Water level dynamics of Amazon wetlands at the watershed scale by satellite altimetry

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Water level dynamics of Amazonwetlands at the watershed scale bysatellite altimetryJoecila Santos Da Silva a b , Frédérique Seyler c , StéphaneCalmant b d , Otto Corrêa Rotunno Filho a , Emmanuel Roux e ,Afonso Augusto Magalhães Araújo f & Jean Loup Guyot g ha Universidade Federal do Rio de Janeiro, UFRJ (COPPE-PEC), CP68506, CEP 21945-970, Rio de Janeiro, Brazilb Université Toulouse, UPS (OMP-PCA), LEGOS, 14 Av, EdouardBelin, F-31400, Toulouse, Francec IRD, S140 Unité ESPACE Montpellier, 500 Rue JF Breton, F-34093,Montpellier, Franced IRD, LEGOS, F-31400, Toulouse, Francee IRD, S140 Unité ESPACE Guyane, Route de Montabo, BP16597323, Cayenne, Guyane, Francef Universidade Federal do Paraná, UFPR (LEMMA), CP 19100, CEP81531-990, Curitiba, Brazilg Université Toulouse, UPS (OMP-SVT), LMTG, 14 Av, EdouardBelin, F-31400, Toulouse, Franceh IRD, LMTG, F-31400, Toulouse, France

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To cite this article: Joecila Santos Da Silva, Frédérique Seyler, Stéphane Calmant, Otto CorrêaRotunno Filho, Emmanuel Roux, Afonso Augusto Magalhães Araújo & Jean Loup Guyot (2012): Waterlevel dynamics of Amazon wetlands at the watershed scale by satellite altimetry, InternationalJournal of Remote Sensing, 33:11, 3323-3353

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International Journal of Remote SensingVol. 33, No. 11, 10 June 2012, 3323–3353

Water level dynamics of Amazon wetlands at the watershed scale bysatellite altimetry

JOECILA SANTOS DA SILVA∗†‡, FRÉDÉRIQUE SEYLER§, STÉPHANECALMANT‡¶, OTTO CORRÊA ROTUNNO FILHO†, EMMANUEL ROUX|,

AFONSO AUGUSTO MAGALHÃES ARAÚJO†† and JEAN LOUP GUYOT‡‡§§†Universidade Federal do Rio de Janeiro, UFRJ (COPPE-PEC), CP 68506, CEP

21945-970, Rio de Janeiro, Brazil‡Université Toulouse, UPS (OMP-PCA), LEGOS, 14 Av, Edouard Belin, F-31400

Toulouse, France§IRD, S140 Unité ESPACE Montpellier, 500 Rue JF Breton, F-34093 Montpellier,

France¶IRD, LEGOS, F-31400 Toulouse, France

|IRD, S140 Unité ESPACE Guyane, Route de Montabo, BP 16597323, Cayenne, GuyaneFrance

††Universidade Federal do Paraná, UFPR (LEMMA), CP 19100, CEP 81531-990,Curitiba, Brazil

‡‡Université Toulouse, UPS (OMP-SVT), LMTG, 14 Av, Edouard Belin, F-31400Toulouse, France

§§IRD, LMTG, F-31400 Toulouse, France

(Received 19 November 2009; in final form 25 August 2010)

In this study we used satellite altimetry to characterize the time and space vari-ations in water stored in or circulating through rivers, floodplains, wetlands andlakes in the major sub-basins of the Amazon basin. Using a specific methodologyto rigorously select original three-dimensional (3D) data from an EnvironmentalSatellite (ENVISAT) mission, water level time series were calculated at the cross-ing path of the satellite tracks with the water bodies. We took advantage of thecontinuous sampling of the water level along the satellite track segments that crossthe watershed to analyse both spatial and temporal relationships between: (i) theriver and its floodplain and (ii) different basins. This work evidences in particularthe existence of water leaking between the Negro and Solimões basins at the highwater stage. It highlights that the phenomenon of a secondary flood peak occurringin the water level series in the Solimões basin at rising water, known as repiquete,is caused by the rain equatorial regime of the northern upstream tributaries of theSolimões River, but is disconnected from the same phenomenon occurring withinthe Rio Negro basin.

1. Introduction

Hydrology mainly deals with continental water reservoirs (CWRs) but is also used tostudy elements of the atmosphere and the oceans that directly affect the movement of

*Corresponding author. Email: [email protected]

International Journal of Remote SensingISSN 0143-1161 print/ISSN 1366-5901 online © 2012 Taylor & Francis

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3324 J. Santos da Silva et al.

water in the Earth system. For the continental watersheds, time and space variationsof the water levels in rivers and wetlands are crucial hydrological observations to bemade (Calmant and Seyler 2006).

As wetlands are the result of climate and geomorphology dynamics, they play animportant role at the watershed scale (Birkett 1995). Wetlands are among the mostproductive environments of the world, providing water and primary productivity(Secretariat de la Convention de Ramsar 1998). They are cradles of biological diversity(Junk and Weber 1996, UNEP 1996, Acreman et al. 2007, Lake and Bond 2007) andcan significantly alter water quality and quantity, for example by sorting the sediments(Meade et al. 1985, Mertes et al. 1996, Dunne et al. 1998), regulating the biogeochem-ical fluxes and changing flood waves (Junk et al. 1989, Richey et al. 1989). As theycan modulate the surface energy and water balance, wetlands also influence climaticstability, with an impact on evapotranspiration processes, CO2 and methane emis-sions (Matthews 2000, Richey et al. 2002, Guerin et al. 2006, Ramillien et al. 2006,Rottenberger 2008). Although wetland systems deserve attention, few attempts havebeen made to monitor their water level dynamics, particularly in the Amazon basin,because of the difficulty of acquiring water level data. Moreover, wetlands are oftendifficult to define, as there are many different types and they cover the transitionalzone between permanently wet and occasionally dry environments.

The Amazon basin is the world’s largest fluvial basin, with a drainage area of6.2 × 106 km2 (5% of the total continental land) and an average water discharge con-tribution to the Atlantic Ocean of 238 × 103 m3 s–1 (Ronchail et al. 2006), representingalmost 15% of the total fresh water discharged to the oceans (Molinier et al. 1995). TheAmazon basin has three main sources for its rivers: (i) the Andes, (ii) the Brazilian andGuyana shields and (iii) the lowlands, and these units cover, respectively, 11%, 44%and 45% of the total basin area (Guyot et al. 1999). The Andean rivers, Marañón–Solimões, and the Madeira tributaries present profiles with dramatic contrasts, froma steep gradient in the Andean domain to a very low river slope on the Amazon low-land: from 20 cm km–1 (Napo River in Peru) to less than 1 cm km–1 (Amazon Riverin Brazil) (Guyot et al. 2007). In the Brazilian and Guyana shields, there are con-nections between the Amazon basin and neighbouring basins: in the north with theOrinoco River (Sternberg 1975) and in the south with the Paraguay River (Sioli 1984).The Casiquiare canal, which links the Amazon to the Orinoco basin, is a well-knownexample of a fluvial system with a drainage that alters direction according to the sea-son (Guyot et al. 1999). River floodplains are the dominant wetland habitat in theAmazon lowland (Guyot et al. 1994). Areas ranging from 90 × 103 km2 (Sippel et al.1998) to 300 × 103 km2 (Junk and Furch 1993) of the Amazon lowland are estimatedto be covered by permanently or seasonally flooded wetlands, known as várzeas. Likemany other large fluvial watersheds, the Amazon basin is poorly monitored and stageinformation is lacking. The monitoring network is composed of conventional rain andflow gauge stations that are currently maintained by the Agência Nacional de Águas(ANA), Brazil. However, the flow gauge network does not provide measurements ofwetland water level because conventional hydrological gauge stations are set at themain river reaches to establish rating curves. The Amazon basin is shown in figure 1.

Radars embarked on board satellites are used to address some limitations of conven-tional monitoring of hydrometeorological variables, such as water level, soil moisture,snow and rain, on or near the land surface. The hydrological variable studied in thiswork is water level. Radar altimetry measurements were initially used to determinethe ocean surface topography. Nevertheless, since satellite-embarked radar altimeters

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Water level dynamics of Amazon wetlands using satellite altimetry 3325

GuyanaVenezuelaColombia

Ecuador

Peru

Bolivia

Brazil

Figure 1. Map of the Amazon basin together with the Negro River basin (green), SolimõesRiver basin (yellow), Madeira River basin (orange), sub-basin Branco and Guaporé (pink)(5 N–20 S/80 W–50 W). Virtual stations defined by the intersection of the river channels over thethree basins (white dots) and the virtual stations used in this study (yellow dots). Gauge stationsare also shown as red dots. The background image is a JERS-1 SAR mosaic of Amazon highwater images. Basin contours are from Seyler et al. (2009c).

were launched in the late 1970s, altimetrists have been investigating the possibilityof applying these data to continental waters (e.g. Calmant and Seyler 2006, Crétauxand Birkett 2006, Alsdorf et al. 2007b, Calmant et al. 2008 and references herein).Radar altimetry of rivers and wetlands has the unique ability to provide stage dataat ungauged locations (Calmant et al. 2008). It uses a single fixed reference datum(Birkett 1998) and is potentially able to estimate the depth of water (Leon et al. 2006).At the beginning of the 1990s, two main families of altimetry missions were devel-oped. The first was the TOPEX/Poseidon (T/P) mission (1992–2006), a joint projectbetween the French Space Agency (CNES) and the American Space Agency (NASA),specifically designed for study of the dynamical processes of the oceans. Its successorsare Jason-1 (launched in 2001) and Jason-2 (launched in 2008). The second familyincludes the European Remote Sensing satellites ERS-1 (1991–1996) and ERS-2 (from1995) and the Environmental Satellite (ENVISAT), launched by the European SpaceAgency (ESA) in 2002.

In the Amazon basin, radar altimetry has proved to be a viable alternative to in situmeasurements to read the periodic measurements of the water level of rivers, includ-ing the associated wetlands (Birkett 1998, Alsdorf et al. 2000, 2001a,b, de OliveiraCampos et al. 2001, Mercier 2001, Alsdorf and Lettenmaier 2003, Berry et al. 2005,Calmant and Seyler 2006, Frappart et al. 2006, Calmant et al. 2008, Roux et al. 2008,2010, Santos da Silva et al. 2010). In addition, it is possible to estimate the water level

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https://www.researchgate.net/publication/215721494_Global_inland_water_monitoring_from_multi-mission_altimetry_Geophys_Res_Lett_3216L16401?el=1_x_8&enrichId=rgreq-1316ebee-9f45-4855-bc42-6a5a3f1962f9&enrichSource=Y292ZXJQYWdlOzIyOTcwMDc3OTtBUzo5ODgwNDk4MDI1Njc3MkAxNDAwNTY4MzUxMjQ5

https://www.researchgate.net/publication/234305197_Measuring_Surface_Water_From_Space?el=1_x_8&enrichId=rgreq-1316ebee-9f45-4855-bc42-6a5a3f1962f9&enrichSource=Y292ZXJQYWdlOzIyOTcwMDc3OTtBUzo5ODgwNDk4MDI1Njc3MkAxNDAwNTY4MzUxMjQ5

https://www.researchgate.net/publication/241439857_Contribution_of_the_TOPEX_NASA_Radar_Altimeter_to_the_global_monitoring_of_large_rivers_and_wetlands?el=1_x_8&enrichId=rgreq-1316ebee-9f45-4855-bc42-6a5a3f1962f9&enrichSource=Y292ZXJQYWdlOzIyOTcwMDc3OTtBUzo5ODgwNDk4MDI1Njc3MkAxNDAwNTY4MzUxMjQ5


https://www.researchgate.net/publication/227765170_Watershed_delineation_for_the_Amazon_sub-basin_system_using_GTOPO30_DEM_and_a_drainage_network_extracted_from_JERS_SAR_images?el=1_x_8&enrichId=rgreq-1316ebee-9f45-4855-bc42-6a5a3f1962f9&enrichSource=Y292ZXJQYWdlOzIyOTcwMDc3OTtBUzo5ODgwNDk4MDI1Njc3MkAxNDAwNTY4MzUxMjQ5

https://www.researchgate.net/publication/43236954_Rating_curves_and_estimation_of_average_water_depth_at_the_upper_Negro_River_based_on_satellite_altimeter_data_and_modeled_discharges?el=1_x_8&enrichId=rgreq-1316ebee-9f45-4855-bc42-6a5a3f1962f9&enrichSource=Y292ZXJQYWdlOzIyOTcwMDc3OTtBUzo5ODgwNDk4MDI1Njc3MkAxNDAwNTY4MzUxMjQ5


slope (Cudlip et al. 1990, Birkett et al. 2002, LeFavour and Asdorf 2005) and the watervolume stored in the floodplains (Frappart et al. 2005, 2008). These studies have beenmostly conducted with the T/P mission.

ENVISAT measurements were used in this study. In the case study of the Curuaiwetland, Cauhope (2004) established relationships between river and floodplain. Leonet al. (2006) proposed a methodology to derive river bed height and slope from bothT/P and ENVISAT data, by estimating the rating curves at the altimetric crossingbetween satellite tracks and rivers. Based on a linear model exploiting ENVISAT dataat a limited number of in situ limnimetric stations along the Negro River, Roux et al.(2008) proposed a methodology to obtain daily time series. On the Curuai floodplain,Bonnet et al. (2008) modelled the transfer of water between river and floodplain, partlybased on altimetric water levels time series. More recently, Santos da Silva et al. (2011)developed studies of water level variability for some Amazon lakes, while Seyler et al.(2008, 2009a,b) studied Llanos de Mojos of the Madeira basin. These studies con-firmed that ENVISAT data sets provide valuable measurements to address water levelvariability for rivers and lakes, allowing monitoring of wetland systems, which is themain focus of this work.

This article presents an analysis of water stage measurement in rivers and wet-lands derived from ENVISAT Radar Altimeter 2 (RA-2) data for the Amazon basin(figure 1). Altimetry values from the satellite are referenced in a global geodetic frame.This provides access to the differences in level between water bodies and to the cycleof connections during high stages and disconnection at low water periods between thedifferent water bodies included in the sub-basins. We analyse the relationships betweenthe river and its floodplain, and between the river and the wetlands in its watershed,through the elevation profile along the track. It is worth noting that such a study couldnot be conducted with a conventional fluviometric network because there are no gaugesample water levels either in the floodplains or in the wetlands of this basin.

2. General description of the study area

Our study sites were all selected in the middle plains region of the Amazon basin.The drainage areas of the Negro, Solimões and Madeira river basins are 0.7 × 106,2.24 × 106 and 1.42 × 106 km2, respectively, corresponding to a total of 70% of theAmazon basin area. The ENVISAT ground-track network crosscuts these basins onmain streams, small tributaries, swamps, wetlands and lakes.

2.1 The Negro River basin

With a mean annual discharge of 29 × 103 m3 s–1, the Negro River is the second largestdischarge tributary of the Amazon after the Madeira River (Molinier et al. 1997). Ithas a drainage area of around 0.7 × 106 km2; rainfall varies from 3500 mm year–1 in theupper basin to 2137 mm year–1 in the lower basin (Villar et al. 2009). This river springsout in Colombia, flows into Venezuela and ends in Brazil, where it crosses the RoraimaLavrado savannas plains and lowland open ombrophilous forests (IBGE 2004). In themiddle reach, the Negro River receives some large tributaries. The largest one is theBranco River running east from the Guyana shield. The floodplains along the banks ofthe Negro River are fairly small because the river is reduced into a channel formed byfaulting (Franzinelli and Igreja 2002). The river transports little suspended sediment,less than 7 × 106 t year–1 (Filizola and Guyot 2009). Frappart et al. (2008) determinedthe seasonal and interannual variations of the total inundated area in the Negro River

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https://www.researchgate.net/publication/252967737_Surface_water_dynamics_in_the_Amazon_Basin_application_of_satellite_radar_altimetry_J_Geophys_Res_107?el=1_x_8&enrichId=rgreq-1316ebee-9f45-4855-bc42-6a5a3f1962f9&enrichSource=Y292ZXJQYWdlOzIyOTcwMDc3OTtBUzo5ODgwNDk4MDI1Njc3MkAxNDAwNTY4MzUxMjQ5

https://www.researchgate.net/publication/229708038_Inundation_Risk_in_Large_Tropical_Basins_and_Potential_Survey_from_Radar_Altimetry_Example_in_the_Amazon_Basin?el=1_x_8&enrichId=rgreq-1316ebee-9f45-4855-bc42-6a5a3f1962f9&enrichSource=Y292ZXJQYWdlOzIyOTcwMDc3OTtBUzo5ODgwNDk4MDI1Njc3MkAxNDAwNTY4MzUxMjQ5




basin for the period 1993–2000. The maximum level is observed from May to Julyand the minimum level from November to February, with a maximum inundated areavarying from 57 × 103 km2 (June 1996) to 42 × 103 km2 (June 1997) and a minimuminundated area varying from 28 × 103 km (January 1997) to 15.7 × 103 km2 (December1997). The Rio Negro basin is shown in figure 1.

The first study site is the Branco River floodplain. The Branco River drains thehillslopes of Roraima State (Brazil) and the Brazil–Guyana border. In the middlecourse, the river crosses a huge low-gradient plain, called the ‘Northern Pantanal’.This wetland is bounded by cliffs that reach a height of 20 m and is surrounded byterra firme, that is, high land that is not inundated during floods (Franzinelli andIgreja 2002). Formed by banks of sediment up to 7 m high, the Rio Branco deltapenetrates like a progressive landform into the Negro valley and dams it; at thislocation, the Negro River bed narrows to a width of merely 2 km (Latrubesse andFranzinelli 2005).

The second site encompasses the Middle Negro River. The lack of alluvial wetlandshas been attributed to deep erosional valleys in the older depositional surfaces of thispart of the river, following a general northwest–southeast direction (Costa et al. 1978).A very large channel with numerous bars forms the Mariuá archipelago, bounded onthe left side by a huge asymmetrical cliff (Latrubesse and Franzinelli 2005).

2.2 The Madeira River basin

The Madeira River basin extends through Bolivia, Peru and Brazil. Although thedrainage area of the Madeira river corresponds to 23% of the total area of the Amazonbasin, its contribution in terms of total discharge is 31.2 × 103 m3 s–1, that is, about15% of the Amazon mean discharge (Molinier et al. 1997). The basin rainfall rangesbetween 2000 and 2200 mm year–1 (Villar et al. 2009). The Madeira River bringsaround 50% of the total suspended sediment (2.8 × 108 t year–1) transported by theAmazon (Filizola and Guyot 2009). The Madeira is characterized by three morpho-logical units, in the following proportions: the Andes (25%), the Brazilian shield (27%)and the Amazon lowland (48%) (Maurice-Bourgoin et al. 2000). Because of thesecharacteristics, high altitudes are encountered in the upstream part of the Madeirabasin, waterfalls in the Brazilian shield, and large floodplains in the lower course. TheMamoré River and its tributary the Guaporé River join the Beni River to form theMadeira. Mamoré flows mostly northwards into the Bolivian territory through exten-sive inundation plains, the Llanos de Mojos, made up of a thousand lakes of all shapesand sizes. The Madeira River basin is shown in figure 1.

The first study site is the Guaporé River. It springs out in the highlands of theParecis plateau, in the State of Mato Grosso, Brazil. From Pontes and Lacerda city, itforms the Bolivia–Brazil border, and runs along the outcrops of the Guaporé shield onits right bank. The river meanders in an alluvial plain bordered with swamps, savannas,inundated forest and surrounded by terra firme forest. This large floodplain varies insize according to the alternating dry and rainy tropical seasons (Ronchail et al. 2005).It can dry up during the austral winter and extend up to 150 × 103 km2 at the end ofthe rainy season (Roche and Fernandez 1988).

The second site is the floodplain of the Amazon basin at the mouth of the MadeiraRiver basin. These seasonally flooded habitats include floating meadows, sandbarscrub, river-edge forest and high flooded forest. During the flooding season, waters

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https://www.researchgate.net/publication/222473672_Water_resources_salinity_and_salt_yields_of_rivers_in_the_Bolivian_Amazon?el=1_x_8&enrichId=rgreq-1316ebee-9f45-4855-bc42-6a5a3f1962f9&enrichSource=Y292ZXJQYWdlOzIyOTcwMDc3OTtBUzo5ODgwNDk4MDI1Njc3MkAxNDAwNTY4MzUxMjQ5

https://www.researchgate.net/publication/227916082_Suspended_sediment_yields_in_the_Amazon_basin_An_assessment_using_the_Brazilian_national_data_set?el=1_x_8&enrichId=rgreq-1316ebee-9f45-4855-bc42-6a5a3f1962f9&enrichSource=Y292ZXJQYWdlOzIyOTcwMDc3OTtBUzo5ODgwNDk4MDI1Njc3MkAxNDAwNTY4MzUxMjQ5

https://www.researchgate.net/publication/223786027_The_late_Quaternary_evolution_of_the_Negro_River_Amazon_Brazil_Implications_for_island_and_floodplain_formation_in_large_anabranching_tropical_systems?el=1_x_8&enrichId=rgreq-1316ebee-9f45-4855-bc42-6a5a3f1962f9&enrichSource=Y292ZXJQYWdlOzIyOTcwMDc3OTtBUzo5ODgwNDk4MDI1Njc3MkAxNDAwNTY4MzUxMjQ5


of main stems drain into these regions, where they remain for several months, mod-ifying the flood peak of the rivers. In the dry season, the water stored in the floodedplains is released back to the main channel.

3. Data

3.1 Conventional gauge data

We used the time series from all the in situ gauges located along the studied river.Measurements have been available since 2002, matching altimetric ENVISAT datarecords. Information reported in table 1 comes from the HidroWeb site (ANA 2008).ANA is the Brazilian entity responsible for the implementation of the national waterresources management system and its regulation.

3.2 Satellite radar altimetry

Radar altimeters installed onboard various satellite missions emit a pulse towards thenadir and receive the echo reflected by the water surface level. The half time span forthe pulse to be reflected back to the altimeter corresponds to the distance ρ run bythe electromagnetic pulse between the satellite and the Earth’s surface, assuming thatthe pulse is propagating at the speed of light. The satellite altitude as with respect toa reference ellipsoid is known accurately by orbitography modelling. Therefore, theheight H of the reflector with respect to the geodetic reference is given at each pass ofthe satellite. Corrections relating to the delayed propagation through the atmosphere,the interaction with the ionosphere, and the solid Earth tides are taken into account:

H = as − ρ + Ciono + Cdry + Cwst + Cst + Cpt, (1)

where Ciono is the correction for delayed propagation through the ionosphere, Cdry andCwet are corrections for delayed propagation in the atmosphere, accounting respec-tively for pressure and humidity variations, and Cst and Cpt are the corrections forcrustal vertical motions, respectively, due to the solid and polar tides. Errors in allthese corrections were not evaluated in this study. A full discussion of the derivationof altimetric heights and their associated errors can be found in Fu and Cazenave(2001).

In the framework of the Earth Observation Programme, ESA launched ENVISATin March 2002. So far, ENVISAT has been the largest satellite built for Earth observa-tion. Data collected by ENVISAT are dedicated to Earth environmental and climatechange analyses. ENVISAT contains 10 instruments that provide an accurate analy-sis of atmosphere, continents, oceans and ice of the planet (Wehr and Attema 2001),including the nadir radar altimeter RA-2. ENVISAT follows a helio-synchronous cir-cular orbit with an inclination of 98.5◦ and a 35-day repeat period. It covers the Earthwithin latitudes of ±81.4◦, with an inter-track distance of approximately 80 km at theEquator. RA-2 is a high precision radar altimeter pointing towards the nadir and oper-ating at two frequencies (Zelli 1999): 13.575 GHz (2.3 cm wavelength, Ku band) and3.2 GHz (3.4 cm wavelength, S band). This dual-frequency system enables estimationof the ionospheric delay. The width of the ground footprint is approximately 3–4 km.

The ENVISAT altimetric data used in this study come from the Centre deTopographie des Océans et de l’Hydrosphère (CTOH 2008), which is a French

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tude

(◦ )L

atit

ude

(◦ )

Nam

eV

irtu

alG

auge

Dis

tanc

e(k

m)∗

Vir

tual

Gau

geV

irtu

alG

auge

H0±

σH

gaug

e(m

)R

MS

(m)

Bar

celo

sV

S_77

9_01

1448

0002

+4−6

2.91

3−6

2.92

9−0

.861

−0.9

6717

.222

±0.

218

0.17

Jatu

aran

aV

S_60

7_01

1503

0000

−14

−59.

538

−59.

648

−3.1

34−3

.063

3.66

7±

0.13

90.

29P

rinc

ipe

daB

eira

VS_

192_

0115

2000

00−3

−64.

409

−64.

425

−12.

443

−12.

427

120.

864

±0.

087

0.30

Not

e:∗ D

ista

nce

betw

een

the

gaug

ean

dth

eE

NV

ISA

Tcr

ossi

ng:+

show

sth

atth

evi

rtua

lsta

tion

isup

stre

amof

the

gaug

e;–

show

sth

atth

evi

rtua

lsta

tion

isdo

wns

trea

mof

the

gaug

e.

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observation service in charge of maintaining a homogeneous and reliable altimet-ric database for the long-term global monitoring of oceans, lakes, rivers and ice.CTOH distributes the geophysical data records (GDRs) provided by the space agen-cies (namely ESA in the case of the ENVISAT satellite), with additional parameters.As far as ENVISAT radar altimetry data are concerned, the additional parametersare atmospheric and ionospheric corrections that are unavailable in the GDRs overcontinents.

The distance between the satellite and the water surface measured by the radar iscalled the range. Four ranges are routinely computed for the RA-2 Ku-band pulses.They come out of four different algorithms used to process the radar echo returned tothe satellite antenna. These algorithms are called retracking algorithms and those ofENVISAT are referred to as the Ocean Retracker, developed by Brown (1977), Ice-1,developed by Wingham et al. (1986) and applied by Bamber (1994), Ice-2, developedby Legrésy and Rémy (1997), and Sea Ice, developed by Laxon (1994). Different algo-rithms are used to best fit the highly variable time distribution of the echo energybounced back by the very different types of surfaces on the Earth. Frappart et al.(2006) compared the performances of these algorithms in delivering reliable water lev-els for land hydrology. They concluded that the Ice-1 algorithm, primarily designed forice sheets, provided the most robust estimated water stages for a sample set extractedon rivers and lakes (various locations within the Amazon basin). According to arecent study (Santos da Silva et al. 2010), when outliers are carefully eliminated fromthe dataset used to compute water level time series, Ice-1 and Ice-2 algorithms per-form similarly. Therefore, we used the range values provided by the Ice-1 retrackingalgorithm.

3.3 Virtual stations and time series

A virtual station (VS) consists of the intersection of a satellite ground track with awater body, making it possible to derive time series of the water stage variations fromthe radar measurements at each pass. However, not all of the radar measurementsreturned by a surface assumed to be inundated are necessarily valid measurements.Several factors can affect the measurements, in particular non-water reflectors, suchas banks, islets and vegetation, can bounce significant energy together with the watersurface and thus may affect the range computation; the water surface at the rim of thefootprint can also dominate the energy received by the satellite instead of the nadirpoint. Frappart et al. (2006) and Santos da Silva et al. (2010) showed that such aneffect of slant measurement could be dealt with by adjusting the level data with aparabola, of which the summit is retained as equivalent to a nadir measurement. Sucha correction was applied to the ENVISAT data when necessary.

To overcome these problems in the selection of the data to be included in the esti-mate of the water height, Virtual Altimetry Station (VALS 2010) software was applied.VALS is a Java-based toolbox that was developed to interactively select altimetry dataat the virtual stations and apply the corrections individually to satellite passes or partsof passes (Santos da Silva et al. 2010). The data processing is performed in three mainsteps. The first step consists of a rough selection guided by imagery, currently GoogleEarth. The second step consists of refining the selection in a cross-sectional view. Thethird step consists of the computation of master points per pass. The median andmean values are computed for each pass using the data subset selected in the secondstep. The 71 VSs studied are listed in tables 2 to 5, and their locations are shownin figure 1.

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https://www.researchgate.net/publication/286387684_The_Average_Impulse_Response_of_a_Rough_Surface_and_Its_Applications?el=1_x_8&enrichId=rgreq-1316ebee-9f45-4855-bc42-6a5a3f1962f9&enrichSource=Y292ZXJQYWdlOzIyOTcwMDc3OTtBUzo5ODgwNDk4MDI1Njc3MkAxNDAwNTY4MzUxMjQ5


Tab

le2.

Lis

tof

the

virt

uals

tati

ons

onth

eG

uapo

réR

iver

.

Vir

tual

stat

ion

Tra

ckno

.Si

teW

idth

(km

)M

ean

long

itud

e(◦ )

Mea

nla

titu

de(◦ )

Mea

nch

ange

(m)

VS_

106_

0110

6G

uapo

réR

iver

0.32

−64.

409

−12.

443

4.37

VS_

106_

0210

6G

uapo

réL

ake

4−6

3.09

0−1

2.96

02.

62V

S_10

6_03

106

Gua

poré

Lak

e5

−63.

117

−13.

078

2.87

VS_

192_

0119

2G

uapo

réR

iver

0.62

−64.

409

−12.

443

8.57

VS_

650_

0165

0G

uapo

réR

iver

0.35

−63.

691

−12.

446

5.18

VS_

650_

0265

0G

uapo

réF

lood

plai

n20

.00

−63.

722

−12.

582

4.91

VS_

951_

0195

1G

uapo

réL

ake

1−6

3.24

5−1

2.29

73.

41V

S_95

1_02

951

Gua

poré

Lak

e2

−63.

202

−12.

483

2.05

VS_

951_

0395

1G

uapo

réL

ake

3−6

3.14

2−1

2.74

84.

07V

S_95

1_04

951

Gua

poré

Lak

e4

−63.

092

−12.

969

2.80

VS_

951_

0595

1G

uapo

réL

ake

6−6

3.05

1−1

3.14

71.

70V

S_95

1_06

951

Gua

poré

Riv

er0.

15−6

3.16

9−1

2.63

05.

35V

S_95

1_07

951

Gua

poré

Tri

buta

ry0.

06−6

3.11

8−1

2.85

33.

62

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Tab

le3.

Lis

tof

the

virt

uals

tati

ons

onth

eB

ranc

oR

iver

.

Vir

tual

stat

ion

Tra

ckno

.Si

teW

idth

(m)

Mea

nlo

ngit

ude

(◦ )M

ean

lati

tude

(◦ )M

ean

chan

ge(m

)

VS_

192_

0219

2It

apar

áR

iver

0.08

−61.

663

−0.0

857.

87V

S_69

3_01

693

Cap

ivar

aR

iver

0.05

−61.

903

1.08

75.

96V

S_69

3_02

693

Mor

roda

água

pret

aSw

amp

−61.

877

0.96

61.

68V

S_69

3_03

693

Mor

roda

água

pret

aSw

amp

−61.

841

0.80

51.

16V

S_69

3_04

693

Cat

rim

aniR

ivie

r0.

13−6

1.77

80.

515

6.92

VS_

693_

0569

3B

ranc

oR

iver

1.00

−61.

742

0.35

48.

44V

S_69

3_06

693

Igar

apé

dola

gogr

ande

0.05

−61.

718

0.24

54.

99V

S_69

3_07

693

Itap

ará

Riv

er0.

08−6

1.64

8−0

.075

7.91

VS_

693_

0869

3It

apar

áIg

arap

é0.

05−6

1.57

3−0

.417

3.59

VS_

693_

0969

3Ja

uape

riR

iver

0.48

−61.

504

−0.7

287.

92

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Tab

le4.

Lis

tof

the

virt

uals

tati

ons

onth

eA

maz

onR

iver

.

Vir

tual

stat

ion

Tra

ckno

.Si

teW

idth

(km

)M

ean

long

itud

e(◦ )

Mea

nla

titu

de(◦ )

Mea

nch

ange

(m)

VS_

063_

0106

3A

maz

onR

iver

5.50

−58.

775

−3.3

3112

.35

VS_

063_

0206

3A

rroz

alL

ake

−58.

749

−3.4

568.

62V

S_06

3_03

063

Mad

eira

Riv

er4.

00−5

8.76

7−3

.372

12.6

7V

S_47

8_01

478

Car

uR

iver

0.03

−58.

712

−3.0

024.

27V

S_47

8_02

478

Uru

buR

iver

10.

10−5

8.75

6−3

.202

9.72

VS_

478_

0347

8U

rubu

Lak

e1

−58.

760

−3.2

217.

31V

S_47

8_04

478

Uru

buL

ake

2−5

8.76

9−3

.261

7.21

VS_

478_

0547

8A

maz

onas

Riv

er4.

00−5

8.74

9−3

.456

10.7

7V

S_47

8_06

478

Mad

eira

Riv

er2.

00−5

8.81

2−3

.455

11.1

1V

S_47

8_07

478

Cur

upir

aL

ake

1−5

8.83

5−3

.560

6.51

VS_

478_

0847

8C

urup

ira

Lak

e2

−58.

854

−3.6

489.

74V

S_47

8_09

478

Cur

upir

aL

ake

3−5

8.86

4−3

.692

10.3

6V

S_47

8_10

478

Cur

upir

aL

ake

4−5

8.87

9−3

.759

7.72

VS_

478_

1147

8C

urup

ira

Riv

er0.

25−5

8.91

0−3

.904

11.5

3V

S_47

8_12

478

Mar

imar

iRiv

er0.

80−5

8.92

3−3

.960

11.2

2V

S_47

8_13

478

Mir

açoe

iro

Riv

er0.

02−5

9.02

9−4

.440

6.21

VS_

607_

0160

7A

maz

onR

iver

6.30

−59.

538

−3.1

3412

.32

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Tab

le5.

Lis

tof

the

virt

uals

tati

ons

onth

eN

egro

Riv

er.

Vir

tual

stat

ion

Tra

ckno

.Si

teW

idth

(m)

Mea

nlo

ngit

ude

(◦ )M

ean

lati

tude

(◦ )M

ean

chan

ge(m

)

VS_

235_

0123

5C

aapi

rang

aSw

amp

−62.

520

0.62

30.

70V

S_23

5_02

235

Caa

pira

nga

Swam

p−6

2.47

10.

401

0.98

VS_

235_

0323

5Ju

tari

Riv

ier

0.15

−62.

398

0.06

92.

23V

S_23

5_04

235

Caa

pira

nga

Swam

p−6

2.51

40.

595

0.91

VS_

235_

0523

5C

aapi

rang

aSw

amp

−62.

508

0.57

00.

88V

S_23

5_06

235

Caa

pira

nga

Swam

p−6

2.46

50.

375

0.72

VS_

235_

0723

5C

aapi

rang

aSw

amp

−62.

452

0.31

40.

84V

S_23

5_08

235

Caa

pira

nga

Swam

p−6

2.44

10.

266

0.68

VS_

235_

0923

5P

irar

ara

Riv

er0.

03−6

2.37

7−0

.027

2.02

VS_

235_

1023

5Ju

tari

Riv

ier

0.04

−62.

279

−0.4

735.

91V

S_73

6_01

736

Águ

abr

anca

Igar

apé

0.03

−62.

115

1.13

03.

15V

S_73

6_02

736

Cat

rim

aniR

ivie

r0.

10−6

2.13

51.

039

5.99

VS_

736_

0373

6N

ovo

Swam

p−6

2.20

20.

735

0.80

VS_

736_

0473

6B

ranq

uinh

oR

iver

0.03

−62.

275

0.40

24.

84V

S_73

6_05

736

Juta

riR

ivie

r0.

17−6

2.35

50.

041

2.07

VS_

736_

0673

6P

irar

ara

Riv

er0.

40−6

2.36

9−0

.027

2.39

VS_

736_

0773

6P

reto

Riv

er0.

03−6

2.42

3−0

.271

4.75

VS_

736_

0873

6Ju

tari

Riv

ier

0.09

−62.

521

−0.7

165.

40V

S_73

6_09

736

Neg

roR

iver

15.0

0−6

2.59

2−1

.041

8.53

VS_

736_

1073

6C

auré

sR

iver

0.07

−62.

653

−1.3

167.

85V

S_73

6_11

736

Uni

niR

iver

0.15

−62.

743

−1.7

286.

57V

S_73

6_12

736

Jaú

Riv

er0.

04−6

2.94

9−2

.663

5.49

VS_

736_

1373

6P

iori

niL

ake

5.00

−63.

160

−3.6

2111

.99

VS_

736_

1473

6So

limõe

sR

iver

4.00

−63.

227

−3.9

2412

.03

VS_

736_

1573

6C

oari

Lak

e5.

70−6

3.25

1−4

.033

13.7

3V

S_73

6_16

736

Pur

usR

iver

0.74

− 63.

619

−5.7

0015

.70

VS_

736_

1773

6M

ucui

mR

iver

0.04

−64.

131

−8.0

078.

59V

S_73

6_18

736

Muc

uim

Riv

er0.

02−6

4.18

9−8

.268

5.58

VS_

736_

1973

6M

adei

raR

iver

2.20

−64.

396

−9.1

9611

.93

VS_

779_

0177

9N

egro

Riv

er14

.55

−62.

912

−0.8

617.

25V

S_77

9_02

779

Uni

niR

iver

0.16

−62.

719

−1.7

416.

24

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In this study, we retained the median and associated mean absolute deviation toconstruct the time series. Furthermore, we found that, given the large number of pos-sible outliers with respect to the few points selected for each pass, the median offersa more robust predictor than the mean. Frappart et al. (2006) came to a similar con-clusion. Finally, a geoid undulation was subtracted from the height value. The geoidused in this study was GGM02C-GRACE, a mean tide solution (Tapley et al. 2004).This geoid model is provided on the T/P ellipsoid. Undulations have been referencedto the GRS80 ellipsoid of WGS84 to be consistent with the ENVISAT heights.

4. Results and discussion

In this study, we took altimetry measurements to assess the stage variations. Only afew gauges are available in the study area; they are all located on rivers and only oneis in the wetlands. Thus, altimetry series are difficult to validate because of the lack ofgauges for comparisons. An in-depth analysis of ENVISAT series in the Amazon basincan be found in Santos da Silva et al. (2010). In this study, we limit the assessment ofthe quality of the stage time series by ENVISAT by presenting two case studies: first,two examples of internal validation of two ENVISAT tracks forming a crossover withshort delays between pairs of measurements; second, comparisons between altimetryseries and in situ reading series with a distance of less than 15 km between the gaugeand the track (table 1).

4.1 VS validation

The case study of two ENVISAT tracks forming a crossover with short delays con-stitutes a good opportunity to check the quality of the altimetry series, since theyprovide data complying with the criterion of independent measurements of the samewater body. Assuming that the water stage changes little between the passes of thetwo tracks, a root mean square (RMS) difference of both passes of a given cyclethroughout the series can be computed. In the Unini River case study, the crossoveris formed by tracks 736 and 779. These tracks pass over the river with a 1.5-day dif-ference (figure 2). The RMS difference between the pass pairs is 18 cm. The secondexample is a wetland, labelled Lake of Guaporé River further on in this study anddisplayed in figure 3. ENVISAT tracks 106 and 951 form a crossover over this lakewith overflies passing 5.5 days apart. The RMS difference between the pass pairs is16 cm. It is noteworthy that this represents a robust estimation of the accuracy of thealtimetry series, since it includes the daily variability of the body stage.

Other series that were computed using pairs of tracks at the Itapará River andAmazon River are shown in figures 4 and 5, respectively. No RMS difference wascalculated for these cases since the lag between pairs of measurements is several dayslong and the hypothesis of stage stability no longer holds.

Figures 6 to 8 show the altimetry series superimposed on readings from the closestin situ gauge with a distance of less than 15 km between the gauge and the track. Giventhe fact that the gauges are not levelled, we established a reference level for the gaugesby simple regression analysis between the altimetry values and gauge readings at thesame dates.

Comparison between the virtual stations and the conventional limnimetric gaugesdo not show any anomalies in any of the following three examples. Altimetric data aremonitoring the seasonal cycle of high and low flood and its interannual variations. Inthe Barcelos case (figure 6), the conventional limnimetric gauge is registering the water

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https://www.researchgate.net/publication/232095595_The_Gravity_Recovery_and_Climate_Experiment_Mission_Overview_and_Early_Results?el=1_x_8&enrichId=rgreq-1316ebee-9f45-4855-bc42-6a5a3f1962f9&enrichSource=Y292ZXJQYWdlOzIyOTcwMDc3OTtBUzo5ODgwNDk4MDI1Njc3MkAxNDAwNTY4MzUxMjQ5


1°S

1°30′S

63°W

62°30′W

VS_779_02

VS_736_11

# 779ENVISAT

# 736ENVISAT

2°S18

20

22

24

26

2002 2003 2004 2005 2006 2007 2008 2009

Wa

ter

leve

l (m

wrt

GG

M0

2C

)

Date

VS_736_11 VS_779_02

Negro River

Unini River

Figure 2. Time series obtained in the Unini River by combining tracks 736 (grey dots) and 779(black dots), which form a crossover over a river channel. The background image is a JERS-1SAR mosaic of Amazon high water images.

63° 0

9′ W

12° 54′ S

13° 03′ S

VS_951_01VS_106_02

# 951ENVISAT

# 106ENVISAT

139

139.5

140

140.5

141

141.5

142

142.5

Wa

ter

leve

l (m

wrt

GG

M0

2C

)

VS_951_01 VS_106_02

Guaporé River

2002 2003 2004 2005 2006 2007 2008 2009

Date

Figure 3. Time series obtained in the lake of Guaporé River by combining tracks 951 (greydots) and 106 (black dots), which form a crossover over a lake. The background image is aJERS-1 SAR mosaic of Amazon high water images.

stage on the southernmost channel of the Negro River, while the altimetric virtualgauge is measuring the water surface elevation on a transect, that is, about 55 kmlong across the Mariuá archipelago, including a multichannel pattern and complexfloodplains with flooded forest. The RMS difference is 17 cm, mostly due to the timevariation in the surface slope between the gauge and the VS.

Virtual station VS_607_01 is located at the crossing of track 607 with the AmazonRiver at 14 km downstream of Jatuarana gauge. The flood and low stages are morepronounced in the VS_607_01 series than they are in the Jatuarana gauge series(figure 7). This difference in stage amplitude is likely to be related to different hydro-logical regimes between the two sites. At this location, the Amazon River dividesinto two branches and flows through a complex of strongly vegetated episodic islandand sand bars known as the ‘Ilha do Careiro’. The conventional limnimetric gauge

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0°

1° 15′ S

61° 4

5′ W

61° 3

0′ W

VS_693_07VS_192_02

# 693ENVISAT

# 192ENVISAT

24

26

28

30

32

2002 2003 2004 2005 2006 2007 2008 2009

Wate

r le

vel (m

wrt

GG

M02C

)

Date

VS_192_02

VS_693_07

Branco River

Itapará River

Figure 4. Time series obtained in the Itapará River by combining tracks 192 (grey dots) and693 (black dots), which form a crossover over a river channel. The background image is aJERS-1 SAR mosaic of Amazon high water images.

58° 4

8′ W

3° 12′ S

3° 24′ S

VS_063_01VS_478_05

# 063ENVISAT

# 478ENVISAT

7

9

11

13

15

17

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Figure 5. Time series obtained in the Amazon River at the mouth of the Madeira River bycombining stages from tracks 478 (grey dots) and 063 (black dots), which form a crossover overa river channel. The background image is a JERS-1 SAR mosaic of Amazon high water images.

is located in the main branch. The secondary branch joins the main stream exactlyunder track 607. The RMS difference between the gauge readings and the height seriesat VS_607_01 is 29 cm. Again, time variation of the surface slope contributes to theRMS difference.

In the case of comparison between measurements at the Principe da Beira gauge andthe VS_192_01, located on the Guaporé River, some precursor events to the flood thathave been registered in the altimetric time series do not appear in the daily readings.Stage values corresponding to the early events in the altimetry series have high mediandispersion. So far, the satellite track is overflying a small tributary for 6 km beforecrossing the main stream. The stage difference between this tributary and the mainstem is probably responsible for the large dispersion in the median values and theearly event peaks registered in the altimetric time series (figure 8). The RMS differenceis 30 cm.

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0° 48′ S

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Figure 6. Comparison between the conventional limnimetric gauge (grey dots) and the virtualstation (black dots) in the Barcelos. The background image is a JERS-1 SAR mosaic of Amazonhigh water images.

3° 06′ S

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Figure 7. Comparison between the conventional limnimetric gauge (grey dots) and virtual sta-tion (black dots) in the Jatuarana. The background image is a JERS-1 SAR mosaic of Amazonhigh water images.

4.2 Relationship between the river and its floodplain

4.2.1 Guaporé River (Madeira basin). Track 650 crosses the Guaporé River and itsfloodplain. The floodplain is covered by lowland open ombrophilous forests (IBGE2004), visible as a bright radar return (table 2 and figure 9(a)) in the JERS-1 syn-thetic aperture radar (SAR) background image. The ENVISAT measurements areprojected following the vertical plane orthogonal to the ∼20 km-long transect, pre-sented in figure 9(b). The altimeter, running from north (N) to south (S), locks onthe river before and after overflying it, creating a first off-nadir effect (figure 9(b)).At high stages, the elevation remains constant throughout the crossing of the entirefloodplain. Conversely, at intermediate and low stages, various parabolas indicate thatoff-nadir distortion affects the measurements successively. Clearly, such effects mustbe corrected one by one to properly calculate water levels for these passes. The three

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Guaporé River

Figure 8. Comparison between the conventional limnimetric gauge (grey dots) and virtualstation (black dots) in the Principe da Beira. The background image is a JERS-1 SAR mosaicof Amazon high water images.

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Guapore River

Figure 9. Comparison between the mainstream and the floodplain in the Gauporé River. (a)Track 650 crosses the Gauporé River and its floodplain. The background image is a JERS-1SAR mosaic of Amazon high water images. (b) The transect of the ENVISAT data. (c) Thetime series extracted at the two extremities of the floodplain. The series over the mainstream isshown with black dots and the series over the floodplain is shown with grey dots.

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solid arrows in figure 9(b) indicate three branches of the river that are permanentlyinundated (narrow sinuous black lines at the south of the floodplain).

The time series displayed in figure 9(c) are extracted at the two ends of the flood-plain. The seasonal water cycle is well phased with multimodal distributions. Therising limb and the recession limb are symmetrical and the level difference betweenhigh and low stage is about 5 m. The difference of water stage observed between thetwo water bodies is 50 to 70 cm. The maximum is observed from February to Apriland the minimum from September to November.

4.2.2 Branco River (Negro basin). Tracks 636 and 192 cross over 206 km of theNorthern Pantanal, a low gradient plain in the Branco River basin. Campinaranavegetation develops around circular swamps of nutrient-poor soil and replaces low-land dense ombrophilous forests in this region (IBGE 2004). Gallery inundated forestsdelineate the rivers (table 3 and figure 10). From north to south, the altimeter lockson the Capivara River, then on the Morro da Água Preta swamps, and lastly crossesthe floodplain of the Branco River (figure 10(a)). This floodplain begins in the RiverCatrimani and extends up to the Itapará River. At the southernmost end of the profile,the Jauaperi River and its tributary Itapará Igarapé, which are visible on the profile,do not belong to the Branco River basin.

The time series are presented in figure 10(b) with the location of the crossoverformed by tracks 693 and 192 over the Itapará River. The series clearly show the yearlyand seasonal cycles of high and low levels with multimodal variations. The average

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(a) (b)

Branco River

Figure 10. Comparison between the mainstream and the floodplain in the Branco River. (a)Tracks 693 and 192 cross the Branco River and the low-gradient plain Northern Pantanal. Thebackground image is a JERS-1 SAR mosaic of Amazon high water images. (b) Time series alongtracks 693 and 192. The series for the right-hand margin, on the northwest side of the BrancoRiver, are shown by continuous lines. The series for the left-hand margin, on the southeast sideof the Branco River, are shown by broken lines.

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fluctuation level of the Capivara River is 6 m. It is 4 m for the Itapará Igarapé and∼8 m for Jauaperi River. As far as the Morro da Água Preta swamps are concerned,the average fluctuation level is ∼1.5 m for both time series, and for the floodplain ofthe Branco River, the average fluctuation level varies from ∼7 to 8.5 m. In particu-lar, it can be seen in the rising limb that the level is continuously accelerating, whilethe recession limb shows that the level is decreasing progressively in the dry season.The maximum levels are observed from May to July while the minimum are observedbetween January and February, except for the year 2005, when the minimum occurredin December.

The elevation profiles formed by the maximum and minimum values of each timeseries along ENVISAT track 693 are shown in figure 11. These profiles are consis-tent with the basin contours found by Seyler et al. (2009c), who state that ItaparáIgarapé and the Jauaperi River, at the southern end of the profile, do not belongto the Branco River basin. At the northern end of the profile, a small variation inwater levels is observed for the interfluve wetlands (swamps) with respect to the riverlevel variation. During the rainy season, a mean slope of 8.14 cm km–1 is measuredalong the 206 km-long segment of ENVISAT track 693, while the floodplain of theBranco River connected to the Catrimani, Branco and Itapará Rivers has a slope of5.90 cm km–1 denivelation over 68 km. During the dry season, the Itapará River isdisconnected from the floodplain.

4.2.3 Amazon River. ENVISAT crosses the Amazon River floodplain at the mouthof the Madeira River along a 165 km-long segment of track 478. This segment includesthe Amazon plain, where there are large flat areas characterized by the absence ofdefinitive drains and covered by dense lowland flooded ombrophilous forests (IBGE2004). This forest is highlighted as bright regions in the image JERS-1 SAR. Thehydrological profile measured by the altimeter along the ENVISAT track is shown

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Figure 11. Elevation of swamps and rivers in relation to the Branco River water stage. Thebasin border (shown in pink) is according to Seyler et al. (2009c).

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Figure 12. Comparison between the mainstream and the floodplain in the Amazon River. (a)Tracks 478 and 063 cross the Amazon River. The background image is a JERS-1 SAR mosaicof Amazon high water images. (b) The transect of the ENVISAT data along track 478. Theyellow arrows indicate the location of the successive virtual stations along the ENVISAT tracks.Crossing of the track with the Amazon River is indicated by a red arrow.

in figure 12(a) and the data are listed in table 4. This profile samples the whole flood-plain, starting with the Urubu River at the northern end, then two lakes, the AmazonRiver, the Madeira River, the Curupira Lake, the Curupira River, the Marimari Riverand the Miraçoeiro River at the southern end. In addition, three virtual stations wereestablished along track 063 at the crossings with the Amazon River, the Madeira Riverand the Arrozal Lake, respectively (figure 12(b)). The corresponding time series arepresented in figure 13. Note that the altimetric time series of the Amazon River ismade up of the two tracks 478 and 063 that cross exactly over the river.

The series presented in figure 13 enables the hydrological regime to be described. Theflood wave presents various peaks during the rising limb, while the recession showsa fast falling limb. In this Amazon floodplain, the flood occurs during May and atthe beginning of July. The water level reaches a maximum altitude of 21 m. Droughtoccurs during October and November down to a minimum altitude of 7 m. The timeseries of the Caru River (dark blue series at the top of figure 13) do not follow thesame hydrological regime, as it belongs to the Uatumã basin, which joins the Amazondownstream of the region analysed.

The elevation of the Amazon floodplain at the mouth of the Madeira River is pre-sented in figure 14. The profiles are formed by the maximum and minimum values

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Figure 13. The time series along tracks 478 and 063 of the Amazon floodplain.

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Figure 14. Elevation of the lakes and the rivers in relation to the Amazon River water stage.

of the successive time series. During flooding, all of the rivers and lakes of the stud-ied area are connected in a unique hydrological system corresponding to the AmazonRiver with a low slope of 0.84 cm km–1. In the dry season, however, there are five differ-ent hydrological patterns. Miraçaoeiro, Marimani and Curupira rivers belong to the

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same hydrological system. Curupira Lake, Urubu River and Caru River are discon-nected from the floodplain, forming individual systems. Conversely, Madeira River,Arrozal Lake and the small lakes between the Amazon River and the Urubu Riverform one system, since they remain connected with the Amazon River even during thedrought.

4.3 Relationship between the river, swamps and lakes within the watershed for theGuaporé River

The different swamps and lakes monitored along track 951 are listed in table 2 andtheir location is shown in figure 15. The time series for lake 1 (VS_951_01), lake 4(VS_951_04) and the Guapore River are presented in figure 15(b). The lowest andhighest stages of the corresponding water bodies are presented in figure 15(c). Thetwo circular-shaped lakes 5 (VS_106_03) and 6 (VS_951_05) at the southern part ofthe image appear disconnected from the alluvial water table of the Guapore River. Theseasonal cycle of the Guaporé River has a symmetrical rising and falling limb withthe maximum flow occurring from February to April and a minimum flow betweenSeptember to November. The mean water level fluctuation is about 5 m. The water lev-els in lakes 1 and 4 precedes both the rising and the decreasing stages of the GuaporéRiver. This suggests that, at this location, the river is supplied by watershed swamps.Probably, it is mainly supplied by the swamps located on the right bank, as they are

12° 24′ S

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Figure 15. Relationship between the lakes and the Guaporé River. (a) Tracks 951 and 106cross the Guaporé River and the lakes. The background image is a JERS-1 SAR mosaic ofAmazon high water images. (b) Lakes 1 and 4 in relation to the Gauporé River. (c) Elevation ofthe lakes in relation to the Gauporé River water stage.

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more extensive than those on the left bank and have almost the same amplitude ofstage variation as the main stream.

4.4 Relationships between basins

4.4.1 Negro River. From north to south, track 736 crosses over 1168 km of thealluvial wetlands in the Middle Negro River (figure 16). From the north, it crossessuccessively small and large unnamed tributaries and the Caapiranga Swamps, and atthe end crosses the Negro River in the Mariuá archipelago. Further south, it crossesthe Solimões (grey dots) and Purus Rivers (bright pink dots), then the Madeira River(dark green dots) in the southernmost part of the segment (table 5). The Unini Rivertime series is made up from crossover measurements of ground tracks 736 and 779. Infigure 16(b), the altimetric time series are given, ranging from Água Branca Igarapé,passing through the Novo and Caapiranga wetland systems, and Jatari, Pirarara,Negro and Unini Rivers extending out to the Madeira River (figure 16(c)). The samehydrological regime is evidenced from Água Branca Igarapé to the Unini River. Thishydrological regime is characterized by a bimodal flood wave, fast rise and slow reces-sion. It should be noted that the flood occurs between April and August and thedrought between September and February. Moreover, the flooded wetlands demon-strate an in-phase temporal pattern. A small variation in water levels (between 0.7

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Figure 16. Height relationships between the swamps and the lakes with the river channelsin the Negro, Solimões and Madeira basins. (a) Tracks 235, 736 and 779 crossing the Negro,Solimões and Madeira Rivers. The background image is a JERS-1 SAR mosaic of Amazonhigh water images. (b) Time series at these crossings from the north to the Negro River (reddots). (c) Time series at these crossings from the south to the Negro River (red dots).

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Latitude (°)

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Figure 17. Elevation of the swamps, lakes and rivers in relation to the water stage of theBranco, Negro, Solimões and Madeira Rivers. The basin borders are according to Seyler et al.(2009c).

and 1 m) is observed for the Novo and Caapiranga wetlands with respect to the otherrivers, Caapiranga wetland being located at the Branco River border.

The elevation profile along the north–south ENVISAT track 736 over the NegroRiver is shown in figure 17. The limits of the Branco, Negro, Solimões and Madeirabasins are marked on this profile. The altimetric track cuts the four basins at almostthe same time (<1 h), representing a transversal section though the basins, highlight-ing the altitudinal relationship between water bodies, within each basin, and amongbasins. Connections can be observed at the high stage between Madeira and Solimõesbasins, and between Solimões and Negro basins. There is a general dissymmetry fromsouth to north, the Negro River being the centre of the general depression. TheSolimões River flows at the northernmost extension of its basin, close to the Negrobasin border. The general decline from the north to the Negro River is only about20 m at the high stage and 30 m at the low stage for a distance of 300 km, that fromthe south to the Solimões River is about 40 m at the high stage and 50 m at the lowstage for a distance of about 500 km.

Continuing with the observation of this crosstrack within the basins, we discussthe temporal relationship of the water levels at the particular location of the trackcrossing. Different time series variations are shown in the figure 18. We have limitedthe discussion to the crossings of the Negro and Solimões Rivers by ENVISAT track736. At the locations of the crossing, the flood peak has occurred at the same timefor the first three years (2003, 2004 and 2005). The time series miss the cycle of theSolimões River 2006 flood peak, which causes an apparent dephasage of the floodpeak. In 2007, the Negro River was still at high water while the Solimões was entering

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Figure 18. Time series of water level characteristics of the Negro and Solimões Rivers.

its recession period. Another striking dephasage that has occurred since the end of2005 is the extremely low stage of the Solimões, while the Negro has not been regis-tering any unusual low stage. From 2005 to 2008, high stages have been reached veryquickly in the Solimões, whereas the water has remained low in the Negro. For someyears, the Negro River has had a secondary flood peak occurring either in December(2002, 2006) or in January (2004) (equatorial regime). In the same years, the phe-nomenon known as repiquete, which is an inflection in the rising stage, has occurred inthe Solimões River. For some other years (the end of 2004 and of 2005), a weak inflec-tion in the rising peak of the Solimões does not correspond to the Negro secondaryflood peak. This suggests that the repiquete is related to the equatorial rain regime ofthe tributaries located upstream of the Negro confluence, rather than being influencedby the Negro River itself.

5. Conclusions

Altimetric data recorded by the radar altimeter RA-2 onboard the ENVISAT missionenabled the quantification of spatial and temporal variations in seasonal wetlands andrivers at the watershed scale. These measurements are referenced to a unique systemthroughout the basin. This allows a basin-wide vision of the various seasonal cycles aswell as the general altitude distribution in space and time of the different water bodiesmaking up the Amazon basin.

Monitoring water levels by altimetry could be extended over the past two decadesby combining the different available missions: the T/P mission has been reprocessedfor continental water studies in the scope of the Contribution de l’Altimétrie Spatialepour l’Hydrologie (CASH 2010) project, while ERS mission data have been repro-cessed by the Ice-2 algorithm, implemented by the Observation of Surface ContinentalAltimetrie Radar (OSCAR) project (Legresy 1995). The next steps should include data

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processing for recent (Jason-2 launched in 2008) or future missions (CryoSat II andAltikA, planned to be launched in 2010).

Although the virtual stations can only be established under satellite ground tracksand under sampling time schedules defined by the altimetric missions, such as 35days for the ENVISAT mission, water level time series with decimetre accuracy wereobtained for wetland and rivers of approximate 20 m width (see tables 4 and 5).

Water level variability was also defined for wetlands and for lakes that were verydifficult to access and with predominant flooded vegetation. In these hydrosystemsthe amplitudes of water level variability are usually small, limited to a few decimetres.As the dynamics of wetlands are more stable, it is possible to select a larger amountof points in the virtual stations, with a more regular temporal sampling and with adetailed spatial and temporal acquisition enriched by a combination of ground tracks.

Such a sampling scheme, although obtained by a nadir track altimeter, can giveus an insight into what might be observed with the future Surface Water OceanTopography (SWOT) altimeter. The SWOT mission is planned for 2020 and will con-duct a comprehensive mapping of flooded areas and water levels, using a technologycalled interferometric altimetry (Alsdorf et al. 2007a). With this mission, the dynamicsof floods will be studied with higher quality and temporal resolution, since bidimen-sional images will be provided with a horizontal resolution of 50–100 m and a timeresolution of 10–20 days.

Such as they are today, altimeter data can provide us with some guidance inthe dominant mechanisms to be considered under the framework of rainfall–runoffmodelling. For instance, with this study, monitoring the spatial and temporal dis-tribution of the saturated areas throughout the basin has become possible. Suchinformation should provide valuable constraints over semi-distributed schemes such asTOPMODEL (Beven and Kirkby 1979). The implications of the availability of thesenew sources of data flows are still to be evaluated. New ways to be incorporated in themodelling methods also need to be defined.

Constraints on the availability of observational data obtained by conventionalhydrometric networks are high, particularly in remote areas or spatially complex areassuch as wetlands. This study shows the feasibility of using altimetric data to circum-vent the scarcity of conventional data, and also suggests new ways of using spatiallydistributed stage measurements in modelling schemes.

AcknowledgementsWe thank the reviewers who greatly helped in rewriting the preliminary version ofthe manuscript. We are grateful for the financial support provided to the first authorby Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Braziland by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Brazil (ref. CAPES/COFECUB no. 516/05), and to the French contributors fundedby the French Centre National d’Etudes Spatiale through the TOSCA programme,project Hydrologie Spatiale. Acquisition of JERS-1 imagery was made possible byNASDA’s Global Rain Forest Mapping Project. We thank the Instituto Brasileiro deGeografia e Estatística (IBGE) for the maps of the region, the Agência Nacional deÁguas (ANA), Brazil for the gauge data and the CTOH (Centre de Topographie desOcéans et de l’Hydrosphère, LEGOS, France) for access to ERS-2 and ENVISATGDRs and additional tropospheric corrections through their online database system,and the European Space Agency (ESA) for granting the use of the data. We are alsograteful to Gerard Cochonneau for his work on the treatment of the altimetric data.

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https://www.researchgate.net/publication/43653829_Passive_microwave_observations_of_inundation_area_and_the_areastage_relation_in_the_Amazon_River_floodplain?el=1_x_8&enrichId=rgreq-1316ebee-9f45-4855-bc42-6a5a3f1962f9&enrichSource=Y292ZXJQYWdlOzIyOTcwMDc3OTtBUzo5ODgwNDk4MDI1Njc3MkAxNDAwNTY4MzUxMjQ5



Water level dynamics of Amazon wetlands at the watershed scale by satellite altimetry

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