The study of river dynamics has long relied on the analysis of traditional in situ hydrographs. This graphical representation of temporal variability at a given location is so ubiquitous that the mere term “hydrograph” is widely recognized as a time series. While such a “temporal hydrograph” is well suited for in situ data analysis, it fails to represent hydrologic variability across space at a given time; a perspective that characterizes satellite-based hydrologic observations. Here we argue that the concept of “spatial hydrograph” should be the focus of its own dedicated scrutiny. We build “space series” of river discharge and present their analysis in the context of peak flow event detection. We propose the use of peak event spatial coverage, referred to as “length”, as an analog to event duration. Our analysis is performed in the Mississippi basin using a dense in situ network. We reveal that peak flow events range in length from around 75 to 1,800 km with a median (mean) value of 330 (520) km along the basin’s largest rivers. Our analysis also suggests that spatial sampling needs to be a factor of 4 (2) finer in resolution than peak flow lengths to detect 81±13% (70±20%) of events and to estimate their length within 84±3% (67±12%) median accuracy. We evaluate the connection between temporal and spatial scales of peak flows and show that events with longer durations also affect larger extents. We finally discuss the implications for the design of satellite missions concerned with capturing floods across space.

The forthcoming Surface Water and Ocean Topography (SWOT) mission will vastly expand measurements of global rivers, providing critical new datasets for both gaged and ungaged basins. SWOT discharge products will provide discharge for all river reaches wider than 100 m, but at lower accuracy and temporal resolution than what is possible in situ. In this paper, we describe how SWOT discharge produced and archived by the US and French space agencies will be computed from measurements of river water surface elevation, width, and slope and ancillary data, along with expected discharge accuracy. We present here for the first time a complete estimate of SWOT discharge uncertainty budget, with separate terms for random (standard error) and systematic (bias) uncertainty components in river discharge timeseries. We expect that discharge uncertainty will be less than 30% for two thirds of global reaches and will be dominated by bias. Separate river discharge estimates will combine both SWOT and in situ data; these “gage constrained” discharge estimates can be expected to have lower systematic uncertainty. Temporal variations in river discharge timeseries will be dominated by random error and are expected to be estimated to within 15% for nearly all reaches, allowing accurate inference of event flow dynamics globally, including in ungaged basins. We believe this level of accuracy lays the groundwork for SWOT to enable breakthroughs in global hydrologic science.

The Physical Oceanography Distributed Active Archive Center (PO.DAAC) has traditionally hosted NASA’s Earth Observing System oceanography datasets, but is expanding its archive to include hydrology datasets from satellites like the upcoming Surface Water and Ocean Topography (SWOT) mission. The SWOT mission, expected to launch later this year (2022), will deliver approximately 20 TB of data per day! Though hydrologic and water resources applications will be enabled at a greater scale than ever before, an increase in data volume requires more efficient and scalable data management technologies. Cloud computing tools and services can help pave the way toward efficiency. By June 2022, PO.DAAC will have enabled all its data to be accessed in the NASA Earthdata Cloud hosted in Amazon Web Services (AWS). Other NASA DAACs are also in the process of migrating their Earth observations to the Earthdata Cloud, which will support seamless access across DAACs and disciplines. PO.DAAC desires to make data access, pre-processing, and analysis as seamless as possible for data users, supporting science and applications users alike with relevant tools and resources. In this presentation, after introducing the PO.DAAC, we highlight a new SWOT-specific data search mechanism (searching via the SWOT River Database (SWORD) pre-defined river reaches) and showcase a cloud computing workflow in the context of hydrologic applications by accessing and analyzing a proxy SWOT dataset, Pre-SWOT Making Earth System Data Records for Use in Research Environments (MEaSUREs) river heights. This cloud workflow can be easily adapted to other PO.DAAC datasets, or further developed with other DAAC data, offering effective guidance and support for a variety of science use cases and applications.