Satellite images and streamgages are the two most common measurement tools used to assemble a record of historical water inundation dynamics, but access to necessary tools and analysis software is often sparse in those parts of the world most vulnerable to extreme hydrologic events. Building off of 2018 work focused on integrating surface water maps and streamgage information, the USGS project Patterns in the Landscape–Analyses of Cause and Effect (PLACE) has incorporated and adapted the USGS Dynamic Surface Water Extent (DSWE) model in the Google Earth Engine (GEE) cloud computing platform to both expand the geographical application of the product beyond the United States and test the model’s ability to deliver synoptic, spatially explicit snapshots of surface water extent in flood-prone parts of the world on monthly and annual scales. DSWE relies on a series of water indices produced using Landsat 30-meter imagery and elevation data to map surface water. The team used GEE cloud computing to access a multi-decadal archive of Landsat 30-meter satellite images to produce and analyze 372 monthly composite maps spanning 1988–2018 for Cambodia, a flood-prone country in Southeast Asia without a comprehensive streamgaging network. Model outputs were evaluated with respect to official USGS outputs in the United States, compared alongside existing surface water maps available in Cambodia, and independently assessed for accuracy at multiple dates across the time series. High-quality results in Cambodia suggest that extending DSWE globally using a cloud computing framework may immediately serve scientists, managers, and planners in a wide array of applications across the globe, while also providing an alternative data creation paradigm to circumvent traditional hardware and software requirements that may be cost prohibitive in less affluent parts of the world.
Annual surface-water maps for Cambodia show differences in surface water extent USGS Dynamic Surface Water Extent (DSWE) model. The two snapshots represent the beginning (1988) and end (2018) of the observed study period.