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Poseidon satellite altimetry. Application to the Amazon basin
C. R. Acad. Sci. Paris, Sciences de la Terre et des planètes / Earth and Planetary Sciences 333 (2001) 633–643 2001 Académie des sciences / Éditions scientifiques et médicales Elsevier SAS. Tous droits réservés S1251-8050(01)01688-3/FLA Géosciences de surface / Surface Geosciences (Hydrologie–Hydrogéologie / Hydrology–Hydrogeology)
Temporal variations of river basin waters from Topex/Poseidon satellite altimetry. Application to the Amazon basin Ilce de Oliveira Camposa,b , Franck Merciera , Caroline Maheua , Gérard Cochonneauc , Pascal Kosuthc, Denizar Blitzkowb, Anny Cazenavea,∗ a Legos–GRGS/Cnes, Observatoire Midi-Pyrénées, 18, av. Édouard-Belin, 31400 Toulouse, France b EP–University of São Paulo, Brazil c LMTG IRD, Lago Sul Brasilia, Brazil
Received 23 July 2001; accepted 18 September 2001 Communicated by Gérard Mégie
Abstract – Although developed and optimised for open oceans, satellite altimetry has the potential to monitor level variations of inland surface waters such as lakes and rivers. Here we present results of water level variations of the Amazon River based on eight years (1993– 2000) of altimetry data of the Topex/Poseidon satellite. We first discuss methods to detect wet surfaces from the altimetric measurements, discriminate between water and dry land, and quantify the accuracy of altimetric measurements over water. Then we show water level fluctuations at selected locations where the satellite crosses the Amazon River. The dominant signal is seasonal, mostly annual, with an amplitude of up to 10–15 m peak to peak. Comparison with in situ measurements indicates that water levels are well measured by Topex/Poseidon during high-water season, unlike low-water season, which suffers from data gaps. We further discuss the interannual component of the signal, which shows two marked minima in 1995 and 1998. The 1998 minimum is interpreted as an effect of the 1997–1998 ENSO event, causing rainfall deficit in the central part of the Amazon basin, hence decrease in water levels. An EOF analysis of precipitation fields over the basin during the 1993–1999 period confirms the rainfall minimum by the end of 1997 for this region. 2001 Académie des sciences / Éditions scientifiques et médicales Elsevier SAS satellite altimetry / Amazon basin / regional hydrology Résumé – Suivi des variations temporelles des niveaux d’eau des fleuves à partir de mesures altimétriques de Topex/Poseidon. Application au bassin de l’Amazone. Bien que développée et optimisée pour le plein océan, l’altimétrie spatiale s’avère être un outil très utile pour étudier les fluctuations temporelles des niveaux d’eau sur les continents, en particulier les niveaux des lacs et des grands fleuves. Dans cette note, nous présentons des séries temporelles de niveaux d’eau sur l’Amazone issues de mesures altimétriques de Topex/Poseidon entre 1993 et 2000. Nous discutons en premier lieu les méthodes permettant de distinguer, dans le signal altimétrique, l’eau des sols secs et de la végétation, ainsi que de quantifier la précision de la mesure altimétrique. Les niveaux de l’Amazone mesurés par Topex/Poseidon sur différents sites présentent une signature saisonnière (principalement annuelle) dominante, avec une amplitude pouvant atteindre 10–15 m pic à pic. Des comparaisons avec des mesures in situ indiquent que Topex/Poseidon restitue correctement les niveaux en période de hautes eaux, ce qui n’est pas toujours le cas en période de basses eaux, quand les mesures valides sont peu nombreuses. Ceci empêche de décrire avec précision le cycle
∗ Correspondence
and reprints. E-mail addresses: [email protected] (C. Maheu), [email protected] (A. Cazenave).
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saisonnier en certaines zones du fleuve. La composante interannuelle du niveau d’eau présente deux minima marqués, en 1995 et 1998. Celui de 1998 constitue un effet de l’événement ENSO de 1997– 1998, dont une des conséquences est un déficit de précipitations au Centre du Bassin amazonien. Une analyse en EOF des champs de pluie sur la période 1993–1999 confirme cette observation. 2001 Académie des sciences / Éditions scientifiques et médicales Elsevier SAS altimétrie spatiale / Amazone / hydrologie régionale
Version abrégée 1. Introduction Le suivi des variations temporelles des niveaux d’eau des fleuves est classiquement réalisé par des enregistrements in situ. Ces variations de niveau peuvent ensuite être exprimées en débits, paramètres intéressants pour l’hydrologue, à l’aide de courbes d’étalonnage. Cependant, la plupart des grands bassins fluviaux couvrent des régions d’accès difficile, ce qui limite considérablement l’installation de réseaux opérationnels de stations hydrographiques in situ. L’altimétrie spatiale offre une alternative intéressante à la mesure régulière des niveaux d’eau des réservoirs continentaux. Plusieurs études ont montré que cette technique possède le potentiel pour mesurer les fluctuations spatio-temporelles des niveaux d’eau sur les mers intérieures, les lacs, les fleuves et mêmes les zones d’inondation permanentes et temporaires [2–4, 6, 13, 15–18]. Dans cette note, nous présentons des séries temporelles de niveau d’eau de l’Amazone, basées sur les mesures altimétriques du satellite Topex/Poseidon entre janvier 1993 et décembre 2000.
2. Le bassin amazonien Le Bassin amazonien est le plus grand bassin hydrographique du monde, tant en terme de superficie (∼ 6 000 000 km2 ) que de débit annuel moyen (∼ 200 000 m3 ·s−1 ). La variabilité du niveau de l’Amazone, de ses affluents et des zones d’inondation est dominée par un cycle saisonnier, conséquence de l’oscillation saisonnière des précipitations sur le bassin. À l’échelle de temps inter-annuelle, la variabilité dominante est associée aux événements ENSO, qui provoquent un déficit de pluie sur la partie centrale du bassin [1, 7–9, 12, 14, 21].
3. Analyse des mesures altimétriques de Topex/Poseidon Nous avons analysé huit années complètes (de janvier 1993 à décembre 2000) de mesures altimétriques de Topex/Poseidon [1]. La figure 1 montre la couverture des traces de Topex/Poseidon sur le Bassin amazonien. Les mesures altimétriques sont d’abord corrigées des différentes erreurs dues à la propagation dans l’ionosphère et la troposphère sèche ainsi que des effets de marées solides. Certaines corrections, nécessaires pour le plein océan, ne sont pas appliquées ici. La correction appelée « troposphérique humide » due à la présence de vapeur
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d’eau dans la basse atmosphère n’est malheureusement pas disponible sur les surfaces continentales et n’est donc pas appliquée. Celle-ci peut atteindre 20–30 cm dans les régions tropicales et varie de façon saisonnière (amplitude de l’ordre de 10 cm). Ceci constitue une source d’erreur notable dans notre analyse. Pour estimer la répétabilité des hauteurs d’eau mesurées, nous avons comparé les séries temporelles de niveau au voisinage d’un point de croisement issues d’une trace montante et d’une trace descendante. Pour être fiable, cette comparaison devrait se faire en un point de croisement situé sur l’eau. Or, une telle situation n’existe pas. Nous avons donc comparé les niveaux d’eau en deux points distants de 15 km (indiqués par des cercles sur la figure 2a). Les séries temporelles correspondantes sont présentées sur la figure 2b, ainsi que les différences. L’écart type des différences est de 82 cm (55 cm lorsque celles-ci sont moyennées sur 150 jours), valeur qui inclut l’incertitude sur la mesure ainsi que la différence des signaux saisonniers entre les deux points. Pour estimer l’exactitude, nous avons comparé les séries temporelles continues de niveau d’eau déduites des mesures Topex/Poseidon avec des mesures in situ issues de stations hydrographiques. Deux exemples sont présentés sur les figures 3a (trace 63) et 3b (trace 139). Sur la figure 3a, on note un bon accord entre les deux courbes. L’écart type des différences est de 45 cm. La figure 3b montre un exemple où Topex/Poseidon ne fournit pas de mesures valides en basses eaux, pour des raisons diverses (réflexions parasites dues aux berges, par exemple). Dans ce cas, le signal saisonnier est mal décrit, en raison de l’absence de mesures en basses eaux. La figure 4 présente les séries temporelles de hauteur d’eau de l’Amazone en deux sites (trace 102 et trace 63). Chaque série est construite en moyennant les mesures de l’intersection d’une trace Topex/Poseidon avec le fleuve. On note un signal saisonnier dominant, atteignant 10–15 m pic à pic dans la partie centrale du bassin. Nous avons étudié la variation inter-annuelle des niveaux en utilisant la hauteur maximale annuelle, correctement mesurée par Topex/Poseidon, pour neuf traces réparties le long de l’axe principal de l’Amazone (figure 5). Sur les courbes de la figure 5, on note deux minima marqués en 1995 et 1998. Le minimum de 1998, uniquement visible sur la partie centrale du bassin, est la conséquence du déficit de précipitation typique des événements ENSO [5, 7, 10, 21]. Une décomposition des champs de précipitation [24] en EOF (Empirical Orthogonal Functions) [19, 22] a été effectuée pour la période 1993–1999. Sur la carte de la structure spatiale du mode 1 (figure 6), on note une ré-
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gion à très fort déficit pluviométrique en 1997–1998 (partie centrale du bassin), en accord avec les analyses des données de pluie lors des événements ENSO antérieurs [5, 7, 9, 10, 14, 21].
4. Conclusion Cette étude montre la capacité de l’altimétrie radar à fournir une information pertinente sur les niveaux d’eau dans les grands bassins hydrographiques. Les principales limitations de la technique apparaissent liées à l’espacement minimal des mesures le long de la trace (de l’ordre de 0,5 km) et à de fréquentes lacunes en matière de mesures
1. Introduction Water balance of large river basins relates changes in water stored in soils, aquifers, rivers, floodplains and wetlands with fluxes like precipitation, evaporation and river runoff. It is thus a measure of regional climate variation, even if anthropogenic factors, such as change in land use, deforestation, irrigation, dam building, etc., may also contribute to the budget. River runoff (and surface water levels) represents an important component of the total water budget over the basin. Being less spatially heterogeneous than precipitation, the runoff signal integrates changes in hydrologic characteristics over a large area and is a particularly valuable indicator of regional climate change over a drainage basin both at seasonal and interannual time scales [17]. Monitoring of river water level is traditionally based on in situ river gauging stations. These level measurements are further expressed in terms of discharge rates that are the hydrologic parameters of interest. However, most of the large river basins are located in remote areas where recording stations are very scarce or even inexistent, a result of either inaccessibility or economical/political restrictions in developing countries. Satellite-based sensors, in particular satellite altimetry, offer a promising alternative for monitoring surface water levels in remote areas. Although satellite altimetry has been developed and optimised for open oceans, several studies have demonstrated its capability of measuring water level change of continental surface reservoirs such as inland seas and lakes [2, 3, 6, 13, 15, 16, 18]. The question of using satellite altimetry for monitoring water level of rivers, floodplains and wetlands has been also addressed [4, 11]. Birkett [4] showed that, in spite of technical limitations, level change of the world’s largest rivers could be monitored by satellite altimetry with a few decimetres precision, from intraseasonal to interannual time scales.
en basses eaux. Des améliorations sensibles devraient être obtenues après correction de la perturbation liée à la troposphère humide, ainsi que par l’exploitation des données altimétriques des satellites ERS-1 et ERS-2, qui offrent une couverture spatiale plus dense que Topex/Poseidon. Les applications incluront l’identification des zones inondées en basses eaux et en hautes eaux, l’estimation des variations saisonnières du volume d’eau stocké en surface du bassin, l’estimation des débits lorsque les courbes hauteur–débit sont connues, l’établissement de référentiels altimétriques homogènes sur les bassins. Au-delà du bassin de l’Amazone, ces applications constituent un enjeu important pour tous les grands bassins fluviaux de la planète.
In this paper, we present results of surface water level fluctuations in the Amazon basin based on altimetry data of the Topex/Poseidon satellite over an 8-year time span. One of the limitations in using radar altimetry to monitor river levels is the ground track resolution (for Topex/Poseidon, the minimal alongtrack ground spacing is ∼ 580 m between each radar echo), preventing narrow rivers to be observed. In addition, vegetation canopy of surrounding terrains and presence of rough topography produce parasite reflections that deteriorate radar echoes, so that unlike in the oceanic domain, the first task here is to identify water reflection in the signal. In this paper, we first discuss the methodology developed for detection of water surfaces. Then we present results of water level time series constructed at selected locations along the Amazon River. Finally, we discuss the observed seasonal and interannual signal, and the relation of the latter with the 1997–1998 ENSO event.
2. Amazon basin The Amazon basin is the largest hydrographic basin in the world, both in terms of area (∼ 6 million km2 ) and mean annual discharge (∼ 200 000 m3 ·s−1 ) that is equivalent to about 20 % of the world’s rivers input to the oceans. The variability of water level of the Amazon River, tributaries and floodplains is dominated by the annual cycle. This seasonal variability is mainly driven by the annual variation in rainfall, itself linked to the variation of atmospheric circulation over South America [20]. Precipitation patterns over the Amazon basin exhibit strong variations from year to year [8, 12]. River flow is mainly the result of rainfall and evaporation with rainfall being the dominant component (e.g., [7]), although relations between runoff and precipitation is complicated by ‘basin memory effects’ in large catchment basins such as the Amazon basin (e.g., [21]). Thus, rainfall variability over the Amazon basin causes changes in surface water levels
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and river discharges. Water vapour flux over the Amazon basin originates predominantly from the equatorial Atlantic Ocean. However, several studies have reported that the predominant interannual variability in the hydrographic parameters (e.g., water level, discharge) in the Amazon basin (mainly northern Amazon Basin) is related to ENSO events [7–9, 12, 14, 21]. Indeed, in most parts of the basin, ENSO years give rise to rainfall deficit and reduced discharges. The only exception is the Madeira basin, located in the southwest of the Amazon Basin, which shows an opposite behaviour, i.e., rainfall excess, during ENSO years [14]. The relation between ENSO and the Amazon basin hydrology should result from modifications in atmospheric circulations and in sea surface temperature distributions in the tropical Pacific [8] that modify the precipitation pattern over the Amazon basin, hence discharge rate of the Amazon river. It is worth mentioning that scarcity of data concerning ground water dynamics over the Amazon basin prevents any estimate of their contribution to river runoff variation through time. Ground waters could indeed partially compensate or damp interannual variability of precipitations.
3. Altimetry data analysis 3.1. Topex/Poseidon data Radar altimetry from space consists of vertical range measurements between the satellite and water level. Difference between the satellite altitude above a reference surface (usually a conventional ellipsoid), determined through precise orbit computation, and satellite-water surface distance, provides measurements of water level above the reference surface. Placed onto a repeat orbit, the altimeter satellite overflies a given region at regular time intervals (called the orbital cycle), during which a complete coverage of the Earth is performed. Here we use altimetry data from the Topex/Poseidon (T/P) satellite (launched in August 1992) to investigate the capability of the altimetric technique to monitor water levels in large hydrographic basins. The temporal resolution of the T/P data is 10 days (the duration of an orbital cycle). Its ground track coverage is 315 km in equatorial regions. Although not as dense as for the ERS-1/2 coverage (about 4 times denser), the existing T/P database has the advantage of providing directly altimetric height measurements over land, unlike the ERS-1/2 missions for which only waveforms are available. We have analysed eight years of Topex/Poseidon altimetry data from January 1993 to December 2000, using the most upgraded GDRs (Geophysical Data Records) made available by the Topex/Poseidon AVISO/Altimetry Data Centre [1].
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The GDRs contain range values derived from averaged radar echoes at 100 ms and 1 s interval, which corresponds to along-track ground spacing of 580 m and 5.8 km, respectively. We used both data sets. In addition to the altimetric heights and radial orbit component, the data set includes a series of environmental and geophysical corrections. Over open ocean these corrections include: ionospheric correction, dry and wet tropospheric correction, solid Earth, ocean and pole tides, ocean tide loading, inverted barometer effect and sea state bias. Models for these corrections have been improved continuously along the T/P lifetime to increase the precision of sea surface heights for open ocean studies, which is currently at the 1-cm level for a single measurement. However, for continental water studies, correction models optimised for open oceans are not necessarily adequate. More problematic is the fact that over continents, some corrections such as the wet tropospheric correction may be simply missing. This problem, along with ground resolution, is the most serious limitation when using the altimetry data for continental water level monitoring. In the present study, we applied the ionospheric, dry tropospheric and solid Earth tide corrections. Pole tide, ocean tide, ocean tide loading, inverted barometer and sea state bias are ocean effects, thus are neglected here. We also corrected the T/P measurements for instrumental drifts and bias as recommended by the T/P project. The ionospheric correction is based on the onboard DORIS measurement of the electronic content. The dry tropospheric correction is accurately computed from ECMWF (European Centre for Medium Range Weather Forecast) surface pressure fields. The wet tropospheric correction derived from the onboard Topex Microwave Radiometer (TMR) over oceans, is generally missing over land. The TMR instrument has a large footprint (43.4 km in diameter for the 18 GHz channel). When the satellite overflies rivers, the TMR footprint always encompasses surrounding lands, making the atmospheric water vapour measurement unusable. Over land, the tropospheric correction is in principle available from outputs of global circulation models such as the ECMWF operational analysis. Unfortunately, in the AVISO database, this data is missing over most of the land surfaces, in particular over the Amazon basin. We looked at the PODAAC database (the U.S. equivalent of AVISO for the T/P project) that provides the ECMWF-based tropospheric correction as a backup to the TMR correction. However, we noted that along the T/P tracks, this correction is set to a constant value over several thousands kilometres, with in some cases abrupt discontinuities between the constants. The reason for such behaviour is unclear. Considering the suspicious tropospheric correction given by the PODAAC database
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over the Amazon basin, we decided not to apply it. Not correcting for the wet tropospheric effect introduces a systematic bias up to 20–30 cm in tropical regions, plus a seasonal error of about 10 cm amplitude. The systematic bias is not problematic here, because we are studying relative water level time series. This is unlike the seasonal error, which represents a serious limitation to the results so far obtained. 3.2. Methodology for detecting and measuring open water bodies in the Amazon basin Radar echoes over land surfaces are hampered by interfering reflections due to water, vegetation canopy and rough topography. As a consequence, waveforms (e.g., the power distribution of the radar echo within the range window) may not have the simple broad-peaked shape seen over ocean surfaces, but can be complex, multi-peaked, preventing from precise determination of the altimetric height. In the case of the Amazon basin, which is extremely flat, problems may arise from interference between the vegetation canopy and water from wetlands, floodplains, tributaries and main river. Outside wet areas, most radar echoes are rejected automatically. Thus to discriminate between water surface and dry land, we considered a series of tests. – Test 1 (‘measurement density’ test): along a given satellite track, we considered the whole set of 10-day repeat passes over the eight years; at the mean position of each along track 1 s measurement, we counted the total number of valid data; for that purpose, we considered all available measurements (at 10-day intervals over the eight years) inside successive circles of 3-km radius; points (i.e., circles) with less than 50 % valid measurements were rejected. – Test 2 (‘water level fluctuation’ test): over some selected tracks, we constructed surface level time series over the 8-year time span. Points with clear seasonal signal (annual amplitude at least of 1 m) allow mapping open water bodies as intersected by T/P tracks, while dry/vegetated surfaces do not exhibit any detectable seasonal height variation. – Test 3 (‘backscatter coefficient’ test): at each location along the track, we constructed time series of the radar backscatter coefficient (denoted by σ0 ); wet surfaces have typically radar backscatter coefficients higher than 20 dB [4] and we used this threshold to select wet areas. Test 1 (‘measurement density’ test) enlightened a general trend of lower response density over rivers than over surrounding wetlands. The reason for this phenomenon is still under investigation and could be linked to saturation of the onboard altimeter instrument by high intensity echo. Until now this
test does not allow a reliable mapping of open water bodies in the Amazon basin. Tests 2 and 3 give more reliable results in terms of detection of open water surfaces. In figure 1, the wet areas detected by T/P using the ‘backscatter coefficient’ test are depicted by yellow/red colours. These wet areas include not only main river and tributaries, but also flooded areas in high water season and permanent wetlands. It is worth mentioning that in some cases, valid data at main river-T/P track intersections are lacking (see figure 1). It would be necessary to examine the raw radar data (waveforms) to understand why altimetric measurements have been deleted over the river. A possible explanation is the specular nature of the reflection over the river, leading to a Dirac-type returned waveform that on onboard tracking algorithm is unable to identify as a valid measurement. This will be the object of a future investigation.
4. Water level time series along the Amazon River 4.1. Validation of the T/P-derived water level series; comparison with in situ level gauging data Validation of the T/P time series was performed by: (1) comparison of water level time series nearby a crossover point between ascending and descending profiles, and (2) comparison with ground-based water level data from gauge stations. Crossover comparison between ascending and descending profiles is a classical method to estimate internal precision of altimetric heights over ocean surfaces. However, it must be underlined that such measurements by ascending and descending profiles occur with a 1- to 5-day lag time and river water level has evolved between theses two measurements. Applying the method to T/P data in the Amazon basin requires the crossover to be located over a wet surface. A single crossover meets (approximately) this condition. Figure 2a shows the position of the cross over nearby the Japurá River. It is not exactly located over the river, but about 10 km north on land. We thus decided to compare the water level time series at locations where the two T/P tracks cross the river (distant by about 15 km). In figure 2b are shown the two T/P water level measurement time series. They superimpose quite well. However the rms difference calculated for all 134 common measurement dates is 82 cm. This rms difference can be interpreted as the T/P instantaneous measurement mean error on this section of the Japurá River, whose high value is probably due to the narrowness of the river (500 m). For longer time spans, the mean error will be lower. If
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Figure 1. Topographic map of the Amazon basin on which are superimposed the Topex/Poseidon satellite tracks (identified by their number). The color variation along the tracks represents the backscatter coefficient (σ0 ) intensity (in dB). Water detection is supposed to correspond to color other than blue. The highest values of σ0 (red color) coincide in general with the main river and tributaries. When away from rivers, high σ0 values allow detection of flooded plains and wetlands. Yellow boxes correspond to areas selected in the present analysis. Black dots show the position of the in situ water gauges: stations of Tabatinga (box 1), Itapeua (box 3), Manacapuru and Manaus (west and east respectively; box 4) and Parintins (box 5). Figure 1. Carte du bassin amazonien et traces de Topex/Poseidon. Les couleurs le long des traces indiquent l’intensité du paramètre de rétrodiffusion du signal radar. Les cercles noirs indiquent la position des stations in situ : stations de Tabatinga (région 1), Itapeua (région 3), Manacapuru et Manaus (d’ouest en est ; région 4) et Parintins (région 5).
smoothed through a 150-day running average, the difference time series has indeed a lower rms, of 55 cm. In situ measurement comparison used data from the permanent network of more than 200 gauging stations implemented by ANEEL (Agência Nacional de Energia Elétrica, Brazil) along the main river and tributaries of the Amazon basin since the early 1970s. Full circles in figure 1 indicate positions of the gauging stations used in this study. The gauging level measurements are referred to some arbitrary reference while the T/P level measurements are relative to the ellipsoid. Two methods were developed to compare raw T/P measurements (every 10 days) with gauging stations measurements (every day). First method consists of removing to each time series its 8-year average and then comparing resulting ‘zero mean’ T/P and in situ
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time series. This method presents two drawbacks: the T/P measurement density is higher during high flow period than during low flow period, which leads to an overestimation in the mean T/P water level; the gauging station can be far from the T/P track intersection, resulting in a difference in signal amplitude (annual water level fluctuations). A second method was developed to minimize these drawbacks: it reconstructs an ‘in situ’ continuous time series of water levels on the T/P track using a linear regression between T/P measurements and the selected gauging station. This allows us comparing raw T/P series and reconstructed in-situ series. For both methods the rms error between T/P measurements and the in situ series (observed or reconstructed) quantifies the dispersion of T/P measurements and can be interpreted as the
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(a)
(b)
Figure 2. a. Map showing the position of a crossover between ascending and descending T/P track. b. Comparison of the two T/P-based water level time series at the positions identified by the white circles. The residual time series is also shown. Figure 2. a. Point d’intersection d’une trace montante et descendante au voisinage du fleuve Japura. b. Séries temporelles de niveau d’eau, mesurées par T/P aux points indiqués par les cercles blancs sur la figure 2a. La courbe des différences est aussi présentée.
(a)
(b)
Figure 3. Comparison of the T/P-derived water level time series (dots) and reconstructed time series from the in situ gauging information (solid curve). a. Track 63, box 4 (the difference time series is also shown). b. Track 139, box 5. Figure 3. Séries temporelles de niveau d’eau de l’Amazone, mesurées par T/P (points) et reconstruites à partir de mesures in situ (courbe en trait plein). a. Trace 63 (la courbe des différences est aussi présentée). b. Trace 139.
accuracy of T/P measurements. We have compared the T/P-derived and the reconstructed (i.e., based on method 2) continuous time series, at two locations: track 63 (box 4, figure 3a) and track 139 (box 5, figure 3b). Figure 3a presents the water level time series at the intersection between the Amazon River and the T/P track numbered 63, re-built from the gauging station information located 8.5 km upstream in Manaus
(solid curve), the T/P measurements (data at 1 s), and the difference time series. The computed rms of the differences is 45 cm. Except for the site of Tabatinga (290◦ E, 4◦ S; box 1), where the rms accuracy is poorer (∼ 80 cm), similar rms values are found at the other two sites: Itapeua (297◦ E, 4◦ S; box 3) and Manacapuru (299.5◦ E, 3.5◦ S; box 4). In the latter two cases, the distances between T/P track intersection and in
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Figure 4. Amazon River water level time series derived from the T/P data at two sites: track 102 and 63 (in m). Dots are T/P data at 10-day intervals spatially averaged over the river width while the solid curve is constructed by interpolation with a Spline function. Figure 4. Séries temporelles de niveau d’eau de l’Amazone, mesurées par T/P (traces 102 et 63). Les points représentent les hauteurs T/P par cycle, moyennées sur la largeur du fleuve. La courbe en trait plein est obtenue par interpolation par une fonction Spline.
situ station are 7 and 70 km, respectively. Considering the large annual amplitude of the river level variation (10 to 15 m peak to peak), a half-meter accuracy of the T/P data is very encouraging. The estimated rms accuracy is fully consistent with previous estimates made by Birkett [4] with 3.7 years of data. She reported an rms accuracy of 60 cm at three comparison sites. It is worth noting that the rms accuracy is higher over floodplains (on the order of 20 cm) than over the river (see [4]). The reason for this is unclear and currently under investigation. Figure 3b presents another example at the intersection between main river and track numbered 139. In this case, we clearly see the inadequacy of T/P in providing data in low water season. The reason for this failure is not yet understood, as the river width does not change significantly between low flow and high flow periods. Inspection of T/P waveforms may help clarifying why data are lacking in low water season at some locations. 4.2. Seasonal variations of the T/P-derived water level time series Here we present water level time series for two selected tracks (track 102, box 1 and track 63, box 4) along the main river where T/P measurements consistently cover the seasonal cycle. Figure 4 shows the T/P-derived water level time series at the T/P track intersections. Dots represent spatial averages over the river width at 10-day interval. A solid curve is obtained by interpolation with a Spline function. The curves exhibit a clear seasonal signal, mostly annual, of up to 10–15 m peak-to-peak amplitude. Maximum signal is observed from May to July,
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Figure 5. Interannual water level time series derived from the T/P data in high water season at the intersection of nine selected tracks (identified by number) (in m). Figure 5. Variations interannuelles du niveau de l’Amazone en périodes de hautes eaux pour neuf sites.
which corresponds to the maximum flood period. This high water period follows the main rainy season by about three months. The most upstream curve (box 1) presents a mostly unimodal seasonal cycle with a slight secondary maximum, occurring by the end of the year. The water level curve for track 63 (box 4) shows a single maximum, with a regular annual cycle. These results are fully consistent with information given by the gauging stations. At sites where T/P succeeds in measuring water level in low water season, the whole seasonal cycle can be correctly described. 4.3. Interannual variations To study the interannual signal, we considered the local series of maximum annual water levels, which are well detected by nine different T/P tracks along the main stream of the Amazon River. To construct the interannual time series we averaged over six successive T/P cycles, water level data on both sides of the seasonal maximum. This procedure leads to a single water level value per year. Corresponding time series are shown in figure 5. Looking at figure 5, two main features are visible: (1) there is a well marked deficit in year 1995, particularly intense in the western portion of the river, and (2) a clear deficit in water level can be observed in 1998 downstream from the confluence of the Negro and Solimões Rivers. The latter minimum can be related to the major 1997–1998 ENSO event. Indeed several studies have established that changes in atmospheric circulation over the tropical Pacific associated with ENSO cause variation in the atmospheric circulation and precipitation regime over South America, hence in Amazon River discharge [10, 13, 15]. In particular major ENSO events
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produce pronounced low discharge over the central portion of the Amazon River [21]. This will be discussed in more details in the next section.
5. Precipitation over the Amazon basin The water balance in a river basin can be described by the water budget equation [23]: ∂S/∂t = P –E–R, which links fluxes with changes in storage. R is the total discharge across the basin boundary (positive for net outflow). R is usually taken to be river discharge at the basin outlet. P is precipitation, E is evapotranspiration from the land surface and S represents the water stored in the basin, which includes vegetation moisture, soil moisture, groundwater storage and surface storage (lakes, rivers, flooded areas and snow where appropriate). t is the time. R and P are the most easily quantifiable variables in the equation above. Water level time series over the Amazon basin carry information on both superficial storage in open water bodies (lakes, rivers and flooded areas) and river discharges, as water level in a river section is related to river discharge in this section. In most river basins, and in particular in the Amazon basin, site discharge is measured directly, as frequently as possible, in order to establish and update specific level–discharge relationship (rating curve). Discharge time series are then deduced from continuously measured water level time series, using the rating curve. Unfortunately the water level time series derived from T/P altimetry cannot be expressed in terms of discharge records, due to the lack of field calibration at the corresponding sites. However, these water levels give valuable and appropriate spatial and temporal information on relative discharge variations, since at first order the rating curve can be approximated by a linear relation. On the assumption that streamflow records integrate spatial hydrological variability within the catchment basin, several studies have compared precipitation records to Amazon River discharge to detect common temporal variability patterns of climatic origin [5, 21]. In order to get some insight into the precipitation variability in the region and its possible influence on the Amazon River level, we analysed precipitation fields from Xie and Arkin [24] over the time span 1993–1999. Note that over a basin as large as the Amazon basin, stream flow results from the integrated contribution of precipitation over the catchment basin. The precipitation data set consists of monthly precipitation grids (mesh size of 2.5◦ × 2.5◦ ). To highlight both temporal and spatial variability, we applied an Empirical Orthogonal Function (EOF) analysis [19, 22] to the gridded precipitation fields. At each grid mesh, the seasonal signal (annual and semi annual cycles) has been removed in order to show up the interannual fluctuations. The EOF method is based on
a decomposition of a spatio-temporal varying field (here precipitation) into an uncorrelated linear combination of functions of the original variables, ranked by variance. The method implicitly assumes that the first few modes of the EOF decomposition define the dominant patterns of the signal. Figure 6 shows the spatial pattern and temporal evolution of the first EOF mode of precipitation in the Amazon basin. It explains 42 % of the total variance. The temporal curve exhibits a very strong minimum at the end of 1997 corresponding to the 1997–1998 ENSO. According to the spatial map, the largest rainfall deficit is observed in the north-central part of the basin, east of the Negro and Solimoes confluence. It is precisely along the Amazon River portion extending between Manaus (300◦ E, 3◦ S) and Obidos (304◦ E, 2◦ S) that the T/Pderived water levels show a marked minimum in 1998 (note that curves shown in figure 5 are based on single yearly values). The T/P-based results are in full agreement with previous observations of hydrographic parameters (water level, discharge) showing interannual variability coupled to ENSO events. These oscillations in river discharge are explained by the eastward shift of the descending branch of the zonal circulation over the equatorial Pacific during ENSO years, leading to a suppression of convection hence precipitation over the central Amazon basin [21]. The second mode (20 % of the total variance; not shown) presents an extremum in 1995 in its temporal curve, corresponding to a minimum of precipitation over the Amazon River west of longitude 305◦ E. Again, this corresponds to the observed 1995 water level minimum seen in the T/P data.
6. Discussion This study has shown that satellite altimetry, in particular the T/P mission, has the capability to monitor temporal variations in surface water level for large river basins, provided that the water area is large enough for parasite land signal not to contaminate the altimetric measurement. The main limitation of the altimetric techniques over the Amazon basin appears to be the lack of water detection during lowwater season. We have also indicated that present water height measurements are not optimised due to the fact that the wet troposphere correction is lacking. This can be improved however by collecting integrated water vapour data from outputs of operational global circulation models (or their re-analyses). Although very demanding, this would significantly improve the accuracy of continental water height measurements. Other interesting perspectives can be envisaged to the future. They concern the use of ERS1 and ERS-2 data whose spatial coverage is about
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Figure 6. First mode of the EOF decomposition of precipitation anomalies over the Amazon basin (seasonal terms removed). Top: map of the spatial pattern (amplitude in mm·d−1 ). Bottom: temporal curve for 1993–1999. Figure 6. Mode 1 de la décomposition en EOF des anomalies de précipitation sur le bassin amazonien entre 1993 et 1999 (après retrait des variations saisonnières). En haut, carte spatiale (amplitude en mm·j−1 ) ; en bas, courbe temporelle.
four times denser than T/P (although their temporal resolution is 35 days instead of 10 days). As mentioned above, altimetric heights were not provided over land. They can be computed however by retracking waveforms. We are currently analysing waveforms from the whole ERS-1 and ERS-2 missions to provide a height database over continental surfaces. This 10-year long time series will be soon ex-
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tended with the observations of ENVISAT, scheduled to be launched in early 2002 on the same orbit as ERS-1 and ERS-2, i.e., with the same spatiotemporal coverage. With such a data set, it will be possible to monitor the spatio-temporal water level variations over rivers, floodplains and wetlands, with a ground resolution of about 50 km. The applications include automatic detection of water over the
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drainage basin and systematic mapping of wet areas in low and high water seasons, the estimate of seasonal and interannual water mass change involved in surface water level fluctuations with inference on the regional water balance and hydrology, and the esti-
mate of discharge rate from altimetry-derived water levels when rating curves are established. These perspectives are not only of interest for the Amazon basin but more generally for other major river basins in the world.
Acknowledgements. This work was supported by the University of São Paulo and the Federal University of Uberlândia (Brazil), CNES, CNRS, IRD, the University of Toulouse and ANEEL through the HiBAm Programme. It received significant support from CAPES–Cofecub (Collaboration universitaire franco-brésilienne) and PNTS (Programme national de télédétection spatiale). We thank P. Vincent, F. Rémy, and N. MognardCampbell for useful discussions on the T/P data processing over land. We also thank J.-L. Guyot and N. Filizola for helpful informations about the Amazon Basin. Finally we are grateful to P. Ribstein for careful review of the original manuscript.
References [1] AVISO/Altimetry, AVISO user handbook for merged Topex/Poseidon products, AVI-NT-02-101, Edition 3.0, Centre national d’études spatiales, France, 1996, 195 p. [2] Birkett C.M., Radar altimetry: a new concept in monitoring lake level changes, EOS Trans., AGU 75 (24) (1994) 273–275. [3] Birkett C.M., The contribution of Topex/Poseidon to the global monitoring of climatically sensitive lakes, J. Geophys. Res. 100 (1995) 25179–25204. [4] Birkett C.M., Contribution of the Topex NASA radar altimeter to the global monitoring of large rivers and wetlands, Water Resour. Res. 34 (5) (1998) 1223–1239. [5] Cauduro Dias de Paiva E.M., Clarke R.T., Time trends in rainfall records in Amazonia, Bull. Am. Meteorol. Soc. 76 (1995) 2203–2211. [6] Cazenave A., Bonnefond P., DoMinh K., Caspian sea level from Topex/Poseidon altimetry: level now falling, Geophys. Res. Lett. 24 (1997) 881–884. [7] Costa M.H., Foley J.A., A comparison of precipitation data sets for the Amazon basin, Geophys. Res. Lett. 25 (1998) 155–158. [8] Costa M.H., Foley J.A., Trends in the hydrological cycle of the Amazon basin, J. Geophys. Res. 104 (1999) 14189–14198. [9] Guyot J.-L., Callede J., Molinier M., Guimaraes V., Oliveira E. de, La variabilité hydrologique actuelle du bassin de l’Amazone ; conséquences climatiques et hydrologiques des événements ENSO à l’échelle régionale et locale, Inamhi–Orstom, Quito, 1997.
[13] Mercier F., Cazenave A., Maheu C., Interannual lake level variations in Africa from Topex–Poseidon satellite altimetry, Global Planet. Change (in press). [14] Molinier M., Guyot J.-L., Cochonneau G., Guimaraes V., La variabilité hydrologique du bassin amazonien et la circulation atmosphérique océanique, Hydrological and geochemical processes in large scale river basins, Manaus, 1999. [15] Morris C.S., Gill S.K., Evaluation of the Topex/Poseidon altimeter system over the Great Lakes, J. Geophys. Res. 99 (1994) 24527–24539. [16] Morris C.S., Gill S.K., Variation of Great Lakes waters from Geosat altimetry, Water Resour. Res. 30 (1994) 1009–1017. [17] National Water Assessment Group, Water, the potential consequences of climate variability and change, US Department of the Interior, Washington DC, 2001. [18] Ponchaut F., Cazenave A., Continental lake level variations from Topex/Poseidon (1993–1996), C. R. Acad. Sci. Paris, série IIa 326 (1998) 13–20. [19] Preisendorfer R.W., in: Mobley C. (Ed.), Principal component analysis in meteorology and oceanography, Elsevier, New York, 1998, 418 p. [20] Rao V.B., Cavalcanti I.F.A., Hada K., Annual variation of rainfall over Brazil and water vapor characteristics over South America, J. Geophys. Res. 101 (1996) 26539–26551. [21] Richey J.E., Nobre C., Deser C., Amazon River discharge and climate variability, Science 246 (1989) 101–102.
[10] Guyot J.-L. et al., Hydrological characteristics of the Amazon Basin, Hydrol. Process. (submitted).
[22] Toumazou V., Cretaux J.-F., Using a Lanczos eigen solver in the computation of Empirical Orthogonal Functions, Mon. Weather Rev. 129 (5) (2001) 1243–1250.
[11] Koblensky C.J., Clarke R.T., Brenner A.C., Frey H., Measurement of river level variations with satellite altimetry, Water Resour. Res. 29 (1993) 1839–1848.
[23] Wood E.F., Lettenmaier D., Liang X., Nijssen B, Wetzel S.W., Hydrological modeling of continental-scale basins, Annu. Rev. Earth Planet. Sci. 25 (1997) 279–300.
[12] Marengo J., Tomasella J., Uvo C.R., Trends in stream flow and rainfall in tropical South America: Amazonia, eastern Brasil and Northwestern Peru, J. Geophys. Res. 103 (1998) 1175–1783.
[24] Xie P., Arkin P.A., An intercomparison of gauge observations and satellite estimates of monthly precipitation, J. Appl. Meteorol. 34 (1995) 1143–1160.
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Temporal variations of river basin waters from Topex/Poseidon satellite altimetry. Application to the Amazon basin Ilce de Oliveira Camposa,b , Franck Merciera , Caroline Maheua , Gérard Cochonneauc , Pascal Kosuthc, Denizar Blitzkowb, Anny Cazenavea,∗ a Legos–GRGS/Cnes, Observatoire Midi-Pyrénées, 18, av. Édouard-Belin, 31400 Toulouse, France b EP–University of São Paulo, Brazil c LMTG IRD, Lago Sul Brasilia, Brazil
Received 23 July 2001; accepted 18 September 2001 Communicated by Gérard Mégie
Abstract – Although developed and optimised for open oceans, satellite altimetry has the potential to monitor level variations of inland surface waters such as lakes and rivers. Here we present results of water level variations of the Amazon River based on eight years (1993– 2000) of altimetry data of the Topex/Poseidon satellite. We first discuss methods to detect wet surfaces from the altimetric measurements, discriminate between water and dry land, and quantify the accuracy of altimetric measurements over water. Then we show water level fluctuations at selected locations where the satellite crosses the Amazon River. The dominant signal is seasonal, mostly annual, with an amplitude of up to 10–15 m peak to peak. Comparison with in situ measurements indicates that water levels are well measured by Topex/Poseidon during high-water season, unlike low-water season, which suffers from data gaps. We further discuss the interannual component of the signal, which shows two marked minima in 1995 and 1998. The 1998 minimum is interpreted as an effect of the 1997–1998 ENSO event, causing rainfall deficit in the central part of the Amazon basin, hence decrease in water levels. An EOF analysis of precipitation fields over the basin during the 1993–1999 period confirms the rainfall minimum by the end of 1997 for this region. 2001 Académie des sciences / Éditions scientifiques et médicales Elsevier SAS satellite altimetry / Amazon basin / regional hydrology Résumé – Suivi des variations temporelles des niveaux d’eau des fleuves à partir de mesures altimétriques de Topex/Poseidon. Application au bassin de l’Amazone. Bien que développée et optimisée pour le plein océan, l’altimétrie spatiale s’avère être un outil très utile pour étudier les fluctuations temporelles des niveaux d’eau sur les continents, en particulier les niveaux des lacs et des grands fleuves. Dans cette note, nous présentons des séries temporelles de niveaux d’eau sur l’Amazone issues de mesures altimétriques de Topex/Poseidon entre 1993 et 2000. Nous discutons en premier lieu les méthodes permettant de distinguer, dans le signal altimétrique, l’eau des sols secs et de la végétation, ainsi que de quantifier la précision de la mesure altimétrique. Les niveaux de l’Amazone mesurés par Topex/Poseidon sur différents sites présentent une signature saisonnière (principalement annuelle) dominante, avec une amplitude pouvant atteindre 10–15 m pic à pic. Des comparaisons avec des mesures in situ indiquent que Topex/Poseidon restitue correctement les niveaux en période de hautes eaux, ce qui n’est pas toujours le cas en période de basses eaux, quand les mesures valides sont peu nombreuses. Ceci empêche de décrire avec précision le cycle
∗ Correspondence
and reprints. E-mail addresses: [email protected] (C. Maheu), [email protected] (A. Cazenave).
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saisonnier en certaines zones du fleuve. La composante interannuelle du niveau d’eau présente deux minima marqués, en 1995 et 1998. Celui de 1998 constitue un effet de l’événement ENSO de 1997– 1998, dont une des conséquences est un déficit de précipitations au Centre du Bassin amazonien. Une analyse en EOF des champs de pluie sur la période 1993–1999 confirme cette observation. 2001 Académie des sciences / Éditions scientifiques et médicales Elsevier SAS altimétrie spatiale / Amazone / hydrologie régionale
Version abrégée 1. Introduction Le suivi des variations temporelles des niveaux d’eau des fleuves est classiquement réalisé par des enregistrements in situ. Ces variations de niveau peuvent ensuite être exprimées en débits, paramètres intéressants pour l’hydrologue, à l’aide de courbes d’étalonnage. Cependant, la plupart des grands bassins fluviaux couvrent des régions d’accès difficile, ce qui limite considérablement l’installation de réseaux opérationnels de stations hydrographiques in situ. L’altimétrie spatiale offre une alternative intéressante à la mesure régulière des niveaux d’eau des réservoirs continentaux. Plusieurs études ont montré que cette technique possède le potentiel pour mesurer les fluctuations spatio-temporelles des niveaux d’eau sur les mers intérieures, les lacs, les fleuves et mêmes les zones d’inondation permanentes et temporaires [2–4, 6, 13, 15–18]. Dans cette note, nous présentons des séries temporelles de niveau d’eau de l’Amazone, basées sur les mesures altimétriques du satellite Topex/Poseidon entre janvier 1993 et décembre 2000.
2. Le bassin amazonien Le Bassin amazonien est le plus grand bassin hydrographique du monde, tant en terme de superficie (∼ 6 000 000 km2 ) que de débit annuel moyen (∼ 200 000 m3 ·s−1 ). La variabilité du niveau de l’Amazone, de ses affluents et des zones d’inondation est dominée par un cycle saisonnier, conséquence de l’oscillation saisonnière des précipitations sur le bassin. À l’échelle de temps inter-annuelle, la variabilité dominante est associée aux événements ENSO, qui provoquent un déficit de pluie sur la partie centrale du bassin [1, 7–9, 12, 14, 21].
3. Analyse des mesures altimétriques de Topex/Poseidon Nous avons analysé huit années complètes (de janvier 1993 à décembre 2000) de mesures altimétriques de Topex/Poseidon [1]. La figure 1 montre la couverture des traces de Topex/Poseidon sur le Bassin amazonien. Les mesures altimétriques sont d’abord corrigées des différentes erreurs dues à la propagation dans l’ionosphère et la troposphère sèche ainsi que des effets de marées solides. Certaines corrections, nécessaires pour le plein océan, ne sont pas appliquées ici. La correction appelée « troposphérique humide » due à la présence de vapeur
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d’eau dans la basse atmosphère n’est malheureusement pas disponible sur les surfaces continentales et n’est donc pas appliquée. Celle-ci peut atteindre 20–30 cm dans les régions tropicales et varie de façon saisonnière (amplitude de l’ordre de 10 cm). Ceci constitue une source d’erreur notable dans notre analyse. Pour estimer la répétabilité des hauteurs d’eau mesurées, nous avons comparé les séries temporelles de niveau au voisinage d’un point de croisement issues d’une trace montante et d’une trace descendante. Pour être fiable, cette comparaison devrait se faire en un point de croisement situé sur l’eau. Or, une telle situation n’existe pas. Nous avons donc comparé les niveaux d’eau en deux points distants de 15 km (indiqués par des cercles sur la figure 2a). Les séries temporelles correspondantes sont présentées sur la figure 2b, ainsi que les différences. L’écart type des différences est de 82 cm (55 cm lorsque celles-ci sont moyennées sur 150 jours), valeur qui inclut l’incertitude sur la mesure ainsi que la différence des signaux saisonniers entre les deux points. Pour estimer l’exactitude, nous avons comparé les séries temporelles continues de niveau d’eau déduites des mesures Topex/Poseidon avec des mesures in situ issues de stations hydrographiques. Deux exemples sont présentés sur les figures 3a (trace 63) et 3b (trace 139). Sur la figure 3a, on note un bon accord entre les deux courbes. L’écart type des différences est de 45 cm. La figure 3b montre un exemple où Topex/Poseidon ne fournit pas de mesures valides en basses eaux, pour des raisons diverses (réflexions parasites dues aux berges, par exemple). Dans ce cas, le signal saisonnier est mal décrit, en raison de l’absence de mesures en basses eaux. La figure 4 présente les séries temporelles de hauteur d’eau de l’Amazone en deux sites (trace 102 et trace 63). Chaque série est construite en moyennant les mesures de l’intersection d’une trace Topex/Poseidon avec le fleuve. On note un signal saisonnier dominant, atteignant 10–15 m pic à pic dans la partie centrale du bassin. Nous avons étudié la variation inter-annuelle des niveaux en utilisant la hauteur maximale annuelle, correctement mesurée par Topex/Poseidon, pour neuf traces réparties le long de l’axe principal de l’Amazone (figure 5). Sur les courbes de la figure 5, on note deux minima marqués en 1995 et 1998. Le minimum de 1998, uniquement visible sur la partie centrale du bassin, est la conséquence du déficit de précipitation typique des événements ENSO [5, 7, 10, 21]. Une décomposition des champs de précipitation [24] en EOF (Empirical Orthogonal Functions) [19, 22] a été effectuée pour la période 1993–1999. Sur la carte de la structure spatiale du mode 1 (figure 6), on note une ré-
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gion à très fort déficit pluviométrique en 1997–1998 (partie centrale du bassin), en accord avec les analyses des données de pluie lors des événements ENSO antérieurs [5, 7, 9, 10, 14, 21].
4. Conclusion Cette étude montre la capacité de l’altimétrie radar à fournir une information pertinente sur les niveaux d’eau dans les grands bassins hydrographiques. Les principales limitations de la technique apparaissent liées à l’espacement minimal des mesures le long de la trace (de l’ordre de 0,5 km) et à de fréquentes lacunes en matière de mesures
1. Introduction Water balance of large river basins relates changes in water stored in soils, aquifers, rivers, floodplains and wetlands with fluxes like precipitation, evaporation and river runoff. It is thus a measure of regional climate variation, even if anthropogenic factors, such as change in land use, deforestation, irrigation, dam building, etc., may also contribute to the budget. River runoff (and surface water levels) represents an important component of the total water budget over the basin. Being less spatially heterogeneous than precipitation, the runoff signal integrates changes in hydrologic characteristics over a large area and is a particularly valuable indicator of regional climate change over a drainage basin both at seasonal and interannual time scales [17]. Monitoring of river water level is traditionally based on in situ river gauging stations. These level measurements are further expressed in terms of discharge rates that are the hydrologic parameters of interest. However, most of the large river basins are located in remote areas where recording stations are very scarce or even inexistent, a result of either inaccessibility or economical/political restrictions in developing countries. Satellite-based sensors, in particular satellite altimetry, offer a promising alternative for monitoring surface water levels in remote areas. Although satellite altimetry has been developed and optimised for open oceans, several studies have demonstrated its capability of measuring water level change of continental surface reservoirs such as inland seas and lakes [2, 3, 6, 13, 15, 16, 18]. The question of using satellite altimetry for monitoring water level of rivers, floodplains and wetlands has been also addressed [4, 11]. Birkett [4] showed that, in spite of technical limitations, level change of the world’s largest rivers could be monitored by satellite altimetry with a few decimetres precision, from intraseasonal to interannual time scales.
en basses eaux. Des améliorations sensibles devraient être obtenues après correction de la perturbation liée à la troposphère humide, ainsi que par l’exploitation des données altimétriques des satellites ERS-1 et ERS-2, qui offrent une couverture spatiale plus dense que Topex/Poseidon. Les applications incluront l’identification des zones inondées en basses eaux et en hautes eaux, l’estimation des variations saisonnières du volume d’eau stocké en surface du bassin, l’estimation des débits lorsque les courbes hauteur–débit sont connues, l’établissement de référentiels altimétriques homogènes sur les bassins. Au-delà du bassin de l’Amazone, ces applications constituent un enjeu important pour tous les grands bassins fluviaux de la planète.
In this paper, we present results of surface water level fluctuations in the Amazon basin based on altimetry data of the Topex/Poseidon satellite over an 8-year time span. One of the limitations in using radar altimetry to monitor river levels is the ground track resolution (for Topex/Poseidon, the minimal alongtrack ground spacing is ∼ 580 m between each radar echo), preventing narrow rivers to be observed. In addition, vegetation canopy of surrounding terrains and presence of rough topography produce parasite reflections that deteriorate radar echoes, so that unlike in the oceanic domain, the first task here is to identify water reflection in the signal. In this paper, we first discuss the methodology developed for detection of water surfaces. Then we present results of water level time series constructed at selected locations along the Amazon River. Finally, we discuss the observed seasonal and interannual signal, and the relation of the latter with the 1997–1998 ENSO event.
2. Amazon basin The Amazon basin is the largest hydrographic basin in the world, both in terms of area (∼ 6 million km2 ) and mean annual discharge (∼ 200 000 m3 ·s−1 ) that is equivalent to about 20 % of the world’s rivers input to the oceans. The variability of water level of the Amazon River, tributaries and floodplains is dominated by the annual cycle. This seasonal variability is mainly driven by the annual variation in rainfall, itself linked to the variation of atmospheric circulation over South America [20]. Precipitation patterns over the Amazon basin exhibit strong variations from year to year [8, 12]. River flow is mainly the result of rainfall and evaporation with rainfall being the dominant component (e.g., [7]), although relations between runoff and precipitation is complicated by ‘basin memory effects’ in large catchment basins such as the Amazon basin (e.g., [21]). Thus, rainfall variability over the Amazon basin causes changes in surface water levels
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and river discharges. Water vapour flux over the Amazon basin originates predominantly from the equatorial Atlantic Ocean. However, several studies have reported that the predominant interannual variability in the hydrographic parameters (e.g., water level, discharge) in the Amazon basin (mainly northern Amazon Basin) is related to ENSO events [7–9, 12, 14, 21]. Indeed, in most parts of the basin, ENSO years give rise to rainfall deficit and reduced discharges. The only exception is the Madeira basin, located in the southwest of the Amazon Basin, which shows an opposite behaviour, i.e., rainfall excess, during ENSO years [14]. The relation between ENSO and the Amazon basin hydrology should result from modifications in atmospheric circulations and in sea surface temperature distributions in the tropical Pacific [8] that modify the precipitation pattern over the Amazon basin, hence discharge rate of the Amazon river. It is worth mentioning that scarcity of data concerning ground water dynamics over the Amazon basin prevents any estimate of their contribution to river runoff variation through time. Ground waters could indeed partially compensate or damp interannual variability of precipitations.
3. Altimetry data analysis 3.1. Topex/Poseidon data Radar altimetry from space consists of vertical range measurements between the satellite and water level. Difference between the satellite altitude above a reference surface (usually a conventional ellipsoid), determined through precise orbit computation, and satellite-water surface distance, provides measurements of water level above the reference surface. Placed onto a repeat orbit, the altimeter satellite overflies a given region at regular time intervals (called the orbital cycle), during which a complete coverage of the Earth is performed. Here we use altimetry data from the Topex/Poseidon (T/P) satellite (launched in August 1992) to investigate the capability of the altimetric technique to monitor water levels in large hydrographic basins. The temporal resolution of the T/P data is 10 days (the duration of an orbital cycle). Its ground track coverage is 315 km in equatorial regions. Although not as dense as for the ERS-1/2 coverage (about 4 times denser), the existing T/P database has the advantage of providing directly altimetric height measurements over land, unlike the ERS-1/2 missions for which only waveforms are available. We have analysed eight years of Topex/Poseidon altimetry data from January 1993 to December 2000, using the most upgraded GDRs (Geophysical Data Records) made available by the Topex/Poseidon AVISO/Altimetry Data Centre [1].
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The GDRs contain range values derived from averaged radar echoes at 100 ms and 1 s interval, which corresponds to along-track ground spacing of 580 m and 5.8 km, respectively. We used both data sets. In addition to the altimetric heights and radial orbit component, the data set includes a series of environmental and geophysical corrections. Over open ocean these corrections include: ionospheric correction, dry and wet tropospheric correction, solid Earth, ocean and pole tides, ocean tide loading, inverted barometer effect and sea state bias. Models for these corrections have been improved continuously along the T/P lifetime to increase the precision of sea surface heights for open ocean studies, which is currently at the 1-cm level for a single measurement. However, for continental water studies, correction models optimised for open oceans are not necessarily adequate. More problematic is the fact that over continents, some corrections such as the wet tropospheric correction may be simply missing. This problem, along with ground resolution, is the most serious limitation when using the altimetry data for continental water level monitoring. In the present study, we applied the ionospheric, dry tropospheric and solid Earth tide corrections. Pole tide, ocean tide, ocean tide loading, inverted barometer and sea state bias are ocean effects, thus are neglected here. We also corrected the T/P measurements for instrumental drifts and bias as recommended by the T/P project. The ionospheric correction is based on the onboard DORIS measurement of the electronic content. The dry tropospheric correction is accurately computed from ECMWF (European Centre for Medium Range Weather Forecast) surface pressure fields. The wet tropospheric correction derived from the onboard Topex Microwave Radiometer (TMR) over oceans, is generally missing over land. The TMR instrument has a large footprint (43.4 km in diameter for the 18 GHz channel). When the satellite overflies rivers, the TMR footprint always encompasses surrounding lands, making the atmospheric water vapour measurement unusable. Over land, the tropospheric correction is in principle available from outputs of global circulation models such as the ECMWF operational analysis. Unfortunately, in the AVISO database, this data is missing over most of the land surfaces, in particular over the Amazon basin. We looked at the PODAAC database (the U.S. equivalent of AVISO for the T/P project) that provides the ECMWF-based tropospheric correction as a backup to the TMR correction. However, we noted that along the T/P tracks, this correction is set to a constant value over several thousands kilometres, with in some cases abrupt discontinuities between the constants. The reason for such behaviour is unclear. Considering the suspicious tropospheric correction given by the PODAAC database
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over the Amazon basin, we decided not to apply it. Not correcting for the wet tropospheric effect introduces a systematic bias up to 20–30 cm in tropical regions, plus a seasonal error of about 10 cm amplitude. The systematic bias is not problematic here, because we are studying relative water level time series. This is unlike the seasonal error, which represents a serious limitation to the results so far obtained. 3.2. Methodology for detecting and measuring open water bodies in the Amazon basin Radar echoes over land surfaces are hampered by interfering reflections due to water, vegetation canopy and rough topography. As a consequence, waveforms (e.g., the power distribution of the radar echo within the range window) may not have the simple broad-peaked shape seen over ocean surfaces, but can be complex, multi-peaked, preventing from precise determination of the altimetric height. In the case of the Amazon basin, which is extremely flat, problems may arise from interference between the vegetation canopy and water from wetlands, floodplains, tributaries and main river. Outside wet areas, most radar echoes are rejected automatically. Thus to discriminate between water surface and dry land, we considered a series of tests. – Test 1 (‘measurement density’ test): along a given satellite track, we considered the whole set of 10-day repeat passes over the eight years; at the mean position of each along track 1 s measurement, we counted the total number of valid data; for that purpose, we considered all available measurements (at 10-day intervals over the eight years) inside successive circles of 3-km radius; points (i.e., circles) with less than 50 % valid measurements were rejected. – Test 2 (‘water level fluctuation’ test): over some selected tracks, we constructed surface level time series over the 8-year time span. Points with clear seasonal signal (annual amplitude at least of 1 m) allow mapping open water bodies as intersected by T/P tracks, while dry/vegetated surfaces do not exhibit any detectable seasonal height variation. – Test 3 (‘backscatter coefficient’ test): at each location along the track, we constructed time series of the radar backscatter coefficient (denoted by σ0 ); wet surfaces have typically radar backscatter coefficients higher than 20 dB [4] and we used this threshold to select wet areas. Test 1 (‘measurement density’ test) enlightened a general trend of lower response density over rivers than over surrounding wetlands. The reason for this phenomenon is still under investigation and could be linked to saturation of the onboard altimeter instrument by high intensity echo. Until now this
test does not allow a reliable mapping of open water bodies in the Amazon basin. Tests 2 and 3 give more reliable results in terms of detection of open water surfaces. In figure 1, the wet areas detected by T/P using the ‘backscatter coefficient’ test are depicted by yellow/red colours. These wet areas include not only main river and tributaries, but also flooded areas in high water season and permanent wetlands. It is worth mentioning that in some cases, valid data at main river-T/P track intersections are lacking (see figure 1). It would be necessary to examine the raw radar data (waveforms) to understand why altimetric measurements have been deleted over the river. A possible explanation is the specular nature of the reflection over the river, leading to a Dirac-type returned waveform that on onboard tracking algorithm is unable to identify as a valid measurement. This will be the object of a future investigation.
4. Water level time series along the Amazon River 4.1. Validation of the T/P-derived water level series; comparison with in situ level gauging data Validation of the T/P time series was performed by: (1) comparison of water level time series nearby a crossover point between ascending and descending profiles, and (2) comparison with ground-based water level data from gauge stations. Crossover comparison between ascending and descending profiles is a classical method to estimate internal precision of altimetric heights over ocean surfaces. However, it must be underlined that such measurements by ascending and descending profiles occur with a 1- to 5-day lag time and river water level has evolved between theses two measurements. Applying the method to T/P data in the Amazon basin requires the crossover to be located over a wet surface. A single crossover meets (approximately) this condition. Figure 2a shows the position of the cross over nearby the Japurá River. It is not exactly located over the river, but about 10 km north on land. We thus decided to compare the water level time series at locations where the two T/P tracks cross the river (distant by about 15 km). In figure 2b are shown the two T/P water level measurement time series. They superimpose quite well. However the rms difference calculated for all 134 common measurement dates is 82 cm. This rms difference can be interpreted as the T/P instantaneous measurement mean error on this section of the Japurá River, whose high value is probably due to the narrowness of the river (500 m). For longer time spans, the mean error will be lower. If
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Figure 1. Topographic map of the Amazon basin on which are superimposed the Topex/Poseidon satellite tracks (identified by their number). The color variation along the tracks represents the backscatter coefficient (σ0 ) intensity (in dB). Water detection is supposed to correspond to color other than blue. The highest values of σ0 (red color) coincide in general with the main river and tributaries. When away from rivers, high σ0 values allow detection of flooded plains and wetlands. Yellow boxes correspond to areas selected in the present analysis. Black dots show the position of the in situ water gauges: stations of Tabatinga (box 1), Itapeua (box 3), Manacapuru and Manaus (west and east respectively; box 4) and Parintins (box 5). Figure 1. Carte du bassin amazonien et traces de Topex/Poseidon. Les couleurs le long des traces indiquent l’intensité du paramètre de rétrodiffusion du signal radar. Les cercles noirs indiquent la position des stations in situ : stations de Tabatinga (région 1), Itapeua (région 3), Manacapuru et Manaus (d’ouest en est ; région 4) et Parintins (région 5).
smoothed through a 150-day running average, the difference time series has indeed a lower rms, of 55 cm. In situ measurement comparison used data from the permanent network of more than 200 gauging stations implemented by ANEEL (Agência Nacional de Energia Elétrica, Brazil) along the main river and tributaries of the Amazon basin since the early 1970s. Full circles in figure 1 indicate positions of the gauging stations used in this study. The gauging level measurements are referred to some arbitrary reference while the T/P level measurements are relative to the ellipsoid. Two methods were developed to compare raw T/P measurements (every 10 days) with gauging stations measurements (every day). First method consists of removing to each time series its 8-year average and then comparing resulting ‘zero mean’ T/P and in situ
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time series. This method presents two drawbacks: the T/P measurement density is higher during high flow period than during low flow period, which leads to an overestimation in the mean T/P water level; the gauging station can be far from the T/P track intersection, resulting in a difference in signal amplitude (annual water level fluctuations). A second method was developed to minimize these drawbacks: it reconstructs an ‘in situ’ continuous time series of water levels on the T/P track using a linear regression between T/P measurements and the selected gauging station. This allows us comparing raw T/P series and reconstructed in-situ series. For both methods the rms error between T/P measurements and the in situ series (observed or reconstructed) quantifies the dispersion of T/P measurements and can be interpreted as the
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(a)
(b)
Figure 2. a. Map showing the position of a crossover between ascending and descending T/P track. b. Comparison of the two T/P-based water level time series at the positions identified by the white circles. The residual time series is also shown. Figure 2. a. Point d’intersection d’une trace montante et descendante au voisinage du fleuve Japura. b. Séries temporelles de niveau d’eau, mesurées par T/P aux points indiqués par les cercles blancs sur la figure 2a. La courbe des différences est aussi présentée.
(a)
(b)
Figure 3. Comparison of the T/P-derived water level time series (dots) and reconstructed time series from the in situ gauging information (solid curve). a. Track 63, box 4 (the difference time series is also shown). b. Track 139, box 5. Figure 3. Séries temporelles de niveau d’eau de l’Amazone, mesurées par T/P (points) et reconstruites à partir de mesures in situ (courbe en trait plein). a. Trace 63 (la courbe des différences est aussi présentée). b. Trace 139.
accuracy of T/P measurements. We have compared the T/P-derived and the reconstructed (i.e., based on method 2) continuous time series, at two locations: track 63 (box 4, figure 3a) and track 139 (box 5, figure 3b). Figure 3a presents the water level time series at the intersection between the Amazon River and the T/P track numbered 63, re-built from the gauging station information located 8.5 km upstream in Manaus
(solid curve), the T/P measurements (data at 1 s), and the difference time series. The computed rms of the differences is 45 cm. Except for the site of Tabatinga (290◦ E, 4◦ S; box 1), where the rms accuracy is poorer (∼ 80 cm), similar rms values are found at the other two sites: Itapeua (297◦ E, 4◦ S; box 3) and Manacapuru (299.5◦ E, 3.5◦ S; box 4). In the latter two cases, the distances between T/P track intersection and in
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Figure 4. Amazon River water level time series derived from the T/P data at two sites: track 102 and 63 (in m). Dots are T/P data at 10-day intervals spatially averaged over the river width while the solid curve is constructed by interpolation with a Spline function. Figure 4. Séries temporelles de niveau d’eau de l’Amazone, mesurées par T/P (traces 102 et 63). Les points représentent les hauteurs T/P par cycle, moyennées sur la largeur du fleuve. La courbe en trait plein est obtenue par interpolation par une fonction Spline.
situ station are 7 and 70 km, respectively. Considering the large annual amplitude of the river level variation (10 to 15 m peak to peak), a half-meter accuracy of the T/P data is very encouraging. The estimated rms accuracy is fully consistent with previous estimates made by Birkett [4] with 3.7 years of data. She reported an rms accuracy of 60 cm at three comparison sites. It is worth noting that the rms accuracy is higher over floodplains (on the order of 20 cm) than over the river (see [4]). The reason for this is unclear and currently under investigation. Figure 3b presents another example at the intersection between main river and track numbered 139. In this case, we clearly see the inadequacy of T/P in providing data in low water season. The reason for this failure is not yet understood, as the river width does not change significantly between low flow and high flow periods. Inspection of T/P waveforms may help clarifying why data are lacking in low water season at some locations. 4.2. Seasonal variations of the T/P-derived water level time series Here we present water level time series for two selected tracks (track 102, box 1 and track 63, box 4) along the main river where T/P measurements consistently cover the seasonal cycle. Figure 4 shows the T/P-derived water level time series at the T/P track intersections. Dots represent spatial averages over the river width at 10-day interval. A solid curve is obtained by interpolation with a Spline function. The curves exhibit a clear seasonal signal, mostly annual, of up to 10–15 m peak-to-peak amplitude. Maximum signal is observed from May to July,
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Figure 5. Interannual water level time series derived from the T/P data in high water season at the intersection of nine selected tracks (identified by number) (in m). Figure 5. Variations interannuelles du niveau de l’Amazone en périodes de hautes eaux pour neuf sites.
which corresponds to the maximum flood period. This high water period follows the main rainy season by about three months. The most upstream curve (box 1) presents a mostly unimodal seasonal cycle with a slight secondary maximum, occurring by the end of the year. The water level curve for track 63 (box 4) shows a single maximum, with a regular annual cycle. These results are fully consistent with information given by the gauging stations. At sites where T/P succeeds in measuring water level in low water season, the whole seasonal cycle can be correctly described. 4.3. Interannual variations To study the interannual signal, we considered the local series of maximum annual water levels, which are well detected by nine different T/P tracks along the main stream of the Amazon River. To construct the interannual time series we averaged over six successive T/P cycles, water level data on both sides of the seasonal maximum. This procedure leads to a single water level value per year. Corresponding time series are shown in figure 5. Looking at figure 5, two main features are visible: (1) there is a well marked deficit in year 1995, particularly intense in the western portion of the river, and (2) a clear deficit in water level can be observed in 1998 downstream from the confluence of the Negro and Solimões Rivers. The latter minimum can be related to the major 1997–1998 ENSO event. Indeed several studies have established that changes in atmospheric circulation over the tropical Pacific associated with ENSO cause variation in the atmospheric circulation and precipitation regime over South America, hence in Amazon River discharge [10, 13, 15]. In particular major ENSO events
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produce pronounced low discharge over the central portion of the Amazon River [21]. This will be discussed in more details in the next section.
5. Precipitation over the Amazon basin The water balance in a river basin can be described by the water budget equation [23]: ∂S/∂t = P –E–R, which links fluxes with changes in storage. R is the total discharge across the basin boundary (positive for net outflow). R is usually taken to be river discharge at the basin outlet. P is precipitation, E is evapotranspiration from the land surface and S represents the water stored in the basin, which includes vegetation moisture, soil moisture, groundwater storage and surface storage (lakes, rivers, flooded areas and snow where appropriate). t is the time. R and P are the most easily quantifiable variables in the equation above. Water level time series over the Amazon basin carry information on both superficial storage in open water bodies (lakes, rivers and flooded areas) and river discharges, as water level in a river section is related to river discharge in this section. In most river basins, and in particular in the Amazon basin, site discharge is measured directly, as frequently as possible, in order to establish and update specific level–discharge relationship (rating curve). Discharge time series are then deduced from continuously measured water level time series, using the rating curve. Unfortunately the water level time series derived from T/P altimetry cannot be expressed in terms of discharge records, due to the lack of field calibration at the corresponding sites. However, these water levels give valuable and appropriate spatial and temporal information on relative discharge variations, since at first order the rating curve can be approximated by a linear relation. On the assumption that streamflow records integrate spatial hydrological variability within the catchment basin, several studies have compared precipitation records to Amazon River discharge to detect common temporal variability patterns of climatic origin [5, 21]. In order to get some insight into the precipitation variability in the region and its possible influence on the Amazon River level, we analysed precipitation fields from Xie and Arkin [24] over the time span 1993–1999. Note that over a basin as large as the Amazon basin, stream flow results from the integrated contribution of precipitation over the catchment basin. The precipitation data set consists of monthly precipitation grids (mesh size of 2.5◦ × 2.5◦ ). To highlight both temporal and spatial variability, we applied an Empirical Orthogonal Function (EOF) analysis [19, 22] to the gridded precipitation fields. At each grid mesh, the seasonal signal (annual and semi annual cycles) has been removed in order to show up the interannual fluctuations. The EOF method is based on
a decomposition of a spatio-temporal varying field (here precipitation) into an uncorrelated linear combination of functions of the original variables, ranked by variance. The method implicitly assumes that the first few modes of the EOF decomposition define the dominant patterns of the signal. Figure 6 shows the spatial pattern and temporal evolution of the first EOF mode of precipitation in the Amazon basin. It explains 42 % of the total variance. The temporal curve exhibits a very strong minimum at the end of 1997 corresponding to the 1997–1998 ENSO. According to the spatial map, the largest rainfall deficit is observed in the north-central part of the basin, east of the Negro and Solimoes confluence. It is precisely along the Amazon River portion extending between Manaus (300◦ E, 3◦ S) and Obidos (304◦ E, 2◦ S) that the T/Pderived water levels show a marked minimum in 1998 (note that curves shown in figure 5 are based on single yearly values). The T/P-based results are in full agreement with previous observations of hydrographic parameters (water level, discharge) showing interannual variability coupled to ENSO events. These oscillations in river discharge are explained by the eastward shift of the descending branch of the zonal circulation over the equatorial Pacific during ENSO years, leading to a suppression of convection hence precipitation over the central Amazon basin [21]. The second mode (20 % of the total variance; not shown) presents an extremum in 1995 in its temporal curve, corresponding to a minimum of precipitation over the Amazon River west of longitude 305◦ E. Again, this corresponds to the observed 1995 water level minimum seen in the T/P data.
6. Discussion This study has shown that satellite altimetry, in particular the T/P mission, has the capability to monitor temporal variations in surface water level for large river basins, provided that the water area is large enough for parasite land signal not to contaminate the altimetric measurement. The main limitation of the altimetric techniques over the Amazon basin appears to be the lack of water detection during lowwater season. We have also indicated that present water height measurements are not optimised due to the fact that the wet troposphere correction is lacking. This can be improved however by collecting integrated water vapour data from outputs of operational global circulation models (or their re-analyses). Although very demanding, this would significantly improve the accuracy of continental water height measurements. Other interesting perspectives can be envisaged to the future. They concern the use of ERS1 and ERS-2 data whose spatial coverage is about
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Figure 6. First mode of the EOF decomposition of precipitation anomalies over the Amazon basin (seasonal terms removed). Top: map of the spatial pattern (amplitude in mm·d−1 ). Bottom: temporal curve for 1993–1999. Figure 6. Mode 1 de la décomposition en EOF des anomalies de précipitation sur le bassin amazonien entre 1993 et 1999 (après retrait des variations saisonnières). En haut, carte spatiale (amplitude en mm·j−1 ) ; en bas, courbe temporelle.
four times denser than T/P (although their temporal resolution is 35 days instead of 10 days). As mentioned above, altimetric heights were not provided over land. They can be computed however by retracking waveforms. We are currently analysing waveforms from the whole ERS-1 and ERS-2 missions to provide a height database over continental surfaces. This 10-year long time series will be soon ex-
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tended with the observations of ENVISAT, scheduled to be launched in early 2002 on the same orbit as ERS-1 and ERS-2, i.e., with the same spatiotemporal coverage. With such a data set, it will be possible to monitor the spatio-temporal water level variations over rivers, floodplains and wetlands, with a ground resolution of about 50 km. The applications include automatic detection of water over the
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drainage basin and systematic mapping of wet areas in low and high water seasons, the estimate of seasonal and interannual water mass change involved in surface water level fluctuations with inference on the regional water balance and hydrology, and the esti-
mate of discharge rate from altimetry-derived water levels when rating curves are established. These perspectives are not only of interest for the Amazon basin but more generally for other major river basins in the world.
Acknowledgements. This work was supported by the University of São Paulo and the Federal University of Uberlândia (Brazil), CNES, CNRS, IRD, the University of Toulouse and ANEEL through the HiBAm Programme. It received significant support from CAPES–Cofecub (Collaboration universitaire franco-brésilienne) and PNTS (Programme national de télédétection spatiale). We thank P. Vincent, F. Rémy, and N. MognardCampbell for useful discussions on the T/P data processing over land. We also thank J.-L. Guyot and N. Filizola for helpful informations about the Amazon Basin. Finally we are grateful to P. Ribstein for careful review of the original manuscript.
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