Cloud‐based computing, access to big geospatial data, and virtualization, whereby users are freed from computational hardware and data management logistics, could revolutionize remote sensing applications in fluvial geomorphology. Analysis of multitemporal, multispectral satellite imagery has provided fundamental geomorphic insight into the planimetric form and dynamics of large river systems, but information derived from these applications has largely been used to test existing concepts in fluvial geomorphology, rather than for generating new concepts or theories. Traditional approaches (i.e., desktop computing) have restricted the spatial scales and temporal resolutions of planimetric river channel change analyses. Google Earth Engine (GEE), a cloud‐based computing platform for planetary‐scale geospatial analyses, offers the opportunity to relieve these spatiotemporal restrictions. We summarize the big geospatial data flows available to fluvial geomorphologists within the GEE data catalog, focus on approaches to look beyond mapping wet channel extents and instead map the wider riverscape (i.e., water, sediment, vegetation) and its dynamics, and explore the unprecedented spatiotemporal scales over which GEE analyses can be applied. We share a demonstration workflow to extract active river channel masks from a section of the Cagayan River (Luzon, Philippines) then quantify centerline migration rates from multitemporal data. By enabling fluvial geomorphologists to take their algorithms to petabytes worth of data, GEE is transformative in enabling deterministic science at scales defined by the user and determined by the phenomena of interest. Equally as important, GEE offers a mechanism for promoting a cultural shift toward open science, through the democratization of access and sharing of reproducible code.

Applications of Google Earth Engine in fluvial geomorphology for detecting river channel change

Riverbank migration has historically been seen as a risk to infrastructure that can be combated through channelization, bank stabilization, and sediment trapping. The physical processes involved with riverbank erosion and deposition are well defined, yet the solutions to equations that describe these processes are computationally and data intensive over large domains. While current understanding of large-scale river channel mobility largely comes from reach- and watershed-scale observations, we need global observations of riverbank erosion and accretion to broaden our understanding of sediment processes within and across river basins. In this work, we create the first global data set of riverbank erosion for >370,000 km of large rivers using up to 20 years of water classifications from Landsat imagery. We estimate uncertainty by propagating water classification errors through our methods. Globally, we find riverbank erosion for rivers wider than 150 m to have an approximately log-normal distribution with a median value of 1.52 m/yr. Comparing our data set to 25 similar estimates of riverbank migration, we found a normalized mean absolute error of 42% and a bias of 5.8%. We show that river width is the best first-order predictor of riverbank erosion, in agreement with existing literature. We also show that the relationship between width and bank erosion is substantially different among a sample of global river basins and suggest that this is due to second-order influences of geology, hydrology, and human influence. These data will help improve models of sediment transport, support models of bank erosion, and improve our understanding of human modification of rivers. 

Global Observations of Riverbank Erosion and Accretion From Landsat Imagery

Human impact on river planform within the context of multi-timescale river channel dynamics in a Himalayan river system

Detailed assessment of spatial and temporal variations in river channel changes and meander evolution as a preliminary work for effective floodplain management. The example of Sajó River, Hungary.

National-scale assessment of decadal river migration at critical bridge infrastructure in the Philippines

Accounting for uncertainty in remotely-sensed measurements of river planform change

Most people face some level of water insecurity. Wise water management practices to address water security issues typically require data derived from a combination of observation and model data. This data has historically proven difficult to sustainably supply in many areas of the world. We present the design and development of a global, modeled streamflow data source for the Group on Earth Observation (GEO) Global Water Sustainability (GEOGloWS) implemented at the European Centre for Medium-Range Weather Forecasts (ECMWF). This GEOGloWS ECMWF Streamflow Service (GEOGloWS Service) is a solution and prototype to sustainably address this need for data. The GEOGloWS Service centralizes computing and human resources to build a global hydrologic model and exposes data and mapping web services that allow users to consume the resulting data to meet their specific needs. The global hydrologic model produces global 15-day ensemble streamflow forecasts and a historical simulation since January 1979. We present case studies in several countries and research environments which demonstrate the utility of the approach taken by the GEOGloWS Service. The case studies show how the global modeled data are being applied to make informed decisions and advance projects in ways that otherwise would not have been possible. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/jfr3.12859

Advancing global hydrologic modeling with the
 GEOGloWS ECMWF
 streamflow service

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Accounting for uncertainty in remotely-sensed measurements of river planform change

Advancing global hydrologic modeling with the GEOGloWS ECMWF streamflow service