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<h2>NAME</h2>
<em><b>r.sim.water</b></em>  - Overland flow hydrologic simulation using path sampling method (SIMWE)
<h2>KEYWORDS</h2>
raster, flow, hydrology
<h2>SYNOPSIS</h2>
<b>r.sim.water</b><br>
<b>r.sim.water help</b><br>
<b>r.sim.water</b> [-<b>t</b>] <b>elevin</b>=<em>name</em> <b>dxin</b>=<em>name</em> <b>dyin</b>=<em>name</em>  [<b>rain</b>=<em>name</em>]   [<b>rain_val</b>=<em>float</em>]   [<b>infil</b>=<em>name</em>]   [<b>infil_val</b>=<em>float</em>]   [<b>manin</b>=<em>name</em>]   [<b>manin_val</b>=<em>float</em>]   [<b>traps</b>=<em>name</em>]   [<b>vector</b>=<em>name</em>]   [<b>depth</b>=<em>name</em>]   [<b>disch</b>=<em>name</em>]   [<b>err</b>=<em>name</em>]   [<b>outwalk</b>=<em>name</em>]   [<b>nwalk</b>=<em>integer</em>]   [<b>niter</b>=<em>integer</em>]   [<b>outiter</b>=<em>integer</em>]   [<b>density</b>=<em>integer</em>]   [<b>diffc</b>=<em>float</em>]   [<b>hmax</b>=<em>float</em>]   [<b>halpha</b>=<em>float</em>]   [<b>hbeta</b>=<em>float</em>]   [--<b>overwrite</b>]  [--<b>verbose</b>]  [--<b>quiet</b>] 

<h3>Flags:</h3>
<DL>
<DT><b>-t</b></DT>
<DD>Time-series output</DD>

<DT><b>--overwrite</b></DT>
<DD>Allow output files to overwrite existing files</DD>
<DT><b>--verbose</b></DT>
<DD>Verbose module output</DD>
<DT><b>--quiet</b></DT>
<DD>Quiet module output</DD>
</DL>

<h3>Parameters:</h3>
<DL>
<DT><b>elevin</b>=<em>name</em></DT>
<DD>Name of the elevation raster map [m]</DD>

<DT><b>dxin</b>=<em>name</em></DT>
<DD>Name of the x-derivatives raster map [m/m]</DD>

<DT><b>dyin</b>=<em>name</em></DT>
<DD>Name of the y-derivatives raster map [m/m]</DD>

<DT><b>rain</b>=<em>name</em></DT>
<DD>Name of the rainfall excess rate (rain-infilt) raster map [mm/hr]</DD>

<DT><b>rain_val</b>=<em>float</em></DT>
<DD>Rainfall excess rate unique value [mm/hr]</DD>
<DD>Default: <em>50</em></DD>

<DT><b>infil</b>=<em>name</em></DT>
<DD>Name of the runoff infiltration rate raster map [mm/hr]</DD>

<DT><b>infil_val</b>=<em>float</em></DT>
<DD>Runoff infiltration rate unique value [mm/hr]</DD>
<DD>Default: <em>0.0</em></DD>

<DT><b>manin</b>=<em>name</em></DT>
<DD>Name of the Mannings n raster map</DD>

<DT><b>manin_val</b>=<em>float</em></DT>
<DD>Mannings n unique value</DD>
<DD>Default: <em>0.1</em></DD>

<DT><b>traps</b>=<em>name</em></DT>
<DD>Name of the flow controls raster map (permeability ratio 0-1)</DD>

<DT><b>vector</b>=<em>name</em></DT>
<DD>Name of the sampling locations vector points map</DD>

<DT><b>depth</b>=<em>name</em></DT>
<DD>Output water depth raster map [m]</DD>

<DT><b>disch</b>=<em>name</em></DT>
<DD>Output water discharge raster map [m3/s]</DD>

<DT><b>err</b>=<em>name</em></DT>
<DD>Output simulation error raster map [m]</DD>

<DT><b>outwalk</b>=<em>name</em></DT>
<DD>Name of the output walkers vector points map</DD>

<DT><b>nwalk</b>=<em>integer</em></DT>
<DD>Number of walkers, default is twice the no. of cells</DD>

<DT><b>niter</b>=<em>integer</em></DT>
<DD>Time used for iterations [minutes]</DD>
<DD>Default: <em>10</em></DD>

<DT><b>outiter</b>=<em>integer</em></DT>
<DD>Time interval for creating output maps [minutes]</DD>
<DD>Default: <em>2</em></DD>

<DT><b>density</b>=<em>integer</em></DT>
<DD>Density of output walkers</DD>
<DD>Default: <em>200</em></DD>

<DT><b>diffc</b>=<em>float</em></DT>
<DD>Water diffusion constant</DD>
<DD>Default: <em>0.8</em></DD>

<DT><b>hmax</b>=<em>float</em></DT>
<DD>Threshold water depth [m] (diffusion increases after this water depth is reached)</DD>
<DD>Default: <em>0.3</em></DD>

<DT><b>halpha</b>=<em>float</em></DT>
<DD>Diffusion increase constant</DD>
<DD>Default: <em>4.0</em></DD>

<DT><b>hbeta</b>=<em>float</em></DT>
<DD>Weighting factor for water flow velocity vector</DD>
<DD>Default: <em>0.5</em></DD>

</DL>
<h2>DESCRIPTION</h2>
<i>r.sim.water</i> is a landscape scale simulation model 
of  overland  flow   designed for spatially variable terrain, soil, cover 
and  rainfall excess conditions. A 2D shallow water flow is described by 
the  bivariate form of Saint Venant equations. The numerical solution is based
on the concept of duality between the field and particle representation of
the modeled quantity. Green's function Monte Carlo method, used to solve the equation,
provides robustness necessary for spatially variable conditions and high
resolutions   (Mitas and Mitasova 1998).  The key inputs of the model include
elevation (<i>elevin</i> raster map), flow gradient vector given by
first-order partial derivatives of elevation field (<i>dxin</i> and <i>dyin</i> raster maps), rainfall
excess rate (<i>rain</i> raster map or <i>rain_val</i> single value) 
and a surface  roughness coefficient given by Manning's n 
(<i>manin</i> raster map or <i>manin_val</i> single value). Partial
derivatives raster maps can be computed along with interpolation of a DEM using
the -d option in <a href="v.surf.rst.html">
v.surf.rst</a> module. If elevation raster is already provided, partial derivatives
can  be computed using <a href="r.slope.aspect.html">r.slope.aspect</a> module. 
Partial derivatives are used to determine the direction and magnitude of water flow velocity. 
To include a predefined direction of flow, map algebra can be used 
to replace terrain-derived partial derivatives with pre-defined
partial derivatives in selected grid cells such as man-made channels, ditches
or culverts. Equations (2) and (3) from 
<a href="http://skagit.meas.ncsu.edu/~helena/gmslab/reports/cerl99/rep99.html">
this report</a> can be used to compute partial derivates 
of the predefined flow using its direction given by aspect and slope.
<br><p>
The module automatically converts horizontal distances from feet to metric system using
database/projection information. Rainfall excess is defined as rainfall intensity
- infiltration rate and should be provided in [mm/hr].
<!-- and can be  computed using   several available infiltration
models (e.g.  Green-Ampt,  Holtan, etc.). (<font color="#ff0000">   find
infiltration module in GRASS  - topmodel, casc2d</font> )-->
 Rainfall intensities are usually available from  meteorological  stations. 
Infiltration rate depends  on soil properties and  land cover. It  varies in space and time.
For saturated  soil and steady-state  water flow it can be estimated  using
saturated hydraulic  conductivity rates  based on field measurements or using
reference values which can be found in literature.
Optionally, user can provide an overland flow infiltration rate map 
<i>infil</i> or a single value <i>infil_val</i> in [mm/hr]
that control the rate of infiltration for the already flowing water, effectively 
reducing the flow depth and discharge.
Overland flow can be further controled by permeable check dams or similar type of structures,
the user can provide a map of these structures and their permeability ratio
in the map <i>traps</i> that defines the probability of particles to pass
through the structure (the values will be 0-1).
<br> </p>
<p>
Output includes a water depth raster map <i>depth</i>  in [m], 
anda water discharge raster map <i>disch</i> in [m3/s]. Error of the numerical
solution can  be analyzed using the <i>err</i> raster map  (the resulting water depth is an average, 
and err is its RMSE). The output vector points map <i>outwalk</i> can be used to analyze and visualize 
spatial distribution of walkers at different simulation times (note that 
the resulting water depth is based on the density of these walkers). Number 
of the output walkers is controled by the <i>density</i> parameter, which controls
how many walkers used in simulation should be written into the output. 
<!--(<font color="#ff0000"> toto treba upresnit/zmenit, lebo nwalk ide prec</font>). -->
Duration of simulation is controled by the <i>niter</i> parameter.  The default value 
is 10 minutes, reaching the steady-state may require much longer time, 
depending on the time step, complexity of terrain, land cover and size of the area. 
Output water depth and discharge maps can be saved during simulation using 
the time series flag <i>-t</i> and <i>outiter</i> parameter 
defining the time step in minutes for writing output files. 
Files are saved with a suffix representing time since the start of simulation in seconds 
(e.g. wdepth.500, wdepth.1000).<br>
<P>
Overland flow is routed based on partial derivatives  of elevation
field or other landscape features influencing water flow. Simulation
equations include a diffusion term (<i>diffc</i> parameter) which enables 
water flow to overcome elevation depressions or obstacles when water depth exceeds 
a threshold water depth value (<i>hmax)</i>, given in [m]. When it is reached,  
diffusion term increases as given by <i>halpha</i> and advection term 
(direction of flow) is given as "prevailing" direction of flow computed
as  average of flow directions from the previous <i>hbeta</i> number of grid cells.
<br>
<h2>
NOTES</h2>
<p>
A 2D shallow water flow is described by the  bivariate form of Saint
Venant equations (e.g., Julien et al., 1995). The continuity of water
flow relation is coupled with the momentum conservation equation and
for a shallow water overland flow, the hydraulic radius is approximated
by the normal flow depth. The system of  equations is closed using the
Manning's relation. Model assumes that the flow is close to the kinematic
wave approximation, but we include a diffusion-like term to incorporate the
impact of diffusive wave effects. Such an incorporation of diffusion
in the water flow  simulation is not new and  a similar term has been obtained
in  derivations of diffusion-advection equations  for overland flow, e.g.,
 by Lettenmeier and Wood, (1992). In our  reformulation,  we simplify the
diffusion coefficient to a constant and we use a modified diffusion term.
The diffusion constant which we have used is rather small (approximately
one order of magnitude smaller than the reciprocal Manning's  coefficient)
and therefore the resulting flow is close to the kinematic regime. However,
the diffusion term improves  the kinematic solution, by overcoming small
shallow pits common in  digital elevation models (DEM) and by smoothing out
the flow over slope  discontinuities or abrupt changes in Manning's coefficient
(e.g., due  to a road, or other anthropogenic changes in elevations or cover).
</p>
<p><b>
Green's function stochastic method of solution.</b> The Saint Venant
equations are solved by a stochastic method called Monte Carlo (very
similar to Monte Carlo methods in computational fluid dynamics or to
quantum Monte  Carlo  approaches for  solving the Schrodinger equation (Schmidt
and Ceperley,  1992,  Hammond  et al., 1994; Mitas, 1996)). It is assumed
that these equations  are a  representation of stochastic processes with
diffusion and drift  components  (Fokker-Planck equations). </p>
The Monte Carlo technique has several unique advantages which are
becoming   even more important due to new developments in computer  technology. 
Perhaps   one of the most significant Monte Carlo properties  is robustness 
which enables  us to solve the equations for complex  cases, such as discontinuities
in the coefficients of differential  operators (in our case, abrupt slope
or cover changes, etc). Also,  rough solutions can be estimated rather
quickly,    which allows us to  carry out preliminary quantitative studies
or to rapidly    extract  qualitative trends by parameter scans. In addition,
the stochastic     methods are tailored to the new generation of computers
as they provide    scalability from a single workstation to large parallel
machines due to   the independence of sampling points. Therefore, the methods
are useful  both for everyday exploratory work using a desktop computer and
for  large, cutting-edge applications using high performance computing. <br>
<h2>
SEE ALSO</h2>
<a href="v.surf.rst.html">v.surf.rst</a>
<a href="r.slope.aspect.html">r.slope.aspect</a>
<a href="r.sim.sediment.html">r.sim.sediment</a>

<h2>
AUTHORS</h2>
Helena Mitasova, Lubos Mitas<br>
North Carolina State University<br>
<a href="mailto:hmitaso@unity.ncsu.edu">hmitaso@unity.ncsu.edu</a><br>
<br>
Jaroslav Hofierka<br>
GeoModel, s.r.o. Bratislava, Slovakia <br>
<address><a href="mailto:hofi@geomodel.sk">
hofierka@geomodel.sk</a>
</address>
<br>
Chris Thaxton<br>
North Carolina State University<br>
csthaxto@unity.ncsu.edu<br>
<address><a href="mailto:csthaxto@unity.ncsu.edu">
csthaxto@unity.ncsu.edu</a>
</address>
<h2>
REFERENCES</h2>
<P>
<a href="http://skagit.meas.ncsu.edu/~helena/gmslab/papers/II.6.8_Mitasova_044.pdf">
Mitasova, H., Thaxton, C., Hofierka, J., McLaughlin, R., Moore, A., Mitas L., 2004,</a> 
Path sampling method for modeling overland water flow, sediment transport 
and short term terrain evolution in Open Source GIS. 
In: C.T. Miller, M.W. Farthing, V.G. Gray, G.F. Pinder eds., 
Proceedings of the XVth International Conference on Computational Methods in Water 
Resources (CMWR XV), June 13-17 2004, Chapel Hill, NC, USA, Elsevier, pp. 1479-1490.
<P>
<a href="http://skagit.meas.ncsu.edu/~helena/gmslab/gisc00/duality.html">
Mitasova H, Mitas, L., 2000, Modeling spatial processes in multiscale framework: 
exploring duality between particles and fields, </a>
plenary talk at GIScience2000 conference, Savannah, GA. 
<P>
Mitas, L., and Mitasova, H., 1998, Distributed soil erosion simulation 
for effective erosion prevention. Water Resources Research, 34(3), 505-516.
<P>
<a href="http://skagit.meas.ncsu.edu/~helena/gmslab/papers/LLEmiterev1.pdf">
 Mitasova, H., Mitas, L., 2001, Multiscale soil erosion simulations for land use management, </a>
In: Landscape erosion and landscape evolution modeling, Harmon R. and Doe W. eds., 
Kluwer Academic/Plenum Publishers, pp. 321-347.
<p>
<a href="http://www.grassbook.org">
Neteler, M. and Mitasova, H., 2008, Open Source GIS: A GRASS GIS Approach. Third Edition.</a>
The International Series in Engineering and Computer Science: Volume 773. Springer New York Inc, p. 406.
<P>
Last changed: Date: 2008/02/16 15:55:10 $<p></p>
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