FIGURES
Figure 8.1.
Modeling of steady state surface water depth in terrain
with depression under uniform soil and cover conditions: a) input data -
10m resolution DEM, b) water depth as a function of upslope
contributing area computed using D8 algorithm (r.watershed
in GRASS5), solves the flow through depression geometrically by continuing
the flow in the selected direction, b) function
of upslope contributing area computed using vector-grid algorithm (Mitasova
et al. 1996, r.flow), depression fucntions as a sink, c) 2D kinematic wave
solution of continuity equation (SIMWE with diffussion->0), water accumulates
in depression to unrealistic depths, d) 2D approximate diffusive wave solution
(SIMWE), water fills depression, creating a pond, and flows out, e) 2D
approximate diffusive wave solution, channel with a given gradient
controls the flow through the depression.
color version
Figure 8.2. Path sampling method.
Figure 8.3. Impact of DEM artifacts on modeling of surface
water flow: a) water flow over a DEM created by TIN with artificial dams in valleys, b)
water flow over a DEM with vertical precision of 1m, with artificial steps along contours
and plateaus, c) water flow over a DEM interpolated by RST (GRASS5.0)
with vertical precision 0.01m.
this figure is under development
Figure 8.4 Net erosion/deposition pattern computed by USPED within a general GIS.
Figure 8.5 Snapshots from a dynamic model of water depth distribution in a relatively flat field (2.5x4.5km) draped as color over the DEM (6m resolution, 30-times vertical exaggeration), a) 5 min, b) 10 min, c) steady state 50 min (line features represent existing drainage which has not been modeled), d) 12 hr after the rain.
FIGURE 8.6 Terrain evolution and simulation of erosion and deposition pattern on a hillslope with hedges: predicted net erosion/deposition with deposition above and erosion below the hedge after 7 rainfall events. The simulations are done at 0.4m resolution for 80m long hillslope. Red spheres show the measured elevation in 1996, green spheres show elevation in 1993 at the start of simulation.
Figure 8.7 Modeling of surface water flow on a section of Mississipi river using SMS. Figures courtesy Dr. Mingshi Chen, William M. Brown, RiverWeb Museum Consortium http://www.ncsa.uiuc.edu/Cyberia/RiverWeb/Projects/RWMuseum/ Surface-water Modeling System (SMS), predicted flow using the finite element two-dimensional hydrodynamic flow simulation program FESWMS http://www.ems-i.com/sms/, 3D visualization of terrain model and the river was done in GRASS5.0 using NVIZ tool.