# Assessment of surface water storage in the Amazon floodplains by hydrological modelling and earth observation data

Jérémy Guilhen, Clément Fabre, Marie Parrens, Sabine Sauvage, Franck Mercier, Ahmad Al Bitar, Jean-Michel Martinez, José-Miguel Sánchez-Pérez

► To cite this version:

Jérémy Guilhen, Clément Fabre, Marie Parrens, Sabine Sauvage, Franck Mercier, et al. Assessment of surface water storage in the Amazon floodplains by hydrological modelling and earth observation data. Journal of Hydrology, 2026, 666, pp.134758. 10.1016/j.jhydrol.2025.134758. hal-05449843

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Research papers

# Assessment of surface water storage in the Amazon floodplains by hydrological modelling and earth observation data

Jérémy Guilhen<sup>a,b</sup> <sup>●</sup>, Clément Fabre<sup>a,*</sup> <sup>●</sup>, Marie Parrens<sup>c,d</sup>, Sabine Sauvage<sup>a,*</sup>, Franck Mercier<sup>b</sup>, Ahmad Al Bitar<sup>c</sup>, Jean-Michel Martinez<sup>e</sup>, José-Miguel Sánchez-Pérez<sup>a,●</sup>

## ARTICLE INFO

This manuscript was handled by Dr. Emma-
nouil Anagnostou, Editor-in-Chief, with the
assistance of Yiwen Mei, Associate Editor

Keywords:
Floodplains
Amazon
SWAT
Water surface
Altimetry

## ABSTRACT

Inland surface waters in tropical regions are fundamental to global hydrological and biogeochemical cycles, yet the quantification of floodplain freshwater storage and its variability remains limited. This study provides a basin-wide assessment of the spatiotemporal dynamics of floodplain water storage in the Amazon Basin over the period 2000–2018. We developed an integrated framework combining in situ discharge measurements, multi-sensor remote sensing datasets, and process-based hydrological modelling to quantify daily floodplain hydro-dynamics at high spatial resolution. Floodplain extent was derived from L-band passive microwave observations from Soil Moisture and Ocean Salinity (SMOS) satellite, while water surface elevation was constrained using altimetry data (Jason-2/3 and Sentinel-3). The hydrological model (SWAF-FP), specifically improved to represent floodplain–river interactions, was calibrated at eight gauging stations and evaluated using approximately 4,000 altimetry-based floodplain water level records. Model performance demonstrated high skill, with a mean $R^2$ of 0.83 for discharge and $R^2 > 0.62$ for floodplain water surface elevation. The results indicate a mean annual surface water storage of 1800 ± 854 km³ across the Amazon Basin, with pronounced seasonal cycles and interannual stability at the basin scale. The Negro and Madeira sub-basins were identified as the dominant contributors to total floodplain storage, accounting for approximately 480 km³ (33.6 %) and 312 km³ (12.5 %), respectively. This study provides the first consistent long-term quantification of Amazonian floodplain water storage dynamics at daily resolution. The results emphasize the critical role of floodplains in regulating basin-scale hydrological fluxes and offer a robust observational–modeling framework for improving large-scale hydrological and Earth system models in tropical regions.

## 1. Introduction

The Amazon Basin, covering approximately 6.1 million km², is the largest river basin in the world and one of the most hydrologically complex regions on Earth. Its hydrological system is characterized by intense precipitation, vast floodplains, intricate topography, and strong seasonal and interannual variability in surface water storage and discharge. Seasonal flooding plays a central role in regulating water fluxes, connecting river channels with extensive floodplain systems, and influencing carbon cycling, energy exchange, and ecosystem functioning. Fleischmann et al. (2022) estimated that nearly one third of the

lowland Amazon Basin is seasonally inundated. While most wetlands are floodplains subject to river overflow, extensive interfluvial wetlands are also seasonally flooded by local rainfall and runoff (Junk et al., 2011). Floodplains and wetlands provide multiple ecosystem services and support millions of people living in the basin. They sustain fisheries, agriculture, and transportation (Jardim et al., 2020), and maintain rich biodiversity adapted to the annual flood pulse (Junk et al., 1989). Moreover, Amazon surface waters play a key role in the global carbon cycle, acting as both a source and a sink of CO₂ (Abril et al., 2014; Raymond et al., 2013; Amaral et al., 2020) and as the largest natural tropical source of methane (Kirschke et al., 2013; Melack et al., 2004;

<footnote><sup>a</sup> Université de Toulouse, Toulouse INP, CNRS, IRD, CRBE, Toulouse, France</footnote>

<footnote><sup>b</sup> Collecte Localisation Satellites, France</footnote>

<footnote><sup>c</sup> Centre d'Etudes Spatiales de la Biosphère (CESBIO), CNES, Université de Toulouse (UPS), Toulouse, France</footnote>

<footnote><sup>d</sup> Dynafor, Université de Toulouse, INRAE, INPT, INP-PURPAN, Castanet-Tolosan, France</footnote>

<footnote><sup>e</sup> Institut de Recherche pour le Développement, Laboratoire GET (IRD, CNRS, UPS, CNES), Toulouse, France</footnote>

<footnote>* Corresponding authors.</footnote>

<footnote>E-mail addresses: clement.fabre@univ-tlse3.fr (C. Fabre), sabine.sauvage@univ-tlse3.fr (S. Sauvage).</footnote>
<figure><img src="image_2.png" /><figcaption>**Fig. 1.** The Amazon catchment and its main subbasins. Gauging stations for each main hydrosystems are shown with (A) Obidos for the Amazon River, (B) Manacapuru for the Solimões River, (C) Fazendo Vista Alegre for the Madeira River, (D) Serrinha for the Negro River, (E) Atalaya for the Ucayali River, (F) Labrea for the Purus River, (G) Caracarai for the Branco River, and (H) Borja for the Marañón River. The mainstem corresponds to the Solimões-Amazon main stream.</figcaption></figure>

Pangala et al., 2017; Pison et al., 2013). Understanding the spatial and temporal dynamics of floodplain inundation and storage is therefore crucial not only for hydrology but also for climate modelling, carbon accounting, and sustainable resource management.

Despite its importance, the quantification of floodplain water storage in the Amazon Basin remains limited in both spatial coverage and temporal resolution. Earlier studies provided basin-scale surface water volume estimates in the range of 1,200–1,800 km³ (Papa et al., 2013; Tourian et al., 2018), but these estimates relied on sparse in situ observations and coarse-resolution remote sensing. Local studies have captured specific events or sub-basins—such as the Negro Basin (Frappart et al., 2005), exceptional droughts (Frappart et al., 2012), or floodplain cross-sections (Alsdorf et al., 2010)—but do not provide a coherent basin-wide, long-term assessment. The main difficulty arises from the limited availability of in situ data in remote tropical environments. Gauging stations are sparse and often poorly distributed, which makes direct measurements of floodplain–river water exchanges and storage impractical at large scales. Remote sensing techniques offer an effective alternative, yet each comes with inherent trade-offs. Optical sensors such as Landsat are constrained by persistent cloud cover and canopy obstruction. Synthetic Aperture Radar (SAR) sensors can

penetrate vegetation (Hess et al., 2003, 2015), but their revisit time is too long to capture flood dynamics at sub-monthly scales. To overcome these limitations, several studies have integrated active and passive microwave observations (Aires et al., 2013; Papa et al., 2010; Parrens et al., 2017; Prigent et al., 2020), and more recently GNSS-R has shown potential for monthly inundation mapping (Setti and Tabibi, 2024). L-band passive microwave sensors such as SMOS (Kerr et al., 2001) and SMAP (Entekhabi et al., 2010) offer improved capacity to detect inundation beneath vegetation. In particular, the SWAF-HR dataset derived from SMOS provides weekly inundation probability maps over the Amazon Basin since 2010 (Parrens et al., 2019), offering new opportunities for more detailed and continuous floodplain monitoring. However, these observations alone cannot fully resolve water storage dynamics without integration into process-based hydrological models.

Advances in large-scale hydrological modelling have improved the ability to represent floodplain dynamics (Archfield et al., 2015; Bierkens, 2015; Decharme et al., 2012; Paiva et al., 2013a; Yamazaki et al., 2013). However, accurate delineation of flood inundation remains a key source of uncertainty, particularly in data-scarce tropical regions. Hydrodynamic models require high-resolution discharge and water level data (Liu et al., 2019), which are not available across much of the
<figure><img src="image_3.png" /><figcaption>Fig. 2. General flow chart of the methodology used in this study.</figcaption></figure>

Amazon. In ungauged basins, hydrologic–hydraulic modelling frame-
works are used as alternatives, but they are highly sensitive to digital
elevation model (DEM) quality, with topographic errors propagating
into biases in flood extent and storage volume estimates (Baugh et al.,
2013; Paiva et al., 2013b; Trigg et al., 2012; Yamazaki et al., 2015). The
Soil and Water Assessment Tool (SWAT) is a widely used, semi-
distributed, physically based model applied at watershed and basin
scales worldwide (Gassman et al., 2014; Santini et al., 2025; Santini,
2020). It has been applied extensively for hydrological and biogeo-
chemical analyses from small to large watersheds (Janjić and Tadić,
2023). Its recent enhancement, the SWAT Floodplain module (SWAT-FP;
Guilhen et al., 2022), allows explicit representation of floodplain–river
interactions and lateral inundation processes. This new module has
already been validated and applied to the Amazon basin in Guilhen
et al., 2022. Using SWAT-FP to the Amazon basin represents a novel
opportunity to improve basin-scale floodplain hydrodynamics simula-
tions, quantify floodplain water storage volumes and so, reduce un-
certainties in water storage assessments.

This study aims to advance the understanding and quantification of
floodplain water storage dynamics in the Amazon Basin by integrating
remote sensing observations with process-based hydrological modelling.
Specifically, the objectives are to: (i) simulate floodplain–river exchange
and water storage dynamics at daily temporal resolution using the
SWAT-FP model over the period 2000–2018. Simulation results were
evaluated using discharge observations and satellite altimetry mea-
surements of floodplain water levels. (ii) quantify seasonal and inter-
annual variability in floodplain water storage and identify sub-basin
contributions to basin-scale hydrological dynamics. Through the com-
bination of new high-resolution inundation datasets and an advanced
floodplain modelling framework, this study provides the first pluri-
annual, basin-wide estimates of Amazonian floodplain water storage
at daily resolution. The resulting insights contribute to improved un-
derstanding of Amazon hydrology, better parameterization of land–
atmosphere interactions, and enhanced representation of floodplain
processes in large-scale Earth system and hydrological models.

## 2. Methodology

2.1. *Study site*

The Amazon Basin spans around 6 million km² across nine South American countries (Fig. 1). It delivers an average discharge of about 215,000 m³ s⁻¹ to the Atlantic Ocean, corresponding to ca. 18 % of the global freshwater discharge to the oceans. Precipitation is abundant throughout the year with about 2,300 mm y⁻¹ (Espinoza Villar et al., 2009). Climatic conditions can vary considerably depending on the region. Indeed, the watershed is crossed by the Equator and is influenced by components of both the Northern and Southern hemispheres. In the Southern Hemisphere, the rainy season typically extends from December to May, and the dry season from June to November. However, the precipitation regime is then highly contrasted (Ronchail and Gal laire, 2006; Espinoza Villar et al., 2009) and is influenced by the North-South Oscillation of the Intertropical Convergence Zone (ITCZ).

The Amazon Basin is the world's most extensive hydrological system, both in terms of discharge and drainage area. With a complex wetland system, the flood pulse of the Amazon varies between 280,000 km² and 633,500 km² (Hess et al., 2015; Fleischmann et al., 2022). However, over the last three decades, extreme hydroclimatic events have intensified, particularly for extreme flood events in the central region (Gloor et al., 2013; Barichivich et al., 2018). In Amazonia, severe droughts and floods are not recent phenomena, as they have been known to occur in the past (Marengo et al., 2016; Granato-Souza et al., 2019). Nevertheless, these extreme phenomena tend to intensify (Espinoza et al., 2024) with important spatial variability (Paca et al., 2020). In parallel, a rise of approximately one meter in the average annual water level in the central Amazon region has been observed over the last century (Barichivich et al., 2018). At the same time, in the southern part of the Amazon Basin, an increase in the length of dry seasons has been noticed (Marengo et al., 2018). Such extreme hydroclimatic events (droughts and floods) are mainly associated with the El Niño-Southern Oscillation (ENSO). However, extreme hydroclimatic events occur across the Amazon Basin at different spatial and temporal scales (Marengo et al., 2016). Monomodal flood pulses characterise the hydrological cycles of the large Amazonian
<table><caption>**Table 1**
Input data used for SWAT-FP.</caption><thead><tr><th>Data</th><th>Product</th><th>Spatial resolution</th><th>Source</th></tr></thead><tbody><tr><td>Digital Elevation Model</td><td>SRTM DEM v3</td><td>500 m</td><td>https://srtm.csi.cgiar.org/srtmdata/</td></tr><tr><td>Soils</td><td>HWSD</td><td>1 km</td><td>https://www.fao.org</td></tr><tr><td>Land use</td><td>GLC 2000</td><td>1 km</td><td>https://forobs.jrc.ec.europa.eu/products/glc2000/glc2000.php</td></tr><tr><td>Precipitations</td><td>TRMM 3B42-v7</td><td>0.25°</td><td>https://disc.gsfc.nasa.gov</td></tr><tr><td>Air temperatures</td><td>ERA-interim</td><td>0.80°</td><td>https://www.ecmwf.int</td></tr><tr><td>Floodplain delineation</td><td>SWAF-HR</td><td>0.01°</td><td>Parrens et al. (2019)</td></tr></tbody></table>

rivers, which exhibit high amplitudes, inducing a distinct annual seasonality in the connected floodplains that cover over 750,000 km² (Junk et al., 2011).

## 2.2. General methodology

This paper uses a modified version of the Soil and Water Assessment Tool (SWAT) hydrological model to estimate daily volumes stored and transported in alluvial wetlands of the Amazon. Fig. 2 presents the general methodology employed in the paper to estimate daily floodplain volumes in the Amazon. The different steps and tools used are detailed in the following sections.

## 2.3. The SWAT-FloodPlain (SWAT-FP) module

SWAT is an agro-hydro-environmental model developed by the USDA Agricultural Research Service (USDA-ARS; Temple, TX, USA) and Texas A&M AgriLife Research (College Station, TX, USA; Arnold et al., 1998). It is a conceptual, semi-distributed, and physically based hydrological model initially designed to predict the impact of human activities and agricultural management on water and sediment yields. SWAT is frequently used on the world's main rivers, and its performance has already been evaluated at several catchment scales and in various pedoclimatic conditions (Faramarzi et al., 2013; Abbaspour et al., 2015; Krysanova and White, 2015). Details on the SWAT functioning are available online (https://swatmodel.tamu.edu/).

The implementation of a SWAT project is essentially based on three main layers: a DEM, a soil layer, and a land cover/use layer. Table 1 details the input data used for SWAT-FP on the Amazon catchment. Concerning the DEM, although the original product offers a spatial resolution of 90 m, we decided to downgrade it to 500 m to facilitate calculations.

Regarding meteorological data, we utilised the Tropical Rainfall Measurement Mission (TRMM; Huffman et al., 2007) for precipitation and the ERA-Interim product for temperatures, which comprises a reanalysis of past atmospheric data from the European Centre for Medium-Range Weather Forecasts and real-time measurements. Even if a significant bias exists in high mountain areas (Strauch et al., 2017), several authors note the relevance of using these products in hydrological modelling of the Amazon (Collischonn et al., 2008; Casimiro et al., 2009; Zulkafli et al., 2014; Correa et al., 2017; Towner et al., 2019).

The new SWAT-FP model incorporates a hydraulic routing method for streams, as described by Santini (2020). In this current version, the model runs both kinematic and diffusive wave equations. The decision-making for kinematic or diffusive wave equations is supported by the calculation for each reach at every time step of the Froude number (Fr), proposed by Simons and Şentürk (1992):

$$Fr = \frac{v_m}{\sqrt{g \cdot h_{rch}}}$$

where $v_m$ is the mean flow velocity (m s⁻¹), g is the gravity acceleration (m s⁻²), and $h_{rch}$ is the water height in the reach (m). If Fr is inferior to 1, the model runs a diffusive wave equation; otherwise, the model performs a kinematic wave routing.

The floodplain module is constituted by a fully connected floodplain network where, in addition to lateral fluxes between the reach and the floodplain, water may be routed to the downstream floodplain by a kinematic wave equation (Guilhen et al., 2022). Thus, at each time step, SWAT-FP, unlike SWAT, simulates the water stocks retained in alluvial wetlands and also allows flood propagation by transferring volumes from an upstream alluvial wetland to one further downstream, without recirculating through the main watercourse.

In situ river discharge data were used to calibrate the model. They were obtained from the Critical Zone Observatory Hydrologie du Bassin Amazonien (HyBam; https://www.so-hybam.org), a long-term monitoring program that has maintained 35 gauging stations in the Amazon catchment since 2003. All data are available at https://www.so-hybam.org for each gauging station over the Amazon Basin. In our study, eight gauging stations located at the outlet of the main hydrosystems of the Amazon catchment (Fig. 1) were selected for calibration. The calibration was performed manually based on expertise by comparing it to observed data. Appendix 1 details the calibrated parameters. Most of these parameters were calibrated at the scale of large sub-catchments (Fig. 1) as well as by hydro-morphological classification, i.e. one value for lowland basins and another for high mountain basins. Most of these parameters fell within the range of previous studies, except for the CANMX parameter, which was increased to represent the specific functioning of the Amazonian canopy. Details on the conceptualisation of SWAT-FP are available in Guilhen et al. (2022).

## 2.4. Floodplain delineation

As mentioned above, floodplain delineation is crucial for modelling water flow in major rivers, such as the Amazon, as they can retain water for extended periods of time, sometimes lasting weeks or even months. In the standard version of SWAT, the floodplain is characterised as being five times the width of the stream, which is not representative of the Amazonian context. Guilhen et al. (2022) explored two delineation methods: based on DEM or based on the Soil Water Fraction at High Resolution (SWAF-HR).

In this article, we will focus on the method using SWAF-HR, which provides the probability of a pixel being inundated. Pixels with a likelihood of inundation above 1 % are deemed to belong to the floodplain. SWAF-HR is a spatially disaggregated version of SWAF (1 km resolution). The latter is obtained using a contextual model based on the Soil Moisture and Ocean Salinity (SMOS, Kerr et al., 2010) brightness temperature and provides a water surface fraction over the Amazon Basin from 2010 to date (Parrens et al., 2019).

## 2.5. Validation of the model

### 2.5.1. Altimetry for inland waters monitoring

Satellite radar altimetry is a well-established method for monitoring water from space. It measures the time taken by a radar pulse to travel from the satellite to the Earth's surface and back to the sensor. By precisely recording this two-way travel time and knowing the satellite's orbital height, the surface elevation can be estimated as:

$$h_{surface} = h_{satellite} - (R + C)$$

where $h_{satellite}$ is the satellite's altitude above a reference ellipsoid, R is the measured range between the satellite and the surface, and C represents various geophysical and instrumental corrections (e.g.,
<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Comparison of discharge of in situ gauging stations from the HyBAM network (black dots) and simulated discharge of SWAT-FP (red line) for the different locations from 2000 to 2018. (A) The Amazon River at Obidos, (B) The Solimões River at Manacapuru, (C) The Madeira River at Fazendo Vista Alegre, (D) The Negro River at Serrinha, (E) The Ucayali River at Atalaya, (F) The Purus River at Labrea, (G) The Branco River at Caracarai, and (H) The Marañón River at Borja. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

ionospheric, tropospheric, and tidal effects).

Conventional, or Low-Resolution Mode (LRM), radar altimetry transmits radar pulses at a relatively low frequency and averages multiple echoes over a wide footprint (typically several kilometers), producing a one-dimensional measurement of surface elevation along the satellite ground track. This mode was used by early altimetry missions such as TOPEX/Poseidon, Jason-2, and Jason-3 (Kumar et al., 2025).

In contrast, SAR altimetry, also known as Delay-Doppler altimetry, coherently processes a sequence of radar echoes to reduce the along-

track footprint (to a few hundred meters) and enhance the signal-to-noise ratio. This technique, implemented on missions such as CryoSat-2 and Sentinel-3A/B, allows finer spatial resolution and improved performance over inland water bodies, coastlines, and heterogeneous surfaces.

### 2.5.2. Altimetry river heights

On the one hand, Alsdorf et al. (2010) suggested that local runoff originates only from 10 % to 20 % of the Amazon floodplain water
<figure><img src="image_5.png" /><figcaption>Fig. 4. Comparison between daily river water heights simulated by SWAT-FP and observed data from Jason 2–3 (a and b) and from Sentinel-3A (c and d). Station locations can be found in Fig. 5.</figcaption></figure>

volume. On the other hand, most of the Amazon Basin floodplains present a shallow slope (between 0.03 % and 0.06 %). Therefore, we assumed that at the scale of the Amazon Basin, the water level in the floodplain is a direct proxy of the water volume. Altimetry data was used to validate both water levels in the river and the floodplain simulated by the model. In this study, we used the data of Jason-2 (2009–2017; repeat cycle: 10 days), Jason-3 (2017–2019; repeat cycle: 10 days) and Sentinel-3A (2016–2019; repeat cycle: 27 days). Jason-2 and Jason-3 provide conventional (LRM) altimetry data, while Sentinel-3A operates in SAR mode, enabling more detailed detection of water surface elevations across the Amazon Basin.

Over inland waters, the returned radar waveform typically shows a sharp rise corresponding to the water surface. The waveform shape is then retracked using dedicated algorithms to accurately determine the range to the reflecting surface. However, in complex environments such as river–floodplain systems, the signal can be affected by surrounding land, vegetation, or multiple reflecting surfaces. Therefore, choosing an appropriate retracking algorithm is critical to ensure accurate water height retrievals.

In this study, we used the ICE-1 retracker (Mercier et al., 2002; Frappart et al., 2006; Calmant et al., 2013), which has been widely applied to continental water bodies and shown to perform well in the Amazon Basin. The resulting altimetry-derived water levels were used to validate both river and floodplain water levels simulated by the model.

### 2.5.3. Altimetry floodplain heights

The validation of water storage capacity in floodplains using altimetry data is based on the methodology developed in Guilhen et al. (2022). The validation relies on Jason-2 (2009–2017) and Jason-3 (2017–2019) data. To specifically focus on the floodplains, a filter was applied to the backscattering values of the series, and only data with a backscattering value greater than 20 dB were selected. Moreover, the acquisition of the data was extended to the floodplain polygons, rather than the usual OLTC (Open Loop Tracking Command) on rivers. Overall, the water storage capacity of the entire Amazon Basin was estimated at a daily time step over the last decade, from 2009 to 2018.

### 2.5.4. Model evaluation

Commonly used performance indices in hydrology were selected for calibrating discharge: the Nash–Sutcliffe Efficiency (NSE) and the coefficient of determination (R²) were applied to determine the quality of the modelling at daily time step for river discharge and the Root Mean Squared Error (RMSE) and R² for the comparison between the simulated water heights in the reach and floodplain and the water level measured by altimetry:

$$NSE = 1 - \frac{\sum_{i=1}^{n} (O - S)^2}{\sum_{i=1}^{n} (O - \bar{O})^2} \qquad (1)$$

$$RMSE = \sqrt{\frac{\sum_{i=1}^{n} (O - S)^2}{N}} \qquad (2)$$

$$R^2 = \frac{\sum_{i=1}^{n} (O - \bar{O})(S - \bar{S})}{\left(\sum_{i=1}^{n} (O - \bar{O})^2\right)^{0.5} \left(\sum_{i=1}^{n} (S - \bar{S})^2\right)^{0.5}} \qquad (3)$$

where O is the observed value, $\bar{O}$ is the mean of the observed values, S is the simulated value, $\bar{S}$ is the mean of the simulated values, and N is the number of observed data. NSE > 0.50 and R² > 0.60 are judged satisfactory (Moriasi et al., 2015).

## 3. Results and discussion

### 3.1. Calibration based on river discharge

Fig. 3 compares river discharges measured by the in-situ gauging stations of the HyBAM network and the river discharges simulated with SWAT-FP. Daily discharges are correctly estimated for most of the main rivers of the basin except for the Marañón River. The performances are generally very satisfactory, with an average R² of 0.83 and an average NSE of 0.50 for all stations (0.74 when excluding the Marañón station).

Simulations underestimate river discharges in the basin heads, such as at the Negro, Branco, Marañón, and Ucayali outlets, where they struggle to reproduce flood peaks. Several hypotheses seem to explain these weaker representations. First, the estimation of precipitation appears to be the main limiting factor. The underestimation of precipitation by TRMM is an established phenomenon in high mountain areas (Dinku et al., 2010; Casimiro et al., 2009; Zulkafli et al., 2014; Vallejo-Bernal et al., 2021). An extreme forcing of the parameters could have tackled this problem. Nevertheless, we preferred keeping the parameters in a realistic range. The second hypothesis follows from the first and is linked to the overflow effect, i.e., the lateral transfer of water from the river channel to the adjacent floodplain. When the floodplain is flooded, part of the water from the reach is transferred to the floodplain. This phenomenon propagates from reach to reach and could store large amounts of water. The combined effect of these two processes can explain the underestimation of flows in the piedmont basins. However, this alteration disappears downstream, where precipitation estimation is better.
<figure><img src="image_6.png" /><figcaption>**Fig. 5.** Spatial representation of all Jason 2–3 (n = 1,313) and Sentinel-3A (n = 3,102) virtual stations in the basin and associated histogram. The colour code refers to the R² value. Only R² with a significant associated p-value were selected.</figcaption></figure>

## 3.2. Validation based on water height records in rivers by spatial altimetry

### 3.2.1. Comparisons of water heights in rivers with Jason 2–3 and Sentinel-3A altimeters

The model was validated using altimetry data from Jason-2, Jason-3 and Sentinel-3A. A total of 1,313 virtual river stations were created for Jason 2–3, and 3,102 virtual stations were designed for Sentinel-3A. Fig. 4 shows the comparisons for four sub-basins between the daily river water height simulations and the observations with Jason 2–3 and Sentinel 3-A, with a well-represented temporality and satisfactory results. Fig. 5 is a spatial representation of all virtual stations in the basin, with the value of the correlation coefficient colour-coded to indicate the relationship between water height observed by altimetry and that simulated by SWAT-FP. The left part shows the histogram of these coefficients for the virtual stations in the watershed. Jason 2–3 returned an average R² of 0.62 ± 0.14 (p-value < 0.01) and an average RMSE of 0.75 ± 0.32. Concerning Sentinel-3A, the average R² for all stations is 0.75 ± 0.14 (p-value < 0.01), and the average RMSE is 0.65 ± 0.54. Both results

would tend to show the relevance of using Jason 2–3 and Sentinel-3A data to validate the water heights simulated by SWAT. However, because a Sentinel-3A virtual station comprises only about twenty comparison points, examining results from Sentinel-3A comparisons as general indicators rather than validation data is preferable.

On the one hand, the R² values are above 0.7 in the central area of the basin, particularly on the Madeira River. Most stations returned R² above 0.4 (>98.5 % for both cases), and the majority of R² values are above 0.6 for Jason 2–3 (61.5 %) and Sentinel-3A (74.4 %), respectively. Low R² values appear in mountainous areas, such as the Andes, or in areas where rivers are located in deep valleys. These areas are unsuitable for conventional altimetry due to the difficulties in identifying water zones in these basin parts.

### 3.2.2. Comparison of water heights in alluvial areas

The results of the SWAT-FP models are very satisfactory for modeling flows and water heights in the streams of the Amazon watershed. Nevertheless, this work focuses on estimating water storage in alluvial
<figure><img src="image_7.png" /><figcaption>**Fig. 6.** Comparison between daily floodplain water heights simulated by SWAT-FP and observed data from Jason 2–3 (a and b) and from Sentinel-3A (c and d). Station locations are in Fig. 7.</figcaption></figure>

wetlands. Therefore, this section focuses on virtual stations located in alluvial areas. Fig. 6 shows the comparisons for four sub-basins between the daily floodplain water height simulations and observations from Jason 2–3 and Sentinel-3-A, demonstrating well-represented temporal-ity and satisfactory results. Fig. 7 represents the spatial evaluation of Jason 2–3 and Sentinel-3A virtual stations in floodplains. These virtual stations do not cover the entire basin, as the floodplain delineation is forced with the SWAF-HR mask. For the 840 virtual stations of Jason 2–3, the average $R^2$ is 0.61 ± 0.16 and the average RMSE is 0.72 ± 0.28. For the 2,570 virtual stations of Sentinel-3A, the average $R^2$ is 0.75 ± 0.14 and the average RMSE is 0.67 ± 0.81. The indicators highlight satisfactory results and are substantially identical to those for rivers in the previous sections. Indeed, the quality of the representation of water height dynamics in the reach directly impacts that of the floodplains. Despite the few intrinsic flaws in conventional altimetry, it remains a robust tool for validating our modelling exercise. At the same time, SAR altimetry has also demonstrated its full performance for this application, thanks in particular to a finer resolution. Nevertheless, revisiting times and data acquisition periods for SAR may constitute a barrier to considering it as a validation method.

### 3.3. Spatial and temporal variability of flooded volumes

Fig. 8 shows the monthly averages of flooded volumes in the Amazon catchment. On average, over the entire Amazon Basin, September, October, and November are the driest months, resulting in little to no water stored in the floodplains. The flood begins between December and January, flooding the plains mainly in the Andean tributaries of the Madeira River. All the Andean basins, i.e., the upstream tributaries of the Madeira, the Marañón, and the Ucayali, overflow in February, with maximum flooding occurring around March. During March to May, the water volumes stored in the Andean basins floodplains decrease, and the flooding occurs in the central floodplains of the Purús and the Madeira. The flood wave continues, passing through the Amazon mainstem with a maximum occurring around May. After June, the floodplains storage gradually reduces. An offset can be highlighted for the northern tributaries, i.e., the Negro and the Branco Rivers, where the flooding occurs from May to September.

We aggregated the Fig. 8 information for the Amazon watershed and its main subsystems, resulting in daily time-step dynamics of water volumes stored in the respective floodplains (Fig. 9). Flood dynamics across the entire watershed show interannual stability. The average annual free water stock in the floodplains amounts to 1,800 km³ for the whole basin, corresponding to ca. 30 % of the average flow at the watershed outlet. The systems that contribute most to the flooding

are the Madeira and the Branco/Negro, with average overflow volumes of around 312 km³ and 480 km³. These two systems have a staggered effect on flooding, with the Madeira in the Southern Hemisphere triggering floods during the first half of the year and the Branco/Negro in the Northern Hemisphere, where floods occur during the second half. Concerning other subsystems, the Purús floodplains store about 150 km³, the Marañón and Ucayali floodplains store about 90 km³, and the Tapajós floodplains present marginal stored volumes of 31 km³. Finally, the water storage reaches 280 km³ for the Amazon mainstem floodplains.

Fig. 9b illustrates the monthly average contribution of each subsystem to the total flooded volume from 2000 to 2018. The contribution of the main hydrosystems is 5.5 % for the Ucayali and Marañón Rivers, 12.5 % for the Madeira, 33.6 % for the Negro River, 6.1 % for the Purús, 1 % for the Tapajós, and 14.7 % for the mainstem. The other floodplains (mostly within the Solimões subcatchment) contribute to 26.3 % of the overall flood. The contribution of the Ucayali and Marañón is relatively stable throughout the year. From January to April, most of the flooded volume (~30 %) is located in the Madeira Basin. During this period, the mainstem contributes approximately 16 %, the Branco/Negro system contributes around 13 %, while the other plains account for 21 % of the total stock. From June to November, the Branco/Negro contributes the most to the flood (>50 %) with a maximum in September. The basins of the Southern Hemisphere (e.g., the Purús and the Tapajós) do not contribute between August and November. In contrast, the maximum contribution occurs in February and March, with 16 % (13 % and 3 % for the Purús and the Tapajós, respectively). For the mainstem, its contribution to the overall flood is low between October and November (6–8 %). It increases to 16 % between December and April and reaches 18 % after May, when the Amazon overflow occurs.

## 4. Discussion

### 4.1. SWAT-FP potential

This paper demonstrates the potential of SWAT-FP to model water flows in complex systems, such as the Amazon, at a daily time step. Several works have previously evaluated these stocks in different ways. For example, the evaluations made with the MGB model (Paiva et al., 2013b) give satisfactory results. However, the SWAT model has the additional potential to refine our understanding of the biogeochemical fluxes in the Amazon system. Indeed, unlike most hydrological models applied on the Amazon River, SWAT and its extensions allow for the simulation of sediment and nutrients dynamics (e.g., nitrogen and carbon).
<figure><img src="image_8.png" /><figcaption>Fig. 7. Spatial representation of all Jason 2–3 (n = 617)) and Sentinel-3A (n = 2,752) virtual stations in the basin and associated histogram. The colour code refers to the R² value (2009–2018). Only R² with a significant associated p-value were selected.</figcaption></figure>

Only a few studies have been conducted with SWAT on the basin, as the model was not adapted due to its poor consideration of water overflow, which plays a crucial role in the Amazon. The SWAT-FP model proposed in Guilhen et al. (2022), and in this paper, addresses this challenge, enabling the study of various complex biogeochemical issues in the world's largest catchment. In fact, by also adding the impact of floodplains on sediment and nutrient dynamics into the model, we could then better understand and quantify the corresponding fluxes which are of major interest for the Amazon basin as well as for the Atlantic Ocean (Gévaudan et al., 2022; Louchard et al., 2021, 2023).

## 4.2. Model limits

### 4.2.1. Temporal variability

The model validation is based on the data measured by Jason 2–3 and Sentinel 3-A for fixed virtual stations. However, the temporal evolution of the reaches sometimes leads to offsets between the virtual station and the river location, reducing the performance of SWAT-FP. For example, the Ucayali sub-catchment (Fig. 10) has a relatively flat relief, making it a suitable area for altimetry applications. However, the results are not convincing for Jason 2–3. Here, the river is divided into several branches, and multiple studies have noted significant migration rates (Monegaglia et al., 2018; Walcker et al., 2021). Fig. 4 shows the Jason 2 altimetric profiles on the Ucayali River for 2009 and 2015. On each profile, a black arrow positions the virtual station according to the
<figure><img src="image_9.png" /><figcaption>Fig. 8. Spatial representation of monthly interannual flooded volume averages from 2000 to 2018.</figcaption></figure>

value of the backscatter coefficients. In 2009, the station latitude was at – 10.558° and moved to – 10.527° in 2015. This means that the position of the virtual station has shifted by about 3.1 km along the trace after six years. However, in our analysis, the position of the virtual station is fixed during the period (2009–2019). Considering the rate of river migration in the construction of the virtual altimetric stations could improve our results.

### 4.2.2. Spatial heterogeneity

The conceptualization of the SWAT-FP model assumes that the estimated water depth in alluvial wetlands allows for the estimation of the water volume in these areas as discussed in previous studies (Fassoni-Andrade et al., 2023; Fleischmann et al., 2020). This assumption is reasonable in the sense that the relief is actually homogeneous at the basin scale. However, SWAT-FP does not seem to represent well the areas of the basin with relief (Fig. 7). This discrepancy between observations and model may be due to a misrepresentation of the alluvial plains by SWAT-FP.

Indeed, one area within 2°N, -4°S, -76°W, -74°W particularly caught our attention. The comparisons between observed and modelled data are generally less good in this northeastern part of the basin. By comparing the altitudes returned by the satellite with those of the DEM, it is evident that the two are significantly different (up to 400 m). This error could be linked to the tracking instructions of the Jason satellites. These instructions, originating from a DEM source, enable the altimeter to more accurately adapt to the topographic variations of the surface under the track. However, particularly at the beginning of the Sentinel-3A mission, some areas were incorrectly parameterised, and the satellite could not correctly measure the height of these targets. These tracking instructions are, however, regularly corrected when the onboard DEM is updated. The points affected by this issue have been removed as much as possible from the analysis.

Unlike Jason 2-3, upstream parts of the basin return a priori better results for Sentinel-3A. Several hypotheses can explain this

phenomenon. First, the differences in the density of measurement points in the time series of the virtual stations may artificially inflate the performance indices (RMSE and R²). Second, Synthetic Aperture Radar (SAR) technology enables the visualisation of watercourses with widths down to 100 m. Additionally, the rectangular footprint of the altimeter enables better signal acquisition on this type of target and reduces pollution from the surrounding environment.

### 4.2.3. Validation data

Moreover, the performances of SWAT-FP also depend on the quality of satellite data and their capability to correctly estimate water levels in the Amazon basin. Although dense forest and cloudy or rainy weather can affect some types of remote sensing data, the Sentinel-3A radar altimeter operates in Ku- and C-band and is largely insensitive to cloud cover and rainfall. Previous studies have shown that precipitation events do not significantly alter the radar waveform or the derived water surface height. In the Amazon Basin, the altimeter footprints are centred over wide open-water channels, so canopy interference is negligible, and the return signal primarily originates from the water surface. Consequently, the quality of the satellite-derived water heights is not substantially affected by forest or weather conditions in this region.

## 4.3. General comparison with past research

Few studies exist on estimating the water volumes of floodplains in the Amazon watershed (Tourian et al., 2018; Sorribas et al., 2020). Tourian et al. (2018) conducted a watershed-scale estimate of the storage capacities of the Amazon floodplains. Their estimate is based on the use of gravimetry via the GRACE satellite and is compared to our results in Table 2. The volumes are pretty similar, except for areas 503 and 506, which are applicable at both the entire watershed and subsystem scales.

At the scale of the entire Amazon Basin, there is a slight deviation of 2%. For the other zones defined by Tourian et al. (2018), the results are also close, with deviations going from 7% to 86%. The low similarity
<figure><img src="image_10.png" /><figcaption>**Fig. 9.** (a) Daily flooded volumes in each subsystem of the Amazon and (b) contribution (%) of each subsystem to the monthly flooded volume (between 2000 and 2018). The mainstream floodplains correspond to the floodplains surrounding the Solimões-Amazon mainstream shown in Fig. 1, while “others” stand mainly for the other floodplains within the Solimões subcatchment.</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>**Fig. 10.** Jason 2 altimetric profiles on the Ucayali River for 2009 (left) and 2015 (right). The red colour represents a high probability of the presence of a watercourse. The black arrows indicate the potential position of the virtual station. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
<table><caption>**Table 2**
Comparison between flooded volumes simulated by SWAT-FP and Tourian et al. (2018).</caption><thead><tr><th>Areas defined by Tourian et al. (2018)</th><th>Estimates (km³) from Tourian et al. (2018)</th><th>Estimates by SWAT-FP (km³)</th><th>Relative difference (%)</th></tr></thead><tbody><tr><td>500</td><td>654 ± 12</td><td>494</td><td>-25 %</td></tr><tr><td>501</td><td>297 ± 3</td><td>266</td><td>-10 %</td></tr><tr><td>502</td><td>360 ± 10</td><td>312</td><td>-13 %</td></tr><tr><td>503</td><td>83 ± 4</td><td>154</td><td>+86 %</td></tr><tr><td>504</td><td>28 ± 1</td><td>32</td><td>+14 %</td></tr><tr><td>505</td><td>127 ± 7</td><td>146</td><td>+15 %</td></tr><tr><td>506</td><td>36 ± 1</td><td>10.5</td><td>-71 %</td></tr><tr><td>507</td><td>272 ± 7</td><td>217</td><td>-20 %</td></tr><tr><td>508</td><td>15 ± 1</td><td>16</td><td>+7 %</td></tr><tr><td>Full basin</td><td>1,766 ± 47</td><td>1,800</td><td>+2 %</td></tr></tbody></table>

rates correspond to zones 503 and 506, which are located in the Juruá River (a sub-catchment of the Solimões River) and the northwestern part of the Negro River. These results can be explained in our case by a slight extension of the floodplain and a low SWAF-HR detection in these areas. Therefore, our floodplain delineation may result in lower modelled water stocks than actual ones. In the other cases, deviations are lower than 25 %. These differences can be explained by the resolution of GRACE, which remains relatively coarse and by underestimates of water surfaces by SWAF-HR. Finally, previous studies on the distribution of water storage contributions across the watershed (Kim et al., 2009; Han et al., 2009) suggested that surface water accounts for 61–73 % of the terrestrial water storage anomaly in the Amazon Basin, while our study returns a value of 63 %.

By focusing on the Madeira Basin, Ovando et al. (2018) found open-water stocks of 10–94 km³ of flooded water at the level of the great Bolivian Mamoré plain during the 2014 flood. In our study, the Mamoré floodplain stored 6.5–85.6 km³ during the 2014 flood. For the upstream parts of the Negro River, Fleischmann et al. (2020) estimated a flooded volume of 8.6–43 km³ for the interfluvial wetlands on the Branco River and 12–45 km³ for the main floodplain of the upstream parts of the Negro River, leading to a total water storage of 20.6–88 km³. Although it is not possible in our study to distinguish the different typologies of floodplains, we find flooded volumes varying from 13.6 to 91.6 km³ over the same area. Our method returns a greater amplitude. Indeed, using a 1-D model and SWAT-FP formalisms does not accurately represent the drainage network of interfluvial wetlands, which can lead to an over-estimation of volumes. Lateral connectivity plays a crucial role in Amazon floodplains, yet it is often overlooked in many studies (Hunter et al., 2007). Nevertheless, some studies tackle this issue. For example, Frappart et al., (2005, 2008) demonstrate that the alluvial plains of the Negro Basin are characterised by a seasonal variation in storage, ranging from 170 to 331 km³. In our case, the volume varies from 309 to 615 km³.

At the Amazon scale, Frappart et al. (2012) demonstrate that the

major drought that occurred during the 2003–2007 period resulted in a reduction in stored water volumes. Thus, the Amazon watershed experienced a decrease of –129.4 km³ stored in 2005, with variable local effects: –36.7 km³ on the mainstem, –11.5 km³ on the Madeira, and –3.6 km³ on the Tapajós. At the same time, Papa et al. (2013) estimated that the impact of this drought, compared to a reference period from 1993 to 2007, will have induced a decrease in lowland stocks of –210 km³ across the basin. With the same periods of study, we observe decreases in open-water stocks in floodplains of –171.2 km³ for the entire basin, –30.9 km³ for the Amazon mainstem, –26 km³ for the Madeira, and –3.9 km³ for the Tapajós.

### 4.4. Effects of extreme weather events on floods

Between 2000 and 2019, the Amazon was subjected to extreme climatic events. These events are mainly due to the consequences of ENSO. We tried to quantify their effects on the flooded volumes of the main hydrosystems of the Amazon. We focused on specific years where the ENSO effects were significant and analysed annual volume anomalies (Table 3). The intense El Niño episode in late 2015-early 2016 caused a deficit of –607 km³ in the entire basin floodplains, with local decreases of –70 km³ for the mainstem, –157 km³ for the Madeira, –181 km³ for the Negro, –57 km³ for the Purús, –13 km³ for the Tapajós, and –16 km³ for the Ucayali and Marañón basins. These results are coherent with Parrens et al., 2023. Climatic events do not affect the entire basin similarly.

Climatic events do not affect the entire basin similarly. For example, 2011 was characterised by an intense La Niña episode leading to intense precipitation and floods (Espinoza et al., 2012). During this event, the Peruvian Amazon basin was particularly affected (Marengo and Espinoza, 2016). Table 3 shows that the annual volume anomalies across the entire basin and the Negro basin are positives (+29 km³ and 81.4 km³, respectively). However, some tributaries, such as the Madeira or the Purús, show negative anomalies (–34.1 km³ and –10.8 km³, respectively). These results are in accordance with those found in Parrens et al., 2023. In these two basins during the flooded season, intense precipitation was recorded but does not implicate exceptional floodplains inundations.

Additionally, it is worth noting that the effects of particularly intense episodes can potentially persist for several years. The 2016 El Niño is the most significant over the simulation period, which is also noticeable in the shallow values of the anomalies. However, 2017 is characterised mainly by a La Niña-type episode, and the anomalies are still negative for most systems and the basin.

### 4.5. Potential SWAT-FP – SWOT coupling

Several innovative tools will help improve estimates of the Amazon hydrological cycle. Among them, the Surface Water Ocean Topography (SWOT) satellite provides unparalleled precision in measuring surface

<caption>**Table 3**</caption>

Average annual volume anomalies in km³ for the entire watershed and its subsystems. The colour code indicates red for El Niño years, blue for La Niña years, and black for transition years.

<table><thead><tr><th>Year</th><th>Full basin</th><th>Madeira</th><th>Negro</th><th>Mainstem</th><th>Purus</th><th>Tapajós</th><th>Ucayali/Maranón</th></tr></thead><tbody><tr><td>2000</td><td>108.5</td><td>-54.0</td><td>78.6</td><td>33.1</td><td>-2.6</td><td>2.5</td><td>3.2</td></tr><tr><td>2003</td><td>-93.8</td><td>-60.8</td><td>-38.6</td><td>4.5</td><td>17.1</td><td>-5.3</td><td>12.2</td></tr><tr><td>2005</td><td>-177.4</td><td>-34.6</td><td>-18.0</td><td>-28.0</td><td>-33.7</td><td>-5.1</td><td>-10.8</td></tr><tr><td>2006</td><td>303.7</td><td>59.0</td><td>125.6</td><td>33.0</td><td>-5.2</td><td>8.8</td><td>21.0</td></tr><tr><td>2007</td><td>99.2</td><td>35.7</td><td>23.7</td><td>15.1</td><td>-18.2</td><td>-2.3</td><td>30.9</td></tr><tr><td>2009</td><td>339.6</td><td>82.0</td><td>47.6</td><td>37.5</td><td>58.3</td><td>5.3</td><td>10.1</td></tr><tr><td>2010</td><td>-196.2</td><td>5.2</td><td>-60.1</td><td>-40.9</td><td>3.4</td><td>-3.6</td><td>-27.</td></tr><tr><td>2011</td><td>29.2</td><td>-34.1</td><td>81.4</td><td>-7.4</td><td>-10.8</td><td>8.8</td><td>-18.9</td></tr><tr><td>2015</td><td>-210.7</td><td>-35.2</td><td>-137.1</td><td>-8.0</td><td>-10.4</td><td>-6.4</td><td>2.9</td></tr><tr><td>2016</td><td>-607.3</td><td>-157.6</td><td>-181.3</td><td>-70.8</td><td>-57.4</td><td>-13.7</td><td>16.2</td></tr><tr><td>2017</td><td>-108.1</td><td>-56.9</td><td>-37.4</td><td>-12.4</td><td>-8.1</td><td>5.1</td><td>2.6</td></tr></tbody></table>
water levels. Data from SWOT provides crucial information to improve hydrological model predictions, as the one presented in this paper. A first study has enabled the identification of the impact of the 2023 drought on water levels in the Amazon Basin (Moreira et al., 2025). In this paper, the flooding extension zones are based on SWAF-HR (Parrens et al., 2019). With SWOT, this information will be available directly from observations, which would allow to better constrain this input variable determining the operation of SWAT-FP. Combining the temporal revisit of SWAF-HR water surface and the high resolution and accuracy of SWOT water levels may prove to be a winning combination to better inform hydrobiogeochemical models like SWAT-FP.

## 5. Conclusion

This paper proposes significant advances in quantifying the dynamics of the water volumes exchanged between the river and the alluvial plains in the Amazon Basin. These quantifications are essential for understanding a complex hydrological system like the Amazon, where the volumes exchanged between the river and the alluvial plain play a crucial role in the hydrological dynamics at small and large scales. We found an interannual surface water storage in the Amazon Basin of 1,800 km3, with significant spatial and temporal variabilities. The main contributors to the flooding are the Madeira and the Negro basins, with a surface water storage equal to 312 km³ (12.5%) and 480 km³ (33.6%) on average, respectively. Finally, SWAT-FP provides a relevant modeling of surface water storage dynamics in floodplains at the scale of the Amazon Basin.

## CRediT authorship contribution statement

**Jérémy Guilhen:** Writing – original draft, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.
**Clément Fabre:** Writing – original draft, Visualization. **Marie Parrens:** Writing – review & editing, Validation, Conceptualization. **Sabine Sauvage:** Writing – review & editing, Supervision, Conceptualization.
**Franck Mercier:** Visualization, Validation, Supervision, Methodology.
**Ahmad Al Bitar:** Writing – review & editing, Validation. **Jean-Michel Martinez:** Writing – review & editing, Validation, Resources. **José-Miguel Sánchez-Pérez:** Writing – review & editing, Supervision, Conceptualization.

## 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.

## Acknowledgment

This work was funded by the HydroSIM project (Hydrologie Spatiale, In situ et Modélisation), the European Space Agency (ESA) in the framework of the Expert Support Laboratories, the program Terre Océan Surfaces Continentales et Atmosphere (TOSCA, France), and the Centre Aval de Traitement des Données SMOS (CATDS). The CATDS has also provided the SMOS L3 dataset. This work is part of SWOT-AVAL.

## Appendices

<table><caption>Appendix 1. Calibrated SWAT parameters over the 2000–2018 period. Most of the parameters were calibrated at the scale of a large sub-catchment shown in Fig. 1 as well as by hydro-morphological classification, e.g., plain against mountainous catchments</caption><thead><tr><th>File</th><th>Parameter</th><th>Description</th><th>Range</th><th>Calibrated value</th></tr></thead><tbody><tr><td rowspan="7">.gw</td><td>GW_DELAY</td><td>Groundwater flow delay (days)</td><td>0–500</td><td>31–180</td></tr><tr><td>ALPHA_BF</td><td>Recession factor for the shallow aquifer (days<sup>-1</sup>)</td><td>0–1</td><td>0.005–0.03</td></tr><tr><td>GWQMN</td><td>Depths of water in aquifer for return flow to occur (mm H<sub>2</sub>O)</td><td>0–5,000</td><td>500–2,500</td></tr><tr><td>GWREVAP</td><td>Revaporation coefficient</td><td>0.02–0.2</td><td>0.05–0.1</td></tr><tr><td>REVAPMN</td><td>Depths of water in aquifer for revaporation to occur (mm H<sub>2</sub>O)</td><td>0–1,000</td><td>250–1,000</td></tr><tr><td>RECHRG_DP</td><td>Percolation to the deep aquifer</td><td>0–1</td><td>0.05</td></tr><tr><td>ALPHA_BF_D</td><td>Recession factor for the deep aquifer (days<sup>-1</sup>)</td><td>0–1</td><td>0</td></tr><tr><td>.mgt</td><td>CN2</td><td>SCS runoff curve number for moisture condition II</td><td>35–98</td><td>35–98</td></tr><tr><td rowspan="6">.hru</td><td>OV_N</td><td>Manning coefficient for slope flow</td><td>0.01–30</td><td>0.05–0.15</td></tr><tr><td>CANMX</td><td>Maximum canopy storage (mm H<sub>2</sub>O)</td><td>0–100</td><td>200</td></tr><tr><td>ESCO</td><td>Soil evaporation compensation factor</td><td>0–1</td><td>0.6–0.95</td></tr><tr><td>EPCO</td><td>Soil evaporation compensation factor</td><td>0–1</td><td>0.5–0.9</td></tr><tr><td>SURLAG</td><td>Surface runoff delay (days)</td><td>1–24</td><td>1–10</td></tr><tr><td>SOS1</td><td>Transition month to the rainy season</td><td>1–12</td><td>1–12</td></tr><tr><td rowspan="2">.sub</td><td>SOS2</td><td>Transition month to the dry season</td><td>1–12</td><td>1–12</td></tr><tr><td>CH_N2</td><td>Manning coefficient for river flow</td><td>0.01–0.3</td><td>0.01–0.15</td></tr><tr><td>.rte</td><td>CH_N3</td><td>Manning coefficient for floodplains</td><td>0.01–0.3</td><td>0.01–0.15</td></tr></tbody></table>

## Data availability

Data will be made available on request.

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