Prepare v1.1.0

docs/changelog.rst

@@ -6,24 +6,30 @@ Distinction is made between new methods (Added), changes to existing methods (Ch
 The format is based on `Keep a Changelog`_, and this project adheres to
 `Semantic Versioning`_.
 
-v1.0.4 (Unreleased)
+v1.1.0 (Unreleased)
 ===================
 
 Added
 -----
 - improved subgrid tables that account for the wet fraction of the cell (#160)
 - add source points at headwater locations with `SfincsModel.setup_river_inflow` (#170)
+- it's now possible to provide a format/precision in `SfincsModel.write_forcing` (#197)
+- river bed levels for burn-in can now be provided at point locations in `SfincsModel.setup_subgrid` (#209)
 
 Changed
 -------
 - improved subgrid tables are saved as NetCDF (#160)
+- improved weighting of adjacent cells in u/v points for determining representative Manning roughness and conveyance depth (#200)
 - In `SfincsModel.setup_river_inflow`, in case of a confluence within a user-defined buffer of the
-  model boundary the confluence rather than both tributaries is selected as inflow point. (#202)
+  model boundary, the confluence rather than both tributaries is selected as inflow point. (#202)
+- turned on "baro", the atmospheric pressure term in the momentum equation, in sfincs.inp by default (#208)
+- the expected variable names for wind and pressure forcing have been changed to "wind10_u", "wind10_v" and "press_msl" to match hydromt-core conventions (#211)
 
 Fixed
 -----
 - rounding errors in `workflows.tile_window` which resulted in erronuous slippy-tiles (#178)
 - "active geometry column to use has not been set" error for GeoDataFrame (#180)
+- added nodata value to raster before writing (#199)
 
 
 v1.0.3 (3 January 2024)

docs/user_guide/sfincs_build_update.rst

@@ -147,4 +147,4 @@ See `Example: Build from Script <../_examples/build_from_script.ipynb>`_ for a m
     Example: Build from CLI <../_examples/build_from_cli.ipynb>
     Example: Build from script <../_examples/build_from_script.ipynb>
     Example: Setup model forcing <../_examples/example_forcing.ipynb>
-    Example: Working with data <../_examples/example_datasources.ipynb>
+    .. Example: Working with data <../_examples/example_datasources.ipynb>

envs/hydromt-sfincs-dev.yml

@@ -8,8 +8,8 @@ dependencies:
 - cartopy
 - descartes
 - ffmpeg
-- geopandas>=0.8
-- hydromt>=0.9.1, <0.10
+- geopandas>=1.0
+- hydromt>=0.10, <0.11
 - jupyterlab
 - matplotlib
 - nbsphinx

hydromt_sfincs/__init__.py

@@ -3,7 +3,7 @@
 from os.path import dirname, join, abspath
 
 
-__version__ = "1.0.4.dev"
+__version__ = "1.1.0"
 
 DATADIR = join(dirname(abspath(__file__)), "data")
 

hydromt_sfincs/plots.py

@@ -73,8 +73,8 @@ def plot_forcing(forcing: Dict, **kwargs):
             df.plot(drawstyle="steps", ax=axes[i])
         elif (
             name.startswith("press")
-            or name.startswith("wind_u")
-            or name.startswith("wind_v")
+            or name.startswith("wind10_u")
+            or name.startswith("wind10_v")
         ):
             df.plot.line(ax=axes[i])
         elif name.startswith("wnd"):

hydromt_sfincs/sfincs.py

@@ -61,7 +61,7 @@ class SfincsModel(GridModel):
         "press_2d": ("netamp", {"barometric_pressure": "press_2d"}),
         "wind_2d": (
             "netamuamv",
-            {"eastward_wind": "wind_u", "northward_wind": "wind_v"},
+            {"eastward_wind": "wind10_u", "northward_wind": "wind10_v"},
         ),
     }
     _FORCING_SPW = {"spiderweb": "spw"}  # TODO add read and write functions
@@ -87,8 +87,8 @@ class SfincsModel(GridModel):
         "precip": {"standard_name": "precipitation", "unit": "mm.hr-1"},
         "precip_2d": {"standard_name": "precipitation", "unit": "mm.hr-1"},
         "press_2d": {"standard_name": "barometric pressure", "unit": "Pa"},
-        "wind_u": {"standard_name": "eastward wind", "unit": "m/s"},
-        "wind_v": {"standard_name": "northward wind", "unit": "m/s"},
+        "wind10_u": {"standard_name": "eastward wind", "unit": "m/s"},
+        "wind10_v": {"standard_name": "northward wind", "unit": "m/s"},
         "wnd": {"standard_name": "wind", "unit": "m/s"},
     }
 
@@ -292,7 +292,7 @@ def setup_grid_from_region(
         # create grid from region
         # NOTE keyword rotated is added to still have the possibility to create unrotated grids if needed (e.g. for FEWS?)
         if rotated:
-            geom = self.geoms["region"].unary_union
+            geom = self.geoms["region"].union_all()
             x0, y0, mmax, nmax, rot = utils.rotated_grid(
                 geom, res, dec_origin=dec_origin, dec_rotation=dec_rotation
             )
@@ -1079,11 +1079,11 @@ def setup_river_outflow(
             if np.any(self.mask == 2) and btype == "outflow":
                 gdf_msk2 = utils.get_bounds_vector(self.mask)
                 # NOTE: this should be a single geom
-                geom = gdf_msk2[gdf_msk2["value"] == 2].unary_union
+                geom = gdf_msk2[gdf_msk2["value"] == 2].union_all()
                 gdf_out = gdf_out[~gdf_out.intersects(geom)]
             # remove outflow points near source points
             if "dis" in self.forcing and len(gdf_out) > 0:
-                geom = self.forcing["dis"].vector.to_gdf().unary_union
+                geom = self.forcing["dis"].vector.to_gdf().union_all()
                 gdf_out = gdf_out[~gdf_out.intersects(geom)]
 
         # update mask

hydromt_sfincs/subgrid.py

@@ -39,21 +39,17 @@ def read(self, file_name, mask):
 
         # get number of levels, point and uv points
         self.nlevels, self.nr_cells, self.nr_uv_points = (
-            ds.dims["levels"],
-            ds.dims["np"],
-            ds.dims["npuv"],
+            ds.sizes["levels"],
+            ds.sizes["np"],
+            ds.sizes["npuv"],
         )
 
         # find indices of active cells
         index_nm, index_mu1, index_nu1 = utils.find_uv_indices(mask)
         active_indices = np.where(index_nm > -1)[0]
-        active_indices_u = np.where(index_mu1 > -1)[0]
-        active_indices_v = np.where(index_nu1 > -1)[0]
 
         # convert 1D indices to 2D indices
-        active_z = np.unravel_index(active_indices, grid_dim, order="F")
-        active_u = np.unravel_index(active_indices_u, grid_dim, order="F")
-        active_v = np.unravel_index(active_indices_v, grid_dim, order="F")
+        active_cells = np.unravel_index(active_indices, grid_dim, order="F")
 
         # Initialize the data-arrays
         # Z points
@@ -96,32 +92,60 @@ def read(self, file_name, mask):
 
         # Now read the data and add it to the data-arrays
         # use index_nm of the active cells in the new dataset
-        self.z_zmin[active_z] = ds["z_zmin"].values.flatten()
-        self.z_zmax[active_z] = ds["z_zmax"].values.flatten()
-        self.z_volmax[active_z] = ds["z_volmax"].values.flatten()
+        self.z_zmin[active_cells] = ds["z_zmin"].values.flatten()
+        self.z_zmax[active_cells] = ds["z_zmax"].values.flatten()
+        self.z_volmax[active_cells] = ds["z_volmax"].values.flatten()
         for ilevel in range(self.nlevels):
-            self.z_level[ilevel, active_z[0], active_z[1]] = ds["z_level"][
+            self.z_level[ilevel, active_cells[0], active_cells[1]] = ds["z_level"][
                 ilevel
             ].values.flatten()
 
         # now use index_mu1 and index_nu1 to put the values of the active cells in the new dataset
         var_list = ["zmin", "zmax", "ffit", "navg"]
         for var in var_list:
             uv_var = ds["uv_" + var].values.flatten()
-            self.u_zmin[active_u] = uv_var[index_mu1[active_indices_u]]
-            self.v_zmin[active_v] = uv_var[index_nu1[active_indices_v]]
+
+            # Dynamically set the attribute for self.u_var and self.v_var
+            u_attr_name = f"u_{var}"
+            v_attr_name = f"v_{var}"
+
+            # Retrieve the current attribute values
+            u_array = getattr(self, u_attr_name)
+            v_array = getattr(self, v_attr_name)
+
+            # Update only the active indices
+            u_array[active_cells] = uv_var[index_mu1[active_indices]]
+            v_array[active_cells] = uv_var[index_nu1[active_indices]]
+
+            # Set the modified arrays back to the attributes
+            setattr(self, u_attr_name, u_array)
+            setattr(self, v_attr_name, v_array)
 
         var_list_levels = ["havg", "nrep", "pwet"]
         for var in var_list_levels:
             for ilevel in range(self.nlevels):
                 uv_var = ds["uv_" + var][ilevel].values.flatten()
-                self.u_havg[ilevel, active_u[0], active_u[1]] = uv_var[
-                    index_mu1[active_indices_u]
+
+                # Dynamically set the attribute for self.u_var and self.v_var
+                u_attr_name = f"u_{var}"
+                v_attr_name = f"v_{var}"
+
+                # Retrieve the current attribute values
+                u_array = getattr(self, u_attr_name)
+                v_array = getattr(self, v_attr_name)
+
+                # Update only the active indices
+                u_array[ilevel, active_cells[0], active_cells[1]] = uv_var[
+                    index_mu1[active_indices]
                 ]
-                self.v_havg[ilevel, active_v[0], active_v[1]] = uv_var[
-                    index_nu1[active_indices_v]
+                v_array[ilevel, active_cells[0], active_cells[1]] = uv_var[
+                    index_nu1[active_indices]
                 ]
 
+                # Set the modified arrays back to the attributes
+                setattr(self, u_attr_name, u_array)
+                setattr(self, v_attr_name, v_array)
+
         # close the dataset
         ds.close()
 
@@ -431,8 +455,8 @@ def build(
             Threshold depth in SFINCS model, by default 0.01 m
         q_table_option : int, optional
             Option for the computation of the representative roughness and conveyance depth at u/v points, by default 2.
-            1: "old" weighting method, compliant with SFINCS < v2.1.0, taking the avarage of the adjecent cells
-            2: "improved" weighting method, recommended for SFINCS >= v2.1.0, that takes into account the wet fractions of the adjacent cells
+            1: "old" weighting method, compliant with SFINCS < v2.1.1, taking the avarage of the adjecent cells
+            2: "improved" weighting method, recommended for SFINCS >= v2.1.1, that takes into account the wet fractions of the adjacent cells
         manning_land, manning_sea : float, optional
             Constant manning roughness values for land and sea,
             by default 0.04 and 0.02 s.m-1/3
@@ -1066,7 +1090,7 @@ def subgrid_q_table(
     # Determine level size (metres)
     dlevel = (zmax - zmin) / (nlevels - 1)
 
-    # Option can be either 1 ("old, compliant with SFINCS < v2.1.0") or 2 ("new", recommended SFINCS >= v2.1.0)
+    # Option can be either 1 ("old, compliant with SFINCS < v2.1.") or 2 ("new", recommended SFINCS >= v2.1.)
     option = option
 
     # Loop through levels

hydromt_sfincs/utils.py

@@ -221,7 +221,7 @@ def read_xy(fn: Union[str, Path], crs: Union[int, CRS] = None) -> gpd.GeoDataFra
 
 
 def read_xyn(fn: str, crs: int = None):
-    df = pd.read_csv(fn, index_col=False, header=None, delim_whitespace=True).rename(
+    df = pd.read_csv(fn, index_col=False, header=None, sep="\s+").rename(
         columns={0: "x", 1: "y"}
     )
     if len(df.columns) > 2:
@@ -280,7 +280,7 @@ def read_timeseries(fn: Union[str, Path], tref: Union[str, datetime]) -> pd.Data
         Dataframe of timeseries with parsed time index.
     """
     tref = parse_datetime(tref)
-    df = pd.read_csv(fn, delim_whitespace=True, index_col=0, header=None)
+    df = pd.read_csv(fn, index_col=0, header=None, sep="\s+")
     df.index = pd.to_datetime(df.index.values, unit="s", origin=tref)
     df.columns = df.columns.values.astype(int)
     df.index.name = "time"
@@ -800,7 +800,7 @@ def read_sfincs_map_results(
             continue
         if "x" in ds_map[var].dims and "y" in ds_map[var].dims:
             # drop to overwrite with ds_like.raster.coords
-            ds_face[var] = ds_map[var].drop(["xc", "yc"])
+            ds_face[var] = ds_map[var].drop_vars(["xc", "yc"])
         elif ds_map[var].ndim == 0:
             ds_face[var] = ds_map[var]
         else:

hydromt_sfincs/workflows/bathymetry.py

@@ -257,7 +257,7 @@ def burn_river_rect(
         # get gdf_riv outside of mask and buffer these lines
         # then merge with gdf_riv_mask to get the full river mask
         gdf_mask = gpd.GeoDataFrame(
-            geometry=[gdf_riv_mask.buffer(0).unary_union],
+            geometry=[gdf_riv_mask.buffer(0).union_all()],
             crs=gdf_riv_mask.crs,
         )  # create single polygon to clip
         gdf_riv_clip = gdf_riv.overlay(gdf_mask, how="difference")
@@ -319,7 +319,7 @@ def burn_river_rect(
     # merge river lines > z points are interpolated along merged line
     if gdf_riv.index.size > 1:
         gdf_riv_merged = gpd.GeoDataFrame(
-            geometry=[linemerge(gdf_riv.unary_union)], crs=gdf_riv.crs
+            geometry=[linemerge(gdf_riv.union_all())], crs=gdf_riv.crs
         )
         gdf_riv_merged = gdf_riv_merged.explode(index_parts=True).reset_index(drop=True)
     else:

hydromt_sfincs/workflows/flwdir.py

@@ -56,7 +56,7 @@ def river_centerline_from_hydrography(
     gdf_riv = gpd.GeoDataFrame.from_features(feats, crs=da_flwdir.raster.crs)
     # clip to mask and remove empty geometries
     if gdf_mask is not None and isinstance(gdf_mask, gpd.GeoDataFrame):
-        gdf_riv = gdf_riv.to_crs(gdf_mask.crs).clip(gdf_mask.unary_union)
+        gdf_riv = gdf_riv.to_crs(gdf_mask.crs).clip(gdf_mask.union_all())
     gdf_riv = gdf_riv[~gdf_riv.is_empty]
     # create river network from gdf to get distance from outlet 'rivlen'
     # length of river segments
@@ -144,7 +144,7 @@ def river_source_points(
         gdf_mask = gdf_mask.to_crs("epsg:3857")
 
     # clip river to model gdf_mask
-    gdf_riv = gdf_riv.to_crs(gdf_mask.crs).clip(gdf_mask.unary_union)
+    gdf_riv = gdf_riv.to_crs(gdf_mask.crs).clip(gdf_mask.union_all())
     # keep only lines
     gdf_riv = gdf_riv[
         [t.endswith("LineString") for t in gdf_riv.geom_type]
@@ -162,7 +162,7 @@ def river_source_points(
         return gpd.GeoDataFrame()
 
     # remove lines that fully are within the buffer of the mask boundary
-    bnd = gdf_mask.boundary.buffer(buffer).unary_union
+    bnd = gdf_mask.boundary.buffer(buffer).union_all()
     gdf_riv = gdf_riv[~gdf_riv.within(bnd)]
 
     # get source points 1m before the start/end of the river
@@ -175,10 +175,10 @@ def river_source_points(
     # get points that do not intersect with up/downstream end of other river segments
     # use a small buffer of 5m around these points to account for dx and avoid issues with imprecise river geometries
     if src_type in ["inflow", "headwater"]:
-        pnts_ds = gdf_ds.buffer(5).unary_union
+        pnts_ds = gdf_ds.buffer(5).union_all()
         gdf_pnt = gdf_up[~gdf_up.intersects(pnts_ds)].reset_index(drop=True)
     elif src_type == "outflow":
-        pnts_up = gdf_up.buffer(5).unary_union
+        pnts_up = gdf_up.buffer(5).union_all()
         gdf_pnt = gdf_ds[~gdf_ds.intersects(pnts_up)].reset_index(drop=True)
 
     # get buffer around gdf_mask, in- and outflow points should be within this buffer

pyproject.toml

@@ -12,8 +12,8 @@ authors = [
 ]
 dependencies = [
     "affine",
-    "geopandas>=0.8",
-    "hydromt>=0.9.1, <0.11",
+    "geopandas>=1.0",
+    "hydromt>=0.10, <0.11",
     "numba",
     "numpy<2.0", # temp pin until bottleneck release v1.4
     "pandas",