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).
8
10 raster, hydrology, soil, flow, overland flow, model
11
13 r.sim.water
14 r.sim.water --help
15 r.sim.water [-ts] elevation=name dx=name dy=name [rain=name]
16 [rain_value=float] [infil=name] [infil_value=float] [man=name]
17 [man_value=float] [flow_control=name] [observation=name]
18 [depth=name] [discharge=name] [error=name] [walkers_output=name]
19 [logfile=name] [nwalkers=integer] [niterations=integer] [out‐
20 put_step=integer] [diffusion_coeff=float] [hmax=float] [hal‐
21 pha=float] [hbeta=float] [random_seed=integer] [nprocs=integer]
22 [--overwrite] [--help] [--verbose] [--quiet] [--ui]
23 Flags:
25 -t
26 Time-series output
27 -s
29 Generate random seed
30 Automatically generates random seed for random number generator
31 (use when you don’t want to provide the seed option)
32 --overwrite
34 Allow output files to overwrite existing files
35 --help
37 Print usage summary
38 --verbose
40 Verbose module output
41 --quiet
43 Quiet module output
44 --ui
46 Force launching GUI dialog
47 Parameters:
49 elevation=name [required]
50 Name of input elevation raster map
51 dx=name [required]
53 Name of x-derivatives raster map [m/m]
54 dy=name [required]
56 Name of y-derivatives raster map [m/m]
57 rain=name
59 Name of rainfall excess rate (rain-infilt) raster map [mm/hr]
60 rain_value=float
62 Rainfall excess rate unique value [mm/hr]
63 Default: 50
64 infil=name
66 Name of runoff infiltration rate raster map [mm/hr]
67 infil_value=float
69 Runoff infiltration rate unique value [mm/hr]
70 Default: 0.0
71 man=name
73 Name of Manning’s n raster map
74 man_value=float
76 Manning’s n unique value
77 Default: 0.1
78 flow_control=name
80 Name of flow controls raster map (permeability ratio 0-1)
81 observation=name
83 Name of sampling locations vector points map
84 Or data source for direct OGR access
85 depth=name
87 Name for output water depth raster map [m]
88 discharge=name
90 Name for output water discharge raster map [m3/s]
91 error=name
93 Name for output simulation error raster map [m]
94 walkers_output=name
96 Base name of the output walkers vector points map
97 Name for output vector map
98 logfile=name
100 Name for sampling points output text file. For each observation
101 vector point the time series of sediment transport is stored.
102 nwalkers=integer
104 Number of walkers, default is twice the number of cells
105 niterations=integer
107 Time used for iterations [minutes]
108 Default: 10
109 output_step=integer
111 Time interval for creating output maps [minutes]
112 Default: 2
113 diffusion_coeff=float
115 Water diffusion constant
116 Default: 0.8
117 hmax=float
119 Threshold water depth [m]
120 Diffusion increases after this water depth is reached
121 Default: 0.3
122 halpha=float
124 Diffusion increase constant
125 Default: 4.0
126 hbeta=float
128 Weighting factor for water flow velocity vector
129 Default: 0.5
130 random_seed=integer
132 Seed for random number generator
133 The same seed can be used to obtain same results or random seed can
134 be generated by other means.
135 nprocs=integer
137 Number of threads which will be used for parallel compute
138 Default: 1
139
141 r.sim.water is a landscape scale simulation model of overland flow
142 designed for spatially variable terrain, soil, cover and rainfall
143 excess conditions. A 2D shallow water flow is described by the bivari‐
144 ate form of Saint Venant equations. The numerical solution is based on
145 the concept of duality between the field and particle representation of
146 the modeled quantity. Green’s function Monte Carlo method, used to
147 solve the equation, provides robustness necessary for spatially vari‐
148 able conditions and high resolutions (Mitas and Mitasova 1998). The key
149 inputs of the model include elevation (elevation raster map), flow gra‐
150 dient vector given by first-order partial derivatives of elevation
151 field (dx and dy raster maps), rainfall excess rate (rain raster map or
152 rain_value single value) and a surface roughness coefficient given by
153 Manning’s n (man raster map or man_value single value). Partial deriva‐
154 tives raster maps can be computed along with interpolation of a DEM
155 using the -d option in v.surf.rst module. If elevation raster map is
156 already provided, partial derivatives can be computed using
157 r.slope.aspect module. Partial derivatives are used to determine the
158 direction and magnitude of water flow velocity. To include a predefined
159 direction of flow, map algebra can be used to replace terrain-derived
160 partial derivatives with pre-defined partial derivatives in selected
161 grid cells such as man-made channels, ditches or culverts. Equations
162 (2) and (3) from this report can be used to compute partial derivates
163 of the predefined flow using its direction given by aspect and slope.
164 The module automatically converts horizontal distances from feet to
166 metric system using database/projection information. Rainfall excess is
167 defined as rainfall intensity - infiltration rate and should be pro‐
168 vided in [mm/hr]. Rainfall intensities are usually available from
169 meteorological stations. Infiltration rate depends on soil properties
170 and land cover. It varies in space and time. For saturated soil and
171 steady-state water flow it can be estimated using saturated hydraulic
172 conductivity rates based on field measurements or using reference val‐
173 ues which can be found in literature. Optionally, user can provide an
174 overland flow infiltration rate map infil or a single value infil_value
175 in [mm/hr] that control the rate of infiltration for the already flow‐
176 ing water, effectively reducing the flow depth and discharge. Overland
177 flow can be further controlled by permeable check dams or similar type
178 of structures, the user can provide a map of these structures and their
179 permeability ratio in the map flow_control that defines the probability
180 of particles to pass through the structure (the values will be 0-1).
181 Output includes a water depth raster map depth in [m], and a water dis‐
183 charge raster map discharge in [m3/s]. Error of the numerical solution
184 can be analyzed using the error raster map (the resulting water depth
185 is an average, and err is its RMSE). The output vector points map out‐
186 put_walkers can be used to analyze and visualize spatial distribution
187 of walkers at different simulation times (note that the resulting water
188 depth is based on the density of these walkers). The spatial distribu‐
189 tion of numerical error associated with path sampling solution can be
190 analysed using the output error raster file [m]. This error is a func‐
191 tion of the number of particles used in the simulation and can be
192 reduced by increasing the number of walkers given by parameter nwalk‐
193 ers. Duration of simulation is controlled by the niterations parame‐
194 ter. The default value is 10 minutes, reaching the steady-state may
195 require much longer time, depending on the time step, complexity of
196 terrain, land cover and size of the area. Output walker, water depth
197 and discharge maps can be saved during simulation using the time series
198 flag -t and output_step parameter defining the time step in minutes for
199 writing output files. Files are saved with a suffix representing time
200 since the start of simulation in minutes (e.g. wdepth.05, wdepth.10).
201 Monitoring of water depth at specific points is supported. A vector map
202 with observation points and a path to a logfile must be provided. For
203 each point in the vector map which is located in the computational
204 region the water depth is logged each time step in the logfile. The
205 logfile is organized as a table. A single header identifies the cate‐
206 gory number of the logged vector points. In case of invalid water
207 depth data the value -1 is used.
208 Overland flow is routed based on partial derivatives of elevation field
210 or other landscape features influencing water flow. Simulation equa‐
211 tions include a diffusion term (diffusion_coeff parameter) which
212 enables water flow to overcome elevation depressions or obstacles when
213 water depth exceeds a threshold water depth value (hmax), given in [m].
214 When it is reached, diffusion term increases as given by halpha and
215 advection term (direction of flow) is given as "prevailing" direction
216 of flow computed as average of flow directions from the previous hbeta
217 number of grid cells.
218
220 A 2D shallow water flow is described by the bivariate form of Saint
221 Venant equations (e.g., Julien et al., 1995). The continuity of water
222 flow relation is coupled with the momentum conservation equation and
223 for a shallow water overland flow, the hydraulic radius is approximated
224 by the normal flow depth. The system of equations is closed using the
225 Manning’s relation. Model assumes that the flow is close to the kine‐
226 matic wave approximation, but we include a diffusion-like term to
227 incorporate the impact of diffusive wave effects. Such an incorporation
228 of diffusion in the water flow simulation is not new and a similar term
229 has been obtained in derivations of diffusion-advection equations for
230 overland flow, e.g., by Lettenmeier and Wood, (1992). In our reformula‐
231 tion, we simplify the diffusion coefficient to a constant and we use a
232 modified diffusion term. The diffusion constant which we have used is
233 rather small (approximately one order of magnitude smaller than the
234 reciprocal Manning’s coefficient) and therefore the resulting flow is
235 close to the kinematic regime. However, the diffusion term improves the
236 kinematic solution, by overcoming small shallow pits common in digital
237 elevation models (DEM) and by smoothing out the flow over slope discon‐
238 tinuities or abrupt changes in Manning’s coefficient (e.g., due to a
239 road, or other anthropogenic changes in elevations or cover).
240 Green’s function stochastic method of solution.
242 The Saint Venant equations are solved by a stochastic method called
243 Monte Carlo (very similar to Monte Carlo methods in computational fluid
244 dynamics or to quantum Monte Carlo approaches for solving the Schro‐
245 dinger equation (Schmidt and Ceperley, 1992, Hammond et al., 1994;
246 Mitas, 1996)). It is assumed that these equations are a representation
247 of stochastic processes with diffusion and drift components
248 (Fokker-Planck equations).
249 The Monte Carlo technique has several unique advantages which are
251 becoming even more important due to new developments in computer tech‐
252 nology. Perhaps one of the most significant Monte Carlo properties is
253 robustness which enables us to solve the equations for complex cases,
254 such as discontinuities in the coefficients of differential operators
255 (in our case, abrupt slope or cover changes, etc). Also, rough solu‐
256 tions can be estimated rather quickly, which allows us to carry out
257 preliminary quantitative studies or to rapidly extract qualitative
258 trends by parameter scans. In addition, the stochastic methods are tai‐
259 lored to the new generation of computers as they provide scalability
260 from a single workstation to large parallel machines due to the inde‐
261 pendence of sampling points. Therefore, the methods are useful both for
262 everyday exploratory work using a desktop computer and for large, cut‐
263 ting-edge applications using high performance computing.
264
266 Spearfish region:
267 g.region raster=elevation.10m -p
268 r.slope.aspect elevation=elevation.10m dx=elev_dx dy=elev_dy
269 # synthetic maps
270 r.mapcalc "rain = if(elevation.10m, 5.0, null())"
271 r.mapcalc "manning = if(elevation.10m, 0.05, null())"
272 r.mapcalc "infilt = if(elevation.10m, 0.0, null())"
273 # simulate
274 r.sim.water elevation=elevation.10m dx=elev_dx dy=elev_dy rain=rain man=manning infil=infilt nwalkers=5000000 depth=depth
275 Figure: Water depth map in the Spearfish (SD) area
277
279 If the module fails with
280 ERROR: nwalk (7000001) > maxw (7000000)!
281 then a lower nwalkers parameter value has to be selected.
282
284 · Mitasova, H., Thaxton, C., Hofierka, J., McLaughlin, R., Moore,
285 A., Mitas L., 2004, Path sampling method for modeling overland
286 water flow, sediment transport and short term terrain evolution
287 in Open Source GIS. In: C.T. Miller, M.W. Farthing, V.G. Gray,
288 G.F. Pinder eds., Proceedings of the XVth International Confer‐
289 ence on Computational Methods in Water Resources (CMWR XV),
290 June 13-17 2004, Chapel Hill, NC, USA, Elsevier, pp. 1479-1490.
291 · Mitasova H, Mitas, L., 2000, Modeling spatial processes in mul‐
293 tiscale framework: exploring duality between particles and
294 fields, plenary talk at GIScience2000 conference, Savannah, GA.
295 · Mitas, L., and Mitasova, H., 1998, Distributed soil erosion
297 simulation for effective erosion prevention. Water Resources
298 Research, 34(3), 505-516.
299 · Mitasova, H., Mitas, L., 2001, Multiscale soil erosion simula‐
301 tions for land use management, In: Landscape erosion and land‐
302 scape evolution modeling, Harmon R. and Doe W. eds., Kluwer
303 Academic/Plenum Publishers, pp. 321-347.
304 · Hofierka, J, Mitasova, H., Mitas, L., 2002. GRASS and modeling
306 landscape processes using duality between particles and fields.
307 Proceedings of the Open source GIS - GRASS users conference
308 2002 - Trento, Italy, 11-13 September 2002. PDF
309 · Hofierka, J., Knutova, M., 2015, Simulating aspects of a flash
311 flood using the Monte Carlo method and GRASS GIS: a case study
312 of the Malá Svinka Basin (Slovakia), Open Geosciences. Volume
313 7, Issue 1, ISSN (Online) 2391-5447, DOI:
314 10.1515/geo-2015-0013, April 2015
315 · Neteler, M. and Mitasova, H., 2008, Open Source GIS: A GRASS
317 GIS Approach. Third Edition. The International Series in Engi‐
318 neering and Computer Science: Volume 773. Springer New York
319 Inc, p. 406.
320
322 v.surf.rst, r.slope.aspect, r.sim.sediment
323
325 Helena Mitasova, Lubos Mitas
326 North Carolina State University
327 hmitaso@unity.ncsu.edu
328 Jaroslav Hofierka
330 GeoModel, s.r.o. Bratislava, Slovakia
331 hofierka@geomodel.sk
332 Chris Thaxton
334 North Carolina State University
335 csthaxto@unity.ncsu.edu
336 Last changed: $Date: 2016-09-20 11:18:44 +0200 (Tue, 20 Sep 2016) $
338
340 Available at: r.sim.water source code (history)
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344 © 2003-2019 GRASS Development Team, GRASS GIS 7.6.0 Reference Manual
346GRASS 7.6.0 r.sim.water(1)