Abstract
Mapping surface water over time provides the spatially explicit information essential for hydroclimatic research focused on droughts and flooding. Hazard risk assessments and water management planning also rely on accurate, long-term measurements describing hydrologic fluctuations. Stream gages are a common measurement tool used to better understand flow and inundation dynamics, but gage networks are incomplete or non-existent in many parts of the world. In such instances, satellite imagery may provide the only data available to monitor surface water changes over time. Here, we describe an effort to extend the applicability of the USGS Dynamic Surface Water Extent (DSWE) model to non-US regions. We leverage the multi-decadal archive of the Landsat satellite in the Google Earth Engine (GEE) cloud-based computing platform to produce and analyze 372 monthly composite maps and 31 annual maps (January 1988–December 2018) in Cambodia, a flood-prone country in Southeast Asia that lacks a comprehensive stream gage network. DSWE relies on a series of spectral water indices and elevation data to classify water into four categories of water inundation. We compared model outputs to existing surface water maps and independently assessed DSWE accuracy at discrete dates across the time series. Despite considerable cloud obstruction and missing imagery across the monthly time series, the overall accuracy exceeded 85% for all annual tests. The DSWE model consistently mapped open water with high accuracy, and areas classified as “high confidence” water correlate well to other available maps at the country scale. Results in Cambodia suggest that extending DSWE globally using a cloud computing framework may benefit scientists, managers, and planners in a wide array of applications across the globe.
Highlights
Surface water dynamics are typically monitored through time series of water flow derived from stream gage data
To investigate the usefulness of Dynamic Surface Water Extent (DSWE) time series in Cambodia, we generate both a comprehensive set of monthly inundation maps that we evaluate against corresponding Joint Research Centre (JRC) products and a set of annual composites maps that we compare to measures of vegetation greenness derived from Landsat
Time-series of accurate surface water maps can provide critical insights into the timing and localized consequences of weather extremes. These maps are valuable in the many flood-prone countries that lack extensive stream gage networks for tracking hydrologic dynamics
Summary
Surface water dynamics are typically monitored through time series of water flow derived from stream gage data. The assembly of accurate, precise, and frequent (sub-daily) measurements of streamflow in diverse watercourses is invaluable for managing water supplies and anticipating flood events. Point-based stream measurements do not directly provide the spatial extent of surface water change (e.g., flood footprints) on the landscape. The spatially explicit mapping of surface water change is recognized as an acceptable method to empirically train, validate, and improve flood inundation predictions [5]. In addition to hydroclimatic research focused on droughts and flooding, accurate maps of surface water extent are valuable inputs into analyses of flora and fauna species’ sensitivity [6,7] and assessments of hazard risk at the community scale [8,9], as well as in water management and economic planning and forecasting [10]
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