High‐resolution raster hydrography maps are a fundamental data source for many geoscience applications. Here we introduce MERIT Hydro, a new global flow direction map at 3‐arc sec resolution (~90 m at the equator) derived from the latest elevation data (MERIT DEM) and water body data sets (G1WBM, Global Surface Water Occurrence, and OpenStreetMap). We developed a new algorithm to extract river networks near automatically by separating actual inland basins from dummy depressions caused by the errors in input elevation data. After a minimum amount of hand editing, the constructed hydrography map shows good agreement with existing quality‐controlled river network data sets in terms of flow accumulation area and river basin shape. The location of river streamlines was realistically aligned with existing satellite‐based global river channel data. Relative error in the drainage area was <0.05 for 90% of Global Runoff Data Center (GRDC) gauges, confirming the accuracy of the delineated global river networks. Discrepancies in flow accumulation area were found mostly in arid river basins containing depressions that are occasionally connected at high water levels and thus resulting in uncertain watershed boundaries. MERIT Hydro improves on existing global hydrography data sets in terms of spatial coverage (between N90 and S60) and representation of small streams, mainly due to increased availability of high‐quality baseline geospatial data sets. The new flow direction and flow accumulation maps, along with accompanying supplementary layers on hydrologically adjusted elevation and channel width, will advance geoscience studies related to river hydrology at both global and local scales.

Expanding the role of rivers: The surfaces of rivers and streams are interfaces for a host of chemical exchanges with the atmosphere and biosphere. For instance, carbon dioxide outgassing from rivers is estimated to be equivalent to one-fifth of combined emissions from fossil fuel combustion and cement production. Allen and Pavelsky used satellite imagery to estimate the surface area of rivers and streams (see the Perspective by Palmer and Ruhi). The stunning map that they generated results in an upward revision, by about one-third, to the total surface area of rivers and streams on Earth. Science , this issue p. 585 ; see also p. 546

The elevations of water surfaces hold important information on the earth's oceans and land surface waters. Ocean sea surface height is related to the internal change of the ocean's density and mass associated with ocean circulation and its response to climate change. The flow rates of rivers and volume changes of lakes are crucial to freshwater supplies and the hazards of floods and drought resulting from extreme weather and climate events. The Surface Water and Ocean Topography (SWOT) Mission is a new satellite using advanced radar technology to make headway in observing the variability of the elevation of water surfaces globally, providing fundamentally new information previously not available to the study of earth's waters. Here, we provide the first results of SWOT over oceans, rivers, and lakes. We demonstrate the potential of the mission to address science questions in oceanography and hydrology.

Spatiotemporally continuous global river discharge estimates across the full spectrum of stream orders are vital to a range of hydrologic applications, yet they remain poorly constrained. Here we present a carefully designed modeling effort (Variable Infiltration Capacity land surface model and Routing Application for Parallel computatIon of Discharge river routing model) to estimate global river discharge at very high resolutions. The precipitation forcing is from a recently published 0.1° global product that optimally merged gauge‐, reanalysis‐, and satellite‐based data. To constrain runoff simulations, we use a set of machine learning‐derived, global runoff characteristics maps (i.e., runoff at various exceedance probability percentiles) for grid‐by‐grid model calibration and bias correction. To support spaceborne discharge studies, the river flowlines are defined at their true geometry and location as much as possible—approximately 2.94 million vector flowlines (median length 6.8 km) and unit catchments are derived from a high‐accuracy global digital elevation model at 3‐arcsec resolution (~90 m), which serves as the underlying hydrography for river routing. Our 35‐year daily and monthly model simulations are evaluated against over 14,000 gauges globally. Among them, 35% (64%) have a percentage bias within ±20% (±50%), and 29% (62%) have a monthly Kling‐Gupta Efficiency ≥0.6 (0.2), showing data robustness at the scale the model is assessed. This reconstructed discharge record can be used as a priori information for the Surface Water and Ocean Topography satellite mission's discharge product, thus named “Global Reach‐level A priori Discharge Estimates for Surface Water and Ocean Topography”. It can also be used in other hydrologic applications requiring spatially explicit estimates of global river flows.

The upcoming Surface Water and Ocean Topography (SWOT) satellite mission, planned to launch in 2022, is the first mission to focus on measuring hydrological processes in Earth's surface water. As such, SWOT will vastly expand observations of global rivers ≥100 m wide. SWOT will provide a variety of data products, including a global vector river product containing water surface elevation (WSE), width, slope, and estimated discharge. Practical application and consistency of the SWOT vector products requires a prior global river network database divided into reaches. Here, we introduce the SWOT River Database (SWORD). SWORD will serve as the framework for the SWOT river vector products consisting of river reaches (∼10 km long) and nodes (∼200 m spacing). We generate SWORD by combining several global river‐ and satellite‐related data sets into one congruent product. When defining river reaches, we incorporate natural and human‐created river obstructions, basin boundaries, tributary junctions, and SWOT orbit track information. SWORD contains a total of 213,485 reaches and 10.7 million nodes. Globally, 77.3% of river reach lengths are between 10 and 20 km with a median reach length of 10.5 km. 95% of river reaches ≥10 km will have sufficient SWOT observations to provide discharge estimates at least once per orbit cycle. SWORD also contains many useful hydrologic and morphological attributes and is designed to be expandable in the future. Even before the launch of SWOT, it can serve as a framework for global hydrologic analyses using models, in situ measurements, and additional satellite observations.

Remote sensing approaches to measuring inland water quality date back nearly 50 years to the beginning of the satellite era. Over this time span, hundreds of peer-reviewed publications have demonstrated promising remote sensing models to estimate biological, chemical, and physical properties of inland waterbodies. Until recently, most of these publications focused largely on algorithm development as opposed to implementation of those algorithms to address specific science questions. This slow evolution contrasts with terrestrial and oceanic remote sensing, where methods development in the 1970s led to publications focused on understanding spatially expansive, complex processes as early as the mid-1980s. This review explores the progression of inland water quality remote sensing from methodological development to scientific applications. We use bibliometric analysis to assess overall patterns in the field and subsequently examine 236 key papers to identify trends in research focus and scale. The results highlight an initial 30 year period where the majority of publications focused on model development and validation followed by a spike in publications, beginning in the early-2000s, applying remote sensing models to analyze spatiotemporal trends, drivers, and impacts of changing water quality on ecosystems and human populations. Recent and emerging resources, including improved data availability and enhanced processing platforms, are enabling researchers to address challenging science questions and model spatiotemporally explicit patterns in water quality. Examination of the literature shows that the past 10–15 years has brought about a focal shift within the field, where researchers are using improved computing resources, datasets, and operational remote sensing algorithms to better understand complex inland water systems. Future satellite missions promise to continue these improvements by providing observational continuity with spatial/spectral resolutions ideal for inland waters.