HAUTEUR_CRUZOE”: difference in elevation between the 0 of
the staff gauge and the position of the radar (in millimetres). The water level is then
calculated as the difference between “HAUTEUR_CRUZOE” and
the distance measured by the radar.
“INTERVALLE_CLEAN_TURBI”: interval between
two consecutive cleaning operations of the turbidimeter using a small brush (in
hours).
“SEUIL_TURB_PRELEV_ISCO”:
turbidity threshold above which the automatic water sampler starts its
regular sampling (in FNU).
“SEUIL_H_PRELEV_ISCO”: water
level threshold above which the automatic water sampler starts its regular
sampling (in millimetres).
“INTERVALLE_ISCO”: time interval between two samples (in
min). Must be a multiple of the scan time (10 min in this study).
“SEUIL_MEM_HDD”: threshold of the remaining
memory on the hydrophone's HDD below which a collection must be planned (in
gigabytes).
The button “charger la configuration actuelle” triggers the reading of
the “config.ini” file on the FTP by the data logger. This file contains the
values of the variables currently loaded by the RIPLE platform.
The button “charger l'ancienne configuration” triggers the reading of
the file “config_old.ini” on the FTP by the data logger, in
which there is a backup of (
The “Supervision” menu allows users to remotely control the proper functioning of the platform. The data displayed in this menu are read from the Riple_SAV.txt file that RIPLE uploads to the FTP server (Fig. 6). These are technical variables concerning the control unit (reference identifiers, OS version, internal battery voltage, PakBus address), the name of the current program of the control unit, the status of the power supply and temperature in the electrical cabinet of the RIPLE platform, the data collected (number of measurement, date and time of measurement, watchdog errors, skipped scans, error with the SDA14 card, status of the SD memory card and of the HDD of the hydrophone).
For example, it is possible to check the status of the power supply of the
platform by looking at the battery voltage time series
(Fig. 11). The temperature measured by the data
logger, i.e. the temperature inside the cabinet, must also be controlled,
especially in winter (take care if air temperature is below
Overview (from downstream) of the river section where the RIPLE
platform is located on
Some parameters of the RIPLE platform can be configured remotely from the “Configuration” menu of the interface (see Fig. 12). This menu is only used by RIPLE's main administrators. To modify other variables, e.g. duration of a scan or measurement time on each instrument, it is necessary to modify the Campbell program of the data logger. It is also possible to do it remotely but it is preferable that it remains exceptional.
Raw data derived from the “Riple_DATA.txt” file
for the 8 to 9 August 2017 in Bourg d'Oisans (Romanche river).
To date, the RIPLE platform has been tested on two river sites located in the
French Alps: the Romanche in Bourg d'Oisans (45.1158
The first site corresponds to a large embanked river typical of anthropomorphized
alpine valleys: the presence of dams upstream and dikes giving rise to a very
rectilinear river. The width of the river is about 30 m; the depth is
typically between 0.5 and 1 m at low flows. SSC typically changes between 0
and 10 g L
The second site corresponds to an ungauged site located in a more pristine
river in the Southern Alps where sediment loads can be high (max SSC
During these 2 years of testing on two sites, the platform has worked
properly, recording a large data set that will be of great interest for the
understanding of sediment transport processes in alpine rivers.
Figure 14 shows the raw data measured by six sensors
for the event of 8 to 9 August 2017 on the Romanche river in Bourg d'Oisans.
The data are provided by the following sensors: water level radar, surface
velocity radar, SuSix MJK turbidity sensor, conductivity probe, echo sounder
and hydrophone. Figure 14 focuses on the same flood event as Fig. 9, which
was only a screenshot of the user web interface. Additionally, the videos
(provided by the large-scale particle image velocimetry digital camera),
photos (provided by the control camera), the two text files
“Riple_DATA.txt” (data from all instruments) and
“Riple_SAV.txt” (data that enable users to remotely control the
proper functioning of the platform), and the raw data of the hydrophone are
open-access data for this flood event through the following link:
The use of RIPLE data is in progress. For example, a current work is being done to combine radar surface velocity measurements with LSPIV velocity measurements to estimate the mean channel velocity and identify the moments when the geometry of the river is significantly modified by deposition and erosion processes.
The characteristics of the presented platform dedicated to monitoring erosion in mesoscale rivers result from a 15-year expertise in hydrometric and sediment measurements within the IGE laboratory and more broadly within the research laboratories in the Grenoble and Lyon communities and local companies such as EDF. The platform has been designed to be applied preferably to rivers in mountainous areas, but it is adjustable and transferable to lowland rivers. Through this study, emphasis was put on water discharge as this elementary variable is not trivial to monitor in situ. Further developments are needed to improve this measurement, especially in rivers where geomorphological processes are very active. A future objective is to develop methods to continuously and directly access discharge data, with a very limited use of gauging and human resources. In addition to the set of measurements provided by the presented platform, a major challenge for the next decade will be the development of methods, allowing users to monitor continuously the bathymetry within the river cross section.
For fine sediment transport, the generalization of the use of the turbidimeter associated with automatic river water sampling for the calibration of turbidity–SSC relationships has allowed for a fairly rapid extension of the monitoring of suspended sediment fluxes during the last decades. However, it is still necessary to improve the spatialization of this measurement within the river cross section, particularly in the case of coarse silt- or sandy-sized particles, which often do not have a homogeneous concentration profile within the water column but rather an increasing concentration profile with depth. It seems necessary to develop non-intrusive technologies for the measurement of suspended sediment fluxes to increase the robustness of the measurement and reduce in situ maintenance. Furthermore, the measurement of the physical characteristics of particles is important because it provides information on transport capacity and deposition processes, on the spatial origin of eroded particles and indicates the propensity of particles to transport adsorbed substances (nutrients, metals, organic products, microorganisms, micro plastics, etc.). These measurements must be performed under conditions that are close to the in situ environment to avoid subsequent flocculation or disaggregation processes.
Concerning coarse sediment transported by bedload, recent metrological developments make it possible to start considering continuous and high frequency monitoring of fluxes and physical characteristics of particles (size distribution) using proxies and inversion models. Passive acoustic and passive seismic monitoring methods are experiencing on-going developments. The first results point to a promising future, although the difficulty lies in validating these methods. Indeed, in situ sampling of transported coarse particles is difficult to carry out and cannot be automated. In this study, the choice was made to integrate passive acoustics technology. These measurements of bedload correspond to a strong demand from the scientific community and more generally from society.
Finally, water quality is partially taken into account in the RIPLE platform through the measurement of electrical conductivity and water temperature and also through the automatic sampling of river water which allows workers, after a filtration step in the laboratory, to carry out any type of analysis from the filtered phase, the so-called dissolved phase (chemical, microbiological, DNA, etc.). Automatic sampling makes it possible to collect water during flood periods or during low-water conditions. The collected samples are also useful for performing analyses (chemical, microbiological, DNA, etc.) on the particulate phase.
In the end, we want to show that the RIPLE platform is a unifying tool that contributes to multidisciplinary studies on understanding the functioning of the critical zone. This is the way this tool has been designed and will continue to evolve. The RIPLE platform is in constant evolution: new innovative instruments are integrated when they have been validated and are in a development phase that allows their integration. Recent examples of integration are the SCAF and the hydrophone. Remote sensing instruments (e.g. radiometers) could also be added to perform non-intrusive turbidity measurements. RIPLE is an autonomous low-power instrument platform which transmits real-time data to a remote server, and it can be controlled remotely, enabling users to fully exploit its potential. The visualization software interface that has been developed allows for an easy follow-up of all measured variables and a beginning of data quality control.
The CRITEX project (“Challenging equipments for the temporal and spatial exploration of the Critical Zone at the catchment scale”) has made it possible to purchase an aquatic drone, the FoRiver 1, manufactured by RiverDrone SARL, which offers the opportunity to plan spatial campaigns of certain variables at “hot moments” (low water level, flood, hydraulic flushing). The aquatic drone can thus carry submerged instruments (e.g. conductivity probe, turbidimeter, echo sounder, hydrophone, automatic sampler) to perform measurement campaigns of the same variables as those measured by the RIPLE platform at other points in the cross section or at other points in the profile along the river at different moments.
The underlying research data of the article are accessible through the following link:
YM developed and implemented the integration of the RIPLE platform and developed the visualization interface. ME and GN supervised the work of YM. TG contributed to the integration of the hydrophone into RIPLE. CL and BM contributed to the integration of SCAF into RIPLE. AH contributed to the integration of the control camera and the LSPIV camera into RIPLE. YM and GN supervised the installation of RIPLE on the Romanche site, with the help of the IGE technical service. RB and GN supervised the installation of RIPLE on the Galabre site, with the help of the IGE technical service. RB and GF are responsible for the maintenance of RIPLE. RB supervises RIPLE on a daily basis. GN wrote the article using as main basis the technical documentation of the platform written by YM. All the co-authors reviewed the document and contributed more specifically to certain sections.
The authors declare that they have no conflict of interest.
Yoann Michielin benefited from an engineer contract from the CRITEX project. The work of Thomas Geay was funded by a research program between Electricité de France (EDF) and Université de Grenoble (GIPSA-Lab/IGE). The development of this platform was carried out with the support of the technical service of the IGE. Most of the instruments presented in this study are the property of the CNRS. They are part of the national park of instruments for the study of the critical zone set up as part of the CRITEX project.
The authors thank Norbert Silvera for his help in integrating the PASS sampler into the RIPLE platform. The authors also thank the French research infrastructure OZCAR (Observatoires de la Zone Critique, Applications et Recherche), the Draix-Bléone Observatory, EDF, the AD Isère Drac Romanche and the department of Alpes-de-Haute-Provence for allowing the RIPLE platform to be installed on the Romanche and Galabre rivers.
This research was supported by the EQUIPEX CRITEX project (grant no. ANR-11-EQPX-0011, PIs Jérôme Gaillardet and Laurent Longuevergne). The deployment of the RIPLE platform in the field was also supported by Labex OSUG@2020 (grant no. ANR10 LABX56) and the Institut National des Sciences de l'Univers (grant no. INSU/CNRS).
This paper was edited by Anette Eltner and reviewed by John Gray and one anonymous referee.