Representing Floodplain Inundation in an Earth System Model
Researchers use a macroscale inundation formulation to improve modeling of terrestrial surface hydrology in the Amazon basin.
Extreme events such as river inundation have extraordinary effects on terrestrial hydrology and aquatic ecosystems, but surface hydrology in basins with evident inundation can present modeling challenges. A research team led by scientists at the U.S. Department of Energy’s Pacific Northwest National Laboratory implemented a method to represent floodplain inundation in a river transport model.
Researchers extended the Model for Scale Adaptive River Transport (MOSART), a key component of the DOE Accelerated Climate Modeling for Energy (ACME) earth system model, to include a macroscale inundation parameterization (formulation) for representing floodplain inundation. The extended model demonstrated improvement in modeling terrestrial surface hydrology in the Amazon River basin, where seasonal floods occur every year, with large impacts on the regional water and biogeochemical cycles. By representing floodplain inundation and refining geomorphological parameters and the river flow representation, researchers improved modeling of streamflow and inundation extent, which provides a foundation for predicting the impacts of global change on water resources and flood hazards in earth system models.
In this research, scientists implemented a macroscale inundation parameterization and integrated it with the MOSART surface-water transport model. When rivers overflowed their banks, the inundation parameterization estimated the amount of the river-floodplain water exchange, as well as the flooded area within each grid cell or watershed. Researchers applied the model to the Amazon basin, where floodplain inundation is a key component of surface water dynamics and plays an important role in water, energy, and carbon cycles. Scientists addressed four aspects of the challenges in continental-scale modeling of surface hydrology by (1) reducing the vegetation-induced biases (offsets from observations) in the digital elevation model data; (2) improving the approach for estimating channel cross-sectional geometry to better represent the spatial variability in channel geometry; (3) accounting for how riverbed resistance to river ﬂow varies with the river size; and (4) considering the backwater effects to improve simulation of river flow in gentle-slope reaches. Researchers evaluated the model performance by using in situ streamflow records and satellite data of water level and inundation area. A sensitivity study showed that representing floodplain inundation, as well as refining floodplain topography, channel geometry and river flow representation, could significantly improve modeling of surface hydrology in the Amazon basin.
Pacific Northwest National Laboratory
- Earth System Modeling
- Accelerated Climate Modeling for Energy