update docs

hydromt_wflow/wflow.py

@@ -262,34 +262,34 @@ def setup_rivers(
         This component sets the all river parameter maps.
 
         The river mask is defined by all cells with a mimimum upstream area threshold
-        `river_upa` [km2].
+        ``river_upa`` [km2].
 
         The river length is defined as the distance from the subgrid outlet pixel to
-        the next upstream subgrid outlet pixel. The `min_rivlen_ratio` is the minimum
+        the next upstream subgrid outlet pixel. The ``min_rivlen_ratio`` is the minimum
         global river length to avg. cell resolution ratio and is used as a threshold in
         window based smoothing of river length.
 
         The river slope is derived from the subgrid elevation difference between pixels at a
-        half distance `slope_len` [m] up- and downstream from the subgrid outlet pixel.
+        half distance ``slope_len`` [m] up- and downstream from the subgrid outlet pixel.
 
         The river manning roughness coefficient is derived based on reclassification
-        of the streamorder map using a lookup table `rivman_mapping_fn`.
+        of the streamorder map using a lookup table ``rivman_mapping_fn``.
 
-        The river width is derived from the nearest river segment in `river_geom_fn`.
+        The river width is derived from the nearest river segment in ``river_geom_fn``.
         Data gaps are filled by the nearest valid upstream value and averaged along
-        the flow directions over a length `smooth_len` [m]
+        the flow directions over a length ``smooth_len`` [m]
 
-        The river depth is calculated using the `rivdph_method`, by default powlaw:
+        The river depth is calculated using the ``rivdph_method``, by default powlaw:
         h = hc*Qbf**hp, which is based on qbankfull discharge from the nearest river
-        segment in `river_geom_fn` and takes optional arguments for the hc
+        segment in ``river_geom_fn`` and takes optional arguments for the hc
         (default = 0.27) and hp (default = 0.30) parameters. For other methods see
         :py:meth:`hydromt.workflows.river_depth`.
 
-        If `river_routing` is set to "local-inertial", the bankfull elevantion map can be
+        If ``river_routing`` is set to "local-inertial", the bankfull elevantion map can be
         conditioned based on the average cell elevation ("wflow_dem") or subgrid outlet pixel
         elevation ("dem_subgrid"). The subgrid elevation might provide a better representation
         of the river elevation profile, however in combination with local-inertial land routing
-        (see `hydromt.setup_floodplains`) the subgrid elevation will likely overestimate the
+        (see :py:meth:`setup_floodplains`) the subgrid elevation will likely overestimate the
         floodplain storage capacity. Note that the same input elevation map should be used for
         river bankfull elevation and land elevation when using local-inertial land routing.
 
@@ -343,7 +343,7 @@ def setup_rivers(
         workflows.river_bathymetry
         hydromt.workflows.river_depth
         pyflwdir.FlwdirRaster.river_depth
-        hydromt.setup_floodplains
+        setup_floodplains
         """
         self.logger.info(f"Preparing river maps.")
 
@@ -472,17 +472,17 @@ def setup_floodplains(
     ):
         """
         This components adds floodplain information to the model schematistation. The user can
-        define what type of floodplains are required (1D or 2D), through the `floodplain_type`
+        define what type of floodplains are required (1D or 2D), through the ``floodplain_type``
         argument.
 
-        If `floodplain_type` is set to "1d", a floodplain profile is derived for every river
+        If ``floodplain_type`` is set to "1d", a floodplain profile is derived for every river
         cell. It adds a map with floodplain volume per flood depth, which is used in the wflow
         1D floodplain schematisation.
 
-        Note, it is important to use the same river uparea value as used in the `setup_rivers`
+        Note, it is important to use the same river uparea value as used in the :py:meth:`setup_rivers`
         method.
 
-        If `floodplain_type` is set to "2d", this component adds a hydrologically conditioned
+        If ``floodplain_type`` is set to "2d", this component adds a hydrologically conditioned
         elevation (hydrodem) map for land routing (local-inertial). For this options, landcells
         need to be conditioned to D4 flow directions otherwise pits may remain in the land
         cells.
@@ -494,13 +494,14 @@ def setup_floodplains(
         Additionally, note that the same input elevation map should be used for river bankfull
         elevation and land elevation when using local-inertial land routing.
 
-        Requires `setup_rivers` to be executed beforehand (with `river_routing` set to
+        Requires :py:meth:`setup_rivers` to be executed beforehand (with ``river_routing`` set to
         `local-inertial`).
 
         Adds model layers:
 
         * **floodplain_volume** map: map with floodplain volumes, has flood depth as third
         dimension [m3] (for 1D floodplains)
+
         * **hydrodem** map: hydrologically conditioned elevation [m+REF] (for 2D floodplains)
 
         Parameters
@@ -1031,7 +1032,7 @@ def setup_gauges(
           used to set the maximum distance to snap to the mask.
         * snapping based on upstream area matching: : ``snap_uparea=True``. The gauge locations
           are snapped to the closest matching upstream area value. Requires gauges_fn to have
-          an `uparea` [km2] column. The closest value will be looked for in a cell window of size ``wdw``
+          an ``uparea`` [km2] column. The closest value will be looked for in a cell window of size ``wdw``
           and the difference between the gauge and the closest value should be smaller than ``rel_error``.
 
         If ``derive_subcatch`` is set to True, an additional subcatch map is derived from
@@ -2540,9 +2541,9 @@ def write_forcing(
         time_units="days since 1900-01-01T00:00:00",
         **kwargs,
     ):
-        """write forcing at `fn_out` in model ready format.
+        """write forcing at ``fn_out`` in model ready format.
 
-        If no `fn_out` path is provided and path_forcing from the  wflow toml exists,
+        If no ``fn_out`` path is provided and path_forcing from the  wflow toml exists,
         the following default filenames are used:
 
             * Default name format (with downscaling): inmaps_sourcePd_sourceTd_methodPET_freq_startyear_endyear.nc

hydromt_wflow/workflows/basemaps.py

@@ -259,13 +259,14 @@ def topography(
     method: str = "average",
     logger=logger,
 ):
-    """Returns topography maps (see list below) at model resolution based on gridded 
-    elevation data input. 
+    """Returns topography maps (see list below) at model resolution based on gridded
+    elevation data input.
+
+    The following topography maps are calculated:
+
+    - elevtn : average elevation [m]
+    - lndslp : average land surface slope [m/m]
 
-    The following topography maps are calculated:\
-    - elevtn : average elevation [m]\
-    - lndslp : average land surface slope [m/m]\
-    
     Parameters
     ----------
     ds : xarray.DataArray

hydromt_wflow/workflows/glaciers.py

@@ -20,11 +20,12 @@ def glaciermaps(
 ):
     """Returns glacier maps (see list below) at model resolution.
 
-    The following glacier maps are calculated:\
-    - wflow_glacierareas: glacier IDs [ID]\
-    - wflow_glacierfrac: area fraction of glacier per cell [-]\
-    - wflow_glacierstore: storage (volume) of glacier per cell [mm]\
-    
+    The following glacier maps are calculated:
+
+    - wflow_glacierareas: glacier IDs [ID]
+    - wflow_glacierfrac: area fraction of glacier per cell [-]
+    - wflow_glacierstore: storage (volume) of glacier per cell [mm]
+
     Parameters
     ----------
     gdf : geopandas.GeoDataFrame

hydromt_wflow/workflows/landuse.py

@@ -22,7 +22,8 @@ def landuse(da, ds_like, df, logger=logger, params=None):
     The parameter maps are prepared based on landuse map and
     mapping table as provided in the generic data folder of hydromt.
 
-    The following topography maps are calculated:\
+    The following topography maps are calculated:
+
     - TODO
 
     Parameters

hydromt_wflow/workflows/soilgrids.py

@@ -283,37 +283,38 @@ def constrain_M(M, popt_0, M_minmax):
 def soilgrids(ds, ds_like, ptfKsatVer, soil_fn, logger=logger):
     """
     Returns soil parameter maps at model resolution based on soil properties from SoilGrids datasets.
-    Both soilgrids 2017 and 2020 are supported. Soilgrids 2017 provides soil properties at 7 specific depths, while soilgrids_2020 provides soil properties averaged over 6 depth intervals. 
-    Ref: Hengl, T., Mendes de Jesus, J., Heuvelink, G. B. M., Ruiperez Gonzalez, M., Kilibarda, 
-    M., Blagotic, A., et al.: SoilGrids250m: Global gridded soil information based on machine learning, 
+    Both soilgrids 2017 and 2020 are supported. Soilgrids 2017 provides soil properties at 7 specific depths, while soilgrids_2020 provides soil properties averaged over 6 depth intervals.
+    Ref: Hengl, T., Mendes de Jesus, J., Heuvelink, G. B. M., Ruiperez Gonzalez, M., Kilibarda,
+    M., Blagotic, A., et al.: SoilGrids250m: Global gridded soil information based on machine learning,
     PLoS ONE, 12, https://doi.org/10.1371/journal.pone.0169748, 2017.
-    Ref: de Sousa, L.M., Poggio, L., Batjes, N.H., Heuvelink, G., Kempen, B., Riberio, E. and Rossiter, D., 2020. 
+    Ref: de Sousa, L.M., Poggio, L., Batjes, N.H., Heuvelink, G., Kempen, B., Riberio, E. and Rossiter, D., 2020.
     SoilGrids 2.0: producing quality-assessed soil information for the globe. SOIL Discussions, pp.1-37.
-    https://doi.org/10.5194/soil-2020-65 
-
-    The following soil parameter maps are calculated:\
-    - `thetaS`            : average saturated soil water content [m3/m3]\
-    - `thetaR`            : average residual water content [m3/m3]\
-    - `KsatVer`           : vertical saturated hydraulic conductivity at soil surface [mm/day]\
-    - `SoilThickness`     : soil thickness [mm]\
-    - `SoilMinThickness`  : minimum soil thickness [mm] (equal to SoilThickness)\
-    - `M`                 : model parameter [mm] that controls exponential decline of KsatVer with soil depth 
-                            (fitted with curve_fit (scipy.optimize)), bounds of M are checked\
-    - `M_`                : model parameter [mm] that controls exponential decline of KsatVer with soil depth 
-                            (fitted with numpy linalg regression), bounds of `M_`are checked\
-    - `M_original`        : `M` without checking bounds\
-    - `M_original_`       : `M_`without checking bounds\
-    - `f`                 : scaling parameter controlling the decline of KsatVer [mm-1] (fitted with curve_fit (scipy.optimize)), bounds are checked\
-    - `f_`                : scaling parameter controlling the decline of KsatVer [mm-1] (fitted with numpy linalg regression), bounds are checked\
+    https://doi.org/10.5194/soil-2020-65
+
+    The following soil parameter maps are calculated:
+
+    - `thetaS`            : average saturated soil water content [m3/m3]
+    - `thetaR`            : average residual water content [m3/m3]
+    - `KsatVer`           : vertical saturated hydraulic conductivity at soil surface [mm/day]
+    - `SoilThickness`     : soil thickness [mm]
+    - `SoilMinThickness`  : minimum soil thickness [mm] (equal to SoilThickness)
+    - `M`                 : model parameter [mm] that controls exponential decline of KsatVer with soil depth
+                            (fitted with curve_fit (scipy.optimize)), bounds of M are checked
+    - `M_`                : model parameter [mm] that controls exponential decline of KsatVer with soil depth
+                            (fitted with numpy linalg regression), bounds of `M_`are checked
+    - `M_original`        : `M` without checking bounds
+    - `M_original_`       : `M_`without checking bounds
+    - `f`                 : scaling parameter controlling the decline of KsatVer [mm-1] (fitted with curve_fit (scipy.optimize)), bounds are checked
+    - `f_`                : scaling parameter controlling the decline of KsatVer [mm-1] (fitted with numpy linalg regression), bounds are checked
     - `c_0`               : Brooks Corey coefficient [-] based on pore size distribution index at depth
-                            1st soil layer (100 mm) wflow_sbm\
-    - `c_1`               : idem `c_0` at depth 2nd soil layer (400 mm) wflow_sbm\
-    - `c_2`               : idem `c_0` at depth 3rd soil layer (1200 mm) wflow_sbm\
-    - `c_3`               : idem `c_0` at depth 4th soil layer (> 1200 mm) wflow_sbm\
-    - `KsatVer_[z]cm`     : KsatVer [mm/day] at soil depths [z] of SoilGrids data [0.0, 5.0, 15.0, 30.0, 60.0, 100.0, 200.0]\
-    - `wflow_soil`        : USDA Soil texture based on percentage clay, silt, sand mapping: [1:Clay, 2:Silty Clay, 3:Silty Clay-Loam, 4:Sandy Clay, 5:Sandy Clay-Loam, 6:Clay-Loam, 7:Silt, 8:Silt-Loam, 9:Loam, 10:Sand, 11: Loamy Sand, 12:Sandy Loam]\
-                
-    
+                            1st soil layer (100 mm) wflow_sbm
+    - `c_1`               : idem `c_0` at depth 2nd soil layer (400 mm) wflow_sbm
+    - `c_2`               : idem `c_0` at depth 3rd soil layer (1200 mm) wflow_sbm
+    - `c_3`               : idem `c_0` at depth 4th soil layer (> 1200 mm) wflow_sbm
+    - `KsatVer_[z]cm`     : KsatVer [mm/day] at soil depths [z] of SoilGrids data [0.0, 5.0, 15.0, 30.0, 60.0, 100.0, 200.0]
+    - `wflow_soil`        : USDA Soil texture based on percentage clay, silt, sand mapping: [1:Clay, 2:Silty Clay, 3:Silty Clay-Loam, 4:Sandy Clay, 5:Sandy Clay-Loam, 6:Clay-Loam, 7:Silt, 8:Silt-Loam, 9:Loam, 10:Sand, 11: Loamy Sand, 12:Sandy Loam]
+
+
     Parameters
     ----------
     ds : xarray.Dataset
@@ -548,16 +549,17 @@ def soilgrids(ds, ds_like, ptfKsatVer, soil_fn, logger=logger):
 
 def soilgrids_sediment(ds, ds_like, usleK_method, logger=logger):
     """
-    Returns soil parameter maps for sediment modelling at model resolution based on soil 
+    Returns soil parameter maps for sediment modelling at model resolution based on soil
     properties from SoilGrids dataset.
 
-    The following soil parameter maps are calculated:\
-        - PercentClay: clay content of the topsoil [%]\
-        - PercentSilt: silt content of the topsoil [%]\
-        - PercentOC: organic carbon in the topsoil [%]\
-        - ErosK: mean detachability of the soil (Morgan et al., 1998) [g/J]\
-        - USLE_K: soil erodibility factor from the USLE equation [-]\
-    
+    The following soil parameter maps are calculated:
+
+        - PercentClay: clay content of the topsoil [%]
+        - PercentSilt: silt content of the topsoil [%]
+        - PercentOC: organic carbon in the topsoil [%]
+        - ErosK: mean detachability of the soil (Morgan et al., 1998) [g/J]
+        - USLE_K: soil erodibility factor from the USLE equation [-]
+
     Parameters
     ----------
     ds : xarray.Dataset