Floodplain Mapping using TINs Triangulated Irregular Networks TINs
- Slides: 39
Floodplain Mapping using TINs • Triangulated Irregular Networks (TINs) • Representation of stream channels using TINs • Floodplain delineation using HEC-HMS, HEC-RAS and Arc. View
TIN with Surface Features Classroom Waller Creek UT Football Stadium
A Portion of the TIN
Input Data for this Portion Mass Points Soft Breaklines Hard Breaklines
TIN Vertices and Triangles
TIN Surface Model Waller Creek Street and Bridge
3 -D Scene
3 -D Scene with Buildings
Floodplain Mapping using TINs • Triangulated Irregular Networks (TINs) • Representation of stream channels using TINs • Floodplain delineation using HEC-HMS, HEC-RAS and Arc. View
River Modeling w River hydraulic modeling provides a tool to study and gain understanding of hydraulic flow phenomena w Topographic data describe the geometry of the simulated river system and permit the establishment of model topology w HEC-RAS, MIKE 11 all hydraulic models require channel information for model development
River Morphology
Flood Inundation
Floodplain Delineation
Floodplain Delineation
Channel and Cross-Section Direction of Flow Channel Cross-Section
Profile. Lines Types 1 - Thalweg 2 - Left. Bank 3 - Right. Bank 4 - Left. Flood. Line 5 - Right. Flood. Line Profile. Lines and Cross. Sections are linked through Channel_ID
TIN as a source of cross-sections
Cross. Sections
Elements of a Cross-Section w. Geometry w. Identifier w. Property w. Georeference w. Supplementary
Floodplain Mapping using TINs • Triangulated Irregular Networks (TINs) • Representation of stream channels using TINs • Floodplain delineation using HEC-HMS, HEC-RAS and Arc. View
Floodplain Mapping Approach HEC-HMS HEC-RAS Flow discharge CRWR-Pre. Pro Parameters Schematic Geometric data Arc. View HEC-Geo. Ras Water surface profiles
Purpose • Integrate/Validate existing tools for floodplain determination and visualization. – Reduce the dependence on field data. – Improve the floodplain analyses capabilities (lower costs and more accuracy).
Digital Spatial Data • Digital elevation model (DEM). • Stream definition. HEC-HMS HEC-RAS Arc. View
CRWR-Pre. Pro • Watershed delineation. • Reach/Watershed parameters determination. HEC-HMS HEC-RAS Arc. View
HEC-HMS: Flow Determination HEC-HMS HEC-RAS Arc. View
HMS-RAS Connection HMS Junctions RAS Cross-sections HEC-HMS HEC-RAS Arc. View
HMS-RAS Connection HMS Hydrograph (0500, 3559. 6) RAS Flow Data HEC-HMS HEC-RAS Arc. View
Digital Terrain Model: TIN • Observed points and breaklines for constructing a triangular irregular network (TIN). HEC-HMS HEC-RAS Arc. View
Digital Terrain Model: TIN • Embedding Buildings into the TIN. HEC-HMS HEC-RAS Arc. View
GIS-RAS Connection • Stream centerline. • Banks. • Flow paths. • Cross sections. HEC-HMS HEC-RAS Arc. View
GIS-RAS Connection • Location of cross sections. HEC-HMS HEC-RAS Arc. View
Hydraulic Modeling with HEC-RAS • Cross-section extracted from the TIN. • RAS stream geometry. HEC-HMS HEC-RAS Arc. View
Hydraulic Modeling with HEC-RAS • Resulting water elevations. HEC-HMS HEC-RAS Arc. View
Floodplain Mapping • Floodplain for 500 cfs. HEC-HMS HEC-RAS Arc. View
Floodplain Mapping • 2 -D floodplain animation (500 – 5, 000 cfs).
Floodplain Mapping • 3 -D floodplain animation.
Limitations • Bridges/culverts: - depend on field data. - data input by hand.
Limitations • The accuracy obtained from our TIN is not good enough.
Solutions • New technologies (i. e. LADAR) are improving the quality of the digital terrain representations. Source: digital representation of NYC generated by ASI and published by ESRI.
- Triangulated irregular network
- Triangulated irregular network
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- Triangulated graph
- The associative mapping is costlier than direct mapping.
- Forward mapping vs backward mapping
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- Floodplain bench
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- Nile floodplain
- How is a floodplain formed
- Source zone transition zone and floodplain zone
- Parishes of the mississippi floodplain region
- Mesopotamia jeopardy
- Nile floodplain
- Utility mapping using gis
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- Matlab neural network toolbox
- Image super resolution using deep convolutional networks
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