1r.sim.water(1) Grass User's Manual r.sim.water(1)
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6 r.sim.water - Overland flow hydrologic simulation using path sampling
7 method (SIMWE)
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10 raster, flow, hydrology
11
13 r.sim.water
14 r.sim.water help
15 r.sim.water [-t] elevin=name dxin=name dyin=name [rain=name]
16 [rain_val=float] [infil=name] [infil_val=float] [manin=name]
17 [manin_val=float] [traps=name] [vector=name] [depth=name]
18 [disch=name] [err=name] [outwalk=name] [nwalk=integer]
19 [niter=integer] [outiter=integer] [density=integer] [diffc=float]
20 [hmax=float] [halpha=float] [hbeta=float] [--overwrite] [--ver‐
21 bose] [--quiet]
22 Flags:
24 -t
25 Time-series output
26 --overwrite
28 Allow output files to overwrite existing files
29 --verbose
31 Verbose module output
32 --quiet
34 Quiet module output
35 Parameters:
37 elevin=name
38 Name of the elevation raster map [m]
39 dxin=name
41 Name of the x-derivatives raster map [m/m]
42 dyin=name
44 Name of the y-derivatives raster map [m/m]
45 rain=name
47 Name of the rainfall excess rate (rain-infilt) raster map [mm/hr]
48 rain_val=float
50 Rainfall excess rate unique value [mm/hr]
51 Default: 50
52 infil=name
54 Name of the runoff infiltration rate raster map [mm/hr]
55 infil_val=float
57 Runoff infiltration rate unique value [mm/hr]
58 Default: 0.0
59 manin=name
61 Name of the Mannings n raster map
62 manin_val=float
64 Mannings n unique value
65 Default: 0.1
66 traps=name
68 Name of the flow controls raster map (permeability ratio 0-1)
69 vector=name
71 Name of the sampling locations vector points map
72 depth=name
74 Output water depth raster map [m]
75 disch=name
77 Output water discharge raster map [m3/s]
78 err=name
80 Output simulation error raster map [m]
81 outwalk=name
83 Name of the output walkers vector points map
84 nwalk=integer
86 Number of walkers, default is twice the no. of cells
87 niter=integer
89 Time used for iterations [minutes]
90 Default: 10
91 outiter=integer
93 Time interval for creating output maps [minutes]
94 Default: 2
95 density=integer
97 Density of output walkers
98 Default: 200
99 diffc=float
101 Water diffusion constant
102 Default: 0.8
103 hmax=float
105 Threshold water depth [m] (diffusion increases after this water
106 depth is reached)
107 Default: 0.3
108 halpha=float
110 Diffusion increase constant
111 Default: 4.0
112 hbeta=float
114 Weighting factor for water flow velocity vector
115 Default: 0.5
116
118 r.sim.water is a landscape scale simulation model of overland flow
119 designed for spatially variable terrain, soil, cover and rainfall
120 excess conditions. A 2D shallow water flow is described by the bivari‐
121 ate form of Saint Venant equations. The numerical solution is based on
122 the concept of duality between the field and particle representation of
123 the modeled quantity. Green's function Monte Carlo method, used to
124 solve the equation, provides robustness necessary for spatially vari‐
125 able conditions and high resolutions (Mitas and Mitasova 1998). The
126 key inputs of the model include elevation (elevin raster map), flow
127 gradient vector given by first-order partial derivatives of elevation
128 field (dxin and dyin raster maps), rainfall excess rate (rain raster
129 map or rain_val single value) and a surface roughness coefficient
130 given by Manning's n (manin raster map or manin_val single value). Par‐
131 tial derivatives raster maps can be computed along with interpolation
132 of a DEM using the -d option in v.surf.rst module. If elevation raster
133 is already provided, partial derivatives can be computed using
134 r.slope.aspect module. Partial derivatives are used to determine the
135 direction and magnitude of water flow velocity. To include a prede‐
136 fined direction of flow, map algebra can be used to replace terrain-
137 derived partial derivatives with pre-defined partial derivatives in
138 selected grid cells such as man-made channels, ditches or culverts.
139 Equations (2) and (3) from this report can be used to compute partial
140 derivates of the predefined flow using its direction given by aspect
141 and slope.
142 The module automatically converts horizontal distances from feet to
144 metric system using database/projection information. Rainfall excess is
145 defined as rainfall intensity - infiltration rate and should be pro‐
146 vided in [mm/hr]. Rainfall intensities are usually available from
147 meteorological stations. Infiltration rate depends on soil proper‐
148 ties and land cover. It varies in space and time. For saturated
149 soil and steady-state water flow it can be estimated using saturated
150 hydraulic conductivity rates based on field measurements or using
151 reference values which can be found in literature. Optionally, user
152 can provide an overland flow infiltration rate map infil or a single
153 value infil_val in [mm/hr] that control the rate of infiltration for
154 the already flowing water, effectively reducing the flow depth and dis‐
155 charge. Overland flow can be further controled by permeable check dams
156 or similar type of structures, the user can provide a map of these
157 structures and their permeability ratio in the map traps that defines
158 the probability of particles to pass through the structure (the values
159 will be 0-1).
160 Output includes a water depth raster map depth in [m], anda water dis‐
163 charge raster map disch in [m3/s]. Error of the numerical solution can
164 be analyzed using the err raster map (the resulting water depth is an
165 average, and err is its RMSE). The output vector points map outwalk can
166 be used to analyze and visualize spatial distribution of walkers at
167 different simulation times (note that the resulting water depth is
168 based on the density of these walkers). Number of the output walkers is
169 controled by the density parameter, which controls how many walkers
170 used in simulation should be written into the output. Duration of sim‐
171 ulation is controled by the niter parameter. The default value is 10
172 minutes, reaching the steady-state may require much longer time,
173 depending on the time step, complexity of terrain, land cover and size
174 of the area. Output water depth and discharge maps can be saved during
175 simulation using the time series flag -t and outiter parameter defining
176 the time step in minutes for writing output files. Files are saved
177 with a suffix representing time since the start of simulation in sec‐
178 onds (e.g. wdepth.500, wdepth.1000).
179 Overland flow is routed based on partial derivatives of elevation
181 field or other landscape features influencing water flow. Simulation
182 equations include a diffusion term (diffc parameter) which enables
183 water flow to overcome elevation depressions or obstacles when water
184 depth exceeds a threshold water depth value (hmax), given in [m]. When
185 it is reached, diffusion term increases as given by halpha and advec‐
186 tion term (direction of flow) is given as "prevailing" direction of
187 flow computed as average of flow directions from the previous hbeta
188 number of grid cells.
189
191 A 2D shallow water flow is described by the bivariate form of Saint
192 Venant equations (e.g., Julien et al., 1995). The continuity of water
193 flow relation is coupled with the momentum conservation equation and
194 for a shallow water overland flow, the hydraulic radius is approximated
195 by the normal flow depth. The system of equations is closed using the
196 Manning's relation. Model assumes that the flow is close to the kine‐
197 matic wave approximation, but we include a diffusion-like term to
198 incorporate the impact of diffusive wave effects. Such an incorporation
199 of diffusion in the water flow simulation is not new and a similar
200 term has been obtained in derivations of diffusion-advection equations
201 for overland flow, e.g., by Lettenmeier and Wood, (1992). In our
202 reformulation, we simplify the diffusion coefficient to a constant and
203 we use a modified diffusion term. The diffusion constant which we have
204 used is rather small (approximately one order of magnitude smaller than
205 the reciprocal Manning's coefficient) and therefore the resulting flow
206 is close to the kinematic regime. However, the diffusion term improves
207 the kinematic solution, by overcoming small shallow pits common in
208 digital elevation models (DEM) and by smoothing out the flow over slope
209 discontinuities or abrupt changes in Manning's coefficient (e.g., due
210 to a road, or other anthropogenic changes in elevations or cover).
211 Green's function stochastic method of solution. The Saint Venant equa‐
213 tions are solved by a stochastic method called Monte Carlo (very simi‐
214 lar to Monte Carlo methods in computational fluid dynamics or to quan‐
215 tum Monte Carlo approaches for solving the Schrodinger equation
216 (Schmidt and Ceperley, 1992, Hammond et al., 1994; Mitas, 1996)). It
217 is assumed that these equations are a representation of stochastic
218 processes with diffusion and drift components (Fokker-Planck equa‐
219 tions). The Monte Carlo technique has several unique advantages which
220 are becoming even more important due to new developments in computer
221 technology. Perhaps one of the most significant Monte Carlo proper‐
222 ties is robustness which enables us to solve the equations for com‐
223 plex cases, such as discontinuities in the coefficients of differen‐
224 tial operators (in our case, abrupt slope or cover changes, etc).
225 Also, rough solutions can be estimated rather quickly, which allows
226 us to carry out preliminary quantitative studies or to rapidly
227 extract qualitative trends by parameter scans. In addition, the sto‐
228 chastic methods are tailored to the new generation of computers as
229 they provide scalability from a single workstation to large parallel
230 machines due to the independence of sampling points. Therefore, the
231 methods are useful both for everyday exploratory work using a desktop
232 computer and for large, cutting-edge applications using high perfor‐
233 mance computing.
234
236 v.surf.rst r.slope.aspect r.sim.sediment
237
239 Helena Mitasova, Lubos Mitas
240 North Carolina State University
241 hmitaso@unity.ncsu.edu
242 Jaroslav Hofierka
243 GeoModel, s.r.o. Bratislava, Slovakia
244 hofierka@geomodel.sk
245 Chris Thaxton
246 North Carolina State University
247 csthaxto@unity.ncsu.edu
248 csthaxto@unity.ncsu.edu
249
251 Mitasova, H., Thaxton, C., Hofierka, J., McLaughlin, R., Moore, A.,
252 Mitas L., 2004, Path sampling method for modeling overland water flow,
253 sediment transport and short term terrain evolution in Open Source GIS.
254 In: C.T. Miller, M.W. Farthing, V.G. Gray, G.F. Pinder eds., Proceed‐
255 ings of the XVth International Conference on Computational Methods in
256 Water Resources (CMWR XV), June 13-17 2004, Chapel Hill, NC, USA, Else‐
257 vier, pp. 1479-1490.
258 Mitasova H, Mitas, L., 2000, Modeling spatial processes in multiscale
260 framework: exploring duality between particles and fields, plenary talk
261 at GIScience2000 conference, Savannah, GA.
262 Mitas, L., and Mitasova, H., 1998, Distributed soil erosion simulation
264 for effective erosion prevention. Water Resources Research, 34(3),
265 505-516.
266 Mitasova, H., Mitas, L., 2001, Multiscale soil erosion simulations for
268 land use management, In: Landscape erosion and landscape evolution mod‐
269 eling, Harmon R. and Doe W. eds., Kluwer Academic/Plenum Publishers,
270 pp. 321-347.
271 Neteler, M. and Mitasova, H., 2008, Open Source GIS: A GRASS GIS
273 Approach. Third Edition. The International Series in Engineering and
274 Computer Science: Volume 773. Springer New York Inc, p. 406.
275 Last changed: Date: 2008/02/16 15:55:10 $
277 Full index
279 © 2003-2008 GRASS Development Team
281GRASS 6.3.0 r.sim.water(1)