Extensive floodplains throughout the Amazon basin support important ecosystem services and influence global water and carbon cycles. A recent change in the hydroclimatic regime of the region, with increased rainfall in the northern portions of the basin, has produced record-breaking high water levels on the Amazon River mainstem. Yet, the implications for the magnitude and duration of floodplain inundation across the basin remain unknown. Here we leverage state-of-the-art hydrological models, supported by in-situ and remote sensing observations, to show that the maximum annual inundation extent along the central Amazon increased by 26% since 1980. We further reveal increased flood duration and greater connectivity among open water areas in multiple Amazon floodplain regions. These changes in the hydrological regime of the world’s largest river system have major implications for ecology and biogeochemistry, and require rapid adaptation by vulnerable populations living along Amazonian rivers.

Dams and reservoirs are human-made infrastructures that have attracted increasing attention because of their societal and environmental significance. Towards better management and conservation of reservoirs, a dataset of reservoir-catchment characteristics is needed, considering that the amount of water and material flowing into and out of reservoirs depends on their locations on the river network and the properties of the upstream catchment. To date, no dataset exists for reservoir-catchment characteristics. The aim of this study is to develop the first database featuring reservoir-catchment characteristics for 3254 reservoirs with storage capacity totaling 682 595 km³ (73.2 % of reservoir water storage capacity in China) to support the management and conservation of reservoirs in the context of catchment level. To ensure a more representative and accurate mapping of local variables of large reservoirs, reservoir catchments are delineated into full catchments (their full upstream contributing areas) and intermediate catchments (subtracting the area contributed by upstream reservoirs from the full upstream part of the current reservoir). Using both full catchments and intermediate catchments, characteristics of reservoir catchments were extracted, with a total of 512 attributes in six categories (i.e., reservoir and catchment body characteristics, topography, climate, soil and geology, land cover and use, and anthropogenic activity characteristics). Besides these static attributes, time series of 15 meteorological variables of catchments were extracted to support hydrological simulations for a better understanding of drivers of reservoir environment change. Moreover, we provide a comprehensive and extensive reservoir dataset on water level (data available for 20 % of 3254 reservoirs), water surface area (99 %), storage anomaly (92 %), and evaporation (98 %) from multisource satellites such as radar and laser altimeters and images from Landsat and Sentinel satellites. These products significantly enhance spatial and temporal coverage in comparison to existing similar products (e.g., 67 % increase in spatial resolution of water level and 225 % increase in storage anomaly) and contribute to our understanding of reservoir properties and functions within the Earth system by incorporated national or global hydrological modeling. In situ data of 138 reservoirs are employed in this study as a valuable reference for evaluation, thus enhancing our confidence in the data quality and enhancing our understanding of the accuracy of current satellite datasets. Along with its extensive attributes, the Reservoir dataset in China (Res-CN) can support a broad range of applications such as water resources, hydrologic/hydrodynamic modeling, and energy planning. Res-CN is on Zenodo through https://doi.org/10.5281/zenodo.7664489 (Shen et al., 2022c).

Continental‐scale river hydrodynamic modeling helps to understand the global hydrological cycle, and model evaluation is essential for calibration and performance assessment. Although many models have been evaluated using several variables separately, methods for the integrated multivariable assessment have yet to be established. We proposed an integrated multivariable evaluation method and an overall basin skill score (OSK) metric, which integrates the sub‐basin skill score, calculated by combining multiple variables' normalized Nash‐Sutcliffe efficiency values. The sub‐basin approach and OSK assess the model performance at the sub‐basin and basin scales and overcome the observation‐related limitations of different spatial dimensions. Evaluation of the CaMa‐Flood global river model for the Amazon Basin considering three variables (discharge, water surface elevation, and flooded area) and two baseline topography data (Shuttle Radar Topography Mission (SRTM) and Multi‐Error‐Removed Improved‐Terrain (MERIT) digital elevation models (DEMs)), shows that the proposed methodology can overcome the limitation of single‐variable evaluation in identifying multiple best parameter sets due to low sensitivity. CaMa‐Flood considering MERIT DEM achieves a single optimal parameter set with a maximum OSK of 0.57 compared with 0.52 for the SRTM DEM, and it shows the importance of accuracy of topography data for improvement in the performance of river hydrodynamic modeling. The proposed multivariable integrated evaluation with better topography data proved to help reduce equifinality and estimate the best parameter by maintaining the physical relationships among variables.

Water resource management has faced challenges in recent decades due to limited in situ observations and the limitations of hydrodynamic modeling. Data assimilation techniques have been proposed to improve hydrodynamic model outputs of local rivers (river length ≤ 1500 km) using synthetic observations of the future Surface Water and Ocean Topography (SWOT) satellite mission to overcome limited in situ observations and the limitations of hydrodynamic modeling. However, large-scale data assimilation schemes require computationally efficient filtering techniques, such as the Local Ensemble Transformation Kalman Filter (LETKF). Expansion of the assimilation domain to maximize observations is limited by error covariance caused by limited ensemble size in complex river networks, such as the Congo River. Therefore, we tested the LETKF algorithm in a continental-scale river (river length > 1500 km) using a physically based empirical localization method to maximize the observations available while filtering error covariance areas. Physically based empirical local patches were derived separately for each river pixel, considering spatial auto-correlations. An observing system simulation experiment (OSSE) was performed using empirical localization parameters to evaluate the potential of our method for estimating discharge. We found our method could improve discharge estimates considerably without affected from error covariance while fully using the available observations. We compared this experiment using empirical localization parameters with conventional fixed-shape local patches of different sizes. The empirical local patch OSSE showed the lowest normalized root mean square error of discharge for the entire Congo basin. Extending the conventional local patch without considering spatial auto-correlation results in very large errors in LETKF assimilation due to error covariance between small tributaries. The empirical local patch method has the potential to overcome the limitations of conventional local patches for continental-scale rivers using SWOT observations.

Quantifying continental-scale river discharge is essential for understanding the terrestrial water cycle, but it is susceptible to errors caused by a lack of observations and the limitations of hydrodynamic modeling. Data assimilation (DA) methods are increasingly used to estimate river discharge in combination with emerging river-related remote sensing products (e.g., water surface elevation (WSE), water surface slope, river width, and flood extent). However, directly comparing simulated WSE to satellite altimetry data remains challenging (e.g., because of large biases between simulations and observations or uncertainties in parameters), and large errors can be introduced when satellite observations are assimilated into hydrodynamic models. In this study we performed direct, anomaly, and normalized value assimilation experiments to investigate the capacity of DA to improve river discharge within the current limitations of hydrodynamic modeling. We performed hydrological DA using a physically based empirical localization method applied to the Amazon basin. We used satellite altimetry data from ENVISAT, Jason 1, and Jason 2. Direct DA was the baseline assimilation method and was subject to errors due to biases in the simulated WSE. To overcome these errors, we used anomaly DA as an alternative to direct DA. We found that the modeled and observed WSE distributions differed considerably (e.g., differences in amplitude, seasonal flow variation, and a skewed distribution due to limitations of the hydrodynamic models). Therefore, normalized value DA was performed to improve discharge estimation. River discharge estimates were improved at 24 %, 38 %, and 62 % of stream gauges in the direct, anomaly, and normalized value assimilations relative to simulations without DA. Normalized value assimilation performed best for estimating river discharge given the current limitations of hydrodynamic models. Most gauges within the river reaches covered by satellite observations accurately estimated river discharge, with the Nash–Sutcliffe efficiency (NSE) > 0.6. The amplitudes of WSE variation were improved in the normalized DA experiment. Furthermore, in the Amazon basin, normalized assimilation (median NSE =0.50) improved river discharge estimation compared to open-loop simulation with the global hydrodynamic model (median NSE =0.42). River discharge estimation using direct DA methods was improved by 7 % with calibration of river bathymetry based on NSE. The direct DA approach outperformed the other DA approaches when runoff was considerably biased, but anomaly DA performed best when the river bathymetry was erroneous. The uncertainties in hydrodynamic modeling (e.g., river bottom elevation, river width, simplified floodplain dynamics, and the rectangular cross-section assumption) should be improved to fully realize the advantages of river discharge DA through the assimilation of satellite altimetry. This study contributes to the development of a global river discharge reanalysis product that is consistent spatially and temporally.

Understanding spatial and temporal variations in terrestrial waters is key to assessing the global hydrological cycle. The future Surface Water and Ocean Topography (SWOT) satellite mission will observe the elevation and slope of surface waters at 50°) and also downstream river reaches at low latitudes. High assimilation efficiency in downstream river reaches was related to both local state correction and the propagation of corrected hydrodynamic states from upstream river reaches. Accurate global river discharge estimates were obtained (Kling‐Gupta efficiency [KGE] > 0.90) in river reaches with >270 accumulated overpasses per SWOT cycle when no model error was assumed. Introducing model errors decreased this accuracy (KGE ≈ 0.85). Therefore, improved hydrodynamic models are essential for maximizing SWOT information. These synthetic experiments showed where discharge estimates could be improved using SWOT observations. Further advances are needed for global‐scale data assimilation.