Mixed-Element Mesh for the Coweeta Watershed

This workflow provides a complete working example to develop an streamaligned mixed-element mesh for Coweeta watershed. Long quad elements with pentagons at junctions are placed along NHDPlus flowlines to represent rivers/streams. Rest of the domain is meshed with standard TIN.

It uses the following datasets:

• NHD Plus for the watershed boundary and hydrography.

• NED for elevation

• NLCD for land cover/transpiration/rooting depths

• GLYHMPS geology data for structural formations

• SoilGrids 2017 for depth to bedrock and soil texture information

• SSURGO for soil data, where available, in the top 2m.

This workflow creates the following files:

• Mesh file: Coweeta.exo, includes all labeled sets

# these can be turned on for development work
%load_ext autoreload
%autoreload 2

## FIX ME -- why is this broken without importing netcdf first?
import netCDF4

# setting up logging first or else it gets preempted by another package
import watershed_workflow.ui
watershed_workflow.ui.setup_logging(1)

import os,sys
import logging
import numpy as np
from matplotlib import pyplot as plt
from matplotlib import cm as pcm
import shapely
import pandas as pd
import geopandas as gpd
import cftime, datetime
pd.options.display.max_columns = None

import watershed_workflow 
import watershed_workflow.config
import watershed_workflow.sources
import watershed_workflow.utils
import watershed_workflow.plot
import watershed_workflow.mesh
import watershed_workflow.regions
import watershed_workflow.meteorology
import watershed_workflow.land_cover_properties
import watershed_workflow.resampling
import watershed_workflow.condition
import watershed_workflow.io
import watershed_workflow.sources.standard_names as names

# set the default figure size for notebooks
plt.rcParams["figure.figsize"] = (8, 6)

Input: Parameters and other source data

Note, this section will need to be modified for other runs of this workflow in other regions.

# Force Watershed Workflow to pull data from this directory rather than a shared data directory.
# This picks up the Coweeta-specific datasets set up here to avoid large file downloads for 
# demonstration purposes.
#
def splitPathFull(path):
    """
    Splits an absolute path into a list of components such that
    os.path.join(*splitPathFull(path)) == path
    """
    parts = []
    while True:
        head, tail = os.path.split(path)
        if head == path:  # root on Unix or drive letter with backslash on Windows (e.g., C:\)
            parts.insert(0, head)
            break
        elif tail == path:  # just a single file or directory
            parts.insert(0, tail)
            break
        else:
            parts.insert(0, tail)
            path = head
    return parts

cwd = splitPathFull(os.getcwd())

# REMOVE THIS PORTION OF THE CELL for general use outside of Coweeta -- this is just locating 
# the working directory within the WW directory structure
if cwd[-1] == 'Coweeta':
    pass
elif cwd[-1] == 'examples':
    cwd.append('Coweeta')
else:
    cwd.extend(['examples','Coweeta'])
# END REMOVE THIS PORTION

# Note, this directory is where downloaded data will be put as well
data_dir = os.path.join(*(cwd + ['input_data',]))
def toInput(filename):
    return os.path.join(data_dir, filename)

output_dir = os.path.join(*(cwd + ['output_data',]))
def toOutput(filename):
    return os.path.join(output_dir, filename)

work_dir = os.path.join(*cwd)
def toWorkingDir(filename):
    return os.path.join(work_dir, filename)
       

# Set the data directory to the local space to get the locally downloaded files
# REMOVE THIS CELL for general use outside fo Coweeta
watershed_workflow.config.setDataDirectory(data_dir)

## Parameters cell -- this provides all parameters that can be changed via pipelining to generate a new watershed. 
name = 'Coweeta'
coweeta_shapefile = './Coweeta/input_data/coweeta_basin.shp'

# Geometric parameters
# -- parameters to clean and reduce the river network prior to meshing
simplify = 60                   # length scale to target average edge 
ignore_small_rivers = 2         # remove rivers with fewer than this number of reaches -- important for NHDPlus HR 
prune_by_area_fraction = 0.01   # prune any reaches whose contributing area is less than this fraction of the domain

# -- mesh triangle refinement control
refine_d0 = 200
refine_d1 = 600

#refine_L0 = 75
#refine_L1 = 200

refine_L0 = 125
refine_L1 = 300

refine_A0 = refine_L0**2 / 2
refine_A1 = refine_L1**2 / 2

min_angle = 32 # degrees


# Simulation control
# - note that we use the NoLeap calendar, same as DayMet.  Simulations are typically run over the "water year"
#   which starts August 1.
start = cftime.DatetimeNoLeap(2010,8,1)
end = cftime.DatetimeNoLeap(2011,8,1)


# Global Soil Properties
min_porosity = 0.05 # minimum porosity considered "too small"
max_permeability = 1.e-10 # max value considered "too permeable"
max_vg_alpha = 1.e-3 # max value of van Genuchten's alpha -- our correlation is not valid for some soils

# a dictionary of output_filenames -- will include all filenames generated
output_filenames = {}

# Note that, by default, we tend to work in the DayMet CRS because this allows us to avoid
# reprojecting meteorological forcing datasets.
crs = watershed_workflow.crs.daymet_crs

# get the shape and crs of the shape
coweeta_source = watershed_workflow.sources.ManagerShapefile(coweeta_shapefile, id_name='BASIN_CODE')
coweeta = coweeta_source.getShapes(out_crs=crs)

# set up a dictionary of source objects
#
# Data sources, also called managers, deal with downloading and parsing data files from a variety of online APIs.
sources = watershed_workflow.sources.getDefaultSources()
sources['hydrography'] = watershed_workflow.sources.hydrography_sources['NHDPlus HR']

#
# This demo uses a few datasets that have been clipped out of larger, national
# datasets and are distributed with the code.  This is simply to save download
# time for this simple problem and to lower the barrier for trying out
# Watershed Workflow.  A more typical workflow would delete these lines (as 
# these files would not exist for other watersheds).
#
# The default versions of these download large raster and shapefile files that
# are defined over a very large region (globally or the entire US).
#
# DELETE THIS SECTION for non-Coweeta runs
dtb_file = os.path.join(data_dir, 'soil_structure', 'DTB', 'DTB.tif')
geo_file = os.path.join(data_dir, 'soil_structure', 'GLHYMPS', 'GLHYMPS.shp')

# GLHYMPs is a several-GB download, so we have sliced it and included the slice here
sources['geologic structure'] = watershed_workflow.sources.ManagerGLHYMPS(geo_file)

# The Pelletier DTB map is not particularly accurate at Coweeta -- the SoilGrids map seems to be better.
# Here we will use a clipped version of that map.
sources['depth to bedrock'] = watershed_workflow.sources.ManagerRaster(dtb_file)

# END DELETE THIS SECTION

# log the sources that will be used here
watershed_workflow.sources.logSources(sources)

Basin Geometry

In this section, we choose the basin, the streams to be included in the stream-aligned mesh, and make sure that all are resolved discretely at appropriate length scales for this work.

the Watershed

coweeta

	AREA	PERIMETER	CWTBASINNA	CWTBASIN_1	BASIN_CODE	SPOT	LABEL	geometry	ID	name
0	1.626020e+07	17521.768	2	1	1	-9999	Coweeta Hydrologic Lab	POLYGON ((1443453.518 -645937.256, 1443489.888...	1	1

# Construct and plot the WW object used for storing watersheds
watershed = watershed_workflow.split_hucs.SplitHUCs(coweeta)
watershed.plot()

<Axes: >

the Rivers

# download/collect the river network within that shape's bounds
reaches = sources['hydrography'].getShapesByGeometry(watershed.exterior, crs, out_crs=crs)
rivers = watershed_workflow.river_tree.createRivers(reaches, method='hydroseq')

watershed_orig, rivers_orig = watershed, rivers
sources['hydrography'].name, sources['hydrography'].source

('NHDPlus HR', 'HyRiver.NHDPlusHR')

# plot the rivers and watershed
def plot(ws, rivs, ax=None):
    if ax is None:
        fig, ax = plt.subplots(1, 1)
    ws.plot(color='k', marker='+', markersize=10, ax=ax)
    for river in rivs:
        river.plot(marker='x', markersize=10, ax=ax)

plot(watershed, rivers)

# keeping the originals for plotting comparisons
def createCopy(watershed, rivers):
    """To compare before/after, we often want to create copies.  Note in real workflows most things are done in-place without copies."""
    return watershed.deepcopy(), [r.deepcopy() for r in rivers]
    

watershed, rivers = createCopy(watershed_orig, rivers_orig)

# simplifying -- this sets the discrete length scale of both the watershed boundary and the rivers
watershed_workflow.simplify(watershed, rivers, refine_L0, refine_L1, refine_d0, refine_d1)

# simplify may remove reaches from the rivers object
# -- this call removes any reaches from the dataframe as well, signaling we are all done removing reaches
#
# ETC: NOTE -- can this be moved into the simplify call?
for river in rivers:
    river.resetDataFrame()

# Now that the river network is set, find the watershed boundary outlets
for river in rivers:
    watershed_workflow.hydrography.findOutletsByCrossings(watershed, river)

plot(watershed, rivers)

watershed.df.columns

Index(['AREA', 'PERIMETER', 'CWTBASINNA', 'CWTBASIN_1', 'BASIN_CODE', 'SPOT',
       'LABEL', 'geometry', 'ID', 'name', 'outlet'],
      dtype='object')

# this generates a zoomable map, showing different reaches and watersheds, 
# with discrete points.  Problem areas are clickable to get IDs for manual
# modifications.
m = watershed.explore(marker=False)
for river in rivers_orig:
    m = river.explore(m=m, column=None, color='black', name=river['name']+' raw', marker=False)
for river in rivers:
    m = river.explore(m=m)
    
m = watershed_workflow.makeMap(m)
m

Make this Notebook Trusted to load map: File -> Trust Notebook

Mesh Geometry

Discretely create the stream-aligned mesh. Download elevation data, and condition the mesh discretely to make for better topography.

# Refine triangles if they get too acute

# width of reach by stream order (order:width)
widths = dict({1:8,2:12,3:16})

# create the mesh
m2, areas, dists = watershed_workflow.tessalateRiverAligned(watershed, rivers, 
                                                            river_width=widths,
                                                            refine_min_angle=min_angle,
                                                            refine_distance=[refine_d0, refine_A0, refine_d1, refine_A1],
                                                            diagnostics=True)

# get a raster for the elevation map, based on 3DEP
dem = sources['DEM'].getDataset(watershed.exterior, watershed.crs)['dem']

# provide surface mesh elevations
watershed_workflow.elevate(m2, dem, method='linear')

# Plot the DEM raster
fig, ax = plt.subplots()

# Plot the DEM data
im = dem.plot(ax=ax, cmap='terrain', add_colorbar=False)

# Add colorbar
cbar = plt.colorbar(im, ax=ax, shrink=0.8)
cbar.set_label('Elevation (m)', rotation=270, labelpad=15)

# Add title and labels
ax.set_title('Digital Elevation Model (DEM)', fontsize=14, fontweight='bold')
ax.set_xlabel('X Coordinate')
ax.set_ylabel('Y Coordinate')

# Set equal aspect ratio
ax.set_aspect('equal')

plt.tight_layout()
plt.show()

In the pit-filling algorithm, we want to make sure that river corridor is not filled up. Hence we exclude river corridor cells from the pit-filling algorithm.

# hydrologically condition the mesh, removing pits
river_mask=np.zeros((len(m2.conn)))
for i, elem in enumerate(m2.conn):
    if not len(elem)==3:
        river_mask[i]=1     
watershed_workflow.condition.fillPitsDual(m2, is_waterbody=river_mask)

There are a range of options to condition river corridor mesh. We hydrologically condition the river mesh, ensuring unimpeded water flow in river corridors by globally adjusting flowlines to rectify artificial obstructions from inconsistent DEM elevations or misalignments. Please read the documentation for more information

# conditioning river mesh
#
# adding elevations to the river tree for stream bed conditioning
watershed_workflow.condition.setProfileByDEM(rivers, dem)

# conditioning the river mesh using NHD elevations
watershed_workflow.condition.conditionRiverMesh(m2, rivers[0])

# plotting surface mesh with elevations
fig, ax = plt.subplots()
ax2 = ax.inset_axes([0.65,0.05,0.3,0.5])
cbax = fig.add_axes([0.05,0.05,0.9,0.05])

mp = m2.plot(facecolors='elevation', edgecolors=None, ax=ax, linewidth=0.5, colorbar=False)
cbar = fig.colorbar(mp, orientation="horizontal", cax=cbax)
ax.set_title('surface mesh with elevations')
ax.set_aspect('equal', 'datalim')

mp2 = m2.plot(facecolors='elevation', edgecolors='white', ax=ax2, colorbar=False)
ax2.set_aspect('equal', 'datalim')

xlim = (1.4433e6, 1.4438e6)
ylim = (-647000, -647500)

ax2.set_xlim(xlim)
ax2.set_ylim(ylim)
ax2.set_xticks([])
ax2.set_yticks([])

ax.indicate_inset_zoom(ax2, edgecolor='k')

cbar.ax.set_title('elevation [m]')

plt.show()

WARNING:matplotlib.axes._base:Ignoring fixed y limits to fulfill fixed data aspect with adjustable data limits.

# add labeled sets for subcatchments and outlets
watershed_workflow.regions.addWatershedAndOutletRegions(m2, watershed, outlet_width=250, exterior_outlet=True)

# add labeled sets for river corridor cells
watershed_workflow.regions.addRiverCorridorRegions(m2, rivers)

# add labeled sets for river corridor cells by order
watershed_workflow.regions.addStreamOrderRegions(m2, rivers)

for ls in m2.labeled_sets:
    print(f'{ls.setid} : {ls.entity} : {len(ls.ent_ids)} : "{ls.name}"')

10000 : CELL : 2593 : "1"
10001 : CELL : 2593 : "1 surface"
10002 : FACE : 76 : "1 boundary"
10003 : FACE : 5 : "1 outlet"
10004 : FACE : 5 : "surface domain outlet"
10005 : CELL : 184 : "river corridor 0 surface"
10006 : CELL : 16 : "stream order 3"
10007 : CELL : 46 : "stream order 2"
10008 : CELL : 122 : "stream order 1"

Surface properties

Meshes interact with data to provide forcing, parameters, and more in the actual simulation. Specifically, we need vegetation type on the surface to provide information about transpiration and subsurface structure to provide information about water retention curves, etc.

NLCD for LULC

We’ll start by downloading and collecting land cover from the NLCD dataset, and generate sets for each land cover type that cover the surface. Likely these will be some combination of grass, deciduous forest, coniferous forest, and mixed.

# download the NLCD raster
nlcd = sources['land cover'].getDataset(watershed.exterior.buffer(100), watershed.crs)['cover']

# what land cover types did we get?
logging.info('Found land cover dtypes: {}'.format(nlcd.dtype))
logging.info('Found land cover types: {}'.format(set(list(nlcd.values.ravel()))))

# create a colormap for the data
nlcd_indices, nlcd_cmap, nlcd_norm, nlcd_ticks, nlcd_labels = \
      watershed_workflow.colors.createNLCDColormap(np.unique(nlcd))
nlcd_cmap

making colormap with: [np.uint8(21), np.uint8(22), np.uint8(23), np.uint8(41), np.uint8(42), np.uint8(43), np.uint8(52), np.uint8(71), np.uint8(81), np.uint8(127)]
making colormap with colors: [(0.86666666667, 0.78823529412, 0.78823529412), (0.84705882353, 0.57647058824, 0.50980392157), (0.92941176471, 0.0, 0.0), (0.40784313726, 0.66666666667, 0.38823529412), (0.10980392157, 0.38823529412, 0.18823529412), (0.70980392157, 0.78823529412, 0.5568627451), (0.8, 0.72941176471, 0.4862745098), (0.8862745098, 0.8862745098, 0.7568627451), (0.85882352941, 0.84705882353, 0.23921568628), (1.0, 1.0, 1.0)]

from_list

under

bad

over

fig, ax = plt.subplots(1,1)
nlcd.plot.imshow(ax=ax, cmap=nlcd_cmap, norm=nlcd_norm, add_colorbar=False)
watershed_workflow.colors.createIndexedColorbar(ncolors=len(nlcd_indices), 
                               cmap=nlcd_cmap, labels=nlcd_labels, ax=ax) 
ax.set_title('Land Cover')
plt.show()

# map nlcd onto the mesh
m2_nlcd = watershed_workflow.getDatasetOnMesh(m2, nlcd, method='nearest')
m2.cell_data = pd.DataFrame({'land_cover': m2_nlcd})

# double-check that nan not in the values
assert 127 not in m2_nlcd

# create a new set of labels and indices with only those that actually appear on the mesh
nlcd_indices, nlcd_cmap, nlcd_norm, nlcd_ticks, nlcd_labels = \
      watershed_workflow.colors.createNLCDColormap(np.unique(m2_nlcd))

making colormap with: [np.uint8(21), np.uint8(22), np.uint8(23), np.uint8(41), np.uint8(42), np.uint8(43), np.uint8(81)]
making colormap with colors: [(0.86666666667, 0.78823529412, 0.78823529412), (0.84705882353, 0.57647058824, 0.50980392157), (0.92941176471, 0.0, 0.0), (0.40784313726, 0.66666666667, 0.38823529412), (0.10980392157, 0.38823529412, 0.18823529412), (0.70980392157, 0.78823529412, 0.5568627451), (0.85882352941, 0.84705882353, 0.23921568628)]

mp = m2.plot(facecolors=m2_nlcd, cmap=nlcd_cmap, norm=nlcd_norm, edgecolors=None, colorbar=False)
watershed_workflow.colors.createIndexedColorbar(ncolors=len(nlcd_indices), 
                               cmap=nlcd_cmap, labels=nlcd_labels, ax=plt.gca()) 
plt.show()

# add labeled sets to the mesh for NLCD
nlcd_labels_dict = dict(zip(nlcd_indices, nlcd_labels))
watershed_workflow.regions.addSurfaceRegions(m2, names=nlcd_labels_dict)

nlcd_labels_dict

{np.uint8(21): 'Developed, Open Space',
 np.uint8(22): 'Developed, Low Intensity',
 np.uint8(23): 'Developed, Medium Intensity',
 np.uint8(41): 'Deciduous Forest',
 np.uint8(42): 'Evergreen Forest',
 np.uint8(43): 'Mixed Forest',
 np.uint8(81): 'Pasture/Hay'}

for ls in m2.labeled_sets:
    print(f'{ls.setid} : {ls.entity} : {len(ls.ent_ids)} : "{ls.name}"')

10000 : CELL : 2593 : "1"
10001 : CELL : 2593 : "1 surface"
10002 : FACE : 76 : "1 boundary"
10003 : FACE : 5 : "1 outlet"
10004 : FACE : 5 : "surface domain outlet"
10005 : CELL : 184 : "river corridor 0 surface"
10006 : CELL : 16 : "stream order 3"
10007 : CELL : 46 : "stream order 2"
10008 : CELL : 122 : "stream order 1"
21 : CELL : 106 : "Developed, Open Space"
22 : CELL : 2 : "Developed, Low Intensity"
23 : CELL : 2 : "Developed, Medium Intensity"
41 : CELL : 1340 : "Deciduous Forest"
42 : CELL : 47 : "Evergreen Forest"
43 : CELL : 1088 : "Mixed Forest"
81 : CELL : 8 : "Pasture/Hay"

MODIS LAI

Leaf area index is needed on each land cover type – this is used in the Evapotranspiration calculation.

# download LAI and corresponding LULC datasets -- these are actually already downloaded, 
# as the MODIS AppEEARS API is quite slow
#
# Note that MODIS does NOT work with the noleap calendar, so we have to convert to actual dates first
start_leap = cftime.DatetimeGregorian(start.year, start.month, start.day)
end_leap = cftime.DatetimeGregorian(end.year, end.month, end.day)
modis_data = sources['LAI'].getDataset(watershed.exterior, crs, start_leap, end_leap)

Building request for bounds: ['-83.4826', '35.0237', '-83.4178', '35.0782']
Requires files:
 ... /home/ecoon/code/watershed_workflow/repos/master/examples/Coweeta/input_data/land_cover/MODIS/modis_LAI_08-01-2010_08-01-2011_35.0782x-83.4826_35.0237x-83.4178.nc
 ... /home/ecoon/code/watershed_workflow/repos/master/examples/Coweeta/input_data/land_cover/MODIS/modis_LULC_08-01-2010_08-01-2011_35.0782x-83.4826_35.0237x-83.4178.nc
files exist locally.

assert modis_data['LAI'].rio.crs is not None
print(modis_data['LULC'].rio.crs, modis_data['LULC'].dtype)

EPSG:4269 float64

# MODIS data comes with time-dependent LAI AND time-dependent LULC -- just take the mode to find the most common LULC
modis_data['LULC'] = watershed_workflow.data.computeMode(modis_data['LULC'], 'time_LULC')

# now it is safe to have only one time
modis_data = modis_data.rename({'time_LAI':'time'})

# remove leap day (366th day of any leap year) to match our Noleap Calendar
modis_data = watershed_workflow.data.filterLeapDay(modis_data)

# plot the MODIS data -- note the entire domain is covered with one type for Coweeta (it is small!)
fig, axs = plt.subplots(1,2, figsize=(12,6))
modis_data['LULC'].plot.imshow(ax=axs[0])
modis_data['LAI'][0].plot.imshow(ax=axs[1])

<matplotlib.image.AxesImage at 0x7ca359f5ad50>

# compute the transient time series
modis_lai = watershed_workflow.land_cover_properties.computeTimeSeries(modis_data['LAI'], modis_data['LULC'], 
                                                                      polygon=watershed.exterior, polygon_crs=watershed.crs)

modis_lai

	time	Deciduous Broadleaf Forests LAI [-]
0	2010-08-01 00:00:00	3.243011
1	2010-08-05 00:00:00	4.847312
2	2010-08-09 00:00:00	3.476344
3	2010-08-13 00:00:00	4.193548
4	2010-08-17 00:00:00	3.186022
...	...	...
88	2011-07-16 00:00:00	6.363441
89	2011-07-20 00:00:00	4.729032
90	2011-07-24 00:00:00	4.132258
91	2011-07-28 00:00:00	5.263441
92	2011-08-01 00:00:00	5.395699

93 rows × 2 columns

# smooth the data in time
modis_lai_smoothed = watershed_workflow.data.smoothTimeSeries(modis_lai, 'time')

# save the MODIS time series to disk
output_filenames['modis_lai_transient'] = toOutput(f'{name}_LAI_MODIS_transient.h5')
watershed_workflow.io.writeTimeseriesToHDF5(output_filenames['modis_lai_transient'], modis_lai_smoothed)
watershed_workflow.land_cover_properties.plotLAI(modis_lai_smoothed, indices='MODIS')

# compute a typical year
modis_lai_typical = watershed_workflow.data.computeAverageYear(modis_lai_smoothed, 'time', output_nyears=10, 
                                                                  start_year=2000)

output_filenames['modis_lai_cyclic_steadystate'] = toOutput(f'{name}_LAI_MODIS_CyclicSteadystate.h5')
watershed_workflow.io.writeTimeseriesToHDF5(output_filenames['modis_lai_cyclic_steadystate'], modis_lai_typical)
watershed_workflow.land_cover_properties.plotLAI(modis_lai_typical, indices='MODIS')

Crosswalk of LAI to NLCD LC

crosswalk = watershed_workflow.land_cover_properties.computeCrosswalk(modis_data['LULC'], nlcd, method='fractional area')

# Compute the NLCD-based time series
nlcd_lai_cyclic_steadystate = watershed_workflow.land_cover_properties.applyCrosswalk(crosswalk, modis_lai_typical)
nlcd_lai_transient = watershed_workflow.land_cover_properties.applyCrosswalk(crosswalk, modis_lai_smoothed)

watershed_workflow.land_cover_properties.removeNullLAI(nlcd_lai_cyclic_steadystate)
watershed_workflow.land_cover_properties.removeNullLAI(nlcd_lai_transient)
nlcd_lai_transient

None LAI [-] False
Open Water LAI [-] False
Perrenial Ice/Snow LAI [-] False
Developed, Medium Intensity LAI [-] True
Developed, High Intensity LAI [-] False
Barren Land LAI [-] False
None LAI [-] False
Open Water LAI [-] False
Perrenial Ice/Snow LAI [-] False
Developed, Medium Intensity LAI [-] True
Developed, High Intensity LAI [-] False
Barren Land LAI [-] False

	time	Developed, Open Space LAI [-]	Developed, Low Intensity LAI [-]	Developed, Medium Intensity LAI [-]	Deciduous Forest LAI [-]	Evergreen Forest LAI [-]	Mixed Forest LAI [-]	Shrub/Scrub LAI [-]	Grassland/Herbaceous LAI [-]	Pasture/Hay LAI [-]
0	2010-08-01 00:00:00	3.441091	3.441091	0.0	3.441091	3.441091	3.441091	3.441091	3.441091	3.441091
1	2010-08-05 00:00:00	4.188735	4.188735	0.0	4.188735	4.188735	4.188735	4.188735	4.188735	4.188735
2	2010-08-09 00:00:00	4.219150	4.219150	0.0	4.219150	4.219150	4.219150	4.219150	4.219150	4.219150
3	2010-08-13 00:00:00	3.846493	3.846493	0.0	3.846493	3.846493	3.846493	3.846493	3.846493	3.846493
4	2010-08-17 00:00:00	3.173528	3.173528	0.0	3.173528	3.173528	3.173528	3.173528	3.173528	3.173528
...	...	...	...	...	...	...	...	...	...	...
88	2011-07-16 00:00:00	4.643113	4.643113	0.0	4.643113	4.643113	4.643113	4.643113	4.643113	4.643113
89	2011-07-20 00:00:00	4.774706	4.774706	0.0	4.774706	4.774706	4.774706	4.774706	4.774706	4.774706
90	2011-07-24 00:00:00	4.864055	4.864055	0.0	4.864055	4.864055	4.864055	4.864055	4.864055	4.864055
91	2011-07-28 00:00:00	5.026882	5.026882	0.0	5.026882	5.026882	5.026882	5.026882	5.026882	5.026882
92	2011-08-01 00:00:00	5.365335	5.365335	0.0	5.365335	5.365335	5.365335	5.365335	5.365335	5.365335

93 rows × 10 columns

# write the NLCD-based time series to disk
output_filenames['nlcd_lai_cyclic_steadystate'] = toOutput(f'{name}_LAI_NLCD_CyclicSteadystate.h5')
watershed_workflow.io.writeTimeseriesToHDF5(output_filenames['nlcd_lai_cyclic_steadystate'], nlcd_lai_cyclic_steadystate)

output_filenames['nlcd_lai_transient'] = toOutput(f'{name}_LAI_NLCD_{start.year}_{end.year}.h5')
watershed_workflow.io.writeTimeseriesToHDF5(output_filenames['nlcd_lai_transient'], nlcd_lai_transient)

Subsurface Soil, Geologic Structure

NRCS Soils

# get NRCS shapes, on a reasonable crs
nrcs = sources['soil structure'].getShapesByGeometry(watershed.exterior, watershed.crs, out_crs=crs)

nrcs

	mukey	geometry	residual saturation [-]	Rosetta porosity [-]	van Genuchten alpha [Pa^-1]	van Genuchten n [-]	Rosetta permeability [m^2]	thickness [m]	permeability [m^2]	porosity [-]	bulk density [g/cm^3]	total sand pct [%]	total silt pct [%]	total clay pct [%]	source	ID	name
0	545800	MULTIPOLYGON (((1444285.044 -650166.508, 14442...	0.177165	0.431041	0.000139	1.470755	8.079687e-13	2.03	3.429028e-15	0.307246	1.297356	66.356250	19.518750	14.125000	NRCS	545800	NRCS-545800
1	545801	MULTIPOLYGON (((1442388.536 -648199.759, 14423...	0.177493	0.432741	0.000139	1.469513	8.184952e-13	2.03	3.247236e-15	0.303714	1.292308	66.400000	19.300000	14.300000	NRCS	545801	NRCS-545801
2	545803	MULTIPOLYGON (((1443525.158 -646896.263, 14435...	0.172412	0.400889	0.000150	1.491087	6.477202e-13	2.03	2.800000e-12	0.379163	1.400000	66.799507	21.700493	11.500000	NRCS	545803	NRCS-545803
3	545805	MULTIPOLYGON (((1447110.457 -649608.807, 14471...	0.177122	0.388687	0.000083	1.468789	3.412748e-13	2.03	2.800000e-12	0.384877	1.400000	46.721675	41.778325	11.500000	NRCS	545805	NRCS-545805
4	545806	MULTIPOLYGON (((1447025.694 -649572.401, 14470...	0.177122	0.388687	0.000083	1.468789	3.412748e-13	2.03	2.800000e-12	0.384877	1.400000	46.721675	41.778325	11.500000	NRCS	545806	NRCS-545806
5	545807	MULTIPOLYGON (((1444160.857 -649303.56, 144415...	0.177122	0.388687	0.000083	1.468789	3.412748e-13	2.03	2.800000e-12	0.384877	1.400000	46.721675	41.778325	11.500000	NRCS	545807	NRCS-545807
6	545811	MULTIPOLYGON (((1443086.069 -650398.94, 144305...	0.185628	0.387114	0.000166	1.473045	4.803887e-13	2.03	1.830706e-12	0.329821	1.488889	69.043320	17.115010	13.841670	NRCS	545811	NRCS-545811
7	545812	POLYGON ((1443088.752 -650445.293, 1443072.215...	0.185628	0.387114	0.000166	1.473045	4.803887e-13	2.03	1.830706e-12	0.329821	1.488889	69.043320	17.115010	13.841670	NRCS	545812	NRCS-545812
8	545813	MULTIPOLYGON (((1445811.72 -649787.828, 144582...	0.183468	0.398767	0.000127	1.445858	4.296896e-13	2.03	6.219065e-14	0.349442	1.410667	60.007287	26.226047	13.766667	NRCS	545813	NRCS-545813
9	545814	MULTIPOLYGON (((1444217.022 -650169.549, 14442...	0.183216	0.399168	0.000125	1.445793	4.285058e-13	2.03	6.060887e-14	0.346424	1.406875	59.510029	26.771221	13.718750	NRCS	545814	NRCS-545814
10	545815	MULTIPOLYGON (((1444285.044 -650166.508, 14442...	0.177808	0.408626	0.000162	1.498482	7.226966e-13	2.03	3.794887e-14	0.319402	1.396429	70.272487	16.465168	13.262346	NRCS	545815	NRCS-545815
11	545817	MULTIPOLYGON (((1443634.169 -650237.131, 14436...	0.161194	0.457152	0.000168	1.559659	1.846434e-12	2.03	2.800000e-12	0.242266	1.198030	76.538424	12.010837	11.450739	NRCS	545817	NRCS-545817
12	545818	MULTIPOLYGON (((1442794.683 -650515.877, 14427...	0.176724	0.454692	0.000151	1.479294	1.162758e-12	2.03	2.800000e-12	0.305511	1.230523	70.847537	13.736515	15.415948	NRCS	545818	NRCS-545818
13	545819	MULTIPOLYGON (((1443778.887 -649765.75, 144377...	0.179179	0.453829	0.000149	1.469380	1.077383e-12	2.03	2.800000e-12	0.313860	1.237111	69.872443	14.111475	16.016082	NRCS	545819	NRCS-545819
14	545820	MULTIPOLYGON (((1443488.297 -648842.039, 14434...	0.176724	0.454692	0.000151	1.479294	1.162758e-12	2.03	2.800000e-12	0.305511	1.230523	70.847537	13.736515	15.415948	NRCS	545820	NRCS-545820
15	545829	MULTIPOLYGON (((1446898.317 -649639.18, 144686...	0.195475	0.370974	0.000131	1.409385	2.366513e-13	2.03	2.091075e-12	0.421416	1.535417	57.760157	27.569263	14.670580	NRCS	545829	NRCS-545829
16	545830	MULTIPOLYGON (((1444950.908 -649847.317, 14449...	0.195431	0.370396	0.000132	1.409349	2.367865e-13	2.03	1.689101e-12	0.420388	1.538287	58.042625	27.326604	14.630771	NRCS	545830	NRCS-545830
17	545831	MULTIPOLYGON (((1443496.468 -648121.713, 14435...	0.195360	0.369401	0.000134	1.409332	2.370381e-13	2.03	1.729105e-12	0.418625	1.543205	58.526856	26.910618	14.562525	NRCS	545831	NRCS-545831
18	545835	MULTIPOLYGON (((1443970.533 -648236.437, 14439...	0.202983	0.420460	0.000125	1.404309	3.889099e-13	2.03	9.069462e-13	0.375338	1.377167	59.117274	21.086002	19.796724	NRCS	545835	NRCS-545835
19	545836	MULTIPOLYGON (((1446235.889 -649531.372, 14462...	0.225964	0.380189	0.000118	1.356194	1.478829e-13	2.03	8.686821e-13	0.410934	1.542610	53.155082	25.296698	21.548220	NRCS	545836	NRCS-545836
20	545837	MULTIPOLYGON (((1445218.33 -648593.814, 144520...	0.219777	0.382865	0.000117	1.368452	1.688837e-13	2.03	9.140653e-13	0.353400	1.523322	53.594200	25.951006	20.454794	NRCS	545837	NRCS-545837
21	545838	MULTIPOLYGON (((1445518.682 -648674.879, 14455...	0.224883	0.381193	0.000114	1.359591	1.486964e-13	2.03	9.090539e-13	0.348524	1.534344	52.199770	26.439637	21.360593	NRCS	545838	NRCS-545838
22	545842	MULTIPOLYGON (((1444928.723 -646359.362, 14449...	0.193278	0.412219	0.000142	1.434838	4.783588e-13	2.03	9.952892e-13	0.386946	1.400000	64.415764	18.601478	16.982759	NRCS	545842	NRCS-545842
23	545843	MULTIPOLYGON (((1443481.962 -646966.156, 14434...	0.200565	0.409655	0.000118	1.409272	3.388668e-13	2.03	9.952892e-13	0.387980	1.400000	56.982759	24.706897	18.310345	NRCS	545843	NRCS-545843
24	545852	MULTIPOLYGON (((1442523.044 -649134.447, 14425...	0.174940	0.395697	0.000127	1.464370	4.885423e-13	2.03	1.811809e-12	0.466299	1.404089	60.280000	28.050000	11.670000	NRCS	545852	NRCS-545852
25	545853	MULTIPOLYGON (((1443770.78 -649877.479, 144376...	0.174940	0.395697	0.000127	1.464370	4.885423e-13	2.03	1.811809e-12	0.466299	1.404089	60.280000	28.050000	11.670000	NRCS	545853	NRCS-545853
26	545854	MULTIPOLYGON (((1443312.547 -650408.487, 14433...	0.174940	0.395697	0.000127	1.464370	4.885423e-13	2.03	1.811809e-12	0.466299	1.404089	60.280000	28.050000	11.670000	NRCS	545854	NRCS-545854
27	545855	POLYGON ((1446239.537 -646604.35, 1446233.251 ...	0.149582	0.384703	0.000247	1.928427	2.349139e-12	2.03	6.238368e-12	0.235555	1.474297	84.857230	9.099160	6.043611	NRCS	545855	NRCS-545855
28	545857	MULTIPOLYGON (((1445019.574 -650052.884, 14450...	0.175471	0.416598	0.000147	1.484106	7.390878e-13	2.03	4.981053e-15	0.405556	1.350000	67.400000	19.600000	13.000000	NRCS	545857	NRCS-545857
29	545859	MULTIPOLYGON (((1446199.661 -646550.119, 14461...	0.209607	0.381582	0.000108	1.389431	1.854826e-13	2.03	1.107558e-12	0.265930	1.502800	51.495222	30.403882	18.100896	NRCS	545859	NRCS-545859
30	545860	MULTIPOLYGON (((1445904.904 -648371.627, 14459...	0.210433	0.385251	0.000105	1.390548	1.880541e-13	2.03	1.035014e-12	0.269704	1.488030	50.640394	30.852217	18.507389	NRCS	545860	NRCS-545860
31	545861	MULTIPOLYGON (((1445925.111 -646332.769, 14459...	0.202264	0.393882	0.000117	1.404424	2.644035e-13	2.03	1.968721e-12	0.309901	1.455172	55.476355	26.989163	17.534483	NRCS	545861	NRCS-545861
32	545863	POLYGON ((1446644.991 -646140.171, 1446630.756...	0.208942	0.381575	0.000109	1.390351	1.885234e-13	2.03	1.131635e-12	0.270988	1.502407	51.766596	30.261553	17.971851	NRCS	545863	NRCS-545863
33	545874	MULTIPOLYGON (((1446310.135 -649449.476, 14463...	0.208675	0.399614	0.000135	1.395264	2.920696e-13	2.03	1.023505e-12	0.365616	1.466059	60.603941	19.792611	19.603448	NRCS	545874	NRCS-545874
34	545875	MULTIPOLYGON (((1445646.413 -648834.921, 14456...	0.208675	0.399614	0.000135	1.395264	2.920696e-13	2.03	1.023505e-12	0.365616	1.466059	60.603941	19.792611	19.603448	NRCS	545875	NRCS-545875
35	545876	POLYGON ((1445827.955 -647533.912, 1445813.719...	0.184037	0.452018	0.000141	1.449088	9.100580e-13	2.03	2.800000e-12	0.324877	1.247752	67.266810	15.562931	17.170259	NRCS	545876	NRCS-545876
36	545878	MULTIPOLYGON (((1442974.458 -650476.405, 14429...	0.196924	0.425599	0.000125	1.416495	4.633338e-13	1.85	2.282599e-12	0.364041	1.346733	59.965519	21.381982	18.652500	NRCS	545878	NRCS-545878
37	545882	POLYGON ((1446277.275 -646404.037, 1446259.685...	0.184498	0.361656	0.000091	1.439689	2.057343e-13	2.03	2.800000e-12	0.280000	1.530000	45.300000	43.200000	11.500000	NRCS	545882	NRCS-545882
38	545885	MULTIPOLYGON (((1443029.67 -648603.973, 144299...	0.165660	0.400572	0.000183	1.574297	9.869368e-13	2.03	2.800000e-12	0.326502	1.413645	74.559606	15.282759	10.157635	NRCS	545885	NRCS-545885
39	545886	MULTIPOLYGON (((1442200.566 -647585.979, 14422...	0.165660	0.400572	0.000183	1.574297	9.869368e-13	2.03	2.800000e-12	0.306355	1.413645	74.559606	15.282759	10.157635	NRCS	545886	NRCS-545886
40	545887	POLYGON ((1442719.417 -648589.931, 1442679.863...	0.165660	0.400572	0.000183	1.574297	9.869368e-13	2.03	2.800000e-12	0.306355	1.413645	74.559606	15.282759	10.157635	NRCS	545887	NRCS-545887

# create a clean dataframe with just the data we will need for ATS
def replace_column_nans(df, col_nan, col_replacement):
    """In a df, replace col_nan entries by col_replacement if is nan.  In Place!"""
    row_indexer = df[col_nan].isna()
    df.loc[row_indexer, col_nan] = df.loc[row_indexer, col_replacement]
    return

# where poro or perm is nan, put Rosetta poro
replace_column_nans(nrcs, 'porosity [-]', 'Rosetta porosity [-]')
replace_column_nans(nrcs, 'permeability [m^2]', 'Rosetta permeability [m^2]')

# drop unnecessary columns
for col in ['Rosetta porosity [-]', 'Rosetta permeability [m^2]', 'bulk density [g/cm^3]', 'total sand pct [%]',
            'total silt pct [%]', 'total clay pct [%]']:
    nrcs.pop(col)
    
# drop nans
nan_mask = nrcs.isna().any(axis=1)
dropped_mukeys = nrcs.index[nan_mask]

# Drop those rows
nrcs = nrcs[~nan_mask]

assert nrcs['porosity [-]'][:].min() >= min_porosity
assert nrcs['permeability [m^2]'][:].max() <= max_permeability
nrcs

# check for nans
nrcs.isna().any()

mukey                          False
geometry                       False
residual saturation [-]        False
van Genuchten alpha [Pa^-1]    False
van Genuchten n [-]            False
thickness [m]                  False
permeability [m^2]             False
porosity [-]                   False
source                         False
ID                             False
name                           False
dtype: bool

# Compute the soil color of each cell of the mesh
# Note, we use mukey here because it is an int, while ID is a string
soil_color_mukey = watershed_workflow.getShapePropertiesOnMesh(m2, nrcs, 'mukey', 
                                                         resolution=50, nodata=-999)

nrcs.set_index('mukey', drop=False, inplace=True)

unique_soil_colors = list(np.unique(soil_color_mukey))
if -999 in unique_soil_colors:
    unique_soil_colors.remove(-999)

# retain only the unique values of soil_color
nrcs = nrcs.loc[unique_soil_colors]

# renumber the ones we know will appear with an ATS ID using ATS conventions
nrcs['ATS ID'] = range(1000, 1000+len(unique_soil_colors))
nrcs.set_index('ATS ID', drop=True, inplace=True)

# create a new soil color and a soil thickness map using the ATS IDs
soil_color = -np.ones_like(soil_color_mukey)
soil_thickness = np.nan * np.ones(soil_color.shape, 'd')

for ats_ID, ID, thickness in zip(nrcs.index, nrcs.mukey, nrcs['thickness [m]']):
    mask = np.where(soil_color_mukey == ID)
    soil_thickness[mask] = thickness
    soil_color[mask] = ats_ID

m2.cell_data['soil_color'] = soil_color
m2.cell_data['soil thickness'] = soil_thickness

# plot the soil color
# -- get a cmap for soil color
sc_indices, sc_cmap, sc_norm, sc_ticks, sc_labels = \
      watershed_workflow.colors.createIndexedColormap(nrcs.index)

mp = m2.plot(facecolors=m2.cell_data['soil_color'], cmap=sc_cmap, norm=sc_norm, edgecolors=None, colorbar=False)
watershed_workflow.colors.createIndexedColorbar(ncolors=len(nrcs), 
                               cmap=sc_cmap, labels=sc_labels, ax=plt.gca()) 
plt.show()

Depth to Bedrock from SoilGrids

dtb = sources['depth to bedrock'].getDataset(watershed.exterior, watershed.crs)['band_1']

# the SoilGrids dataset is in cm --> convert to meters
dtb.values = dtb.values/100.

# map to the mesh
m2.cell_data['dtb'] = watershed_workflow.getDatasetOnMesh(m2, dtb, method='linear')

gons = m2.plot(facecolors=m2.cell_data['dtb'], cmap='RdBu', edgecolors=None)
plt.show()

GLHYMPs Geology

glhymps = sources['geologic structure'].getShapesByGeometry(watershed.exterior.buffer(1000), watershed.crs, out_crs=crs)
glhymps = watershed_workflow.soil_properties.mangleGLHYMPSProperties(glhymps,
                                              min_porosity=min_porosity, 
                                              max_permeability=max_permeability, 
                                              max_vg_alpha=max_vg_alpha)

# intersect with the buffered geometry -- don't keep extras
glhymps = glhymps[glhymps.intersects(watershed.exterior.buffer(10))]
glhymps

	ID	name	source	permeability [m^2]	logk_stdev [-]	porosity [-]	van Genuchten alpha [Pa^-1]	van Genuchten n [-]	residual saturation [-]	geometry
1	1793338	GLHYMPS-1793338	GLHYMPS	3.019952e-11	1.61	0.05	0.001	2.0	0.01	MULTIPOLYGON (((1377015.684 -683020.036, 13769...

# quality check -- make sure glymps shapes cover the watershed
print(glhymps.union_all().contains(watershed.exterior))
glhymps

True

	ID	name	source	permeability [m^2]	logk_stdev [-]	porosity [-]	van Genuchten alpha [Pa^-1]	van Genuchten n [-]	residual saturation [-]	geometry
1	1793338	GLHYMPS-1793338	GLHYMPS	3.019952e-11	1.61	0.05	0.001	2.0	0.01	MULTIPOLYGON (((1377015.684 -683020.036, 13769...

# clean the data
glhymps.pop('logk_stdev [-]')

assert glhymps['porosity [-]'][:].min() >= min_porosity
assert glhymps['permeability [m^2]'][:].max() <= max_permeability
assert glhymps['van Genuchten alpha [Pa^-1]'][:].max() <= max_vg_alpha

glhymps.isna().any()

ID                             False
name                           False
source                         False
permeability [m^2]             False
porosity [-]                   False
van Genuchten alpha [Pa^-1]    False
van Genuchten n [-]            False
residual saturation [-]        False
geometry                       False
dtype: bool

# note that for larger areas there are often common regions -- two labels with the same properties -- no need to duplicate those with identical values.
def reindex_remove_duplicates(df, index):
    """Removes duplicates, creating a new index and saving the old index as tuples of duplicate values. In place!"""
    if index is not None:
        if index in df:
            df.set_index(index, drop=True, inplace=True)
    
    index_name = df.index.name

    # identify duplicate rows
    duplicates = list(df.groupby(list(df)).apply(lambda x: tuple(x.index)))

    # order is preserved
    df.drop_duplicates(inplace=True)
    df.reset_index(inplace=True)
    df[index_name] = duplicates
    return

reindex_remove_duplicates(glhymps, 'ID')
glhymps

	ID	name	source	permeability [m^2]	porosity [-]	van Genuchten alpha [Pa^-1]	van Genuchten n [-]	residual saturation [-]	geometry
0	(1793338,)	GLHYMPS-1793338	GLHYMPS	3.019952e-11	0.05	0.001	2.0	0.01	MULTIPOLYGON (((1377015.684 -683020.036, 13769...

# Compute the geo color of each cell of the mesh
geology_color_glhymps = watershed_workflow.getShapePropertiesOnMesh(m2, glhymps, 'index', 
                                                         resolution=50, nodata=-999)

# retain only the unique values of geology that actually appear in our cell mesh
unique_geology_colors = list(np.unique(geology_color_glhymps))
if -999 in unique_geology_colors:
    unique_geology_colors.remove(-999)

# retain only the unique values of geology_color
glhymps = glhymps.loc[unique_geology_colors]

# renumber the ones we know will appear with an ATS ID using ATS conventions
glhymps['ATS ID'] = range(100, 100+len(unique_geology_colors))
glhymps['TMP_ID'] = glhymps.index
glhymps.reset_index(drop=True, inplace=True)
glhymps.set_index('ATS ID', drop=True, inplace=True)

# create a new geology color using the ATS IDs
geology_color = -np.ones_like(geology_color_glhymps)
for ats_ID, tmp_ID in zip(glhymps.index, glhymps.TMP_ID):
    geology_color[np.where(geology_color_glhymps == tmp_ID)] = ats_ID

glhymps.pop('TMP_ID')

m2.cell_data['geology_color'] = geology_color
                            

geology_color_glhymps.min()

<xarray.DataArray 'index' ()> Size: 8B
array(0)

xarray.DataArray

'index'

• 0

  array(0)

• Coordinates: (0)
• Indexes: (0)
• Attributes: (0)

Combine to form a complete subsurface dataset

bedrock = watershed_workflow.soil_properties.getDefaultBedrockProperties()

# merge the properties databases
subsurface_props = pd.concat([glhymps, nrcs, bedrock])

# save the properties to disk for use in generating input file
output_filenames['subsurface_properties'] = toOutput(f'{name}_subsurface_properties.csv')
subsurface_props.to_csv(output_filenames['subsurface_properties'])
subsurface_props

	ID	name	source	permeability [m^2]	porosity [-]	van Genuchten alpha [Pa^-1]	van Genuchten n [-]	residual saturation [-]	geometry	mukey	thickness [m]
100	(1793338,)	GLHYMPS-1793338	GLHYMPS	3.019952e-11	0.050000	0.001000	2.000000	0.010000	MULTIPOLYGON (((1377015.684 -683020.036, 13769...	NaN	NaN
1000	545800	NRCS-545800	NRCS	3.429028e-15	0.307246	0.000139	1.470755	0.177165	MULTIPOLYGON (((1444285.044 -650166.508, 14442...	545800.0	2.03
1001	545801	NRCS-545801	NRCS	3.247236e-15	0.303714	0.000139	1.469513	0.177493	MULTIPOLYGON (((1442388.536 -648199.759, 14423...	545801.0	2.03
1002	545803	NRCS-545803	NRCS	2.800000e-12	0.379163	0.000150	1.491087	0.172412	MULTIPOLYGON (((1443525.158 -646896.263, 14435...	545803.0	2.03
1003	545805	NRCS-545805	NRCS	2.800000e-12	0.384877	0.000083	1.468789	0.177122	MULTIPOLYGON (((1447110.457 -649608.807, 14471...	545805.0	2.03
1004	545806	NRCS-545806	NRCS	2.800000e-12	0.384877	0.000083	1.468789	0.177122	MULTIPOLYGON (((1447025.694 -649572.401, 14470...	545806.0	2.03
1005	545807	NRCS-545807	NRCS	2.800000e-12	0.384877	0.000083	1.468789	0.177122	MULTIPOLYGON (((1444160.857 -649303.56, 144415...	545807.0	2.03
1006	545813	NRCS-545813	NRCS	6.219065e-14	0.349442	0.000127	1.445858	0.183468	MULTIPOLYGON (((1445811.72 -649787.828, 144582...	545813.0	2.03
1007	545814	NRCS-545814	NRCS	6.060887e-14	0.346424	0.000125	1.445793	0.183216	MULTIPOLYGON (((1444217.022 -650169.549, 14442...	545814.0	2.03
1008	545815	NRCS-545815	NRCS	3.794887e-14	0.319402	0.000162	1.498482	0.177808	MULTIPOLYGON (((1444285.044 -650166.508, 14442...	545815.0	2.03
1009	545818	NRCS-545818	NRCS	2.800000e-12	0.305511	0.000151	1.479294	0.176724	MULTIPOLYGON (((1442794.683 -650515.877, 14427...	545818.0	2.03
1010	545819	NRCS-545819	NRCS	2.800000e-12	0.313860	0.000149	1.469380	0.179179	MULTIPOLYGON (((1443778.887 -649765.75, 144377...	545819.0	2.03
1011	545820	NRCS-545820	NRCS	2.800000e-12	0.305511	0.000151	1.479294	0.176724	MULTIPOLYGON (((1443488.297 -648842.039, 14434...	545820.0	2.03
1012	545829	NRCS-545829	NRCS	2.091075e-12	0.421416	0.000131	1.409385	0.195475	MULTIPOLYGON (((1446898.317 -649639.18, 144686...	545829.0	2.03
1013	545830	NRCS-545830	NRCS	1.689101e-12	0.420388	0.000132	1.409349	0.195431	MULTIPOLYGON (((1444950.908 -649847.317, 14449...	545830.0	2.03
1014	545831	NRCS-545831	NRCS	1.729105e-12	0.418625	0.000134	1.409332	0.195360	MULTIPOLYGON (((1443496.468 -648121.713, 14435...	545831.0	2.03
1015	545835	NRCS-545835	NRCS	9.069462e-13	0.375338	0.000125	1.404309	0.202983	MULTIPOLYGON (((1443970.533 -648236.437, 14439...	545835.0	2.03
1016	545836	NRCS-545836	NRCS	8.686821e-13	0.410934	0.000118	1.356194	0.225964	MULTIPOLYGON (((1446235.889 -649531.372, 14462...	545836.0	2.03
1017	545837	NRCS-545837	NRCS	9.140653e-13	0.353400	0.000117	1.368452	0.219777	MULTIPOLYGON (((1445218.33 -648593.814, 144520...	545837.0	2.03
1018	545838	NRCS-545838	NRCS	9.090539e-13	0.348524	0.000114	1.359591	0.224883	MULTIPOLYGON (((1445518.682 -648674.879, 14455...	545838.0	2.03
1019	545842	NRCS-545842	NRCS	9.952892e-13	0.386946	0.000142	1.434838	0.193278	MULTIPOLYGON (((1444928.723 -646359.362, 14449...	545842.0	2.03
1020	545843	NRCS-545843	NRCS	9.952892e-13	0.387980	0.000118	1.409272	0.200565	MULTIPOLYGON (((1443481.962 -646966.156, 14434...	545843.0	2.03
1021	545853	NRCS-545853	NRCS	1.811809e-12	0.466299	0.000127	1.464370	0.174940	MULTIPOLYGON (((1443770.78 -649877.479, 144376...	545853.0	2.03
1022	545854	NRCS-545854	NRCS	1.811809e-12	0.466299	0.000127	1.464370	0.174940	MULTIPOLYGON (((1443312.547 -650408.487, 14433...	545854.0	2.03
1023	545855	NRCS-545855	NRCS	6.238368e-12	0.235555	0.000247	1.928427	0.149582	POLYGON ((1446239.537 -646604.35, 1446233.251 ...	545855.0	2.03
1024	545857	NRCS-545857	NRCS	4.981053e-15	0.405556	0.000147	1.484106	0.175471	MULTIPOLYGON (((1445019.574 -650052.884, 14450...	545857.0	2.03
1025	545859	NRCS-545859	NRCS	1.107558e-12	0.265930	0.000108	1.389431	0.209607	MULTIPOLYGON (((1446199.661 -646550.119, 14461...	545859.0	2.03
1026	545860	NRCS-545860	NRCS	1.035014e-12	0.269704	0.000105	1.390548	0.210433	MULTIPOLYGON (((1445904.904 -648371.627, 14459...	545860.0	2.03
1027	545861	NRCS-545861	NRCS	1.968721e-12	0.309901	0.000117	1.404424	0.202264	MULTIPOLYGON (((1445925.111 -646332.769, 14459...	545861.0	2.03
1028	545874	NRCS-545874	NRCS	1.023505e-12	0.365616	0.000135	1.395264	0.208675	MULTIPOLYGON (((1446310.135 -649449.476, 14463...	545874.0	2.03
1029	545875	NRCS-545875	NRCS	1.023505e-12	0.365616	0.000135	1.395264	0.208675	MULTIPOLYGON (((1445646.413 -648834.921, 14456...	545875.0	2.03
1030	545876	NRCS-545876	NRCS	2.800000e-12	0.324877	0.000141	1.449088	0.184037	POLYGON ((1445827.955 -647533.912, 1445813.719...	545876.0	2.03
1031	545878	NRCS-545878	NRCS	2.282599e-12	0.364041	0.000125	1.416495	0.196924	MULTIPOLYGON (((1442974.458 -650476.405, 14429...	545878.0	1.85
1032	545882	NRCS-545882	NRCS	2.800000e-12	0.280000	0.000091	1.439689	0.184498	POLYGON ((1446277.275 -646404.037, 1446259.685...	545882.0	2.03
1033	545885	NRCS-545885	NRCS	2.800000e-12	0.326502	0.000183	1.574297	0.165660	MULTIPOLYGON (((1443029.67 -648603.973, 144299...	545885.0	2.03
999	999	bedrock	n/a	1.000000e-16	0.050000	0.000019	3.000000	0.010000	None	NaN	NaN

Extrude the 2D Mesh to make a 3D mesh

# set the floor of the domain as max DTB
dtb_max = np.nanmax(m2.cell_data['dtb'].values)
m2.cell_data['dtb'] = m2.cell_data['dtb'].fillna(dtb_max)

print(f'total thickness: {dtb_max} m')
total_thickness = 50.

total thickness: 19.65074688662983 m

# Generate a dz structure for the top 2m of soil
#
# here we try for 10 cells, starting at 5cm at the top and going to 50cm at the bottom of the 2m thick soil
dzs, res = watershed_workflow.mesh.optimizeDzs(0.05, 0.5, 2, 10)
print(dzs)
print(sum(dzs))

[0.05447108 0.07161305 0.11027281 0.17443206 0.26441087 0.36782677
 0.45697337 0.49999999]
2.0000000000000004

# this looks like it would work out, with rounder numbers:
dzs_soil = [0.05, 0.05, 0.05, 0.12, 0.23, 0.5, 0.5, 0.5]
print(sum(dzs_soil))

2.0

# 50m total thickness, minus 2m soil thickness, leaves us with 48 meters to make up.
# optimize again...
dzs2, res2 = watershed_workflow.mesh.optimizeDzs(1, 10, 48, 8)
print(dzs2)
print(sum(dzs2))

# how about...
dzs_geo = [1.0, 2.0, 4.0, 8.0, 11, 11, 11]
print(dzs_geo)
print(sum(dzs_geo))

[2.71113048 5.85796948 9.43304714 9.99786869 9.99998875 9.99999546]
48.0
[1.0, 2.0, 4.0, 8.0, 11, 11, 11]
48.0

# layer extrusion
DTB = m2.cell_data['dtb'].values
soil_color = m2.cell_data['soil_color'].values
geo_color = m2.cell_data['geology_color'].values
soil_thickness = m2.cell_data['soil thickness'].values


# -- data structures needed for extrusion
layer_types = []
layer_data = []
layer_ncells = []
layer_mat_ids = []

# -- soil layer --
depth = 0
for dz in dzs_soil:
    depth += 0.5 * dz
    layer_types.append('constant')
    layer_data.append(dz)
    layer_ncells.append(1)
    
    # use glhymps params
    br_or_geo = np.where(depth < DTB, geo_color, 999)
    soil_or_br_or_geo = np.where(np.bitwise_and(soil_color > 0, depth < soil_thickness),
                                 soil_color,
                                 br_or_geo)

    layer_mat_ids.append(soil_or_br_or_geo)
    depth += 0.5 * dz
    
# -- geologic layer --
for dz in dzs_geo:
    depth += 0.5 * dz
    layer_types.append('constant')
    layer_data.append(dz)
    layer_ncells.append(1)
    
    geo_or_br = np.where(depth < DTB, geo_color, 999)

    layer_mat_ids.append(geo_or_br)
    depth += 0.5 * dz

# print the summary
watershed_workflow.mesh.Mesh3D.summarizeExtrusion(layer_types, layer_data, 
                                            layer_ncells, layer_mat_ids)

# downselect subsurface properties to only those that are used
layer_mat_id_used = list(np.unique(np.array(layer_mat_ids)))
subsurface_props_used = subsurface_props.loc[layer_mat_id_used]
subsurface_props_used

	ID	name	source	permeability [m^2]	porosity [-]	van Genuchten alpha [Pa^-1]	van Genuchten n [-]	residual saturation [-]	geometry	mukey	thickness [m]
100	(1793338,)	GLHYMPS-1793338	GLHYMPS	3.019952e-11	0.050000	0.001000	2.000000	0.010000	MULTIPOLYGON (((1377015.684 -683020.036, 13769...	NaN	NaN
999	999	bedrock	n/a	1.000000e-16	0.050000	0.000019	3.000000	0.010000	None	NaN	NaN
1000	545800	NRCS-545800	NRCS	3.429028e-15	0.307246	0.000139	1.470755	0.177165	MULTIPOLYGON (((1444285.044 -650166.508, 14442...	545800.0	2.03
1001	545801	NRCS-545801	NRCS	3.247236e-15	0.303714	0.000139	1.469513	0.177493	MULTIPOLYGON (((1442388.536 -648199.759, 14423...	545801.0	2.03
1002	545803	NRCS-545803	NRCS	2.800000e-12	0.379163	0.000150	1.491087	0.172412	MULTIPOLYGON (((1443525.158 -646896.263, 14435...	545803.0	2.03
1003	545805	NRCS-545805	NRCS	2.800000e-12	0.384877	0.000083	1.468789	0.177122	MULTIPOLYGON (((1447110.457 -649608.807, 14471...	545805.0	2.03
1004	545806	NRCS-545806	NRCS	2.800000e-12	0.384877	0.000083	1.468789	0.177122	MULTIPOLYGON (((1447025.694 -649572.401, 14470...	545806.0	2.03
1005	545807	NRCS-545807	NRCS	2.800000e-12	0.384877	0.000083	1.468789	0.177122	MULTIPOLYGON (((1444160.857 -649303.56, 144415...	545807.0	2.03
1006	545813	NRCS-545813	NRCS	6.219065e-14	0.349442	0.000127	1.445858	0.183468	MULTIPOLYGON (((1445811.72 -649787.828, 144582...	545813.0	2.03
1007	545814	NRCS-545814	NRCS	6.060887e-14	0.346424	0.000125	1.445793	0.183216	MULTIPOLYGON (((1444217.022 -650169.549, 14442...	545814.0	2.03
1008	545815	NRCS-545815	NRCS	3.794887e-14	0.319402	0.000162	1.498482	0.177808	MULTIPOLYGON (((1444285.044 -650166.508, 14442...	545815.0	2.03
1009	545818	NRCS-545818	NRCS	2.800000e-12	0.305511	0.000151	1.479294	0.176724	MULTIPOLYGON (((1442794.683 -650515.877, 14427...	545818.0	2.03
1010	545819	NRCS-545819	NRCS	2.800000e-12	0.313860	0.000149	1.469380	0.179179	MULTIPOLYGON (((1443778.887 -649765.75, 144377...	545819.0	2.03
1011	545820	NRCS-545820	NRCS	2.800000e-12	0.305511	0.000151	1.479294	0.176724	MULTIPOLYGON (((1443488.297 -648842.039, 14434...	545820.0	2.03
1012	545829	NRCS-545829	NRCS	2.091075e-12	0.421416	0.000131	1.409385	0.195475	MULTIPOLYGON (((1446898.317 -649639.18, 144686...	545829.0	2.03
1013	545830	NRCS-545830	NRCS	1.689101e-12	0.420388	0.000132	1.409349	0.195431	MULTIPOLYGON (((1444950.908 -649847.317, 14449...	545830.0	2.03
1014	545831	NRCS-545831	NRCS	1.729105e-12	0.418625	0.000134	1.409332	0.195360	MULTIPOLYGON (((1443496.468 -648121.713, 14435...	545831.0	2.03
1015	545835	NRCS-545835	NRCS	9.069462e-13	0.375338	0.000125	1.404309	0.202983	MULTIPOLYGON (((1443970.533 -648236.437, 14439...	545835.0	2.03
1016	545836	NRCS-545836	NRCS	8.686821e-13	0.410934	0.000118	1.356194	0.225964	MULTIPOLYGON (((1446235.889 -649531.372, 14462...	545836.0	2.03
1017	545837	NRCS-545837	NRCS	9.140653e-13	0.353400	0.000117	1.368452	0.219777	MULTIPOLYGON (((1445218.33 -648593.814, 144520...	545837.0	2.03
1018	545838	NRCS-545838	NRCS	9.090539e-13	0.348524	0.000114	1.359591	0.224883	MULTIPOLYGON (((1445518.682 -648674.879, 14455...	545838.0	2.03
1019	545842	NRCS-545842	NRCS	9.952892e-13	0.386946	0.000142	1.434838	0.193278	MULTIPOLYGON (((1444928.723 -646359.362, 14449...	545842.0	2.03
1020	545843	NRCS-545843	NRCS	9.952892e-13	0.387980	0.000118	1.409272	0.200565	MULTIPOLYGON (((1443481.962 -646966.156, 14434...	545843.0	2.03
1021	545853	NRCS-545853	NRCS	1.811809e-12	0.466299	0.000127	1.464370	0.174940	MULTIPOLYGON (((1443770.78 -649877.479, 144376...	545853.0	2.03
1022	545854	NRCS-545854	NRCS	1.811809e-12	0.466299	0.000127	1.464370	0.174940	MULTIPOLYGON (((1443312.547 -650408.487, 14433...	545854.0	2.03
1023	545855	NRCS-545855	NRCS	6.238368e-12	0.235555	0.000247	1.928427	0.149582	POLYGON ((1446239.537 -646604.35, 1446233.251 ...	545855.0	2.03
1024	545857	NRCS-545857	NRCS	4.981053e-15	0.405556	0.000147	1.484106	0.175471	MULTIPOLYGON (((1445019.574 -650052.884, 14450...	545857.0	2.03
1025	545859	NRCS-545859	NRCS	1.107558e-12	0.265930	0.000108	1.389431	0.209607	MULTIPOLYGON (((1446199.661 -646550.119, 14461...	545859.0	2.03
1026	545860	NRCS-545860	NRCS	1.035014e-12	0.269704	0.000105	1.390548	0.210433	MULTIPOLYGON (((1445904.904 -648371.627, 14459...	545860.0	2.03
1027	545861	NRCS-545861	NRCS	1.968721e-12	0.309901	0.000117	1.404424	0.202264	MULTIPOLYGON (((1445925.111 -646332.769, 14459...	545861.0	2.03
1028	545874	NRCS-545874	NRCS	1.023505e-12	0.365616	0.000135	1.395264	0.208675	MULTIPOLYGON (((1446310.135 -649449.476, 14463...	545874.0	2.03
1029	545875	NRCS-545875	NRCS	1.023505e-12	0.365616	0.000135	1.395264	0.208675	MULTIPOLYGON (((1445646.413 -648834.921, 14456...	545875.0	2.03
1030	545876	NRCS-545876	NRCS	2.800000e-12	0.324877	0.000141	1.449088	0.184037	POLYGON ((1445827.955 -647533.912, 1445813.719...	545876.0	2.03
1031	545878	NRCS-545878	NRCS	2.282599e-12	0.364041	0.000125	1.416495	0.196924	MULTIPOLYGON (((1442974.458 -650476.405, 14429...	545878.0	1.85
1032	545882	NRCS-545882	NRCS	2.800000e-12	0.280000	0.000091	1.439689	0.184498	POLYGON ((1446277.275 -646404.037, 1446259.685...	545882.0	2.03
1033	545885	NRCS-545885	NRCS	2.800000e-12	0.326502	0.000183	1.574297	0.165660	MULTIPOLYGON (((1443029.67 -648603.973, 144299...	545885.0	2.03

# extrude
m3 = watershed_workflow.mesh.Mesh3D.extruded_Mesh2D(m2, layer_types, layer_data, 
                                             layer_ncells, layer_mat_ids)

print('2D labeled sets')
print('---------------')
for ls in m2.labeled_sets:
    print(f'{ls.setid} : {ls.entity} : {len(ls.ent_ids)} : "{ls.name}"')

print('')
print('Extruded 3D labeled sets')
print('------------------------')
for ls in m3.labeled_sets:
    print(f'{ls.setid} : {ls.entity} : {len(ls.ent_ids)} : "{ls.name}"')

print('')
print('Extruded 3D side sets')
print('---------------------')
for ls in m3.side_sets:
    print(f'{ls.setid} : FACE : {len(ls.cell_list)} : "{ls.name}"')
    

2D labeled sets
---------------
10000 : CELL : 2593 : "1"
10001 : CELL : 2593 : "1 surface"
10002 : FACE : 76 : "1 boundary"
10003 : FACE : 5 : "1 outlet"
10004 : FACE : 5 : "surface domain outlet"
10005 : CELL : 184 : "river corridor 0 surface"
10006 : CELL : 16 : "stream order 3"
10007 : CELL : 46 : "stream order 2"
10008 : CELL : 122 : "stream order 1"
21 : CELL : 106 : "Developed, Open Space"
22 : CELL : 2 : "Developed, Low Intensity"
23 : CELL : 2 : "Developed, Medium Intensity"
41 : CELL : 1340 : "Deciduous Forest"
42 : CELL : 47 : "Evergreen Forest"
43 : CELL : 1088 : "Mixed Forest"
81 : CELL : 8 : "Pasture/Hay"

Extruded 3D labeled sets
------------------------
10000 : CELL : 38895 : "1"

Extruded 3D side sets
---------------------
1 : FACE : 2593 : "bottom"
2 : FACE : 2593 : "surface"
3 : FACE : 1140 : "external sides"
10001 : FACE : 2593 : "1 surface"
10002 : FACE : 1140 : "1 boundary"
10003 : FACE : 75 : "1 outlet"
10004 : FACE : 75 : "surface domain outlet"
10005 : FACE : 184 : "river corridor 0 surface"
10006 : FACE : 16 : "stream order 3"
10007 : FACE : 46 : "stream order 2"
10008 : FACE : 122 : "stream order 1"
21 : FACE : 106 : "Developed, Open Space"
22 : FACE : 2 : "Developed, Low Intensity"
23 : FACE : 2 : "Developed, Medium Intensity"
41 : FACE : 1340 : "Deciduous Forest"
42 : FACE : 47 : "Evergreen Forest"
43 : FACE : 1088 : "Mixed Forest"
81 : FACE : 8 : "Pasture/Hay"

# save the mesh to disk
output_filenames['mesh'] = toOutput(f'{name}.exo')
try:
    os.remove(output_filenames['mesh'])
except FileNotFoundError:
    pass
m3.writeExodus(output_filenames['mesh'])

You are using exodus.py v 1.20.10 (seacas-py3), a python wrapper of some of the exodus library.

Copyright (c) 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021 National Technology &
Engineering Solutions of Sandia, LLC (NTESS).  Under the terms of
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Opening exodus file: /home/ecoon/code/watershed_workflow/repos/master/examples/Coweeta/output_data/Coweeta.exo
Closing exodus file: /home/ecoon/code/watershed_workflow/repos/master/examples/Coweeta/output_data/Coweeta.exo

Exodus Library Warning/Error: [ex_put_set] in file '/home/ecoon/code/watershed_workflow/repos/master/examples/Coweeta/output_data/Coweeta.exo'
	Warning: extra list was ignored for element set 10000 in file id 131072