Potentials and limitations of Sentinel-3 for river discharge assessment

Journal Pre-Proof Potentials and limitations of sentinel-3 for river discharge assessment Angelica Tarpanelli, Stefania Camici, Karina Nielsen, Luca Brocca, Tommaso Moramarco, Jérôme Benveniste PII: DOI: Reference:

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Please cite this article as: Tarpanelli, A., Camici, S., Nielsen, K., Brocca, L., Moramarco, T., Benveniste, J., Potentials and limitations of sentinel-3 for river discharge assessment, Advances in Space Research (2019), doi: https://doi.org/10.1016/j.asr.2019.08.005

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JOURNAL PRE-PROOF

POTENTIALS AND LIMITATIONS OF SENTINEL-3 FOR RIVER DISCHARGE ASSESSMENT

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Angelica Tarpanelli1, Stefania Camici1, Karina Nielsen2, Luca Brocca1, Tommaso Moramarco1, Jérôme

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Benveniste3

Research Institute for Geo-Hydrological Protection, National Research Council, Via Madonna Alta 126, 06128

Technical University of Denmark National Space Institute (DTU-Space), 2800 Kongens Lyngby, Denmark; email:

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[email protected] 3

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Perugia, Italy; e-mail: [email protected]

European Space Agency, Centre for Earth Observation (ESA-ESRIN), Largo Galileo Galilei, 00044 Frascati, Italy; e-

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mail: [email protected]

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ABSTRACT

The monitoring of rivers is not the primary objective of the Sentinel-3 mission. The first

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satellite of the constellation was launched in February 2016 and so far no study has investigated the joint use of altimeter, near-infrared and thermal sensors for discharge estimation. Nevertheless,

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similar sensors onboard other platforms have showed their ability to estimate river discharge also in scarcely gauged areas. The advantage of altimetry lies in the observation of water surface elevation,

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which can be proficiently used in approaches based on rating curve, empirical formulae or hydraulic

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modeling. Even though their use is limited, near-infrared sensors are successfully used to detect the variability of river discharge thanks to their high capacity to discriminate water from land. Thermal sensors are nearly completely unused, but the unique study that uses the difference in temperature of the river water between day and night for the estimation of water level, encourages its use for river discharge assessment as well. To improve the estimation of river discharge and foster studies that are aimed at monitoring ungauged rivers, the combination of the sensors is considered a viable path. The aim of this manuscript is to review these studies to show the limitations and the potentials

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JOURNAL PRE-PROOF of each sensor onboard the Sentinel-3 satellite and to investigate the added value of using these three sensors co-located on the same platform for river discharge monitoring.

INTRODUCTION

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Key Words: River discharge, radar altimetry, inland water, Sentinel-3

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River discharge is listed as one of the Essential Climate Variables by the Global Climate Observing System (GCOS) (GCOS, 2011) as the basis to identify the main processes in the global

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water cycle and climate systems, as well as in the water resource management and in the framework of forecasting of extreme events such as floods and droughts (Di Baldassarre & Uhlenbrook, 2011).

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Notwithstanding its importance, river discharge monitoring is an open issue. The existing insitu gauged networks represent a tool for quantifying the instantaneous water volume in many river

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channels. However, we have a surprisingly poor knowledge of the present-day spatial and temporal dynamics of surface river discharge. The measurement of river discharge is indirect and consists of

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in situ observations of water level and flow velocity. River discharge (and water level), traditionally obtained via ground-based observation networks, suffers from well-known inherent problems: high

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costs of installation and maintenance, sparse coverage (often limited by political instead of

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hydrological boundaries), slow dissemination of data, heterogeneous temporal coverage, damage to the stations during floods, absence of stations in remote areas, and absence of management strategy (Biancamaria et al., 2011; Siddique-E-Akbor et al., 2011). Developing new procedures for river discharge estimation based on satellite remote sensing technology is becoming urgent. In the last two decades, successful use of satellite sensors for hydrological applications has been demonstrated (Koblinsky et al., 1993; Birkett et al., 1998; Zakharova et al., 2006; Smith and Pavelsky, 2008; Van Dijk et al., 2016; Tarpanelli et al., 2018). Although not without limitations, satellite radar altimeters, near-infrared and thermal sensors have 2

JOURNAL PRE-PROOF demonstrated their potential in the monitoring of river discharge with Sentinel-3 and the possibility to have the three sensors onboard the same platform represents a significant advantage over a single sensor. The combination of the different characteristics of the sensors themselves and their capability to observe the Earth under different aspects can enhance the ability to monitor rivers

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overcoming the limitations of single sensors.

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Envisat, the ESA satellite in orbit from March 2002 to April 2012 carried the three

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instruments mentioned above. Some studies have investigated the Envisat capability to monitor

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flow of inland water by exploiting the RA-2 altimeter and the MERIS sensor (Getirana and PetersLidard, 2013; Domeneghetti et al., 2014; Tarpanelli et al., 2018). As a successor of Envisat, the

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Sentinel-3 mission has the objective to provide continuity of Envisat type measurement capability in Europe (Donlon et al., 2012) with improved performance for all the instruments. During the

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calibration/validation phase, product verification and validation have been carried out confirming the overall excellent performance of the optical payload of Ocean and Land Colour Instrument,

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OLCI, and Sea and Land Surface Temperature Radiometer, SLSTR (Nieke and Mavrocordatos, 2017). Concerning satellite altimetry, the new delay-Doppler (also called Synthetic Aperture Radar

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(SAR)) technology is expected to provide a better water level measurement compared to conventional altimeters (Raney et al., 1998) due to the high along-track resolution that may enable

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the measurement of high-resolution water level transects across rivers. A recent study by

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Normandin et al. (2018) demonstrated the evolution of the performances of satellite altimetry measurements from the conventional altimeters Jason-1, Jason-2, Jason-3, ERS-2, and Envisat, to the new generation altimeters SARAL (AltiKa) and Sentinel-3A (SRAL). The inter-comparison between the altimetry-based water levels against 19 ground stations in the Inner Niger Delta showed an increased accuracy in the water level estimates for the recent missions, Jason-3, SARAL/AltiKa and Sentinel-3A, indicating an improvement thanks to the use of the Ka-band for SARAL/AltiKa and of the SAR mode for Sentinel-3A.

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JOURNAL PRE-PROOF In this context, the purpose of the paper is to evaluate the potential of the Sentinel-3 mission as well as its limitations due to the technical characteristics of the sensors, for the derivation of river discharge. A review of studies that used altimeter, near infrared and thermal sensors in hydrological applications and, specifically, for river discharge monitoring is carried out. The paper discusses the

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use of single sensor data and shows some attempts to combine multiple data sources to improve and

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overcome the limitations due to the use of a single sensor. The classification of the studies analyzed

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is based on the sensor used for the estimation of river discharge as shown in Figure 1. Finally,

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conclusions aim at underlining the limitations and the potentiality of the described approaches and

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provide suggestions for the application of Sentinel-3 data.

Figure 1: classification of the studies for the river discharge assessment, based on the sensors used to measure the hydraulic variables. For the symbol, see the text.

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USE OF ALTIMETRY FOR RIVER DISCHARGE ASSESSMENT The radar altimeter is the most used remote sensing sensor for the estimation of river

discharge. Since the first precision satellites were launched in the 90s, after some promising trials in the 70s and 80s, great progress has been made for the use of altimetry in inland water applications. 4

JOURNAL PRE-PROOF Original studies (Raney, 1998; Birkett et al., 2002; Frappart et al., 2006; Biancamaria et al., 2017) contributed to advance the scientific knowledge and to modify the technological aspects of the traditional hydrological/hydraulic models due to the new source of data. The progress made for flow quantification allowed for the development of techniques based on three different approaches:

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(i) rating curves between altimetric water height and in situ discharge observations; (ii)

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hydraulic/empirical equations with remote sensing-derived hydraulic variables (width, channel

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bottom, slope, depth); (iii) hydrological and hydraulic modeling also including data assimilation

the estimation of river discharge by radar altimetry. Rating curve

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2.1

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techniques. For each group, the main studies are described to illustrate the developments reached on

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A rating curve is a functional law between the water depth and the discharge expressed as:

Qt   a ht   aH t   H 0  b

b

(1)

where Q is the river discharge, h is the water depth expressed as the difference between the water

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level, H, and the null-discharge elevation, H0 (corresponding to the channel bottom of the cross-

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section), a and b are parameters related to the geometry of the channel cross-section and to the roughness coefficient. Eq. (1) is typically applied to estimate river discharge at monitored ground stations with known geometry, where water level measurements are continuously recorded.

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Water level from radar altimetry can substitute ground observations, both in case of

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discontinued in situ gauged stations and ungauged basins. For both cases, the estimation of the rating curve is not straightforward. Indeed, the three typical situations to be addressed are: 1) water level from altimetry and river discharge are observed at the same cross-section but they are not available in the same period, 2) altimetry track over the river is far from the gauged station, and 3) discharge observations are lacking.

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JOURNAL PRE-PROOF In the first case, the rating curve evaluation can be done by adopting specific approaches that consider the pairs of water level and discharge not concurrent (i.e. not acquired at the same date) but having the same frequency, that is the quantile function (Tourian et al., 2013). When the measurements of water level and discharge are collected in a common period of

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observation, the main issue is related to the distance between the altimetry ground-track crossing

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the river (i.e., virtual station, VS) and the location of the in situ gauged station. Sometimes the

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distance is several kilometers upstream or downstream, where the cross-sectional area can be very

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different. To link the in situ and satellite water level, a linear regression can be derived between the gauged water height at the in situ station, Hobs, and the altimetric water height, Halt, yielding to: (2)

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H obs    H alt  

where  and  are the coefficients of the linear regression. Eq.2 is applied if gage site has a

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discontinuity in water level time series allowing us to acquire data by leveraging altimetry retrieval. This is the procedure applied in various studies such as Coe and Birkett (2004) at N’Djamena in the

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Lake Chad with Topex/Poseidon (coefficient of correlation equal to 0.98 between the two water height time series) and Bogning et al. (2018) for the Lambaréné station along the Ogooué River

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basin (Central Africa) with altimetry data of ERS-2, Envisat, Jason-2, Jason-3, SARAL/AltiKa, CryoSat-2 and Sentinel-3 (coefficient of correlation ranging from 0.74 with ERS-2 to 0.98 with

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CryoSat-2 and root mean square errors from 1.05 to 0.25 m again with ERS-2 and CryoSat-2,

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respectively). Alternatively, Kouraev et al. (2004) for the Ob’ river inferred the rating curves by leveraging Topex/Poseidon water levels at the satellite tracks and the river discharge recorded at the Salekhard gauged site, located 65 km upstream. The mean errors in the estimation of river discharge at daily scale was about 8%, whereas at monthly scale it reached 17%. The same approach was applied in other studies (Dubey et al., 2015; Zakharova et al., 2006) such as Papa at al. (2010) who related the river discharge observed at the Hardinge site on the Ganga River and at the Bahadurabad site on the Brahmaputra River with the water level retrieved by Topex/Poseidon, ERS-2 and Envisat at virtual stations located at several kilometers from the gauged sites (from 25 to 60 km). The mean 6

JOURNAL PRE-PROOF error on the estimated daily discharge derived from altimetry ranged from 15% using Topex/Poseidon over the Brahmaputra to 36% using ERS-2 over the Ganga. The discharge time series for the same stations have been extended with the Jason-2 water level time series in Papa et al. (2012) with mean errors of about 6.5% and 13%, respectively.

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In the case of ungauged sites (i.e., discharge observations are lacking), a possible solution is

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to simulate discharge with hydrological models (Shao et al., 2018). Leon et al. (2006) constructed

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rating curves with the heights derived from Envisat and Topex/Poseidon and the river discharges

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estimated through the Muskingum-Cunge flow routing model for 21 stations of the Upper Negro River. Successively, Getirana et al. (2009) derived rating curve for 12 virtual stations in the Branco

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River, fitting a power-law relation between Envisat-derived water level and discharge modelled by the MGB-IPH hydrological model. Similarly, Paris et al. (2016) estimated the rating curve for

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almost 1000 virtual stations in the Amazon River, with the water level derived from Envisat and Jason-2 and the discharge simulated with the MGB-IPH model. Getirana and Peters-Lidard (2013)

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evaluated river discharge by rating curve based on Envisat water level and simulated discharge provided by the modeling system composed of a land surface model and a global river routing

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scheme in 135 stations over the Amazon basin. In the evaluation of the rating curve, a one-to-one relationship between water depth and

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discharge is typically considered. This means that each single value of depth is linked to a single

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value of discharge and vice versa (see Eq. 1). Zakharova et al. (2019) demonstrated that by using the Jason-2 and Jason-3 for retrieving water level over the three tributaries of the Lena River and the ground observed discharge at the outlet of the basin, at the Kusur station, it is possible to infer a reliable rating curve for the estimation of monthly and annual discharge along the river reach. However, the rating curve shows a hysteresis loop with rising limb and recession limb. This means that for the same value of river discharge two water levels are observed, one lower for the rising limb and one higher for the recession limb. Only a few studies took the existence of the loop in the relationship into account (Zakharova et al., 2019, on the Lena river). 7

JOURNAL PRE-PROOF 2.2

Empirical equations Based on the traditional laws of hydraulics and by monitoring some key variables by satellite,

the literature reports several approaches for estimating river discharge. These algorithms use one or more variables sensed by satellite such as water surface width (surface area), channel slope, and/or

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flow depth.

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Smith (1997) reviewed the first studies on the use of satellite observations for stage and

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discharge estimation. He listed the first attempts to use radar altimetry to estimate water surface

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level and described the method to correlate inundation area with river discharge. The big obstacle at that time was the availability of a large dataset of satellite images at high resolution to infer the

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empirical rating curve. Successively, Bjerklie et al. (2003; 2005) developed empirical formulas to derive river discharge for single and braided channels, with satellite remote sensing data (optical,

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SAR and altimeters). Specifically, width and slope data were extracted from aerial photos and SAR imagery. Other attempts have been done with the use of width, slope and surface velocity by

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AirSAR imagery. Results showed a quite large percentage error (72%) using local discharge data. Among the relationships described in Bjerklie et al. (2003), the following equation has been

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successfully used in the literature (e.g., Birkinshaw et al., 2014; Tarpanelli et al., 2013a): Q  7.22  B1.02h1.74S 0.35

(3)

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where B and h are the width and the depth of the equivalent cross sections and S is the slope of the

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hydraulic gradient. It is worth noting that the empirical formulas cannot be extended to every river and proper coefficients must be calibrated for the evaluation of a more accurate final product. In Bjerklie et al. (2018), Jason-2 satellite altimetry data are used for surface water height and

river reach slope estimation, whereas reach mean flow width has been extracted by Landsat images at two locations in the Yukon River (Arctic region). The discharge was calculated through two physically based flow resistance equations, the Manning equation (Eq. 4) and the Prandtl-von Karman universal flow velocity distribution equation (Eq. 5):

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JOURNAL PRE-PROOF Q  n 1 ARh2 / 3 S 1/ 2

(4)

 0.5   h  Q  2.5Bhav ghavS   ln av   1     h0  

(5)

where A is the cross-sectional flow area, Rh is the hydraulic radius (flow area/wet perimeter), g is

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the gravitational constant, 9.81 m/s2, y0 is the roughness height. hav is the average depth computed

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    

(6)

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  1 hav  H  H 0 1      1  bs

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as:

where bs is the channel shape coefficient (bs=2 for a parabolic shape of the cross-section). Comparing the estimated discharge with the USGS in-situ measured value, the Manning equation

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reproduces the hydraulic relation better than Prandtl-von Karman equation. The main issue remains

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the estimation of the bottom of the section, H0, that needs to be calibrated by using ground observations. As for the ground measurements, the estimation of the bottom represents a generic problem also for the use of altimetry-derived water level. Most of the empirical or hydraulic

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formulae need a cross-section survey to estimate the shape and the bed of the river in order to

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evaluate the flow area that is involved in the calculation of river discharge. The absence of

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measurements of the cross-section geometry can be a deterrent to applying this kind of procedure.

Hydraulic models of complex nature

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2.3

Water level measurements provided by radar altimetry in the continental environment

hydraulic modeling and the estimation of river discharge at the downstream section of a river reach. Approaches can vary from simple to complex hydraulic models, and from steady to unsteady flow. Typically, altimetry-derived water levels are integrated with flood modelling for the calibration of the roughness parameters, for the validation of the results and within assimilation approaches.

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JOURNAL PRE-PROOF A simple methodology to estimate the discharge along rivers, even those poorly gauged, was proposed by Tarpanelli et al. (2013a) that used water level measurements derived from ERS-2 and Envisat satellite altimetry in the Rating Curve Model (RCM) (Moramarco et al., 2001; Moramarco et al., 2005). The RCM estimates the flow conditions in a river section using only flow depths

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recorded at one site and discharges observed at an upstream river section, taking the significant

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lateral inflow contributions into account. The approach has been applied to the Po River at Piacenza

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station with flow depths and discharge continuously recorded, to estimate discharge at two VSs

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located 190 and 207 km downstream, where only water levels derived by the radar altimetry are measured and the cross-section geometry is known. The performance of the simulated discharge

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compared to those observed in terms of Nash-Sutcliffe efficiency, NSE was on average 0.82 at the gauged station of Sermide and 0.79 at Pontelagoscuro. The same model was used by Birkinshaw et

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al. (2010) to derive a good estimation of river discharge in several sections of the Mekong River by using water level data from ERS-2 and Envisat (NSE ranging from 0.893 to 0.935).

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More complex modelling studies have used radar altimetry time series to calibrate the coefficient that describes the roughness conditions (e.g., the Manning coefficient) of the main

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channel or the floodplain (Domeneghetti et al., 2014; Garambois et al., 2017). Domeneghetti et al. (2014) used water levels from ERS-2 and Envisat to calibrate the main channel roughness

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parameter of a quasi-2D hydraulic model (HEC-RAS) along a reach of 137 km along the Po River.

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By trying different configurations of the use of satellite and ground water level measurements, they concluded that the integration of the satellite datasets with traditional in situ observations fosters the trustworthiness and reliability of the hydraulic model. The availability of satellite measurements along the river has proven effective for estimating the roughness coefficient in a continuous way. Schneider et al. (2018) calibrated the friction coefficient by using the water levels from the long-repeat orbit CryoSat-2 satellite and the onedimensional model, MIKE 11 (DHI, 2015). The characteristics of CryoSat-2 allowed to calibrate the Manning roughness coefficient each 10 km over the main channel of the Po River, showing the 10

JOURNAL PRE-PROOF high potential of such satellite mission with respect to ground observations. The average root mean square error between CryoSat-2 and in situ observations was found to be 0.38 m, lower than 0.59 and 0.87 m found by Tarpanelli et al., 2013a, with previous satellite altimetry missions ERS-2 and Envisat in the same Po River.

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An example of the floodplain coefficient calibration is provided by the study of Yan et al.

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(2014) who, in a reach of the Danube River, used Envisat water levels and SRTM topography in

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supporting flood level predictions through a 2-D hydraulic model, LISFLOOD-FP (Bates et al.,

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2010). The missing cross-section geometry forced the calibration of the floodplain roughness coefficient and the channel bed elevation below the SRTM elevation, demonstrating the added

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value of the satellite information for data-poor areas in the estimation of a flood event inundation. Concerning ungauged rivers, the study of Schneider et al. (2017) exploited CryoSat-2 data over the

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Brahmaputra River in combination with Envisat virtual station data to calibrate the shape and the bottom elevation of cross-sections used as geometric boundary of the hydrodynamic model MIKE

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11. The calibrated model was able to reproduce water level–discharge relationships at the outlet of the basin with good accuracy with the NSE equal to 0.81 for the validation period, even though a

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bias of about 11% of overestimation was found. However, poor performances were obtained for the interior catchments.

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Despite the good performance obtained with the use of satellite-derived water level, its

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measurement uncertainty influences the performance of hydrodynamic models. Domeneghetti et al. (2015) analyzed the influence of the error of ERS-2 and Envisat water levels in the calibration of the quasi-2D hydraulic model in HEC-RAS along the Po River. They found that Envisat outperforms ERS-2 in estimating the roughness coefficient, probably due to the better performance of the former in the assessment of the water level. It is expected that with SAR mode on Sentinel-3 the accuracy of the water level measurements will improve, with consequently improvement of the model performances.

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JOURNAL PRE-PROOF Integration of satellite remote sensing data may be combined in operational modeling systems, but usually the spatial or temporal resolution is not suitable for these applications. Moreover, the hydrological quantities of interest are not directly measured by satellite and often their temporal sampling does not satisfy the prediction requirements (typically daily or sub-daily).

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A solution is the combination of remote sensing data, i.e. satellite radar altimetry, with

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hydrodynamic models in a data assimilation framework. Some examples are described over the

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Amazon River (Paiva et al., 2013; Emery et al., 2018), the Brahmaputra Basin in South Asia

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(Michailovsky et al., 2013), and the Zambezi River (Michailovsky et al., 2014). Typically, radar altimetry data from virtual stations are assimilated in a routing model driven by the output of a

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calibrated rainfall runoff model. The assimilation technique (e.g. Extended Kalman filter or Ensemble Kalman Filter) is used to update the state vectors of a hydrodynamic river model (water

2.4

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evaluation of the river discharge.

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levels, river discharge or water volumes) with consequent improvement of the performance in the

SWOT mission and the Discharge Team Algorithm Group

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The Surface Water and Ocean Topography (SWOT) satellite mission scheduled for launch in 2021 is expected to measure rivers wider than 100 m at global scale, with a goal of estimating

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discharge for river wider than 50 m (Biancamaria et al., 2016; Pavelsky et al., 2014). This

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capability is due to the Ka-band radar interferometer (KaRIn) that allows better spatial resolution with respect to the other acquisition modes (Biancamaria et al., 2016). The mission utilizes wide swath altimetry to provide spatially distributed water surface elevations, but also slope and water mask (hence, river top width), i.e. two important variables for estimating river discharge, differently from the nadir altimeters that measure water level only over the satellite tracks. Multiple approaches have been proposed to estimate river discharge starting from these observations, but the transferability of such methods to the traditional altimeters is quite difficult, provided that other 12

JOURNAL PRE-PROOF satellite sensors (i.e. optical or topographical missions) are jointly used to measure the other variables. The basic equations are the same: the inversion of flow-law equations (Eq. 4) and the conservation of the mass for a river reach, termed Mass-conserved Flow-Law Inversion, McFLI. A

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comparison of 5 McFLI algorithms, over 19 major rivers across the world, was carried out by

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Durand et al. (2016). The set of algorithms included: at-many-stations hydraulic geometry

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(AMHG; Gleason & Smith, 2014), GaMo (Garambois & Monnier, 2015), MetroMan (Durand et

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al., 2014), mean-annual flow and geomorphology (MFG) and mean flow and constant roughness (MFCR).

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AMHG approach is based on the traditional law of Leopold and Maddock (1953) according to which hydraulic variables as width, B, depth, h and flow velocity, v, can be expressed in terms of

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river discharge, Q, following Eq.s (7-8-9): B  eQ l

v  kQ m

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h  cQ f

(7) (8) (9)

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where e, c, k, l, f, m are constant parameters that follows the constrain: e  c  k  1 and l  f  m  1 . Gleason and Smith (2014) found that the coefficients (e, c, k, l, f, m) are not independent empirical

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parameters, but log-linearly related along a river.

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The other approaches, GaMo and MetroMan, use the Eq. (4) written as follows: Q  n 1  A0  A

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B 2 / 3 S 1/ 2

(10)

where A0 is the cross-sectional flow area beneath the lowest height measurement, and A is the change in cross-sectional area. While the variations of the flow area, the width and the slope may be remotely measured, the roughness coefficient, n, and A0 are unknown and different approaches have been developed to estimate these two parameters. A0 and n are both optimized to preserve continuity

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JOURNAL PRE-PROOF between the observed reaches using a constrained nonlinear steepest-descent optimization in GaMo, and the Metropolis algorithm in MetroMan. The Mean-Annual Flow and Geomorphology approach used the wide-channel approximation based on the Eq. (4) rewritten as:

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Q  n 1 Bh 5 / 3 S 1/ 2

(11)

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The two parameters to be optimized are flow resistance, n, and the elevation of zero flow, H0,

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inside the variable h (see the Eq. 1). The former is computed from a relation that scales a mean

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value of the roughness from observations of width and stage provided across a number of USGS field data and gauges: x

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 hB  n  c 0 n0   hB

(12)

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where c0 and x are empirical coefficients, the overbar indicates time averages and n0 is the reference value for n given by n=0.22·S00.18, with S0 the channel bottom slope. Estimating the discharge by a

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water balance model, n and H0 are calibrated, whereas B is calculated in order to match the time series of discharge estimates.

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The mean flow and constant roughness approach assumes constant n=0.03 m-1/3s and uses the mean annual discharge derived by water balance model to calibrate A0 by Eq. (10). Generally, no

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single algorithm achieved high performance on more than six rivers and the median relative root

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mean square error of all methods is 55%. In a successive study by Hagemann et al. (2017) the Manning equation is combined with the

AMHG laws in a Bayesian method. Results show improvement in terms of performance indices, even though for some sites the best solution is to switch between the Manning + AMHG and the Manning-only, based on the characteristics of the river width and the rating curves. By using the synthetic SWOT observations numerous attempts demonstrated the feasibility to implement a hydrodynamic model with data assimilation in the Ohio River (Andreadis et al., 2007; Yoon et al., 2012; both with LISFLOOD-FP), in the Garonne River (Oubanas et al., 2018 with the 14

JOURNAL PRE-PROOF full Saint Venant equations), and in the Ganges-Brahmaputra-Meghna River (Paiva et al., 2015; with diffusive wave approximation of Saint Venant’s equations).

USE OF NEAR INFRARED SENSORS FOR RIVER DISCHARGE

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ESTIMATION

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The large use of near infrared sensors in literature is justified by the high potential of these

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sensors to discriminate water, vegetation, sediments and chlorophyll. However, other applications for flood dynamics and discharge estimation have been developed in the last few years. For

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example, the relationship between MODIS band 2 and river discharge was developed for the first time in Brakenridge et al. (2005). For the White River, in Indiana, USA, the authors used the

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MODIS near infrared band because it provides an excellent water/land discrimination. They calculated the ratio between a calibrated reflectance (land pixel) and the measured reflectance

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(water pixel), and they compared this ratio versus the discharge measured at a U.S. Geological Survey gauged station at Petersburg. Using a polynomial relationship, they assessed river discharge

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with a coefficient of determination, R2, equal to 0.92. The physical process inherent to this approach is that lower reflectance values in band 2 are associated with larger water surface area. Remote

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sensing signal measurements are sensitive to flow area change. The monitoring of water surface

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area takes advantage of the spatial coverage provided by remote sensing. The surface area of river reach is also less prone to local variation in riverbed geometry. Therefore, higher values of the ratio (calibrated reach/measured reach) indicate higher reach water surface areas, higher river stages, and, thus, higher river discharges (Brakenridge et al., 2005). The calibration pixel is chosen to remove all the effects due to the noise (e.g., atmosphere) that can affect the measurement of the reflectance in the measured water pixel. Successively, the same approach was used by Tarpanelli et al. (2013b) who analyzed 7 years of daily MODIS images for a reach of Po River (Italy) including four gauged stations. They found a better correlation between the reflectance ratio by the MODIS 15

JOURNAL PRE-PROOF images and flow velocity (range from 0.67 to 0.77) compared to river discharge (range from 0.65 to 0.75) and derived a regional law able to describe the velocity along the Po River. The considerable advantage to have daily maps at medium resolution of MODIS images is positively useful also to derive flooded areas (Khan et al., 2011; Auynirundronkool et al., 2012; Li

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et al., 2013; Martinis et al., 2013; Huang et al., 2014; Pekel et al., 2014; Bergé-Nguyen and

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Crétaux, 2015). However, a few studies have linked directly surface water with river discharge

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(Smith et al., 1995; 1996; Ticehurst et al., 2014). Smith and Pavelsky (2008) used MODIS band 2

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signal to discriminate between water and not water and they derived the effective width as the surface area extracted by MODIS divided by the length of the river reach. A direct correlation

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between remotely sensed effective widths and daily discharges at the Kusur station on the Lena River, revealed a power-law relationship between the two variables (coefficient of determination,

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R2 equal to 0.71). The same experiment but with a shift of some days and a measurement observed kilometers downstream, maintained high correlation (R2 equal to 0.81). Moreover, analyzing for

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different reach length the variation of the parameter of the exponential law that relates effective width to river discharge, authors showed that such parameter converges to a stable value at length

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scale two or three times the width of the Lena River floodplain. This can be an encouraging result for supposing a “regional” rating curve valid for an entire river and/or for similar rivers.

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According to Van Dijk et al. (2016), the main assumption is that if water extent increases, the

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river discharge increases but the relationship is not unique. It depends on the relationship between discharge, stage and inundation extent. Differently from the previous studies that used NIR band 2 of MODIS, Van Dijk et al. (2016) tested the potential of the shortwave band 7 (SWIR) to estimate monthly river discharge through the measurements of water extent for more than 8800 gauges. They calculated the fraction of water extent, we, as the ratio: we 

 M   dry  water   dry

(13)

16

JOURNAL PRE-PROOF where M is the band 7 reflectance, dry the fifth percentile highest reflectance and water the reflectance of surface water. The water extent has been related to the in situ monthly river discharge obtaining a mean value of correlation coefficient equal to 0.43 for all the stations with values greater 0.6 for more than 2100 stations. Interestingly, they did not observe a differentiation of the

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performance based on the width of the rivers. Specifically, they obtained better results for broad and

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low gradient river systems with large and variable discharge, while poorer results were found for

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narrow rivers in high relief terrain with small and less variable discharge. The temporal variability

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of discharge seems the higher factor affecting the correlation with river discharge. Concerning the geomorphological and hydroclimatological patterns, better performances were found for large and

were obtained in arid and temperate regions.

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unregulated lowland rivers, particularly in tropical and boreal climate zones, while poor results

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Landsat Thematic Mapper (TM) was used by Gleason et al. (2014) for deriving river discharge through at-many-stations hydraulic geometry (AMHG) method (described above). The

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analysis of 34 rivers in different physiographic and climatologic settings confirms that the method can retrieve river discharge quite well with median relative root mean square error of 20%, even if

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some particular exception should be done: braided rivers, rivers where the exponent l (Eq. 7) is low and rivers displaying extreme variability in discharge (arid climate) have a problem to derive

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reliable estimates of discharge.

4

USE OF THERMAL SENSORS FOR RIVER DISCHARGE ESTIMATION The study of Parinussa et al. (2016) suggested that the differences between daytime Land

Surface Temperature, LST, and night time LST is a proxy of flood inundation (Ordoyne and Friedl, 2008). Indeed, under dry conditions, shallow water bodies has low thermal inertia and emit a small heat flux at night time. This means that the difference between daytime and night time (ΔLST) is large. Under wet conditions, large inundation leads to more emissive heat flux at night time due to 17

JOURNAL PRE-PROOF the higher thermal inertia of the water body. Pham et al. (2018) exploited this different behavior of the water bodies and demonstrated that large ΔLST values indicate lower water levels, whereas smaller ΔLST values occurring when river levels increase. They first demonstrated this inverse relationship using in situ measurements of water level and ΔLST from MODIS satellite. Then, the

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approach was applied to a number of virtual stations of Jason-2 satellite altimetry over the Mekong

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River. A regression model was used to predict daily water level in order to supplement the 10-day

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Jason-2 altimetry data. The modelled daily water levels provide a root mean square error between

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0.3 m to 0.6 m when compared against in situ data, and between 1.4 m to 1.9 m against satellite altimetry. Results show the potential of the thermal bands to produce high temporal resolution water

5

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levels for hydrological applications.

COMBINATION OF RADAR ALTIMETRY AND OPTICAL/NEAR-

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INFRARED SENSORS

The availability of a large number of satellite sensors expands the possibilities to observe and

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monitor the Earth and represents a valid support for the measurements of hydraulic variables. The combination of different satellite sensors can improve the temporal or spatial information even if

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the acquisitions of the sensors is not simultaneous. Examples in the literature show the benefits of

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using different types of satellite data for the river discharge assessment and revealed a step forward in the monitoring of completely ungauged areas (Jung et al., 2013; Pan et al., 2016) Based on MODIS and altimetry data, Sichangi et al. (2016) developed two simplified

empirical formulas to derive river discharge. In the first formula river discharge is expressed in terms of river stage, whereas in the second formula river width is considered as well. Stage and width are retrieved from altimetry (Envisat and Jason-2) and from MODIS respectively. Observed discharge at 14 gauged stations from 8 globally distributed major rivers are considered as case studies and used to calibrate the parameters of the empirical formulas. Better performances are 18

JOURNAL PRE-PROOF obtained by considering both width and stage with NSE ranging between 0.60 and 0.97. The worst performances are observed in the artic rivers (Lena and Yenisey rivers), due to frozen water in the period from November to April. A synergy between MODIS and altimetry is also investigated exploiting both physical

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(Tarpanelli et al., 2015) and machine learning approaches (Tarpanelli et al., 2018). Based on the

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strong connection between MODIS reflectance ratio and the flow velocity of the river, Tarpanelli et

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al. (2015) integrated this information with the flow areas of the cross-section derived by the

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altimetric measurements from Envisat, to derive river discharge at a specific site of the Po River, where long time series of in situ observations is available. The product of flow area and velocity

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was considered both with and without the bathymetric information of the river cross-section. The results demonstrated that the integration of two types of satellite data can provide river discharge

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with an error of 36% and NSE equal to 0.75 if the bathymetric survey is available, and an error of 38% and NSE equal to 0.72 if the bathymetry is estimated by entropy theory (Moramarco et al.,

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2013). In another example of integration of optical and altimetry sensors, Tarpanelli et al. (2018) used two independent artificial neural networks (ANN) to connect the satellite reflectance ratio

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from MODIS (Aqua and Terra), MERIS and altimetry (ERS-2, Topex/Poseidon, Envisat, CryoSat-2 and Jason-2) along the Po River (at Pontelagoscuro) and the Niger River (at Lokoja). The ANNs

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have been trained with in situ discharges observed at two cross-sections each one along the two

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rivers. The validation results showed that, the more sensors that are involved, the better is the obtained performance. In the Niger river the total discharge is given with NSE equal to 0.98 (0.78 if the seasonality is removed) and a correlation of 0.99; whereas in the Po River the discharge is estimated with NSE equal to 0.83 and correlation of 0.91. Huang et al. (2018) used a total of 1237 images from Landsat, Sentinel-1 and Sentinel-2 to extract river width and satellite altimetry from Jason-2/3 and SARAL/AltiKa to derive time series of water level in the narrow river channels of the high-mountain regions of the Upper Brahmaputra River characterized by complex terrain and limited observations. The width and the water level are 19

JOURNAL PRE-PROOF used to retrieve values of discharges separately by using two power function equations, whereas a third equation considers the combination of both. The combination of several hydraulic variables provided river discharge with a higher accuracy with compared to just considering single variables.

LIMITATIONS OF THE REMOTE SENSING APPROACHES AND

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6

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PROPOSED SOLUTIONS

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The above description listed the recent literature on the use of satellite remote sensing measurements to estimate the river discharge. In the following section, we describe the limitations

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and challenges of the approaches distinguishing between the ones related to the traditional hydraulic

6.1

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from the ones linked to the uncertainty of remote sensing data. Challenges related to hydraulic laws

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6.1.1 Rating curves

A simple rating curve is a one-to-one relationship between water depth and discharge, which

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is valid for a specific cross-section in specific hydraulic and vegetation conditions. Temporary changes of the reference regime, due to backwater effects in non-uniform flows, variable

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downstream boundary condition, seasonal vegetation growth and changes in reach or control

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section geometry, may impose the use of different rating curves (Le Coz, 2012). Moreover, the stability of the cross-sections that are assumed constant over time even for ten to twenty years is an invalid hypothesis especially for river characterized by solid transport. In these cases the rating curve can change dramatically after a flood event and the river discharge assessment can be severely compromised. A few studies that use satellite data take in account the existence of the loop (or hysteresis) in the relationship between water level and discharge that, if neglected, can induce large errors in the

20

JOURNAL PRE-PROOF evaluation of river discharge especially for unsteady flow (Domeneghetti et al., 2012; Paris et al., 2016). In addition, due to the costs and danger to carry out flow velocity measurements (hence discharge), few measurements are available for high water levels, thus providing large errors

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(Moramarco et al., 2004). In absence of these measurements, the high flows of the rating curve are

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extrapolated by physically based models that utilize a detailed representation of channel geometry

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and floodplain topography or statistical approach, with a consequent uncertainty due to the model

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parameters and the modelling scheme adopted.

All these sources of errors are tough to remove. A possible solution is to become aware of the

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errors and not provide a deterministic value of river discharge but a probabilistic value that

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represents the uncertainty degree of the approach (Barbetta et al., 2017; Paris et al., 2016). 6.1.2 Empirical equations and hydraulics models

Most of the studies are based on simple relationships that link the hydraulic variables (water

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level, width, slope) with the available river discharge measurements. When traditional hydraulic laws are introduced, they are often used in a simplified form due to the complexity to estimate the

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parameters or the characteristics of the river cross-section (Singh, 2003). River channel patterns may have an influence on the coefficients and the exponents. Single channel, meanders, mountain

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or straight channels affect the parameters of the hydraulic relationships in different ways (Chitale et

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al., 1973). The drainage area also has an influence on the coefficients of the general relationships (Stall and Yang, 1970), but this aspect is often not considered. The coefficients are mostly averaged to give rise to formulae suitable for multiple case studies so that they can be extended even to poorly gauged sites. Concerning the empirical laws, generally, the roughness coefficient is assumed to be constant along a river reach, even if it is known that it can change not only from one site to another, as demonstrated by hydraulic modelling studies (e.g., Schneider et al., 2018), but it can be very 21

JOURNAL PRE-PROOF different at a given cross-section going from low to high flow. Since the depth and velocity are functions of roughness, the power function model proposed by Leopold and Maddock (1953) is not valid when the rate of change in roughness is not uniform (Richards, 1973; 1976). Recently, studies proposed the idea to not consider the roughness coefficient as a physical parameter but as a

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statistical parameter used to compensate the lack of accuracy in the description of the riverbed

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geometry (Horritt and Bates, 2002; Pappenberger et al., 2005; Di Baldassarreet al., 2010). The

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consequence is that the roughness coefficients assume values very different from those reported in

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the literature for natural channels.

An important aspect that often is neglected by the empirical studies is the overflows of the

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banks that drastically change the relationships with the river discharge. The exponents of the empirical law for high flow conditions can be different from those for low flow conditions (Rhodes,

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1978) and they can also change from location to location on the same river and from river to river. They depend on the sediments and boundary conditions of the flow (Phillips and Harlin, 1984).

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One of the remaining issues is the water depth that cannot be measured by satellite. Radar altimetry provides the water surface elevation of the rivers but without a topographical survey of a

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specific section, the knowledge of the cross-section bottom geometry for the evaluation of flow areas or water depth is undetermined. Some studies have tried to approximate bathymetry through

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morphological characteristics (Mersel et al., 2013; Domeneghetti., 2016; Paris et al., 2016) or

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entropy theory (Moramarco et al., 2013) but the topic is still ambitious and tedious. 6.2

Challenges related to remote sensing data

The described above insights and their relation to the traditional hydraulics, are well known to

the hydraulic and hydrology community. In the cited studies, which try to assess river discharge by satellite data, the uncertainty of the remotely sensed measurements should be added to the approximations and simplifications of the hydraulic relationships, undermining the estimates of the final product of discharge. 22

JOURNAL PRE-PROOF On one hand, even if characterized by high frequent revisit time (nearly daily) and large spatial coverage, near infrared and thermal sensors have restrictions to be used for narrow rivers due to the spatial resolution of the images that ranges from 250 m to 1 km. On the other hand, one of the main limitations of the radar altimeter is the revisit time.

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Current and past missions are characterized by periods of sampling of 10, 27 or 35 days that are too

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long for detecting the flow dynamics of interest at higher frequencies in most rivers. For small

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channel networks such as the European river basins, this temporal resolution, along with the inter-

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track distance (80 or 300 km at the equator), is not considered suitable for the river monitoring and for supplying the need of forecasting activities or water resource management. In addition, since

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they are optimized for observing the ocean surface, over continental land most of the current radar altimeters have limitations on the echoes reflected from the land surface (problem of multiple peaks

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caused by the multiple reflections from water and rough topography) (Biancamaria et al., 2017). In this context, merging more information coming from different sensors or different missions

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(but same sensors) can represent the best way to solve the issue. For example, Tourian et al. (2016) transferred the water level information coming from different satellite altimetry missions towards a

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specific location through the hydraulic concept of wave travel time. The solution has been very productive to improve the sampling frequency to 3-4 days in case of radar altimetry analysis.

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Alternatively, as stressed in Section 5, the combination of radar altimetry with other sensors,

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such as optical and/or thermal sensors, may be a way to overcome the limitations due to the spatial and temporal resolution of single sensors. The strong differences of reflectance or backscatter of water with respect to land are a discriminant for all the sensors here examined: altimeter, near infrared and thermal. Each of the sensors is able to detect a hydraulic and geometric characteristic describing the dynamic of the flow through measurements of water elevation, reflectance or temperature. The combination of these measurements can be exploited to indirectly determine the variation of water volume inside the channel represented by the river discharge.

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7

BENEFITS AND POTENTIALITY OF SENTINEL-3

7.1

Sentinel-3 mission: sensors and main characteristics Sentinel-3 is one of the satellites central to the European Commission’s Copernicus program,

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designed to deliver data and imagery of the Earth. For mission details see Donlon et al. (2012). The

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main characteristics of the mission and the onboard sensors are described here.

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The mission includes four identical satellites, A and B, launched on 16 February 2016 and on

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25 April 2018, respectively, orbiting in constellation for optimum global coverage and data delivery and C and D, planned to be launched as soon as satellites A and B needs to be substituted. The primary mission of Sentinel-3 is designed to measure systematically sea-surface topography, ocean

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wave heights, wind speed and the thickness of sea-ice, sea- and land-surface temperature, sea- and

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land-surface color, to monitor and understand large-scale global dynamics, to support ocean forecasting systems and for environmental and climate monitoring (Aschbacher et al., 2012). As secondary objective, the mission aims to monitor wildfires, to provide information on the land use

carries

several

instruments

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Sentinel-3

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and vegetation indices and to measure the height of rivers and lakes. To reach these objectives,

(https://www.esa.int/Our_Activities/Observing_the_Earth/Copernicus/Sentinel-3/): • The Sea and Land Surface Temperature Radiometer (SLSTR), that measures global sea- and

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land-surface temperatures to an accuracy of better than 0.3 K. Based on heritage from Envisat’s

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Advanced Along Track Scanning Radiometer (AATSR), it uses a dual-view along-track-scanning approach and delivers measurements at a spatial resolution of 500 m for visible/near-infrared and short-wavelength infrared channels and at 1 km for the thermal infrared channels. It operates across 9 wavelength bands including 2 channels dedicated for the fire monitoring. • The Ocean and Land Colour Instrument (OLCI) is based on heritage from Envisat’s Medium Resolution Imaging Spectrometer (MERIS) instrument and features 21 distinct bands tuned to specific ocean color, vegetation and atmospheric correction measurement requirements. It has a 24

JOURNAL PRE-PROOF spatial resolution of 300 m for all measurements and a swath width of 1270 km, overlapping the SLSTR swath. OLCI is a support to the monitoring of ocean ecosystems, to agriculture and crop management, and it provides estimates of atmospheric aerosol and clouds. • A topography system, which includes a dual-frequency (Ku- and C-band) synthetic aperture

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radar (SAR) altimeter, based on technologies used on ESA's Earth Explorer CryoSat mission, a

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microwave radiometer for atmospheric correction and a DORIS receiver for orbit positioning. It

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provides measurements at a resolution of approximately 300 m in the along-track direction. It

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measures sea surface height, wave height and surface wind speed over the oceans. It measures also the topography of ice sheets, sea ice thickness, river and lake levels, as well as the land topography.

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The important feature in the Sentinel-3 altimeter, which was not present on CryoSat, is the open loop tracking command (OLTC) that positions the receiving window at the target’s altitude thanks

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to the onboard DORIS and an uploaded digital elevation model (DEM). This mode is very efficient on difficult terrain like rivers and lakes, as long as the DEM is well known a priori, as the closed

reflectors nearby.

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loop tracking command sometimes fails to keep the lock on the target at nadir due to bright

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Sentinel-3 instruments revisit time with one satellite in orbit it is less than 3 days for OLCI, less than 2 days for SLSTR and 27 days for the topography package. With two satellites, land and

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ocean coverage is reached within 1 and 2 days, respectively, at the Equator, improving with

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increasing latitude. The altimetry mission has the same revisit time but has a double track density, Sentniel-3B being placed in the middle of the Sentinel-3A inter-track distance. All data are free of charge and open to users worldwide. 7.2

Comparison between the sensors onboard Sentinel-3 with respect to the past sensors As new generation satellite, Sentinel-3 mounts sensors with equivalent or better performance

compared to its predecessors. A single SLSTR has increased the dual view swath width from 500 to 740 km and this provides a mean global coverage revisit time at the equator of 1.9 days (one 25

JOURNAL PRE-PROOF spacecraft) or 0.9 days (two spacecraft). The enlarged single view swath width of 1400 km provides a mean global coverage revisit time at the equator of 1 day (one spacecraft) or half a day (two spacecraft). The higher on-ground resolution of 0.5 km at nadir (instead of 1 km) for all VIS and SWIR channels improve the land and clouds daytime observations. Two added channels (at

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wavelengths of 2.25 and 1.375 microns) in the SWIR band to allow improved cloud and aerosol

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detection to give more accurate SST/LST retrievals. It is expected that the applications for

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hydrology can benefit from all these improvements.

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As a heritage of MERIS, OLCI has similar spatial resolution (280 – 300m) but acquires in higher number of spectral bands (21 with respect to 15). Acquiring in similar bands the sensors has

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the same capability to measure reflectance and hence it can support hydrological applications as MERIS (or MODIS) did. Moreover, the characteristics of the OLCI swath minimize the impact of

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sun glint contamination effects with respect to MERIS. An advantage for the hydrological applications is represented by the improved coverage of the global land that is less than 3 days with

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one satellite compared to approximately 15 days for MERIS. Finally, also the overlap with the SLSTR instrument swath and simultaneous acquisitions facilitate the use of OLCI and SLSTR in

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synergy. So far, no attempt has been done with OLCI for the estimation of river discharge as described in Section 3, but thanks of the improved characteristics of the sensors, similar or better

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performance than those obtained with MERIS are expected.

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The SRAL sensor of Sentinel-3 is very similar to the altimeter instrument of the CryoSat

mission, even though it does not include the interferometry mode. SRAL operates in High Resolution Mode (commonly called SAR) for 100% of the coverage for all types of surfaces and in Low Resolution Mode (LRM) only as a back-up mode. Differently from CryoSat, the Sentinel-3 orbit altitude and inclination is almost the same as Envisat. The revisit time has improved from 35 days (Envisat) to 27 days (Sentinel-3) and the two Sentinel-3 satellites allow a spatial coverage twice as dense with respect to previous altimeter missions. A couple of studies on Sentinel-3 SRAL have already demonstrated the improved capability to measure river water level with reduced 26

JOURNAL PRE-PROOF uncertainty (Normandin et al., 2018; Bogning et al., 2018). These promising results encourage the use of the SAR technology on Sentinel-3 for increasingly narrow rivers to identify their field of application. The SAR mode described above is in fact the unfocused SAR mode. Processors performing the fully focused SAR mode have been developed (Egido and Smith, 2017; Guccione et

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al., 2018), but are still being tuned at this time, and, while they require huge computing power, they

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could theoretically bring the along-track resolution to half the antenna diameter, that is 50 cm, for

How river discharge estimation can benefit from Sentinel-3 sensors data

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7.3

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which one of the first beneficiaries will be inland water level monitoring.

One objective of the Sentinel-3 mission is to provide continuity of Envisat type measurements

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capability in Europe (Donlon et al., 2012). Being an evolution of the Envisat satellite, Sentinel-3

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represents a continuum also in the evaluation of hydrological applications. The improved characteristics of the sensors (see Paragraph 7.2) feed the idea that Sentinel-3 can be a valuable support in river discharge estimation, and also provide an added value to the existing state of the art.

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If extended to multiple case studies, the Sentinel-3 mission can support the SWOT mission by helping to explore more and more rivers on a global scale.

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Moreover, the added value of the combination with other sensors (near infrared or thermal) represents a significant and important advantage because the instruments are onboard the same

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platform. At each overpass, Sentinel-3 is able to observe the Earth with different views and from

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the combination of these observations a significant impact can be obtained in many applications for which river discharge data are needed, such as water resource management, climate change, improving the understanding of the water cycle, to mention a few. Mainly, it may represent a strong support to the preservation of settlements in developing countries (in Africa or in Asia), in which poor living conditions often are worsened by extreme flood or drought events. Thanks to the long duration of the mission, studies on the development of an operational flood forecasting and drought

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JOURNAL PRE-PROOF monitoring scheme to help with a rapid emergency response and decision-making processes can be feasible and desirable.

ACKNOWLEDGEMENTS: A.T., S.C., K.N., L.B. and T.M. acknowledge the support of

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the European Space Agency through the RIDESAT Project (ESA contract 4000125543/18/I-NB).

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Andreadis, K.M., Clark, E.A., Lettenmaier, D.P., Alsdorf, D.E., 2007. Prospects for river discharge and depth estimation through assimilation of swath-altimetry into a raster-based hydrodynamics model. Geophys. Res. Lett. 34(10), L10403. https://doi.org/10.1029/2007GL029721 Aschbacker, J., Milagro-Perez M.P., 2012. The European Earth monitoring (GMES) programme: Status and perspectives. Remote Sens. Env., 120, 3–8. Auynirundronkool, K., Chen, N., Peng, C., Yang, C., Gong, J., Silapathong, C., 2012. Flood detection and mapping of the Thailand Central plain using RADARSAT and MODIS under a sensor web environment. Int. J. Appl. Earth Obs. 14, 245–255. https://doi.org/10.1016/j.jag.2011.09.017 Barbetta, S., Coccia, G., Moramarco, T., Brocca, L., and Todini E. 2017. Improving the effectiveness of realtime flood forecasting through Predictive Uncertainty estimation: the multi-temporal approach, J. of Hydrol. 51, 555–576. https://doi.org/10.1016/j.jhydrol.2017.06.030 Bates, P. D., Horritt, M. S., and Fewtrell, T. J., 2010. A simple inertial formulation of the shallow water equations for efficient twodimensional flood inundation modelling. J. Hydrol. 387(1–2), 33–45. https://doi.org/10.1016/j.jhydrol.2010.03.027 Bergé-Nguyen, M., Cretaux, J.F., 2015. Inundations in the Inner Niger Delta: Monitoring and Analysis Using MODIS and Global Precipitation Datasets. Remote Sens. 7(2), 2127–2151. https://doi.org/10.3390/rs70202127 Biancamaria, S., Hossain, F., Lettenmaier, D. P. (2011) Forecasting transboundary river water elevations from space. Geophys Res. Lett. 38, L11401. Biancamaria, S., Lettenmaier, D.P., Pavelsky, T.M., 2016. The SWOT mission and its capabilities for land hydrology. In Remote Sensing and Water Resources. Springer, Cham, pp. 117–147. https://doi.org/10.1007/s10712-015-9346-y Biancamaria, S., Frappart, F., Leleu, A.S., Marieu, V., Blumstein, D., Desjonquères, J.D., Boy, F., Sottolichio, A., Valle-Levinson, A., 2017. Satellite radar altimetry water elevations performance over a 200 m wide river: Evaluation over the Garonne River. Adv. Space Res. 59(1), 128–146. http://doi.org/10.1016/j.asr.2016.10.008 Birkett, C.M., 1998. Contribution of the TOPEX NASA Radar Altimeter to the global monitoring of large rivers and wetlands. Water Resour. Res. 34(5), 1223–1239. https://doi.org/10.1029/98WR00124 Birkett, C.M., Mertes, L., Dunne, T., Costa, M.H., Jasinski, M.J. 2002. Surface water dynamics in the Amazon Basin: Application of satellite radar altimetry. J. Geophys. Res., 107(D20), 8059. http://doi.org/10.1029/2001JD000609. Birkinshaw, S.J., O’Donnell, G.M., Moore, P., Kilsby, C.G., Fowler, H.J., Berry, P.A.M., 2010. Using satellite altimetry data to augment flow estimation techniques on the Mekong River. Hydrol. Process. 24, 3811–3825. https://doi.org/10.1002/hyp.7811 Birkinshaw, S.J., Moore, P., Kilsby, C.G., O'Donnell, G.M., Hardy, A.J., Berry, P.A.M., 2014. Daily discharge estimation at ungauged river sites using remote sensing. Hydrol. Process. 28(3), 1043– 1054. https://doi.org/10.1002/hyp.9647 Bjerklie, D.M., Dingman, S.L., Vorosmarty, C.J., Bolster, C.H., Congalton, R.G., 2003. Evaluating the potential for measuring river discharge from space. J. Hydrol. 278(1-4), 17–38. https://doi.org/10.1002/hyp.9647 Bjerklie, D.M., Moller, D., Smith, L.C., Dingman, S.L., 2005. Estimating discharge in rivers using remotely sensed hydraulic information. J. Hydrol. 309(1-4), 191–209. http://doi.org/10.1016/j.jhydrol.2004.11.022

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