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Providing reliable estimates of streamflow and hydrological fluxes is a major challenge for water resources management over national and transnational basins in South America. Global hydrological models and land surface models are a possible solution to simulate the terrestrial water cycle at the continental scale, but issues about parameterization and limitations in representing lowland river systems can place constraints on these models to meet local needs. In an attempt to overcome such limitations, we extended a regional, fully coupled hydrologic–hydrodynamic model (MGB; Modelo hidrológico de Grandes Bacias) to the continental domain of South America and assessed its performance using daily river discharge, water levels from independent sources (in situ, satellite altimetry), estimates of terrestrial water storage (TWS) and evapotranspiration (ET) from remote sensing and other available global datasets. In addition, river discharge was compared with outputs from global models acquired through the eartH2Observe project (HTESSEL/CaMa-Flood, LISFLOOD and WaterGAP3), providing the first cross-scale assessment (regional/continental  ×  global models) that makes use of spatially distributed, daily discharge data. A satisfactory representation of discharge and water levels was obtained (Nash–Sutcliffe efficiency, NSE > 0.6 in 55 % of the cases) and the continental model was able to capture patterns of seasonality and magnitude of TWS and ET, especially over the largest basins of South America. After the comparison with global models, we found that it is possible to obtain considerable improvement on daily river discharge, even by using current global forcing data, just by combining parameterization and better routing physics based on regional experience. Issues about the potential sources of errors related to both global- and continental-scale modeling are discussed, as well as future directions for improving large-scale model applications in this continent. We hope that our study provides important insights to reduce the gap between global and regional hydrological modeling communities.

Being one of the most vulnerable regions in the world, the Ganges–Brahmaputra–Meghna delta presents a major challenge for climate change adaptation of nearly 200 million inhabitants. It is often considered as a delta mostly exposed to sea-level rise and exacerbated by land subsidence, even if the local vertical land movement rates remain uncertain. Here, we reconstruct the water-level (WL) changes over 1968 to 2012, using an unprecedented set of 101 water-level gauges across the delta. Over the last 45 y, WL in the delta increased slightly faster (∼3 mm/y), than global mean sea level (∼2 mm/y). However, from 2005 onward, we observe an acceleration in the WL rise in the west of the delta. The interannual WL fluctuations are strongly modulated by El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) variability, with WL lower than average by 30 to 60 cm during co-occurrent El Niño and positive IOD events and higher-than-average WL, by 16 to 35 cm, during La Niña years. Using satellite altimetry and WL reconstructions, we estimate that the maximum expected rates of delta subsidence during 1993 to 2012 range from 1 to 7 mm/y. By 2100, even under a greenhouse gas emission mitigation scenario (Representative Concentration Pathway [RCP] 4.5), the subsidence could double the projected sea-level rise, making it reach 85 to 140 cm across the delta. This study provides a robust regional estimate of contemporary relative WL changes in the delta induced by continental freshwater dynamics, vertical land motion, and sea-level rise, giving a basis for developing climate mitigation strategies.

The African continent hosts some of the largest freshwater systems worldwide, characterized by a large distribution and variability of surface waters that play a key role in the water, energy and carbon cycles and are of major importance to the global climate and water resources. Freshwater availability in Africa has now become of major concern under the combined effect of climate change, environmental alterations and anthropogenic pressure. However, the hydrology of the African river basins remains one of the least studied worldwide and a better monitoring and understanding of the hydrological processes across the continent become fundamental. Earth Observation, that offers a cost-effective means for monitoring the terrestrial water cycle, plays a major role in supporting surface hydrology investigations. Remote sensing advances are therefore a game changer to develop comprehensive observing systems to monitor Africa’s land water and manage its water resources. Here, we review the achievements of more than three decades of advances using remote sensing to study surface waters in Africa, highlighting the current benefits and difficulties. We show how the availability of a large number of sensors and observations, coupled with models, offers new possibilities to monitor a continent with scarce gauged stations. In the context of upcoming satellite missions dedicated to surface hydrology, such as the Surface Water and Ocean Topography (SWOT), we discuss future opportunities and how the use of remote sensing could benefit scientific and societal applications, such as water resource management, flood risk prevention and environment monitoring under current global change.Article HighlightsThe hydrology of African surface water is of global importance, yet it remains poorly monitored and understoodComprehensive review of remote sensing and modeling advances to monitor Africa’s surface water and water resourcesFuture opportunities with upcoming satellite missions and to translate scientific advances into societal applications

The unprecedented hydraulic visibility of rivers surfaces deformation with SWOT satellite offers tremendous information for improving hydrological‐hydraulic models and discharge estimations for rivers worldwide. However, estimating the uncertain or unknown parameters of hydraulic models, such as inflow discharges, bathymetry, and friction parameters, poses a high‐dimensional inverse problem, which is ill‐posed if based solely on altimetry observations. To address this, we couple the hydraulic model with a semi‐distributed hydrological model, to constrain the ill‐posed inverse problem with sufficiently accurate initial estimates of inflows at the network upstreams. A robust variational data assimilation of water surface elevation (WSE) data into a 1D Saint‐Venant river network model, enables the inference of inflow hydrographs, effective bathymetry, and spatially distributed friction at network scale. The method is demonstrated on the large, complex, and poorly gauged Maroni basin in French Guiana. The pre‐processing chain enables (a) building an effective hydraulic model geometry from drifting ICESat‐2 WSE altimetry and Sentinel‐1 width; (b) filtering noisy SWOT Level 2 WSE data before assimilation. A systematic improvement is achieved in fitting the assimilated WSE (85% cost improvement), and in validating discharge at 5 gauges within the network. For assimilation of SWOT data alone, 70% of data‐model fit is in [−0.25;0.25m] $[-0.25;\\,0.25\\,\\mathrm{m}]$ and the discharge NRMSE ranges between 0.05 and 0.18 (18%–71% improvement from prior). The high density of SWOT WSE enables the inferrence of detailed spatial variability in channel bottom elevation and friction, and inflows timeseries. The approach is transferable to other rivers networks worldwide.

Land surface models (LSMs) are widely used to study the continental part of the water cycle. However, even though their accuracy is increasing, inherent model uncertainties can not be avoided. In the meantime, remotely sensed observations of the continental water cycle variables such as soil moisture, lakes and river elevations are more frequent and accurate. Therefore, those two different types of information can be combined, using data assimilation techniques to reduce a model's uncertainties in its state variables or/and in its input parameters. The objective of this study is to present a data assimilation platform that assimilates into the large-scale ISBA-CTRIP LSM a punctual river discharge product, derived from ENVISAT nadir altimeter water elevation measurements and rating curves, over the whole Amazon basin. To deal with the scale difference between the model and the observation, the study also presents an initial development for a localization treatment that allows one to limit the impact of observations to areas close to the observation and in the same hydrological network. This assimilation platform is based on the ensemble Kalman filter and can correct either the CTRIP river water storage or the discharge. Root mean square error (RMSE) compared to gauge discharges is globally reduced until 21 % and at Óbidos, near the outlet, RMSE is reduced by up to 52 % compared to ENVISAT-based discharge. Finally, it is shown that localization improves results along the main tributaries.

In 2015, an emergency state was declared in Bolivia when Poopó Lake dried up. Climate variability and the increasing need for water are potential factors responsible for this situation. Because field data are missing over the region, no statements are possible about the influence of mentioned factors. This study is a preliminary step toward the understanding of Poopó Lake drought using remote sensing data. First, atmospheric corrections for Landsat (FLAASH and L8SR), seven satellite derived indexes for extracting water bodies, MOD16 evapotranspiration, PERSIANN-CDR and MSWEP rainfall products potentiality were assessed. Then, the fluctuations of Poopó Lake extent over the last 26 years are presented for the first time jointly, with the mean regional annual rainfall. Three main droughts are highlighted between 1990 and 2015: two are associated with negative annual rainfall anomalies in 1994 and 1995 and one associated with positive annual rainfall anomaly in 2015. This suggests that other factors than rainfall influenced the recent disappearance of the lake. The regional evapotranspiration increased by 12.8% between 2000 and 2014. Evapotranspiration increase is not homogeneous over the watershed but limited over the main agriculture regions. Agriculture activity is one of the major factors contributing to the regional desertification and recent disappearance of Poopó Lake.

The Surface Water and Ocean Topography (SWOT) mission offers novel global observations of river hydrodynamics, yet its performance across varied river morphologies remains understudied. We evaluate SWOT node and raster products over a ∼210 km stretch of the Ganga River, India, during the fast‐sampling phase, using GNSS‐based continuous measurements, two in situ stations, and four altimetry virtual stations. Node products show slightly better water surface elevation (WSE) accuracy than raster products, with RMSEs of 0.19 m (GNSS), 0.09 m (in situ station, Varanasi), and 0.88 m (virtual stations), though quality filters reduce temporal resolution. Raster data, however, captures 2D WSE variability, enhancing spatial sampling in wide river cross‐sections. SWOT‐derived water surface slopes (WSS) yield mixed results across SWORD reaches (RMSE: 2.54 cm/km). Strong backscatter from main river channel waters (mean +13.34 dB) aids width retrieval. Our analysis highlights SWOT's potential for river hydrodynamic applications while underscoring the need of evaluation in complex riverine environments.

The Amazon river basin has been recently affected by extreme climatic events, such as the exceptional drought of 2005, with significant impacts on human activities and ecosystems. In spite of the importance of monitoring freshwater stored and moving in such large river basins, only scarce measurements of river stages and discharges are available and the signatures of extreme drought conditions on surface freshwater dynamics at the basin scale are still poorly known. Here we use continuous multisatellite observations of inundation extent and water levels between 2003 and 2007 to monitor monthly variations of surface water storage at the basin scale. During the 2005 drought, the amount of water stored in the river and floodplains of the Amazon basin was ∼130 km3 (∼70%) below its 2003-7 average. This represents almost a half of the anomaly of minimum terrestrial water stored in the basin as estimated using the Gravity Recovery and Climate Experiment (GRACE) data.

Since the late 1990s, rising sea levels around the Torres Islands (north Vanuatu, southwest Pacific) have caused strong local and international concern. In 2002-2004, a village was displaced due to increasing sea incursions, and in 2005 a United Nations Environment Programme press release referred to the displaced village as perhaps the world's first climate change \"refugees.\" We show here that vertical motions of the Torres Islands themselves dominate the apparent sea-level rise observed on the islands. From 1997 to 2009, the absolute sea level rose by 150 + /-20 mm. But GPS data reveal that the islands subsided by 117 + /-30 mm over the same time period, almost doubling the apparent gradual sea-level rise. Moreover, large earthquakes that occurred just before and after this period caused several hundreds of mm of sudden vertical motion, generating larger apparent sea-level changes than those observed during the entire intervening period. Our results show that vertical ground motions must be accounted for when evaluating sea-level change hazards in active tectonic regions. These data are needed to help communities and governments understand environmental changes and make the best decisions for their future.

The capabilities of radar altimetry to measure inland water bodies are well established, and several river altimetry datasets are available. Here we produced a globally distributed dataset, the Global River Radar Altimeter Time Series (GRRATS), using Envisat and Ocean Surface Topography Mission (OSTM)/Jason-2 radar altimeter data spanning the time period 2002–2016. We developed a method that runs unsupervised, without requiring parameterization at the measurement location, dubbed virtual station (VS) level, and applied it to all altimeter crossings of ocean-draining rivers with widths >900 m (>34 % of the global drainage area). We evaluated every VS, either quantitatively for VS locations where in situ gages are available or qualitatively using a grade system. We processed nearly 1.5 million altimeter measurements from 1478 VSs. After quality control, the final product contained 810 403 measurements distributed over 932 VSs located on 39 rivers. Available in situ data allowed quantitative evaluation of 389 VSs on 12 rivers. The median standard deviation of river elevation error is 0.93 m, Nash–Sutcliffe efficiency is 0.75, and correlation coefficient is 0.9. GRRATS is a consistent, well-documented dataset with a user-friendly data visualization portal, freely available for use by the global scientific community. Data are available at https://doi.org/10.5067/PSGRA-SA2V1 (Coss et al., 2016).