Florian Livernaux

Sidonie Laviéville

Pascale Guiffrey

Jean-Marc Campagne

Eric Leclerc

Enan$oselec$ve Aldol Reac$on with Difluoroenoxysilanes Catalyzed by Ca$onic Palladium Aqua Complexes

Keywords: fluorinated molecules, aldol reacCon, palladium catalysis, enanCoselecCvity, chiral ligands

The enanCoselecCve addiCon of difluoroenoxysilanes to aromaCc aldehydes, catalyzed by caConic palladium aqua complexes, has been described. The reacCon is operaConally simple, does not require anhydrous condiCons and performs at room temperature. The palladium catalysts, previously discovered and reported by Sodeoka, are easily prepared in two steps from PdCl2(MeCN)2. The aldol compounds were obtained in good yields and with enanComeric raCos up to 93:7, the reacCon being more efficient using electron-rich aldehydes.

Introduc/on

The fluorine rush that occurred in the past thirty years resulted in the very high occurrence of this element in bioacCve structures, since approximately 40% of agrochemicals and 25% of the drugs released on the market feature at least one fluorine atom. [1,2] This tremendous representaCon finds its origin in the fascinaCng and unique properCes of the fluorine atom (strong electronegaCvity combined with a small size) and of the C-F bond (very strong and short). [3] These features results in major modificaCons of the lipophilicity, the pKa, the metabolic stability and the binding affiniCes with a receptor, thus potenCally improving the pharmacodynamic and pharmacokineCc properCes of judiciously designed fluorinated drugs. [1,4] Such a growing importance of fluorinated molecules was therefore accompanied by a franCc research work on the development of methodologies for the preparaCon of a wide range of fluorinated building-blocks. [5] TrifluoromethylaCon, [6] difluoromethylaCon, [7] fluoromethylaCon reacCons [8] and fluorinaCon processes [9] have been thoroughly invesCgated. Among difluoromethylaCon or difluoromethylenaCon strategies, i.e. the direct introducCon of a CF2Y group, aldol-type reacCons of difluoromethylcarbonyl compounds [Y = C(O)X] have held the ahenCon of our research group and of many others. The synthesis of difluorinated aldol and Mannich products through Mukaiyama-type reacCons from difluoroenoxysilanes has indeed been long studied. [10] AlternaCve methods using more stable precursors to difluoroenolates have also been devised, such as the trifluoroacetate release strategy and other decarboxylaCve methods. [11,12] Our own contribuCon to these "non-difluoroenoxysilane" approaches relied on one-pot strategies from CF3TMS and acylsilanes or on the use of , -difluoro--trimethylsilylacetamides. [13] Some catalyCc enanCoselecCve addiCons of difluoroenolate precursors to various carbonyl-type electrophiles have been reported, [10a,10d-e,10h,11b,12a] but examples of classical aldol reacCon on standard aldehydes remain scarce (scheme 1). [11e,14] We wish to report herein a novel catalyCc enanCoselecCve addiCon of difluoroenoxysilanes onto aromaCc aldehydes, using caConic palladium aqua complexes.

Results and Discussion

First aiming for the development of a catalyCc enanCoselecCve version of the addiCon of -(trimethylsilyl)difluoroacetamides to aldehydes,[13a] we decided to examine the acCvity of the caConic palladium complexes developed by Sodeoka. These "diaquo" and " -hydroxo" complexes indeed catalyzed the addiCon of silyl enol ethers to aldehydes and imines in a highly enanCoselecCve manner. [15] This approach relies on the generaCon of a palladium enolate through acCvaCon of the O-Si bond by the H2O ligand of the pre-catalyst and the possibility of acCvaCng the C-Si bond of -(trimethylsilyl)difluoroacetamides was ahracCve. Sodeoka's catalysts were prepared according the reported procedures using either (R)-TolBINAP or (R)-DTBMSegPhos as the chiral ligand (scheme 2). [15] It should be menConed that the " -hydroxo" complex could not be prepared when using (R)-DTBMSegPhos as the ligand, the considerable steric hindrance of this bis-phosphine probably prevenCng the formaCon of a dinuclear complex.

Scheme 2. Ca.onic palladium complexes prepara.on.

These catalysts were however inacCve for the addiCon of -(trimethylsilyl)difluoroacetamides to aldehydes (scheme 2). Using cat 1 or cat 4, either in THF or in DMF and even upon heaCng, and benzaldehyde as the electrophile, no conversion to the corresponding aldol was ever observed. If the Lewis bases of an ion-pair catalysts such as TBAT were effecCve promoters for this reacCon, the H2O or OH-ligands of the caConic complexes could not trigger the C-Si bond scission and the generaCon of the palladium enolate. The combinaCon of the palladium catalyst cat 1 with a mild fluoride source (CsF or KF) results in an efficient reacCon but yielding a racemic product, presumably through a direct fluoride-

(a) Addition to acylpyridine N-oxides :

[10a] N O O R 1 R 2 F F OTMS R 3 + Cu(BF 4 ) 2 /BOX N O HO R 1 R 2 F F O R 3
up to 93% ee (b) Addition to isatins : [10h,12c] F

4 : L = (R)-TolBinap cat 5 : L = (R)-SegPhos NaOH cat 1 : L = (R)-TolBinap cat 2 : L = (R)-SegPhos cat 3 : L = (R)-DTBMSegPhos
promoted addiCon as already reported.[13a] Since such a catalyCc system was never applied to difluoroenoxysilanes, the reacCon of 1a with benzaldehyde was ahempted under similar condiCons (scheme 3). We were pleased to observe the formaCon of the aldol compound 2a in 23% yield arer 24h of reacCon (and complete consumpCon of 1a) and in an encouraging enanComeric raCo (er) of 78:22, using the aqua complex cat 1 as the catalyst in THF. The -OH complex Cat 4 induced a slower reacCon but with a sensible improvement in the enanCoselecCvity (84:16 er).

Scheme 3. Preliminary study.

These preliminary results prompted us to begin a survey of the reacCon condiCons in order to improve the yields and enanCoselecCvity of this reacCon (table 1). A first examinaCon of the solvent influence was performed using the aqua complex cat 1 as the catalyst, demonstraCng the need for a polar media since the best result was obtained in DMF (entries 1-4). It should be menConed at that point that a mixture of 2a and of its corresponding TMS ether 2a-TMS was generally obtained arer reacCon, which prompted us to perform a systemaCc acidic hydrolysis with aq. HCl in methanol before work-up. We then tried to improve the conversion in aldehyde under the same condiCons. It should be menConed that 1a was completely consumed and that the conversion to 2a was accompanied by the formaCon of ,difluoroacetophenone, the hydrolysis product of 1a. The use of a large excess of 1a and an increase of the concentraCon allowed the complete conversion of the aldehyde and 2a was obtained in 68% yield and 86:14 er (entry 5). At that point, other polar solvents (DMSO, NMO and NMP) were tested under these opCmized condiCons, bringing no improvement compared to the result in DMF (entries 6-8). A catalyst survey was then conducted and -OH complex Cat 4 only brought a much slower reacCon with no improvement in enanCoselecCvity (entry 9). Using SegPhos as the ligand, the aqua complex Cat 2 led to a decreased enanCoselecCvity (entry 10) while -OH complex Cat 5 once again slowed down the reacCon (entry 11). The best result at that point of the study was obtained using DTBMSegPhos as the ligand since catalyst cat 3 allowed the formaCon of 2a in 84% yield and 87:13 er (entries 12-13). The reacCon remained a bit slow, even under improved condiCons, and increasing the temperature only led to an increased hydrolysis of 1 and to a drop in enanCoselecCvity. We thus decided to examine the influence of addiCves that might promote the catalyst turnover and limit the extent of a background reacCon. A proCc addiCve appeared as an interesCng opCon as it might help to release the palladium catalyst arer addiCon of the palladium enolate to benzaldehyde. t-BuOH, TFE and HFIP proved to be interesCng promoters of the reacCon since the reacCon Cme was shortened to 24h and gave rise to a cleaner reacCon (entries [14][15][16][17]. The best result was obtained with HFIP and 2a was obtained in 89% yield and 89:11 er (entry 16). This addiCve was tested back using -OH complex Cat 4 as the catalyst as it gave interesCng enanCoselecCviCes in our first experiments but the enanCoselecCvity was sCll inferior to the one induced by cat 3. It should be menConed at that point that several control experiments were performed in order to assess the extent of a background reacCon promoted either by residual impuriCes of the catalyst or by the addiCves. (TolBINAP)PdCl2 and AgBF4, precursors of cat 1, were tested as catalysts for the reacCon between 1a and benzaldehyde in DMF but gave rise to only traces of 2a arer 48h. However, prolonged reacCon Cmes induced a massive hydrolysis of 1a. HFIP and TFE, used as addiCves in the improved reacCon condiCons, were tested as well and promoted a complete hydrolysis of 1a, along with less than 10% of 2a. If a racemic background reacCon remains a possible drawback of this catalyCc system, the hydrolyCc stability of 1a appears to be a much bigger issue.

Table 1. Optimization of the Aldol Reaction. [a] Entry cat x solvent time concentration additive Yield (%) [b] er [c] 1 cat 1 [a] On 0.5 mmol scale (benzaldehyde). [b] Relative to benzaldehyde. [c] Measured by chiral HPLC using a Daicel IB column eluted with n-hexane/i-PrOH 98:2. A scope of the reacCon was then performed based on the improved condiCons depicted in table 1, entry 16, using a panel of electron-rich and electron-poor aromaCc aldehydes, isaCns, as well as difluoroenoxysilanes 1b (Ar1 = 4-ClC6H4) and 1c (Ar1 = 4-MeOC6H4) (scheme 4). In contrast with Wolf's work using the trifluoroacetate release strategy, the enanCoselecCvity is strongly dependent on the subsCtuCon pahern of the aromaCc aldehyde.

[11e] The reacCon is parCcularly efficient using electron-rich aldehydes, giving rise to the highest enanCoselecCviCes. Indeed, the addiCon to benzaldehydes subsCtuted in para posiCon by electron-donaCng group (2e, 2g-i, 2r and 4a-b) or to heteroaromaCc aldehydes (2s-t) provided enanComeric raCos from 86:14 to 93:7.

[16] These levels are comparable to those obtained with Wolf's method.[11e] Despite three examples below 80:20 er (2b, 2d and 2k), all other subsCtuCon paherns yield compounds with enanComeric raCos in the 80:20 to 84:16 range, whatever the posiCon or the EDG or EWG nature of the subsCtuent. In contrast, the addiCon to a parCcularly electron-poor aromaCc aldehyde such as para-nitrobenzaldehyde led to irreproducible results and complex reacCon mixtures from which the product could only be isolated in poor yields and enanCoselecCviCes. One remarkable feature is the tolerance of the reacCon to the presence of a free hydroxy group on the aldehyde, as demonstrated by the efficient reacCon on vanillin or 4-hydroxybenzaldehyde, which yielded respecCvely 2p and 2q with good enanCoselecCviCes (respecCvely 87:13 and 91:9 er).

ReacCve ketones such as isaCn and N-methylisaCn were also tested and led to very fast reacCons but only with poor enanCoselecCviCes (3a-b), while standard ketones such as acetophenone led to a sluggish reacCon. It should be menConed at that point that a few ahempts of addiCon on aliphaCc aldehydes, cyclohexanecarboxaldehyde, or pivalaldehyde led only to degradaCon products and hydrolysis of 1a. Finally, the use 1b (Ar1 = 4-ClC6H4) as the nucleophile gave results comparable to 1a (4a-b). In contrast, the more reacCve and less stable enoxysilane 1c (Ar1 = 4-MeOC6H4) led to a faster reacCon but compounds 5a-b were obtained in poor enanComeric raCos despite good isolated yields. The reacCon was also conducted in the absence of addiCve in order to circumvent a possible background reacCon but this modificaCon only slightly improved the enanCoselecCvity.

b The absolute (S) configura.on of 2,4 and 5 was assessed by comparison of HPLC chromatograms obtained on a Chiralcel OD column for compound 2e, by comparison with Wolf's reported reten.on .mes for the same compound on the same column (see SI for details).[11e]

A plausible catalyCc cycle is depicted in scheme 5 and is inspired from the previously reported mechanism proposals (scheme 5).

[15] The generaCon of palladium enolate 1a-Pd from 1a and cat 3, releasing TMSOH and HBF4, was already demonstrated by Sodeoka. Arer addiCon of this enolate to the aldehyde, the corresponding palladium alkoxide 2-Pd can react with subtrate 1a to release the TMS-protected product 2-TMS and regenerate the catalyCc species 1a-Pd (pathway a), which is in agreement with the presence of the TMS ether in the mixture prior to acidic hydrolysis (vide supra). However, this transfer step might be sluggish and the acceleraCng effect provided by HFIP could be explained by the turnover mechanism depicted in pathway b. Indeed, HFIP could act as a proton donor to release directly alcohol 2 and the catalyCc species cat 3-HFIP, the laher being able to react with 1a to generate a new palladium enolate 1a-Pd and TMS-protected HFIP.

Conclusion

In summary, we have developed a robust, operaConally simple, enanCoselecCve addiCon of difluoroenoxysilanes to aromaCc aldehydes, catalyzed by caConic palladium aqua complexes. The reacCon is tolerant to proCc funcConal groups and efficient using hydroxy-subsCtuted benzaldehydes. The enanComeric raCos range from 76:24 to 93:7 using aromaCc aldehydes and the reacCon performs best on electron-rich substrates. In contrast, poor enanCoselecCviCes are obtained using isaCns as electrophiles. Our approach is however one of the few enanCoselecCve aldol reacCons involving difluoroenolates and standard aldehydes and, as such, a complementary approach to the range of asymmetric addiCons of fluoroenolate-type nucleophiles to carbonyl-electrophiles.

Experimental Sec/on

General Methods. Aldehydes are disClled prior use when relevant. Unless otherwise specified, all other commercial materials were used without further purificaCon. Anhydrous DMF 99.8% Acroseal® was used for the aldol reacCons. Flash column chromatography was performed with the aid of a Biotage Isolera Automated Flash Chromatography System using prepacked SNAP Ultra or Sfär cartridges (10 g). Infrared spectra were recorded with a Perkin-Elmer FT-IR Spectrum 100; absorpCons are given in wave numbers (cm-1). MelCng points were recorded on a Stuart SMP50 automaCc melCng point apparatus. 1H, 13C and 19F spectra were recorded with a Bruker Ultra shield 400 plus. 1H chemical shirs are reported in delta ( ) units in parts per million (ppm) relaCve to the singlet at 7.26 ppm for CDCl3 (residual CHCl3) or to the quintuplet at 3.31 ppm for CD3OD (undeuterated methanol). [17] 13C chemical shirs are reported in ppm relaCve to the central line of the triplet at 77.16 ppm for CDCl3 or to the heptuplet at 49.00 ppm for CD3OD. [17] 19F chemical shirs are calibraCng using C6F6 as an external standard. The mulCpliciCes are designated as s = singlet; d = doublet; t = triplet; q = quartet; quin = quintuplet; m = mulCplet; br = broad ; app = apparatus and combinaCons thereof. All coupling constants (J values) are reported in Hertz (Hz). High-resoluCon mass spectra were obtained from the Laboratoire de Mesure Physique (Montpellier university) using a Bruker MicroTof QII. All difluoroenoxysilanes 1 were prepared according to the same literature procedure. [18] All palladium complexes were prepared according to All racemic compounds were prepared from 1 and the corresponding aldehyde according to the herein reported Pd-catalyzed method but using a palladium complex made from racemic TolBINAP. [15b-d] General Procedure for the Aldol Reac/on. To a soluCon of difluoroenoxysilane 1 (1.5 mmol) and aldehyde (0.5 mmol) in DMF (1 mL) were added {[DTBMSegPhosPd(OH2)2]2+,(BF4-)2} (0.075 g, 0.05 mmol) and HFIP (0.105 mL, 1.0 mmol). The vial was flushed with argon and the reacCon mixture was sCrred for 24h at rt. Arer the reacCon was complete (TLC monitoring pentane/EtOAc 8:2), MeOH (2 mL) and 1N HCl (1 mL) were then added and the soluCon was sCrred for 20 mn. The reacCon mixture was poured in a separatory funnel containing saturated aqueous NaHCO3 (20 mL) and extracted with Et2O (3 x 10 mL). The combined organic layers were washed with water (4 x 20 mL) and brine (10 mL), dried over sodium sulfate and evaporated under reduced pressure. The crude mixture was purified by flash chromatography using a Biotage Isolera (10 g SNAP Ultra or Sfär cartridge, EtOAc/pentane mixture) to afford the Ctle compound. Compounds 2a, 2c-e, 2g, 2l and 2s were previously described by Wolf,[11e] while compounds 2t and 3a-b were previously described by Zhou.

[10g]

1 ,

 1 THF, 24h : 23%, 78:22 er cat 4, THF, 48h : 18%, 84:16 er 1a 2a (With CsF or KF as additive : racemic product)

Scheme 4 .

 4 Scheme 4. Scope of the Aldol Reac.on. Reac.on condi.ons : Ar2CHO (0.5 mmol), 1(1.5 mmol), HFIP (1 mmol), cat 3 (0.05 mmol), DMF(1 mL). The enan.omeric ra.o (er) of the purified product was determined measured by chiral HPLC using a Daicel IB, IC or AS-H column eluted with n-hexane/i-PrOH 98:2, 90:10 or 80:20. b The absolute (S) configura.on of 2,4 and 5 was assessed by comparison of HPLC chromatograms obtained on a Chiralcel OD column for compound 2e, by comparison with Wolf's reported reten.on .mes for the same compound on the same column (see SI for details).[11e]

  Scheme 5. Proposed mechanisms.

  Scheme 1. Cataly.c enan.oselec.ve addi.ons of difluoroenolates to C=O electrophiles.

Acknowledgements

The Centre NaConal de la Recherche ScienCfique (CNRS) and the Ecole NaConale Supérieure de Chimie de Montpellier (ENSCM) are gratefully acknowledged for their support.


Spatio-temporal variations of water volume over inundated areas located in a large river basin have been determined using combined observations from a multi-satellite inundation dataset, the Topex/Poseidon (T/P) altimetry satellite, and in situ hydrographic stations for the water levels over rivers and floodplains. We computed maps of monthly surface water volume change over the period of common availability of T/P and the multisatellite data (1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000). The basin of the Negro River, the largest tributary in terms of discharge to the Amazon River, was selected as a test site. A strong seasonal signal is observed with minima in October and maxima in June. A strong interannual component is also present, particularly important during ENSO years. The surface water change was estimated to be 167 ± 39 km 3 between October 1995 (low water) and June 1996 (high water). This result is consistent with previous estimates obtained for the 1995-1996 hydrological cycle over the same area using the JERS mosaic data. The surface water volume change is then compared to the total water volume change inferred from the GRACE satellite for an average annual cycle. The total water storage for the Negro River basin is almost equally partitioned between surface water and the combination of soil moisture and groundwater.

The water volume changes are also evaluated using in-situ discharge measurements and the GPCP precipitation product (correlation of 0.61). The results show the high potential for the new technique to provide valuable information to improve our understanding of large river basin hydrologic processes.

I. INTRODUCTION AND BACKGROUND

Terrestrial waters represent less than 1 % of the total amount of water on Earth.

However, they have crucial impact on terrestrial life and human needs, and play a major role in climate variability [START_REF] Cosandey | Hydrologie continentale[END_REF][START_REF] Perrier | Le cycle de l'eau et les activités au sein de l'espace rural[END_REF].

Among the various reservoirs in which fresh water on land is stored (ice caps, glaciers, snow pack, soil moisture, groundwater, etc.), surface waters (rivers, lakes, reservoirs, wetlands and inundated areas) play a crucial role in the global biochemical and the hydrological cycles [ [START_REF] Marsily | Eaux continentales[END_REF]. Even if wetlands and floodplains cover about 6 % of the Earth surface [START_REF] Oecd | Guidelines for aid agencies for improved conservation and sustainable use of tropical and sub-tropical wetlands, Organisation for Economic Cooperation and Development, Development Assistance Committee: Guidelines on Aid and Environment[END_REF], they have a substantial impact on flood flow alteration, sediment stabilization, water quality, groundwater recharge and discharge [START_REF] Maltby | Wetland management goals: wise use and conservation[END_REF]Bullock and Acreman, 2003]. Variations in the extent of inundated surfaces and wetlands also contribute to the interannual variability of methane surface emissions [START_REF] Richey | Outgassing from Amazonian rivers and wetlands as a large tropical source of atmospheric CO2[END_REF][START_REF] Bousquet | Contribution of anthropogenic and natural sources to atmospheric methane variability[END_REF]. Moreover, floodplain inundation is an important regulator of river hydrology owing to storage effects along channel reaches.

Extensive floodplains along large South American rivers, such as Amazon, Paraná or Orinoco, have a significant role in the hydrological cycle of fluvial basins. Transport of water and sediments by rivers is substantially modified during residence of river water in floodplains. During its stay in these inundation areas, river water from is not only delayed in its transit to sea and affected by evapotranspiration, but it is also often subject to large biogeochemical changes due to sedimentation, nutrient uptake by the biota, and modifications of redox conditions [Hamilton et al., 2002]. The water storage in these wetlands and its outflow represent a significant part of the water balance in the basin [START_REF] Richey | Sources and routing of the Amazon River flood wave[END_REF][START_REF] Alsdorf | Amazon floodplain water level changes measured with interferometric SIR-C radar[END_REF].

Analysis of the flow and storage of fresh water over land is thus a key issue for understanding the terrestrial branch of the global water cycle, and is now recognized to have major importance for climate research as well as for inventory and management of water resources [Bullock and Acreman, 2003]. However, given that a large number of the world floodplains and wetlands are only inundated during some portion of the year, our current knowledge of the inter-seasonal and inter-annual variability of the land surface water storage cycle at the regional to global scales is still rather incomplete [START_REF] Matthews | Wetlands in Atmospheric methane: its role in the global environment[END_REF].

Quantifying temporal variations in water volume stored in river floodplains has many practical applications, and further, would help improve understanding of the controlling mechanisms, inundation extent and their influence on the river water budget. For inundated areas permanently or temporarily connected to main channels, determining water volume variations is equivalent to estimating the potential water volume stored and/or released by a valley reach during flood stage. These water volume variations are an important variable for hydrodynamic modeling of river flow and the determination of river transport capacity. For inundated areas that never connect to the main channel, volume variations are essentially a function of baseflow variations, inputs from the local basin and rainfall. In all cases, the inundated area is a buffer zone between the river and the upland watershed, and its water volume variation represents the flood pulse of floodplains as expressed by [START_REF] Junk | The flood pulse concept in river floodplain systems[END_REF]. It is also a key biogeochemical and ecological characteristic, which unfortunately, cannot be easily measured in the field [START_REF] Alsdorf | Tracking fresh water from space[END_REF].

Until recently, estimates of floodplain volume variations within large river basins have essentially relied on hydrological models [START_REF] Coe | Long-term simulations of discharge and floods in the Amazon Basin[END_REF][START_REF] Winsemius | Comparison of two model approaches in the Zambezi River basin with regard to model reliability and identifiability[END_REF]].

In situ gauge measurements have helped to quantify the movement of water (discharge, height) in river channels, but provide comparatively little information about the spatial dynamics, height variations and volume storage of surface water, within floodplains and wetlands. In addition, some regions are completely ungauged and the number of groundbased stations has dramatically decreased during the last decade [START_REF] Alsdorf | Tracking fresh water from space[END_REF]. Lacking spatial measurements of surface water volumetric changes, hydrological models are unable to properly represent the effects of surface storage on river discharge [START_REF] Alsdorf | Water storage of the central Amazon floodplain measured with GIS and remote sensing imagery[END_REF][START_REF] Alsdorf | Spatial and temporal complexity of the Amazon flood measured from space[END_REF].

In recent years, remote sensing techniques have clearly shown the capability to monitor components of the water balance in large river basins [START_REF] Famiglietti | Remote Sensing of Terrestrial Water Storage, Soil Moisture and Surface Waters[END_REF]. They are particularly very useful to surface water hydrology investigations [START_REF] Smith | Satellite remote sensing of river inundation area, stage and discharge: a review[END_REF][START_REF] Alsdorf | Spatial and temporal complexity of the Amazon flood measured from space[END_REF] as they provide a unique mean to observe continuously large regions and are the only alternative to the lack of in situ data in remote areas. For example, satellite altimetry has been used for systematic monitoring of water levels of large rivers, lakes and floodplains (e.g., [START_REF] Birkett | Contribution of the TOPEX NASA radar altimeter to the global monitoring of large rivers and wetlands[END_REF][START_REF] Mercier | Interrannual lake level fluctuations (1993-1999) in Africa from Topex/Poseidon : connections with ocean-atmosphere interactions over the Indian Ocean[END_REF][START_REF] Maheu | Water level fluctuations in the Plata basin (South America) from Topex/Poseidon satellite altimetry[END_REF].

Synthetic Aperture Radar (SAR) Interferometry [START_REF] Alsdorf | Interferometric radar measurements of water level changes on the Amazon floodplain[END_REF][START_REF] Alsdorf | Amazon floodplain water level changes measured with interferometric SIR-C radar[END_REF] and passive and active microwave observations [START_REF] Sippel | Passive microwave observations of inundation area and the area/stage relation in the Amazon River floodplain[END_REF]] also give crucial information on land surface water dynamics. Recently, the development of a multisatellite technique to estimate water surface extent shows great potential; for example, [START_REF] Prigent | Global inundation dynamics inferred from multiple satellite observations, 1993-2000[END_REF] now offers the first global estimates of monthly inundation extents over most of a decade (1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000) and at a 0.25° horizontal resolution. In addition, new space gravity missions, such as the GRACE mission, offer for the first time the possibility of directly measuring the spatio-temporal variations of total terrestrial water storage [START_REF] Wahr | Time-variable gravity from GRACE : first results[END_REF]Tapley et al., 2004b;Chen et al., 2005a,b;[START_REF] Ramillien | Time variations of the land water storage from an inversion of 2 years of GRACE geoids[END_REF][START_REF] Seo | Terrestrial water mass load changes from Gravity Recovery and Climate Experiment (GRACE)[END_REF]Lettenmaier and Famiglietti, 2006;[START_REF] Syed | Terrestrial Hydroclimatology from GRACE and GLDAS, in revision[END_REF]. [START_REF] Frappart | Floodplain water storage in the Negro River basin estimated from microwave remote sensing of inundation area and water levels[END_REF]Frappart et al. [ , 2006a] ] estimate water storage changes using remote sensing by combining high resolution imagery-derived inundation extents and altimetry-derived water level measurements in the Negro River and Mekong basins respectively. The technique showed encouraging results, but suffered from a lack of temporal coverage, as the SAR data from JERS are only available for two months in 1995-1996. In this paper, we propose a new approach to estimate river volume changes in a large drainage basin that combines estimates of spatial and temporal surface water extent from a multi-satellite technique [START_REF] Prigent | Global inundation dynamics inferred from multiple satellite observations, 1993-2000[END_REF] with water level measurements from satellite altimetry.

The method is developed and presented as a case study over the Negro river in the Amazon basin. The results of this study will improve our understanding of large river basin hydrologic processes and modeling.

II. THE STUDY REGION

The Negro River sub-basin (700,000 km 2 , Fig. 1) occupies 12% of the Amazon basin.

It is the largest tributary to the Amazon River and ranks as the fifth largest river in the world for its water discharge [START_REF] Meade | Backwater effects in the Amazon River basin of Brazil[END_REF][START_REF] Molinier | Hydrologie du basin de l'Amazone[END_REF]. The Negro joins the Solimões River to form the Amazon River downstream from Manaus, and drains about 700,000 km² of Colombia (10%), Venezuela (6%), Guyana (2%) and Brazil (82%).

It extends from 73.25° to 59.35° longitude West and from 5.4° North to 3.35° latitude South. It is characterized by the dark color of its water, due to high content in dissolved organic matter and a low sediment load [START_REF] Sternberg | The Amazon River of Brazil[END_REF], and is a low gradient river, which partly accounts for the considerable extent of the floodplains (along with the large amount of precipitation). Rainfall in the sub-basin varies greatly both in space and time.

Mean annual precipitation rates vary by more than 50% within the Negro river basin, from less than 2000 mm/yr (minimum values less than 1700 mm/yr are recorded in the northern part of the Branco River basin), to between 2250 and 2500 mm/yr near Manaus and up to 3000 mm/yr in the northwest [START_REF] Liebmann | Interannual variability of the rainy season and rainfall in the Brazilian Amazon basin[END_REF]. The timing of the rainy season differs widely along a south to north gradient: the beginning of the rainy season occurs in December in the south and in March or April in the north, whereas the rainy period ends from May to October [START_REF] Marengo | Onset and end of the rainy season in the Brazilian Amazon basin[END_REF].

III. DATASETS

A. Multi-satellite-derived inundation dataset

The methodology developed to quantify the extent and seasonality of land surface inundation at the global scale with a suite of satellites is described in detail in Prigent et al. [2001a;2001b;2007]. The satellite observations used to derive the inundation dataset cover a large portion of the electromagnetic spectrum:

1) passive microwave emissivities at 37 GHz (0.81 cm), estimated from SSM/I by removing the contributions of the atmosphere (water vapor, clouds, rain) and the modulation by the surface temperature, using ancillary data from visible and infra-red satellite observations from the International Satellite Cloud Climatology Project (ISCCP) [START_REF] Rossow | Advances in understanding clouds from ISCCP[END_REF] and the National Center for Environment Prediction (NCEP)

reanalysis [START_REF] Kalnay | The NCEP/NCAR 40-year reanalysis project[END_REF]. The emissivity calculation is detailed in [START_REF] Prigent | Land surface microwave emissivities over the globe for a decade[END_REF].

2) ERS active microwave instrument backscattering coefficients at 5.25 GHz (5.71 cm).

3) AVHRR visible (0.58 -0.68µm) and near-infrared (0.73-1.1µm) reflectances and derived NDVI, from the NDVI AVHRR 8 km product, generated under the joint NASA and NOAA Earth Observing System Pathfinder project [START_REF] James | The Pathfinder AVHRR land data set: An improved coarse resolution data set for terrestrial monitoring[END_REF],

A full description of each satellite observation and the potential of merged satellite data to study inundated surfaces can be found in Prigent et al., [2001b].

The method to estimate surface water extent is a three-step process. First, each satellite data set is pre-processed. All the satellite observations are mapped onto an equal area grid of 0.25 o ×0.25 o resolution at the equator, chosen for consistency with global climate studies and compatibility with the other satellite spatial resolutions.

Second, an unsupervised clustering algorithm is used to merge the three sets of satellite observations. This algorithm has already shown the potential to characterize large-scale features of the density and seasonality of vegetation [Prigent et al., 2001b] and it is refined for surface water detection. A sensitivity analysis has been conducted to select an optimal subset of observations that are used as input to the clustering scheme for water surface detection. Examination of the resulting cluster maps enables the selection of the classes that are related to inundated areas [Prigent et al., 2001a].

The last step consists of quantifying the fractional inundation extent within each pixel previously classified as inundated. Detection of inundation primarily relies on the passive microwave land-surface signal from SSM/I. Inundated regions are characterized by a decrease of microwave emissivities and an increase of the emissivity polarization difference, even under dense canopy conditions. Because the ERS scatterometer shows minimal response to the presence of inundation, but is very sensitive to vegetation density [Prigent et al., 2001 b], it is used to assess vegetation contributions to the passive microwave observations. A linear mixture model that relies on the statistical relationship between polarization differences in emissivities and backscatter is used to quantify inundated area.

Global monthly-mean maps of inundation extent are created with a 0.25 o ×0.25 o spatial resolution at the equator. The technique is globally applicable without any tuning for individual environments [START_REF] Prigent | Global inundation dynamics inferred from multiple satellite observations, 1993-2000[END_REF]. Case studies for specific regions and environments are presented for India by Papa et al. [2006b],for the Ob river basin in Papa et al. [2007a] and for the large Siberian Watersheds in Papa et al. [2007b]. This dataset has been also recently used for hydrologic and climatic analyses, such as the evaluation of the methane surface emissions models [START_REF] Bousquet | Contribution of anthropogenic and natural sources to atmospheric methane variability[END_REF], the validation of river flooding scheme performances in land surface models [START_REF] Decharme | A new river flooding scheme for global climate applications Off-line validation over South America[END_REF].

B. Topex/Poseidon (T/P)-derived water levels

The T/P radar altimeter is the first dual frequency sensor of a joint French and U.S.

mission, whose goal is to substantially improve our understanding of global ocean dynamics by making accurate measurements of the ocean surface topography [START_REF] Fu | Satellite altimetry and Earth science, a handbook of techniques and applications[END_REF]. It operates in Ku and C bands, at frequencies (wavelengths) of 13.6

GHz (2.3 cm) and 5.3 GHz (5.8 cm), respectively. During its 10-day repeat cycle, T/P has provided along-track nadir measurements of Earth surface elevation (ocean and continental surfaces) between 66° latitude north and 66° latitude south since it was launched in October 1992. Radar altimetry entails vertical range measurements between the satellite and the Earth surface. The water levels are given by the difference between the satellite orbit information and the range [START_REF] Fu | Satellite altimetry and Earth science, a handbook of techniques and applications[END_REF] or altimetric height.

We use the Geophysical Data Records (standard ocean data, AVISO data base) which are commonly used for the monitoring of water levels over rivers and floodplains (e.g., [START_REF] Birkett | Contribution of the TOPEX NASA radar altimeter to the global monitoring of large rivers and wetlands[END_REF][START_REF] Birkett | Surface water dynamics in the Amazon Basin : Application of satellite radar altimetry[END_REF][START_REF] Maheu | Water level fluctuations in the Plata basin (South America) from Topex/Poseidon satellite altimetry[END_REF]. The altimetry data have been corrected for the classical geophysical and environmental corrections needed over land (see for instance Frappart et al., 2006b). The accuracy of T/P derived water levels in the Negro River basin is discussed in section IV A.

C. In-situ water level time series

The Brazilian Water Agency (Agencia Nacional de Aguas or ANA) is in charge of managing a network of 571 gauging stations in the Brazilian part of the Amazon basin (http://www.ana.gov.br). At each station, daily measurements of water stage are collected, and daily estimates of discharge are produced using rating curves, obtained from periodic (sometimes several times a year) simultaneous measures of stage and discharge. Among these 571 inventoried gauges, 46 are located in the Negro River subbasin and 25 of them have records over the last 20 years. From these 25 in-situ gauge stations, only 8 are leveled and thus can be used for this study (Fig. 1). We also used daily river discharges from Jatuarana (1 st in-situ station downstream the confluence of Solimões and Negro rivers) and Manacapuru (outlet of the Solimões River) to estimate by difference the monthly river discharge in Manaus.

D. GRACE-derived land water solutions

The GRACE mission, launched in March 2002, is devoted to measuring spatiotemporal changes in Earth's gravity field that results mainly from water mass redistribution among the surface fluid envelopes [Tapley et al., 2004b]. Several recent studies have shown that GRACE data over the continents provide important new information on the total land water storage (surface waters, soil moisture and underground waters, and where appropriate on snow mass) [Tapley et al., 2004a;[START_REF] Wahr | Time-variable gravity from GRACE : first results[END_REF]Chen et al., 2005a,b;[START_REF] Ramillien | Time variations of the land water storage from an inversion of 2 years of GRACE geoids[END_REF][START_REF] Rodell | Estimating ground water storage changes in the Mississippi river basin using GRACE[END_REF][START_REF] Schmidt | GRACE observations of changes in continental water storage[END_REF][START_REF] Yeh | Remote sensing of groundwater storage changes in Illinois using the Gravity Recovery and Climate Experiment (GRACE)[END_REF].

We use the land water and snow solutions derived from the inversion of 35 GRACE geoids from the third data release by GeoForschungZentrum (GFZ-RL03), as presented in [START_REF] Ramillien | Time variations of the land water storage from an inversion of 2 years of GRACE geoids[END_REF][START_REF] Ramillien | Time variations of the regional evapotranspiration rate from Gravity Recovery and Climate Experiment (GRACE) satellite gravimetry[END_REF]. These solutions range from February 2003 to February 2006, with a few missing months (June 2003 and January 2004) and a spatial resolution of 400 km. Studies made with previous and less accurate releases of GRACE products estimated the error on the land water storage to be 18 mm for 750 km spatial average GRACE based land water solutions [START_REF] Wahr | Time-variable gravity from GRACE : first results[END_REF] and [START_REF] Ramillien | Time variations of the land water storage from an inversion of 2 years of GRACE geoids[END_REF]2006] found an error 15 mm as the final a posteriori uncertainty on the land water solutions, with spatial resolution of 660 km.

E. Precipitation from GPCP

The Global Precipitation Climatology Project (GPCP), established in 1986 by the World Climate Research Program, provides data that quantify the distribution of precipitation over the whole globe [START_REF] Adler | The Version 2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979-Present)[END_REF]. We use here the Satellite-Gauge

Combined Precipitation Data product of GPCP Version 2 data for evaluating our estimates of monthly surface water volume variations in the Negro River basin. The GPCP products we are using are monthly means with a spatial resolution of 2.5° of latitude and longitude and are available from January 1979 to present. Over land surfaces, the uncertainty in the rate estimates from GPCP is generally lower than over the oceans due to the in situ gauge input (in addition to satellite) from the GPCC (Global Precipitation Climatology Center). Over land, validation experiments have been conducted in a variety of location worldwide and suggest that while there are known problems in regions of persistent convective precipitation, non precipitating cirrus or regions of complex terrain, the estimates uncertainties range between 10%-30% [START_REF] Adler | The Version 2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979-Present)[END_REF].

IV. METHODS

A. Water level time series derived from T/P altimetry

Along the satellite tracks shown in Fig. 1, we have considered 86 altimetry stations at which we have computed water level time-series from T/P altimetry data. These altimetry stations are identified in Fig. 1 by the white dots. The altimetry stations selected here are those which provide good quality water level time-series (according to data editing and error bars associated with each time-series -see below) and few data gaps. Most stations are located over the floodplains. Other stations correspond to intersections of the satellite tracks with the river.

To construct a water level time series, we consider all 10 Hz altimetry data along a portion of satellite track. The intersections between satellite tracks and rivers or floodplains were determined using the flooded areas identified by a classification of the JERS-1 dual mosaics [START_REF] Frappart | Floodplain water storage in the Negro River basin estimated from microwave remote sensing of inundation area and water levels[END_REF]. Once selected, the data are expressed in terms of water height (water level) above the geoid (for that purpose, the GRACE geoid GGM02C, complete to degree and order 150, has been used; [START_REF] Tapley | GGM02 -an improved Earth gravity field model from GRACE[END_REF]. For each intersection between the river (or the floodplain) and the satellite ground track, we define a so-called 'altimetry station', represented by a rectangular window. Outliers are deleted using a 3-σ criterion over the whole time span of analysis. For each 10 day cycle, the water level at a given virtual station is obtained by computing the median of all the high-rate data (10 Hz) included in the rectangular window. This process, repeated for each cycle, allows the construction of a water level time-series at the virtual station. The dispersion in L1 norm is given by the estimator known as Median Absolute Deviation:

∑ = - - = N i med i x x N x MAD 1 1 1 ) ( (1)
where N: number of observations, x i : i th observation, x med : median of the observations.

The accuracy of T/P water level time-series over river and floodplains has been discussed in several previously published papers (i.e. [START_REF] Birkett | Contribution of the TOPEX NASA radar altimeter to the global monitoring of large rivers and wetlands[END_REF][START_REF] Oliveira Campos | Temporal variations of river basin waters from Topex/Poseidon satellite altimetry ; application to the Amazon basin[END_REF][START_REF] Birkett | Surface water dynamics in the Amazon Basin : Application of satellite radar altimetry[END_REF][START_REF] Maheu | Water level fluctuations in the Plata basin (South America) from Topex/Poseidon satellite altimetry[END_REF]. For this study, the accuracy of the altimetry derived water levels over the Negro River was previously estimated by Over the western/eastern flowing parts of the river network, the T/P tracks cross the river nearly perpendicularly, allowing clear separation of the contributions of the river mainstream from those of associated floodplains (see Fig. 1). As a consequence, the interpolation in the along-track direction follows the difference of water levels between the mainstream and the floodplain. In the cross-track direction, interpolation over several tens of kilometers will only reflect the mean slope of the river.

Over the northern/southern flowing parts of the river network, the T/P tracks run parallel to the river. In these cases, depending on the choice of the geographical coordinates of the virtual station, the time series can be influenced by the elevation variation within the adjacent floodplain [START_REF] Birkett | Surface water dynamics in the Amazon Basin : Application of satellite radar altimetry[END_REF]. These authors did not report obvious amplitude or phase differences due to the inclusion of some floodplain areas. The water levels drops between the mainstream and the inundated floodplain, as reported by [START_REF] Alsdorf | Water storage of the central Amazon floodplain measured with GIS and remote sensing imagery[END_REF] using interferometric SAR observations on the Amazon floodplain (lower than 11 cm), are generally lower than the altimeter-derived water level dispersion.

C. Surface water volume

The variation of water volume corresponds to the difference of surface water levels integrated over the inundated surface. These variations δV(t i , t i-1 ), between two consecutive months numbered i and i -1, over the floodplain S, are the sum of the products of the difference of surface water levels δh j (i , i -1), with j = 1, 2, . . . inside S, by the elementary surfaces R e 2 and the percentage of inundation P j :

) sin( ) 1 , , , ( ) 1 , ( 2 j S j j j e i i h P R i i V θ λ θ δ δλδθ δ ∑ ∈ - = - (3)
where δλ and δθ are the sampling grid steps along longitude λ and latitude θ (0.22 • ), respectively, and R e the mean radius of the Earth (6378 km). The surface and total water volume variations are expressed in km 3 /month.

The error of the method was estimated using:

( ) ∑ = + = n i i i i i h d S h dS V d 1 δ δ δ (4)
where: dδV is the error on the water volume variation (δV), S i is the i th elementary surface, δh i is the i th elementary water level variation between two consecutive months, dS i is the error on the i th elementary surface, and dδh i is the error on the i th elementary water level variation between two consecutive months.

The error sources include misclassifications, T/P altimetry measurements and the linear interpolation method. The maximum error on the volume variation can be estimated as:

) ( max ) ( max max max max h S h S V δ δ δ ∆ + ∆ ≤ ∆ (5)
where: ∆(δV max ) is the maximum error on the water volume variation (δV), S max is the maximum flooded surface, δh max is the maximum water level variation between two consecutive months, ∆S max is the maximum error for the flooded surface, and ∆(δh max ) is the maximum error for the water level variation between two consecutive months.

V. RESULTS

A. Spatial distribution of inundated areas

The inundated area fractions for the Negro River basin were extracted from the [START_REF] Prigent | Global inundation dynamics inferred from multiple satellite observations, 1993-2000[END_REF] dataset. Note that lacking additional external information, the technique captures, but does not discriminate among, inundated wetlands, rivers, small lakes, irrigated agriculture. [START_REF] Prigent | Global inundation dynamics inferred from multiple satellite observations, 1993-2000[END_REF] showed that this multisatellite inundation dataset exhibits very realistic distributions, with major inundated wetlands well delineated for all latitudes and environments and are considered consistent with existing independent static inventories [Prigent et al, 2001a;2007]. Moreover, their seasonal and inter-annual variability has been evaluated over different environments and over different large river basins with respect to rain rate and snow estimates, altimeter river height levels, and were found consistent with other related hydrological variables such as in situ river discharge. The only possible independent evaluation of the accuracy at regional scale was performed by comparisons with high-resolution images from Synthetic Aperture Radar and indicated only a likely underestimation of the extent of the small wetlands (< 80 km 2 , ie ~10% of a ~800 km 2 pixel of our equal-area grid (see figure 6 and 7 of [START_REF] Prigent | Global inundation dynamics inferred from multiple satellite observations, 1993-2000[END_REF]). The seasonal and interannual variations of total inundated area in the Negro River basin are presented on Fig. 3. The maximum is observed from May to July whereas the minimum is observed from November to February. Fig. 3 shows important interannual variability with maximum inundated area varying from 57,000 km 2 (June 1996) to 42,000 km 2 (June 1997) and minimum inundated area varying from 28,000 km 2 (January 1997) to 15,700 km 2 (December 1997), while 1998 is characterized by an important peak of inundated area (53,500 km 2 ) which occurs after a year of very small inundation area. The small inundation in 1997 and large inundation in 1998 corresponds to the 1997/1998 El Niño Southern Oscillation (ENSO) event and its opposite (1998/1999 La Niña) which are associated with decreases and increases of water levels and discharge in the Negro River basin [START_REF] Guyot | La variabilité hydrologique actuelle dans le bassin de l'Amazone[END_REF]] and water storage in the whole Amazon basin [START_REF] Stuck | ENSO impact on simulated South American hydro-climatology[END_REF]].

Long-term and higher spatial resolution surveys of wetland extent over large regions are very scarce. SAR imagery can provide estimates with much better spatial resolution than the multisatellite inundation product but suffers from a lack of temporal coverage.

One study of flooding in the Amazon basin for both low-water (September-October 1995) and high-water (May-June 1996) conditions is that of [START_REF] Frappart | Floodplain water storage in the Negro River basin estimated from microwave remote sensing of inundation area and water levels[END_REF] based on 100-m resolution L-band SAR observations from the Japanese Earth Resources Satellite-1 (JERS-1). Table 1 compares our estimates with the [START_REF] Frappart | Floodplain water storage in the Negro River basin estimated from microwave remote sensing of inundation area and water levels[END_REF] results during low (October 1995) and high water (June 1996) for the Negro River. The total SAR-derived flooded area is 36,000 km 2 (159,000 km 2 ) for this 700,000 km 2 region as compared to our results of 26,000 km 2 (57,000 km 2 ) for low (high) water stage. The difference in total flooded area is larger during high water stage. With its much better spatial resolution, the SAR can more accurately estimate small areas that are flooded in generally dry conditions or small dry areas in generally flooded conditions, whereas our lower resolution observations may miss some fractional coverage. Nevertheless, due to the low sensibility of L-Band to smooth surfaces, is responsible for a loss of accuracy for

the discrimination between open water surfaces, bare soils and low vegetated areas [START_REF] Martinez | Mapping of Flood Dynamics and Spatial Distribution of Vegetation in the Amazon Floodplain Using Multitemporal Sar Data[END_REF]. Besides, sensitivity analysis showed that the classification accuracy is highly dependant on the number of images used [START_REF] Martinez | Mapping of Flood Dynamics and Spatial Distribution of Vegetation in the Amazon Floodplain Using Multitemporal Sar Data[END_REF]. The classification results used here for delineating the floodplains in the Negro River basin were obtained with only two acquisitions of SAR images (JERS-1 double mosaic), i.e., the worst configuration in terms of accuracy.

The SAR-derived product presents a greater sensitivity to small amounts of water under dense canopy cover than the multisatellite product. The inundation of the upper Negro (Cucui and Sao Felipe sub-basins) and Uaupes (Serrinha sub-basin) are underestimated by the multisatellite dataset. Without the flooded area of these three subbasins, the SAR-derived flooded areas are now 25,000 km 2 and 95,000 km 2 for low and high water stage respectively. The two estimates are almost identical for low water stage but the SAR-derived remains greater at high water stage.

Additionally, as shown in [START_REF] Prigent | Global inundation dynamics inferred from multiple satellite observations, 1993-2000[END_REF], during the low water stage, more pixels with low fractional inundation are detected by the SAR relative to our product, but also there is a tendency for our analysis to overestimate the higher fractional inundations as well. However, both of these biases are smaller during high water stage. This result may be explained by more frequent areas with small fractional inundation during low water stage, as expected, but also by the more frequent occurrence of small dry patches in areas with large fractional inundation. However, a very strict detection threshold produces a systematic overestimate, whereas a less strict threshold allows for both overestimates and underestimates, producing a small average error. Likewise, our lowerresolution product can yield a less biased estimate using a finite detection threshold as we did. We note that the SAR product still has some artifacts that have not been removed

(radar speckle, lagged gain changes) and that the boundary between flooded and dry signals is still somewhat ambiguous [START_REF] Hess | Dual-season mapping of wetland inundation and vegetation for the central Amazon basin[END_REF], as in our analysis, so there is uncertainty in the detection threshold for SAR as well.

B. Water level time series

The 86 T/P altimetry stations where water level time series can be constructed are unevenly distributed across the basin (Fig. 1). Thirty-two are located on rivers and 54 on

wetlands. An important goal of this dataset is to give valuable information on water levels This relatively large number of stations (94 encompassing 86 altimetric and 8 in-situ), distributed over river channels and wetlands, is necessary to estimate accurately water volume variations. [START_REF] Frappart | Floodplain water storage in the Negro River basin estimated from microwave remote sensing of inundation area and water levels[END_REF] reported that the precision is better on the river channels than on the inundated areas and floodplains. Minimum standard deviations of around 10 cm are measured on downstream rivers with large open water areas when the pulse emitted by the altimeter is not scattered by vegetation, whereas maximum standard deviations of around 60 cm is observed on flooded areas covered with dense vegetation.

C. Water volume variations

The monthly flood maps indicate that the flood period in the Negro River generally ranges from May to August, whereas low water period ranges from September to February. As an example for the 95-96 hydrological cycle, Fig. 5 shows maps of interpolated water levels for October 1995 and June 1996, which corresponds to the minimum and maximum respectively. ∆S max = 10% (cf. [START_REF] Prigent | Global inundation dynamics inferred from multiple satellite observations, 1993-2000[END_REF] of 60,000 km², ∆(δh max ) = 0.5 m, maximum dispersion of the altimeter measurements.

For the whole study zone, we obtained a maximum error of 39 km 3 over the period October 1995 -June 1996 for a total positive variation of 167 km 3 , that is to say an error of 23%.

Comparisons have been made with the results obtained combining T/P altimetry and JERS-1 imagery [START_REF] Frappart | Floodplain water storage in the Negro River basin estimated from microwave remote sensing of inundation area and water levels[END_REF]. The results of this comparison are presented in Table 2. The total SAR-derived flooded volume difference between low and high water stages is 320 km 3 , that is to say almost two times larger than our current results. If the sub-basins of Cucui, Sao Felipe and Serrinha are not included in the estimate for the reasons previously mentioned, the maximum storage is 220 km 3 . For the same study area, our result is 30% lower than the maximum storage variations estimated by [START_REF] Frappart | Floodplain water storage in the Negro River basin estimated from microwave remote sensing of inundation area and water levels[END_REF]. The difference of maximum of surface water storage variations between the two methods mainly come from the difference between the flooded area estimates.

We then compared the mean annual cycle of monthly surface storage variations for 1993-2000 period with the mean annual cycle of monthly changes in total water storage from GRACE for 2003-2005 (Figure 7). We notice that the average monthly GRACE- We also demonstrate the complementarity between several types of remote sensing data: multisatellite inundation dataset, water levels derived from radar altimetry and GRACE measurements of the total water storage. For the first time, a decomposition into several components of the total water storage from GRACE is shown to be possible.

These results will have implications for better monitoring the water cycle, and in particular improvements can be expected when additional data from current and future satellite missions are used. The combination of data from the present radar altimeters (T/P, ERS-1&2, Jason-1, ENVISAT RA-2) will allow better sampling of water level variations over rivers and floodplains in both time and space. The use of the future soil moisture products derived from the SMOS [START_REF] Kerr | SoilMoisture Retrieval from Space: The Soil Moisture and Ocean Salinity (SMOS) Mission[END_REF] and future soil moisture missions will allow for further decomposition of the change in total water storage into its surface water, soil moisture and remaining storage reservoirs (e.g. groundwater).

Table Captions

Table 1: Estimates of the flooded areas in the Negro River basin for October 1995 and June 1996 from the multisatellite product, a classification of JERS-1 images, the same classification without the upper Negro (Cucui and Sao Felipe sub-basins) and Uaupes (Serrinha sub-basin).

Table 2: Estimates of the water volume variations in the Negro River basin between October 1995 and June 1996 from the multisatellite product, a classification of JERS-1 images, the same classification without the upper Negro (Cucui and Sao Felipe subbasins) and Uaupes (Serrinha sub-basin).

Figure Captions

Frappart

  et al. [2005]. Minimum standard deviations of around 10 cm are measured on downstream rivers with large open water areas during high water when the pulse emitted by the altimeter is not scattered by vegetation, whereas maximum standard deviations of around 50 cm are observed on flooded areas covered with dense vegetation during low water.B. Water level mapsMonthly maps of water level can be estimated over the Negro River basin. As the temporal resolution of the inundation map is one month, we create monthly averages of the water levels for each altimetry or in situ station. For a given month during the flood season, water levels were linearly interpolated over the flooded zones of the Negro River basin. A pixel of 25 km x 25 km is considered inundated when its percentage of inundated area is greater than 0. Maps of interpolated surface water levels with 25 km resolution have been constructed for each month between January 1993 and December 2000. As mentioned above, water levels are expressed with respect to the geoid. For the purpose of hydrological interpretation of water volumes, referring to the topography would be best as far as floodplains are concerned. However, available topographic databases are not necessarily precise enough for the present study.The monthly maps of water level were produced by bilinear interpolation scheme to estimate the water level for each grid point.The upstream portion of the Negro River is orientated North/South. It flows from West to East after the confluence with the Uaupes, a West/East flowing tributary, until Manaus, the outlet of the Negro River. The Branco River, the largest tributary of the Negro River, flows North/South. Two types of cross-section are observed: the satellite crosses the river and the satellite track runs along the river. As T/P crosstrack (or intertrack) is 315 km at the Equator, large floodplain areas are not measured by the altimeter.

Fig. 2

 2 Fig.2shows the average fractional inundation in the Negro River basin for 1994. The

  for unmonitored regions of the Negro River basin such as the Uaupes River flowing from the Colombian part of the watershed, on the western side of the Negro basin (four stations on the river and one on the floodplain), the upper Negro, near the Venezuelan border and the divide with Orinoco basin (15 mainly on a large unmonitored inundated area), or the right bank of Negro River (9 stations). Examples of water level time series derived from radar altimetry are presented on Fig. 4.

Figure 6

 6 Figure6presents the differences between two consecutive months of the mean

  derived total water volume changes present almost in-phase fluctuations with the average monthly changes in surface water volume computed in the present study. The maxima and minima of the time-series are observed the same month, May and October respectively. The maximum surface water volume change represents between a third and a half of the total and the minimum roughly a half. A comparison between water volume stored in the floodplains of the Negro River basin and the water volume that flows to Manaus (outlet of the basin) was performed.The time series of monthly changes in surface water volume and monthly integrated discharge are presented in Figure8. The two time series present the same range of variations (between -80 km 3 /month and 50 km 3 /month). Nevertheless, no obvious relationship can be found between these two parameters.[START_REF] Frappart | Floodplain water storage in the Negro River basin estimated from microwave remote sensing of inundation area and water levels[END_REF] already observed this lack of clear relationship between potential storage capacity within the floodplain and integrated discharge during the flood season.

Figure 9

 9 Figure 9 compares the time series of the monthly changes in surface water volume

Figure 1 :

 1 Figure 1: Overview map of the Negro River basin in South America. The Negro River

Figure 2 :

 2 Figure 2: Extent of inundation as estimated from the multi-satellite dataset for 1994 (% of

Figure 3 :

 3 Figure 3: Monthly inundated surfaces (km 2 ) in the Negro River basin between January

Figure 4 :

 4 Figure 4: Examples of time series of water level for river (upper panel) and floodplain

Figure 5 :

 5 Figure 5: Water level maps (with reference to GGM02C geoid) for October 1995 (a) and

Figure 6 :Figure 7 :

 67 Figure 6: Variation of surface water volume change from T/P radar altimetry and multi-

Figure 8 :

 8 Figure 8: Monthly variation of surface water volume change from T/P radar altimetry and

Figure 9 :

 9 Figure 9: Monthly variation of surface water volume change from T/P radar altimetry and

Figure 10 :

 10 Figure 10: Monthly variation of surface water volume change from T/P radar altimetry

  Figure 1: 1

  Figure 3:

Figure

  Figure 6:

1 Table 1 :

 11

	Flooded area (km 2 )	Multi-satellite	JERS-1 JERS-1 Negro downstream
	Low water (Oct 95) 26,000	36,000	25,000
	High water (Jun 96) 57,000 159,000	95,000
				2

Table 2 :

 2

Acknowledgments

The authors would like to acknowledge the CTOH (Centre de Topographie des Océans et de l'Hydrosphère) at LEGOS for the provision of the Topex/Poseidon GDR dataset and the HYBAM project for the in-situ gauge measurements. The first and third authors were supported by NASA Gravimetry Recovery and Climate Experiment (GRACE) Hydrology grants.

Figure 10: