A bit of cleanup and reworking on cmf3d-shapefile.py

README.rst

@@ -17,7 +17,8 @@ Modelling water and solute fluxes
 ===================================
 
 cmf is a programming library to create hydrological models, which are highly modular and connectible to other
-models developed using a multiple hypotheses background. Although written in C++, its primary usage is to be compiled as an extension to other programming languages, using SWIG. Researchers can build individual models,
+models developed using a multiple hypotheses background. Although written in C++, its primary usage is to be compiled
+as an extension to other programming languages, using SWIG. Researchers can build individual models,
 targeting their scientific question by using the library objects like water storages, boundary conditions,
 fluxes and solvers. cmf uses the finite volume method to set up a wide range of models of water flow through
 your study area. Resulting models can range from **lumped conceptual models** to **fully distributed darcian models**
@@ -73,17 +74,17 @@ the formulation of different hypotheses. The principle of the connection of CMF
 disciplines is shown by Kraft et al. (2010), and Haas et al. (2012) show the relevance of tightly connected
 models of transport and turnover for the emission of greenhouse gases from ecosystems.
 
- - Beven, K., 2006. Searching for the Holy Grail of scientific hydrology. Hydrol.Earth Syst.Sci 10, 609-618.
- - Beven, K.J., 2002. Towards an alternative blueprint for a physically-based digitally simulated hydrologic response modelling system. Hydrol.Proc. 16, 189-206.
- - Buytaert, W., Reusser, D., Krause, S., Renaud, J.P., 2008. Why can't we do better than Topmodel? Hydrol.Proc. 22, 4175-4179.
- - Clark, M.P., Kavetski, D., Fenicia, F., 2011. Pursuing the method of multiple working hypotheses for hydrological modeling. Water Resour.Res 47.
- - Haas, E., Klatt, S., Fröhlich, A., Kraft, P., Werner, C., Kiese, R., Grote, R., Breuer, L., Butterbach-Bahl, K., 2012. LandscapeDNDC: a process model for simulation of biosphere-atmosphere-hydrosphere exchange processes at site and regional scale. Landscape Ecol. DOI 10.1007/s10980-012-9772-x
- - Kirchner, J., 2006. Getting the right answers for the right reasons: Linking measurements, analyses, and models to advance the science of hydrology. Water Resour.Res. 42, W03S04, doi:10.1029/2005WR004362.
- - Kraft, P., Multsch, S., Vache, K. B., Frede, H.-G. and Breuer, L.: Using Python as a coupling platform for integrated catchment models, Adv. Geosci., 27, 51-56, doi:10.5194/adgeo-27-51-2010, 2010.
- - Kraft, P., 2012. A hydrological programming language extension for integrated catchment models, Dissertation, Justus-Liebig-Universität, Gießen, 16 March. [online] Available from: http://geb.uni-giessen.de/geb/volltexte/2012/8759/
- - Kraft, P., Vache, K. B., Frede, H.-G. and Breuer, L.: A hydrological programming language extension for integrated catchment models, Environ. Model. Softw., 26, 828-830, doi:10.1016/j.envsoft.2010.12.009, 2011.
- - Qu, Y.Z., Duffy, C.J., 2007. A semidiscrete finite volume formulation for multiprocess watershed simulation. Water Resour.Res. 43, W08419, doi:10.1029/2006WR005752.
- - Seibert, J., McDonnell, J.J., 2002. On the dialog between experimentalist and modeler in catchment hydrology: Use of soft data for multicriteria model calibration. Water Resour.Res. 38, doi: 10.1029/2001WR000978.
- - Sivapalan, M., 2003. Process complexity at hillslope scale, process simplicity at the watershed scale: is there a connection? Hydrol.Proc. 17, 1037-1041.
- - Tetzlaff, D., McDonnell, J.J., Uhlenbrook, S., McGuire, K.J., Bogaart, P.W., Naef, F., Baird, A.J., Dunn, S.M., Soulsby, C., 2008. Conceptualizing catchment processes: simply too complex? Hydrol.Proc. 22, 1727-1730.
- - Vache, K.B., McDonnell, J.J., 2006. A process-based rejectionist framework for evaluating catchment runoff model structure. Water Resour.Res. W02409, doi:10.1029/2005WR004247.
+- Beven, K., 2006. Searching for the Holy Grail of scientific hydrology. Hydrol.Earth Syst.Sci 10, 609-618.
+- Beven, K.J., 2002. Towards an alternative blueprint for a physically-based digitally simulated hydrologic response modelling system. Hydrol.Proc. 16, 189-206.
+- Buytaert, W., Reusser, D., Krause, S., Renaud, J.P., 2008. Why can't we do better than Topmodel? Hydrol.Proc. 22, 4175-4179.
+- Clark, M.P., Kavetski, D., Fenicia, F., 2011. Pursuing the method of multiple working hypotheses for hydrological modeling. Water Resour.Res 47.
+- Haas, E., Klatt, S., Fröhlich, A., Kraft, P., Werner, C., Kiese, R., Grote, R., Breuer, L., Butterbach-Bahl, K., 2012. LandscapeDNDC: a process model for simulation of biosphere-atmosphere-hydrosphere exchange processes at site and regional scale. Landscape Ecol. DOI 10.1007/s10980-012-9772-x
+- Kirchner, J., 2006. Getting the right answers for the right reasons: Linking measurements, analyses, and models to advance the science of hydrology. Water Resour.Res. 42, W03S04, doi:10.1029/2005WR004362.
+- Kraft, P., Multsch, S., Vache, K. B., Frede, H.-G. and Breuer, L.: Using Python as a coupling platform for integrated catchment models, Adv. Geosci., 27, 51-56, doi:10.5194/adgeo-27-51-2010, 2010.
+- Kraft, P., 2012. A hydrological programming language extension for integrated catchment models, Dissertation, Justus-Liebig-Universität, Gießen, 16 March. [online] Available from: http://geb.uni-giessen.de/geb/volltexte/2012/8759/
+- Kraft, P., Vache, K. B., Frede, H.-G. and Breuer, L.: A hydrological programming language extension for integrated catchment models, Environ. Model. Softw., 26, 828-830, doi:10.1016/j.envsoft.2010.12.009, 2011.
+- Qu, Y.Z., Duffy, C.J., 2007. A semidiscrete finite volume formulation for multiprocess watershed simulation. Water Resour.Res. 43, W08419, doi:10.1029/2006WR005752.
+- Seibert, J., McDonnell, J.J., 2002. On the dialog between experimentalist and modeler in catchment hydrology: Use of soft data for multicriteria model calibration. Water Resour.Res. 38, doi: 10.1029/2001WR000978.
+- Sivapalan, M., 2003. Process complexity at hillslope scale, process simplicity at the watershed scale: is there a connection? Hydrol.Proc. 17, 1037-1041.
+- Tetzlaff, D., McDonnell, J.J., Uhlenbrook, S., McGuire, K.J., Bogaart, P.W., Naef, F., Baird, A.J., Dunn, S.M., Soulsby, C., 2008. Conceptualizing catchment processes: simply too complex? Hydrol.Proc. 22, 1727-1730.
+- Vache, K.B., McDonnell, J.J., 2006. A process-based rejectionist framework for evaluating catchment runoff model structure. Water Resour.Res. W02409, doi:10.1029/2005WR004247.

demo/cmf2d.py

@@ -42,12 +42,12 @@ def load_meteo(project):
 
     # Load climate data from csv file
     # could be simplified with numpy's 
-    csvfile =  open('data/climate.csv')
+    csvfile = open('data/climate.csv')
     csvfile.readline() # Read the headers, and ignore them
     for line in csvfile:
         # split the line in to columns using commas
         columns = line.split(',')
-        # Get the values, but ignore the date, we have begin and steo
+        # Get the values, but ignore the date, we have begin and step
         # of the data file hardcoded
         # If you don't get this line - it is standard Python, I would recommend the official Python.org tutorial
         for timeseries,value in zip([rain,meteo.Tmax,meteo.Tmin,meteo.rHmean,meteo.Windspeed,meteo.Sunshine],

demo/cmf3d-shapefile.py

@@ -59,63 +59,102 @@ def load_meteo(project):
     # Use the meteorological station for each cell of the project
     project.use_nearest_meteo()
 
-
-def soiltype(depth):
-    """
-    Creates a retention curve for a depth below ground
-    using an exponential Ksat decline
-    :param depth: depth below ground in m
-    :return: Retention curve
-    """
-    return cmf.BrooksCoreyRetentionCurve(ksat=15 * np.exp(-depth),
-                                         porosity=0.5,
-                                         _b=5.5,
-                                         theta_x=0.35)
-
-def build_cell(c, layercount=0):
+class Model:
     """
-    Shapes and makes a cell, by adding layers and connections
-    :param c: cmf.Cell
-    :return: None
+    The 3d model based on irregular polygons from a shapefile
     """
 
-    for d in [0.05, 0.1, 0.2, 0.3, 0.5, 0.75, 1.0, 1.3, 1.7, 2.][-layercount:]:
-        c.add_layer(d, soiltype(d))
-    c.saturated_depth = 5.
-    c.surfacewater_as_storage()
-    # use Richards connection
-    c.install_connection(cmf.Richards)
-    c.install_connection(cmf.MatrixInfiltration)
-    c.install_connection(cmf.CanopyOverflow)
-    c.install_connection(cmf.SimpleTindexSnowMelt)
-    c.install_connection(cmf.PenmanMonteithET)
-
-
-def create_project(subsurface_lateral_connection,
-                   surface_lateral_connection,
-                   layercount=0):
-
-    p = cmf.project()
-    shp = Shapefile('data/soil_lu_gw.shp')
-
-    # Create cells
-    for feature in shp:
-        c = cmf.geometry.create_cell(p, feature.shape, feature.HEIGHT, feature.OID)
-        build_cell(c, layercount)
-
-    # Build topology
-    cmf.geometry.mesh_project(p, verbose=True)
-
-    cmf.connect_cells_with_flux(p, subsurface_lateral_connection)
-    cmf.connect_cells_with_flux(p, surface_lateral_connection)
-
-    load_meteo(p)
-
-    return p
-
-
-p = create_project(cmf.DarcyKinematic, cmf.KinematicSurfaceRunoff, 1)
-
-print(cmf.describe(p))
+    def build_cell(self, c, layercount=0):
+        """
+        Shapes and makes a cell, by adding layers and connections
+        :param c: cmf.Cell
+        :return: the now enhanced cell
+        """
+
+        def soiltype(depth):
+            """
+            Creates a retention curve for a depth below ground
+            using an exponential Ksat decline
+            :param depth: depth below ground in m
+            :return: Retention curve
+            """
+            return cmf.BrooksCoreyRetentionCurve(ksat=15 * np.exp(-depth),
+                                                 porosity=0.5,
+                                                 _b=5.5,
+                                                 theta_x=0.35)
+
+        for d in [0.05, 0.1, 0.2, 0.3, 0.5, 0.75, 1.0, 1.3, 1.7, 2.][-layercount:]:
+            c.add_layer(d, soiltype(d))
+        c.saturated_depth = 1.
+        c.surfacewater_as_storage()
+        # use Richards connection
+        c.install_connection(cmf.Richards)
+        c.install_connection(cmf.GreenAmptInfiltration)
+        c.install_connection(cmf.CanopyOverflow)
+        c.install_connection(cmf.SimpleTindexSnowMelt)
+        c.install_connection(cmf.PenmanMonteithET)
+        return c
+
+    def connect_outlet_cell(self, outlet_cell: cmf.Cell, subsurface_lateral_connection,
+                            surface_lateral_connection):
+        o = outlet_cell.surfacewater
+        for neighbor, width in outlet_cell.neighbors:
+            for l in neighbor.layers:
+                subsurface_lateral_connection(l, o, width)
+            surface_lateral_connection(neighbor.surfacewater, o, width)
+
+    def get_outflow(self, t):
+        return sum(o.surfacewater(t) for o in self.outlet_cells)
+
+    def get_rainfall(self, t):
+        return sum(c.get_rainfall(t)/c.area for c in self.project) * 1000
+
+    def __init__(self, subsurface_lateral_connection,
+                       surface_lateral_connection=None,
+                       layercount=0):
+        """
+        Creates the cmf project
+
+        :param subsurface_lateral_connection: Type of lateral subsurface connection, eg. cmf.Darcy
+        :param surface_lateral_connection:  Type of lateral surface flux connection, eg. cmf.KinematicSurfaceRunoff or None
+        :param layercount: Number of layers in the subsurface
+        :return:
+        """
+
+        p = cmf.project()
+        shp = Shapefile('data/vollnkirchner_bach_cells.shp')
+
+        # Create cells
+        outlet_cells = []
+        for feature in shp:
+            c = cmf.geometry.create_cell(p, feature.shape, feature.HEIGHT, feature.OID, with_surfacewater=False)
+
+            if feature.LANDUSE_CU.starts_with('outlet'):
+                outlet_cells.append(c)
+            else:
+                self.build_cell(c, layercount)
+
+        # Build topology
+        cmf.geometry.mesh_project(p, verbose=True)
+
+        cmf.connect_cells_with_flux(p, subsurface_lateral_connection)
+        if surface_lateral_connection:
+            cmf.connect_cells_with_flux(p, surface_lateral_connection)
+
+        for o_cell in outlet_cells:
+            self.connect_outlet_cell(o_cell, subsurface_lateral_connection, surface_lateral_connection)
+
+
+
+
+
+        load_meteo(p)
+
+        self.project = p
+        self.outlet_cells = outlet_cells
+
+if __name__ == '__main__':
+
+    p = Model(cmf.DarcyKinematic, cmf.KinematicSurfaceRunoff, 1)
 
 

demo/data/vollnkirchner_bach_cells.prj

@@ -0,0 +1 @@
+PROJCS["ETRS_1989_UTM_Zone_32N",GEOGCS["GCS_ETRS_1989",DATUM["D_ETRS_1989",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",500000.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",9.0],PARAMETER["Scale_Factor",0.9996],PARAMETER["Latitude_Of_Origin",0.0],UNIT["Meter",1.0]]