Provided by: grass-doc_7.8.2-1build3_all bug

NAME

       r.sim.water  - Overland flow hydrologic simulation using path sampling method (SIMWE).

KEYWORDS

       raster, hydrology, soil, flow, overland flow, model

SYNOPSIS

       r.sim.water
       r.sim.water --help
       r.sim.water   [-ts]   elevation=name  dx=name  dy=name   [rain=name]    [rain_value=float]
       [infil=name]   [infil_value=float]   [man=name]   [man_value=float]    [flow_control=name]
       [observation=name]           [depth=name]           [discharge=name]          [error=name]
       [walkers_output=name]     [logfile=name]     [nwalkers=integer]      [niterations=integer]
       [output_step=integer]       [diffusion_coeff=float]       [hmax=float]      [halpha=float]
       [hbeta=float]     [random_seed=integer]     [nprocs=integer]     [--overwrite]    [--help]
       [--verbose]  [--quiet]  [--ui]

   Flags:
       -t
           Time-series output

       -s
           Generate random seed
           Automatically  generates  random  seed for random number generator (use when you don’t
           want to provide the seed option)

       --overwrite
           Allow output files to overwrite existing files

       --help
           Print usage summary

       --verbose
           Verbose module output

       --quiet
           Quiet module output

       --ui
           Force launching GUI dialog

   Parameters:
       elevation=name [required]
           Name of input elevation raster map

       dx=name [required]
           Name of x-derivatives raster map [m/m]

       dy=name [required]
           Name of y-derivatives raster map [m/m]

       rain=name
           Name of rainfall excess rate (rain-infilt) raster map [mm/hr]

       rain_value=float
           Rainfall excess rate unique value [mm/hr]
           Default: 50

       infil=name
           Name of runoff infiltration rate raster map [mm/hr]

       infil_value=float
           Runoff infiltration rate unique value [mm/hr]
           Default: 0.0

       man=name
           Name of Manning’s n raster map

       man_value=float
           Manning’s n unique value
           Default: 0.1

       flow_control=name
           Name of flow controls raster map (permeability ratio 0-1)

       observation=name
           Name of sampling locations vector points map
           Or data source for direct OGR access

       depth=name
           Name for output water depth raster map [m]

       discharge=name
           Name for output water discharge raster map [m3/s]

       error=name
           Name for output simulation error raster map [m]

       walkers_output=name
           Base name of the output walkers vector points map
           Name for output vector map

       logfile=name
           Name for sampling points output text file. For each observation vector point the  time
           series of sediment transport is stored.

       nwalkers=integer
           Number of walkers, default is twice the number of cells

       niterations=integer
           Time used for iterations [minutes]
           Default: 10

       output_step=integer
           Time interval for creating output maps [minutes]
           Default: 2

       diffusion_coeff=float
           Water diffusion constant
           Default: 0.8

       hmax=float
           Threshold water depth [m]
           Diffusion increases after this water depth is reached
           Default: 0.3

       halpha=float
           Diffusion increase constant
           Default: 4.0

       hbeta=float
           Weighting factor for water flow velocity vector
           Default: 0.5

       random_seed=integer
           Seed for random number generator
           The  same  seed  can be used to obtain same results or random seed can be generated by
           other means.

       nprocs=integer
           Number of threads which will be used for parallel compute
           Default: 1

DESCRIPTION

       r.sim.water 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 (elevation raster map), flow
       gradient  vector  given  by  first-order partial derivatives of elevation field (dx and dy
       raster maps), rainfall excess rate (rain raster map or  rain_value  single  value)  and  a
       surface  roughness  coefficient  given  by Manning’s n (man raster map or man_value single
       value). Partial derivatives raster maps can be computed along with interpolation of a  DEM
       using  the  -d  option  in v.surf.rst module. If elevation raster map is already provided,
       partial derivatives can be computed using r.slope.aspect 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  this  report  can  be  used  to
       compute  partial  derivates of the predefined flow using its direction given by aspect and
       slope.

        Figure: Simulated water flow in a rural area showing the areas with highest  water  depth
       highlighting streams, pooling, and wet areas during a rainfall event.

       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].  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 infil or a single value infil_value  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  controlled  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  flow_control  that  defines  the
       probability of particles to pass through the structure (the values will be 0-1).

       Output  includes  a  water depth raster map depth in [m], and a water discharge raster map
       discharge in [m3/s]. Error of the numerical solution  can  be  analyzed  using  the  error
       raster  map  (the  resulting  water depth is an average, and err is its RMSE).  The output
       vector points map output_walkers 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).  The spatial distribution  of  numerical  error  associated
       with  path  sampling solution can be analysed using the output error raster file [m]. This
       error is a function of the number of particles used in the simulation and can  be  reduced
       by  increasing  the number of walkers given by parameter nwalkers.  Duration of simulation
       is controlled by the niterations 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 walker, water depth and  discharge  maps
       can  be  saved  during  simulation using the time series flag -t and output_step 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 minutes (e.g. wdepth.05, wdepth.10).
       Monitoring of water depth at specific points is supported. A vector map  with  observation
       points and a path to a logfile must be provided. For each point in the vector map which is
       located in the computational region the water depth  is  logged  each  time  step  in  the
       logfile.  The  logfile  is  organized  as a table. A single header identifies the category
       number of the logged vector points.  In case of invalid water depth data the value  -1  is
       used.

       Overland flow is routed based on partial derivatives of elevation field or other landscape
       features  influencing  water  flow.  Simulation  equations  include   a   diffusion   term
       (diffusion_coeff  parameter) which enables water flow to overcome elevation depressions or
       obstacles when water depth exceeds a threshold water depth value  (hmax),  given  in  [m].
       When  it  is  reached,  diffusion  term  increases  as  given by halpha and advection term
       (direction of flow) is given as "prevailing" direction of flow computed as average of flow
       directions from the previous hbeta number of grid cells.

NOTES

       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).

       Green’s function stochastic method of solution.
       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).

       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.

EXAMPLE

       Using the North Carolina full sample dataset:
       # set computational region
       g.region raster=elev_lid792_1m -p
       # compute dx, dy
       r.slope.aspect elevation=elev_lid792_1m dx=elev_lid792_dx dy=elev_lid792_dy
       # simulate (this may take a minute or two)
       r.sim.water elevation=elev_lid792_1m dx=elev_lid792_dx dy=elev_lid792_dy depth=water_depth disch=water_discharge nwalk=10000 rain_value=100 niter=5
       Now, let’s visualize the result using rendering to a file (note the further management  of
       computational region and usage of d.mon module which are not needed when working in GUI):
       # increase the computational region by 350 meters
       g.region e=e+350
       # initiate the rendering
       d.mon start=cairo output=r_sim_water_water_depth.png
       # render raster, legend, etc.
       d.rast map=water_depth_1m
       d.legend raster=water_depth_1m title="Water depth [m]" label_step=0.10 font=sans at=20,80,70,75
       d.barscale at=67,10 length=250 segment=5 font=sans
       d.northarrow at=90,25
       # finish the rendering
       d.mon stop=cairo

        Figure: Simulated water depth map in the rural area of the North Carolina sample dataset.

ERROR MESSAGES

       If the module fails with
       ERROR: nwalk (7000001) > maxw (7000000)!
       then a lower nwalkers parameter value has to be selected.

REFERENCES

           ·   Mitasova,  H.,  Thaxton,  C.,  Hofierka,  J., McLaughlin, R., Moore, A., Mitas L.,
               2004, 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.

           ·   Mitasova H, Mitas, L., 2000, Modeling spatial processes in  multiscale  framework:
               exploring  duality  between  particles  and  fields, plenary talk at GIScience2000
               conference, Savannah, GA.

           ·   Mitas, L., and  Mitasova,  H.,  1998,  Distributed  soil  erosion  simulation  for
               effective erosion prevention. Water Resources Research, 34(3), 505-516.

           ·   Mitasova,  H.,  Mitas,  L., 2001, Multiscale soil erosion simulations for land use
               management, In: Landscape erosion and landscape evolution modeling, Harmon R.  and
               Doe W. eds., Kluwer Academic/Plenum Publishers, pp. 321-347.

           ·   Hofierka, J, Mitasova, H., Mitas, L., 2002. GRASS and modeling landscape processes
               using duality between particles and fields. Proceedings of the Open source  GIS  -
               GRASS users conference 2002 - Trento, Italy, 11-13 September 2002.  PDF

           ·   Hofierka,  J.,  Knutova,  M.,  2015, Simulating aspects of a flash flood using the
               Monte Carlo method  and  GRASS  GIS:  a  case  study  of  the  Malá  Svinka  Basin
               (Slovakia),  Open  Geosciences.  Volume  7, Issue 1, ISSN (Online) 2391-5447, DOI:
               10.1515/geo-2015-0013, April 2015

           ·   Neteler, M. and Mitasova, H., 2008, Open Source GIS: A GRASS GIS  Approach.  Third
               Edition.   The  International  Series  in Engineering and Computer Science: Volume
               773. Springer New York Inc, p. 406.

SEE ALSO

        v.surf.rst, r.slope.aspect, r.sim.sediment

AUTHORS

       Helena Mitasova, Lubos Mitas
       North Carolina State University
       hmitaso@unity.ncsu.edu

       Jaroslav Hofierka
       GeoModel, s.r.o. Bratislava, Slovakia
       hofierka@geomodel.sk

       Chris Thaxton
       North Carolina State University
       csthaxto@unity.ncsu.edu

SOURCE CODE

       Available at: r.sim.water source code (history)

       Main index | Raster index | Topics index | Keywords index | Graphical index | Full index

       © 2003-2019 GRASS Development Team, GRASS GIS 7.8.2 Reference Manual