A.-T Tran

Guillaume Nguyen

T Dramais

J Van Emmerik

Némery

B Camenen
email: benoit.camenen@inrae.fr

N Gratiot

J.-A Cohard

F Gard

V Q Tran

A.-T Nguyen

G Dramais

T Van Emmerik

J Némery

Monitoring discharge in a tidal river using water level observations: application to the Saigon River, Vietnam

Keywords: Saigon River, water level, water discharge, tidal river, flood, stage-fall-discharge rating curve

Monitoring discharge in a tidal river using water level observations: Application to the Saigon River, Vietnam

Introduction

A good understanding of the hydrological cycle, and discharge in particular, in tidal rivers enables reliable forecasting and decision making by researchers and policy makers. Some priorities are generally put on the protections against floods, saline intrusion and the dynamics of pollutants because of the social, economic and political stakes they are linked to. Hydrological cycles in Low Elevation Coastal (and deltaic) Zone (LECZ, 0-10 masl) differ a lot from their upstream environments due to the tidal influence [START_REF] Van Driel | Vulnerability assessment of deltas in transboundary river basins[END_REF]. Although this is a strategic zone at the interface between land and ocean, physical and environmental data collected in this environment remain sparse because of inherent logistical difficulties due to the unsteadiness of the flow [START_REF] Taniguchi | Optimizing the waterenergy-food nexus in the Asia-Pacific Ring of Fire[END_REF]. On one hand, the river water discharge is influenced by tidal dynamics, which modulate hydrodynamics at high and low frequency and can reshape the geomorphology, with feedback loops on hydrodynamics [START_REF] Mao | Tides and tidal currents in the Pearl River Estuary[END_REF]. On the other hand, estuarine and deltaic zones are highly influenced by peak fresh water discharges, which are themselves modulated by tidal asymmetry [START_REF] Sassi | River flow controls on tides and tide-mean water level profiles in a tidal freshwater river[END_REF].

In the floodplain of the Saigon River, which is part of the LECZ of the south of Vietnam, vulnerability could be assessed according to four factors [START_REF] Mcgranahan | The rising tide: Assessing the risks of climate change and human settlements in low elevationcoastal zones[END_REF][START_REF] Van Driel | Vulnerability assessment of deltas in transboundary river basins[END_REF] : (i) Relative Sea Level Rise (RSLR) due to climate change and natural and anthropogenic subsidence;

(ii) Wetland ecological threat; (iii) Population pressure: and (iv) Delta governance (adaptivity, participation, fragmentation). The Saigon River flows through Ho Chi Minh City (HCMC), a highly populated area, where population density can reach up to 30,000 inhabitants/km 2 [START_REF] Nguyen | Nutrient dynamics and eutrophication assessment in the tropical riversystem of Saigon -Dongnai (southern Vietnam)[END_REF].

The unprecedented growth of HCMC induces pressure on the environment and especially water resources [START_REF] Van Leeuwen | The challenges of water governance in Ho Chi Minh City[END_REF].

A major issue in such regions is to measure the instantaneous discharge for a tidal river and to evaluate the residual discharge of the tide-affected river. Indeed, a simple stage-discharge relationship cannot be used because of the downstream influence of the tide. Stage-fall-discharge (SFD) rating curves are traditionally and successfully used to compute discharge at sites where variable backwater effects could affect a classical stage-discharge relationship [START_REF] Rantz | An empirical method of determining momentary discharge of tide-affected streams. Water-Supply Paper 1586[END_REF]. Such methods have recently improved thanks to Bayesian methods [START_REF] Petersen-Øverleir | Bayesian analysis of stagefall-discharge models for gauging stations affected by variable backwater[END_REF][START_REF] Mansanarez | Bayesian analysis of stage-fall-discharge rating curves and their uncertainties[END_REF]. However, the relation between stage, slope, and discharge has generally been considered too complex in tidal rivers to attempt to obtaining accurate discharges. [START_REF] Rantz | An empirical method of determining momentary discharge of tide-affected streams. Water-Supply Paper 1586[END_REF] proposed a graphical method using multiple correlation, which remains difficult to apply, especially when the tidal influence leads to discharge fluctuations as large as the residual discharge.

That is the reason why there exists nearly no hydrometric stations worldwide in such rivers. Recently, acoustic sensing techniques (through HADCP, Horizontal Acoustic Doppler Current Profiler) have been implemented successfully in some tidal rivers such as the Sacramento River, California [START_REF] Ruhl | Monitoring alternatives at the Sacramento River at Freeport, California: results ofthe 2002-2004 pilot study[END_REF] or in the rivers Berau and Mahakam in East Kalimantan, Indonesia [START_REF] Hoitink | Continuous measurements of discharge from a horizontal acoustic Doppler current profiler in a tidal river[END_REF][START_REF] Sassi | Discharge estimation from H-ADCP measurements in a tidal river sub-ject to sidewall effects and a mobile bed[END_REF]. Such techniques generally correspond to a velocity index method. Similar method using an ADP (Acoustic Doppler Profiler) current meter was deployed on the Tanshui River, Taiwan [START_REF] Chen | Real-time discharge measurement in tidal streams by an index velocity[END_REF] or fixed ADCPs (Acoustic Doppler Current Profiler) on the Yangtze River at Xuliujing, China [START_REF] Zhao | Multiprofile discharge estimation in the tidal reach of Yangtze Estuary[END_REF][START_REF] Mei | Large addition of freshwater to the tidal reaches of the yangtze(changjiang) river[END_REF]. These methods remain however expensive (around 30keexcluding structural works and maintenance) and difficult to display in an urban place due to risk of vandalism.

In a recent work, [START_REF] Camenen | Estimation d'une courbe de tarage hauteur-dénivelée-débit pour une rivière influencée par la marée[END_REF] proposed to apply a stage-falldischarge rating curve to estimate instantaneous discharges in tidal rivers.

Such simple model can be calibrated using intense discharge campaigns achieved over a full tide cycle, which are easily available nowadays thanks to the ADCP technology. The first application of the method was based on field measurements made on the Saigon River in September 2016 and was particularly successful [START_REF] Camenen | Estimation d'une courbe de tarage hauteur-dénivelée-débit pour une rivière influencée par la marée[END_REF]. It presented some limitations for a very asymetric tidal wave (close to a tidal bore), which is not the case for the Saigon River.

In this paper, this simple low-cost water level-based discharge monitoring is applied the Saigon River for a two-year period. A calibration and validation of the method are first presented. The monitoring of two hydrological seasons from January 2017 to Dec 2019 is then discussed. Results obtained allow for examining and discussing two specific aspects : first, what is the hydrological pattern of the Saigon River at different time scales, from the event scale to years, and how is it influenced by the sea level ; second, how much the rainfall regime is directly influencing the Saigon River dynamics, in particular, how does the river react to extreme events?

Material and methods

Study site

The Saigon River is located in the South of Vietnam, in a low elevation coastal zone, i.e. between 0 and 10 m above mean sea level (MSL, Figure 1). The Saigon River takes its source in Cambodia and is 225 km long.

Its catchment area has a surface of 4717 km 2 [START_REF] Nguyen | Nutrient dynamics and eutrophication assessment in the tropical riversystem of Saigon -Dongnai (southern Vietnam)[END_REF]. The Saigon River is actually a complex river system, subject to several human and environmental interactions, including many canals while it crosses Ho Chi Minh City (HCMC) megalopolis, before flowing into the Dong Nai River and the coastal waters. Upstream the megalopolis, the river is regulated by the Dau Tieng dam, which was built during the 1980's, in order to mitigate saline intrusion and secure the fresh water supply uptake station of HCMC.

The situation of HCMC is all the more critical as 65% of the city is located at an altitude of 1.5 m above MSL [START_REF] Scussolini | Adaptation to sea level rise: Amultidisciplinary analysis for ho chiminh city, vietnam[END_REF][START_REF] Vachaud | Flood-related risks in ho chi minh city and ways of mitigation[END_REF].

The flow of the Saigon River is predominantly driven by tidal currents, which affects both the water level and water discharge, with regular exfiltration of water in some urban districts during high spring tides. Over the year, precipitation follow two contrasted seasons: the dry season, usually ex- tending from November to April and the wet season from May to October, which gathers about 80% of the total rain [START_REF] Nguyen | Nutrient dynamics and eutrophication assessment in the tropical riversystem of Saigon -Dongnai (southern Vietnam)[END_REF]. Rain event can be severe and precipitation record can locally exceed 300 mm/day, even 400 mm/day. In this paper, we focus on one of the most severe rainy events that experienced the city. It occurred in November 2018, when Usagi typhoon hit HCMC. During this event, there was an increase of more than 300 mm in rainfall which caused flooding, material and human damages reported in several newspapers.

Water discharge estimation

The instantaneous water discharge was estimated by applying a stage-falldischarge (SFD) rating curve adapted from the general Manning-Strickler law, previously tested and validated by [START_REF] Camenen | Estimation d'une courbe de tarage hauteur-dénivelée-débit pour une rivière influencée par la marée[END_REF]. This model is based on the assumption of a pseudo-uniform flow and a prismatic section, which is valid for a low slope river with slowly varying water depth.

Considering an almost constant flow between two water level measures, the Manning-Strickler equation can be applied : Another assumption made here is that the river section and reach are stable with no significant bed changes. It was verified by comparing the different bathymetries from both ADCP campaigns.

Q = KA w R 2/3 h √ S (
Two hydrological stations are needed to evaluate the hydraulic slope of the river: the water level z up and z dn were measured at Phu Cuong (upstream station) and Bach Dang then Thao Dien stations (downstream stations), which are named "PC", "BD", and "TD" hereafter (see Fig. 1a). Distance between stations is around L = 42 km (PC-BD) and L = 35 km (PC-TD), which is sufficient to have a significant difference in altitude and to estimate slope with a good resolution for this low slope system. Due to tides and flow oscillation, the slope oscillates between positive and negative values.

Also, the slope in Equation (1) (S = (z up -z dn )/L) does not correspond to the local slope at the point in which the discharge is evaluated, i.e. at Phu Cuong (PC). Indeed, because of the distance between the two stations, the flow discharge is slightly different at the two stations. However, this spatial offset can be translated as a lag time ∆t. As a consequence, the discharge estimation at PC can be written :

Q(t) = KA w (z up (t))R h (z up (t)) 2/3 |S(t + ∆t)| S(t + ∆t) |S(t + ∆t)| (2)
By specifying the river cross-section at PC (assuming it is relevant for the whole reach), one can easily evaluate A w and R h as a function of the water level z up . In Equation ( 2), two parameters need to be calibrated :

-K, the Strickler coefficient of the river reach supposed homogeneous between PC and BD, -∆t, a priori negative since the tide progresses from downstream to upstream.

The Strickler coefficient could vary depending on the discharge due to additional head losses at low flows (emergence of sills) or at high flows (flood-plain interaction). Nevertheless, such cases were not observed for the specific case of the Saigon River.

Water level measurement

To apply Equation ( 2 stations and water levels measured by the SIHYMECC at the staff gauges for the selected period between 24th to 27th of June 2017.

Residual discharge

In order to study the net water discharge in the Saigon River and evaluate the hydrological cycle, the instantaneous water discharge needs to be averaged over four tide periods (T tide = 12.4 hours) to filter the 1st order semi-diurnal tidal signal from the data-series. As a consequence,

Q n (t 0 ) = t 0 +2T tide t 0 -2T tide Q(t)dt.
In the same way, the tidal averaged water level can be obtained such as z ta (t 0 ) = t 0 +2T tide t 0 -2T tide z(t)dt. The acquisition of data at high frequency (every 10 minutes) and over a longer period (two hydrological seasons) is a prerequisite to such evaluation.

Calibration and validation of the SFD rating curve

Calibration of the model using ADCP campaigns

Two Acoustic Doppler Current Profiler (ADCP) campaigns have been led

in September 2016 and March 2017 with a Rio Grande 600 kHz [START_REF] Dinehart | Repeated surveys by acoustic Doppler current profiler for flow and sediment dynamics in a tidal river[END_REF]. During 24 hours and for every hour, one gauging, i.e. three transects, was realized at PC with a boat and a georeferenced ADCP mounted on it. ADCP campaigns were used to calibrate the water discharge estimation, calculated with Equation (2) [START_REF] Camenen | Estimation d'une courbe de tarage hauteur-dénivelée-débit pour une rivière influencée par la marée[END_REF]. K and ∆t were calibrated such as the modelled discharge fit to observed data (see also appendix Appendix A.1). Also, since it is very difficult to evaluate the exact vertical reference level of each station, these campaigns were used to optimize the measured difference z up -z dn using an additional parameter ∆z (i.e. z up -z dn = (z up -z dn ) measured + ∆z)

To ensure the robustness of the hydraulic model, calibration were realized during both wet (September 2016) and dry (March 2017) seasons. These ADCP campaigns were also used to determine the river cross-section characteristics, to calculate A w and R h as a function of water level. Error in ADCP measurements were evaluated at 10% using a minimum error value of 100 m 3 /s since the conditions were adverse [START_REF] Le Coz | Estimating the uncertainty of streamgauging techniques using in situ collaborative interlaboratory experiments[END_REF]. We found the best results using a Manning-Strickler coefficient K = 26 m 1/3 /s and ∆t = -2.0 hours (see Appendix Appendix A.1). Results are presented in Fig. 3. Very good results can be observed for the September 2016 campaign.

Some slight error may be observed at the end of the first flow peak (Fig. 3a, at around 21 h, local time) but it is due to a pressure gauge outside water. For the 2017 campaign, results are not as accurate but still in good agreement with data. The clear asymmetric semi-diurnal tidal signal for this specific day may explain some of the differences [START_REF] Camenen | Estimation d'une courbe de tarage hauteur-dénivelée-débit pour une rivière influencée par la marée[END_REF].

Validation of the model using other discharge estimations

The monthly campaigns made by SIHYMECC also include some discharge estimation every hour for 48 h. They applied the velocity index method by measuring the water level and the depth-averaged velocity u index at one location y index [START_REF] Chen | Real-time discharge measurement in tidal streams by an index velocity[END_REF] :

Q = α index u index A w (3)
with α index = 0.8 a calibration coefficient, α index = U/u index where U = Q/A w the section-averaged velocity. Again, since conditions are adverse, we roughly estimated the error from this method equal to 15% plus a minimum error of 150 m 3 /s based on [START_REF] Ruhl | Computation of discharge using the index-streamflow: method in tidally affected areas[END_REF].

The corresponding water discharge estimated from Equation ( 2) are in good agreement with data from SIHYMECC (Fig. 4). The model tends to yield smaller peak values for the inward discharge (negative), even if this trend is not observed for all tidal cycles. This tendency is more pronounced for the 2018 data (Fig. 4b). This could be the consequence of either a varying flow repartition throughout the river section during the ebb and flow not caught by the index velocity method or a bias on the slope estimation by the proposed model based on a slope downstream of the station.

(a) (b)

A two-year monitoring of the Saigon River

Water levels

The water level time series, which covers two hydrological years, from January 2017 to December 2018 are reported in Figure 5a. For both stations, the reference level is the one of the staff gauge, which explains the existence of some negative values.

In each panel, one can see that the tidal signal clearly predominates with a strong semi-diurnal harmonic. 5b). The cyclicity is evidenced on both PC and TD stations, who present synchronous fluctuations, with a time laps of about 1 hour and 10 minutes, which corresponds to the duration of tide wave propagation between the two stations. The tidal magnitude is smaller at PC station (by 17%) than at TD station, which is physically consistent with quadratic friction dissipation: the further upstream the station is, the less it is influenced by tidal waves. Tidal range was measured to oscillate between -2.00 m and 1.50 m at both TD and PC stations.

A first glimpse of seasonality can be observed in the two-year time-series.

One can observe lower water levels in the rainy season (May to September) and higher values in the dry season (November to March). Semi-diurnal tidal forcing clearly prevails on the Saigon River. As illustrated in Figures 2 and5, the 14-days cycle presented both symmetric and asymmetric tides; a pattern which is particularly well developed in the estuaries located in the south of Vietnam [START_REF] Chen | Real-time discharge measurement in tidal streams by an index velocity[END_REF].

Continuous discharge estimation

By applying Equation ( 2), one can easily calculate the discharge at PC using water levels from both PC and TD stations. Water discharge is dominated by tidal semi-diurnal harmonics, as for water level data-series (Figure 6). Instantaneous discharge ranged from -2000 to 2000 m 3 /s. The oscillation of the magnitude of tides, between spring and neap tides, is also wellidentified (Figure 6b). Maxima appears at every syzygy between the sun, the moon and the earth. Overall, the analysis of instantaneous water discharge highlighted the predominant forcing of tides on any other forcing, despite contrasted and well-marked dry and rainy seasons.

Residual discharge

All data from the SIHYMECC field campaigns (which correspond to a 48 h experiment ≈ 4T tide ) were compared to the proposed model (Figure 7a).

As expected, since the model yields smaller amplitudes (see Figure 4), one (a) generally finds a good correlation but with few outliers, predominantly above the 1:1 line for negative discharge and bellow the 1:1 line for positive discharge (i.e. |Q model |lsim|Q(CEM )|). One should note also the many zero values for the SIHYMECC (CEM) data, which may be the consequence of a human bias (while reading the staff gauge) and partly explains uncertainties in experimental data and some of the scatter in the model response. A comparison of the net water discharge measured by the SIHYMECC and modelled by our group is reported in Figure 7b for the 18 SIHYMECC campaigns available.

One can realize the order of magnitude of difference between maximum in-stantaneous discharges and net discharges, which corresponds to the red box in Figure 7a. Despite the existence of few outliers, modelled points for net water discharge are well correlated with the estimation from measured data.

As expected, since the model yields smaller amplitudes, the net discharge estimations are generally smaller. The existence of outliers is not surprising looking at the sensitivity of such calculation while net discharge is one order of magnitude smaller than peak values (positive and negative). But surprisingly, while SIHYMECC estimations of the net discharge are always positive, the proposed model leads sometimes to negative values. Although difficult to explain, these negative values may results from a combination of factors, such a the asymmetrical tides and complex exchanges with groundwater and with the canal network. The regular occurrence of salt intrusions up to HCMC does confirm the possible occurrence of a net negative water discharge. Also, as discussed above, the model is very sensitive to a possible error in water level estimation a PC and BD, TD stations. Hence, for the net discharge, we evaluated the error equal to 20 m 3 /s per cm of error in the vertical axis (See Appendix Appendix A.3).

The net discharge time series over this two-year experiment is presented in The seasonality of precipitation in this tropical humid low lying area is expectedly a main driver of the annual hydrological cycle, but as shown previously, this driver is masked by the tidal forcing (Figure 8). The wet season is expectedly linked with a higher water discharge and a refill of the groundwater. At the opposite, the dry season is expectedly linked with a decrease of the net water discharge. This general pattern was broadly described by [START_REF] Nguyen | Nutrient dynamics and eutrophication assessment in the tropical riversystem of Saigon -Dongnai (southern Vietnam)[END_REF], but needs to be detailed and should be understood on a physically based characterization of the main drivers. One Monthly cumulated precipitation, tide-averaged water level along the Saigon and Dong Nai rivers and monthly averaged net-discharge are presented in Figure 9 for the 2017 and 2018 periods. 2017 and 2018 rainy seasons are well-marked, with an increase in precipitation from June to October. In 2018, precipitations are particularly high and extended in October and November. The tide-averaged water level follows the rain pattern for both increasing and decreasing phases, with a shift of two to three months.

Q [m 3 /s] Q net (t) smooth Q net (t) Q net (CEM) exp. Q net (CEM) mod.
Concerning the averaged water discharge, it was measured to fluctuate between -100 to 500 m 3 /s (no value is given for January 2018 since there was a too long gap in data), with a magnitude that was quite different for both hydrological years. In 2017, one can noticed that there exists an expected time lag between rain (first) and river discharge (few weeks later) but there is also some delayed and complex interactions with groundwater and/or water level.

The rainy season, which started in May, was followed by a rise in the net water discharge only one month later (June) and then, by an increase of the tide-averaged water level few weeks later (July, August). Also, one observed a drop in net discharge in August and September, while precipitation remains high followed by a rise in net discharge while precipitation dropped. It seems there are strong exchanges of water with groundwater and/or floodplain with a possible recharge from the groundwater and/or floodplain in May, August and September and a restitution to the river in November and December.

However, [START_REF] Van | Groundwater and river interaction parameter estimation in Saigon River, Vietnam[END_REF] estimated possible exchanges of water between the aquifer and the Saigon River of approximately 0.02 m 3 /s per km, which appears negligible. Effects of the large drainage and canal system around the Saigon River (i.e. of the Saigon floodplain) may prevail here. Also, anthropogenic influences such as Dau Tieng dam management or water supply pumping may significantly affect the net Saigon river water discharge. In 2018, cumulated rain also led to an increase of the net water discharge, but the rise of the Saigon River discharge was much faster and almost correlated to precipitation, which lets presume that soil saturation was higher and led to a direct response of the river to precipitation. Even if rain initiated late in the season, as compared with year 2017 (May and June in 2018 instead of April and May in 2017), the shape and cumulative rain during the rainy season was quite comparable to 2017. The net water discharge responded similarly to 2017 in June 2018. However, in 2018 the response to precipitation is stronger with a monthly averaged net discharge above 400 m 3 /s in July and August. Surprisingly, in October and November, while precipitation is particularly high in fall and superimposed with the extreme typhoon event in late November, one can observe a decrease of the net discharge. Significant floods have been observed during this period; a large part of the water volume from precipitation may have been spread over the flood plain.

Looking at the tide-averaged water level (Figure 9b) measured at Phu Cuong (PC), Thao Dien (TD) or Phu An (PA), but also at Vam Sat (station located downstream on the Dong Nai River at approximately 20 km from the see shore), a clear annual fluctuation can be observed with high water levels from October to April. Such high values yield an averaged negative slope leading some counter-effects on the net discharge (see Appendix Appendix A.3). Interestingly, these fluctuations are not correlated to precipitations and could partially explain the deficit in net water discharge compared to precipitation. We suspect these fluctuations to be a consequence of the peak flow season of the Mekong River, which occurs in October in coastal floodplain and could influence sea water levels next to its river mouth (Chen et al., 2012a;[START_REF] Ho | Integrated urban flood risk management approach in contextof uncertainties: case study Ho Chi Minh city[END_REF][START_REF] Ha | Water and suspended sediment budgets in the Lower Mekong from high-frequency measurements (2009-2016[END_REF].

(a)

Effects of long-term precipitation

To get further in the analysis of the interaction between the Saigon River discharge and its surrounding floodplain, we examined a pluriannual monthly rainfall series (Figure 10). This series was associated with the severe Niño event in 2015-2016 [START_REF] Thirumalai | Extreme temperatures in Southeast Asia caused by El Niño and worsened by global warming[END_REF][START_REF] Ha | Water and suspended sediment budgets in the Lower Mekong from high-frequency measurements (2009-2016[END_REF]. In Figure 10 The 2017-2018 and 2018-2019 hydrological seasons that are reported in Figure 9 support the hypothesis of a phase of recovery after these pluri-annual droughts. The 2017 wet season showed a priori an atypical hydrological response, with a normal rainy season from May to October, that is not in correlation with water discharge due to strong exchanges with groundwater.

There is indeed a shift between the discharge increase and the precipitation increase. This may result of years of deficit in the groundwater.

In 2018, the precipitation-river interaction was noticeably different. It seems the groundwater recovered from the drought leading to a more typical response of the river to precipitation. We may hypothesize that regional recovery of rain during the last wet seasons (2016-2018) led to a recharge of the groundwater table, and further of the river, presumably because the floodplain had fully recovered from the pluri-annual drought that hit the region from 2013 to 2016.

Conclusion

Tidal rivers and their floodplains are environments that are hardly monitored although they concentrate most of the human activities and form habitats for rich ecosystems. Flood risk is a particular issue for such environment and requires a good monitoring and a good understanding of its hydrological functioning.

Here, we applied a stage-fall-discharge rating curve adapted from the general Manning-Strickler law [START_REF] Camenen | Estimation d'une courbe de tarage hauteur-dénivelée-débit pour une rivière influencée par la marée[END_REF] to assess the instantaneous water discharge on the Saigon River. For the first time, the Saigon River dynamics was recorded at high frequency over two hydrological years, including the heaviest rain event ever recorded. Such tool appears to be a very efficient and low cost system for estimating discharge in tidal rivers as soon as the tidal wave is not too asymmetric and the river bed is stable (no general erosion nor aggradation). One issue remains the estimation of the slope since the model is sensitive to possible errors in water level measurements. This is particularly true for the Saigon River, which is tide dominated.

The analysis of the data-series highlights the driving role of tides on water mixing and water dynamics at hourly to monthly timescales. Once filtered from tidal effects, the averaged water level and water discharge points out a rather small contribution of run-off and a potential significant impact of flood plain and anthopogenic structures (Dau Tieng dam, canals, water pumping, etc.) to the hydrosystem response. The study of river response during Usagi typhoon illustrated how much the floodplain can attenuate the direct impact of intense precipitations and how critical is the sea water level to evacuate the flood. Also, the seasonal variability of the tidal-averaged water levels showed a possible influence of the nearby Mekong high flows that reduces the slope and limits the net discharge.

This study remains a preliminary study, which needs to be completed with some further studies on groundwater and flood plain dynamics, including the complex canal network in HCMC to get an accurate evaluation of the vulnerability of the megalopolis to flooding during extreme events and how this vulnerability will evolve during coming decades under a worrying context of subsidence and sea level rise.

both ADCP campaigns in September 2016 and March 2917 (see Figure 3), the estimation of the error was made using the mean square error :

E(Q) = 1 n Σ n i=1 [Q mod. (t i ) -Q meas. (t i )] 2 (A.1)
where n is the number of gauging data, Q mod. the modelled discharge and Q meas. the measured discharge, and t i the time of each of these gauging.

As can be observed in In Figure A.16 is presented the errors in discharge estimations for an error of 1 cm in the upstream water level. Surprisingly, effects are very similar (opposite) to the error in the downstream water level. Indeed, the sensitivity on the water slope is much higher than the sensitivity on the hydraulic radius or wet section.

Figure 1 :

 1 Figure 1: Location of the study site: the low elevation coastal zone of the Saigon

  1) with Q the water discharge [m 3 /s], K the Manning-Strickler coefficient [m 1/3 /s], R h = A w /P w the hydraulic radius [m], A w the wet section [m 2 ], P w the wet perimeter [m], and S the energy slope [-] assumed equal to the water slope.

  ), CTD Diver sensors were installed at PC and BD stations in September 2016, as reported in Figure1. They measure pressure, conductivity and temperature every 10 minutes. Conductivity and temperature were considered as interesting proxy to interpret data series and were used to operate the post processing, but were not used directly in this study. PC sensor was immersed in the river bank of the Saigon River, close to Phu Cuong city, TD sensor was immersed in the heart of the megalopolis of Ho Chi Minh City, in Thao Dien district. Both sensors were downloaded at a bimonthly basis. Due to logistical constraints, significant risks of theft and of mechanical deterioration, TD station was displaced twice with a corresponding adjustment of parameters in Equation (2) (L and ∆t). The sensor was initially installed in Bach Dang, in the city centre in September 2016. It was moved to Boat House (BH), 8.5 km upstream of the Bach Dang point, from January 2017 to the 8th of March 2017. Then, since 15th March 2017, the sensor is located in Thao Dien Village (TD). Distance between those two last sites is only 0.9 km. To compensate water level measurements from atmospheric pressure fluctuations, a barometer was installed at the CARE center (Ho Chi Minh University of Technology, see Figure 1). Some drifts of the water level measurements were observed due to fine deposit accumulation leading to over-pressure. Water levels at PC and TD were thus corrected based on monthly campaigns made by Sub Institute of Hydrometeorology and Climate Change (SIHYMECC, also named CEM hereafter for Center of Environmental Monitoring) of Ho Chi Minh City. During these campaigns, water levels were measured every hour for three days at the staff gauge. In Figure 2, one can observed a very good agreement between CTD Diver data and SIHYMECC data (the water level H corresponds the the difference between the water surface level and a reference level of the station).

Figure 2 :

 2 Figure 2: Comparison between water levels H measured at PC (a) and TD (b)

Figure 3 :

 3 Figure 3: Water discharge Q comparison between model results and ADCP mea-

Figure 4 :

 4 Figure 4: Water discharge Q estimated at PC (blue line) in comparison to SI-

Figure 5 :

 5 Figure 5: Two-year water level time series measured at PC and TD (a) with a zoom

Figure 6 :

 6 Figure 6: Two-year water discharge time-series (a) with a zoom in September 2018

Figure 8 .Figure 7 :

 87 Figure 8. Compared to monthly SIHYMECC campaigns, we observe more

  can clearly observe the seasonality of net discharge with peak values during the wet season in Figure8. In 2017, the peak net discharge reached 300 m 3 /s and lasted only for two months in the beginning of June and the end of July. In 2018, net discharges exceeded 200 m 3 /s from June to August with maximum values up to 400 m 3 /s. It then decreased before a second peak was reached in early October. Flood extended for more than four months, leading to one of the most humid year of the decade (Fig.8and Fig.10).

Figure 8 :

 8 Figure 8: Net discharge Q net estimate for the January 2017 -December 2019 period

Figure 9 :

 9 Figure 9: Monthly rainfall P M (a), tide-averaged water levels H net (b) and monthly

  , one can observed a regular decrease of annual precipitation from 2009 to 2012, a minimum from 2013 to 2015 and an increase until 2019. The 2013-2015 period thus corresponded to a deficit of rain, which was evidenced by socio-political impacts described in newspapers, particularly during the dry season 2015-2016. This dry season was reported as the worst drought ever reported in 98 years of monitoring in the floodplains of Mekong and Saigon-Dong Nai hydrosystems.

Figure 10 :

 10 Figure 10: Monthly rainfall P M at HCMC from 2009 to 2019.

4. 5 .

 5 Effects of an extreme event : the Usagi typhoonOn November 25th 2018, HCMC was hit by Usagi typhoon. Arriving from the East-south east, it reached the coast, at 50 km of Ho Chi Minh City (Ba Ria-Vung Tau Province) in the morning. It continued moving Northwest ward inland over HCMC area as a tropical depression. According to the forecast department of the Southern Centre for Hydro-Meteorological Forecasting (SCHMF), this was the highest ever recorded rainfall in a 24-hours period in the megalopolis. Districts of HCMC recorder cumulated daily rain between 293 to 408 mm for independent stations of measurements. While there is no debate concerning the intensity of Usagi event and the severity of damages, there is a clear interest in understanding how the hydrosystem physically behaved during and just after those exceptional rains. The paradoxical result is the lack of direct response of the Saigon River to the extreme precipitations. As shown in Figure5, none of the two hydrological stations recorded any consistent rise of the water level during the typhoon, neither any flash rise of the river discharge (Figure6). The only noticeable impact recorded by our CTD sensors was a slight reduction of the conductivity at the PC station, which dropped of a few percent when Usagi typhoon hit the region (not shown in this paper). Could we conclude that the typhoon had no effects on the river dynamics? Has the Dau Tieng dam totally regulated the flow? By comparing data from November 2017 and November 2018, one can observe that despite a rise of rainfall by a factor six, mean water level and net discharge reduced during this period by 15% (0.77 m in Nov. 2017 and 0.66 m in Nov. 2018) and 50% (65 m 3 /s in Nov. 2017 and 30 m 3 /s in Nov. 2018), respectively. It clearly shows that floods observed in some districts of the city during the event were not directly driven by the overflow of water from the river, but certainly by intense run off over impervious streets (Figure??). Also, we should not underestimate the potential impact of the Dau Tieng dam management; large amount of water may have been retained during the flood and slowly reinjected in the river during the following weeks. Finally, since tide-average water levels in the Saigon River are relatively higher in late fall (Fig.9), and because of the storm surge, evacuation of water may have been more difficult. This can be explained by the morphology of the delta, which is very flat and close to the sea shore. Results from Scussolini et al. (2017) showed the influence of the sea level on HCMC flood risk. Ho et al. (2014) argued that main hydrological explanations for the flood risk on HCMC area are the upstream floods including those of the Dong Nai and Mekong delta, local rainfall, land subsidence, and sea level rise. Our results indicate that local rainfall and sea water level may be the most impacting factors.

Figure

  Figure A.11: Sensitivity of the Strickler coefficient K (a) and time shift ∆t (b) on

Acknowledgements

This study was supported by the Rhône-Alpes region through the CMIRA Coopera financial support. We also wish to thank all the interns who worked on that topic throughout the last two years : Rémi Saillard, Lucas Barbieux, Roman Smoliakov and Swann Benaksas, as well as everyone involved in the two 24 hours field campaigns, which made the calibration possible. We are grateful to Dr. Nguyen Phuoc Dan from CARE center (HCMUT) and Dr Nguyen Van Hong from the sub-institute of hydrometeorology and climate change center (SIHYMECC) for providing us the field measurements of water level and water discharges.

Appendix A.1. Error calculation on experimental data

A first sensitivity analysis was made to optimize main model parameters (Strickler coefficient K and the time shift ∆t) to experimental data. For lead to some underestimation of the peak discharge values. Indeed, the error calculation is biased by the more numerous data points around the zero discharge value, which are slightly better predicted using a smaller value of the Strickler coefficient (see also Figure 3). Also, the effect of ∆t appears not as sensitive. It is also important to realize that water level data present a frequency of 10 min (1 min in September 2016), which does not allow to

Appendix A.3. Sensitivity analysis on input

The only input data of the model (Equation ( 2)) are the water levels measured at the downstream and upstream stations. The water level measured downstream will only affect the slope estimation whereas the water level upstream (at the reference station PC) will also affect the estimation of the wet section and hydraulic radius.

In Figure A.15 is tested an error of 1 cm in the downstream water level on the estimated discharge. The effect is inversely proportional to the discharge and errors can reach 300 m 3 /s for Q(t) ≈ 0 assuming an error of 1 cm. The asymmetrical shape of the figure is due the the time shift on the slope. On the other hand, error on the net discharge is quite limited but not negligible with an error of ±20 m 3 /s approximately.


F Lauters
email: fabien.thollet@inrae.fr

Long term high frequency sediment observatory in an alpine catchment: the Arc-Isère rivers, France

Keywords: long term observatory, database, suspended sediment concentration, turbidity, water discharge, sediment fluxes, Alpine rivers

We present a dataset on to the Arc-Isère long-term environmental research observatory, which is part of the Rhône Basin Long Term Environmental Research Observatory. This alpine catchment located in the French Alps is characterized by high Suspended Particulate Matter (SPM) in anthropogenized valleys. Suspended Sediment Concentrations (SSC) naturally observed in the river are very high, ranging from a few tens of milligrams per litre at low flow to tens of grams per litre during major natural hydrological events (floods, debris flows) or river dam hydraulic flushes. One research objective related to this site is to better understanding the SSC dynamics along the river using a system of nested catchments (Arvan, Arc, and Isère) in order to assess both temporal and spatial dynamics. The data allow the quantification of fine sediment yields and also the evaluation of possible morphological changes due to fine sediment deposition or resuspension. Additionally, the observatory database support studies on contaminants (either dissolved or particulate contaminants). Our monitoring

DATA SET NAME

High frequency suspended sediment flux database on a French Alpine river system.

OBJECTIVES AND SITE DESCRIPTION

The Arc-Isère observatory is located in the French Alps (outlet of the observatory near Grenoble: longitude 5.7689497E / latitude 45.1974416N / altitude 207.8 m). The site was designed to study alpine rivers that carry large amounts of fine sediments, typically yielded by erosive badland sub-catchments such as in the Arvan river system. The bed river sediment material consists of a mixture of coarse (gravel and cobble) and fine (sand, silt and clay) particles. The Arc and Isère rivers were significantly modified due to anthropogenic changes such as dykes linked to the construction of motorways and railways, numerous hydroelectric diversions and dams, and sediment mining. It resulted in the formation of a system of alternate gravel bars within a constrained river [START_REF] Jaballah | Alternate bar development in an alpine river following engineering works[END_REF]. In addition, a decrease of pasture land area and restoration works within the catchment modified the relative input quantity of the different types of sediments to the river. Since the end of the 20th century, there has been a relative shortage of coarse particle input, and an excess of fine particles. The resulting imbalance led to erosion of the main channel, aggradation and fixation of gravel bars due to massive fine sediment deposits and riparian vegetation growth [START_REF] Jourdain | Morphodynamics of alternate bars in thepresence of riparian vegetation[END_REF].

The hydrological monitoring and resulting data from the catchments are useful in the assessment of:

• The characterization of deposition and erosion of fine sediments over gravel bars along alpine rivers;

• The assessment of the exchanges of fine sediments between flow and bed matrix (infiltration, exfiltration);

• The understanding of the spatial and temporal variations of fine sediment transport including sand in the river system;

• The quantification of the forcing effects of bar morphology, hydrology, sediment supply, riparian vegetation on fine sediment dynamics;

• The study of the contaminant transport.

The Arc-Isère river system is a reference site of the ZABR (Zone Atelier du Bassin du Rhône [Rhône Basin Long Term Environmental Research Observatory], https://www.zabr.assograie.org/) that has been monitored for more than 15 years [START_REF] Mano | Suspended Particle Matter dynamics in four alpine watersheds (France): influence of climatic regime and optimization of load calculation[END_REF][START_REF] Némery | Carbon and suspended sediment transport in an impounded alpine river (Isère, France)[END_REF][START_REF] Antoine | Downstream erosion and deposition dynamics of fine suspended sediments due to dam flushing[END_REF].

Six hydro-sedimentary stations (see Figure 1) have been established in a system of nested catchments (Arvan, Arc and Isère catchments), and provide data for both water discharge and sediment concentration (operated by INRAE, IGE). The network is completed by stations operated by partners (EDF, DREAL Rhône-Alpes Auvergne: Directions Régionales, de l'Environnement, de l'Aménagement et du Logement [Regional board for the environment, planning and housing]). All the partners work in synergy, for example in choosing the location of the stations, to better investigate the whole catchment.

The three catchments have distinct characteristics (see Table 1): the Arvan River is a steep mountain torrent, highly responsive to precipitation and snow melt events. The Arc River is a mountain river with an average slope of 1%, with several river dams. Regular dam flushes (yearly if no significant flood occurs), produce substantial fine sediment transport. Lastly, the Isère River is a larger piedmont river where gravel bars are affected by fine sediment deposits and vegetation spreading and where flood risk is a significant issue. The Arc and the Arvan catchments produce substantial yields of suspended particulate matter (SPM), which is transported along the river system or temporarily stored over gravel bars or within dam reservoirs.

DESCRIPTION OF HYDRO-SEDIMENTARY STATIONS

Water discharge and suspended sediment concentrations (SSC) are measured at each station. Data are recorded with time steps varying from two to thirty minutes. Times steps are adapted to accurately measure natural variations of these parameters.

Flow discharge time series are based on water level measurements recorded continuously on a datalogger. Using different gauging methods on each station (see Table 2), rating curves were established by applying either a classical stage/discharge relation or the index velocity method coupled to a stage/area relation with an average velocity [START_REF] Thollet | Mesure de débit en rivière par station radar hauteur / vitesse selon la méthode de la vitesse témoin [Streamflow monitoring at stage / velocity radar stations using the index velocity method[END_REF]. The uncertainty of the calculated discharge is determined from the mean deviation between the measured flow rates and the rating curve using a dynamic approach described in Morlot et al. (2014) or a bayesian method (Baratin; [START_REF] Dramais | Advantages of a mobile LSPIV method for measuring flood discharges and improving stage-discharge curves[END_REF][START_REF] Le Coz | Combining hydraulic knowledge and uncertain gaugings in the estimation of hydrometric rating curves: a Bayesian approach[END_REF] depending on the station. Suspended Sediment Concentrations (SSC) are measured indirectly from a turbidimeter signal (see Table 3). Hach Lange SC100 or SC200 controllers are used in addition to numerical Solitax SC probes, all equipped with a mechanical cleaning system (wiper). Sensors use the infrared scattered light method with the optical response being dependent on sediment characteristics in the water.

Therefore, a site-and sensor-specific calibration across a wide range of concentrations was established for each location. Each site relationship, SSC = f(turbidity), is asssumed linear with a single segment.

The calibration of this relationship is done using water samples obtained using ISCO 3700 or Sigma SD900 automatic samplers controlled by the turbidimeter (i.e. triggered if a minimum turbidity value is reached with a frequency of one sample every 30 min unless the turbidity drops again below the minimum value) or by the datalogger (i.e. triggered for a specific programmed period with a frequency of one sample every 30 min). SSC were determined by filtration of a precise volume of each water sample (ranging from 50 to 500 mL) through pre-weighed fiberglass filters and by drying them at 105 °C for 2 hours (Standard NF EN 872, ISO 4365, 2005). Results are expressed in mg L -1 , and the uncertainty on triplicates was estimated to be below 5%. Sand was recently excluded from the analysis [START_REF] Dramais | Comparaisons de méthodes pour la mesure des matières en suspension dans les cours d'eau en présence de sable [Methods comparison for river suspended sediment measurements containing sand[END_REF]. The evaluation of the SPM fluxes as a product of discharge and concentration is premised on SPM concentration being homogeneous throughout the river section, which is not the case for sand concentration. Moreover, since turbidity is inversely sensitive to the size of the particles [START_REF] Thollet | Influence de la granulométrie des particules sur la mesure par turbidimétrie des flux de matières en suspension dans les cours d'eau [Influence of particle grain size to the suspended sediment concentration measurement by turbidity[END_REF], the inclusion of sand particles may significantly bias the turbidity/SSC relationships.

A particular effort has been made to quantify uncertainties of turbidimetric measurement and associated variables. The uncertainty of the calculated SSC is determined from the mean deviation between the measured SSC and the SSC obtained with the turbidity calibration curve (table 3). Taking into account the calibration curve and sample analysis, uncertainties on high frequency SSC data series are variable, generally between 10 to 20% [START_REF] Navratil | Global uncertainty analysis of suspended sediment monitoring using turbidimeter in a small mountainous river catchment[END_REF][START_REF] Arnaud | Influence de la mesure du débit sur l'incertitude liée au calcul de flux de mes et de carbone organique particulaire. Application sur un cours d'eau alpin (l'Isère à Grenoble, France)[END_REF]. More precisely, on the Isère River calculated uncertainties reach 20% for SSC below 2 g/L and 8% for SSC above 2 g/L [START_REF] Némery | Retour d'expérience sur l'utilisation de la turbidité en rivière de montagne [Feedback on the use of turbidity in mountainous rivers[END_REF]. Because the turbidimeter is sensitive to the variation in size of suspended particles (Thollet, 2014), the established relationship turbidity/SSC can be unstable for stations that are relatively close to sediment production areas, for example on the Arvan River or on the Arc River at Pontamafrey. We also observed for these stations a seasonal effect according to hydrological event types (storm event, snow melt, flood event, low flow). At Pontamafrey, depending on the event type, the calibration coefficient for turbidity/SSC varies between 0.8 and 1.6. In contrast, on the Isère River at Grenoble, grain sorting with flow propagation yields a more stable grain size distribution and a more stable relationship. Sampling is carried out regularly to ensure there is no change in the relationship between turbidity and SSC (see Table 4). Such sampling is also necessary when a turbidity probe is replaced or after maintenance. Results of SSC analysis are displayed on the SSC time-series to check the accuracy of the turbidity/SSC relationships and to better appreciate the samplings frequency.

DATA PROCESSING AND ONLINE STORAGE

Raw data is processed manually and individually for each station from one to four times a year, using DP+, a specific software from French hydrometric services (only available to the French hydrometric community), spreadsheets and custom R programs. Processing includes removal of duplicated data, identifying gaps and aberrant points, applying linear corrections when staff gauges show data drifts etc. The process takes into account all the observations (maintenance and events that affected data values, staff gauge readings) made by technical staff during station visits. A quality level code is assigned to each data value related to the metrology and the corrections made during data processing.

The quality codes are categorised as "valid", "doubtful", "estimated", a "no code" (because of no traceability for example). When no data are reported, the code is either "gap" (no measurement) or "invalid" (irrelevant measurement that was removed). Gaps and invalid measurements are marked as -9999 and they are graphically identified with a red line.

DATA AVAILABILITY

The time series are made available on a public online database named "Base de Données des Observatoires en Hydrologie (BDOH)" [START_REF] Branger | Le projet base de données pour les observatoires en hydrologie : un outil pour la bancarisation, la gestion et la mise à disposition des données issues des observatoires hydrologiques de long terme à Irstea [Database for hydrological observatories: a tool for storage, management and access of data produced by the long-term hydrological observatories of Irstea[END_REF] for Database of Hydrologic observatories (https://bdoh.irstea.fr/ARC-ISERE/) with a DOI reference (10.17180/OBS.ARC-ISERE). The database is available in French and in English. This database gives access to data in a homogeneous form that can be used by all scientists. Data can be browsed and visualized without limitation by anyone. However, an individual registration is requested to be able to download data. This allows us to keep in touch with data users, and let them know when updates occur. In addition, the registration provides us some statistics about the different user profiles that support our requests for funds to maintain and develop the BDOH application. Registered users can select and freely download the data they are interested in with functionalities like time step interpolations, average calculation. Data are exported as flat text files that contain all the necessary metadata (producer, variable, unit, time zone, conversion factors) and can be processed by any software (Excel, R, Matlab, etc.). Data managers also have access to additional functionalities, such as detection of duplication or inconsistency when importing new data in BDOH, rating curve management for stage to flow discharge or turbidity to SCC conversions, automatic calculations of derived data including integrated fluxes, and reporting tools. These tools help them to maintain complete, consistent and quality-controlled datasets.

Currently 37 users are registered on the Arc-Isère observatory. Since the database was created in 2013, an average of 70 downloads have been made every year.

CONCLUSION

Acquiring this high-frequency database on the Arc-Isère observatory requires a significant investment in equipment, but above all an investment of technical and scientific labor and expertise. The length of the time-series exceeding a decade allows a study of sediment transport processes at inter-annual scales in connection with meteorological variability and land use planning. A nested basin scale approach is also relevant for the detailed spatio-temporal study of the flow and sediment propagation along the river for specific events such as spring (snowmelt) floods, debris flows, or hydraulic dam flushes. It also allows identification of deposition or resuspension processes, which are fundamental in the construction of hydro-sedimentary models. Finally, this study site, while highly developed for the production of hydroelectricity, is characterized by a mountain river with extreme suspended solid transport, and fine sediment deposit and vegetation issues. Thus, the database is of interest for river managers and water operators but also and above all for research at the regional level of the Rhône basin and the Mediterranean basin. 2005-2019 99% † Percentage of the time series period without gap (0 means no data, 100% means no gap). Gaps are represented in the data product as -9999 values. ‡ The station was moved 5 km upstream in 2019 because of the installation of a new hydraulic power station. Since there is no significant tributary on this section, the discharge could be affected only due to the small difference in the catchment area. ‡ ‡ Only water level from 2015 to 2019. Flow discharge will be soon available for 2020.

Accepted Article

Accepted Article

Accepted Article

F

Table 1 :

 1 Mean interannual Suspended sediment fluxes (SSF) on the Arc-Isère catchment (https://dx.doi.org/10.17180/OBS.ARC-ISERE)

	Station / river	Start year	Catchment area (km²)	Annual average SSF (kt)	Specific SSF (t/km²)
	Saint Jean d'Arves / Arvan	2010	58	50.3	867
	St Jean de Maurienne/ Arvan	2009	222	250	1126
	Pontamafrey / Arc	2009	1510	515 †	341 †
	Chamousset / Arc	2011	2000	1200 †	600 †
	Montmélian / Isère	2010	4840	1500	310
	Grenoble -Campus / Isère	2006	5720	1440	251
	† Because of hydropower plants and water diversions, specific fluxes at Pontamafrey and at Chamousset are underevaluated
	and overevaluated, respectively.

Table 2 :

 2 Main characteristics on flow discharge measurements

	Station / river (Owner)		Frequenc y (min)	Range (min-max in m 3 /s)	Technology	Gauging methods and devices	Uncertainty	Data availability over the period †
	Saint Jean					Ott C31 current meter
	d'Arves ‡ / Arvan (DREAL Auvergne Rhône-		6	0.4-35	Stage Radar Cruzoe Paratronic (France) and velocity Radar RQ30 Sommer (Austria)	(Germany), water surface velocity with radar SVR Decatur (Finland)	10% b	2010-2015 93%
	Alpes)
						Electromagnetic current
	St Jean de Maurienne / Arvan (INRAE)	2	0.2-80	Stage & velocity radar sensors Sommer RQ24 (Austria)	meter Flo-Mate 2000 with radar SVR Decatur Marsh Mc Birney (UK) , water surface velocity	20% b	2010-2018 73%
						(Finland)
	Pontamafrey / Arc (EDF)	6	1.6-356	Pressure sensor Hydrologic LPN8 (France)	ADCP Teledyne Rio Grande (USA)	5% b	2011-2020 97%
	Chamousset / Arc (DREAL Auvergne Alpes) Rhône-	6	2.6-427	Stage & velocity radar sensors Sommer RQ30 (Austria)	Gauging truck equipped with OttC31 current (Finland) radar SVR Decatur meter, ADCP Teledyne Rio Grande (USA), water surface velocity with	10% a	2011-2019 98%
	Montmélian				Gauging truck equipped
	/ Isère					with OttC31 current
	(DREAL Auvergne		6	26-780	Pressure sensor Hydrologic LPN8 (France)	meter, ADCP Teledyne Rio Grande (USA)	10% a	2009-2015 ‡ ‡ 99%
	Rhône-
	Alpes)
						Gauging via cableways	5% a
	Grenoble -				with Ott C31 current
	Campus Isère	/	30	33-866	Pressure sensor Ott PLS 0-10 m (Germany)	meter (Germany), ADCP Teledyne Rio Grande
	(EDF / IGE)					(USA), ADCP SonTek M9
						(USA)

Table 3 :

 3 Main characteristics on sedimentary measurements Percentage of the period without gap (0% means no data; 100% means no gap). Gaps are represented in the data product as -9999 values ‡ The turbidity sensor at Arvan amont is wintered to prevent from freezing. Only half of the year is taken into account. Winter flows are assumed to be negligible.

	Station / river (Owner)		Frequency (min)	Range (min-max in g/L)	Technology (Turbidimeter -Datalogger -Sampler)	Uncertainty	Data availability / for the period †
	Saint	Jean		0-114	Hach Lange SC100 & Solitax 0-150 g/L		2010-2020
	d'Arves ‡ / Arvan	2		(Germany) -Ott Duosens (Germany) -	10 to 20%	53%
	(INRAE)				Automatic sampler Sigma SD900 (
	St	Jean	de		0-132	Hach Lange SC100 & Solitax 0-150 g/L		2009-2018
	Maurienne/ Arvan	2		(Germany) -Ott Logosens (Germany) -	15 to 25%	76%
	(INRAE)				Automatic sampler Sigma SD900
	Pontamafrey / Arc (INRAE / EDF)	2	0-137.6	Hach Lange SC100 & Solitax 0-150 g/L Automatic sampler ISCO 3700 (USA) (Germany) -Ott NetDL (Germany)-	10 to 20%	2009-2020 95%
	Chamousset / Arc (INRAE / EDF)	2	0-83.5	Hach Lange SC100 & Solitax 0-150 g/L Automatic sampler ISCO 3700 (USA) (Germany) -Ott NetDL (Germany) -	8 to 20%	2011-2020 91%
	Montmélian	/		0-31.4	Hach Lange SC100 & Solitax 0-50 g/L		2009-2019
	Isère			60		(Germany) -Ott NetDL (Germany) -	15 to 25%	67%
	(EDF)					Automatic sampler ISCO 3700 (USA)
	Grenoble Campus / Isère (IGE / EDF)	-	30	0-46.9	Hach Lange SC200 & Solitax 0-50 g/L (USA) (Germany) -Campbell Scientific CR1000 (USA) -Automatic sampler ISCO 3700	8% to 20%	2006-2019 99%
		†

Table 4 :

 4 Calibrations of turbidimeter and sampling

	Station / river	Scale set period	Turbidity/SSC coefficient	R²	Minimum SSC (mg/L)	Maximum SSC (mg/L)	Nb of sample analyzed
		04/2010 -01/2014	1.00	0.91	4	45880	170
	Saint Jean d'Arves / Arvan	01/2014 -01/2015 01/2015 -12/2017 12/2017 -12/2018	4.01 0.76 1.05	0.91 0.900 0.96	12 15 21	4290 173278 32794	27 103 71
		12/2018 -in progress	0.6	0.70	5	22374	94
		10/2009 -08/2012	1.25	0.95	44	82580	215
	St Jean de Maurienne / Arvan Pontamafrey / Arc Chamousset / Arc Montmélian / Isère Grenoble -Campus / Isère Accepted Article	08/2012 -04/2013 04/2013 -04/2014 04/2014 -05/2015 05/2015 -01/2017 01/2017 -11/2017 11/2017 -08/2018 08/2020 -in progress 04/2009 -05/2010 02/2011 -04/2019 04/2019 -in progress 05/2011 -10/2012 10-2012 -05/2017 05/2017 -06/2017 06/2017 -in progress 01/2011 -04/2017 05/2017 -06/2017 06/2017 -in progress 03/2006 -05/2010 05/2010 -01/2012 01/2012 -10/2012 10/2012 -03/2015 03/2015 -05/2017 05/2017-09/2017 09/2017 -08/2018 11/2018-in progress	0.82 0.95 0.80 0.88 1.09 0.87 -0.99 1.08 1.32 1.08 1.03 0.75 1.00 0.91 0.90 1.15 1.07 0.93 1.07 0.72 0.54 0.66 0.66 0.88	0.98 0.98 0.86 0.96 0.98 0.99 -0.95 0.92 0.93 0.98 0.92 0.99 0.89 0.91 0.72 0.83 0.98 0.93 0.98 0.91 0.96 0.95 0.92 0.96	9 172 27 35 7 8 -40 2 300 2 6 79 1 21 100 32 6 4 6 4 3 41 9 10	27713 32658 8832 16637 83963 43758 -52486 45000 23000 18349 19818 6895 15920 3071 430 88000 13700 10210 13700 7140 6731 6478 28448 5578	43 73 153 67 79 75 1 110 680 116 215 205 18 161 215 22 386 678 530 678 565 298 145 161 149

Acknowledgements

The study took place on an experimental site of the ZABR (Zone Atelier du Bassin du Rhône) which is a CNRS-labelled site (https://www.zabr.assograie.org/). We would like to thank the guest editor Stephen Sebestyen and the two anonymous reviewers for their significant input to improve the quality of this paper. We are also grateful to the students and professors of ENSE3 who contribute to the flow measurements at Grenoble-Campus station and to the hydrologists of EDF, which is a partner in the production of the measurements of this station.

Funding

The observatory is mostly financed thanks to internal funds from research institutes, universities, research institutes, and operational companies (INRAE, IGE, Grenoble INP, ZABR, EDF). It is also supported by the French National Research Agency (ANR) under the grant ANR-18-CE01-0019-01 (Deposition and Erosion of fine sediments in Alpine Rivers -DEAR-project) and by other project funds (EC2CO Bioheffect Structurante Initiative).

Contributors

Contributors to this observatory are INRAE, IGE, EDF, and DREAL Auvergne-Rhône-Alpes. One to three persons/month per station are needed to maintain the instruments, monitoring, and data processing.

Data ownership

Flow discharge and SSC data for each station belong to the producer (contributor), i.e. INRAE, IGE, EDF, DREAL Auvergne-Rhône-Alpes as presented in Tables 2 and3.