Drivers and impacts of sediment deposition in Amazonian floodplains

Domain: earth_science
Published: 2025-04-02
Editorial outcome: major_revision

Abstract
The Amazon River carries enormous amounts of sediment from the Andes mountains, much of which is deposited in its floodplains. However, accurate quantification of the sediment sink at fine spatiotemporal scales is still challenging. Here, we present a high-resolution hydrodynamic-sediment model to simulate sediment deposition in a representative Amazon/Solimões floodplain. The process is found to be jointly driven by inundation, suspended sediment concentration in the Amazon River, and floodplain hydrodynamics and only weakly correlated with inundation level. By upscaling the sediment deposition rate (1.33 ± 0.24 kg m−2 yr−1), we estimate the trapping of 77.3 ± 13.9 Mt (or 6.1 ± 1%) of the Amazon River sediment by the Amazon/Solimões floodplains every year. Widespread deforestation would reduce the trapping efficiency of the floodplains over time, exacerbating downstream river aggradation. Additionally, we show that the deposition of sediment-associated organic carbon plays a minor role in fueling carbon dioxide and methane emissions in the Amazon.

Introduction
The Amazon River is the world’s largest hydrological system with a drainage area of 6.1 million km2 1. It delivers 17.8% of global freshwater (with an annual discharge rate of 6642 km3 yr−1)2 and roughly 1200 million tons (Mt) of sediment to the Atlantic Ocean each year3,4. Sediments transported from their origins in Guyana/Brazilian Shield and Andes to the Atlantic Ocean undergo a complex journey with repeated cycles of deposition and resuspension5. A substantial portion of the sediment does not make its way to the ocean but is instead deposited over Amazonian floodplains6. This deposition helps shape the diverse riparian and lacustrine landscapes comprising 14% of the lowland area (<500 m altitude) in the Amazon Basin7,8.

Sediment deposition affects river geomorphology9 and ecosystems10, and enriches the soil of the Amazon Basin with organic carbon and nutrients. This sustains high biodiversity and productivity in the region8,11 while fueling carbon dioxide (CO2) and methane (CH4)12,13 emissions. Despite its importance, our understanding of sediment deposition drivers in Amazonian floodplains remains limited. Existing studies suggest that the Amazon River inundation controls sediment deposition in the floodplains near the Andes14, where coarser sands are rapidly deposited during high water periods. However, this observation may not be applicable to the downstream floodplains, such as the Amazon/Solimões, which comprise the majority of Amazonian floodplain area. As the river progresses downstream, sediment composition transitions to finer sediments due to preferential deposition of coarse sediments, sediment attrition, and dilution by finer sediments from black-water tributaries15. Thus, sediment deposition in the Amazon/Solimões floodplains is less likely to be influenced by river inundation magnitude and more so by local factors, such as inundation phases, river sediment concentration, and floodplain hydrodynamics.

Human impacts also remain a poorly understood influence on sediment deposition in Amazonian floodplains. Despite low population density, sediment dynamics in the Amazon are not immune to anthropogenic disturbance16–18, especially from dam construction and land use change. Numerous dams on the mainstream and tributaries of the Amazon River have already trapped substantial amounts of sediment19–21. It is estimated that many proposed hydropower plants in the Amazon, without prioritizing hydrological connectivity, can potentially reduce sediment transport to downstream river reaches by up to 100%22. Additionally, the Amazon Basin is witnessing widespread deforestation activities23–25, particularly in the south and southeast regions26. Soil erosion increased 300% in the sub-basins of the Madeira, Solimões, Xingu, and Tapajós due to the rapid expansion of cropland and rangeland26. Climate change is another critical driver of changes in Amazon sediment dynamics14,27,28. Increasing trends of suspended sediment concentrations (SSC) in the Amazon River could be the result of the increase of annual peak flow over the past two decades29,30. However, future projections show that the Amazon will become drier with time28. The aforementioned factors culminate in complex and changing trends in sediment dynamics for the Amazon River and its surrounding floodplain. To better understand these dynamics, there is a critical need for a modeling framework which accurately represents the interactions among different drivers.

The sediment budget of the Amazon River is well measured via a network of gauge stations on the mainstream and major tributaries4 and remote sensing observations31, but accurate representation of sediment dynamics within Amazonian floodplains remains challenging. Both in-situ data and estimates of SSC derived from remote sensing imagery have limited ability to resolve the processes in sufficient spatial and temporal resolution. For example, though the underlying drivers of sediment dynamics, such as inundation, fluctuate usually on a daily basis and vary significantly in space across the complex landscapes at a 10-m scale32, in-situ sediment sampling was only occasionally conducted in specific floodplain locations for short time periods33,34. Meanwhile, despite the broad coverage from remote sensing technologies over the Amazon Basin floodplains31,35,36, the data resolution remains coarse and its uncertainty is often high due to the empirical nature of the methodology, signal degradation by cloud cover, macrophytes, and shallow sediment beds37–40. Current measures of sediment-associated particulate organic carbon (POC) deposition also lack sufficient spatial and temporal details. In particular, they are too coarse to determine which landscape and oxic condition POC is dominantly deposited in Amazonian floodplains41, and this is a determinant factor for the transformation pathways of POC in floodplain soils42,43.

Numerical sediment modeling that couples a high-fidelity two-dimensional (2-D) hydrodynamic model with a detailed sediment model offers a way to overcome these limitations44. Previous applications of 2-D hydrodynamic models in the central Amazon Basin45,46 as well as its floodplains47,48 have shown promising performance in resolving inundation extent, water depth, and the river-floodplain flow exchange. But these hydrodynamic models were used either solely for flooding simulations32,49 or together with sparse SSC data for quantifying the overall sediment budget in a floodplain40. Here, we used a recently developed hydrodynamic model, now coupled with sediment dynamics, to simulate high-resolution sediment deposition.

We selected a representative site of the Solimões floodplains, Janauacá, to investigate sediment dynamics and sediment/carbon deposition in Amazonian floodplains. Janauacá (3.2–3.25°, 60.23–60.13°) is a medium-size Amazonian floodplain along the right bank of Amazon/Solimões River, 40 km upstream from the confluence of the Amazon River and Rio Negro at Manaus, Brazil (Fig. 1a). This area is a typical subsystem of the Amazon River, consisting of floodplain forests, wetlands, river channels, and a lake. While its water stage follows a monomodal cycle, the water circulation pattern within the floodplain demonstrates significant complexities32. Janauacá has a small drainage area (~786 km2)50, receiving annual precipitation accumulations of ~2000 mm and little freshwater inflow from upstream rivers51. This floodplain connects to the Amazon River via a perennial channel (12 km in length; 100–200 m width) with levees located in its northeastern section, where exchanging fluxes occur during rising and falling water periods. The flooded area varies from 23 to 39 km2 during low water and high water, respectively. The water stage in Janauacá changes accordingly between below 11–24 m. The floodplain is progressively filled with sediment52, which mainly sources from the “white-water” Amazon/Solimões River loaded with suspended sediments from the Andes. In contrast, the upland catchment around the southern lake drains with black and clear water streams of low suspended solids concentration33. The sediment-rich white water has critical impacts on the downstream soil fertility53. In-situ sampling of water level, velocity, sediment, other suspended matters, and carbon fluxes was established in Janauacá over the last few years33 which helps improve the understanding of Amazonian floodplains. The data have been used to quantify CO2 and CH4 dynamics54 and their response to vegetation and inundation55. The hydrodynamic model has been rigorously validated in representing floodplain hydrodynamics, including both water stage and flow current32. Our advanced model enables the estimation of sediment deposition in Amazonian floodplains at unprecedented spatial and temporal details. It also facilitates disentangling the drivers of sediment deposition and its impact on carbon cycling in the Amazon.

Results
The hydrodynamic-sediment coupled model performs well in simulating water level and SSC in the Janauacá floodplain (Fig. 1b–e). As measured, the simulated water level in our computational domain follows four hydrological periods (LW: low water; RW: rising water; HW: high water; FW: falling water, see Method section for definitions) of the main course of the Amazon River tightly (Supplementary Fig. S2). The observed and modeled SSC are consistent in different locations of the floodplain, including the upstream river channel, the downstream river channel, and the open lake area (Fig. 1d). The SSC dynamics exhibit an annual monomodal cycle but asynchronous relationships with the hydrological cycle, with the water level peak lagged by over 4 months from the annual maximum SSC. Specifically, high and low SSC concentrations occur during RW and HW periods, respectively. The impacts of the water and sediment influx from upstream rivers on SSC dynamics are minor, whereas the largest contribution is from the Amazon River (Supplementary Fig. S5).

Sediment deposition in Janauacá is driven jointly by the hydrological cycle, SSC variations in the Amazon River, and floodplain hydrodynamics (Fig. 2). The monthly variations of the total sediment deposition in the floodplain and the sediment trapping ratio closely follow that of sediment influx from the Amazon River rather than water level (Fig. 2a, b). In Janauacá, sediment influx is shaped by the asynchronous variations of water level and SSC in the Amazon River (Supplementary Fig. S2). On average, 0.17 Mt yr−1 of sediment was deposited on the Janauacá floodplain from 2007 to 2016, or equivalently 0.033 Mt km−1 yr−1 of sediment was deposited per unit river length as the adjacent boundary of Janauacá with the Amazon River is about 5 km long.

The interannual variability of sediment deposition in the floodplain, however, shows a weak negative correlation (−0.36) against water level, the indicator of river flooding magnitude. Conversely, it shows a strong positive correlation (0.94) with the SSC in the Amazon River (Fig. 2c). Consequently, sediment deposition in the floodplain remained unexpectedly intensive during the years of extremely low water levels in 2010 and 2011 when the river SSC was high, and fell to the lowest in 2015 when the water level was normal but SSC was the lowest.

Due to the effect of floodplain hydrodynamics, the simulated sediment deposition shows large and distinct spatial variability during the four hydrological periods, with rates ranging from less than 1 kg m−2 yr−1 in the areas with persistently fast flows to over 5 kg m−2 yr−1 in the deeper lake areas (Fig. 2d, g). These results fall within the reported range of 0.5 to 8.8 kg m−2 yr−1 for different Amazonian floodplains6,34,56,57, as detailed in the Discussion section. Unlike the modeled SSC that reached a maximum during RW, the deposition rate reached elevated values of >2 kg m−2 yr−1 in both the RW and HW periods (Fig. 2e, f). Between these two periods, the sediment deposition during HW is more widespread to the lake and wetland units in the floodplain (Fig. 2e, f), implying its higher relevance to the sediment and carbon budgets of these landscape components. Particularly, the model shows that the rising water supplied a dominant amount of sediments to the floodplain (Supplementary Fig. S4b). These intruded sediments were only deposited to the deep water area of Janauacá Lake during the subsequent HW, several months after RW (Fig. 2f), when the slowing of water movement favored sediment deposition (Supplementary Fig. S6c). In regions adjacent to WLBC3 where flows are persistently fast during RW and HW (Supplementary Figs. S6b and S6c), the deposition rates are reduced due to continuous sediment resuspension and transport (Fig. 2e, f). Overall, the annual sediment deposition can exceed 5 kg m−2 yr−1 in the deep water lake.

Despite its short length (about 5 km) along the Amazon River, the Janauacá floodplain alone can trap up to 0.09% of the Amazon River sediment (Fig. 2b). This high trapping ratio highlights the efficient retention of the sediment influx by the floodplain. Of the 0.34 Mt mean annual sediment influx through the river-floodplain boundary (Fig. 2a), ~11.7% was deposited in the floodplain (Supplementary Fig. S10) and the rest was returned to the Amazon River during FW (Fig. 2b). Like sediment deposition, the sediment trapping ratio of the floodplain also reached the peak values in the RW period (Fig. 2b).

Anthropogenic disturbances from river damming and land use change exert critical and nonlinear impacts on sediment deposition in Amazonian floodplains (Fig. 3, and see “Method” section for experiment design of the counterfactual scenarios). By reducing and increasing the SSC level in the Amazon River to respectively mimic the impacts from damming and deforestation, our model predicts that a reduction of SSC by reservoir trapping or a small increase of SSC by moderate deforestation in the Amazon would cause the same proportional changes (roughly linear increase) of sediment deposition in the Janauacá floodplain (Fig. 3b). In other words, under these perturbation levels, the sediment retention ratio in the floodplain does not change. However, if the SSC level in the Amazon River increases substantially due to deforestation, the gain of sediment deposition in the floodplain would gradually lose to the surge of river sediment influx over time (Fig. 3c and Supplementary Fig. S16), causing higher fractions of river sediment to transport downstream instead of being deposited in the floodplain. Our analysis shows that this reduction of sediment retention in the floodplain is a result of floodplain bed evolution (Supplementary Figs. S12–S14). Under the extreme scenario of agricultural expansion (60% increase in SSC), the trapping efficiency of the floodplain is predicted to decline by 7% by the tenth year of the simulation, as the floodplain elevation near its open boundary rises by more than 0.5 m (Supplementary Fig. S12). It is worth noting that the human-induced alteration of SSC levels will make little changes to the seasonal and inter-annual variabilities of sediment deposition and sediment exchange flux (Fig. 3a, b and Supplementary Fig. S11b), although their seasonal variations are intensified. The seasonal change of SSC implies a more dynamic response of the deposition rate, with shifts in SSC peaks causing notable increases in sediment influx during HW (Supplementary Fig. S15a, c, and e).

With sediment deposition, considerable biologically active POC from the Amazon River was also deposited in the floodplain. However, quantitatively, the deposited POC per year is markedly lower than the annual soil organic carbon input from ecosystem primary production (Fig. 4a, b). For the lake area where POC deposition is the strongest, the modeled annual mean POC deposition rate is only 0.064 ± 0.013 g C m−2 day−1, which is one order of magnitude lower than the measured in-lake net primary production (NPP) in Janauacá and other Amazonian floodplain lakes, for instance 5.7–29.2 g C m−2 day−1 in Janauacá Lake33, 0.01–0.72 g C m−2 day−1 in the floodplain lakes of central Amazon13, and 0.55 g C m−2 day−1 in the nearby Lake Calado58 (Fig. 4a). Sediment deposition plays an even less important role in the carbon budget of the Janauacá wetland area (Fig. 4b). The modeled annual mean POC deposition rate is 0.012 ± 0.002 g C m−2 day−1, which is two to three orders of magnitude smaller than the measured wetland NPP of 26 ± 5 g C m−2 day−159 and 1.5 g C m−2 day−1 in the nearby, smaller Calado floodplain60. If considering the whole Janauacá floodplain, the estimated POC deposition rate is also considerably smaller than the derived NPP from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite (4.4 g C m−2 day−1) and the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) multi-model ensemble (2.5 g C m−2 day−1) (Fig. 4b; and see “Method” section).

With the measured oxygen levels and high-resolution water level and POC deposition simulations, we delineated a detailed map of oxygen exposure time (OET) of the deposited POC in the floodplain (Fig. 4c; and see “Method” section for the OET calculation). Within the lake zone (as defined in Fig. 1a), over 80% of the deposited POC has OET less than 10 days and over 90% has OET less than 1 month (Fig. 4d). The short OETs can be attributed to the concentration of POC deposition in deep lake waters (Fig. 2) as well as the shallow depths of oxycline in the Janauacá Lake33. Therefore, when sediment deposition occurs in the Janauacá Lake, the associated POC would likely experience an oxygen-deficient environment subsequently which favors carbon burial and anaerobic carbon oxidation, such as methanogenesis61,62. Conversely, outside of the lake zone, such as in wetlands and rivers, the deposited POC has much longer OET. For instance, over 60% of the deposited POC experiences longer than 100 days of OET in the non-lake area (Fig. 4d), an environment favoring aerobic carbon oxidation62.

Methods
Hydrodynamic-sediment coupled model
An numerical model is developed based on the TELEMAC-MASCARET modeling system that couples hydrodynamics and suspended sediment transport processes. TELEMAC-MASCARET is an open-source integrated suite of solvers for use in the field of free-surface flow82. The system tightly couples a shallow water module (TELEMAC-2D) and a new sediment transport module (GAIA)83 at every timestep (i.e., 30 s). TELEMAC-2D solves 2-dimensional (2-D) shallow water equations (SWE) and advection diffusion equations (ADE) for suspended sediment transport. GAIA computes the bed evolution and the erosion (E) and deposition (D) fluxes (kg s−1 m−2) following84,85:

E = M \tau_b (1 - \tau / \tau_{ce}), (1)

D = w_s C_b (1 - \tau_b / \tau_{cd}), (2)

where M is the erodibility coefficient or Krone-Partheniades erosion law constant (kg s−1 m−2), \tau_{ce} and \tau_{cd} are the critical shear stress for erosion and deposition (N m−2), repsectively. \tau_b = 0.5 \rho C_f (U^2 + V^2) is the bottom shear stress (N m−2), where C_f is the friction coefficient, and U and V are velocities in the Cartesian system. w_s is the settling velocity of the sediment particles and C_b is defined as the sediment concentration at the interface between suspended load and bed load, which is the SSC in our case since bed load is not considered. Both E and D contribute to the change of bed elevation.

The hydrodynamic component of the coupled model was previously configured by Pinel32 based on the 30-m resolution digital elevation model (DEM) derived from Shuttle Radar Topographic Mission (SRTM)50. The bias over permanently inundated areas in the SRTM DEM was addressed by using the elevation data from the NASA’s Ice, Cloud, and Land Elevation Satellite (ICEsat) Geoscience Laser Altimeter System (GLAS). The SRTM correction also used bathymetric data from high water levels in June 2012, collected with an ADCP and GPS-linked echo sounder, along with sampling from May 2008 and August 2006. Meanwhile, vegetation-induced biases were corrected using a wetland map from ref. 7 and a MODIS-derived vegetation height dataset from ref. 86. These corrected ground elevations were integrated using the ANUDEM v5.3 algorithm, constrained by a drainage channel network identified from Landsat imagery. For a detailed methodology, see ref. 50. The computational domain of Janauacá is delineated as the floodable region with an elevation lower than 29 m (4.6 m above the observed maximum water level)32. The mesh was developed with a horizontal resolution of 23–77 m32. The domain includes 8 inflow boundaries (BCF) and 3 water level boundaries (WLBC), the latter of which connect to the mainstem of the Amazon River (Fig. 1a). The delineation of WLBCs is based on ALOS-1/PALSAR images. The model domain is classified into various topological regions based on the dual-season wetlands map7 (Supplementary Fig. S1), where the corresponding Manning’s roughness coefficients are defined following87 and are calibrated to ensure minimum bias in the hydrodynamic simulation32. The upland discharge and water level boundary data are obtained from a hydrologic model51 and the observation gauge at Manacapuru (60°33’13.8”, 03°18’51.0") from the ORE-HYBAM database4, (https://hybam.obs-mip.fr/), respectively. The water level at the WLBCs is bias-corrected, using the difference in bed elevation from the Manacapuru gauge. Wind was excluded from the simulation due to the unavailability of vegetative sub-canopy wind, which is essential for accurate modeling in this context. This hydrodynamic model, as calibrated and validated against observations of water level, velocity, and flood extent over multiple hydrological years, provides a prerequisite for coupling with a sediment transport model that preserves the full dynamics.

Our simulation considers depth-integrated SSC, as the water depth in the Janauacá floodplain is typically much shallower than that in the Amazon River mainstem. Even in the deeper lake areas, peak water depths reach about 10 m during HW and reduce to less than 5 m during LW (Fig. S3), with SSC showing minimal variation across the shallower water column40,88. The daily SSC at the water level boundaries (Supplementary Fig. S2a) was derived from the near-surface observation at Manacapuru from the ORE-HYBAM database4, with sample gaps filled using satellite-derived estimates following40. Outliers that are 4 standard deviations away from the mean were removed from the satellite-derived SSC estimates. A Lowess filter was applied with specified parameters (fraction=0.02, iterations=0, degree of polynomial=1) to ensure a smoothed SSC boundary condition and eliminate unrealistic variations that may cause numerical instability issues. There are no significant tributaries between the Manacapuru gauge and the WLBCs that would alter the sediment load along this stretch of the river. The satellite data39 also implies no significant difference between SSC at Manacapuru and at WLBCs (Supplementary Fig. S18). However, due to the lack of direct SSC measurements at WLBCs, there is an inherent uncertainty in the SSC boundary condition. While there are no SSC samples collected from the upstream boundaries, the lake’s upland catchment drains black water streams with low SSC (<20 mg L−1)33. Thus, the SSC values are assumed as 20 mg L−1 at the BCF1 and BCF8 and 40 mg L−1 at the rest BCFs. The water level data is used to define 4 hydrological periods of low water (LW), rising water (RW), high water (HW), and falling water (FW): LW is when water level <15 m, HW is when water level >20 m, and RW and FW are the periods with water level between 15 m and 20 m (Supplementary Fig. S2b).

In-situ samples collected at Manacapuru along the Solimões River88 and in a similar floodplain, i.e., Lake Grande de Curuai89, imply that the composition of suspended sediment is dominated by silt and clay (<63 μm), with the median particle diameter (D50) of 12~49 μm. Thus, we define 3 cohesive sediment classes in GAIA. The first two classes correspond to clay or fine silt from the Amazon/Solimões River, and the third class is sourced from the upstream catchment. We use the default sediment density of 2650 kg m−3 for all classes. Please note that since significant flocculation only occurs where the Amazon sediments meet the fine sediments from brackish-water tributaries27, our model does not account for flocculation processes, which are observed to enhance the settling velocity of fine sediments in many river systems90.

GAIA allows the change of lake morphology from sedimentation processes. The cumulative bed evolution is computed internally in the model computation using the deposition obtained in Equation (2) and a default bottom layer density of 50 kg m−3. Since the model does not output deposition rate directly, we estimated the deposition per unit area from the computed bed evolution. The deposition rate per unit length is the total deposition of Janauacá over the length of its open boundaries connecting to the Amazon River (~5 km). The sediment trapping ratio is defined as the ratio of the Amazon River sediment trapped by the floodplain, which is calculated by dividing the deposited sediment in the floodplain by the river sediment load measured at Manacapuru.

The modeled hydrodynamics in the Janauacá floodplain has been previously validated in ref. 50. This study re-validates the model against the water level observation at two gauges. The validation of modeled sediment dynamics in Amazonian floodplains remains challenging due to the dearth of both SSC and sediment deposition data. Here, the modeled sediment dynamics are validated using the SSC in-situ samples collected at three sites, which are all 0.5 m below the water surface, within Janauacá (Fig. 1a)33,54,55, and the measured sediment deposition rate in floodplains with similar geographic settings6,34,89, as there is no direct measurement of sediment deposition in the local region. At the downstream gauge, the Amazon River dominates the SSC variation, whereas in the open lake and upstream regions, wind effects may lead to sediment resuspension during low-flow seasons. The coupled hydrodynamic and sediment transport simulation is performed from 2007 to 2016. The first year is considered as model spin-up and is excluded from the analysis. To upscale the modeled deposition, the total floodplain area within the Amazon/Solimões River mainstem drainage basin is computed using the flooded wetland extent during high flood conditions7,91 with the basin boundary delineated in ref. 92. The amount of sediment deposition in the floodplains within the drainage basin of Amazon/Solimões River mainstem can be estimated using the total floodplain area and the modeled deposition rate per unit area.

Anthropogenic disturbance
To quantitatively assess the impact of anthropogenic disturbance from dam construction19–21 and land use change23,26 on sediment deposition, we performed multiple numerical experiments by perturbing the SSC value at the WLBCs. In general, dams trap large amounts of sediment, resulting in decreased SSC in the downstream reaches30,93. 94,95 reported a 20–30% reduction of SSC in the Madeira River downstream of the Santo Antônio Dam. 22 implies that the dams, built or proposed in the central Amazon Basin present a trapping efficiency of 0–60%. Following their estimate, the potential impact of dam construction is quantified by experiments with the boundary SSC reduced by 10%, 20%, 30%, 40%, 50% and 60%. Furthermore, excessive agricultural expansion and livestock activities cause deforestation in the Amazon Basin. The resultant land use and land cover change intensifies soil losses from erosion, increasing downstream SSC23. The impact of deforestation is estimated by increasing the boundary SSC by 10–60% based on the increased soil erosion and sediment delivery rate in Solimões River26. While it is not entirely clear how anthropogenic disturbances affect the seasonal variations in SSC, these potential impacts were assessed through experiments, including reducing variation amplitude by 60% and enhancing the SSC magnitude by 60% during peak periods. Although the timing of the SSC peak is influenced by climatic rather than anthropogenic forcing, supplementary experiments were conducted by advancing the SSC peaks 30, 60, and 90 days towards the water level peak to assess how interactions between climate and human activities might affect the sediment deposition in Amazonian floodplains.

Carbon dynamics
The impact of sediment deposition on carbon dynamics of Amazonian floodplains is assessed through the estimation of particulate organic carbon (POC) deposition and oxygen exposure time (OET) of deposited POC. While the present TELEMAC-GAIA model does not directly simulate POC deposition, it can be reasonably estimated using simulated sediment deposition rate and measured POC concentration in suspended sediment of the Amazon River.

D_{POC} = D \times f_{POC}, (3)

where f_{POC} is the POC concentration measured from suspended load samples collected from channel depth profiles at Manacapuru during two sampling campaigns in June 2005 and March 2006 that respectively correspond to HW and RW periods70. The value is 1.27% ± 0.21%. It should be noted that due to the dependence of POC adherence on sediment grain size96, the use of a unified POC ratio across all sediment classes carries substantial uncertainty.

OET is the major driver of POC burial efficiency and its mineralization pathways. High OET corresponds to fast mineralization of deposited POC and strong CO2 production through aerobic oxidation. To the contrary, low OET corresponds to slow mineralization of deposited POC and strong CH4 production through anaerobic oxidation. For deposited POC, OET is estimated using the modeled water depth and measured depth-resolved dissolved oxygen (DO) levels following the steps below. First, we identify the oxycline—the depth below which DO concentrations fall below 2 mg L−1—using DO data collected during the four hydrological periods by Amaral33 at the wind-exposed (WE) site of Janauacá. The estimated oxycline depths are 0.5 m, 2 m, 3.5 m, 1.2 m for LW, RW, HW, and FW, respectively. We assume that the measured DO is uniformly distributed horizontally across the WE site and the identified depths separate aerobic and anaerobic sediment. Second, we compare the modeled water depth at each grid cell to the estimated low DO depths and define OET of each grid cell as the amount of days in a year that the sediment is above the low DO depths. The estimated OET is calculated based solely on the pre-burial oxidation condition for the settled POC and does not account for post-deposition oxidation processes that can deplete oxygen in the sediment layers. As a result, our estimation tends to be conservative.

For Amazonian floodplains, sediment/soil OC has two sources: autochthonous OC from primary production within the floodplain and allochthonous OC from POC deposition. We estimate autochthonous OC input in the floodplain from published net primary production (NPP) data for the lake area (Fig. 1a) and wetland area (non-lake flooded area) of Janauacá. The wetland area is covered by macrophytes and flooded forest (Supplementary Fig. S1). For the wetland area, the NPP estimates are available from in-situ data at Janauacá33, from a comparable and smaller floodplain58–60, satellite-derived MODIS product97 and ISIMIP multi-model ensemble98. It should be noted that due to their coarse resolutions, the MODIS and ISIMIP estimates would include primary production from upland forests. We used the NPP of CARAIB, DLEM, JULES-B1, LPJmL, ORCHIDEE, VEGAS, and VISIT from ISIMIP2a simulations that are forced by the GSWP3 climate data. The ISIMIP model outputs have the resolution of 0.5° and are archived monthly from 1971 to 2010. The annual NPP at the grid nearest to our domain since 2000 is used for comparison. The monthly NPP derived from MODIS from 2008 to 2016, with the spatial resolution of 500 m, is averaged annually over the model’s computational domain. For the lake area, the NPP estimates are available from local measurements33 and similar floodplain lakes13,34,58.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "What is the reference for this value?\nPage 6, “years of extremely low water levels in 2010 and 2011”: It is unclear why the years evaluated in this paper were chosen. Is this limited by SSC data availability? Are these years anomalous due to storms or climate cyclicity? If satellite measurements are available that match field sampling well, why not examine sediment transport over a longer period of time to have more statistically robust results?\nPage 6, “the deposition rate reached elevated values”: To get from sediment concentration to sediment deposition rates in a specific you multiply the SSC by an average settling velocity. Grain size and SSC both vary with depth in the channel, so how does your model account for this? Does the SSC from your datasets represent a surface value or do you do some normalization based on flow depth and shear stress?\nPage 6, “deep water area of Janauaca Lake”: What are the constraints on lake bathymetry? The model appears to assume it is constant.\nPage 6, “For 0.34 Mt yr−1 of the mean annual sediment influx (Figure 2a)", "reviewer_2": "This manuscript is much improved after what appears to be careful attention and response to the comments of the three reviewers. Many of my primary concerns have been satisfied, although the following relatively minor issues remain.\nAbstract: Andes mountains (missing an “s” on mountains)\nAbstract: the state encompassing Manaus is Amazonas, which might be a better fit than Amazônia in this context. Elsewhere you use anglicized words to refer to the greater Amazon area (e.g., Amazonian) so it’s a bit of a divergence to see the Portuguese usage here. This is admittedly a minor point, and one of the authors is Brazilian, so perhaps it’s fine.\nL6: I brought up the 15% vs the 6.1% you reference in the abstract in my last review. It would be useful here to hedge this number at this location by pointing out the difference in domain for these two numbers, especially since you use the exact same phrasing (Amazonian floodplains) in the abstract and on this line to refer to two different areal domains. (I do appreciate the expanded discussion later in the paper about the potential limitations/uncertainties regarding your new estimate.)\nFigure 1: I appreciate the addition of (e) but remain suspect of the goodness of fit between modeled and observed SSCs since this is on a log-log scale. Please indicate if the black line is a best fit or 1:1 line in both (c) and (e).\nL133: I suggest “reached a maximum” instead of “topped” here\nL152: I’m confused by the numbers here. Is this the same 15% referenced from [6] in the intro? As written, they appear to refer to two different quantities, that is, fractional deposition rate of mainstem sediment on floodplains vs retention of sediment influx to the floodplain itself. Is the 15% just a coincidence on both of these numbers? I don’t see 11.7% in Figure 2B anywhere.\nFigure 3: Please double check the y-axis label of subplot c. Do the units work?\nL232: I remain confused by the use of 11.7 here and 6.1 in the abstract. I believe they are referring to different quantities (see comment on L152) but as written it is hard to distinguish and keep track of the ratio of deposition to the mainstem flux and ratio of retention of the inflow to the floodplain. I suggest clarifying to indicate which is which when numbers are used.\nFigure S7: What is the meaning of the dashed lines in the figures? Are they best fit lines? If so I am surprised by their orientations, especially in (a) and (c), since the r values are negative and the slopes should be, as well.\nFinally, the figshare link includes Python pickle (.pkl) files. As described at https://docs.python.org/3/library/pickle.html the pickle module is not secure, and users should only unpickle data they trust, since pickle files can execute arbitrary code on a user’s machine. This makes it an inappropriate format for supplemental files for the manuscript, and I strongly suggest providing data in a more standard format like netCDF which does not have the security implications of pickle files. For this reason, I have not opened the files, so it’s possible something even simpler than netCDF like CSV or JSON would be appropriate.", "reviewer_3": "nPage 12, “tropical floodplains”: I would assume the magnitude of deposition should be set more by tectonics than climate, since subsiding basins with aggrading rivers should have significant floodplain deposition rates regardless of if they are in the tropics. Please clarify this point.", "reviewer_13": "The introduction needs to include more prior work on floodplain outgassing and clarify that you focus on particulate OC. Rivers, wetlands, and dry floodplain soils can all release CO2 and CH4 but this can come from oxidation of POC or DOC. For instance, I would expect DOC to be more readily emitted as CO2 or CH4 from rivers and wetlands while POC should track with fine sediment deposition and is more likely to be sequestered in floodplains and avoid oxidation.\nPage 3, “accurate estimation of sediment dynamics in Amazonian floodplains with sufficient spatial and temporal details still remains challenging.”: It is not clear to me what spatial and temporal resolution you require to understand biogeochemical cycling and what specific knowledge gap this study hopes to fill.\nPage 4, “we used a recently developed and well validated model”: It would be very helpful to give more information about the model you used and where it has been validated. Stating here that the model was already validating for hydraulics at this field site is important since the reader does not get that information until the Methods.\nPage 4, “the simulated water level in the floodplain follows four hydrological periods”: I was quite confused by the extent of the floodplain boundary versus the model domain, which should be defined and consistent with maps in the supplement.\nPage 6, “the mean annual deposition rate per unit length of Amazon River", "reviewer_retained”": "Where does the rest of the sediment go? Does it bypass the floodplain and return to the Amazon River or does it flow upstream on tributaries?\nPage 7: I would appreciate a paragraph that includes discussion of the spatial and temporal patterns in SSC and deposition and discusses causes of their variability in detail. I feel like the results gloss over this and just discuss parameters calculated for the whole floodplain, like the sediment trapping ratio.\nPage 8, “except in the case of elevating SSC by 60% in which excessive erosion would occur during HW”: I don’t understand why elevating SSC would increase erosion. If input SSC is higher wouldn’t that cause increased deposition? This is an interesting and non-intuitive result that deserves more discussion so the reader can understand this effect.\nPage 9, “measured in-lake net planktonic primary production (NPP)”: It is worth separating out different types of carbon. SSC can contain petrogenic OC, which is more recalcitrant and could be important for long-term OC storage in floodplain deposits, while the biospheric OC produced by NPP could be more labile.\nTop of page 10: This paragraph should be motivated in the introduction. Stating that you want to determine if sediment POC deposition matters in floodplains and particularly in lakes would help to focus the introductory paragraphs.\nPage 10, “POC deposition at the Januaca floodplain is also considerably smaller than the derived NPP”: This paragraph makes it sound like the POC deposition flux doesn’t matter. It's important to be very clear if it does, and if so, over what time and spatial scales.\nPage 10, “the shallow depths of the oxycline”: Be specific. How shallow? Does the oxycline depth vary throughout the year?\nPage 12, “tropical floodplains”: I would assume the magnitude of deposition should be set more by tectonics than climate, since subsiding basins with aggrading rivers should have significant floodplain deposition rates regardless of if they are in the tropics. Please clarify this point.\nPage 12, “sediment deposition in the floodplains could account for a 6.1%±1% of sediment discharge by Amazon River”: How does this compare to the rates of sediment transport and storage computed by Repasch et al. (2020)? Other work by Repasch and Scheingross could provide insight into OC oxidation during POC storage in the Amazonian floodplain.\nRepasch, M., Wittmann, H., Scheingross, J. S., Sachse, D., Szupiany, R., Orfeo, O., et al. (2020). Sediment transit time and floodplain storage dynamics in alluvial rivers revealed by meteoric 10Be. Journal of Geophysical Research: Earth Surface, 125(7), e2019JF005419. https://doi.org/10.1029/2019JF005419\nPage 12, “river SSC and floodplain hydrodynamics", "reviewer_stage”": "Based on the Exner equation you should get most rapid erosion and deposition when you have a spatial or temporal change in SSC so this offset makes sense to me. I think setting up a testable hypothesis in the Introduction based on sediment transport conservation of mass would help the reader to understand this temporal offset more clearly.\nPage 13, “sediment would be deposited over the vegetated area [60", "reviewer_61": "This should vary depending on the delta and the magnitudes of riverine versus tidal and storm-driven sediment fluxes. I don't think the references here are particularly convincing, since the Mississippi River deposits enough sediment within its channel that it is very superelevated.\nPage 13, “exacerbating channel aggradation and reducing flood capacity”: This is an important point but requires some more detail. Reaches could gradually adjust to increased SSC in other ways such as migrating more rapidly, steepening, or avulsing.\nPage 14, “The POC deposition rate on the wetland area of Amazonian floodplains is relatively small”: Again, I am not sure how impactful the carbon story is. The authors argue that the methane flux could be important, unlike the CO2 fluxes. However, they do not offer any specific numbers for Amazon CH4 from other sources or make their own measurements of CO2 or CH4 outgassing. This is important because CH4 could be produced at depth in lakes but then be oxidized to CO2 by methanotrophs, in which case the flux may not matter. In addition, CO2 and CH4 have different residence times in the atmosphere so it's important to say what timescales these fluxes occur over.\nPage 14, “input of deposited POC to the soil carbon pools”: The authors seem to assume that adding POC will increase CH4 production. Is this the case for floodplain lakes in the Amazon? Other limitations could be reaction kinetics, microbial abundance, and type of POC input. Since the lakes have such high biospheric POC input from photosynthesis I do not expect that adding a small amount of recalcitrant POC would significantly affect lake CH4 emissions.\nPage 14, “This Amazonian floodplain is a typical subsystem of Amazon River,”: There is some justification for why you picked this floodplain, but it would be helpful to know how common lakes are along the Amazon. Was this motivated by findings in your prior work? Perhaps the entire “Study site” section could be moved into the main text and describe more of the earlier results at this site.\nPage 16, “Shuttle Radar Topographic Mission (SRTM)”: SRTM data is for the top of vegetation. I went through the references and found that you corrected for this in prior work, but that should be mentioned in this manuscript as well.\nPage 16, “Manning’s roughness coefficient”: How did you delineate these regions and pick a Manning's roughness value for each? Did you pick a value independently based on field observations or iterate on different values to find a good fit of the model to the data? How sensitive was the model to delineating these domains and picking different Manning's n?\nPage 16, “A Lowess filter”: Please specify which filter parameters were used.\nPage 17, “…high water (HW) and falling water (FW)”: How did you pick these periods? Was it arbitrary or was there a quantitative threshold?\nPage 17, “we define 3 cohesive sediment classes in GAIA”: Do you include the effects of flocculation here? Mud is rarely transported as isolated particles in rivers and this would increase settling velocities and likely decrease the time spent in oxidating environments.\nPage 17, “The deposition rate per unit area is estimated from the bed evolution rate and the default bottom layer density of 50 kg m3.”: I did not understand how the deposition rate was calculated from the bed evolution rate since the equations presented have it being calculated from sediment concentrations and settling velocity. Is the bottom layer density the water + sediment in the bedload layer? Or is that the bottom of the river?\nPage 17, “SSC in-situ samples”: How were these samples collected? Using a Niskin bottle, Van Dorn sampler, etc.\nPage 19, equation 3: Part of the results in Bouchez et al. referenced here is that POC tends to stick to finer grained sediment. Since you are using 3 different grain size classes and sources for sediment, it makes sense to assign each a different f_POC if they are statistically significant.\nPage 19, “the identified depths separate aerobic and anaerobic sediment.”: Rapid burial can also produce anaerobic environments under sediment since oxygen will have to diffuse down into the layers of lake mud. I am unclear if the calculations consider post-deposition oxidation or only consider oxidation while POC is settling out of the water column.\nPage 20, “macrophytes”: Please define the difference between macrophytes and forest.\nFigure 1: I would like panels added to a figure showing the locations of the floodplain and where the gages are placed, similar to Figure S1 but in the main text. I am not familiar with this region and the geometry of the rivers and lake is important to understand the results. In addition, please put the circles and triangles on the figure legend. From the figure, it looks like there was a big pulse of sediment from upstream in January 2016 that the model did not capture very well… why is that? Does this affect the results?\nFigure 2: I don't see these transects mentioned in the caption marked on Figure S1. Also, this caption is the first mention of POC versus floodplain carbon in general. Be sure to define POC and distinguish it from DOC.\nFigure 3: It is not clear to me why the authors used a polynomial fit, and in particular a cubic equation. Comparing the linear and polynomial fits requires at least reporting R^2 values for each fit to all the data. Attributing the change in behavior for very high SSC inputs could inform what relationship you expect here and motivate picking a more complicated function than a linear fit. For example, deposition is proportional to SSC*w_s. If the settling velocity is somehow proportional to SSC (maybe you pick up larger sediment at higher SSC?) then you could propose that w_s ~ SSC and D ~ SSC^2.\nFigure S1: How did the authors select this computational domain? Please put an arrow on the figure to indicate the direction each river is flowing. It would be helpful to have a topographic map showing the boundaries of the floodplain and have the areas shaded in.\nFigure S2: This image is still confusing to try and understand what is considered the floodplain versus the model domain. Much of the area appears to be a lake (permanent water), though the boundaries of the lake does not agree with Figure S1. It is not clear how the areas for forest, inundated forest, etc. were delineated and the methods need to be explained in the caption or Methods section. One possible source could be inundation frequencies maps accessible at https://global-surface-water.appspot.com/map.\nFigure S3: I am not sure how well the satellite measurements and field sampling agree, so a cross-plot of field versus satellite measurements is needed to validate this approach.\nFigure S5: There are gaps visible between Amazon riverbank and the start of the model domain that seem important since most of the water and sediment is coming from the Amazon. Some mention of how much water flows through the secondary channel connected to the main channel versus overbank sheet flow would be helpful in addition to the delineation of fluxes in through each boundary already done in the main text.\nFigure S6: Be clear which of these lines is the sediment and water from the Amazon. The main text mentions that flow into the modeling domain is positive while out is negative but it would be good to annotate the figure to remind the reader. Also, it appears that there is a net transport of water and sediment upstream (flowing backwards) into the tributaries to the lake – is that correct?\nFigure S9: It's not clear how this map was produced compared to Figure S2. Is the floodplain here equivalent to the wetlands in that caption?", "reviewer_author_response": "We thank the reviewer for the valuable comments and recommendations. We have carefully addressed the reviewer’s suggestions as follows:\nR1C1:\nThe authors scale up their deposition rate to a stretch of the Solimoes/Amazon River. However, there is probably still a lot of deposition and exchange happening in smaller-order rivers. Can the authors further contextualize their results across all rivers in the Amazon basin or at least comment on how the estimate they are providing does not include other rivers and is thus an underestimate of the true amount of floodplain sediment deposition in this basin. This is an important point, because the deposition amount is not just 6% of the Amazon sediment flux, it is much higher if you account for the deposition happening in other floodplains.\nReviewer 2\nThank you for the opportunity to review this paper, which describes a modeling effort to determine sedimentation in a floodplain on the Amazon River, Brazil. Overall, the paper is easy to follow and describes an interesting study on floodplain of a globally important river. The study seems interesting and is of importance to quantifying and constraining Amazonian sediment dynamics, but I do not believe it currently merits publishing in Nature Communications.\nReviewer 3\nThis manuscript uses a numerical model that couples 2D shallow-water hydrodynamics with cohesive sediment entrainment and deposition to predict how much sediment is deposited on the floodplain of the Amazon River near Lake Janauaca´. The results indicate that most suspended sediment comes from the Amazon River and flows through a secondary channel before being deposited in the lake, and that there is a 4-month delay between peak floods in the Amazon and peak sediment deposition. In addition, the results indicate that a small fractionofparticulateOCisdepositedaspartofSSC,butitisunclearifthishasasignificant effect on regional biogeochemical cycling since NPP by algae in the lake greatly outpaces POC deposition.\nOverall, I found the model results interesting, but I did not think the manuscript described the modeling in sufficient detail to fully interpret the results. This study builds on significantpreviousworkatthesite, butthemanuscriptdoesnotstandonitsownaswritten. The introduction did not lay out clear knowledge gaps or testable hypotheses for the study, so it is not clear to me how significant or transferable the results are to other floodplains. Predicting fine-scale patterns of sedimentary deposition is at the frontier of modeling for river floodplains, but the paper requires significant rewriting to make this study’s contribution and broader impacts clear. Given these considerations, I recommend this manuscript for major revisions followed by re-review.", "reviewer_2016": "nPage 10, “POC deposition at the Januaca floodplain is also considerably smaller than the derived NPP”: This paragraph makes it sound like the POC deposition flux doesn’t matter. It's important to be very clear if it does, and if so, over what time and spatial scales.", "reviewer_2020": "nPage 12, “river SSC and floodplain hydrodynamics, do not synchronize with the temporal and spatial variations of the water stage”: Based on the Exner equation you should get most rapid erosion and deposition when you have a spatial or temporal change in SSC so this offset makes sense to me. I think setting up a testable hypothesis in the Introduction based on sediment transport conservation of mass would help the reader to understand this temporal offset more clearly.", "reviewer_6": "nPage 6, “For 0.34 Mt yr-1 of the mean annual sediment influx (Figure 2a), 11.7±7.2% was retained”: Where does the rest of the sediment go? Does it bypass the floodplain and return to the Amazon River or does it flow upstream on tributaries?", "reviewer_framework": "nAmazonian floodplains play an important role in the global CO and CH cycles (Murguia- 2 4 Flores et al., 2023). Due to the enormous amount of sediment deposition in the floodplains, it is reasonable to speculate that the deposition of sediment-associated POC could be important for the regional CO and CH emissions (Saunois et al., 2019; Guilhen et al., 2020). However, our 2 4 results do not support this hypothesis. Compared to the wetland soil and lake sediment carbon that are sourced from ecosystem primary production, the soil/sediment carbon input due to POC deposition is considerably smaller. Therefore, POC deposition in Amazon floodplains is unlikely to contribute to the high CO and CH emissions observed in the region, despite its crucial role 2 4 in the supply of critical nutrients to the floodplain soils (Petsch et al., 2023). Furthermore, because Amazonian floodplains are one of several sediment deposition hotspots in the world, we suspect that POC deposition also only plays a minor role in fueling CO and CH emissions in 2 4 other productive floodplains across the globe. However, for low-productivity floodplains in high latitudes, fluvial POC deposition remains important for regional CO and CH emissions (Herbst 2 4 et al., 2024).\nAlthough sediment deposition in Amazonian floodplains is not strongly tied to regional CH 4 emissions, due to its strong spatial heterogeneity and temporal variations it can influence the spatial and temporal variability of CH emissions within each floodplain. For instance, we find 4 that the POC deposition rate in the lake area is approximately five times higher than that in the wetland area. For the deepest lake area, the POC deposition rate during HW can reach 0.57 g C m−2 day−1, which becomes comparable to the measured in-lake NPP. With limited oxygen exposure, a substantial fraction of these biologically active POC could be oxidized to CH during 4 the HW period through methanogenesis (Segers, 1998; Sobek et al., 2009). As lake CH models 4 rarely include this carbon source, they would likely fail to capture the related CH emission pulses 4 from floodplain lakes in the Amazon (Tan et al., 2024). Therefore, our results underscore the importance to couple floodplain hydrodynamic, POC deposition, and CH dynamics for more 4 realistic CH modeling in floodplain lakes.\nR3C17:\nPage 10, “the shallow depths of the oxycline”: Be specific. How shallow? Does the oxycline depth vary throughout the year?"}, "author_response": "We thank the reviewer for the valuable comments and recommendations. We have carefully addressed the reviewer’s suggestions as follows:\nR1C1:\nAbstract: Andes mountains (missing an “s” on mountains)\nResponse:\nCorrected.\nR1C2:\nAbstract: the state encompassing Manaus is Amazonas, which might be a better fit than Amazônia in this context. Elsewhere you use anglicized words to refer to the greater Amazon area (e.g., Amazonian) so it’s a bit of a divergence to see the Portuguese usage here. This is admittedly a minor point, and one of the authors is Brazilian, so perhaps it’s fine.\nResponse:\nCorrected to “Amazonas” as suggested.\nR1C3:\nL6: I brought up the 15% vs the 6.1% you reference in the abstract in my last review. It would be useful here to hedge this number at this location by pointing out the difference in domain for these two numbers, especially since you use the exact same phrasing (Amazonian floodplains) in the abstract and on this line to refer to two different areal domains. (I do appreciate the expanded discussion later in the paper about the potential limitations/uncertainties regarding your new estimate.)\nResponse:\nWe appreciate the reviewer pointing out this potential confusion. We have revised the text in both the abstract and introduction to clarify these differences in domain and ensure consistent terminology.\nRevised Abstract: “By upscaling the sediment deposition rate (1.33±0.24 kg m−2 yr−1), we estimate that 77.3±13.9 Mt of sediment could be deposited in the floodplains along the Amazon/Solimo˜es River mainstem every year, representing 6.1%±1% of sediment discharge from the river.”\nThe 15% value provides an example of the ratio of sediment trapped along the passing 390 km-long between Itacoatiara and O´bidos. While this figure is frequently cited in the literature, we consider it region-specific and not necessarily appropriate as a reference in our study. To avoid potential confusion, we have removed this value from the revised introduction.\nRevised Introduction: “A substantial portion of the sediment does not make its way to the ocean but is instead deposited over Amazonian floodplains (Mangiarotti et al., 2013). ”\nR1C4:\nFigure 1: I appreciate the addition of (e) but remain suspect of the goodness of fit between modeled and observed SSCs since this is on a log-log scale. Please indicate if the black line is a best fit or 1:1 line in both (c) and (e).\nResponse:\nThanks for the comment. The use of log-log scale to compare the modeled and observed SSCs was because SSC values are significantly larger at the Downstream station but much smaller at the other two stations. In the revision, the scale has been reverted from log scale to a normal scale. The black dashed line in both (c) and (e) indicate 1:1 line. We added this information to the caption of Figure 1.\nR1C5:\nL133: I suggest “reached a maximum” instead of “topped” here\nResponse:\nCorrected as suggested.\nR1C6:\nL152: I’m confused by the numbers here. Is this the same 15% referenced from [6] in the intro? As written, they appear to refer to two different quantities, that is, fractional deposition rate of mainstem sediment on floodplains vs retention of sediment influx to the floodplain itself. Is the 15% just a coincidence on both of these numbers? I don’t see 11.7% in Figure 2B anywhere.\nResponse:\nWe apologize for the confusion. The number is indeed the same 15% referenced in the introduction (Mangiarotti et al., 2013). A noted in our response to R1C3, this value is the sediment trapping ratio of the floodplains along a 390 km Amazon River reach between Itacoatiara and O´bidos. Notably, the sediment trapping ratio is defined in the Methodology section as: “The sediment trapping ratio is defined as the ratio of the Amazon River sediment trapped by the floodplain, which is calculated by dividing the deposited sediment in the floodplain by the river sediment load measured at Manacapuru.” As emphasized in the revision, 11.7% is the percentage of the 0.34 Mt mean annual sediment influx deposited in the floodplain. Because the sediment influx through the river-floodplain boundary is only a fraction of the sediment discharge of the Amazon River, this value is not the sediment trapping ratio and the comparison of 15% vs. 11.7% is problematic. Since the calculation of sediment trapping ratio depends on the location and length of river reaches, to ensure a more cautious comparison, we have removed this specific comparison from the revised manuscript. However, we retain the comparison based on the deposition rate reported in Mangiarotti et al. (2013).\n“Of the 0.34 Mt mean annual sediment influx through the river-floodplain boundary (Figure 2a), ∼11.7% was deposited in the floodplain (Supplementary Figure S10) and the rest was returned to the Amazon River during FW (Figure 2b).”\nMoreover, we have also added a new figure showing the deposition ratio over the simulation years as Supplementary Figure S10, where in the caption we defined the deposition ratio as the proportion of sediment deposited in the floodplain relative to the sediment influx.\nR1C7:\nFigure 3: Please double check the y-axis label of subplot c. Do the units work?\nResponse:\nOurapologies. Therewasatypointhey-axislabel,whichshouldbeD /D −SSC /SSC .\nexp baseline exp baseline\nThis typo has been corrected in the revision.\nR1C8:\nL232: I remain confused by the use of 11.7 here and 6.1 in the abstract. I believe they are referring to different quantities (see comment on L152) but as written it is hard to distinguish and keep track of the ratio of deposition to the mainstem flux and ratio of retention of the inflow to the floodplain. I suggest clarifying to indicate which is which when numbers are used.\nResponse:\nThanksforthecomment. Thetwonumbersindeedrepresentthequantitiesnotedbythereviewer. Toimproveclarity, wehaveaddedthedefinitionofthedepositionratio(i.e., 11.7%)inthecaption ofSupplementaryFigureS10. PleaseseeourresponsetoR1C6formoreexplanations. Inresponse to this comment, the sentence has been rephrased to:\n“This result highlights that floodplain lakes are a major sink of water kinetic energy in the Amazon Basin and thus largely responsible for the trapping of ∼11.7% of sediment influx from the Amazon River.”\nR1C9:\nFigure S7: What is the meaning of the dashed lines in the figures? Are they best fit lines? If so I am surprised by their orientations, especially in (a) and (c), since the r values are negative and the slopes should be, as well.\nResponse:\nWe apologize for the confusion and agree that the 1:1 lines in Figure S7 are not appropriate. We have updated this figure with best-fit lines.\nR1C10:\nFinally, the figshare link includes Python pickle (.pkl) files. As described at https:// docs.python.org/3/library/pickle.html the pickle module is not secure, and users should only unpickle data they trust, since pickle files can execute arbitrary code on a user’s machine. This makes it an inappropriate format for supplemental files for the manuscript, and I strongly suggest providing data in a more standard format like netCDF which does not have the security implications of pickle files. For this reason, I have not opened the files, so it’s possible something even simpler than netCDF like CSV or JSON would be appropriate.\nResponse:\nThank you for the suggestion. We have uploaded the files in CSV format and provided a detailed description in a README file. The original simulation output, which exceeds 450 GB in size, will be made available upon request.\nReviewer 2\nThe authors made numerous changes to address earlier comments from myself (Reviewer 3) and the other reviewers, resulting in a much-improved manuscript. The re-submitted product presents a much more detailed and nuanced picture of floodplain sedimentation and POC dynamics in the Amazon.\nResponse:\nWe sincerely thank the reviewer for the positive feedback and comments. We have carefully addressed the reviewer’s recommendations as follows:\nR2C1:\nThe authors argue that POC deposition does not matter when compared to NPP, except where it is deposited under anoxic conditions, where it might be oxidized to CH4. The degree that methane production in Amazonian floodplain lakes occurs and is significant in the regional and global climate is still not clear to me. I would be fine with the authors just reporting a negative result – that floodplain POC is not significant compared to organic input from primary productivity in the Amazon – to ensure that the OC interpretations are well-supported and described consistently. Right now the paper has two main takeaways (drivers of floodplain sediment deposition and potential OC emissions as methane) and I think the paper works well as a sedimentation study without flashy results about methane emissions.\nResponse:\nThank you for raising this point regarding the potential significance of POC deposition in influencing CH emissions and its role in the broader context of regional and global climate. We 4 acknowledge in the abstract that the evidence we present primarily supports the conclusion that “the deposition of sediment-associated organic carbon only plays a minor role in fueling carbon dioxide and methane emissions from Amazonian floodplains.”\nIn the last paragraph of the discussion, we have rephrased the text to clarify that while the role of POC deposition to CH emissions in specific conditions (e.g., anoxic lake areas) cannot be 4 ignored, once put into the context of annual CH emissions over the whole floodplain, the role of 4 this process is negligible relative to that of NPP. Additionally, we have de-emphasized the CH 4 modeling implications to ensure that the focus remains on sediment deposition drivers.\n“AlthoughsedimentdepositioninAmazonianfloodplainsisnotanimportantdriverofregional CH emissions, due to its strong spatial heterogeneity and seasonal variations, this process can 4 influence CH emissions under specific conditions (i.e., deep lake areas during HW) within each 4 floodplain. For instance, we find that the POC deposition rate in the lake area is approximately five times higher than that in the wetland area. For the deepest lake area, the POC deposition rate during HW can reach 0.57 g C m−2 day−1, which becomes comparable to the measured in-lake NPP. With limited oxygen exposure, a substantial fraction of these biologically active POC could be oxidized to CH during 4 the HW period through methanogenesis (Segers, 1998; Sobek et al., 2009). As lake CH models 4 rarely include this carbon source, they would likely fail to capture the related CH emission pulses 4 from floodplain lakes in the Amazon (Tan et al., 2024). Therefore, our results underscore the importance to couple floodplain hydrodynamic, POC deposition, and CH dynamics for more 4 realistic CH modeling in floodplain lakes. However, since 4 4 deep lake areas only comprise a small fraction of the entire floodplains and the HW period only lasts 2∼3 months, the overall contribution of POC deposition to regional annual CH emissions 4 is minor.”\nR2C2:\nLine 20: Unclear what “sediment attrition” means. Maybe “sediment abrasion?”\nResponse:\nThank you for the comment. “sediment attrition” refers to the the reduction in size and quantity of sediment particles due to processes like grinding or scraping, while “abrasion” specifically refers to the wearing away of sediment particles against fixed surfaces. As our intent here is to emphasize broadly on the reduction of sediment size as they travel downstream, we prefer the use of “attrition”.\nR2C3:\nLine 32: I don’t fully understand how dams would cause a 100% decrease in sediment transport. Does that mean no sediment would come from the Amazon to the ocean? This could be rephrased to be more clear.\nResponse:\nFlecker et al.(2022) shows that many dams proposed in sediment-richriver reaches in the western Amazonhavehighsedimenttrappingefficienciesofupto100%. Toavoidconfusion, werephrased this sentence to “It is estimated that many proposed hydropower plants in the Amazon, without prioritizing hydrological connectivity, can potentially reduce sediment transport to downstream river reaches by up to 100% (Flecker et al., 2022).”\nR2C4:\nLine 65: “Local floodplains” – local to the Amazon or somewhere else? I found this wording confusing.\nResponse:\nSorry for the confusion. “Local floodplains” refer to the floodplain local to the central Amazon Basin. In the revision, this sentence is rephrased to “Previous applications of 2-D hydrodynamic models in the central Amazon Basin as well as its floodplains ...”\nR2C5:\nFigure 1: The context imagery in the Panel A insets are really hard to see. I would use outlines of the continent and basin rather than imagery. In Panel B, it seems like there is an error in your boundary conditions to cause these flat sections of model results. Please explain why this is and if it matters in the main text. Panel E – please add error bars to the points. This will make the green points look like they have a much better fit because the SSC error bars are quite large. Caption – add a first sentence describing the figure overall before going into each panel.\nResponse:\nThanks for the comment. In the revised Figure 1, we have updated the insets in Panel A to enhance readability. In Panel B, the bias in the simulated water levels arises from the complex bathymetry of the Amazon floodplains, which cannot be fully resolved even with a 30-m resolution. This bias is generally less than 1 m during four out of five hydrological years, reaching approximately 3 m only in 2010. It does not affect the analysis, as the hydrodynamic simulation has been rigorously validated across various locations (Pinel et al., 2020), and the bias is localized to this specific area close to the river channel bank. In the revised manuscript, we have clarified the cause of this bias: “In (b), the bias in the simulated water level at WL A during LW is due to the unresolved floodplain bathymetry at this specific location.” In Panel E, error bars have been added to the green dots for improved clarity. Additionally, the scale has been reverted from log scale to a normal scale. An introductory sentence has been added to the figure caption.\nR2C6:\nLine 131: It might be helpful to set the reader’s expectations for what deposition rates should scale to. Each of the reviewers had slightly different ideas about this, so reminding the reader what you expect here might help.\nResponse:\nAgree. To address this, we have added a brief discussion of expected deposition rates in similar Amazonian floodplains, based on previous studies, and used these as a reference to contextualize our modeled results. Please note that a more detailed comparison of the deposition rates is provided in the Discussion section.\n“Due to the effect of floodplain hydrodynamics, the simulated sediment deposition shows large and distinct spatial variability during the four hydrological periods, with rates ranging from less than 1 kg m−2 yr−1 in the areas with persistently fast flows to over 5 kg m−2 yr−1 in the deeper lake areas (Figures 2d-2g). These results fall within the reported range of 0.5 to 8.8 kg m−2 yr−1 for Amazonian floodplains (Bourgoin et al., 2007; Mangiarotti et al., 2013; Forsberg et al., 1989; Smith et al., 2003), as detailed in the Discussion section.”\nR2C7:\nFigure 2: Please make the color bars go to negative values to capture the full range of deposition rates from your model outputs.\nResponse:\nIn the revised Figure 2, the colormap scale has been updated to -2.5∼2.5 kg m−2 yr−1.\nR2C8:\nLines 272-294: This is a long paragraph with two different estimations of sediment trapping in floodplains. Integrating the two estimates, with one as an upper and the other as a lower bound, would help condense this paragraph and improve the flow of ideas.\nResponse:\nThank you for your suggestion. We understand the importance of integrating the two estimates for clarity. However, we believe the 6.1% estimate based on the floodplain area is more robust. The river-length-based estimate of 211 Mt per year, while also informative, may be less accurate due to its inherent assumption. To address your comment, we have restructured the paragraph into two paragraphs to improve the flow and focus. We emphasized the 6.1% estimate as the primary result while briefly mentioning the alternative estimate for context.\n“By upscaling the sediment deposition rate to the floodplain area within the drainage basin of theAmazon/Solim˜oesRiver mainstem(i.e., 58,103km2), we estimatethattheAmazon/Solim˜oes floodplains could trap a total amount of 77.3±13.9 Mt of sediment per year and 0.98±0.18 Mt of POC per year using the POC/sediment mass ratio of 1.27% (Bouchez et al., 2014). Given 1200 Mt sediment drained to the Atlantic Ocean by the Amazon River per year (Martinelli et al., 1989), sediment deposition in the floodplains could account for a 6.1±1% of sediment discharge by the Amazon River. Despite a small percentage of the total sediment load retained in the Amazon floodplains annually, this sediment can be subject to extensive biogeochemical processing due to prolonged floodplain storage, as indicated by the observed long transit times (Repasch et al., 2020).\nThe estimated deposition is influenced by several uncertainties, such as the variability in floodplain trapping efficiency and the potential overestimation of the floodplain area (Fleischmann et al., 2022). Moreover, Janauac´a may not represent all floodplains along the Solim˜es River, as there are sites with varying ratios of local drainage extent to lake area (Forsberg et al., 1988; Sobrinho et al., 2016). Our upscaling estimation focuses on the mainstem of the Solimo˜es/Amazon River but excludes all tributaries, assuming that Janauac´a is only representative of floodplains adjacent to the mainstem. This figure likely underestimates the total sediment deposition in the basin, because the floodplains of the tributaries could also trap a substantial amount of sediment (Trigg et al., 2012). For comparison, using the simulated river-length-specific deposition rate of 0.033 Mt km−1 yr−1 and the total mainstem length of 3349 km for the Solim˜oes/Amazon River from HydroRIVERS (Lehner and Grill, 2013), an alternative river-length-based upscaling approach yields a much higher total deposition estimate of 211 Mt per year. However, this figure is likely an overestimate because floodplains are not always present along the river and on both sides.”\nR2C9:\nLine 355: Why did you pick 29 m as the elevation bound of the floodplain? Please say why and/or reference prior work.\nResponse:\nThe computational domain delineation was initially carried out in Pinel et al. (2020), where a value of 29 m, which is 4.6 m above the observed maximum water level, was chosen. This value is considered sufficient to ensure that no water from the Amazon River will exceeded this level. This information is clarified in the revision: “The computational domain of Janauac´a is delineated as the floodable region with an elevation lower than 29 m (4.6 m above the observed maximum water level) (Pinel et al., 2020).”\nR2C10:\nLine 481: Why did you pick these values for the oxycline? Please briefly describe why.\nResponse:\nWe use a dissolved oxygen (DO) value of 2 mg L−1, a standard value commonly referenced in the literature (e.g., (Winton et al., 2023)), to determine the oxycline from the DO data collected during the four hydrological periods by Amaral et al. (2018). In the revision, the sentence has been rephrased to improve clarity:\n“First, we identify the oxycline—the depth below which DO concentrations fall below 2 mg L−1—using DO data collected during the four hydrological periods by Amaral et al. (2018) at the wind-exposed (WE) site of Janauac´a. The estimated oxycline depths are 0.5 m, 2 m, 3.5 m, 1.2 m for LW, RW, HW and FW, respectively.”\nR2C11:\nFigure S2: I appreciate that the values for these thresholds were added to the main text, but I still don’t understand why these values were selected. Is there a quantitative justification for the thresholds, for instance splitting up the annual hydrograph into 4 equal time periods?\nResponse:\nThanks for the comment. The four hydrological periods are typically defined from water level time series in Amazon floodplains (Pinel et al., 2020; Rudorff et al., 2018). Here, the thresholds are chosen because (a) they ensure comparable periods within a hydrological year, and (b) the high water threshold is near the top of the riverbank. This information has been added to the caption of Figure S2."}]

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