ORI GIN AL PA PER

Monitoring Continental Surface Waters by SatelliteAltimetry

Stephane Calmant Æ Frederique Seyler Æ Jean Francois Cretaux

Received: 30 March 2008 / Accepted: 9 December 2008 / Published online: 24 January 2009� Springer Science+Business Media B.V. 2009

Abstract The monitoring of continental water stages is a requirement for meeting human

needs and assessing ongoing climatic changes. However, regular gauging networks fail to

provide the information needed for spatial coverage and timely delivery. Although the

space missions discussed here were not primarily dedicated to hydrology, 18 years of

satellite altimetry have furnished complementary data that can be used to create hydro-

logical products, such as time series of stages, estimated discharges of rivers or volume

change of lakes, river altitude profiles or leveling of in situ stations. Raw data still suffer

uncertainties of one to several decimeters. These require specific reprocessing such as

waveform retracking or geophysical correction editing; much work still remains to be

done. Besides, measuring the flow velocity appears feasible owing to SAR interferometer

techniques. Inundated surfaces, and the time variations of their extent, are currently almost

routinely computed using satellite imagery. Thus, the compilation of the continuous efforts

of the scientific community in these various investigative directions, such as recording

from space the discharges of rivers or the change in water volume stored in lakes, can be

foreseen in the near future.

Keywords Satellite altimetry � Hydrology � Rivers and lakes

1 Introduction

The radar altimeters employed on altimetry mission satellites transmit a short microwave

pulse in the nadir direction, and the echo reflected by the surface is examined. The time for

the pulse to be reflected back to the altimeter corresponds to the distance (or ‘‘range’’)

travelled by the electromagnetic pulse between the satellite and the Earth’s surface,

S. Calmant (&) � J. F. CretauxLEGOS, UMR CNRS/UT3/IRD/CNES, Universite Paul Sabatier, UT3, 31400 Toulouse, Francee-mail: stephane.calmant@ird.fr

F. SeylerLMTG, UMR CNRS/UT3/IRD, Universite Paul Sabatier, UT3, 31400 Toulouse, France

123

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assuming that the pulse is propagating at the speed of light. The height H of the reflecting

surface is determined by the difference between the satellite orbit (Alt) and the altimeter

range R measurement, with the addition of various corrections that take into account the

time delays related to the propagation of the pulse through the atmosphere and the iono-

sphere and, lastly, correction for solid tidal effects on the Earth:

H ¼ Alt� Rþ DTCþWTCþ ICþ Ts½ � ð1Þ

where DTC is the Dry Tropospheric Correction, WTC the Wet Tropospheric Correction,

IC the Ionospheric Correction and Ts the solid tide correction. In Eq. (1), H is considered

with respect to a mathematical reference ellipsoid, similar to that used for satellite orbits. A

full discussion of the derivation of altimetric heights and their associated errors can be

found in Fu and Cazenave (2001). Hydrology often requires that elevation be expressed in

terms of altitudes instead of ellipsoidal height. In that case, H must be corrected for a geoid

model.

So far, not a single satellite altimetric mission has been designed primarily for moni-

toring continental waters. However, the radar altimetric missions TOPEX/Poseidon (T/P

hereafter), ERS 1 and 2, Jason, GFO and ENVISAT have registered data over the lakes and

the great river basins of the world for about 18 years. How these data could be useful to the

hydrological community, what their actual drawbacks are, how they can be resolved in

time, and how to evaluate their values for different kinds of applications, are all still open

questions. Clearly, the hydrological products derived from altimetry data can be improved,

but it may also be necessary to investigate the usual hydrological practices to make these

products more adequate for hydrology purposes.

The issues at stake concerning the continental water monitoring archive are particu-

larly important and will be reviewed in the first section of this article. From the first report

published by NASA on radar altimetry, in 1969, to the current studies involving radar

altimetry for river and wetland storage and various flow studies, the relentless efforts of

the scientific community will also be reviewed in the second section. In order to provide

the hydrologists’ community with adequate products, teams of geodesists processed such

data and developed databases accessible via the Internet; these are also presented.

Technical issues such as re-tracking, correction algorithms, multi-mission temporal series

building, and error and uncertainty estimations are also presented. Applications of interest

are discussed in the last section of this article. This article also presents the major

advantages and drawbacks of hydrometric data used in hydrology today, either in situ or

spaceborne.

2 The Issues at Stake

Water resources on the continental surface are limited, and the spatial and temporal distri-

bution of this resource does not always meet the most crucial needs. Water resources readily

available for human consumption and for ecosystems are found in lakes and rivers, corre-

spond to only 0.27% of the fresh water and about 0.007% of the total amount of water in the

world (Agencia National das Aguas, http://hidroweb.ana.gov.br/doc/WRMB/part1.htm).

The quantity and quality of the fresh water supplies will be a major problem in the coming

decades. The world consumption of fresh water reached 54% of the total stock in 1995. It is

likely to equal the total fresh water resource available in North Africa and South Asia by

2025, when Asia will be using ten times more water than the rest of the world (World

Resources Institute 2000http://www.wri.org/wri/wr).

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One billion people lack sufficient water for domestic consumption today, and it is

estimated that, in 30 years, 5.5 billion people will be living in areas with moderate to

serious water shortages (Population Reference Bureau 1997). In the 1970s, the emergence

of environmentalism, followed by the adoption of the sustainability principle, in combi-

nation with the threat of a worldwide water shortage in the years to come, has led a number

of countries to completely revise their strategies and governance tools applied to the

integrated management of water resources. The monitoring of continental surface water

stored in rivers, lakes, and wetlands has turned into the scientific hydrologist community’s

primary objective and a major concern for a majority of state organizations.

Unfortunately, our knowledge of the global dynamics of terrestrial surface waters is still

very poor (SWOT, Satellite Water Ocean Topography, http://www.geology.ohio-state.edu/

water/). Yet, studies of the integrated, global nature of the hydrological cycle are essential

to our understanding of natural climate variability and to predict a climatic response to

anthropogenic forcing (Koster et al. 1999). Most international water management groups

underline the need for core hydrological data. Nowadays, monitoring of the continental

water resource (temporal variability of river stages and river discharges) is provided via

hydrologic networks. These networks are organized on a national basis. The challenges

common to most regions include inadequate monitoring networks, gaps in records, a

general decline in the number of stations, chronic under-funding, differences in processing

and quality control, and differences in data policies (WMO 2003). Thus, major issues in the

poorer regions of the world include poor status or outright lack of monitoring networks,

support infrastructure and data quality. For example, the WMO first identified those

National Hydrological Networks in Africa that still maintain a hydrological data archive on

paper. Replies to a questionnaire sent to Hydrological Advisors in 39 countries showed that

82% use paper for archiving their data (WMO 2004), which means that they are not

available to the scientific community or the decision makers.

Maintaining a hydrological network in remote basins is a costly task. To give the

example of the Brazilian part of the Amazon basin network, managed by ANA (Agencia

Nacional de Aguas) and other federal or state entities, there is on average one limnimetric

station for each 7,200 km2, About 270 limnimetric stations are managed by ANA in the

Amazon basin, from which only 210 have been regularly gauged, and only 61% are still

operating today. The cost of this network operation is about 3 million dollars per year, a

huge expense for the country.

Moreover, the question of water resources must be analyzed in the scope of the

changing climate. River and wetland water stages are integrated measurements of the basin

response to climatic stress. The availability of raw data is a key issue here. Presently, these

measurements are much more reliable than the spatially averaged estimation of rain,

evapo-transpiration and infiltration. For example, Roads et al. (2003) compared climate

model outputs with ground-truth data over the continental United States. Using predictions

from various climate models, they found that the runoff predictions are often in error by

50%, and even mismatches with observations as high as 100% were not uncommon. Coe

(2000) found similar results for many of the world’s large river basins.

Thus, these models are now becoming limited as a result of the decline in observations

of water discharge and water storage (Alsdorf et al. 2003). As rivers, lakes and wetlands

form the main fresh water resource, there is a need for a global, homogeneous, continuing

(over many years) monitoring system which delivers fast-access data on continental water

stages and, wherever possible, on water volumes and river discharges too. We now

examine how satellite altimetry can or could bring some responses—although partial—to

the problem of monitoring water stages worldwide.

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3 Pioneering Works

In 1969, NASA established the long-term objectives of radar spatial altimetry from space,

reporting the activities of the Williamstown group on solid Earth and ocean physics (Kaula

1969). In a report on SKYLAB altimeter results, Brown (1977) analyzed the backscattering

and typical waveforms of the Salt Lake of Utah. Miller (1979) showed applications for

determining the water stages of continental lakes from measurements collected by the

GEOS-3 radar altimeter. Pioneering studies used the data collected by Seasat in 1978

(Brooks 1982) and Geosat from 1986 to 1988 (Morris and Gill 1994a), demonstrating the

feasibility of using data from ocean-designed missions for land waters. Mason et al. (1985)

conducted a study of the ability of ERS1 data to measure water stage variations in relation

to climate change.

On continental surfaces, radar altimetry left the domain of prospective studies with the

first studies dedicated to the Great Lakes in the United States, using SEASAT data (Brooks

1982), Geosat (Morris and Gill 1994a), and then T/P (Morris and Gill 1994b; Birkett

1995a). The African Great Lakes have also been the subject of various studies (Birkett

1995b; Cazenave et al. 1997; Ponchaut and Cazenave 1998; Birkett et al. 1999; Mercier

2001; Mercier et al. 2002). The first studies of great river basins began with Koblinsky

et al. (1993) who searched for specular waveforms in the GEOSAT data to estimate the

water levels on four sites on the Amazon. They estimated a 70 cm rms discrepancy

between satellite and in situ measurements, partly attributed to uncertainties in the in situ

record, but mostly due to poor modeling of the orbit. Orbit errors are decreasing with the

new generations of satellite. From the aforementioned 50 cm error estimated for GEOSAT

by Koblinsky et al. (1993), the uncertainty in the radial component of satellite orbits is now

estimated to be 15 cm for ERS-1 and 3 cm for T/P (Le Traon et al. 1995). Now, data

currently used to establish the time series of water stages over the world’s rivers and lakes

originate from the T/P—Jason series (10-day repeat period, respectively since 1992, 2002

and 2008), ENVISAT (following the ERS series started in 1991 with a 35-day repeat

period) missions and, to a lesser extent, the GFO mission. Time series of river stages may

cover a decade (Birkett 1995b, 1998; De Oliveira Campos et al. 2001; Maheu et al. 2003).

Satellite altimetry has also been used to determine the height profiles of rivers. Cudlip

et al. (1992) used SEASAT data to establish an Amazon water profile. They used 32

crossings of the river by the satellite tracks over a 18-day period in July, 1978. These

authors estimated the water level accuracy to be between 10 and 20 cm. More recently,

Birkett et al. (2002) published time series of stage fluctuations over the Amazon basin

using T/P measurements between 1992 and 1999. It was concluded that accuracy might

range from tens of centimeters to several meters (1.1 m average rms). These authors found

that the water-surface gradient of the main stem varies both spatially and temporally, with

values ranging from 1.5 cm/km downstream to 4.0 cm/km for more upstream reaches.

Overall, they demonstrated that altimetric data from the T/P mission could monitor the

transient flood waves of this continental-scale river basin.

4 Available Database for Hydrology

At present, various databases enable the retrieval of time series of water stages of the great

basins of the world. The first database came from the processing of ERS and ENVISAT at

de Montfort University, UK, on behalf of ESA. Two types of product are currently

available: the River Lake Hydrology product (RLH), designed for hydrologists with no

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prior knowledge of radar altimetry and organized in accordance with river/lake crossing

points, with each product corresponding to one crossing point, and the River Lake

Altimetry (RLA) product, for radar altimetry experts. It is structured around orbit or by 35-

day cycle and provides all crossing points for a predefined region (European Space

Agency, http://earth.esa.int/riverandlake/description.htm). Another database is Hydroweb

(http://www.legos.obs-mip.fr/soa/hydrologie/hydroweb/), created in LEGOS (Laboratoire

d’Etudes en Oceanographie et Geodesie Spatiale), Toulouse, France. It collects river

observations (*200 sites mainly using T/P data) and *150 lakes and reservoirs in the

world in a multi-mission processing of the data from ENVISAT, GFO, T/P and Jason. The

third one is the Global Reservoir and Lake Elevation Database. It is maintained by the US

Department of Agriculture (http://www.pecad.fas.usda.gov/cropexplorer/global_reservoir/).

It mainly consists of time series over lakes and reservoirs using the T/P-JASON suite.

Owing to the use of Interim data, e.g., data not fully checked and delivered by the Agencies

before the final product is available, this site is able to propose Near Real Time (NRT)

values. Yet, because it is restricted to the T/P-JASON suite, this database suffers from a

lack of measurements for many sites given the low performance of JASON-1 over con-

tinental waters. The CASH project (Contribution of Spatial Altimetry to Hydrology)

funded by the French Ministry of Research and Technology has produced a retracked T/P

database (http://www.hydrospace-cash.fr/). This product consists of a water level time

series for each river crossing for eight great basins in the world, expressed with respect to

the geoid (EGM96), with the frequency of one ‘‘mean’’ point at every overflight (i.e., every

10 days at best for Topex/Poseidon). The radar ranges used to compute water stages for

this database had their echoes being re-processed using various algorithms, called trackers.

These trackers were those already used routinely by ESA to process the ENVISAT radar

measurements.

5 Issues Affecting the Quality of Altimetry Data for Continental Watersand Error Budgets

The altimetry datasets used by land water investigators were primarily intended and pro-

cessed for other scientific purposes, namely to measure heights of the ocean surface (T/P,

Jason, GFO, part of ERS 1 and 2, and ENVISAT) or ice caps (ERS 1 and 2, and ENVI-

SAT). Using these data turns out to be problematic, in particular for rivers and to a lesser

extent for lakes. Here, we now consider the major points and methods followed by

investigators to overcome them.

The basic measurement of nadir altimetry is the two-way travel time of a pulse emitted

by the antenna onboard the satellite and bounced back by the Earth’s surface. The precision

of the measurement will then strongly depend on the capability to pick up (within the time

window) the radar echo that corresponds to the actual height of the reflector at the nadir of

the antenna. This time is to be found within the spread of the energy received at the

satellite antenna (Fig. 1). For all the current missions, the energy returns of the individual

echoes emitted at about 1–2 kHz are averaged between 10 and 18 Hz (*100 measurement

packets) to form the so-called waveforms that are distributed to the scientific community as

SDRs (Sensor Data Records). The shape of the return waveforms over rivers is highly

variable, different from the consistent return waveforms over oceans (Fig. 2). Smith (1997)

stated that the major difficulty in retrieving ranges over continental waters results from the

variability in shape of the return waveforms when onboard trackers are designed for ocean

waveforms.

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Regular ‘‘ocean-type’’ trackers expect long tail shapes of energy distribution (view A in

Fig. 3) whereas the echoes bouncing off rivers are often specular (Guzkowska et al. 1990)

or a combination of specular echoes (view B in Fig. 3). Thus, in the best case, energy is

received but the range estimate is erroneous or not estimated (views C and D in Fig. 3); in

the worst case, the altimeter loses tracking and subsequent echoes are lost. The antenna-

reflector range is determined by fitting the waveform with a predefined analytical function

(called waveform ‘‘tracking’’). If the analytical function is not well suited to the waveform

shape, this tracking leads to wrong estimates of the height value (or even no estimate at

all). It is worth noting that the radar altimetry data collected by the ongoing ENVISAT

mission are nominally retracked with four algorithms. In turn, retracking the radar

waveforms collected by the ERS 1 & 2, T/P, Jason, and GFO missions requires that some

retracking procedures be conducted. Berry et al. (2005) showed that, by retracking multi-

mission altimeter data with tuned filters, high quality height data can be obtained from the

vast majority of lakes with surface areas greater than 500 km2. Results from the Amazon

Basin also show the great improvement in both quantity and quality of data obtained. The

retracking of the T/P data with ENVISAT algorithms that has been performed by CLS, a

subsidiary of the French Space agency CNES (Centre National d’Etudes Spatiales), for the

CASH project has provided promising results in terms of accuracy improvement and

recovery of data that are missing in the Topex/Poseidon data distributed to users as Merged

Geophysical Data Records (MGDRs, Mercier and Zanife 2006). More in-depth informa-

tion about this key point of tracking algorithms can be found in Calmant and Seyler (2006)

and the references given there.

The second issue is the problem of off-nadir reflections, in particular the hooking effect.

This effect occurs when the ground area illuminated by the radar beam at the nadir of the

Fig. 1 Position of the pulse relative to the reflecting surface and energy received by the satellite. Thevertical axis is positioned at the time of emission of the pulse. The dotted line stands for the theoretical timegiving the correct two-way travel time to be used for the range (half amplitude of the peak)

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antenna is bouncing back little energy in comparison to the energy bounced back by a

water surface at the edge of the radar ground footprint. Thus, the latter is dominant in the

echo returned although lessened by the antenna pattern. The tracking procedure will then

determine the range by fitting this return of energy that traversed a skewed path. Because

the range estimate assumes that the target is at the satellite nadir, this leads to an over-

estimated range, i.e., to an underestimate of the height of the reflecting water surface. This

is not uncommon in continental waters and has to be carefully taken into account because

these measurements can be erroneously interpreted (Frappart et al. 2006a, b). The

geometry of such a mis-measurement is depicted in Fig. 4 and an example of a real case is

given in Fig. 5.

The third geometrical effect impacting accuracy is the slope effect. Indeed, within a

radar footprint a few kilometers wide, the river height can change significantly. This

changes the waveform shape and affects range determination. Also, in braided reaches,

branches may have different heights, which will spread the return time of energy and

contribute to an erroneous range determination.

The width of the radar beam of ocean-oriented missions is several kilometers since a

surface average is needed over oceans to reduce as much as possible height measurement

corruption due to wind driven waves. The drawback for hydrology studies is that echoes

over rivers whose width is less than, say, 1 km—i.e., most of them—are polluted by energy

Fig. 2 Samples of Topex waveforms over rivers in the Amazon basin. a ‘‘Ocean-like’’ waveform processedby the onboard tracker, e.g., the algorithm implemented onboard the satellite to provide a range estimate.b Multi-peak waveform rejected by the tracker. c Specular waveform processed by the tracker. d Specularwaveform, similar to (c), rejected by the tracker

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reflected by riverbanks or islets. The heterogeneity of the reflecting surface is estimated to

be the first cause of error in terms of magnitude (Rosmorduc et al. 2006).

Lastly, the air density, the amount of water vapor and the electrons in the ionosphere

modify the travel time of radar waves throughout the atmosphere. The electron content in

the ionosphere and air pressure are given by independent datasets. However, the amount of

water vapor is estimated using microwave radiometers employed onboard together with the

radar altimeters. Current microwave radiometers fail to estimate the atmospheric content of

water vapor over continents because the signature of the atmospheric water vapor is mixed

with that of ground wetness. Thus, this effect, ranging from a few centimeters to some tens

of centimeters, cannot be accurately corrected for over land waters and corrections can

only be estimated from large scale global datasets such as ECMWF or NCEP. Yet, these

Fig. 3 Waveforms over different water bodies: an example of waveforms over oceanic surfaces—showinga high similarity along the track—is presented in the left-hand figure. An example of waveforms collectedover the Amazon basin, at the confluence of the Solimoes (waters in blue in the false color Landsat image)and Negro river (waters in black in the Landsat image) is presented in the right-hand figure. This examplehighlights the high variability in the shape of the waveforms making necessary that echo retracking isperformed on a case basis. Redrawn after Mercier and Zanife (2006)

Fig. 4 Geometry of the hooking effect. When the satellite is at location A on its orbit, i.e., not yet flyingover the water body, radar energy reaches the water body on the borders of the footprint. If the energybounced back by this water body dominates the total energy returned to the satellite antenna (i.e., when theground at the nadir is poorly reflecting), the retracking procedure will estimate the two-way travel time byfitting this peak of energy, estimating a slant distance (h0) instead of a nadir one (h). The true height, i.e., theone at the nadir will be obtained only when the satellite is a location B on its orbit. The resulting along-trackheights exhibit a characteristic parabola pattern similar to the one presented in Fig. 5

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model-based corrections—although satisfactory in most cases—can suffer from large

errors, in particular in the case of mountain lakes. Mercier and Zanife (2006) have shown

that the errors due to changes in the altitude of the reflecting surface (and thus the thickness

of the atmosphere column) are not taken into account. They also demonstrated that a

computation of the atmospheric corrections such as the DTC based on the use of a Digital

Elevation Model to estimate the altitude of the reflecting surface was not adequate. They

proposed a method where the altitude of the reflecting surface is deduced from the alti-

metric measurement itself. Cretaux et al. (2008) have shown in the case of Lake Issykul-

kul that the Dry Tropospheric Corrections delivered by the T/P, ENVISAT, JASON and

GFO Geophysical Data Records (GDRs) were wrong (Fig. 6), either because the altitude of

the lake was not accounted for in the computation of the reference air pressure or because

this altitude varies erroneously within the Lake surface in the DTM used for the compu-

tation of this reference air pressure, producing an artificial variation of the correction along

the tracks crossing the lake, and hence erroneous height variations over the lake surface. In

this case, the largest difference between corrections on a given day exceeds 60 cm. Such an

error is a major issue when merging water stages derived from different altimetry missions

in order to increase the sampling frequency.

From Eq. (1), it is clear that the quality of the orbit directly enters into the error budget

of the water height. Yet, orbit modeling has dramatically improved over the last decade

(Fig. 7). Thus, in view of the aforementioned error sources, this error can be considered of

less importance for continental waters. This fact is used by web services such as

Cropexplorer to propose NRT preliminary values of water height using interim orbits,

given that the water height finally computed with a more refined orbit will not differ

significantly in regard to the overall uncertainties.

Fig. 5 Example of the hooking effect in the altimetry data (ENVISAT) over a small river in the Amazonbasin (Igarape Nelson Pinheiro, Rio Negro sub-basin). Left view (a) Map of the crossing betweenN. Pinheiro river and ENVISAT track (blue dots). The green polygon stands for the window from whichmeasurements were extracted to be plotted in the right figure. The river appears in white in the JERS mosaic,i.e., highly energetic echoes, since the water echoes are coherently double bounced off of the tree trunkssurrounding the river. Right view (b) cross section displaying where the ENVISAT passes (each black line)over the river and adjacent banks. Note that the capture of echoes bounced by the river surface before andafter the satellite passes over the river itself (green polygon) turns into a parabolic apparent reflector underthe banks on both sides of the river. The data within the green polygon are marked as red dots in the left-hand view

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Several studies have dealt with the estimation of the error budget in the altimetric series

of continental water stages. In particular, Birkett et al. (2002) compared T/P altimetric

series with in situ series. Discrepancies up to several meters have been reported in this

study. However, it should be pointed out that such comparisons suffer severe limitations.

On the one hand, the satellite track usually fails to cross the river directly over a gauging

station. Comparing both series assumes that the water stage varies identically in both

places, but this is often not true. Indeed, water stage variations along a reach are affected

by changes in the cross section geometry. Also, flow routing should be performed to

account for the delay from the location of the gauge and that of the satellite track when the

distance between both locations is significant with respect to flow velocity and sampling

rate. Typically, the delay is 1 day—e.g., the sampling rate of gauging stations—for a flow

to cover 85 km with a 1 m/s wave. Given that water stages tend to vary by several

Fig. 6 Model-derived dry tropospheric correction at lake Issyk-kul (After Cretaux et al. 2008). The pinkband stands for the range of DTC expected from the records of a meteorological station on the lake shore

Fig. 7 Improvement in orbit error from GEOS-3 in 1975 to JASON and ENVISAT in 2002

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centimeters or decimeters within a day, the error induced when the delay is not taken into

account can contribute significantly to the discrepancy between both series. On the other

hand, when the gauge is not leveled, scatter between altimetric and in situ series is

computed with reduced—zero mean series, and the resulting estimate of the discrepancy

does not take into account possible biases, such as the tracker-dependent bias. Roux et al.

(2008) investigated ways of estimating errors in virtual stations through the use of inter-

polated series, i.e., using more than one in situ gauging station and taking into account the

propagation time between the in situ and virtual gauges. They found that the random error

is roughly at the decimeter level except for low water, where the error can be *1 m or

more. This result is also consistent with the results presented by Bercher et al. (2006).

Alternatively, the accuracy of altimetric measurements can be assessed by means of a

comparison with GPS ground truth height measurements right under the satellite track.

Although this technique is widely used to assess the accuracy of altimetric data over

oceanic surfaces (Bonnefond et al. 2003, among others), this kind of fieldwork has seldom

been conducted over rivers and the results published to date, as in Frappart et al. (2006a,

b), are too limited to allow definite conclusions to be drawn as to the determination of the

error budget in altimetric series using this approach. Although intrinsic system errors of

radar measurements are the same over land waters and oceans, e.g., centimetric accuracy,

the overall uncertainty of altimetric measurements over continental waters is now a couple

of decimeters (Birkett et al. 2002; Frappart et al. 2006a, b).

6 Temporal Sampling

The time step of the altimetric series is given by the orbit repeat period. For the current

radar altimetric missions, this period ranges from 10 days for T/P and Jason to 35 days for

ERS-2 and ENVISAT. GFO has an intermediate repeat period of 17 days. In terms of

sampling rate, this is much lower than the time step of in situ measurements usually

collected once or twice a day, or even much more often, every 15 min in automated

networks of developed countries. Bercher et al. (2006) evaluated the amount of informa-

tion lost due to this under-sampling.

To improve this time step, multi-mission series can be constructed. This is often pos-

sible over large lakes that are frequently crossed by several tracks and/or several missions.

For example, JASON, ENVISAT and GFO altogether have overflown Lake Victoria about

20 times each month since 2002 (Fig. 8). Moreover, stage variations of most lake levels do

not vary significantly on a daily basis. Thus, taking into account all the available passes

should lead to a highly satisfactory time sampling of some dozens of the world’s largest

lakes. As far as rivers are concerned, the situation is more critical. In some ‘‘fortunate’’

places where satellite tracks intersect over a river reach, a multi-track ‘‘virtual station’’ can

be created. An example of such a series is given in Fig. 9. These combined series raise the

issue of the type of errors in the different data series. Indeed, the error budget of each

mission includes biases that must be accounted for prior to combining the data from

different missions.

Another method (Fig. 10) consists of interpolating satellite-based altimetry based on a

linear model exploiting data at a limited number of in situ limnimetric stations in order to

obtain time-series with a 1-day sampling period (Roux et al. 2008). The precision of

interpolation has been investigated in terms of methodology, sensitivity to model rapid

stage variations, robustness with regard to missing values and the effect of random noise.

An optimisation method based on a multi-objective criterion (OPT) furnishes the best

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absolute results, as it is the method that is least sensitive to missing values and random

noise, two factors that systematically affect radar altimetry data. Results also show that

taking into account more than one in situ reference station significantly decreases the RMS

errors in the predicted stages. Taking into account time shifts between stations improves

the results too.

Fig. 8 a Satellite tracks crosscutting the surface of Lake Victoria, including GFO, T/P and JASON,ENVISAT and ICEsat. The color-coded surface in the background are the ellipsoidal height variation due togravity used to separate the geographical and temporal variations in the altimetry measurements along thesatellite tracks. b Temporal variations. The color coding of the dots is as follows: GFO in blue, T/P in pink,JASON-1 in black, ENVISAT in red, ICEsat in yellow. The green line stands for in situ measurements

Fig. 9 Example of multi-satellite time series at a crossover formed by the tracks of the ERS and T/Pmissions over the Rio Negro. Note that the three independent series have been adjusted for their relativebiases in order to produce a consistent series

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7 Applications

A range of applications derived from the altimetric measurements of continental water

levels has been performed or is currently underway. A brief review of these applications is

now given.

7.1 Levelling of Hydrological Network Gauge Stations

A number of great river basins are located in remote geographical areas. These basins

include hydrological gauges, where topographic leveling has not been carried out or, in

some cases, leveling uncertainty is too high due to the difficulties in conducting the

conventional processes of terrestrial—spirit—leveling. By way of an example, for the

Amazon basin, most hydrological gauges from the ANA network (Brazilian National

Agency for Water) are located outside the topographic leveling routes of the IBGE

(Brazilian Institute for Geography and Statistics). Additionally, gauge stations within the

hydrographic basin of the Amazon and in the neighboring countries of Brazil are all

unleveled, apart from the Iquitos station in Peru. This is a major drawback for hydrody-

namical modeling of the basin, since these kinds of models require that the hydrographic

parameters of the river, such as bed slope, be entered into the model using a common

altitudinal reference. Cauhope (2004) leveled gauges in the Curuai Varzea and the adjacent

Amazon reach using ENVISAT time series. For the Tapajos River, a tributary of the

Amazon River, Calmant and Seyler (2004) showed that ICESat measurements are par-

ticularly well-suited for gauge leveling. Kosuth et al. (2006) established altimetric

levelling from Topex Poseidon for 97 hydrometric stations along 27,740 km of the

Fig. 10 Example of time series at an ENVISAT virtual station. The daily values are predicted using boththe ENVISAT series and readings at remote stations

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Amazon hydrographic network. Validation has been undertaken for 23 stations, comparing

altimetric data with leveling values obtained from bi-frequency GPS positioning. However,

leveling of other gauge networks worldwide still has to be conducted.

7.2 Estimation of Discharge from Stage Altimetric Measurements

Dense stage and discharge estimations have many uses. For example, discharge values are

essential for water management, extreme flow prediction, and hydrological modeling. Rain

gauges within remote watersheds are often sparsely distributed, although the spatially

averaged estimation of rain is an important parameter for Global Climate Models. As

previously pointed out, a better distribution of the integrated response of the basin to the

incoming rain would allow a better validation of these models. Better-distributed discharge

values could also be used to constrain models of weathering processes and carbon flux

estimations.

Several works have examined the ability of spatial data such as altimetry or imagery to

retrieve river discharge. Recently, Bjerklie et al. (2005) has estimated in-bank river dis-

charge on the basis of hydraulic relationships constrained with remotely sensed width

information and channel slope obtained from topographic maps. Coe and Birkett (2004)

estimated the mean monthly discharge of the Chari River at N’Djamena, Chad. They used

T/P surface water stages upstream from the gauging station calibrated with the ground-

based gauge height and discharge data using simple empirical regression techniques.

Kouraev et al. (2004) estimated discharge for the Ob river (Siberia) along two T/P tracks

crosscutting the river in the vicinity of the Salekhard gauging station. T/P measurements

were found to provide reliable water level (H) time series that could then be used to

estimate water discharge (Q) from the rating curve between H and Q at Salekhard, located

65–70 km away from the T/P tracks. Zakharova et al. (2006) used the same method to

derive discharge at T/P virtual stations of the Amazon main stream. These studies suggest

that remotely sensed river hydraulic data could be used to estimate the discharge at a

specific location directly, if nearby ground-based discharge measurements are used to

develop discharge ratings in conjunction with the remotely observed variable(s). As

pointed out by Bjerklie et al. (2005), discharge ratings developed from ground-based flow

measurements and remotely sensed hydraulic information are site specific. Leon et al.

(2008) developed a model based on a diffusion-cum-dynamic wave propagation assump-

tion, using in situ discharges and radar altimetry data to estimate rating curves at the

satellite track crossings in the Negro River Basin, Amazon. The calibration phase led to

differences of less than 4% between measured and estimated outflows and validation has

yielded less than 10% errors.

By estimating discharges by combining altimetric water stage and remote in situ dis-

charges, denser stage-discharge rating curves within a basin can be obtained. For example,

out of the 571 gauges listed by ANA, 46 are located in the Negro River sub-basin and 25

have complete records covering the last 20 years. Along the T/P tracks, water level time

series were built for 88 T/P crossings with river and floodplains (Frappart et al. 2005).

Based on altimetric heights, 3.5 times more measurement points are available in the Negro

River basin. These points are evenly distributed within the basin, making possible a

regionalization of the water fluxes. Yet, it is worth noting that these rating curves share

with the in situ curves the need for regular updates to account for possible changes in the

hydrological characteristics of the stem.

In addition, SAR interferometry appears to be the most promising technique to retrieve

surface flow velocities (Goldstein and Zebker 1987; Romeiser and Runge 2007; Romeiser

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et al. 2007 among others). Yet, this technique still suffers from technical limitations, such

as being able to estimate the velocity component only in the line-of-sight direction. Thus,

under the assumption that the velocity vector is mostly parallel to the river channel, and

that a global coverage implies that the spacecraft orbits the Earth at large inclination, the

flow velocity in many East–West trending river segments is likely to be poorly resolved.

7.3 Estimation of Spatial and Temporal Variations of Water Storage: Rivers

and Wetlands

As pointed out by Alsdorf et al. (2003), for the past 100 years our understanding of the

hydraulic characteristics and hydrological mass-balances of surface water runoff have

largely been derived from discharge measurements at in-channel gauging stations. Mea-

surement of in-channel discharge unfortunately does not provide the information necessary

for understanding flow and storage in off-river-channel environments, such as wetlands,

floodplains, and anabranches (e.g., braided channels); these environments are increasingly

recognized for their importance in the biogeochemical cycling of waterborne constituents.

Using radar interferometry, Alsdorf et al. (2001) estimated water height changes in an

Amazon lake to be about 12 ± 2.4 cm, and a volume change of 280 106 m3 during the

44 days between the two JERS images used. These authors reviewed a T/P crossing of the

lake and estimated the change in water stage to be of 20 ± 10 cm during the same period.

The T/P nadir measurements might appear less accurate than interferometry but, so far,

only these measurements offer 10-day periodic estimates over more than a decade. In an

extensive study of T/P measurements over the Amazon basin, Birkett et al. (2002) have

successfully distinguished rivers from floodplains in a number of cases. A small phase

offset of a few days in stage variations between river and nearby floodplain has occa-

sionally been observed.

Frappart et al. (2005), determined spatio-temporal variations of water volume over the

main stream and floodplain located in the Negro River basin, using area variation estimates

for a seasonal cycle captured by the Synthetic Aperture Radar (SAR) onboard the Japanese

Earth Resources Satellite (JERS-1), and changes in water level from the T/P altimetry,

combined with in situ hydrographic stations. Similarly, Frappart et al. (2006a, b) moni-

tored the flood propagation along the Mekong River by combining satellite altimetry data

and imagery. A volume variation of 331 km3 was estimated for the whole Negro sub-basin,

enhancing the complex relationship between the volume potentially stored, the inundated

area and the volume flow during the same period. Altimetry data is useful to study the

hydrological cycle of rivers. Also using the T/P data, Maheu et al. (2003) mapped the flood

propagation along strike the La Plata basin, from the Pantanal wetland to the mouth in the

South Atlantic Ocean.

7.4 Water Profiles and Geodynamical Implications

River free surface slope is an important parameter in floodwave propagation models and

sediment transport calculations. For example, slope values of a few centimeters per kilo-

meter were evaluated for the Amazon main stem using barometric estimates of elevation

performed at some gauging stations (Salati and Marques 1984; Sioli 1984; Nordin and

Meade 1986; Meade et al. 1991). Guzkowska et al. (1990) and Cudlip et al. (1992) used 15

of the 32 crossings of the Amazon River by the SEASAT altimeter to provide an estimate

of the elevation profile of the Amazon, whereas Mertes et al. (1996) and Dunne et al.

(1998) used the SEASAT falling stage measurements to calculate 14 gradient values for

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the Amazon main stem. Birkett et al. (2002) used T/P measurements along the main stem

of the Amazon to estimate the spatial and temporal variation of the gradient values.

The slope of the riverbed is an important parameter for modeling river hydrodynamics.

Recently, Leon et al. (2006a) have proposed a methodology to derive stream profiles from

the riverbed’s height and slope. This method takes advantage of the fact that altimetry data

all have a common reference, whereas in situ measurements are only referred to a local

origin, united from one gauge to the other. The height of the river bed at virtual stations is

determined as that height at which discharge vanishes in rating curves established by

combining times series of water height by satellite altimetry with discharges predicted by

routing the flow recorded at remote gauges. To give an example of the potential of

altimetric data to provide river slopes, even for rivers located in semi-arid regions, the

slope of the Godavari river is presented in Fig. 11. The Godavari river is a sacred river that

runs through India. It originates at 1,620 m above mean sea level, runs eastwards for about

1,500 km and empties into the Bay of Bengal. It runs through the Godavari graben, within

steep banks in the upper part of its course. With a mean annual discharge of 3,200 m3/s

(Singh and Swamy 2006) that ranges far below the Amazon basin with its 209,000 m3/s

mean annual discharge, the Godavari river is also an example of the capabilities of satellite

altimetry in retrieving heights for medium-size rivers, at least partly.

7.5 Hydrologic Regime of Ungauged or Poorly Gauged Basins

A very promising application of radar altimetry concerns the characterization of hydrologic

regime of poorly gauged tropical rivers. Leon et al. (2006b) have studied the 200,000 km2

basin of Rio Caqueta, the most important river basin within Colombian Amazonia. Over 32

gauging stations installed in the Rio Caqueta basin, only six stations are active today. This

point is mainly due to political problems in the FARC controlled region. Only one station,

namely Vila Betancourt, near the border of Brazil, has a full history of discharge esti-

mations in the period matching that of the altimetric data. Along the 1,270 km length, 13

virtual stations have been determined, 12 for ENVISAT crossings and only one with T/P.

Together with the river stages along the river, the discharge at the virtual station has been

estimated by hydrodynamic modelling. Hence, stage discharge relationships have been

computed, leading also to estimates of the river depth at the virtual station and to the slope

of the free surface river between virtual stations. The Caqueta river is characterized by a

hydrological regime highly variable in space and time, and an average width of about

Fig. 11 Example of height profile determined by satellite altimetry for a non-levelled river (Godavari,India)

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1.1 km at high stage. Despite these factors that impact the quality of the altimetric data,

discharges have been estimated with a mean error of only 15% and the river depth with a

mean difference of less than 1.1 m. Moreover, a 15% mean error in discharge estimates is

interesting since discharge computed using in situ measurements often has a worse

accuracy since it is extrapolated from rating curves.

7.6 Monitoring Lakes for Climate Change Assessment and the Impact of Human

Activity

The monitoring of levels by satellite altimetry is much better for lakes than it is for rivers.

As stated previously, lake surfaces can be cross cut by several missions and, if large

enough, by several tracks of some missions. Merging these measurements together enables

one to produce high sampling rate time series of the level of the Lake, as displayed in

Fig. 8 for the case of Lake Victoria. Such a procedure requires that the curvature of the

lake surface be computed beforehand in order to have a common height reference all over

the surface of the lake (Fig. 8). In the present case of Lake Victoria, the rms difference

between the altimetry series and the in situ series is 8 cm, after the mean of each series has

been removed.

The assessment of the lake water balance could provide improved knowledge of

regional and global climate change and a quantification of the human stress on water

resources across all continents, as studied by Birkett (2000) in the case of Lake Chad and

adjacent wetlands or by Mercier et al. (2001) and Birkett et al. (1999) to highlight the links

between the levels in the African Great Lakes and oceanic climatic indices. Hostetler

(1995) noted that deep and steep sided lakes are good proxies for high amplitude-low

frequency changes, while shallow water basins are better targets for rapid-low amplitude

changes. Central Asia is a place where many lakes of both types, e.g., large bodies in flat

areas such as the Aral sea and narrow bodies in steep mountainous areas such as the

Karakul and Togtogul lakes, are encountered. Very few of these lakes are monitored with

in situ data. However, many of them can be monitored by satellite altimetry (Fig. 12).

Shallow lakes are also extremely sensitive for revealing decreased water input—pos-

sibly of human origin—and rising evaporation. The Aral sea is typical of such a case and it

has been extensively studied, including using altimetry (Cretaux et al. 2005). The Aral sea

is located in an arid zone characterised by marked differences between summer and winter

temperatures and has low precipitation all year round. Evaporation is approximately ten

times greater than precipitation and the sea being maintained at equilibrium by the

inflowing waters of the Amu Darya and Syr Darya. Around 1960, a decision was made to

develop an intensive cotton and rice economy in its vicinity. In such an arid zone, irrigation

provided the means to reach the planned agricultural objectives of the Soviet Union

government. Large-scale development of ground infrastructure (irrigation channels, res-

ervoirs) started in the 1960s and the volume of water utilised for irrigation increased to

around 100 km3/year, overtaking the annual Amu Daria and Syr Daria inflows. As a result,

the level of the Aral Sea dropped by 13 m between 1960 and 1989 (Fig. 13). This led to the

split of the Aral sea into so-called Small Aral in the north and Big Aral in the south.

Satellite altimetry tracks crosscut both lakes and have measured the level variations since

1992. During that period, Big Aral shrank at a rate of 60–80 cm/year (Cretaux et al. 2005).

The corresponding decrease in surface extent and volume was 67,000 km2 and 1,083 km3

in 1960 (Bortnik 1999) to 16,000 km2 and 100 km3 in 2004 (Cretaux et al. 2005). The

difference between evaporation and precipitation for Big Aral represents an average loss of

25–30 km3/year over the last decade.

Surv Geophys (2008) 29:247–269 263

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Cretaux et al. (2005) also showed that the reduction of lake volume compared to the

hydrological budget deduced from gauges implied an underground water inflow of

5 ± 3 km3/year to the Big Aral. During the last 2 years, the level and volume of the Big

Aral has continuously shrunk with a rate as high as about 10 km3/year in water loss and

1 m/year in decrease of the sea level (Fig. 13). Since 1989, the Small Aral experienced a

different situation since it has continued to be fed by the Syr Darya River and therefore has

Fig. 12 Central Asian lakes and reservoirs monitored by satellite altimetry (http://www.legos.obs-mip.fr/soa/hydrologie/hydroweb/). The image is from a mosaic of Landsat images taken in 1990

Fig. 13 Big Aral sea volume variations taken from in situ measurements and from altimetry data (afterCretaux et al. 2005)

264 Surv Geophys (2008) 29:247–269

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dried up less than the Big Aral (Fig. 14). There are two main explanations: first, the area of

the basin is much smaller and the effect of evaporation is reduced, and, secondly, during

the years 1992 to 1999 a dam was built in the Berg’s strait in order to separate both lakes

and stop the loss of water from the Syr Darya into the desert. This dam was destroyed (and

rebuilt) three times during this period. Aladin et al. (2005) demonstrated that during the

period of 1993 to 1999 the existence of the dam contributed to the restoration of the Small

Aral. In August 2005, a solid dam, funded by World Bank, has been built in the Berg’s

Strait separating the Small and the Big Aral. It induced a 2-m increase in the sea level of

Small Aral since then and, every spring, the release of some water through open gate in the

dam has allowed the regulation the level of the Small Aral to an approximate level of 42 m

(Fig. 13).

8 Conclusions

Altimetric data offer many possibilities for the monitoring of continental waters. Among

them is the ability of tracking either in-channel fluxes or flow and storage in off-river-

channel environments, such as wetlands, floodplains, and anabranches. Also, that all the

series are naturally leveled is a great benefit of satellite altimetry with respect to in situ

gauges. These are critical for surface water balance. The development of methods to

estimate the river discharge using remotely sensed data would provide the means to

increase the streamflow measurement network globally. Typically, in situ data collection

and management activities are undertaken at the national level, where there is a need for

regionally coordinated systems and actions. Worldwide coverage and near-real time

availability of measurement is definitely another major benefit of satellite altimetry with

respect to local measurements.

Fig. 14 Variation of volume of small Aral taken from satellite altimetry (black stars) and from hydrologicalin situ measurements (orange triangles)

Surv Geophys (2008) 29:247–269 265

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The major drawback in the use of altimetric height for water stage monitoring is the

temporal sampling rate. Clearly, the 10-day period of T/P and Jason and the 35-day period

for ENVISAT cannot compete with observations made daily or twice a day at most gauges

around the world. In some applications like flood events, even more frequent data, every

few minutes, are required. On the other hand, over lakes that can be overflown by several

missions and several tracks of each mission, the time sampling can be much better, up to

three to four times per week and then compare with the frequency of in situ sampling.

Another limitation is measurement uncertainty that has been seen to vary from a few

centimeters to some meters or more in the worst cases. With respect to radar altimetry,

accuracy can to a certain extent be improved by adapting the processing of echo wave-

forms to the continental case. This uncertainty is due to the ground point target size which

ranges from kilometers for T/P to a few hundred meters for ENVISAT and 70 m for

ICESat. Roughly speaking, uncertainty decreases with the size of the footprint, as the

combination of water and vegetation or the merging of different water bodies in a single

footprint becomes less likely. When it comes to identifying and separating peaks of energy

reflected by small water bodies, the sensor and echo processing capability becomes a major

issue.

Higher resolution is foreseen for the future missions. It should reach a few hundred

meters for AltiKa, since the footprint in the Ka band radar is smaller than it is in the Ku band

and Cryosat-2 owing to footprint slicing by virtue of interferometry. Lastly, again owing to

interferometry techniques, a full coverage of the continental domain, a resolution of a few

tens of meters and a slope accuracy of 10 lrad along river courses are expected with Surface

Water Ocean Topography (SWOT), the first mission specifically dedicated to the moni-

toring of continental waters (SWOT Homepage: www.geology.ohio-state.edu/water).

The prospects offered by future missions will definitely enhance the ability of spatial

data to be included in models and change the management and monitoring of water

resources. Clearly, satellite altimetry is not likely to replace in situ measurements in the

near future, but a combination of both systems will certainly enhance our ability to monitor

the cycle of surface water from the regional scale to the global scale.

Acknowledgments The authors thank P. Bates, an anonymous reviewer and the Editor for their in-depthreviews that greatly helped to improve this article.

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