Interactive Mapping of Inundation Metrics Using Cloud Computing for Improved Floodplain Conservation and Management

Within large-river ecosystems, floodplains serve a variety of important ecological functions. A recent survey of 80 managers of floodplain conservation lands along the Upper and Middle Mississippi and Lower Missouri Rivers in the central United States found that the most critical information needed to improve floodplain management centered on metrics for characterizing depth, extent, frequency, duration, and timing of inundation. These metrics can be delivered to managers efficiently through cloud-based interactive maps. To calculate these metrics, we interpolated an existing one-dimensional hydraulic model for the Lower Missouri River, which simulated water surface elevations at cross sections spaced (<1 km) to sufficiently characterize water surface profiles along an approximately 800 km stretch upstream from the confluence with the Mississippi River over an 80-year record at a daily time step. To translate these water surface elevations to inundation depths, we subtracted a merged terrain model consisting of floodplain LIDAR and bathymetric surveys of the river channel. This approach resulted in a 29000+ day time series of inundation depths across the floodplain using grid cells with 30 m spatial resolution. Initially, we used these data on a local workstation to calculate a suite of nine spatially distributed inundation metrics for the entire model domain. These metrics are calculated on a per pixel basis and encompass a variety of temporal criteria generally relevant to flora and fauna of interest to floodplain managers, including, for example, the average number of days inundated per year within a growing season. Using a local workstation, calculating these metrics for the entire model domain requires several hours. However, for the needs of individual floodplain managers working at site scales, these metrics may be too general and inflexible. Instead of creating a priori a suite of inundation metrics able to satisfy all user needs, we present the usage of Google's cloud-based Earth Engine API to allow users to define and query their own inundation metrics from our dataset and produce maps nearly instantaneously. This approach allows users to select the time periods and inundation depths germane to managing local species, potentially facilitating conservation of floodplain ecosystems.