Inland surface waters in tropical environments play a major role in the water and carbon cycle. Remote sensing techniques based on passive, active microwave or optical wavelengths are commonly used to provide quantitative estimates of surface water extent from regional to global scales. However, some of these estimates are unable to detect water under dense vegetation and/or in the presence of cloud coverage. To overcome these limitations, the brightness temperature data at L-band frequency from the Soil Moisture and Ocean Salinity (SMOS) mission are used here to estimate flood extent in a contextual radiative transfer model over the Amazon Basin. At this frequency, the signal is highly sensitive to the standing water above the ground, and the signal provides information from deeper vegetation density than higher-frequencies. Three-day and (25 km × 25 km) resolution maps of water fraction extent are produced from 2010 to 2015. The dynamic water surface extent estimates are compared to altimeter data (Jason-2), land cover classification maps (IGBP, GlobeCover and ESA CCI) and the dynamic water surface product (GIEMS). The relationships between the water surfaces, precipitation and in situ discharge data are examined. The results show a high correlation between water fraction estimated by SMOS and water levels from Jason-2 (R > 0.98). Good spatial agreements for the land cover classifications and the water cycle are obtained.

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Research to understand the nitrogen cycle has been thriving. The production of reactive nitrogen by humans exceeds the removal capacity through denitrification of any natural ecosystem. The surplus of reactive nitrogen is also a significant pollutant that can shift biological diversity and distribution, promotes eutrophication in aquatic ecosystems, and affects human health. Denitrification is the microbial respiration in anoxic conditions and is the main process that removes definitively nitrates from the ecosystem by returning of reactive nitrogen (Nr) to the atmosphere as N 2 and N 2 O emissions. This process occurs in the oceans, aquatic ecosystems and temporary flooded terrestrial ecosystems. Wetlands ecosystems are rich in organic matter and they have regular anoxic soil conditions ideal for denitrification to occur. In the current paper, we provide a meta-analysis that aims at exploring how research around global nitrogen, denitrification and wetlands had evolved in the last fifty years. Back in the time, wetland ecosystems were seen as non-exploitable elements of the landscape, and now they are being integrated as providers of ecosystem services. A significant improvement of molecular biology techniques and genetic extraction have made the denitrification process fully understood allowing constructed wetlands to be more efficient and popular. Yet, large uncertainties remain concerning the dynamic quantification of the global denitrification capacity of natural wetland ecosystems. The contribution of the current investigation is to provide a way forward for reducing these uncertainties by the integration of satellite-based Earth Observation (EO) technology with parsimonious physical based models.

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Recent years have seen the development of 1‐D and 2‐D regional‐scale hydrological‐hydrodynamic models, which differ greatly from reach‐scale applications in terms of subgrid assumptions, parameterization, and applied resolution. Although 1‐D and 2‐D comparisons have already been performed at reach and local scales, model differences at regional scale are poorly understood. Moreover, there is a need to improve the coupling between hydrological and hydrodynamic models. It is addressed here by applying the MGB model at 1‐D and 2‐D dimensions for the whole ~700,000 km2 Negro basin (Amazon), which presents different wetland types. Long‐term continuous simulations are performed and validated with multisatellite observations of hydraulic variables. Results showed that both approaches are similarly able to estimate discharges and water levels along main rivers, especially considering parameter uncertainties, but differ in terms of flood extent and volume and water levels in complex wetlands. In these latter, the diffuse flow and drainage patterns were more realistically represented by the 2‐D scheme, as well as wetland connectivity across the basin. The 2‐D model led to higher drainage basinwide, while the 1‐D model was more sensitive to hydrodynamic parameters for discharge and flood extent and had a similar sensitivity for water levels. Finally, tests on the coupling between hydrologic and hydrodynamic processes suggested that their representation in an online way is less important for tropical wetlands than model dimensionality, which largely impacts water transfer and repartition.

Hydrological extremes, in particular floods and droughts, impact all regions across planet Earth. They are mainly controlled by the temporal evolution of key hydrological variables like precipitation, evaporation, soil moisture, groundwater storage, surface water storage and discharge. Precise knowledge of the spatial and temporal evolution of these variables at the scale of river basins is essential to better understand and forecast floods and droughts. In this article, we present recent advances on the capability of Earth observation (EO) satellites to provide global monitoring of floods and droughts. The local scale monitoring of these events which is traditionally done using high-resolution optical or SAR (synthetic aperture radar) EO and in situ data will not be addressed. We discuss the applications of moderate-to low-spatial-resolution space-based observations, e.g., satellite gravimetry (GRACE and GRACE-FO), passive microwaves (i.e. SMOS) and satellite altimetry (i.e. the JASON series and the Copernicus Sentinel missions), with supporting examples. We examine the benefits and drawbacks of integrating these EO datasets to better monitor and understand the processes at work and eventually to help in early warning and management of flood and drought events. Their main advantage is their large monitoring scale that provides a "big picture" or synoptic view of the event that cannot be achieved with often sparse in situ measurements. Finally, we present upcoming and future EO missions related to this topic including the SWOT mission. Keywords Floods • Droughts • Large scale • Terrestrial water storage • GRACE • SMOS • Satellite altimetry • SWOT Water Storage on the Continents: General RemarksFreshwater represents less than 3% of the total amount of water on Earth. On land, freshwater is stored in various reservoirs such as ice caps, snow, glaciers, groundwater, soil moisture (in the unsaturated soil and root zone, i.e. in the upper few metres of the soil (e.g., Hillel 1998)) and surface water bodies (rivers, lakes, man-made reservoirs, wetlands and inundated areas) (Fig. 1). These different storage compartments are in direct interactions with the atmosphere. For example, in the tropical Pacific, long-term droughts and floods are under the influence of the El Niño-Southern Oscillation (ENSO) events (e.g., Ward et al. 2014; Fok et al. 2018 and references therein).