2019 CSDMS meeting-008: Difference between revisions

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|CSDMS meeting abstract title=A mountain-to-coast hydrogeomorphic modeling framework for flood risk prediction
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|CSDMS meeting coauthor first name abstract=Alexander
|CSDMS meeting coauthor last name abstract=Horner-Devine
|CSDMS meeting coauthor institute / Organization=University of Washington
|CSDMS meeting coauthor town-city=Seattle
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|State=Washington
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|CSDMS meeting coauthor first name abstract=Nirnimesh
|CSDMS meeting coauthor last name abstract=Kumar
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|CSDMS meeting coauthor first name abstract=Erkan
|CSDMS meeting coauthor last name abstract=Istanbulluoglu
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|CSDMS meeting coauthor first name abstract=Jacob
|CSDMS meeting coauthor last name abstract=Morgan
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|CSDMS meeting coauthor first name abstract=Christina
|CSDMS meeting coauthor last name abstract=Bandaragoda
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|CSDMS meeting coauthor first name abstract=Jeffrey
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|CSDMS meeting coauthor first name abstract=Zhuoran
|CSDMS meeting coauthor last name abstract=Duan
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|CSDMS meeting coauthor first name abstract=Brian
|CSDMS meeting coauthor last name abstract=Collins
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|CSDMS meeting coauthor first name abstract=Allison
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|CSDMS meeting coauthor institute / Organization=Western Washington University
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{{CSDMS meeting abstract template 2019
|CSDMS meeting abstract=Deposition of sediment from upland sources has the potential to increase flood risk in downstream riverside communities by reducing the carrying capacity of rivers and causing overbank flow. However, the morphodynamic response of rivers to variable upstream sediment supply remains poorly understood, and operational flood models do not account for sediment in flood prediction.
 
We introduce a framework for integrating source-to-sink sediment dynamics using coupled hydrological, hydrodynamic and landscape evolution models to quantify and better predict flooding events.  A Distributed Hydrology Soil Vegetation Model is used to simulate upland streamflow and land coverage over numerical grids of river networks. Modules from the Python toolkit, Landlab, generate and route sediment from mountain sources (i.e. landslides, exposed glacial till) in the same domain. Streamflow and sediment from these upland models are delivered to a Delft3D hydrodynamic, sediment transport and morphodynamic model to characterize the effects of sediment-routing on lowland, coastal floodplains and investigate the impact on flood risk. This modeling framework is tested for three Puget Sound, WA basins: the Nooksack River, Skagit River and Mt. Rainier drainage, where gage analysis performed on historic USGS indicates regional morphodynamic patterns, with potential implications on flood risk. To ensure accurate model-coupling, the model ensemble is tested in an idealized, Landlab-generated domain.
 
Funded by the National Science Foundation.
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Latest revision as of 19:51, 1 April 2019





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A mountain-to-coast hydrogeomorphic modeling framework for flood risk prediction

Shelby Ahrendt, The University of Washington Seattle Washington, United States. shelby.ahrendt@gmail.com
Alexander Horner-Devine, University of Washington Seattle Washington, United States.
Nirnimesh Kumar, University of Washington Seattle Washington, United States.
Erkan Istanbulluoglu, University of Washington Seattle Washington, United States.
Jacob Morgan, University of Washington Seattle Washington, United States.
Christina Bandaragoda, University of Washington Seattle Washington, United States.
Jeffrey Keck, University of Washington Seattle Washington, United States.
Zhuoran Duan, University of Washington Seattle Washington, United States.
Brian Collins, University of Washington Seattle Washington, United States.
Allison Pfeiffer, Western Washington University Bellingham Washington, United States.


Deposition of sediment from upland sources has the potential to increase flood risk in downstream riverside communities by reducing the carrying capacity of rivers and causing overbank flow. However, the morphodynamic response of rivers to variable upstream sediment supply remains poorly understood, and operational flood models do not account for sediment in flood prediction.

We introduce a framework for integrating source-to-sink sediment dynamics using coupled hydrological, hydrodynamic and landscape evolution models to quantify and better predict flooding events. A Distributed Hydrology Soil Vegetation Model is used to simulate upland streamflow and land coverage over numerical grids of river networks. Modules from the Python toolkit, Landlab, generate and route sediment from mountain sources (i.e. landslides, exposed glacial till) in the same domain. Streamflow and sediment from these upland models are delivered to a Delft3D hydrodynamic, sediment transport and morphodynamic model to characterize the effects of sediment-routing on lowland, coastal floodplains and investigate the impact on flood risk. This modeling framework is tested for three Puget Sound, WA basins: the Nooksack River, Skagit River and Mt. Rainier drainage, where gage analysis performed on historic USGS indicates regional morphodynamic patterns, with potential implications on flood risk. To ensure accurate model-coupling, the model ensemble is tested in an idealized, Landlab-generated domain.

Funded by the National Science Foundation.


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