# Monitoring water surface elevation dynamics in the Brazilian Pantanal wetland using radar altimetry

Uelison Mateus Ribeiro, Samuel Corgne, Manuela Grippa, Félix Girard, Sly Wongchuig, Carolina Joana da Silva, Vitor Matheus Bacani, Mauro Henrique Soares da Silva, Frederico Gradella, Damien Arvor

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## To cite this version:

Uelison Mateus Ribeiro, Samuel Corgne, Manuela Grippa, Félix Girard, Sly Wongchuig, et al. Monitoring water surface elevation dynamics in the Brazilian Pantanal wetland using radar altimetry. Remote Sensing Applications: Society and Environment, 2025, 40, pp.101805. 10.1016/j.rsase.2025.101805. halshs-05422331

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Monitoring water surface elevation dynamics in the Brazilian
Pantanal wetland using radar altimetry

Uelison Mateus Ribeiro<sup>a,∗</sup>, Samuel Corgne<sup>b</sup>, Manuela Grippa<sup>c,‡</sup>, Félix Girard<sup>c,§</sup>,
Sly Wongchuig<sup>d,||</sup>, Carolina Joana da Silva<sup>e</sup>, Vitor Matheus Bacani<sup>f</sup>,
Mauro Henrique Soares da Silva<sup>g</sup>, Frederico Gradella<sup>h,||</sup>, Damien Arvor<sup>a</sup>

ARTICLE INFO

Dataset link: https://doi.org/10.5281/zenodo.15571469

Keywords:

Sentinel-3

Sentinel-6

Backwater flooding

Nhecolândia shallow lakes

Taquari megafan

A B S T R A C T

With its complex hydrological dynamics and high diversity of habitats, the Pantanal, a large South American wetland, is increasingly threatened by anthropogenic activities and climate change. In such vulnerable ecosystems, radar altimetry is a key source of Water Surface Elevation (WSE) measurements and, thus, fundamental for monitoring these often remote and poorly gauged environments. This study assesses the potential of the Sentinel-3 and Sentinel-6 missions to monitor hydrological dynamics in the Brazilian Pantanal (May 2016 to March 2024). We first evaluated the agreement between radar altimetry and in situ WSE and then demonstrated the contribution of these data to characterizing three distinct hydrological features across this large tropical wetland. Our results showed strong agreement between altimetry and in situ water levels, with correlation coefficients (R) greater than 0.85 and Root Mean Square Errors (RMSE) below 0.4 m in most cases. Using this extensive radar altimetry network, we demonstrated backwater flooding on the Miranda River, which experiences two annual flood cycles driven by local precipitation and the Paraguay River's flood pulse. This dynamic was disrupted by recent megadroughts. We also detected significant WSE declines in the shallow lakes of the Nhecolândia region, directly linked to the megadroughts, and revealed along-channel variations in seasonal water level patterns across the ungauged Taquari megafan, a distributive fluvial system likely subject to the combined pressures of upland agriculture and climatic extremes. These findings underscore the high potential of radar altimetry for monitoring and understanding complex hydrologic dynamics in vulnerable ecosystems like the Pantanal.

# 1. Introduction

Wetlands provide valuable ecosystem services (de Groot et al., 2016) and are critical to achieving many of the 17 goals and 169 targets of the Sustainable Development Goals (SDGs) adopted in 2015 by the United Nations (Convention on Wetlands,

<footnote><sup>a</sup> UMR LETG 6554 (CNRS, Univ Rennes 2, Univ Brest, Nantes Université), Rennes, 35000, France</footnote>

<footnote><sup>b</sup> French Institute of Pondicherry - UMIFRE 21 CNRS-MEAE/UAR 3330, Puducherry, 605001, India</footnote>

<footnote><sup>c</sup> Geosciences Environnement Toulouse (Université de Toulouse, CNRS, IRD, CNES), Toulouse, 31000, France</footnote>

<footnote><sup>d</sup> Laboratoire d'Etudes en Géophysique et Océanographie Spatiales (LEGOS), Université de Toulouse, CNES/CNRS/IRD/UT3, Toulouse, 31000, France</footnote>

<footnote><sup>e</sup> State University of Mato Grosso (UNEMAT), Research Center for Limnology, Biodiversity, and Ethnobiology of the Pantanal (CELBE), Cáceres, 78216-060, MT, Brazil</footnote>

<footnote><sup>f</sup> Federal University of Mato Grosso do Sul, UFMS/CPTL, Três Lagoas, 79610-100, MS, Brazil</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: uelison.ribeiro@univ-rennes2.fr (U.M. Ribeiro).</footnote>
2021; McInnes, 2016). They supply renewable freshwater, fish, timber and non-timber products, as well as non-consumptive amenities (e.g., bathing, fishing, leisure, and ecotourism) to numerous traditional populations (Junk et al., 2015; Millennium Ecosystem Assessment, 2005). They are essential for groundwater recharge, sediment retention, organic carbon storage, and water purification (Millennium Ecosystem Assessment, 2005). Water storage in inland wetlands (e.g., floodplains, lakes, and reservoirs) limits the impacts of downstream severe floods, while inland deltas safeguard arid areas against droughts (Junk et al., 2015). Furthermore, wetlands provide habitats for high biodiversity and genetic resources, including some of the rarest flora and fauna (Denny, 1994; Junk and Cunha, 2005; Rittenhouse and Peterman, 2016; Guerra et al., 2025).

The Pantanal is a large tropical wetland in the centre of the South American continent, with approximately 150,000 km² (Padovani, 2010). Endowed with high biodiversity, this biome is biogeographically influenced by the surrounding ecosystems of Cerrado (east), Amazonia (north), Atlantic Forest (southeast), and Chaco and Chiquitano Dry Forest (west) (Alho and Silva, 2012). Its ecosystem structure and function are determined by the inundation pattern, named flood pulse (Junk and Cunha, 2005). Waters move slowly southwards so that the flood peak takes 4 months to cross the Pantanal from north to south (Alho, 2005), where it is thus several months delayed from the peak of rainfall (Ivory et al., 2019; Ribeiro et al., 2024). As water accumulates, rivers overflow their banks during the rainy season (November–March), flooding adjacent lowlands and forming shallow lakes, swamps, and marshes (Marengo et al., 2016). The water level then depends on interannual rainfall variability and on hydraulic bottlenecks that generate upstream backwater flooding (Stevaux et al., 2020).

As the functioning of the Pantanal ecosystem is directly linked to annual and multiannual flood cycles (Alho, 2005), changes in hydrological dynamics are expected to affect wildlife diversity and distribution, as well as many wetland ecosystem services (Thielen et al., 2020). In this respect, deforestation, intensive agriculture, and river damming in the surrounding uplands have been singled out as serious threats (Alho et al., 2019; Schulz et al., 2019; Louirival et al., 2025). In addition, climate change represents another major threat to this large tropical wetland (Thielen et al., 2021; de Morais et al., 2022). Especially, recent unprecedented megadrought events (2019–2020 and 2024) have induced changes in the inter- and intra-annual flooding dynamics (Lázaro et al., 2020; Marengo et al., 2021; da Silva et al., 2022) and, combined with exceptionally dry and hot conditions, triggered extensive fire propagation (Marengo et al., 2021) in almost a third of the Pantanal (Libonati et al., 2020). As a result, the fires destroyed from cattle ranches to bridges, had severe impacts on wildlife (Garcia et al., 2021; Soranço et al., 2025), and exacerbated long-standing pressures on traditional livelihoods (da Silva et al., 2014).

In this context, monitoring continental waters is of fundamental importance. In hydrological studies, river water levels are used to qualify river dynamics, monitor and forecast floods, and design water resource infrastructure (Kittel et al., 2021). In addition, variations in water levels are measured to understand the impacts of climate change (e.g., extreme rain and drought events) and anthropogenic activities (e.g., excessive pumping) (Frappart et al., 2021). However, in situ monitoring of continental waters is very heterogeneous throughout the world, often impractical in floodplains, and increasingly scarce (Biancamaria et al., 2017; Kittel et al., 2021; Frappart et al., 2021; Normandin et al., 2018). This statement is especially valid for the Pantanal, as few tributaries of the Paraguay River have more than one gauge station after entering the wetland. For example, the Taquari River, one of the largest megafans in the world, has not had a single active in situ station in the floodplain since 1993.

In such remote and poorly gauged watersheds, satellite radar altimetry represents an important alternative to measure inland water surface elevation (WSE). Initially developed to monitor the ocean's dynamic topography, it has been used to retrieve water levels in rivers (Birkett, 1998; da Silva et al., 2010; Biancamaria et al., 2017; Kittel et al., 2021), lakes (Birkett, 1995; Medina et al., 2008; Crétaux et al., 2011, 2015; Frappart et al., 2021), and wetlands (Birkett, 1998; Papa et al., 2006; Dettmering et al., 2016; Ovando et al., 2018). Hydrological applications range from monitoring lake level changes and computing lake storage to calibration of hydraulic models and assimilation into hydrodynamic river models (Jiang et al., 2017). Improvements in altimeter sensor properties and processing techniques have enabled the monitoring of WSE on small (40–200 m wide) to medium (200–800 m wide) rivers and small lakes (<0.01 km²) (Biancamaria et al., 2017; de Fleury et al., 2023; Girard et al., 2025). For instance, Synthetic Aperture Radar (SAR) altimeters, first launched on-board CryoSat-2, are less affected by land contamination due to high along-track resolution, compared to conventional Low Resolution Mode (LRM) altimeters (Jiang et al., 2020), allowing measurements of WSE in small water bodies (Villadsen et al., 2016).

The Sentinel-3 synthetic aperture radar altimeter (SRAL) sensor is the first satellite altimeter to provide near-global coverage in SAR mode. It has a shorter intertrack interval (52 km) than traditional short-repeated missions such as Jason (315 km) and Envisat (80 km), which can provide a denser network of WSE measurements (Kittel et al., 2021). Another mission to carry an SAR altimeter is Sentinel-6. It occupies the same “reference orbit” as previous missions (i.e., TOPEX/Poseidon, Jason-1/2/3) and also carries an LRM altimeter fully backward-compatible with the measurements of its predecessors, allowing the inter-calibration of its state-of-the-art SAR observations with the historical records (Donlon et al., 2021).

However, while radar altimetry has been validated globally and is extensively used in hydrological studies (Kittel et al., 2021; Yang et al., 2022), its performance and application in the highly dynamic Pantanal ecosystem remain insufficiently explored. Previous applications in this region have been limited in scope, ranging from early TOPEX/POSEIDON studies at a single location on the Paraguay River (Birkett, 1998) to the analysis of a few lakes with Envisat and Saral (Costa et al., 2016). The only comprehensive basin-scale study to date relied on ENVISAT observations (Dettmering et al., 2016).

Given the Pantanal's status as one of the world's largest continuous wetlands and its complex flood pulse dynamics and high ecological vulnerability, improving hydrological monitoring is essential to support conservation and management efforts. Therefore, a dedicated assessment of the Sentinel-3 and Sentinel-6 constellations – and their enhanced spatial resolution and tracking capabilities – is paramount to leverage the full potential of these modern missions for monitoring the Pantanal's hydrological dynamics. To this end, the objectives of this study are twofold. First, we evaluate the accuracy of radar altimetry measurements
of water surface elevation (hereafter named RA-WSE) compared with in situ measurements (hereafter named IS-WSE). Second, we assess the contribution of radar altimetry data to improving the understanding and characterization of three key hydrological features in the Pantanal: (1) backwater effects within the Paraguay River system; (2) the impact of recent megadroughts on shallow lakes in the Nhecolândia region, a unique landscape with > 10,000 lakes; and (3) water level dynamics along the ungauged main channel of the Taquari megafan.

## 2. Study area

The Pantanal is an active sedimentary basin (McGlue et al., 2015; Warren et al., 2014) located within the western central Brazil (78%), Bolivia (18%) and Paraguay (4%) (Tomas et al., 2019). The Brazilian portion (Fig. 1) is in the Upper Paraguay River Basin (UPRB), a catchment area of 361,666 km² in the Brazilian states of Mato Grosso and Mato Grosso do Sul (Alho and Silva, 2012). Its climate is influenced by the Intertropical Convergence Zone and the South American Summer Monsoon (McGlue et al., 2015; Zhou and Lau, 1998). Annual precipitation varies from 1200–1350 mm in the north-northeast to 710–1200 mm in the south-southwestern, including 700 mm in the center of the Pantanal (Assine et al., 2015a). Rainfall is seasonal and concentrated during the austral summer. The average annual temperature is 24 °C, although it can range from –1 °C to 41 °C due to a north-to-south change in the air current between the Andes (east) and the Central Highlands of Brazil (west) (Marengo et al., 2016).

Several bedrock rivers carrying sediments from the northern and eastern plateaus become alluvial rivers within the Pantanal (Assine et al., 2015c). They drain into the Paraguay River and form low-gradient megafans through a natural avulsion (i.e., the process of significant change in the course of a river) and shift sedimentation process in a large depositional tract system (Assine and Soares, 2004). The Paraguay River is the trunk stream of the UPRB. Along this river, three hydraulic bottlenecks (Amolar, Urucum, and Fecho dos Morros) are fundamental to flood wave transmission. The floodplain narrowing, caused by litho-structural control, and the water intake of the main tributaries generate backwater flooding, in which the flood waters spread over the floodplain in the upstream direction as the flood wave reaches each bottleneck (Stevaux et al., 2020). This effect slows the flood wave and increases the water level.

The Urucum bottleneck, for example, is a narrowing of the Paraguay floodplains formed by the Urucum Mountains on the right bank and the fringe of the Miranda fluvial fan on the left (Assine et al., 2015a). Together with discharges from the Taquari Megafan, Miranda, and Negro rivers, this bottleneck creates a backwater effect that reduces the hydraulic slope on the Corumbá reach, the Miranda River, and the southern limit of the Taquari Megafan (Stevaux et al., 2020).

In addition to this backwater effect, shallow lakes are also characteristic of the Pantanal wetland. For example, the southern Pantanal region of Nhecolândia hosts more than 10,000 shallow lakes, among freshwater, brackish, and saline water lakes (Assine et al., 2015b). While freshwater lakes, which are the most numerous, are connected to ephemeral and permanent streams (locally known as *vazantes* and *corixos*, respectively) by seasonal flood waters, brackish to saline water lakes (locally known as *salinas*) are isolated from surface drainage (Assine et al., 2015c).

Finally, another notable geomorphic feature of the Pantanal is the Taquari megafan, a low sinuosity/meandering fan larger than the Okavango delta (Botswana), with approximately 250 km from its apex to its toe (Assine, 2005; Assine et al., 2005). It is characterized by a complex network of lobes and paleochannels and a very low mean gradient (~36 cm/km) (Assine, 2005). After entering the Pantanal, the Taquari River changes from a confined gorge to a distributary, forming an alluvial megafan characterized by avulsion as the primary process within the system (Assine et al., 2015b). Once an important waterway in the Pantanal wetland, the river is now shallow and has many mid-channel bars (Assine et al., 2005). Unfortunately, despite its hydrological importance and ongoing transformations, water levels in the Taquari River have not been measured since 1993, making the use of radar altimetry data essential.

## 3. Data

The Fig. 2 introduces the data and methods used in this study. Two RA-WSE datasets were considered: a ready-to-use dataset from the HYDROWEB database and a complementary dataset generated using the AlTiS software. In addition, a dataset of in situ stations was used for validation purposes, and ancillary data were also necessary to process AlTiS data and analyze hydrological features in the Pantanal.

### 3.1. *In situ water surface elevation (IS-WSE)*

Daily records of water surface elevation from 27 operational gauging stations were freely acquired from the Brazilian National Water Agency ANA (Fig. 3). The gauge stations are distributed along the Paraguay River (11) and its tributary system in the UPRB: Jaurú (1), Cuiabá (7), São Lourenço (2), Piquiri (2), Taquari (1), Miranda (1), and Aquidauana (2) rivers (Fig. 3). Two of these stations are located outside the Pantanal and were included due to their proximity to the plains. One is on the Taquari River and the other on the Aquidauana River, both on the surrounding eastern plateau. Table A.3 presents all gauge stations used in this study. Each gauge station is leveled against a local reference surface representing the zero-altitude mark at the given station. This mark is normally located below the riverbed.
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Brazilian Pantanal Wetland. (a) Urucum Bottleneck: narrow Paraguay River floodplain constrained by the Urucum Mountains (right border) and the fringe of the Miranda fluvial fan (left border); (b) Nhecolândia lakes; (c) Active lobes of the Taquari megafan. Satellite images: Sentinel-2; Tiles 21KVV (18/10/2023), 21KWV (18/10/2023), 21KXV (15/10/2023).</figcaption></figure>

## 3.2. Radar altimetry water surface elevation (RA-WSE)

Radar altimetry WSE time series are extracted at specific locations where satellite ground tracks intersect with water bodies. These locations refer to virtual stations (VSs) (Kittel et al., 2021), which represent point-based measurements along a transect.

The altimetry-based measurements of WSE used in this study were derived from Sentinel-3 and Sentinel-6 (S6) radar altimetry missions (Table 1). Sentinel-3 is a European Space Agency (ESA) mission to support the Copernicus programme. It includes Sentinel-3A (S3A, launched in February 2016) and Sentinel-3B (S3B, launched in April 2018) satellites. These satellites carry dual-frequency SRALs (Ku- and C-band) on board (Donlon et al., 2012). They are on a 98.65° inclination sun-synchronous orbit at 814.5 km altitude,
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Flowchart of the methodology, showing the data used, including the processing chain for the AlTiS dataset; the validation of Hydroweb and AlTiS datasets; and the analysis of key hydrological features in the Pantanal based on the radar altimetry water surface elevation data.</figcaption></figure>

with a 180° phase difference. The Sentinel-3 ground tracks are 52 km apart at the Equator (considering the two satellites), with a 300-m along-track resolution (in SAR mode) and a 27-day revisit interval.

Also part of the Copernicus programme framework, Sentinel-6 includes two satellites with an expected 5-year launch separation. The first satellite, named Sentinel-6 Michael Freilich, was launched on 21 November 2020. It carries the Poseidon-4 dual-frequency (Ku- and C-band) radar altimeter that provides SAR processing in Ku-band and the conventional LRM (Donlon et al., 2021). Sentinel-6 continues previous altimetry missions started in 1992 (TOPEX/Poseidon, Jason-1, Jason-2, and Jason-3), occupying a 66° inclination orbit at 1336 km altitude and with a 10-day repeat cycle. We also considered three multimission time series for Jason-2/Jason-3/Sentinel-6 (J2-J3-S6). Regarding Jason-2 and Jason-3, both are joint missions between the French National Centre for Space Studies (CNES), the National Aeronautics and Space Agency (NASA), the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), and the National Oceanic and Atmospheric Administration (NOAA). These missions operate in LRM on an equatorial ground track spacing of approximately 315 km and with a 10-day repeat cycle. Jason-2 was launched in June 2008 (remaining in its nominal orbit until 3 July 2016) and Jason-3 in January 2016. The former carries the Poseidon-3 radar altimeter, a two-frequency altimeter with Ku (13.575 GHz) and C (5.3 GHz) bands (Desjonquères et al., 2010), and the latter the Poseidon-3B radar altimeter.

In this study, we considered two datasets of RA-WSE derived from S3 and S6 missions:

* Hydroweb virtual stations refer to RA-WSE from the ready-to-use Hydroweb database, freely available at HYDROWEB. In this dataset, 88 WSE time series were found within the Pantanal wetland.

* AlTiS virtual stations refer to RA-WSE generated using Altimetry Time Series (AlTiS) software to monitor a wider variety (different types and sizes) of water bodies. AlTiS is an open-source project under the CeCill License, developed by the Centre of Topography of the Oceans and the Hydrosphere (CTOH) at the Laboratory of Space Geophysical and Oceanographic Studies (LEGOS) (Frappart et al., 2021). It processes Cryosat-2, ERS-2, ENVISAT, GFO, Jason-1/2/3, SARAL, Sentinel-3A/B, TOPEX, Sentinel-6 altimetry, and SWOT-Nadir data. The AlTiS software was downloaded at CTOH (2024a) and the radar altimetry Geographical Data Record (GDR) referenced to the WGS84 ellipsoid was provided freely by CTOH (2024b).

### 3.3. Ancillary data

In addition to the in situ and radar altimetry WSE datasets, we used three ancillary data:
<table><caption>**Table 1**
Specifications of Sentinel-3 and Sentinel-6 radar altimetry missions.</caption><thead><tr><th>Mission</th><th>Sentinel-3<br>(S3)</th><th>Sentinel-6<br>(S6)</th></tr></thead><tbody><tr><td>Space agency</td><td>ESA</td><td>ESA, NASA,<br>EUMETSAT, NOAA</td></tr><tr><td>Radar altimeter</td><td>Synthetic aperture Radar<br>Altimeter (SRAL)</td><td>Poseidon-4</td></tr><tr><td>Acquisition mode</td><td>SAR</td><td>SAR & LRM</td></tr><tr><td>Frequency (GHz)</td><td>13.575 (Ku)<br>5.41 (C)</td><td>13.575 (Ku)<br>5.41 (C)</td></tr><tr><td>Repeat Cycle (days)</td><td>27</td><td>10</td></tr><tr><td>Intertrack Spacing</td><td>52 km<br>(S3A and S3B combined)</td><td>315 km</td></tr><tr><td>Orbit inclination</td><td>98.65° with a 180° phase<br>difference<br>between the two satellites</td><td>66°</td></tr><tr><td>Altitude</td><td>814.5 km</td><td>1336 km</td></tr><tr><td>Along-track Resolution</td><td>300 m (SAR)</td><td>300 m (SAR)</td></tr><tr><td>Launch date</td><td>Feb 2016 (S3A)<br>Apr 2018 (S3B)</td><td>Nov 2020</td></tr></tbody></table>

* **Sentinel ground tracks:** Kml files of Sentinel-3A/B and Sentinel-6 ground tracks are available at ESA and AVISO, respectively.

* **Map of water bodies:** Shapefile of the Pantanal water bodies (rivers, lakes, dams, and reservoirs) produced by the Brazilian National Institute for Space Research (INPE) and available at INPE. The dataset was created through visual interpretation of Landsat-8/OLI imagery (R(5)G(6)B(4) composite, 30 m resolution) at a 1:75,000 scale, with a minimum mapping unit of 1 hectare.

* **Rainfall data:** Monthly precipitation at Miranda station (station code: 2056001, available at ANA)

# 4. Methods

The methodological approach of this study consists of three steps (Fig. 2). First, ready-to-use virtual stations were compiled from the Hydroweb database for the Pantanal, and water surface elevations were extracted at additional relevant locations using the AlTiS software. Second, the RA-WSE data from both sources were statistically validated against in-situ stations. Third, these datasets were analyzed to assess their potential to monitor three key hydrological features within the Pantanal.

## 4.1. Processing of AlTiS virtual stations

Whereas WSE time series from Hydroweb stations were acquired ready-to-use, building the AlTiS virtual station dataset required a 2-step processing of raw radar altimetry data with the AlTiS software.

First, virtual stations were located by overlaying Sentinel ground tracks with the Pantanal map of water bodies. To account for the ±1 km cross-track orbital shift of altimetry satellites (Cretaux et al., 2018), a 1 km buffer was applied around the Sentinel tracks. In total, 80 virtual stations were selected throughout the Pantanal wetland, covering various water bodies (rivers, lakes, and ephemeral streams) of varying sizes.

Second, the RA-WSE time series were generated for each station through a multi-step process. The GRD data were initially loaded into AlTiS alongside a given cross-section file to delineate a coarse VS. The data were then filtered with backscattering coefficient thresholds of 40 (for pre-January 2020 data) and 20 dB (post-January 2020 data) (de Fleury et al., 2023; Girard et al., 2025), followed by an empirical waveform peakiness threshold of 20 (unitless) to reject multi-peak waveforms or dry lake returns (Girard et al., 2025). In some cases (particularly concerning Sentinel-6 measurements), remaining outliers exhibiting deviations of several meters were also removed based on visual inspection. Finally, RA-WSE time series were generated by computing, for each repeat cycle (i.e., for each time the satellite passes over a given location), the median and mean values and their respective median absolute deviation and standard deviation. To minimize the effect of potential residual outliers that may occur due to mixing of backscatter sources (multiple lakes, wet shores, etc.) on the SAR footprint, particularly in the WSE observations of small lakes, the median values of each cycle were used (Frappart et al., 2021).

## 4.2. Statistical validation of RA-WSE vs IS-WSE

Radar altimetry WSE time series at Hydroweb and AlTiS stations were compared with in situ gauge stations. These comparisons were performed between the in situ gauge and the VS on the same river when located within 100 km of each other. To minimize vertical bias, the time series were normalized at mean zero for each comparison by subtracting the mean values of the matching
<figure><img src="image_6.png" /><figcaption>Fig. 3. Distribution of virtual (Sentinel-3A/B, Sentinel-6, Jason-2/Jason-3/Sentinel-6) and in situ stations in the Brazilian Pantanal wetland.</figcaption></figure>

records (Kittel et al., 2021), as satellite and ground data rely on different elevation references. Comparisons were performed by
calculating Pearson's correlation coefficient (R), root mean square error (RMSE), and relative RMSE (RRMSE, computed by dividing
the RMSE by the water level range of the in situ station during the observation period) (Normandin et al., 2018; Frappart et al.,
2021). For validation purposes, only comparisons between virtual and in situ stations within 100 kilometers and on the same river
were considered. This procedure reduced the number of virtual stations eligible for validation to 63 Hydroweb and 17 AlTiS stations.
In cases with more than one gauge station within 100 km of a VS, only the nearest was retained. The R, RMSE, and RRMSE were
also calculated between in situ stations on the same river and within a radius of 100 km to provide a reference for the variations
between stations due to local differences in the morphological aspects of the rivers.

4.3. *Description of water level dynamics within the Pantanal*

We assessed the potential of RA-WSE to monitor three specific hydrological features.

* Backwater flooding in the Miranda River: We investigated the effect of backwaters on the dynamics of the Miranda River. Although WSE in the Miranda River is known to respond rapidly to precipitation (Padovani, 2010), its dynamics are also
<table><caption>**Table 2**
Spatial distribution of in situ stations and radar altimetry (RA) virtual stations (VS) in the Pantanal wetland.</caption><thead><tr><th></th><th>In situ<br>stations</th><th>Hydroweb<br>VS</th><th>AlTiS<br>VS</th><th>RAVS<sup>b</sup></th></tr></thead><tbody><tr><td><strong>Number of (virtual) stations (total)</strong></td><td>27</td><td>88</td><td>80</td><td>151</td></tr><tr><td>Paraguay River (PR)</td><td>11</td><td>25</td><td>8</td><td>27</td></tr><tr><td>Cuiabá River</td><td>7</td><td>10</td><td>4</td><td>12</td></tr><tr><td>Taquari River</td><td>1<sup>a</sup></td><td>9</td><td>2</td><td>10</td></tr><tr><td>São Lourenço River</td><td>2</td><td>8</td><td>1</td><td>8</td></tr><tr><td>Piquiri River</td><td>2</td><td>7</td><td>1</td><td>7</td></tr><tr><td>Miranda River</td><td>1</td><td>5</td><td>2</td><td>5</td></tr><tr><td>Aquidauana River</td><td>2<sup>a</sup></td><td>5</td><td>1</td><td>5</td></tr><tr><td>Other rivers</td><td>1</td><td>18</td><td>8</td><td>23</td></tr><tr><td>Lakes</td><td>0</td><td>1</td><td>39</td><td>40</td></tr><tr><td>Floodplains</td><td>0</td><td>0</td><td>14</td><td>14</td></tr><tr><td><strong>Tributaries of the PR measured</strong></td><td>8</td><td>16</td><td>14</td><td>21</td></tr></tbody></table>

influenced by the “damming” of the Paraguay River at the Urucum bottleneck. Indeed, Paraguay waters flow back towards the Miranda River in the flood peak period of the Paraguay River, which occurs in the dry season in this reach. To assess these relationships, we analyzed RA-WSE time series downstream in the Miranda River (S3A acquisitions) and the Paraguay River (near Ladário, J2-J3-S6 acquisitions) for the period between May 2016 and March 2024. In addition, we calculated cross-correlation functions (CCFs) (Venables and Ripley, 2002; Ripley, 2002) between the monthly mean WSE at the two VSs and the monthly precipitation at Miranda station to assess lead-lag relationships. The analysis was performed using the ccf2() function from the astsa package in R v.4.2.3 software. Standard linear detrending of the time series was performed before cross-correlation.

* **Megadrought effects on Nhecolândia shallow lakes:** We compared the WSE variations before and during the recent megadrought event initiated in 2019 at five AlTiS virtual stations corresponding to five shallow lakes in Nhecolândia. Each period comprises three hydrological years, i.e., from July 2016 to June 2019 and from July 2019 to June 2022.

* **WSE dynamics along the ungauged Taquari River:** We evaluated the potential of Hydroweb stations derived from Sentinel-3A acquisitions to monitor water level patterns along the Taquari River during the 2018–2020 period. Although this river is the main channel of the Taquari megafan and an important tributary of the Paraguay River, in situ monitoring within the Pantanal lowlands was discontinued in the early 1990s.

All WSE time series processing and analyses were performed using standard functions in R v.4.2.3, including time series manipulation using *xts*, statistical analysis using *metrica*, and spatial processing using the *terra* and *sf* packages. Final maps were prepared in QGIS Desktop 3.30.0.

# 5. Results

## 5.1. Spatial distribution of virtual stations

The virtual stations provided a comprehensive source of water level data across the Pantanal wetlands (Table 2; Fig. 3). The combined dataset comprises 151 unique virtual stations after reconciling duplicates between the 88 VSs from the Hydroweb database and the 80 VSs generated by the AlTiS software at the intersections of Sentinel ground tracks and water bodies. This extensive RA-WSE network complements the 27 operational in situ stations within the Pantanal wetland and its surrounding plateaus.

Most Hydroweb stations were located on the Paraguay (25 VSs or 28.4%), Cuiabá (10), Taquari (9), São Lourenço (8), Piquiri (7), Miranda (5), and Aquidauana (5) rivers. Ten other tributaries, including ephemeral streams, had at least one VS. Furthermore, one VS was on a lake. The AlTiS stations were complementary to the Hydroweb dataset and contributed to increasing the variety of types and sizes of monitored water bodies, including lakes (48.75%) ranging from 0.08 to 45.8 km², rivers (33.75%) from 32 to 431 m wide, as well as floodplains and ephemeral streams (17.5%).

## 5.2. Statistical validation of RA-WSE

Due to spatial inclusion criteria (same river and within 100 km of in situ stations), 63 Hydroweb and 17 AlTiS stations were retained for statistical validation. Both Hydroweb and AlTiS stations showed consistent agreement with in situ measurements. For instance, the R values between Hydroweb stations and nearby in situ stations located on the same river were greater than 0.85 in 84.12% of comparisons, with a median RMSE of 36 cm (for river water levels ranging from 1.00 to 6.70 m) and median RRMSE of 10.54% (Fig. 4). Similarly, high correlations (R > 0.85) were also observed in all but two comparisons between AlTiS and in situ stations. For the AlTiS comparisons, the median RMSE was 40 cm and the median RRMSE was 9.57%.

<footnote><sup>a</sup> Two stations (Taquari and Aquidauana rivers) are located on plateau edges, before the rivers enter the Pantanal.</footnote>

<footnote><sup>b</sup> Monitoring locations featuring both Hydroweb and AlTiS stations were counted only once.</footnote>
<figure><img src="image_7.png" /><figcaption>Fig. 4. Performance of Hydroweb and AlTis virtual stations compared to in situ gauge stations in the Brazilian Pantanal wetland.</figcaption></figure>

Despite some high RMSE values in the comparisons between RA-WSE and IS-WSE, their variability was consistent with that observed between the nearby in situ stations in the same rivers (Fig. 4), which showed RMSE ranging from 24 to 101 cm. For instance, elevated RMSE values in these comparisons were primarily associated with large river water level variations (>5.0 m; e.g., Fig. 5(d), (g) and Fig. 6(f)) and differing dynamics between the compared water levels (e.g., Fig. 6(e)), even within the same river and 100 km distance.

### 5.2.1. Hydroweb virtual stations

Along the Paraguay River, all 25 Hydroweb virtual stations showed strong agreement with nearby in situ stations (Fig. 5(c), (e), (g) and Fig. S1). Most of these VSs were derived from S3B acquisitions (15 VSs or 60%), with R ranging from 0.86 to 0.99 and RMSE from 18 to 78 cm; 7 VSs from S3A acquisitions (28%), with R varying from 0.93 to 0.99 and RMSE from 17 to 57 cm; and 3 VSs (12%) of combined satellite acquisitions from S6/J2/J3 missions. For these VSs, R ranged from 0.95 to 0.98 and RMSE from 26 to 50 cm, compared to in situ stations. In the Jaurú River, the sole Hydroweb station (S3B) showed a high correlation (R = 0.91) and moderate RMSE (43 cm) with the Porto Limão station (Fig. 5(a)). This VS, which is downstream on the Jaurú River, near its mouth, showed slightly better agreement with the Descalvados station, on the Paraguay River, with a higher correlation (R = 0.93) and lower RMSE (34 cm).

High correlations between Hydroweb and in situ stations were also observed on the Cuiabá River, ranging from 0.89 to 0.99 (Fig. 6(a) and Fig. S1). Three stations were derived from S3A acquisitions (R: 0.89–0.97; RMSE: 36–50 cm), three from S3B (R: 0.91–0.96; RMSE: 26–53 cm), and four from S6 (R: 0.95–0.99; RMSE: 12–47 cm). Most Hydroweb stations on the São Lourenço and Piquiri rivers, tributaries of the Cuiabá River, also showed good agreement with in situ stations (Fig. S1). For instance, R greater than 0.90 and RMSE varying from 14 to 46 cm were found for six Hydroweb stations on the São Lourenço River and six on the Piquiri River, distributed among S3A, S3B, and S6 acquisitions. However, two of the VSs on the São Lourenço and one on the Piquiri River, all derived from S3A acquisitions, showed moderate agreement (R: 0.51–0.81; RMSE: 64–93 cm; Fig. 6(c) and Fig. S1).
<figure><img src="image_8.png" /><figcaption>Fig. 5. Comparison between water surface elevations (WSE) time series from in situ stations and virtual stations across the Paraguay and Jaurú rivers. RA-WSE: Radar altimetry WSE; IS-WSE: In situ WSE.</figcaption></figure>

In the Taquari River, the only in situ station available is the Coxim station, located a few kilometers before the river enters the wetland. Therefore, only four VSs further upstream were within 100 km of the gauge station. The correlations between these virtual stations and the Coxim station were moderate ($R = 0.66$ and $0.67$ for the two S3A VSs and $R = 0.75$ and $0.76$ for the two S3B VSs; Fig. S1), while RMSE ranged from 19 to 31 cm. Similarly, Hydroweb stations further upstream on the Miranda River were compared with the sole station on this river after entering the Pantanal. Comparisons in this river showed $R$ greater than 0.9 ($R = 0.91$, 0.94, and 0.95 for S3B, S3A, and S3A, respectively), despite a relatively high RMSE, ranging from 43 to 58 cm (Fig. S1). Other VSs, further down the river, were more than 100 km from the in situ station and were not considered for validation. In the Aquidauana River, Hydroweb stations further upstream showed higher correlations ($R = 0.89$, 0.91, and 0.97 for S3B, S3B, and S3A, respectively), and RMSE ranging from 20 to 48 cm (Fig. 6(g) and Fig. S1), compared with Porto Ciriaco and Aquidauana stations. Conversely, Hydroweb stations further downstream did not show good agreement (Fig. S1). Low correlations and high RMSE were found for an S3A VS ($R = 0.29$, RMSE = 79 cm) and an S3B VS ($R = 0.28$, RMSE = 75 cm; Fig. 6(e)), both compared with the Porto Ciriaco station.
<figure><img src="image_9.png" /><figcaption>**Fig. 6.** Comparison between water surface elevation (WSE) time series from in situ stations and virtual stations across the Cuiabá, São Lourenço, Piquiri, Miranda, and Aquidauana rivers. RA-WSE: Radar altimetry WSE; IS-WSE: In situ WSE.</figcaption></figure>

### 5.2.2. AlTiS virtual stations

High correlations were observed in all but two comparisons between AlTiS and the in situ stations carried out on the same river (Fig. 6 and Fig. S2). Consistent agreements were obtained regardless of the altimetry mission from which WSE was retrieved. For instance, WSE observations derived from S3A measurements (4 VSs) showed R ranging from 0.91 to 0.99 and RMSE from 29 to 74 cm; WSE from S3B (8 VSs) showed R ranging from 0.79 to 0.97 and RMSE from 33 to 63 cm; and WSE from S6 (5 VSs) showed R ranging from 0.93 to 0.99 and RMSE from 14 to 40 cm. These comparisons were performed for AlTiS virtual stations in the Jaurú (Fig. 5(b)), Paraguay (e.g., Fig. 5(d), (f), (h)), Cuiabá (e.g., Fig. 6(b)), São Lourenço (Fig. 6(d)), Piquiri, Miranda (Fig. 6(f)) and Aquidauana (Fig. 6(h)) rivers. Most virtual stations were located on the Paraguay River, with a VS from S3A acquisitions, five from S3B, and two from S6. The Cuiabá River had four virtual stations (1 S3A and 3 S6), and the other rivers had one each: the Jaurú, São Lourenço, and Aquidauana rivers had an S3B VS; and the Piquiri and Miranda rivers had an S3A VS.
## 5.3. Hydrological dynamics

### 5.3.1. Backwater flooding in the Miranda River

In the southern Pantanal, the flood peak (May to July) of the Paraguay River occurs several months after the peak of precipitation (November to March) in the headwaters of this large wetland (Andrade et al., 2022; Hamilton et al., 1996). For instance, the water levels near the Ladário station show a 5-month lag time with the precipitation data from the Miranda station ($R_{lag5}$ = 0.53; Fig. 7(a), (b), (c)). In this reach of the Paraguay River, the flood wave coming from the northern part of the Pantanal arrives from June to August and is blocked by the Urucum bottleneck, forming a huge upstream “lake” (Stevaux et al., 2020), ultimately overflowing towards tributaries such as the Miranda River. As a result, the Miranda River presents two flood cycles, one linked to precipitation in the headwaters and the other to the arrival of the flood wave (see, for example, the 2018–2019 hydrological year in the light gray hydrograph in Fig. 7(a)). The rainy-season flood (December to March) and backwater-driven flood (May to June) merge during wet hydrological years (e.g., 1648 mm of rainfall in 2017–2018), creating a prolonged inundation. By June 2018, for example, water levels at the two virtual stations (65 km apart) differed by less than 30 cm, indicating a near-flat water surface gradient across the Corumbá reach–lower Miranda floodplains. In contrast, the flood peak linked to the backwaters is much weaker during dry hydrological years, such as in 2020–2021 (1164 mm of rainfall), when the difference in WSE between the two virtual stations reached 349 cm in June 2021 (i.e., more than ten times larger than the difference observed in June 2018). Therefore, instead of slowing the flow of the Miranda River as the backwater-driven flood usually does in wet years, the low water level of the Paraguay River (which reached 81.48 cm in October 2021, the lowest level of this century) in a dry year contributed to speeding up the drainage and further aggravating the drought effects on the Miranda River, which lasted until February 2022.

Regarding the lag effect, the downstream Miranda River showed a 3-month delay with precipitation data ($R_{lag3}$ = 0.53; Fig. 7(d)), while preceding Paraguay River flow by one to two months ($R_{lag-2}$ = 0.57; Fig. 7(e)). Nevertheless, the CCF may inflate the Miranda River’s lag/lead times due to its two-cycle flood regime. This is particularly true for the lead time with the Paraguay River, as the short distance between the virtual stations makes such prolonged delays hydrologically unlikely.

### 5.3.2. Megadrought effects on Nhecolândia shallow lakes

The prolonged drought that began in 2019 has severely impacted the hydrology of the Pantanal landscapes (Marengo et al., 2021), including the Nhecolândia region. The lakes in this area are inherently shallow, with temporary and permanent freshwater lakes reaching 2 m deep and saline lakes ranging from 2 to 3 m during the dry season (Barbiero et al., 2008). Given these shallow depths, the water level declines observed during the drought period (July 2019–June 2022) were particularly extreme, with many lakes reaching critically low levels (Fig. 8). Median levels in five lakes analyzed here, for example, dropped by 1.54 to 1.93 m relative to pre-drought conditions (July 2016–June 2019; Fig. B.10), desiccating many lakes and pushing others to the brink.

### 5.3.3. WSE dynamics along the ungauged Taquari River

Radar altimetry is currently the only source of continuous WSE records along the main channel of the Taquari megafan. The river is crossed by six S3A, five S3B and one S6 ground tracks, enabling the establishment of up to 12 virtual stations. The RA-WSE time series along the river reveals that the Taquari’s intra-annual water level range is markedly lower than that of the Paraguay River. For instance, during the 2018–2019 hydrological year, WSE variation at a VS on the Paraguay River near Ladário (Fig. 9(a)) exceeded three meters, whereas amplitudes in the Taquari River ranged from 50 cm, in the lower distributary fan (Fig. 9(b)), to 100 cm in the upper fan (Fig. 9(d)), where the river is confined by deposits of abandoned lobes.

Within the distributary fan lobe, the elevated channel–levee system, coupled with crevasses, diverts waters to the plains during the flood season (Assine, 2005), dissipating seasonal signals in the hydrograph. In this region, a significant negative trend in water levels was observed between 2018 and 2020 (Fig. 9(b)). In contrast, the Taquari River dynamics in the upper fan (Fig. 9(d)) retain similarity to pre-floodplain conditions, with persistent seasonality patterns due to channel confinement and minimal water loss to the fan (Assine, 2005).

## 6. Discussion

While statistical validation confirms the high reliability of radar altimetry data, we discuss here some limitations of the methodological approach and suggest prospects for enhancing our understanding of wetland ecosystem functioning.

### 6.1. Methodological challenges: AlTiS processing and inherent data quality limitations

While the Hydroweb database provides good spatial coverage, the use of AlTiS software was crucial for extending this coverage, particularly for lakes in the Nhecolândia region, which are often smaller than 0.5 km². However, this gain came at the cost of scalability. The AlTiS processing required time-consuming, expert-based visual inspection for thresholding and VS establishment, which limited the total number of stations we could feasibly process. Although the backscattering and peakiness coefficient thresholds were effective in filtering the radar altimetry data in most virtual stations, remaining outliers had to be removed based on visual inspection, particularly in VSs from S6 acquisitions, as stricter thresholds would have removed too many valid records. This semi-automated approach, though necessary for quality control and practical for local studies, underscores a constraint in generating large VS networks for small water bodies.
<figure><img src="image_10.png" /><figcaption>**Fig. 7.** Relationship between water surface elevations (WSE) from Hydroweb stations on the Paraguay and Miranda rivers and monthly precipitation at the Miranda climate station. (a) Monthly precipitation (barplot) and water levels from Paraguay (lower hydrograph) and Miranda (upper hydrograph) rivers. (b) Location map (P and M refer to Paraguay and Miranda virtual stations). Cross-Correlation function (CCF) between: (c) Paraguay River's WSE and Rainfall, (d) Miranda River's WSE and Rainfall, and (e) Miranda and Paraguay rivers' levels.</figcaption></figure>

Furthermore, the quality of RA-WSE time series is intrinsically linked to the size and geometry of the target. While effective for lakes and larger rivers, data quality on narrower streams is more susceptible to contamination from the surrounding land, leading to a lower number of valid observations and noisier signals (Normandin et al., 2018). This limitation is exacerbated in meandering rivers, where the interaction between the footprint, river width, and adjacent features like oxbow lakes can lead to multi-peaked waveforms and anomalous retrievals, as observed on the Piquiri River (Fig. 6(c)). The river's relatively narrow width (132 m) likely contributed to the inaccurate estimations during March 2019 and June 2020. Fortunately, the dense cross-track network provided by the dual-satellite Sentinel-3 mission enables cross-validation, allowing for the identification and filtering of such erroneous stations by comparison with neighbors along the same river.
<figure><img src="image_11.png" /><figcaption>Fig. 8. Surface water dynamics of shallow lakes in the Nhecolândia landscape. Water levels: Sentinel-3A; Satellite images: Sentinel-2.</figcaption></figure>

## 6.2. Validation performance and uncertainty analysis

The validation of RA-WSE is inherently constrained by methodological factors. A primary limitation is the unattainability of perfect overlap between virtual and in situ stations. This spatial discrepancy means that natural variations in river morphology – such as differences in stream shape, width, and water level range between the two sites – can inflate RMSE values. Furthermore, tributary inflows or floodplain interactions between the locations can introduce physical differences in the water level signals. This is reflected in our findings, where the RMSE values between RA-WSE and IS-WSE were consistent with those observed between different in situ stations themselves (Fig. 4), suggesting that a significant portion of the observed error originates from this spatial mismatch rather than altimetric inaccuracy itself. For instance, moderate agreement in some comparisons is more likely related to inherent hydrological variations (notably annual range; see Fig. 6(e)) than to data quality. Additionally, despite data normalization, vertical reference discrepancies persist. In situ stations measure river level relative to a local arbitrary datum (gauge zero reference), while virtual stations are referenced to the WGS84 ellipsoid.

Despite these important methodological considerations, the RA-WSE dataset demonstrated consistently strong agreement with in situ records across 63 Hydroweb and 17 AlTiS stations, corroborating the established performance of radar altimetry for inland water level monitoring. Our findings align with previous studies in diverse global wetlands. For instance, Normandin et al. (2018) assessed the performance of seven RA missions (European Remote-Sensing Satellite-2 (ERS-2), ENVIronment SATellite (ENVISAT), Satellite with Argos and AlTika (SARAL), Jason-1, Jason-2, Jason-3 and Sentinel-3A) in the Inner Niger Delta from 1995 to 2017, compared with in situ water levels, and found R greater than 0.8 in 80% of the comparisons and RMSE lower than 40 cm in 48% of the cases. Similarly, Kittel et al. (2021) measured low root-mean-square deviation (RMSD < 32 cm) for six Sentinel-3A and B VSs compared with in situ stations in the Zambezi River. Furthermore, Frappart et al. (2021) found very accurate results (R > 0.85; RMSE < 7 cm) for WSE retrievals from Sentinel-3A and B over seven out of eight lakes in Switzerland, compared to in situ water levels.
<figure><img src="image_12.png" /><figcaption>**Fig. 9.** Water levels (Jan 2018–Dec 2019) from Hydroweb stations along the Taquari River. Virtual stations on the Paraguay River near Ladário (a) and along the Taquari River (b–d).</figcaption></figure>

## 6.3. Insights on the understanding of complex hydrological dynamics in the Pantanal

The dense cross-track network of radar altimetry virtual stations provides a powerful tool to monitor a wide range of hydrological dynamics across the Pantanal. We first demonstrate the potential of RA-WSE to analyze backwater floodings caused by hydraulic bottlenecks along the Paraguay River. While several studies have recognized the importance of this phenomenon in conditioning the Pantanal's hydrological regime (Hamilton, 2002; Assine et al., 2015a; Stevaux et al., 2020), only Stevaux et al. (2020) comprehensively investigated its mechanisms. These authors simulated backwater in the *Amolar* bottleneck using a theoretical model established by the Brazilian Waters Agency (ANA) and validated it with Acoustic Doppler Current Profilers (ADCP) survey during mean (November 2015) and flood (May 2016) water levels. Their findings revealed that a 1 m rise in water level at the *Amolar* station can propagate the backwater effect up to the *Porto Conceição* station (270 km upstream). These seminal results should now be compared and complemented by other approaches that take advantage of the potential of radar altimetry to assess backwater flooding and its interannual variability in the Pantanal, as emphasized in this study.

Building on this application, while we here focused on the upstream section of the Urucum bottleneck, future studies should both advance analysis of this area and extend investigations upstream of the *Amolar* and *Morro dos Feixos* bottlenecks to better characterize the spatial extent and propagation dynamics of this phenomenon. Previous applications of satellite altimetry to backwater analysis demonstrate its value across diverse fluvial systems. In the Amazon River Basin, Zakharova et al. (2006) relied on TOPEX/Poseidon (T/P) satellite altimetry data to understand the relationship between water level and discharge in the mouth of the Negro River, a zone affected by backwaters, while de Paiva et al. (2013) used ENVISAT altimetry data to validate hydrologic/hydrodynamic models, revealing flood wave attenuation by backwater effects. Similarly, Liu et al. (2023) used in situ stations and ICESat-2 multi-beam laser altimetry data to show a unique stage-slope-discharge relationship in backwater-affected zones upstream of river confluences in the Mississippi-Missouri river system. Looking ahead, the recently launched Surface Water and Ocean Topography (SWOT) mission (Biancamaria et al., 2016) incorporates Ka-band radar interferometer (KaRIn) altimetry into water resources monitoring, enabling unprecedented monitoring of small water bodies and complex river systems (Yao et al., 2025). This capability is expected to significantly improve the observations of backwater effects (Liu et al., 2023).

Furthermore, the value of the VS network is particularly critical for monitoring remote areas vulnerable to climate change and anthropogenic pressures (Normandin et al., 2018; de Fleury et al., 2023). Our results demonstrate this capability, revealing significant WSE declines in shallow lakes in the Nhecolândia region and distinct along-channel variations in seasonal water level patterns in the Taquari megafan.

In the Nhecolândia, the observed WSE declines align with recent megadrought events (Thielen et al., 2021). Such extreme events are expected to lead to major changes in the structure and function of lakes, such as water chemistry, light availability, and the
structure of phytoplankton, zooplankton, and submerged aquatic vegetation communities (Havens et al., 2016). Meanwhile, in the Taquari megafan, deforestation and agricultural activities in the highlands have accelerated sedimentary processes (Assine and Soares, 2004; Bergier, 2013). These conditions favor the occurrence of geomorphic events such as the large-scale avulsion currently in progress at the Caronal farm (see location in Fig. 9, which is leading to the abandonment of the main channel segment. The negative water level trend observed downstream from 2018 to 2020 (Fig. 9(b)) is likely a direct hydrological signature of this ongoing avulsion process, an impact that was compounded by the effects of the megadrought.

## 6.4. Broader implications and prospects for wetland monitoring

Radar altimetry-derived WSE estimates can enhance our understanding of the Paraguay River system's complex “channel-floodplain-flood pulse-connectivity” dynamics. This intricate relationship within the vast tropical wetland remains poorly understood (Stevaux et al., 2020). As noted by these authors, management projects initiated by public authorities without this critical knowledge may threaten the sustainability of the Pantanal. A key example is the proposed Hidrovia Paraguay-Paraná (HPP), a major commercial waterway project debated since the late 1980s and resurfaced in recent years. It aims to create a navigable north-south route from Cáceres (Brazil) to Nuevo Palmira (Uruguay) to transport soybeans and other commodities from inland regions in Brazil, Paraguay, and Bolivia to seaports in Argentina and Uruguay for global export. However, by increasing the discharge of the Paraguay River and coupled with the effects of climate change, this project could disrupt essential backflooding processes and likely reduce the spatial extent and duration of the seasonal floods that define the ecosystem (Wantzen et al., 2024).

The RA-WSE network presented here can also facilitate a more in-depth investigation into the relationship between vegetation phenology and hydroclimatic variables at the landscape level. In the Pantanal, distinct flood patterns are a key driver of local vegetation variability (Ivory et al., 2019; Ribeiro et al., 2024) and are therefore fundamental to plant habitat diversity. Given the vulnerability of these ecosystems, unraveling the timing, regulatory mechanisms, and dynamics of floods is essential. This is especially urgent within the context of climate change (Ivory et al., 2019), which is intensifying hydrological extremes (Marengo et al., 2021).

Furthermore, the time series of water levels at virtual stations could provide critical calibration and validation data for hydraulic and hydrological models (Jiang et al., 2017). This potential is particularly relevant for improving the performance of novel flood hazard mapping approaches that rely on machine learning and hybrid GIS-based models (Darvishi, 2025; Solaimani et al., 2023, 2024). Integrating direct, spatially distributed water level measurements from altimetry could significantly enhance the accuracy of such flood simulations and vulnerability assessments, creating a more comprehensive framework for wetland management and conservation planning.

## 7. Conclusions

This study demonstrates the operational feasibility and reliability of satellite radar altimetry (Sentinel-3A/B, Sentinel-6) for monitoring water level dynamics across the Pantanal's complex and heterogeneous landscapes. By integrating 151 virtual stations from the Hydroweb database and a complementary AlTiS-derived dataset, we established a comprehensive monitoring network that captures the diversity of the region's water bodies, from major rivers to shallow isolated lakes. Validation against in situ gauges showed consistently strong agreement ($R > 0.85$ and RMSE < 0.4 m in most cases), underscoring the reliability of the data for scientific and practical applications.

This radar altimetry WSE network provides critical insights into complex hydrological dynamics across the Pantanal. For instance, we demonstrated backwater flooding on the Miranda River, which experiences two annual flood cycles driven by local precipitation and the Paraguay River's flood pulse. We showed that during recent megadroughts, low levels in the Paraguay River disrupted this backwater effect, leading to accelerated drainage of this tributary. The network also proved essential for monitoring remote, vulnerable areas, revealing significant water level declines in the shallow lakes of the Nhecolândia region due to the recent megadroughts and mapping along-channel variations in the ungauged Taquari megafan – a system subject to the combined pressures of upland agriculture and climatic extremes.

This integrated virtual station network establishes radar altimetry as an indispensable tool for impact assessment and sustainable water governance. As we have shown, megadroughts can weaken the Paraguay River's backwater effect, thereby accelerating tributary drainage and ultimately intensifying drought impacts on floodplain ecosystems and local communities. These effects could be severely exacerbated by large-scale projects like the proposed Hidrovia Paraguay-Paraná commercial waterway. The significant WSE declines in Nhecolândia's shallow lakes and the avulsive dynamics of the Taquari megafan further underscore the high susceptibility of these systems. As climate change amplifies hydrological extremes, the continuous monitoring capability provided by such altimetry networks is essential for informed decision-making to protect the entire Pantanal ecosystem.

### CRediT authorship contribution statement

**Uelison Mateus Ribeiro:** Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Samuel Corgne:** Writing – review & editing, Validation, Supervision, Project administration, Methodology, Funding acquisition, Conceptualization. **Manuela Grippa:** Writing – review & editing, Methodology. **Félix Girard:** Writing – review & editing, Methodology. **Sly Wongchuig:** Writing – review & editing, Visualization. **Carolina Joana da Silva:** Writing – review & editing. **Vitor Matheus Bacani:** Writing – review & editing. **Mauro Henrique Soares da Silva:** Writing – review & editing. **Frederico Gradella:** Writing – review & editing. **Damien Arvor:** Writing – review & editing, Validation, Supervision, Project administration, Methodology, Funding acquisition, Conceptualization.
## Ethical statement

No ethical approval was required, as the research did not involve human participants, animal subjects, or direct field interventions.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgments

U. M. R. Ph.D. grant is funded by the Centre National d'Etudes Spatiales (CNES) and the Région Bretagne, France. V. M. B. acknowledges the support of the National Council for Scientific and Technological Development (CNPq, Brazil) through a research productivity fellowship (Grant number 313303/2023-1). This work was also supported by (1) the CNES through the CHOVE-CHUVA Space For Climate Observatory (SCO CHOVE-CHUVA), (2) the CNES through the SEMTI-SENT project, (3) the CNES through the SCO PANTANAL project, (4) the French National Research Agency (ANR) through the TELKANTE LAB project, and (5) by the Foundation to Support the Development of Education, Science and Technology in the State of Mato Grosso do Sul (FUNDECT; Call Fundect 31/2021 – Universal 2021). The authors also wish to thank the anonymous reviewers for their insightful comments and suggestions, which greatly helped improve the quality of this manuscript.

## Appendix A

See Table A.3.

## Appendix B

See Fig. B.10.

<table><caption>**Table A.3**
Gauge stations used in this study.</caption><thead><tr><th>Station Code</th><th>Name</th><th>River</th><th>Lat</th><th>Lon</th></tr></thead><tbody><tr><td>66070004</td><td>Cáceres (Agência Fluvial)</td><td>Paraguay River</td><td>16° 3' 48.06" S</td><td>57° 41' 17.01" W</td></tr><tr><td>66077000</td><td>Porto Limão</td><td>Jaurú River</td><td>16° 8' 40.95" S</td><td>58° 0' 27.98" W</td></tr><tr><td>66090000</td><td>Descalvados</td><td>Paraguay River</td><td>16° 43' 59.78" S</td><td>57° 44' 57.15" W</td></tr><tr><td>66120000</td><td>Porto Conceição</td><td>Paraguay R.-Corixo sn</td><td>17° 8' 31.07" S</td><td>57° 21' 16.36" W</td></tr><tr><td>66125000</td><td>Bela Vista do Norte</td><td>Paraguay River</td><td>17° 38' 33.07" S</td><td>57° 41' 28.76" W</td></tr><tr><td>66270000</td><td>S. Antonio do Leverger</td><td>Cuiabá River</td><td>15° 52' 13.25" S</td><td>56° 4' 37.41" W</td></tr><tr><td>66280000</td><td>Barão de Melgaço</td><td>Cuiabá River</td><td>16° 11' 35.33" S</td><td>55° 58' 0.24" W</td></tr><tr><td>66340000</td><td>Porto Cercado</td><td>Cuiabá River</td><td>16° 30' 43.23" S</td><td>56° 22' 31.29" W</td></tr><tr><td>66360000</td><td>Fazenda São João</td><td>Cuiabá River</td><td>16° 56' 38.78" S</td><td>56° 37' 50.12" W</td></tr><tr><td>66370000</td><td>F. Camargo Correia</td><td>Cuiabá-São Lourenço</td><td>17° 3' 23.5" S</td><td>56° 34' 51.1" W</td></tr><tr><td>66460000</td><td>Acima do Córrego Grande</td><td>Vermelho River</td><td>16° 36' 29.16" S</td><td>55° 12' 23.04" W</td></tr><tr><td>66470000</td><td>São José do Boriréu</td><td>São Lourenço River</td><td>16° 55' 31.08" S</td><td>56° 13' 24.96" W</td></tr><tr><td>66600000</td><td>São Jerônimo</td><td>Piquiri River</td><td>17° 12' 6.12" S</td><td>56° 0' 30.96" W</td></tr><tr><td>66650000</td><td>São José do Piquiri</td><td>Piquiri River</td><td>17° 17' 30.12" S</td><td>56° 23' 13.92" W</td></tr><tr><td>66710000</td><td>Pousada Taiamã</td><td>Cuiabá River</td><td>17° 21' 56.16" S</td><td>56° 46' 28.92" W</td></tr><tr><td>66750000</td><td>Porto Alegre</td><td>Cuiabá River</td><td>17° 37' 27.78" S</td><td>56° 57' 54.81" W</td></tr><tr><td>66800000</td><td>Amolar</td><td>Paraguay River</td><td>18° 2' 26.1" S</td><td>57° 29' 21.9" W</td></tr><tr><td>66810000</td><td>São Francisco</td><td>Paraguay River</td><td>18° 23' 38.04" S</td><td>57° 23' 27.96" W</td></tr><tr><td>66825000</td><td>Ladário</td><td>Paraguay River</td><td>19° 0' 7.39" S</td><td>57° 35' 44.12" W</td></tr><tr><td>66870000</td><td>Coxim</td><td>Taquari River</td><td>18° 30' 28.08" S</td><td>54° 45' 42.84" W</td></tr><tr><td>66895000</td><td>Porto do Manga</td><td>Paraguay River</td><td>19° 15' 30.7" S</td><td>57° 14' 9.3" W</td></tr><tr><td>66910000</td><td>Miranda</td><td>Miranda River</td><td>20° 14' 26.88" S</td><td>56° 23' 57.12" W</td></tr><tr><td>66945000</td><td>Aquidauana</td><td>Aquidauana River</td><td>20° 28' 41.88" S</td><td>55° 48' 5.04" W</td></tr><tr><td>66950000</td><td>Porto Ciriaco</td><td>Aquidauana River</td><td>19° 41' 54.96" S</td><td>56° 16' 51.96" W</td></tr><tr><td>66960008</td><td>Porto Esperança</td><td>Paraguay River</td><td>19° 36' 3" S</td><td>57° 26' 14" W</td></tr><tr><td>66970000</td><td>Forte Coimbra</td><td>Paraguay River</td><td>19° 55' 12.28" S</td><td>57° 47' 28.07" W</td></tr><tr><td>67100000</td><td>Porto Murtinho</td><td>Paraguay River</td><td>2° 42' 8.8" S</td><td>57° 53' 29.9" W</td></tr></tbody></table>

Datum: WGS 84.
<figure><img src="image_13.png" /><figcaption>Fig. B.10. Boxplots of water levels from five shallow lakes before (Jul 2016–Jun 2019) and during (Jul 2019–Jun 2022) the recent megadrought period. A–E Lakes A–E shown in Fig. 8.</figcaption></figure>

## Appendix C. Supplementary data

Supplementary material related to this article can be found online at https://doi.org/10.1016/j.rsase.2025.101805.

### Data availability

Data sources are provided in the manuscript. The radar altimetry dataset generated for this study using the AlTiS software is available in the Zenodo repository at https://doi.org/10.5281/zenodo.15571469.

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