New perspectives in monitoring water resources
in large tropical transboundary basins
based on remote sensing and radar altimetry
FREDERIQUE SEYLER1, STEPHANE CALMANT2, JOECILA DA
SILVA3, JUAN-GABRIEL LEON4, FREDERIC FRAPPART5, MARIEPAULE BONNET5, NAZIANO FILIZOLA6, EMMANUEL
ROUX1,GERARD COCHONNEAU1, ANA-CAROLINA ZOPPAS COSTI7,
EURIDES DE OLIVEIRA8, JEAN-LOUP GUYOT5, PATRICK SEYLER5
1 Institut de Recherches pour le Développement, IRD, US 140 ESPACE, CP 7091, Lago Sul,
71970-320, Brasilia, DF, Brasil, frederique.seyler@ird.fr
2 Université de Toulouse, France, UMR 5566 LEGOS, IRD, CNRS, CNES
3 Universidade Federal do Rio de Janeiro, Brasil
4 Universidade Nacional de Colombia, Colombia
5 Université de Toulouse, France, UMR 5563 LMTG, IRD, CNRS
6 Universidade do Estado de Amazonas, Brasil
7 CPRM Servico Geologico do Brasil, Brasil
8 ANA Agencia Nacional de Aguas, Brasil
Abstract: The combined use of remote sensing and radar altimetry is offering entirely new
perspectives for the monitoring of water resources in large tropical trans-boundary basins.
Based on a study conducted mostly in the region of the Llanos de Mojos, a large complex of
wetlands located within the southernmost extension of the Amazon Basin, at the Brazilian
border with Bolivia and Peru, but also based on previous studies, we give some results that
will illustrate this point of view. First, the results are showing the current limitations of the
radar altimetric missions, which were designed primarily for ocean level or ice caps studies:
essentially the revisit time, the size of the water bodies that can be monitored, and the lack of
reliable data in presence of relief. Nevertheless, they also show that the data processing and
the tools we developed to select appropriately the data, allow retrieving quite accurately the
seasonal variability of the water elevation within the basin selected. Second, we are stating the
absolute necessity of coupling these data with remote sensing images, in order to retrieve
useful information on the hydrologic behaviour of this very complex system. The common
altitudinal reference of the radar altimetry missions is opening new modelling opportunities,
as the river slope is a key parameter for hydrodynamical studies. Last, the spatial distribution
reachable nowadays, and the perspectives offered by the future sensors, are going forward a
global, detailed capability of monitoring wetlands and floodplains, as well as their
relationship with the river flow. The applications resulting from these monitoring tools are of
primarily importance for tropical poorly gauged basins, and we will review as a conclusion
some of them, as infrastructure monitoring and planning, flood and drought monitoring and
forecast, fluvial waterway monitoring and transport planning, fluvial dynamics of the riverbed
and discharge modelling.
Key words: Water Resources, Remote Sensing, Radar Altimetry, Trans-boundary Basins
INTRODUCTION
Radar altimetry missions for ocean studies began with GEOS3, launched by NASA in 1975
with both objectives of defining the structure of the earth's gravitational field and mapping the
ocean surfaces. It was followed by SEASAT in 1978. SEASAT was managed by JPL and had
the first space-borne synthetic aperture radar (SAR) along with a radar altimeter. SEASAT
with 60 cm precision on orbit radial component was the first to be scientifically used for
ocean mapping, although it flew only three months before switching off. It was followed by
GEOSAT, and this successful mission has given way to three lineages of satellite, GFO
(GEOSAT Follow-On), ERS/ENVISAT and Topex/Poseidon/Jason. From Topex/Poseidon
(T/P) launched in 1992, and its 3 cm precision on orbit radial component (Le Traon et al.,
1995), were initiated studies dedicated to inland seas and lakes. In the first PhD dissertation
defended in France on continental water altimetry, Mercier (2001) stated "In spite of great
potentiality in the domain of continental hydrology, (…) the number of team practising
satellite altimetry beyond the border of ocean studies is small". The remark could be made
today, eight years later.
Basically, three teams are processing and distributing radar altimeter data for continental
water studies:
(a) The Global Reservoir and Lakes Monitor Project team supported by NASA and USDA
(United States Department of Agriculture) and leaded by C. Birkett (1995a, 1995b). The
project has used archive data from the T/P mission (1992-2002) and uses now near-real time
radar altimeter data from the Poseidon-2 instrument on-board the Jason-1 and Jason-2
satellites which were launched respectively in December, 2001 and in July, 2008. It delivers
time series of lakes and reservoir surface height variations for about 100 locations around the
world, with an accuracy expected to be better than 10cm rms.
(http://www.pecad.fas.usda.gov/cropexplorer/global_reservoir/).
(b) The River and Lake product team supported by ESA and De Montfort University (UK)
and led by P. Berry (2007).The project is aimed at developing, demonstrating and assessing
an information service based on inland water altimetry (tethys.eaprs.cse.dmu.ac.uk/).
(c) The Hydroweb team at LEGOS (Laboratoire d'Etudes en Géophysique et Océanographie
Spatiale), Toulouse, France and the associated laboratories, in particular the LMTG
(Laboratoire d'Etudes et Transfert en Geologie), which has led the CASH project
(Contribution of Spatial Altimetry for Hydrology) from 2004 to 2006. The objective of the
CASH project was to define the scientific and technological environment necessary to use the
altimetric elevation of continental water surface in complement of conventional gauging
network data in the scope of hydrological studies. Some of the applications developed during
the lifetime of the project will be briefly described hereafter. Today, the CTOH (Centre
Topographique des Océans et de l'Hydrosphere), part of LEGOS, has been approved as
Observation Service and is supported by CNES (Centre National d'Etudes Spatiales), CNRS
(Centre National de Recherche Scientifique) and IRD (Institut de Recherches pour le
Développement), from France. It distributes freely altimetric products along track ameliorated
specifically for continental water, from the missions: T/P, JASON 1 and JASON 2, GFO, and
ENVISAT RA-2. Hydroweb is a database of water levels temporal series over great rivers,
lakes and wetlands distributed by the GOHS (Geodésie, Océanographie et Hydrologie
Spatiale) team of LEGOS (http://www.legos.obs-mip.fr/fr/soa/hydrologie/hydroweb/). CLS
(Collecte Localisation Satellite) also in Toulouse, France, which was partner of the CASH
project, is distributing retracked T/P data (retracked with the four algorithms used in the
ENVISAT RA-2 altimetry products: OCEAN, ICE1, ICE2 and SEAICE) over the 8 great
fluvial basins studied by CASH: Amazon (pilot site), la Plata - Parana, Congo, Doner, Lena,
Ganga - Brahmaputra, Mekong, Yellow River.
(http://ocean.cls.fr/html/cash/donnees/access_fr.html). CLS is currently pursuing the efforts
of adapting the altimetry data retracking algorithms to the specificity of continental waters,
through the PISTACH project (Improved Jason-2 Altimetry Products for Coastal Zones and
Continental Waters), funded by CNES. Part of the CASH project team has been relocated by
IRD in Brazil, in the frame of bilateral cooperation projects, to pursue the applications of
radar altimetry for the Amazon basin hydrology, and the transfer of the developed
methodologies to Brazilian partners. Three main projects are developed:
(i) Amazon basin Hydrological Monitoring from Space (Monitoramento Espacial
Hidrológico na Bacia Amazônica), which is a technical cooperation project between IRD and
ANA (Agencia Nacional de Aguas) approved by the ABC (Agencia Brasileira de
Cooperação). The objective of the project is to build a database of satellite altimetry and
imagery products, with the same standards as the hydrological network of gauges maintained
by ANA.
(ii) Fluvial Dynamics of the system Negro-Solimões-Amazon, developed in the frame
of a bilateral understanding agreement between IRD and CPRM (Serviço Geologico do
Brasil). The project aims at calibrating/validating altimetric data by the way of field
campaigns and installation of permanent equipment under the satellite tracks, with the
ultimate objective of using the altimetric data in fluvial dynamics monitoring and fluvial
evolution prediction.
(iii) Project of Spatial Hydrology for the Amazon Basin (Projeto de Hidrologia
Espacial na bacia Amazônica - PHIESAM). The project involves the creation of a team
dedicated to spatial hydrology studies within the UEA (Universidade do Estado de
Amazonas), with the collaboration of UFAM (Universidade Federal de Amazonas) and INPA
(Instituto Nacional de Pesquisas da Amazônia).
Basically, these projects are sharing the same vision of radar altimetry for hydrology.
Although not optimized for continental water study, water surface elevation measured from
space can be used for hydrological monitoring of great trans-boundary basins. After reviewing
briefly the main applications of radar altimetry to hydrology, we will draw the perspectives
offered for water resources monitoring globally, based on the example of a study developed in
the Llanos de Mojos, at the border between Brazil and Bolivia, with ENVISAT RA2 data and
satellite imagery.
BRIEF HISTORY OF RADAR ALTIMETRY APPLICATIONS TO HYDROLOGY
GEOS3 was the first altimeter to be dedicated to ocean mapping, but yet, some authors sought
to use these new sensors to land mapping and continental water monitoring. Miller (1979)
shown that GEOS3 data could be used to monitor lakes water level. From these pioneer
studies, three main applications have been drawn. A review of the application of radar
altimetry to hydrology can be found in Calmant et al., in press. We will only give here some
salient points:
a) The first line of applications concerns water resource monitoring in relation with climate
and agriculture. The three web sites cited herein are proposing this application line in their
stake statements. The studies in this line are numerous. We can cite Mason et al. (1985),
Morris and Gill, (1994), Birkett, (1995), Ponchaut and Cazenave, (1998), Birkett et al.,
(1999), Birkett (2000), de Oliveira Campos et al., (2001), Mercier et al., (2002). Most of these
studies were conducted over great lakes. A review of radar altimetry over lakes can be found
in Creteaux and Birkett, (2006). The first study on river was conducted by Koblinsky et al.,
(1993) from GEOSAT waveforms. They estimated to 70-cm rms the discrepancy between
satellite and in-situ measurements at four sites on the Amazon. They attributed partly the
uncertainties to the orbit determination, but partly to the in-situ record, and we will comment
that in the next paragraph. The altimetric data that have been most used in these studies have
been those of T/P (10-day repeat period, since 1992), followed by Jason-1 (10-day repeat
period since 2002) and now Jason 2 since July 2008. ENVISAT (following the ERS series
started in 1991 with a 35-day repeat period) mission has been used only in Berry et al.,
(2005), and Frappart et al., (2006) with a purpose of validation on rivers.
b) The second great application of radar altimetry for hydrology is coming from the fact that
radar altimeters paths are crossing water bodies at all types of hydrological situations.
Hydrologic networks in-situ stations are located in narrow, straight sections of the river, as
they are to be gauged from time to time, to estimate discharge and be able to relate river stage
and discharge, in linear stage-discharge relationship known as rating curve. For this reason the
status of wetlands water level is largely unknown globally. The first application of radar
altimetry for monitoring wetlands has been by Cudlip et al., (1992). But it is difficult to
distinguish river and floodplain with T/P data. Birkett et al. (2002) have succeeded in a
number of cases as a phase offset of a few days in stage variations between river and nearby
floodplain has occasionally been observed. Frappart et al. (2005) have determined spatiotemporal variations of water volume over the main stream jointly with the floodplain in the
Negro River basin, using area variation estimates for a seasonal cycle captured by the
Synthetic Aperture Radar (SAR) onboard the Japanese Earth Resources Satellite (JERS-1),
and changes in water level from the T/P altimetry at 88 altimetric stations, combined with 8
in-situ hydrographic stations. A volume variation of 331 km3 was estimated for the whole
Negro sub-basin, enhancing the complex relationship between the volume potentially stored
in the inundated area and the volume flow during the same period. Similarly, Frappart et al.
(2006) monitored the flood propagation along the Mekong River by combining satellite
altimetry data and SPOT4 vegetation imagery. Lastly Frappart et al., (2008) have used
combined satellite imagery to re-estimate the volume of water stored in the inundation plains
of the Negro sub-basin with a better temporal resolution. The first study using ENVISAT data
to examine the relationship between river and floodplain through the differences in water
levels was conducted by Cauhope, (2004). On the same floodplain located in the Amazon
basin, Bonnet et al., (2008) have modelled the transfer of water between river and floodplain
partly based on altimetric water levels time series.
c) The third application is using the unique reference system of the altimetric data for
studying the slope of the rivers and therefore be able to model the hydrodynamics. As for the
other applications the pioneer works were conducted on the Amazon main stem with
SEASAT data (Guskowska et al., 1990; Cudlip et al.,1992; Mertes et al., 1996, and Dunne et
al.,1998) and T/P data (Birkett et al., 2002). With mixed T/P and ENVISAT data, Leon et al.
(2006a and 2006b) have proposed a methodology to derive stream profiles from the river bed
height and slope derived from altimetry, through the estimation of rating curves at the
altimetric virtual stations. Some works have estimated discharge at altimetric stations using
empirical regressions from in situ gauging stations (Coe and Birkett , 2004; Kouraev et al.,
2004; and Zakharova et al., 2006).
The present study is mixing both applications: monitoring of wetlands and estimating slope
and discharge.
DRAWBACKS
The major drawbacks in using radar altimetry for hydrology are often reported as being the
precision of the measurements and the revisit time of the altimeters (10 days for T/P and
Jason and 35 days for ERS/ENVISAT). We can add the size of the water bodies that can be
monitored, and the lack of reliable data in case of steep relief close to the margin and before
the river in the flight direction (Seyler et al., 2008). Seyler et al., (2008) have shown that
seasonal fluctuation of river stage can be captured for rivers width lesser than 100m. As for
precision and revisit time, most of the studies attempting to validate altimetric data have used
comparison with in situ data. We will discuss this notion in this study. For example, Bercher
et al. (2006) evaluated the amount of information lost due to the revisit time of the currently
used radar altimeters under-sampling, compared with daily measurements at in situ stations.
But for some hydrologic applications, as estimation and monitoring of fast flood events,
hourly or even more frequent measurements are needed. Nevertheless, daily in-situ water
stage measurements have never been questioned for most hydrological application. In the
same way, we will try to show in this study, that for some applications, altimetric
measurements are able to monitor seasonal variations of water level in vast inundated transboundary basins, and are very valuable as they are today, without speaking of our need to
adapt and prepare for the future altimetric missions, which will be designed for continental
waters, as the SWOT mission (Alsdorf et al., 2003).
DATA AND METHODS
The study is located at the Brazilian border with Bolivia and Peru. In Brazil the region is
known as the upper Madeira region. Madeira river has four main tributaries, two flowing
through Bolivia: Beni and Mamore rivers, one flowing through Peru and Bolivia: the Madre
de Dios River, and one forming the border between Bolivia and Brasil: The Guapore river.
Mamore and Beni are meandering rivers, flowing mostly from South to North, West of an
extensive inundation plain, called Llanos de Mojos. The Llanos de Mojos region is a large
floodplain with variable extension related to the alternating dry and rainy tropical seasons
(Ronchail et al., 2005), partly dry during Austral winter and reaching 150 000 km² at the end
of the rainy season (Roche and Fernandez, 1988).
Altimetric radar data
The altimetric data used in this study come from the ENVISAT mission, distributed by the
CTOH, specifically the range data retracked by the ICE 1 algorithm. They are along-track
recorded. In order to extract the value of water surface elevation, we used a manual selection
of the altimetric ranges projected in the plane perpendicular to the flow direction (Roux et al.,
in revision). One virtual gauge presents generally several points belonging to the same
satellite cycle. In order to retrieve the time series for the virtual gauge, we used the median
and a median dispersion of the points located across the water body (Santos Da Silva et al.,
2008). Finally, the value of the geoid GGM02C has been added to the elevation value,
referenced to the ellipsoid WGS84 (Tapley et al., 2004). For purpose of validation, the data at
six virtual stations have been compared with the water levels at 6 conventional gauges located
on the Madeira and the Guapore rivers gauges (Porto Velho, Principe da Beira, Pimenteiras,
Abuna, Pedras Negras, Vila Bela de Santissima Trindade, data distributed by ANA,
www.ana.gov.br). Figure 1 is presenting the location of the 31 virtual gauges presented in this
study.
Satellite image data
The satellite images used were:
a) JERS-1 (L Band SAR launched by the National Space Development Agency of Japan
(NASDA) in February, 1992) image mosaic from the Global Rain Forest Mapping (GRFM)
project at 100m resolution (Siqueira et al., 2000).
b) C-Band SRTM (Shuttle Radar Topography Mission) data are DEM (Digital Elevation
Model) data that have been collected throughout a 10-day acquisition period in February
2000. The DEM has a relative vertical error of 5.5 m over land with 90% significance
4
3
2
Madre de Dios
8
1
20
21
6,7
23,24
22
12
25
26
5
10
18
9
11
27
19
28 to 30
Rio Itonamas
Mamore
13
Guapore
31
15
Beni
16
17
14
Fig. 1: Location of the virtual gauges
(Rodriguez, 2005). Errors over open water are largely unknown but were estimated to ±5.51
m by Le Favour and Alsdorf, (2005), after removing of the linear trends.
Geomorphological parameters and discharge estimation
Three main geomorphological parameters have been estimated at the virtual stations, and are
presented Table 2. Each virtual station is identified by a sequential number, the satellite track
number, the river it crosses, and the latitude-longitude of the virtual station's centre given as
the mean of the longitude and latitude for the points constituting the station. The width (L) has
been measured from JERS1 images mosaic by the distance measuring tool of ARCGIS, and is
therefore given with an uncertainty of 100m, being the resolution of the image. The maximum
level difference H (m) has been estimated by the following formula (1):
H = MAX(hi) - MIN(hi)
(1)
hi = median of the water surface altitude at the virtual station for each ENVISAT cycle (m)
Slope (m/m) has been estimated by two methods: The first one is given by
S1 = MIN(hi) - MIN(hj) / dist(SVi to SVj) (2)
SVj being located downstream SVi and dist(SVi to SVj) being the distance between SVi to
SVj in m, and measured by the distance measuring tool of ARCGIS.
S2 = MEAN(MIN(hi) - MIN(hj) / dist(SVi to SVj); MIN(hh) - MIN(hi) / dist(SVh to SVi) (3)
SVh being located upstream and SVj downstream SVi
Bankfull discharge Q1* (m3/s) has been estimated for each virtual station by:
Q* = 4.00·A*1.21·S0.28
(4)
given by Williams (1978) where Q* is the bankfull discharge, A* cross-sectional area at
bankfull and S the slope.
A* has been approximated by:
A* = L * H (5)
Another formula is given by Bjerklie et al., (2003):
Q2* = 0.1676xL*1.86
(6)
The estimation of discharge has been evaluated by comparing with monthly discharges
estimated at two conventional gauges (Table 1).
Table 1: Minimum (Min Q), Mean (Moy Q), and Maximum (Max Q) of monthly discharges
(m3/s) estimated between, respectively, 1970, September - 2007, November at Guaraja-Mirim,
on Mamore river upstream the confluence with the Guapore River, 1967 May - 2008 June at
Porto Velho, on the Madeira river, 331km downstream the confluence Beni-Mamore.
Conventional gauge
Guaraja-Mirim
Min Q
1039
Moy Q
7688
Max Q
21280
Porto Velho
2220
18394
47400
RESULTS AND DISCUSSION
Validation
We can analyze the precision of the altimetric data by analysing the discrepancy between
water levels at conventional gauges and at virtual gauges. Table 2 gives the rms standing for
the discrepancy at 6 couples conventional gauges/altimetric virtual stations The rms is
calculated with respect to the best fitting regression line, which coefficient is provided.
Table 2: Comparison between in situ gauge and virtual gauge.
Conventional
gauge
Porto Velho
Principe
da
Beira
Pimenteiras
Abuna
Pedras Negras
Vila Bela de
Santissima
Trindade
Altimeter track
Distance(km)
Rms (m)
18
Regression coef.
(m/m)
1.06
951
192
5
0.75
1.349
478
278
106
0
30
29
1.02
0.99
0.74
0.170
1.496
0.689
392
0
0.62
0.342
0.395
As we have seen, it is very difficult to assess the precision of the altimetric data by
comparison with conventional gauges, as the first one is averaging the surface elevation
across the channel of the river, and the second one is measuring at one single point located on
one bank of the river. First, the precision of the conventional gauge with respect with the
spatial variability of the river surface is never stated. Koblinsky et al., (1993) have attributed
part of the error in altimetric measurements to the error at in situ gauges, as they have
estimated the discrepancy between two conventional gauges located on the opposite banks of
the Tapajós river. But most of the time, the in situ gauge is given as the absolute reference of
the river surface plane. Second, it is very uncommon to have an altimeter track right above the
conventional gauge taken as reference. When located at some distance from each other, the
river section can change, or unknown amount of water can reach the river between the two
points from an ungauged tributary, or another unknown amount of water can be temporarily
lost in a derivation or an inundation plain. Analysing table 2, we can summarize the validation
situation in three categories:
a) the regression coefficient is very different from 1, which is the case when the river sections
(conventional and altimetric) are not similar: Principe da Beira (regression coefficient of 0.75;
the track is partly crossing over a braided part of the river), Pedras Negras (regression
coefficient of 0.74; the track is crossing in a 30° angle from the river longitudinal profile,
allowing to take in account part of the longitudinal slope of the river in the altimetric
measurement). The last case is that of Vila Bela de Santissima Trindade. The track 393 of
ENVISAT is crossing the Guapore river right above the conventional gauge, but is averaging
the 100m wide channel at this point plus 17km of floodplain, which gives a regression
coefficient of 0.62.
b) the regression coefficient is near 1, which could indicate a similar river section. It is the
case for the comparison made at Porto Velho and Abuna. The Madeira river has an equivalent
width at both conventional and virtual station, and the track is crossing in a near perpendicular
direction from the river flow. But the distance between the two measurements points is large,
18 km for Porto Velho and 30 km for Abuna. In both cases, there are some derivations by
floodplains. For Abuna, the derivations seem very important ones, as seen on JERS images,
and a tributary is joining the river between the conventional gauge and the satellite track.
c) the regression coefficient is near 1, and the distance between the two measurements points
is null. This is the case at Pimenteiras. This is the only case where the precision of the
altimetric data can be assessed: 17cm rms. This is within the range given in Calmant et
Seyler, (2006) for the ENVISAT data (decimetric precision), taking into account the spatial
variability of the river across the channel.
General slope across the wetlands of Llanos de Mojos and between the four rivers
draining the area
Fig 2 presents a comparison between ENVISAT and SRTM data across the Madre de Dios et
Beni rivers, and a NNW-SSE cross cut of the Llanos de Mojos. SRTM and ENVISAT data
are very close from each other across the floodplain and differs 10 to 50 meters when crossing
Beni and Madre de Dios rivers. The difference can be due to a difference of geoid model. We
have chosen the GGM02C model to retrieve altitudes from ENVISAT data, and SRTM
altitude data are given in reference to WGM96 model. Despite these discrepancies, both
dataset are according to place the Llanos de Mojos as a very plane region declining from West
to East between 165 to 150m high. The two rivers Madre de Dios and Beni are draining a
lowest region separated by various reliefs from the vast inundation plain of the Llanos.
Fig 3 presents two cross cuts of ENVISAT tracks. The first one, track 106, at the top of the
figure is cutting the llanos de Mojos from NNW to SSW between the Mamore and the
Guapore rivers. The second one, track 035 is cutting the llanos from NNE to SSW, West of
the Guapore, across the area drained by the Guapore river and its tributaries. Both tracks are
showing a general decline of the altitudes from South to North. The profiles are also
indicating that most part of the inundation plain is drained by various tributaries of the
Guapore river.
Llanos de Mojos is then distributed in two vast wetland systems, located East of the Beni and
Madre de Dios basins, without evident communication with these two watersheds. The two
parts of Llanos de Mojos plateau are: first one located South West of Mamore river, drained
by the Mamore and its tributaries mostly oriented SW-NE, direction enhanced by a string of
Llanos de Mojos
Madre de Dios
Beni
Envisat Track 121
N
Madre de Dios
Envisat Track 665
Beni
Llanos de Mojos
Fig. 2: Cross cut of the rivers Madre de Dios and Beni, and of the Llanos de Mojos from
NNW to SSE. Pink line represents the ENVISAT data (track 665 at the bottom and
track 121 up). Red line represents a height profile of the SRTM data at the same
location than the ENVISAT track. In the middle, the JERS image of the area.
Mamore
Rio Blanco
Guapore
ENVISAT
track 106
ENVISAT
track 035
Rio Machupo
Guapore
Fig. 3: Cross cut of the rivers Mamore and Guapore, and of the Llanos deMojos.
geometrically-shaped lakes. The second one is drained by the Guapore and its tributaries
mostly in a direction SSW-NNE. The two parts are gently sloping NNE. There is no evidence
of communication between the two parts.
Morphological parameters at virtual stations and estimation of discharge
Table 3: Morphological parameters at virtual stations: SV: Number of station localized in
Fig1. Track: ENVISAT track number; River MD: Madre de Dios, B: Beni; M: Mamore; G:
Guapore; B-M confluence Beni Mamore; M-G Confluence Mamore-Guapore L: rivers
draining the geometrically-shaped rivers left bank Mamore; I: Itonamas river; aff. G
tributaries left bank Guapore; mean lat: mean latitude at the virtual station; mean lon: mean
longitude at the virtual station; L: Width (m); H: Maximum level difference (m); S1: Slope
(m/m) calculated by (2); S2: Slope (m/m) calculated by (3); Q*1 full bank discharge (m3/s)
estimated by formula 4 with S1 (2); Q*2 (m3/s) full bank discharge estimated with formula 4
with S2 (3); Q*3 (m3/s) full bank discharge estimated with formula 6.
SV Track River mean lat mean lon L
H
S1
S2
Q*1 Q*2 Q*3
1 207 MD
-11.61 -67.67
600 10.518 0.000119 0.000119 12618 12618 24640
2 665 MD
-11.34 -67.05
1400 8.44 0.000111 0.000115 26476 26717 119144
3 121 MD
-11.08 -66.39
640 10.752 0.000089 0.000100 12912 13355 27782
4 278 B-M
-10.38 -65.38
1420 8.28
0.000260 38426 33351 122329
5 207 B
-13.33 -67.32
350 6.805 0.000087 0.000087 3556 3556 9042
6 665 B
-12.12 -66.88
360 8.53 0.000054 0.000071 4243 4564 9528
7 665 B
-11.88 -66.93
430 8.11 0.000067 0.000061 5253 5106 13259
8 121 B
-11.37 -66.33
390 7.02 0.000083 0.000075 4161 4045 11057
9 665 L
-13.62 -66.53
<100 1.94 0.000119 0.000119 187 187 880
10 121 L
-13.28 -65.90
<100 1.77 0.000162 0.000141 182 175 880
11 579 M
-13.21 -65.19
480 12.32 0.000030 0.000023 8183 7395 16270
12 736 M
-12.45 -65.13
400 11.5 0.000013 0.000030 4649 5841 11591
13 192 M
-14.95 -65.00
370 11.55 0.000046 0.000081 6049 7081 10026
14 650 M
-16.91 -64.72
<100 7.15 0.000116 0.000116 900 900 880
15 106 I
-15.50 -63.68
<100 1.346 0.000124 0.000127 121 122 880
16 564 I
-16.19 -63.12
<100 2.54 0.000131 0.000203 266 301 880
17 20
I
-16.65 -62.50
<100 7.192 0.000276 0.000276 1154 1154 880
18 192 aff G
-12.87 -64.51
100 11.94 0
0.090018 13083 10776 880
19 650 aff G
-12.95 -63.80
100 7.45 0.000037 0.000037 686 686 880
20 736 G-M
-11.95 -65.02
1200 13.307 0.000088 0.000059 21812 19490 42075
21 35
G
-12.12 -64.72
590 10.845 0
0.000030 7765 8695 23881
22 192 G
-12.46 -64.41
430 9.572 0.000039 0.000051 5524 5953 13259
23 493 G
-12.50 -63.92
260 6.04 0.000063 0.000058 1968 1918 5201
24 650 G
-12.45 -63.69
150 5.18 0.000052 0.000050 796 787 1870
25 951 G
-12.63 -63.17
100 5.58 0.000048 0.000054 522 538 880
26 564 G
-13.12 -62.41
100 5.39 0.000060 0.000063 531 539 880
27 20
G
-13.57 -61.79
100 4.85 0.000067 0.000087 481 518 880
28 865 G
-13.54 -61.53
100 3.87 0.000107 0.000088 418 396 880
29 478 Guapore -13.51 -61.06
100 4.6
0.000070 0.000079 457 473 880
30 936 Guapore -13.94 -60.44
100 4.095 0.000088 0.000092 424 430 880
31 392 Guapore -14.88 -59.94
<100 1.54 0.000097 0.000097 133 133 880
Table 4: Full Bank Discharge by rivers MD: Madre de Dios; B: Beni; M: Mamore; G:
Guapore; Sum M-G: Sum of value for Mamore and for Guapore; Md: Mamore after the
confluence with Guapore; Sum 4tr. Sum of the values for the 4 tributaries; Q*1 full bank
discharge (m3/s) estimated by formula 4 with S1 (2); Q*2 full bank discharge (m3/s)
estimated with formula 4 with S2 (3); Q*3 full bank discharge (m3/s) estimated with formula
6.
MD
B
D*1 12912 4161
M
8183
G
7765
Sum M-G Md Sum 4tr.
15918
21812 38885
D*2 13355 4045 7395 8695 15090
D*3 27782 11057 16270 23881 40151
19490 36890
42075 80914
Table 5: Contribution (%) of the four tributaries at the full bank discharge of Madeira river
MD
B
M
G
Md
%total D*1
%total D*2
33
11
21
20
36
36
11
20
24
29
%total D*3
34
14
20
30
22
The bank full discharges estimated from the slope downstream the virtual station (S1) and
from the mean slope upstream and downstream the station (S2) are very close to each other.
Comparing the bank full discharge of the Mamore upstream the confluence with the Guapore
(Md), with the maximum discharge at Guajara-Mirim, the closest value is that of D*1,
calculated only with the downstream slope. The value of D*1 summed for the four tributaries
(sum 4tr., Table 4) is closest too to the values of maximum discharge in Porto Velho. Full
bank discharges calculated with formula 6 (only the width of the river) seem highly
overestimated. Retaining the values of D*1 as the more realistic, it can be assessed that at full
bank, the Madre de Dios contributes for 33% of the discharge of the Madeira river, the Beni
for only 11%, the Mamore before the confluence with the Guapore to 21%, the Guapore to
20%, and the sum of the two at the confluence with the Beni river contributes for 56% of the
Madeira discharge. There are rough estimations, but it could be useful in an ungauged basin,
to have estimates of discharge obtained only from satellite data.
CONCLUSION
Altimetric data as they are today are not well adapted to continental hydrology. Nevertheless,
we can assess that radar altimeter can monitor correctly the seasonal fluctuation of water stage
in great trans-boundary basins. From previous studies, we can state that the width of the river
is not the only criteria to take into account to predict the reliability of the time series. Rivers
less than 100 meters wide can be sampled, provided that they are surrounded by a floodplain,
even covered by flooded forest. Steep relief near and before (in the path direction) the river,
steep longitudinal slope, islands, flow direction along track are major impediment for
obtaining a reliable time series. Except these situations, the time series obtained allow
monitoring the seasonal variation of water stage. Although it is very difficult to assess the
precision of the altimetric data by comparison with conventional gauges, in cross-track
situations at the exact location of a conventional gauge, discrepancy between virtual and
conventional gauges can be analysed, and in all cases, they do not exceed 20cm. In this study,
full bank discharges have been estimated for the four main tributaries of the Rio Madeira.
Estimation of general slope across the Llanos de Mojos have allowed discriminating two main
draining area in this vast wetland complex, one by the Mamore river, and the other by the
Guapore river, without any evidence of communication, at least by superficial runoff, between
the two regions. Beni river, which seems to flow very closely to the Llanos de Mojos complex
on remote sensing images is not related to the wetlands, as they are forming a vast
overhanging plateau gently sloping North West and separated from the Beni river by high
ridges of savannas.
Of course, precision, revisit time, size of the water bodies monitored, conditions of shallow
relief, impose restrictions to the use of satellite radar altimetry to hydrology. Actually, they
limit its use to a range of applications: levelling of conventional unlevelled water gauges;
study of the relationship between the river and its floodplain and between the river and the
swamps and wetlands within the watershed; study of the elevation profile and river slope.
These two last applications have opened entirely new perspectives in the hydrological field:
for example: infrastructure monitoring and planning (in particular monitoring of remote dams
for producing hydropower), flood and drought monitoring and forecast, fluvial waterway
monitoring and transport planning, fluvial dynamics of the riverbed and discharge modelling.
All these applications are to be conducted with joined spatial and conventional monitoring,
but in remote, tropical forested areas, or in vast wetlands, unreachable most of the time, as the
region that is the object of the present study, or in trans-boundary basins where conventional
data are unevenly distributed, or lastly in politically troubled region, radar altimetry
monitoring is of primarily importance. In this prospect, the SWOT mission, which will be
launched around 2015, and will provide a global, quasi-continuous measurement of water
surface area and elevation, will provide very valuable new data sets. A lot of improvements
are yet to be achieved to process the existing data, and it is necessary to carry on other
validation works in distinct environment.
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