Triangulated Irregular Networks and Similarity in Landscape - - PowerPoint PPT Presentation
Triangulated Irregular Networks and Similarity in Landscape Processes Enrique R. Vivoni, Valeri Y. Ivanov, Vanessa Teles, Rafael L. Bras and Dara Entekhabi Massachusetts Institute of Technology AGU Fall Meeting, Session H21F December 10, 2002
Enrique R. Vivoni, Valeri Y. Ivanov, Vanessa Teles, Rafael L. Bras and Dara Entekhabi Massachusetts Institute of Technology
AGU Fall Meeting, Session H21F December 10, 2002
Shuttle Radar Topography Mission (~25 m) and Landsat imagery (~30 m) in Costa Rica
(Courtesy NASA SRTM)
How can we capture abundant high-resolution Earth remote sensing data in hydrologic and geomorphic models?
Large-scale Modeling of Earth Science Systems
How can we minimize the tradeoffs between model resolution, accuracy and computational expense in landscape modeling?
Sub-basin Modeling USGS 30 m Raster DEM 360 m DEM 1260 km2 130 basins Raster Grid Modeling 808 km2 6,238 cells
aggregation
AUS 25 m Raster DEM 1260 km2 26 basins
aggregation
Multiple-resolution TINs provide a flexible data structure for distributed hydrogeomorphic modeling at large scales.
Watershed TIN Modeling Hydrographic TIN Model 808 km2 54,438 nodes (6% 30-m DEM) Regional TIN Modeling Hydrologic TIN Model 500,000 km2 19,805 nodes (4% 1-km DEM) Baron Fork Arkansas-Red River
TINs are a piece-wise linear interpolation of x, y, z points to create triangular elements of varying size using the Delaunay criteria.
Computational Data Structure Voronoi cell Node Edge Triangle
Traditional Approach Hydrographic TINs Hydrologic TINs
Sequential methods for constructing TINs include additional constraining criteria based on the landscape process of interest.
Samples points according to slope criteria Constrains TIN with streams and basin boundary Samples points according to hydrologic criteria
Arc/Info GIS utilized to construct TINs from multiple data sources (DEMs, HRUs, landscape features) using 3 methods.
TIN-Index Analysis Package (TIAP): http://hydrology.mit.edu/tRIBS
DEM processing Landscape forms
Land-surface data
Hydrography
Landscape Indices
processes.
(e.g. TOPMODEL) of terrain processes, usually assuming steady-state.
priori estimate of landscape behavior.
distribution function.
non-arbitrary, physically-based initialization for distributed models.
How can we incorporate knowledge of landscape process
( )
β λ tan ln a =
Slope Criteria Terrain Analysis Wetness Index Hydrologic Criteria
Terrain Analysis Similarity TIN Model
Resolution function
Distribution function
Proximity filter utilized to sample DEM according to landscape index value.
significance (e.g., saturated areas, hollows).
hydrologic response (e.g. flat upslope areas). 60,000 nodes 60,000 nodes
Wetness Index of Beven and Kirkby Landslide Index of Montgomery and Dietrich Erosion Index of Moore and Wilson Example applications: f(a, β)
Owl Creek Watershed
ln (Tc)
− = φ β ρ ρ β tan tan 1 sin
w s c
a T Q
n m c
a T = 0896 . sin 13 . 22 β
( )
β λ tan ln a =
14 CONUS basins using USGS 30-m DEMs and 25-m SRTM DEMs
Hydrographic TIN Performance RMSE between TIN and DEM (meters) Data Reduction factor for TIN (d = nt/ ng)
Statistical Comparison of TINs with Aggregated DEM: Basin Scale
Primary and Secondary Terrain Descriptors
Statistical Comparison of TINs with Aggregated DEM: Continental Scale Mississippi River Basin (3,196,675 km2) HYDRO 1K DEM data (1km resolution) Data reduction (d = 0.03)
TIN-based Real-time Integrated Basin Simulator (tRIBS) Channel-Hillslope Integrated Landscape Development (CHILD)
Basin Streamflow Hydrograph Basin Saturation Fraction tRIBS Comparison over Baron Fork using Traditional and Hydrologic TINs to illustrate impact of resolving saturated areas according to wetness index.
71.58 60.41
Groundwater exfiltration
3.51 6.71
Perched return flow
18.94 27.00
Saturation-excess runoff
5.96 5.88
Infiltration-excess runoff
Hydrologic Traditional Runoff Percentage 2.29 3.07
Std Depth (m)
3.38 4.19
Mean Depth (m)
Hydrologic Traditional Depth to water table
Surface Runoff Dynamics Groundwater Dynamics Surface saturation frequency (%) due to coupled surface-subsurface runoff
CHILD Comparison over Owl Creek using Traditional and Erosion TINs to illustrate impact of resolving areas according to erosion index. Basin Sediment Erosion
Sediment volume (m3) erosion due to diffusion and fluvial processes over 14 year simulation period using rain gauge forcing. Traditional (topographic) TIN Erosion-index TIN
(1) Multiple-resolution TINs provide a flexible data structure for distributed hydrogeomorphic modeling at large scales. (2) A new method for embedding process behavior using a landscape index into a TIN mesh performs well and is theoretically attractive. (3) Statistical and distributed model tests of the TIN terrain products illustrate advantages of capturing process behavior in landscape representation.
(a) Multiple-resolution, nested TINs for modeling across differing scales. (b) Exploring TIN aggregation effect on distributed model output. (c) Generalizing hydrologic and geomorphic indices for multi-purpose modeling. (d) Regional and continental-scale tests of distributed model performance using similarity TINs.
Conclusions Future Directions
Wetness Index Erosion Index Landslide Index Distance between points Index values
n m c
a T = 0896 . sin 13 . 22 β
− = φ β ρ ρ β tan tan 1 sin
w s c
a T Q
Erosion Application in Owl Creek (TX) Landslide Application in Tolt River (WA)
Traditional method Hydrologic method Mean = 4.19 m Std = 3.07 m Mean = 3.38 m Std = 2.29 m