We will briefly discuss working with some of the WRF-Hydro input and output (IO) files. The IO files for WRF-Hydro generally are standard netCDF4 files, and there are many way to work with these data. In this lesson we simply cover a few Python libraries and commands that will be needed for later lessons in this tutorial. This is by no means a comprehensive guide to working with netCDF files.
More information on working with netCDF files can be found on the Unidata website at https://www.unidata.ucar.edu/software/netcdf/.
We are using Python 3 for all exercises in this tutorial. There are also a number of tools developed in R that have similar capabilities, but we have chosen Python here for its ease of use and strong netCDF4 and geospatial processing support.
We are using the Miniconda distribution of Python 3 with the Python libraries listed below and their dependencies. Miniconda is a stripped down version of Anaconda, a Python distribution for scientific computing. You can obtain Miniconda from the Continuum Analytics website at https://conda.io/miniconda.html.
There are many resources for learning more about miniconda, conda, and Python. Answers to just about any question can be found with a little searching on either Google or Stack Overflow.
Below are the libraries we will be using. These libraries have been installed for you if you are running this tutorial in the wrfhydro/training Docker container. Otherwise, you will need to install miniconda and the required python libraries if running on your own system.
NOTE: The libraries listed below are only the required Python libraries. These Python libraries also require a number of system libraries that you may or may not need to install on your own system. Notably, you WILL need the NETCDF4 system library
Required Python libraries:
xarray: xarray is an open-source project for working with self-describing Common Data Model scientific datasets, primarily in netCDF4 format. It eases many of the pain-points in loading, manipulating, and plotting multidimensional arrays. xarray is well documented and you can learn more by reading their documentation at https://xarray.pydata.org/en/stable/ or https://github.com/pydata/xarray.
netCDF4: Library for reading and writing netCDF files. This is a required dependency for xarray if you will be using xarray with netCDF4 datasets.
Below is a brief list of the Python commands we will be running, virtually all of the commands are from the xarray package, indicated by a xr.
prefix preceding the command.
xr.open_dataset('path-to-netcdf-file')
: Open a single netCDF file in xarray.
Note: This command only opens the netCDF file and reads header information, it does not load any of the data payload into memory This is a handy feature of netCDF4 and xarray that allows for viewing basic information about very large netCDF files without loading into memory.
xr.open_mfdataset(list-of-netcdf-files or 'path-to-netcdf-directory', combine=’by_coords’)
: Similar to xr.open_dataset
, xr.open_mfdataset
opens multiple netCDF files as a single dataset, concatenating them along a common dimension(s).
After we have opened the datasets there are a few more methods we will use on these datasets.
my_dataset = xr.open_dataset('path-to-netcdf-file')
my_dataset.info()
: Print information about the netCDF file, similar to ncdump
command line utility.
my_dataset.load()
: Load the netCDF4 data payload into memory
my_dataset.myvariable
: Access a variable named myvariable
from the dataset.
my_dataset.myvariable.plot()
: Plot the variable my variable. Xarray will attempt to guess the axes, and in the case of spatial or timeseries data with only 1 dimension it typically does a good job. However, you may need to specify this manually if not.
There is MUCH more you can do with xarray
, but that covers the basic commands we will use in this training.
In the next section we will go over a couple of basic examples of plotting some of the outputs from our ~/wrf-hydro-training/output/lesson2/run_gridded_default
simulation.
We will start with plotting a couple of variables from our geogrid file.
%%bash
ls ~/wrf-hydro-training/output/lesson2/run_gridded_default/DOMAIN
# Import xarray and turn on fancy HTML representations of datasets
import xarray as xr
xr.set_options(display_style="html")
<xarray.core.options.set_options at 0x7f92b40f0be0>
Open the geogrid dataset
# Load a dataset
geogrid = xr.open_dataset('~/wrf-hydro-training/output/lesson2/run_gridded_default/DOMAIN/geo_em.d01.nc')
Print some info about the dataset
geogrid
<xarray.Dataset> Dimensions: (Time: 1, land_cat: 24, month: 12, soil_cat: 16, south_north: 16, south_north_stag: 17, west_east: 15, west_east_stag: 16) Dimensions without coordinates: Time, land_cat, month, soil_cat, south_north, south_north_stag, west_east, west_east_stag Data variables: Times (Time) |S19 b'0000-00-00_00:00:00' XLAT_M (Time, south_north, west_east) float32 41.42282 ... 41.51908 XLONG_M (Time, south_north, west_east) float32 -73.85333 ... -73.63379 XLAT_V (Time, south_north_stag, west_east) float32 41.418358 ... 41.... XLONG_V (Time, south_north_stag, west_east) float32 -73.8551 ... -73.... XLAT_U (Time, south_north, west_east_stag) float32 41.424145 ... 41.... XLONG_U (Time, south_north, west_east_stag) float32 -73.85928 ... -73... CLAT (Time, south_north, west_east) float32 41.42282 ... 41.51908 CLONG (Time, south_north, west_east) float32 -73.85333 ... -73.63379 MAPFAC_M (Time, south_north, west_east) float32 0.96826696 ... 0.96815115 MAPFAC_V (Time, south_north_stag, west_east) float32 0.9682724 ... 0.9... MAPFAC_U (Time, south_north, west_east_stag) float32 0.9682653 ... 0.9... MAPFAC_MX (Time, south_north, west_east) float32 0.96826696 ... 0.96815115 MAPFAC_VX (Time, south_north_stag, west_east) float32 0.9682724 ... 0.9... MAPFAC_UX (Time, south_north, west_east_stag) float32 0.9682653 ... 0.9... MAPFAC_MY (Time, south_north, west_east) float32 0.96826696 ... 0.96815115 MAPFAC_VY (Time, south_north_stag, west_east) float32 0.9682724 ... 0.9... MAPFAC_UY (Time, south_north, west_east_stag) float32 0.9682653 ... 0.9... E (Time, south_north, west_east) float32 0.000109357774 ... 0.0... F (Time, south_north, west_east) float32 9.6489275e-05 ... 9.66... SINALPHA (Time, south_north, west_east) float32 -0.28458056 ... -0.287... COSALPHA (Time, south_north, west_east) float32 0.95865214 ... 0.9577397 LANDMASK (Time, south_north, west_east) float32 1.0 1.0 1.0 ... 1.0 1.0 XLAT_C (Time, south_north_stag, west_east_stag) float32 41.41969 ...... XLONG_C (Time, south_north_stag, west_east_stag) float32 -73.86105 ..... SINALPHA_U (Time, south_north, west_east_stag) float32 -0.28468728 ... -... COSALPHA_U (Time, south_north, west_east_stag) float32 0.9586204 ... 0.9... SINALPHA_V (Time, south_north_stag, west_east) float32 -0.28448653 ... -... COSALPHA_V (Time, south_north_stag, west_east) float32 0.95868003 ... 0.... LANDUSEF (Time, land_cat, south_north, west_east) float32 0.0 0.0 ... 0.0 LU_INDEX (Time, south_north, west_east) float32 11.0 11.0 ... 11.0 11.0 HGT_M (Time, south_north, west_east) float32 245.67378 ... 227.06079 SOILTEMP (Time, south_north, west_east) float32 282.35 ... 281.94897 SOILCTOP (Time, soil_cat, south_north, west_east) float32 0.0 0.0 ... 0.0 SCT_DOM (Time, south_north, west_east) float32 3.0 3.0 3.0 ... 3.0 3.0 SOILCBOT (Time, soil_cat, south_north, west_east) float32 0.0 0.0 ... 0.0 SCB_DOM (Time, south_north, west_east) float32 3.0 3.0 3.0 ... 3.0 3.0 GREENFRAC (Time, month, south_north, west_east) float32 0.4111168 ... 0... LAI12M (Time, month, south_north, west_east) float32 0.59203833 ... ... Attributes: TITLE: OUTPUT FROM GEOGRID V4.2 SIMULATION_START_DATE: 0000-00-00_00:00:00 WEST-EAST_GRID_DIMENSION: 16 SOUTH-NORTH_GRID_DIMENSION: 17 BOTTOM-TOP_GRID_DIMENSION: 0 WEST-EAST_PATCH_START_UNSTAG: 1 WEST-EAST_PATCH_END_UNSTAG: 15 WEST-EAST_PATCH_START_STAG: 1 WEST-EAST_PATCH_END_STAG: 16 SOUTH-NORTH_PATCH_START_UNSTAG: 1 SOUTH-NORTH_PATCH_END_UNSTAG: 16 SOUTH-NORTH_PATCH_START_STAG: 1 SOUTH-NORTH_PATCH_END_STAG: 17 GRIDTYPE: C DX: 1000.0 DY: 1000.0 DYN_OPT: 2 CEN_LAT: 41.47101 CEN_LON: -73.74365 TRUELAT1: 30.0 TRUELAT2: 60.0 MOAD_CEN_LAT: 41.47101 STAND_LON: -97.0 POLE_LAT: 90.0 POLE_LON: 0.0 corner_lats: [41.42282 41.55638 41.51908 41.385624... corner_lons: [-73.85333 -73.80026 -73.63379 -73.68... MAP_PROJ: 1 MMINLU: USGS NUM_LAND_CAT: 24 ISWATER: 16 ISLAKE: -1 ISICE: 24 ISURBAN: 1 ISOILWATER: 14 grid_id: 1 parent_id: 1 i_parent_start: 1 j_parent_start: 1 i_parent_end: 16 j_parent_end: 17 parent_grid_ratio: 1 sr_x: 1 sr_y: 1 FLAG_MF_XY: 1 FLAG_LAI12M: 1
array([b'0000-00-00_00:00:00'], dtype='|S19')
array([[[41.42282 , 41.420174, ..., 41.388283, 41.385624], [41.431725, 41.42907 , ..., 41.397175, 41.394512], ..., [41.547474, 41.54481 , ..., 41.51286 , 41.51018 ], [41.55638 , 41.553715, ..., 41.521755, 41.51908 ]]], dtype=float32)
array([[[-73.85333 , -73.84149 , ..., -73.699066, -73.687195], [-73.84982 , -73.83795 , ..., -73.695526, -73.683655], ..., [-73.8038 , -73.7919 , ..., -73.64923 , -73.63736 ], [-73.80026 , -73.78836 , ..., -73.64566 , -73.63379 ]]], dtype=float32)
array([[[41.418358, 41.415714, ..., 41.383823, 41.38117 ], [41.427273, 41.42462 , ..., 41.392735, 41.390068], ..., [41.551926, 41.549263, ..., 41.517307, 41.514637], [41.560825, 41.558178, ..., 41.526203, 41.523533]]], dtype=float32)
array([[[-73.8551 , -73.84326 , ..., -73.70087 , -73.688995], [-73.85159 , -73.83972 , ..., -73.697296, -73.685425], ..., [-73.80203 , -73.79013 , ..., -73.64746 , -73.63556 ], [-73.79849 , -73.78659 , ..., -73.64389 , -73.63202 ]]], dtype=float32)
array([[[41.424145, 41.421494, ..., 41.386948, 41.384285], [41.433037, 41.430397, ..., 41.395847, 41.39318 ], ..., [41.5488 , 41.54614 , ..., 41.511524, 41.508846], [41.557705, 41.55505 , ..., 41.520428, 41.517754]]], dtype=float32)
array([[[-73.85928 , -73.84741 , ..., -73.693146, -73.681274], [-73.855774, -73.84387 , ..., -73.689575, -73.677734], ..., [-73.80975 , -73.79785 , ..., -73.64331 , -73.63141 ], [-73.80621 , -73.79431 , ..., -73.63971 , -73.62784 ]]], dtype=float32)
array([[[41.42282 , 41.420174, ..., 41.388283, 41.385624], [41.431725, 41.42907 , ..., 41.397175, 41.394512], ..., [41.547474, 41.54481 , ..., 41.51286 , 41.51018 ], [41.55638 , 41.553715, ..., 41.521755, 41.51908 ]]], dtype=float32)
array([[[-73.85333 , -73.84149 , ..., -73.699066, -73.687195], [-73.84982 , -73.83795 , ..., -73.695526, -73.683655], ..., [-73.8038 , -73.7919 , ..., -73.64923 , -73.63736 ], [-73.80026 , -73.78836 , ..., -73.64566 , -73.63379 ]]], dtype=float32)
array([[[0.968267, 0.96827 , ..., 0.968309, 0.968312], [0.968256, 0.968259, ..., 0.968298, 0.968301], ..., [0.968118, 0.968121, ..., 0.968159, 0.968162], [0.968107, 0.96811 , ..., 0.968148, 0.968151]]], dtype=float32)
array([[[0.968272, 0.968276, ..., 0.968315, 0.968318], [0.968262, 0.968265, ..., 0.968304, 0.968307], ..., [0.968112, 0.968115, ..., 0.968153, 0.968157], [0.968102, 0.968105, ..., 0.968143, 0.968146]]], dtype=float32)
array([[[0.968265, 0.968269, ..., 0.968311, 0.968314], [0.968254, 0.968258, ..., 0.9683 , 0.968303], ..., [0.968116, 0.968119, ..., 0.96816 , 0.968163], [0.968105, 0.968109, ..., 0.96815 , 0.968153]]], dtype=float32)
array([[[0.968267, 0.96827 , ..., 0.968309, 0.968312], [0.968256, 0.968259, ..., 0.968298, 0.968301], ..., [0.968118, 0.968121, ..., 0.968159, 0.968162], [0.968107, 0.96811 , ..., 0.968148, 0.968151]]], dtype=float32)
array([[[0.968272, 0.968276, ..., 0.968315, 0.968318], [0.968262, 0.968265, ..., 0.968304, 0.968307], ..., [0.968112, 0.968115, ..., 0.968153, 0.968157], [0.968102, 0.968105, ..., 0.968143, 0.968146]]], dtype=float32)
array([[[0.968265, 0.968269, ..., 0.968311, 0.968314], [0.968254, 0.968258, ..., 0.9683 , 0.968303], ..., [0.968116, 0.968119, ..., 0.96816 , 0.968163], [0.968105, 0.968109, ..., 0.96815 , 0.968153]]], dtype=float32)
array([[[0.968267, 0.96827 , ..., 0.968309, 0.968312], [0.968256, 0.968259, ..., 0.968298, 0.968301], ..., [0.968118, 0.968121, ..., 0.968159, 0.968162], [0.968107, 0.96811 , ..., 0.968148, 0.968151]]], dtype=float32)
array([[[0.968272, 0.968276, ..., 0.968315, 0.968318], [0.968262, 0.968265, ..., 0.968304, 0.968307], ..., [0.968112, 0.968115, ..., 0.968153, 0.968157], [0.968102, 0.968105, ..., 0.968143, 0.968146]]], dtype=float32)
array([[[0.968265, 0.968269, ..., 0.968311, 0.968314], [0.968254, 0.968258, ..., 0.9683 , 0.968303], ..., [0.968116, 0.968119, ..., 0.96816 , 0.968163], [0.968105, 0.968109, ..., 0.96815 , 0.968153]]], dtype=float32)
array([[[0.000109, 0.000109, ..., 0.000109, 0.000109], [0.000109, 0.000109, ..., 0.000109, 0.000109], ..., [0.000109, 0.000109, ..., 0.000109, 0.000109], [0.000109, 0.000109, ..., 0.000109, 0.000109]]], dtype=float32)
array([[[9.648928e-05, 9.648423e-05, ..., 9.642334e-05, 9.641827e-05], [9.650627e-05, 9.650122e-05, ..., 9.644033e-05, 9.643524e-05], ..., [9.672697e-05, 9.672189e-05, ..., 9.666101e-05, 9.665591e-05], [9.674394e-05, 9.673886e-05, ..., 9.667797e-05, 9.667288e-05]]], dtype=float32)
array([[[-0.284581, -0.284647, ..., -0.287208, -0.287163], [-0.284601, -0.285743, ..., -0.287172, -0.286221], ..., [-0.285152, -0.286347, ..., -0.287831, -0.287842], [-0.2863 , -0.285182, ..., -0.287682, -0.287636]]], dtype=float32)
array([[[0.958652, 0.958632, ..., 0.957868, 0.957882], [0.958646, 0.958306, ..., 0.957879, 0.958164], ..., [0.958482, 0.958126, ..., 0.957681, 0.957678], [0.95814 , 0.958473, ..., 0.957726, 0.95774 ]]], dtype=float32)
array([[[1., 1., ..., 1., 1.], [1., 1., ..., 1., 1.], ..., [1., 1., ..., 1., 1.], [1., 1., ..., 1., 1.]]], dtype=float32)
array([[[41.41969 , 41.417046, ..., 41.3825 , 41.37983 ], [41.42859 , 41.425945, ..., 41.391396, 41.388733], ..., [41.553238, 41.55059 , ..., 41.515965, 41.513294], [41.562153, 41.559494, ..., 41.524868, 41.522194]]], dtype=float32)
array([[[-73.86105 , -73.84918 , ..., -73.694916, -73.683044], [-73.85751 , -73.84564 , ..., -73.691376, -73.679504], ..., [-73.80798 , -73.79608 , ..., -73.64151 , -73.62964 ], [-73.80444 , -73.79254 , ..., -73.63794 , -73.62607 ]]], dtype=float32)
array([[[-0.284687, -0.285717, ..., -0.287101, -0.287112], [-0.284539, -0.285738, ..., -0.287121, -0.287245], ..., [-0.285034, -0.285101, ..., -0.287611, -0.287735], [-0.285222, -0.285177, ..., -0.287857, -0.287585]]], dtype=float32)
array([[[0.95862 , 0.958314, ..., 0.9579 , 0.957897], [0.958664, 0.958308, ..., 0.957894, 0.957857], ..., [0.958517, 0.958498, ..., 0.957747, 0.95771 ], [0.958461, 0.958475, ..., 0.957673, 0.957755]]], dtype=float32)
array([[[-0.284487, -0.284609, ..., -0.287057, -0.287068], [-0.284507, -0.285761, ..., -0.288037, -0.287088], ..., [-0.285302, -0.286104, ..., -0.286685, -0.286639], [-0.286338, -0.286349, ..., -0.28772 , -0.287731]]], dtype=float32)
array([[[0.95868 , 0.958644, ..., 0.957914, 0.95791 ], [0.958674, 0.958301, ..., 0.957619, 0.957904], ..., [0.958438, 0.958199, ..., 0.958025, 0.958039], [0.958129, 0.958125, ..., 0.957714, 0.957711]]], dtype=float32)
array([[[[0. , ..., 0.085859], ..., [0.199663, ..., 0.005892]], ..., [[0. , ..., 0. ], ..., [0. , ..., 0. ]]]], dtype=float32)
array([[[11., 11., ..., 11., 11.], [11., 11., ..., 11., 11.], ..., [11., 11., ..., 11., 11.], [11., 11., ..., 11., 11.]]], dtype=float32)
array([[[245.67378, 246.8656 , ..., 172.53244, 146.55048], [276.03546, 265.66193, ..., 157.50964, 150.35123], ..., [ 88.49963, 88.31935, ..., 249.58205, 209.7987 ], [ 87.94086, 89.25037, ..., 235.75212, 227.06079]]], dtype=float32)
array([[[282.35 , 282.35 , ..., 282.35 , 282.35 ], [282.35 , 282.35 , ..., 282.35 , 282.35 ], ..., [281.97113, 281.96994, ..., 281.9564 , 281.9553 ], [281.9649 , 281.9637 , ..., 281.95004, 281.94897]]], dtype=float32)
array([[[[0., ..., 0.], ..., [0., ..., 0.]], ..., [[0., ..., 0.], ..., [0., ..., 0.]]]], dtype=float32)
array([[[3., 3., ..., 3., 3.], [3., 3., ..., 3., 3.], ..., [4., 4., ..., 3., 3.], [3., 4., ..., 3., 3.]]], dtype=float32)
array([[[[0., ..., 0.], ..., [0., ..., 0.]], ..., [[0., ..., 0.], ..., [0., ..., 0.]]]], dtype=float32)
array([[[3., 3., ..., 3., 3.], [3., 3., ..., 3., 3.], ..., [6., 6., ..., 3., 3.], [3., 6., ..., 3., 3.]]], dtype=float32)
array([[[[0.411117, ..., 0.370281], ..., [0.365939, ..., 0.39 ]], ..., [[0.401117, ..., 0.362797], ..., [0.375555, ..., 0.387175]]]], dtype=float32)
array([[[[0.592038, ..., 0.521875], ..., [0.534459, ..., 0.6 ]], ..., [[0.592038, ..., 0.521875], ..., [0.534459, ..., 0.556187]]]], dtype=float32)
Plot the HGT_M variable, the topographic height in meters for each grid cell
geogrid.HGT_M.plot()
<matplotlib.collections.QuadMesh at 0x7f9272d78eb0>
Plot the LU_INDEX variable, the dominant land-use class index for each grid cell
geogrid.LU_INDEX.plot(cmap="tab20")
<matplotlib.collections.QuadMesh at 0x7f926ac41730>
Plot the SCT_DOM variable, the dominant soil type for each grid cell
geogrid.SCT_DOM.plot(cmap="tab20")
<matplotlib.collections.QuadMesh at 0x7f926ab95610>
So how do you know what these values mean? You can check the parameter tables that come with the code to check lookup values. For example, the MPTABLE.TBL file lists the land cover categories.
cat ~/wrf-hydro-training/output/lesson2/run_gridded_default/MPTABLE.TBL
&noahmp_usgs_veg_categories VEG_DATASET_DESCRIPTION = "USGS" NVEG = 27 / &noahmp_usgs_parameters ! NVEG = 27 ! 1: Urban and Built-Up Land ! 2: Dryland Cropland and Pasture ! 3: Irrigated Cropland and Pasture ! 4: Mixed Dryland/Irrigated Cropland and Pasture ! 5: Cropland/Grassland Mosaic ! 6: Cropland/Woodland Mosaic ! 7: Grassland ! 8: Shrubland ! 9: Mixed Shrubland/Grassland ! 10: Savanna ! 11: Deciduous Broadleaf Forest ! 12: Deciduous Needleleaf Forest ! 13: Evergreen Broadleaf Forest ! 14: Evergreen Needleleaf Forest ! 15: Mixed Forest ! 16: Water Bodies ! 17: Herbaceous Wetland ! 18: Wooded Wetland ! 19: Barren or Sparsely Vegetated ! 20: Herbaceous Tundra ! 21: Wooded Tundra ! 22: Mixed Tundra ! 23: Bare Ground Tundra ! 24: Snow or Ice ! 25: Playa ! 26: Lava ! 27: White Sand ISURBAN = 1 ISWATER = 16 ISBARREN = 19 ISICE = 24 ISCROP = 2 EBLFOREST = 13 NATURAL = 5 LOW_DENSITY_RESIDENTIAL = 31 HIGH_DENSITY_RESIDENTIAL = 32 HIGH_INTENSITY_INDUSTRIAL = 33 !--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- ! 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 !--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- CH2OP = 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, DLEAF = 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, Z0MVT = 1.00, 0.15, 0.15, 0.15, 0.14, 0.50, 0.12, 0.06, 0.09, 0.50, 0.80, 0.85, 1.10, 1.09, 0.80, 0.00, 0.12, 0.50, 0.00, 0.10, 0.30, 0.20, 0.03, 0.00, 0.01, 0.00, 0.00, HVT = 15.0, 2.00, 2.00, 2.00, 1.50, 8.00, 1.00, 1.10, 1.10, 10.0, 16.0, 18.0, 20.0, 20.0, 16.0, 0.00, 0.50, 10.0, 0.00, 0.50, 4.00, 2.00, 0.50, 0.00, 0.10, 0.00, 0.00, HVB = 1.00, 0.10, 0.10, 0.10, 0.10, 0.15, 0.05, 0.10, 0.10, 0.10, 11.5, 7.00, 8.00, 8.50, 10.0, 0.00, 0.05, 0.10, 0.00, 0.10, 0.10, 0.10, 0.10, 0.00, 0.10, 0.00, 0.00, DEN = 0.01, 25.0, 25.0, 25.0, 25.0, 25.0, 100., 10.0, 10.0, 0.02, 0.10, 0.28, 0.02, 0.28, 0.10, 0.01, 10.0, 0.10, 0.01, 1.00, 1.00, 1.00, 1.00, 0.00, 0.01, 0.01, 0.01, RC = 1.00, 0.08, 0.08, 0.08, 0.08, 0.08, 0.03, 0.12, 0.12, 3.00, 1.40, 1.20, 3.60, 1.20, 1.40, 0.01, 0.10, 1.40, 0.01, 0.30, 0.30, 0.30, 0.30, 0.00, 0.01, 0.01, 0.01, MFSNO = 2.50, 2.50, 2.50, 2.50, 2.50, 2.50, 2.50, 2.50, 2.50, 2.50, 2.50, 2.50, 2.50, 2.50, 2.50, 2.50, 2.50, 2.50, 2.50, 2.50, 2.50, 2.50, 2.50, 2.50, 2.50, 2.50, 2.50, ! Row 1: Vis ! Row 2: Near IR RHOL_VIS=0.00, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.07, 0.10, 0.10, 0.10, 0.07, 0.10, 0.07, 0.10, 0.00, 0.11, 0.10, 0.00, 0.10, 0.10, 0.10, 0.10, 0.00, 0.10, 0.00, 0.00, RHOL_NIR=0.00, 0.58, 0.58, 0.58, 0.58, 0.58, 0.58, 0.35, 0.45, 0.45, 0.45, 0.35, 0.45, 0.35, 0.45, 0.00, 0.58, 0.45, 0.00, 0.45, 0.45, 0.45, 0.45, 0.00, 0.45, 0.00, 0.00, ! Row 1: Vis ! Row 2: Near IR RHOS_VIS=0.00, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.00, 0.36, 0.16, 0.00, 0.16, 0.16, 0.16, 0.16, 0.00, 0.16, 0.00, 0.00, RHOS_NIR=0.00, 0.58, 0.58, 0.58, 0.58, 0.58, 0.58, 0.39, 0.39, 0.39, 0.39, 0.39, 0.39, 0.39, 0.39, 0.00, 0.58, 0.39, 0.00, 0.39, 0.39, 0.39, 0.39, 0.00, 0.39, 0.00, 0.00, ! Row 1: Vis ! Row 2: Near IR TAUL_VIS=0.00, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.00, 0.07, 0.05, 0.00, 0.05, 0.05, 0.05, 0.05, 0.00, 0.05, 0.00, 0.00, TAUL_NIR=0.00, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.10, 0.10, 0.25, 0.25, 0.10, 0.25, 0.10, 0.25, 0.00, 0.25, 0.25, 0.00, 0.25, 0.25, 0.25, 0.25, 0.00, 0.25, 0.00, 0.00, ! Row 1: Vis ! Row 2: Near IR TAUS_VIS=0.00, 0.220, 0.220, 0.220, 0.220, 0.220, 0.220, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.000, 0.220, 0.001, 0.000, 0.220, 0.001, 0.001, 0.001, 0.000, 0.001, 0.000, 0.000, TAUS_NIR=0.00, 0.380, 0.380, 0.380, 0.380, 0.380, 0.380, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.000, 0.380, 0.001, 0.000, 0.380, 0.001, 0.001, 0.001, 0.000, 0.001, 0.000, 0.000, XL = 0.000, -0.30, -0.30, -0.30, -0.30, -0.30, -0.30, 0.010, 0.250, 0.010, 0.250, 0.010, 0.010, 0.010, 0.250, 0.000, -0.30, 0.250, 0.000, -0.30, 0.250, 0.250, 0.250, 0.000, 0.250, 0.000, 0.000, CWPVT = 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, C3PSN = 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, KC25 = 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, AKC = 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, KO25 = 3.E4, 3.E4, 3.E4, 3.E4, 3.E4, 3.E4, 3.E4, 3.E4, 3.E4, 3.E4, 3.E4, 3.E4, 3.E4, 3.E4, 3.E4, 3.E4, 3.E4, 3.E4, 3.E4, 3.E4, 3.E4, 3.E4, 3.E4, 3.E4, 3.E4, 3.E4, 3.E4, AKO = 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, AVCMX = 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, AQE = 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, LTOVRC= 0.0, 1.2, 1.2, 1.2, 1.2, 1.30, 0.50, 0.65, 0.70, 0.65, 0.55, 0.2, 0.55, 0.5, 0.5, 0.0, 1.4, 1.4, 0.0, 1.2, 1.3, 1.4, 1.0, 0.0, 1.0, 0.0, 0.0, DILEFC= 0.00, 0.50, 0.50, 0.50, 0.35, 0.20, 0.20, 0.20, 0.50, 0.50, 0.60, 1.80, 0.50, 1.20, 0.80, 0.00, 0.40, 0.40, 0.00, 0.40, 0.30, 0.40, 0.30, 0.00, 0.30, 0.00, 0.00, DILEFW= 0.00, 0.20, 0.20, 0.20, 0.20, 0.20, 0.10, 0.20, 0.20, 0.50, 0.20, 0.20, 4.00, 0.20, 0.20, 0.00, 0.20, 0.20, 0.00, 0.20, 0.20, 0.20, 0.20, 0.00, 0.20, 0.00, 0.00, RMF25 = 0.00, 1.00, 1.40, 1.45, 1.45, 1.45, 1.80, 0.26, 0.26, 0.80, 3.00, 4.00, 0.65, 3.00, 3.00, 0.00, 3.20, 3.20, 0.00, 3.20, 3.00, 3.00, 3.00, 0.00, 3.00, 0.00, 0.00, SLA = 60, 80, 80, 80, 80, 80, 60, 60, 60, 50, 80, 80, 80, 80, 80, 0, 80, 80, 0, 80, 80, 80, 80, 0, 80, 0, 0, FRAGR = 0.00, 0.20, 0.20, 0.20, 0.20, 0.20, 0.20, 0.20, 0.20, 0.20, 0.20, 0.10, 0.20, 0.10, 0.10, 0.00, 0.10, 0.10, 0.10, 0.10, 0.10, 0.10, 0.10, 0.00, 0.10, 0.00, 0.00, TMIN = 0, 273, 273, 273, 273, 273, 273, 273, 273, 273, 273, 268, 273, 265, 268, 0, 268, 268, 0, 268, 268, 268, 268, 0, 268, 0, 0, VCMX25= 0.00, 80.0, 80.0, 80.0, 60.0, 70.0, 40.0, 40.0, 40.0, 40.0, 60.0, 60.0, 60.0, 50.0, 55.0, 0.00, 50.0, 50.0, 0.00, 50.0, 50.0, 50.0, 50.0, 0.00, 50.0, 0.00, 0.00, TDLEF = 278, 278, 278, 278, 278, 278, 278, 278, 278, 278, 278, 268, 278, 278, 268, 0, 268, 268, 0, 268, 268, 268, 268, 0, 268, 0, 0, BP = 1.E15, 2.E3, 2.E3, 2.E3, 2.E3, 2.E3, 2.E3, 2.E3, 2.E3, 2.E3, 2.E3, 2.E3, 2.E3, 2.E3, 2.E3, 1.E15, 2.E3, 2.E3, 2.E3, 2.E3, 2.E3, 2.E3, 2.E3, 1.E15, 2.E3, 1.E15, 1.E15, MP = 9., 9., 9., 9., 9., 9., 9., 9., 9., 9., 9., 6., 9., 6., 9., 9., 9., 9., 9., 9., 9., 9., 9., 9., 9., 9., 9., QE25 = 0., 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.00, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.00, 0.06, 0.00, 0.00, RMS25 = 0.00, 0.10, 0.10, 0.10, 0.10, 0.10, 0.10, 0.10, 0.10, 0.32, 0.10, 0.64, 0.30, 0.90, 0.80, 0.00, 0.10, 0.10, 0.00, 0.10, 0.10, 0.10, 0.00, 0.00, 0.00, 0.00, 0.00, RMR25 = 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 1.20, 0.00, 0.00, 0.01, 0.01, 0.05, 0.05, 0.36, 0.03, 0.00, 0.00, 0.00, 0.00, 2.11, 2.11, 2.11, 0.00, 0.00, 0.00, 0.00, 0.00, ARM = 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, FOLNMX= 0.00, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 0.00, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 0.00, 1.5, 0.00, 0.00, WDPOOL= 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 0.00, 0.00, 1.00, 0.00, 0.00, 1.00, 1.00, 0.00, 0.00, 0.00, 0.00, 0.00, WRRAT = 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 3.00, 3.00, 3.00, 30.0, 30.0, 30.0, 30.0, 30.0, 0.00, 0.00, 30.0, 0.00, 0.00, 3.00, 3.00, 0.00, 0.00, 0.00, 0.00, 0.00, MRP = 0.00, 0.23, 0.23, 0.23, 0.23, 0.23, 0.17, 0.19, 0.19, 0.40, 0.40, 0.37, 0.23, 0.37, 0.30, 0.00, 0.17, 0.40, 0.00, 0.17, 0.23, 0.20, 0.00, 0.00, 0.20, 0.00, 0.00, NROOT = 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 0, 2, 2, 1, 3, 3, 3, 2, 1, 1, 0, 0, RGL = 999.0, 100.0, 100.0, 100.0, 100.0, 65.0, 100.0, 100.0, 100.0, 65.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 100.0, 30.0, 999.0, 100.0, 100.0, 100.0, 100.0, 999.0, 100.0, 999.0, 999.0, RS = 200.0, 40.0, 40.0, 40.0, 40.0, 70.0, 40.0, 300.0, 170.0, 70.0, 100.0, 150.0, 150.0, 125.0, 125.0, 100.0, 40.0, 100.0, 999.0, 150.0, 150.0, 150.0, 200.0, 999.0, 40.0, 999.0, 999.0, HS = 999.0, 36.25, 36.25, 36.25, 36.25, 44.14, 36.35, 42.00, 39.18, 54.53, 54.53, 47.35, 41.69, 47.35, 51.93, 51.75, 60.00, 51.93, 999.0, 42.00, 42.00, 42.00, 42.00, 999.0, 36.25, 999.0, 999.0, TOPT = 298.0, 298.0, 298.0, 298.0, 298.0, 298.0, 298.0, 298.0, 298.0, 298.0, 298.0, 298.0, 298.0, 298.0, 298.0, 298.0, 298.0, 298.0, 298.0, 298.0, 298.0, 298.0, 298.0, 298.0, 298.0, 298.0, 298.0, RSMAX = 5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000., ! Monthly values, one row for each month: SAI_JAN = 0.0, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.2, 0.2, 0.3, 0.4, 0.3, 0.5, 0.4, 0.4, 0.0, 0.2, 0.3, 0.0, 0.1, 0.2, 0.1, 0.0, 0.0, 0.0, 0.0, 0.0, SAI_FEB = 0.0, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.2, 0.2, 0.3, 0.4, 0.3, 0.5, 0.4, 0.4, 0.0, 0.2, 0.3, 0.0, 0.1, 0.2, 0.1, 0.0, 0.0, 0.0, 0.0, 0.0, SAI_MAR = 0.0, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.2, 0.2, 0.3, 0.4, 0.3, 0.5, 0.4, 0.4, 0.0, 0.2, 0.3, 0.0, 0.1, 0.2, 0.1, 0.0, 0.0, 0.0, 0.0, 0.0, SAI_APR = 0.0, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.2, 0.2, 0.3, 0.4, 0.4, 0.5, 0.3, 0.4, 0.0, 0.2, 0.3, 0.0, 0.1, 0.2, 0.1, 0.0, 0.0, 0.0, 0.0, 0.0, SAI_MAY = 0.0, 0.2, 0.2, 0.2, 0.3, 0.3, 0.3, 0.2, 0.2, 0.3, 0.4, 0.4, 0.5, 0.4, 0.4, 0.0, 0.3, 0.3, 0.0, 0.1, 0.2, 0.1, 0.0, 0.0, 0.0, 0.0, 0.0, SAI_JUN = 0.0, 0.3, 0.3, 0.3, 0.4, 0.4, 0.4, 0.2, 0.3, 0.4, 0.4, 0.7, 0.5, 0.5, 0.4, 0.0, 0.4, 0.4, 0.0, 0.2, 0.2, 0.2, 0.0, 0.0, 0.0, 0.0, 0.0, SAI_JUL = 0.0, 0.4, 0.4, 0.4, 0.6, 0.6, 0.8, 0.4, 0.6, 0.8, 0.9, 1.3, 0.5, 0.5, 0.7, 0.0, 0.6, 0.6, 0.0, 0.4, 0.4, 0.4, 0.0, 0.0, 0.0, 0.0, 0.0, SAI_AUG = 0.0, 0.5, 0.5, 0.5, 0.9, 0.9, 1.3, 0.6, 0.9, 1.2, 1.2, 1.2, 0.5, 0.6, 0.8, 0.0, 0.9, 0.9, 0.0, 0.6, 0.6, 0.6, 0.0, 0.0, 0.0, 0.0, 0.0, SAI_SEP = 0.0, 0.4, 0.4, 0.4, 0.7, 1.0, 1.1, 0.8, 1.0, 1.3, 1.6, 1.0, 0.5, 0.6, 1.0, 0.0, 0.7, 1.0, 0.0, 0.7, 0.8, 0.7, 0.0, 0.0, 0.0, 0.0, 0.0, SAI_OCT = 0.0, 0.3, 0.3, 0.3, 0.3, 0.8, 0.4, 0.7, 0.6, 0.7, 1.4, 0.8, 0.5, 0.7, 1.0, 0.0, 0.3, 0.8, 0.0, 0.5, 0.7, 0.5, 0.0, 0.0, 0.0, 0.0, 0.0, SAI_NOV = 0.0, 0.3, 0.3, 0.3, 0.3, 0.4, 0.4, 0.3, 0.3, 0.4, 0.6, 0.6, 0.5, 0.6, 0.5, 0.0, 0.3, 0.4, 0.0, 0.3, 0.3, 0.3, 0.0, 0.0, 0.0, 0.0, 0.0, SAI_DEC = 0.0, 0.3, 0.3, 0.3, 0.3, 0.3, 0.4, 0.2, 0.3, 0.4, 0.4, 0.5, 0.5, 0.5, 0.4, 0.0, 0.3, 0.4, 0.0, 0.2, 0.2, 0.2, 0.0, 0.0, 0.0, 0.0, 0.0, LAI_JAN = 0.0, 0.0, 0.0, 0.0, 0.2, 0.0, 0.4, 0.0, 0.2, 0.3, 0.0, 0.0, 4.5, 4.0, 2.0, 0.0, 0.2, 0.2, 0.0, 0.2, 1.0, 0.6, 0.0, 0.0, 0.0, 0.0, 0.0, LAI_FEB = 0.0, 0.0, 0.0, 0.0, 0.3, 0.0, 0.5, 0.0, 0.3, 0.3, 0.0, 0.0, 4.5, 4.0, 2.0, 0.0, 0.3, 0.3, 0.0, 0.3, 1.0, 0.6, 0.0, 0.0, 0.0, 0.0, 0.0, LAI_MAR = 0.0, 0.0, 0.0, 0.0, 0.3, 0.2, 0.6, 0.2, 0.4, 0.5, 0.3, 0.0, 4.5, 4.0, 2.2, 0.0, 0.3, 0.3, 0.0, 0.3, 1.1, 0.7, 0.0, 0.0, 0.0, 0.0, 0.0, LAI_APR = 0.0, 0.0, 0.0, 0.0, 0.4, 0.6, 0.7, 0.6, 0.7, 0.8, 1.2, 0.6, 4.5, 4.0, 2.6, 0.0, 0.4, 0.6, 0.0, 0.4, 1.3, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, LAI_MAY = 0.0, 1.0, 1.0, 1.0, 1.1, 2.0, 1.2, 1.5, 1.4, 1.8, 3.0, 1.2, 4.5, 4.0, 3.5, 0.0, 1.1, 2.0, 0.0, 0.6, 1.7, 1.2, 0.0, 0.0, 0.0, 0.0, 0.0, LAI_JUN = 0.0, 2.0, 2.0, 2.0, 2.5, 3.3, 3.0, 2.3, 2.6, 3.6, 4.7, 2.0, 4.5, 4.0, 4.3, 0.0, 2.5, 3.3, 0.0, 1.5, 2.1, 1.8, 0.0, 0.0, 0.0, 0.0, 0.0, LAI_JUL = 0.0, 3.0, 3.0, 3.0, 3.2, 3.7, 3.5, 2.3, 2.9, 3.8, 4.5, 2.6, 4.5, 4.0, 4.3, 0.0, 3.2, 3.7, 0.0, 1.7, 2.1, 1.8, 0.0, 0.0, 0.0, 0.0, 0.0, LAI_AUG = 0.0, 3.0, 3.0, 3.0, 2.2, 3.2, 1.5, 1.7, 1.6, 2.1, 3.4, 1.7, 4.5, 4.0, 3.7, 0.0, 2.2, 3.2, 0.0, 0.8, 1.8, 1.3, 0.0, 0.0, 0.0, 0.0, 0.0, LAI_SEP = 0.0, 1.5, 1.5, 1.5, 1.1, 1.3, 0.7, 0.6, 0.7, 0.9, 1.2, 1.0, 4.5, 4.0, 2.6, 0.0, 1.1, 1.3, 0.0, 0.4, 1.3, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, LAI_OCT = 0.0, 0.0, 0.0, 0.0, 0.3, 0.2, 0.6, 0.2, 0.4, 0.5, 0.3, 0.5, 4.5, 4.0, 2.2, 0.0, 0.3, 0.3, 0.0, 0.3, 1.1, 0.7, 0.0, 0.0, 0.0, 0.0, 0.0, LAI_NOV = 0.0, 0.0, 0.0, 0.0, 0.3, 0.0, 0.5, 0.0, 0.3, 0.3, 0.0, 0.2, 4.5, 4.0, 2.0, 0.0, 0.3, 0.3, 0.0, 0.2, 1.0, 0.6, 0.0, 0.0, 0.0, 0.0, 0.0, LAI_DEC = 0.0, 0.0, 0.0, 0.0, 0.2, 0.0, 0.4, 0.0, 0.2, 0.3, 0.0, 0.0, 4.5, 4.0, 2.0, 0.0, 0.2, 0.2, 0.0, 0.2, 1.0, 0.6, 0.0, 0.0, 0.0, 0.0, 0.0, SLAREA=0.0228,0.0200,0.0200,0.0295,0.0223,0.0277,0.0060,0.0227,0.0188,0.0236,0.0258,0.0200,0.0200,0.0090,0.0223,0.0422,0.0390, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, ! Five types, one row for each type (BVOC currently not active). EPS1 = 41.87, 0.00, 0.00, 2.52, 0.04, 17.11, 0.02, 21.62, 0.11, 22.80, 46.86, 0.00, 0.00, 0.46, 30.98, 2.31, 1.63, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, EPS2 = 0.98, 0.00, 0.00, 0.16, 0.09, 0.28, 0.05, 0.92, 0.22, 0.59, 0.38, 0.00, 0.00, 3.34, 0.96, 1.47, 1.07, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, EPS3 = 1.82, 0.00, 0.00, 0.23, 0.05, 0.81, 0.03, 1.73, 1.26, 1.37, 1.84, 0.00, 0.00, 1.85, 1.84, 1.70, 1.21, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, EPS4 = 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, EPS5 = 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, / &noahmp_modis_veg_categories VEG_DATASET_DESCRIPTION = "modified igbp modis noah" NVEG = 20 / &noahmp_modis_parameters ! 1 'Evergreen Needleleaf Forest' -> USGS 14 ! 2, 'Evergreen Broadleaf Forest' -> USGS 13 ! 3, 'Deciduous Needleleaf Forest' -> USGS 12 ! 4, 'Deciduous Broadleaf Forest' -> USGS 11 ! 5, 'Mixed Forests' -> USGS 15 ! 6, 'Closed Shrublands' -> USGS 8 "shrubland" ! 7, 'Open Shrublands' -> USGS 9 "shrubland/grassland" ! 8, 'Woody Savannas' -> USGS 8 "shrubland" ! 9, 'Savannas' -> USGS 10 ! 10, 'Grasslands' -> USGS 7 ! 11 'Permanent wetlands' -> avg of USGS 17 and 18 (herb. wooded wetland) ! 12, 'Croplands' -> USGS 2 "dryland cropland" ! 13, 'Urban and Built-Up' -> USGS 1 ! 14 'cropland/natural vegetation mosaic' -> USGS 5 "cropland/grassland" ! 15, 'Snow and Ice' -> USGS 24 ! 16, 'Barren or Sparsely Vegetated' -> USGS 19 ! 17, 'Water' -> USGS 16 ! 18, 'Wooded Tundra' -> USGS 21 ! 19, 'Mixed Tundra' -> USGS 22 ! 20, 'Barren Tundra' -> USGS 23 ISURBAN = 13 ISWATER = 17 ISBARREN = 16 ISICE = 15 ISCROP = 12 EBLFOREST = 2 NATURAL = 14 LOW_DENSITY_RESIDENTIAL = 31 HIGH_DENSITY_RESIDENTIAL = 32 HIGH_INTENSITY_INDUSTRIAL = 33 !--------------------------------------------------------------------------------------------------------------------------------------------------------------------- ! 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 !--------------------------------------------------------------------------------------------------------------------------------------------------------------------- CH2OP = 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, DLEAF = 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, Z0MVT = 1.09, 1.10, 0.85, 0.80, 0.80, 0.20, 0.06, 0.60, 0.50, 0.12, 0.30, 0.15, 1.00, 0.14, 0.00, 0.00, 0.00, 0.30, 0.20, 0.03, HVT = 20.0, 20.0, 18.0, 16.0, 16.0, 1.10, 1.10, 13.0, 10.0, 1.00, 5.00, 2.00, 15.0, 1.50, 0.00, 0.00, 0.00, 4.00, 2.00, 0.50, HVB = 8.50, 8.00, 7.00, 11.5, 10.0, 0.10, 0.10, 0.10, 0.10, 0.05, 0.10, 0.10, 1.00, 0.10, 0.00, 0.00, 0.00, 0.30, 0.20, 0.10, DEN = 0.28, 0.02, 0.28, 0.10, 0.10, 10.0, 10.0, 10.0, 0.02, 100., 5.05, 25.0, 0.01, 25.0, 0.00, 0.01, 0.01, 1.00, 1.00, 1.00, RC = 1.20, 3.60, 1.20, 1.40, 1.40, 0.12, 0.12, 0.12, 3.00, 0.03, 0.75, 0.08, 1.00, 0.08, 0.00, 0.01, 0.01, 0.30, 0.30, 0.30, MFSNO = 2.50, 2.50, 2.50, 2.50, 2.50, 2.50, 2.50, 2.50, 2.50, 2.50, 2.50, 2.50, 2.50, 2.50, 2.50, 2.50, 2.50, 2.50, 2.50, 2.50, ! Row 1: Vis ! Row 2: Near IR RHOL_VIS=0.07, 0.10, 0.07, 0.10, 0.10, 0.07, 0.07, 0.07, 0.10, 0.11, 0.105, 0.11, 0.00, 0.11, 0.00, 0.00, 0.00, 0.10, 0.10, 0.10, RHOL_NIR=0.35, 0.45, 0.35, 0.45, 0.45, 0.35, 0.35, 0.35, 0.45, 0.58, 0.515, 0.58, 0.00, 0.58, 0.00, 0.00, 0.00, 0.45, 0.45, 0.45, ! Row 1: Vis ! Row 2: Near IR RHOS_VIS=0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.36, 0.26, 0.36, 0.00, 0.36, 0.00, 0.00, 0.00, 0.16, 0.16, 0.16, RHOS_NIR=0.39, 0.39, 0.39, 0.39, 0.39, 0.39, 0.39, 0.39, 0.39, 0.58, 0.485, 0.58, 0.00, 0.58, 0.00, 0.00, 0.00, 0.39, 0.39, 0.39, ! Row 1: Vis ! Row 2: Near IR TAUL_VIS=0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.07, 0.06, 0.07, 0.00, 0.07, 0.00, 0.00, 0.00, 0.05, 0.05, 0.05, TAUL_NIR=0.10, 0.25, 0.10, 0.25, 0.25, 0.10, 0.10, 0.10, 0.25, 0.25, 0.25, 0.25, 0.00, 0.25, 0.00, 0.00, 0.00, 0.25, 0.25, 0.25, ! Row 1: Vis ! Row 2: Near IR TAUS_VIS=0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.220, 0.1105, 0.220, 0.000, 0.220, 0.000, 0.000, 0.000, 0.001, 0.001, 0.001, TAUS_NIR=0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.380, 0.1905, 0.380, 0.000, 0.380, 0.000, 0.000, 0.000, 0.001, 0.001, 0.001, XL = 0.010, 0.010, 0.010, 0.250, 0.250, 0.010, 0.010, 0.010, 0.010, -0.30, -0.025, -0.30, 0.000, -0.30, 0.000, 0.000, 0.000, 0.250, 0.250, 0.250, ! CWPVT = 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, CWPVT = 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, C3PSN = 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, KC25 = 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, AKC = 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, KO25 = 3.E4, 3.E4, 3.E4, 3.E4, 3.E4, 3.E4, 3.E4, 3.E4, 3.E4, 3.E4, 3.E4, 3.E4, 3.E4, 3.E4, 3.E4, 3.E4, 3.E4, 3.E4, 3.E4, 3.E4, AKO = 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, AVCMX = 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, AQE = 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, LTOVRC= 0.5, 0.55, 0.2, 0.55, 0.5, 0.65, 0.65, 0.65, 0.65, 0.50, 1.4, 1.6, 0.0, 1.2, 0.0, 0.0, 0.0, 1.3, 1.4, 1.0, DILEFC= 1.20, 0.50, 1.80, 0.60, 0.80, 0.20, 0.20, 0.20, 0.50, 0.20, 0.4, 0.50, 0.00, 0.35, 0.00, 0.00, 0.00, 0.30, 0.40, 0.30, DILEFW= 0.20, 4.00, 0.20, 0.20, 0.20, 0.20, 0.20, 0.20, 0.50, 0.10, 0.2, 0.20, 0.00, 0.20, 0.00, 0.00, 0.00, 0.20, 0.20, 0.20, RMF25 = 3.00, 0.65, 4.00, 3.00, 3.00, 0.26, 0.26, 0.26, 0.80, 1.80, 3.2, 1.00, 0.00, 1.45, 0.00, 0.00, 0.00, 3.00, 3.00, 3.00, SLA = 80, 80, 80, 80, 80, 60, 60, 60, 50, 60, 80, 80, 60, 80, 0, 0, 0, 80, 80, 80, FRAGR = 0.10, 0.20, 0.10, 0.20, 0.10, 0.20, 0.20, 0.20, 0.20, 0.20, 0.1, 0.20, 0.00, 0.20, 0.00, 0.10, 0.00, 0.10, 0.10, 0.10, TMIN = 265, 273, 268, 273, 268, 273, 273, 273, 273, 273, 268, 273, 0, 273, 0, 0, 0, 268, 268, 268, VCMX25= 50.0, 60.0, 60.0, 60.0, 55.0, 40.0, 40.0, 40.0, 40.0, 40.0, 50.0, 80.0, 0.00, 60.0, 0.00, 0.00, 0.00, 50.0, 50.0, 50.0, TDLEF = 278, 278, 268, 278, 268, 278, 278, 278, 278, 278, 268, 278, 278, 278, 0, 0, 0, 268, 268, 268, BP = 2.E3, 2.E3, 2.E3, 2.E3, 2.E3, 2.E3, 2.E3, 2.E3, 2.E3, 2.E3, 2.E3, 2.E3, 1.E15, 2.E3, 1.E15, 2.E3, 1.E15, 2.E3, 2.E3, 2.E3, MP = 6., 9., 6., 9., 9., 9., 9., 9., 9., 9., 9., 9., 9., 9., 9., 9., 9., 9., 9., 9., QE25 = 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.00, 0.06, 0.00, 0.06, 0.00, 0.06, 0.06, 0.06, RMS25 = 0.90, 0.30, 0.64, 0.10, 0.80, 0.10, 0.10, 0.10, 0.32, 0.10, 0.10, 0.10, 0.00, 0.10, 0.00, 0.00, 0.00, 0.10, 0.10, 0.00, RMR25 = 0.36, 0.05, 0.05, 0.01, 0.03, 0.00, 0.00, 0.00, 0.01, 1.20, 0.0, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 2.11, 2.11, 0.00, ARM = 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, FOLNMX= 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 0.00, 1.5, 0.00, 1.5, 0.00, 1.5, 1.5, 1.5, WDPOOL= 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 0.00, 0.5, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 1.00, 1.00, 0.00, WRRAT = 30.0, 30.0, 30.0, 30.0, 30.0, 3.00, 3.00, 3.00, 3.00, 0.00, 15.0, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 3.00, 3.00, 0.00, MRP = 0.37, 0.23, 0.37, 0.40, 0.30, 0.19, 0.19, 0.19, 0.40, 0.17, 0.285, 0.23, 0.00, 0.23, 0.00, 0.00, 0.00, 0.23, 0.20, 0.00, NROOT = 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 2, 3, 1, 3, 1, 1, 0, 3, 3, 2, RGL = 30.0, 30.0, 30.0, 30.0, 30.0, 100.0, 100.0, 100.0, 65.0, 100.0, 65.0, 100.0, 999.0, 100.0, 999.0, 999.0, 30.0, 100.0, 100.0, 100.0, RS = 125.0, 150.0, 150.0, 100.0, 125.0, 300.0, 170.0, 300.0, 70.0, 40.0, 70.0, 40.0, 200.0, 40.0, 999.0, 999.0, 100.0, 150.0, 150.0, 200.0, HS = 47.35, 41.69, 47.35, 54.53, 51.93, 42.00, 39.18, 42.00, 54.53, 36.35, 55.97, 36.25, 999.0, 36.25, 999.0, 999.0, 51.75, 42.00, 42.00, 42.00, TOPT = 298.0, 298.0, 298.0, 298.0, 298.0, 298.0, 298.0, 298.0, 298.0, 298.0, 298.0, 298.0, 298.0, 298.0, 298.0, 298.0, 298.0, 298.0, 298.0, 298.0, RSMAX = 5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000., 5000., ! Monthly values, one row for each month: SAI_JAN = 0.4, 0.5, 0.3, 0.4, 0.4, 0.3, 0.2, 0.4, 0.3, 0.3, 0.3, 0.3, 0.0, 0.3, 0.0, 0.0, 0.0, 0.2, 0.1, 0.0, SAI_FEB = 0.4, 0.5, 0.3, 0.4, 0.4, 0.3, 0.2, 0.4, 0.3, 0.3, 0.3, 0.3, 0.0, 0.3, 0.0, 0.0, 0.0, 0.2, 0.1, 0.0, SAI_MAR = 0.4, 0.5, 0.3, 0.4, 0.4, 0.3, 0.2, 0.4, 0.3, 0.3, 0.3, 0.3, 0.0, 0.3, 0.0, 0.0, 0.0, 0.2, 0.1, 0.0, SAI_APR = 0.3, 0.5, 0.4, 0.4, 0.4, 0.3, 0.2, 0.4, 0.3, 0.3, 0.3, 0.3, 0.0, 0.3, 0.0, 0.0, 0.0, 0.2, 0.1, 0.0, SAI_MAY = 0.4, 0.5, 0.4, 0.4, 0.4, 0.3, 0.2, 0.4, 0.3, 0.3, 0.3, 0.3, 0.0, 0.3, 0.0, 0.0, 0.0, 0.2, 0.1, 0.0, SAI_JUN = 0.5, 0.5, 0.7, 0.4, 0.4, 0.3, 0.2, 0.4, 0.4, 0.4, 0.4, 0.3, 0.0, 0.4, 0.0, 0.0, 0.0, 0.2, 0.2, 0.0, SAI_JUL = 0.5, 0.5, 1.3, 0.9, 0.7, 0.6, 0.4, 0.7, 0.8, 0.8, 0.6, 0.4, 0.0, 0.6, 0.0, 0.0, 0.0, 0.4, 0.4, 0.0, SAI_AUG = 0.6, 0.5, 1.2, 1.2, 0.8, 0.9, 0.6, 1.2, 1.2, 1.3, 0.9, 0.5, 0.0, 0.9, 0.0, 0.0, 0.0, 0.6, 0.6, 0.0, SAI_SEP = 0.6, 0.5, 1.0, 1.6, 1.0, 1.2, 0.8, 1.4, 1.3, 1.1, 0.9, 0.4, 0.0, 0.7, 0.0, 0.0, 0.0, 0.8, 0.7, 0.0, SAI_OCT = 0.7, 0.5, 0.8, 1.4, 1.0, 0.9, 0.7, 1.1, 0.7, 0.4, 0.6, 0.3, 0.0, 0.3, 0.0, 0.0, 0.0, 0.7, 0.5, 0.0, SAI_NOV = 0.6, 0.5, 0.6, 0.6, 0.5, 0.4, 0.3, 0.5, 0.4, 0.4, 0.4, 0.3, 0.0, 0.3, 0.0, 0.0, 0.0, 0.3, 0.3, 0.0, SAI_DEC = 0.5, 0.5, 0.5, 0.4, 0.4, 0.3, 0.2, 0.4, 0.4, 0.4, 0.3, 0.3, 0.0, 0.3, 0.0, 0.0, 0.0, 0.2, 0.2, 0.0, LAI_JAN = 4.0, 4.5, 0.0, 0.0, 2.0, 0.0, 0.0, 0.2, 0.3, 0.4, 0.2, 0.0, 0.0, 0.2, 0.0, 0.0, 0.0, 1.0, 0.6, 0.0, LAI_FEB = 4.0, 4.5, 0.0, 0.0, 2.0, 0.0, 0.0, 0.2, 0.3, 0.5, 0.3, 0.0, 0.0, 0.3, 0.0, 0.0, 0.0, 1.0, 0.6, 0.0, LAI_MAR = 4.0, 4.5, 0.0, 0.3, 2.2, 0.3, 0.2, 0.4, 0.5, 0.6, 0.3, 0.0, 0.0, 0.3, 0.0, 0.0, 0.0, 1.1, 0.7, 0.0, LAI_APR = 4.0, 4.5, 0.6, 1.2, 2.6, 0.9, 0.6, 1.0, 0.8, 0.7, 0.5, 0.0, 0.0, 0.4, 0.0, 0.0, 0.0, 1.3, 0.8, 0.0, LAI_MAY = 4.0, 4.5, 1.2, 3.0, 3.5, 2.2, 1.5, 2.4, 1.8, 1.2, 1.5, 1.0, 0.0, 1.1, 0.0, 0.0, 0.0, 1.7, 1.2, 0.0, LAI_JUN = 4.0, 4.5, 2.0, 4.7, 4.3, 3.5, 2.3, 4.1, 3.6, 3.0, 2.9, 2.0, 0.0, 2.5, 0.0, 0.0, 0.0, 2.1, 1.8, 0.0, LAI_JUL = 4.0, 4.5, 2.6, 4.5, 4.3, 3.5, 2.3, 4.1, 3.8, 3.5, 3.5, 3.0, 0.0, 3.2, 0.0, 0.0, 0.0, 2.1, 1.8, 0.0, LAI_AUG = 4.0, 4.5, 1.7, 3.4, 3.7, 2.5, 1.7, 2.7, 2.1, 1.5, 2.7, 3.0, 0.0, 2.2, 0.0, 0.0, 0.0, 1.8, 1.3, 0.0, LAI_SEP = 4.0, 4.5, 1.0, 1.2, 2.6, 0.9, 0.6, 1.0, 0.9, 0.7, 1.2, 1.5, 0.0, 1.1, 0.0, 0.0, 0.0, 1.3, 0.8, 0.0, LAI_OCT = 4.0, 4.5, 0.5, 0.3, 2.2, 0.3, 0.2, 0.4, 0.5, 0.6, 0.3, 0.0, 0.0, 0.3, 0.0, 0.0, 0.0, 1.1, 0.7, 0.0, LAI_NOV = 4.0, 4.5, 0.2, 0.0, 2.0, 0.0, 0.0, 0.2, 0.3, 0.5, 0.3, 0.0, 0.0, 0.3, 0.0, 0.0, 0.0, 1.0, 0.6, 0.0, LAI_DEC = 4.0, 4.5, 0.0, 0.0, 2.0, 0.0, 0.0, 0.2, 0.3, 0.4, 0.2, 0.0, 0.0, 0.2, 0.0, 0.0, 0.0, 1.0, 0.6, 0.0, SLAREA=0.0090, 0.0200, 0.0200, 0.0258, 0.0223, 0.0227, 0.0188, 0.0227, 0.0236, 0.0060, 0.0295, 0.0200, 0.0228, 0.0223, 0.02, 0.02, 0.0422, 0.02, 0.02, 0.02, ! Five types, one row for each type (BVOC currently not active). EPS1 = 0.46, 0.00, 0.00, 46.86, 30.98, 21.62, 0.11, 21.62, 22.80, 0.02, 0.815, 0.00, 41.87, 0.04, 0.0, 0.0, 2.31, 0.0, 0.0, 0.0, EPS2 = 3.34, 0.00, 0.00, 0.38, 0.96, 0.92, 0.22, 0.92, 0.59, 0.05, 0.535, 0.00, 0.98, 0.09, 0.0, 0.0, 1.47, 0.0, 0.0, 0.0, EPS3 = 1.85, 0.00, 0.00, 1.84, 1.84, 1.73, 1.26, 1.73, 1.37, 0.03, 0.605, 0.00, 1.82, 0.05, 0.0, 0.0, 1.70, 0.0, 0.0, 0.0, EPS4 = 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, EPS5 = 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, / &noahmp_rad_parameters !------------------------------------------------------------------------------ ! 1 2 3 4 5 6 7 8 soil color index for soil albedo !------------------------------------------------------------------------------ ALBSAT_VIS = 0.15, 0.11, 0.10, 0.09, 0.08, 0.07, 0.06, 0.05 ! saturated soil albedos ALBSAT_NIR = 0.30, 0.22, 0.20, 0.18, 0.16, 0.14, 0.12, 0.10 ! saturated soil albedos ALBDRY_VIS = 0.27, 0.22, 0.20, 0.18, 0.16, 0.14, 0.12, 0.10 ! dry soil albedos ALBDRY_NIR = 0.54, 0.44, 0.40, 0.36, 0.32, 0.28, 0.24, 0.20 ! dry soil albedos ALBICE = 0.80, 0.55 ! albedo land ice: 1=vis, 2=nir ALBLAK = 0.60, 0.40 ! albedo frozen lakes: 1=vis, 2=nir OMEGAS = 0.8 , 0.4 ! two-stream parameter omega for snow BETADS = 0.5 ! two-stream parameter betad for snow BETAIS = 0.5 ! two-stream parameter betaI for snow EG = 0.97, 0.98 ! emissivity soil surface 1-soil;2-lake / &noahmp_global_parameters ! atmospheric constituants CO2 = 395.e-06 !co2 partial pressure O2 = 0.209 !o2 partial pressure ! runoff parameters used for SIMTOP and SIMGM: TIMEAN = 10.5 !gridcell mean topgraphic index (global mean) FSATMX = 0.38 !maximum surface saturated fraction (global mean) ! adjustable parameters for snow processes Z0SNO = 0.002 !snow surface roughness length (m) (0.002) SSI = 0.03 !liquid water holding capacity for snowpack (m3/m3) (0.03) SNOW_RET_FAC = 5.e-5 !snowpack water release timescale factor (1/s) SWEMX = 1.00 !new snow mass to fully cover old snow (mm) !equivalent to 10mm depth (density = 100 kg/m3) TAU0 = 1.e6 !tau0 from Yang97 eqn. 10a GRAIN_GROWTH = 5000. !growth from vapor diffusion Yang97 eqn. 10b EXTRA_GROWTH = 10. !extra growth near freezing Yang97 eqn. 10c DIRT_SOOT = 0.3 !dirt and soot term Yang97 eqn. 10d BATS_COSZ = 2.0 !zenith angle snow albedo adjustment; b in Yang97 eqn. 15 BATS_VIS_NEW = 0.95 !new snow visible albedo BATS_NIR_NEW = 0.65 !new snow NIR albedo BATS_VIS_AGE = 0.2 !age factor for diffuse visible snow albedo Yang97 eqn. 17 BATS_NIR_AGE = 0.5 !age factor for diffuse NIR snow albedo Yang97 eqn. 18 BATS_VIS_DIR = 0.4 !cosz factor for direct visible snow albedo Yang97 eqn. 15 BATS_NIR_DIR = 0.4 !cosz factor for direct NIR snow albedo Yang97 eqn. 16 RSURF_SNOW = 50.0 !surface resistence for snow [s/m] RSURF_EXP = 5.0 !exponent in the shape parameter for soil resistance option 1 / &noahmp_crop_parameters ! NCROP = 5 ! 1: Corn ! 2: Soybean ! 3: Sorghum ! 4: Rice ! 5: Winter wheat DEFAULT_CROP = 0 ! The default crop type(1-5); if zero, use generic dynamic vegetation !---------------------------------------------------------- ! 1 2 3 4 5 !---------------------------------------------------------- PLTDAY = 130, 111, 111, 111, 111, ! Planting date HSDAY = 280, 300, 300, 300, 300, ! Harvest date PLANTPOP = 78.0, 78.0, 78.0, 78.0, 78.0, ! Plant density [per ha] - used? IRRI = 0.0, 0.0, 0.0, 0.0, 0.0, ! Irrigation strategy 0= non-irrigation 1=irrigation (no water-stress) GDDTBASE = 10.0, 10.0, 10.0, 10.0, 10.0, ! Base temperature for GDD accumulation [C] GDDTCUT = 30.0, 30.0, 30.0, 30.0, 30.0, ! Upper temperature for GDD accumulation [C] GDDS1 = 60.0, 50.0, 50.0, 50.0, 50.0, ! GDD from seeding to emergence GDDS2 = 675.0, 718.0, 718.0, 718.0, 718.0, ! GDD from seeding to initial vegetative GDDS3 = 1183.0, 933.0, 933.0, 933.0, 933.0, ! GDD from seeding to post vegetative GDDS4 = 1253.0, 1103.0, 1103.0, 1103.0, 1103.0, ! GDD from seeding to intial reproductive GDDS5 = 1605.0, 1555.0, 1555.0, 1555.0, 1555.0, ! GDD from seeding to pysical maturity C3C4 = 2, 1, 2, 2, 2, ! photosynthetic pathway: 1. = c3 2. = c4 Aref = 7.0, 7.0, 7.0, 7.0, 7.0, ! reference maximum CO2 assimulation rate PSNRF = 0.85, 0.85, 0.85, 0.85, 0.85, ! CO2 assimulation reduction factor(0-1) (caused by non-modeling part,e.g.pest,weeds) I2PAR = 0.5, 0.5, 0.5, 0.5, 0.5, ! Fraction of incoming solar radiation to photosynthetically active radiation TASSIM0 = 8.0, 8.0, 8.0, 8.0, 8.0, ! Minimum temperature for CO2 assimulation [C] TASSIM1 = 18.0, 18.0, 18.0, 18.0, 18.0, ! CO2 assimulation linearly increasing until temperature reaches T1 [C] TASSIM2 = 30.0, 30.0, 30.0, 30.0, 30.0, ! CO2 assmilation rate remain at Aref until temperature reaches T2 [C] K = 0.55, 0.55, 0.55, 0.55, 0.55, ! light extinction coefficient EPSI = 12.5, 12.5, 12.5, 12.5, 12.5, ! initial light use efficiency Q10MR = 2.0, 2.0, 2.0, 2.0, 2.0, ! q10 for maintainance respiration FOLN_MX = 1.5, 1.5, 1.5, 1.5, 1.5, ! foliage nitrogen concentration when f(n)=1 (%) LEFREEZ = 268, 268, 268, 268, 268, ! characteristic T for leaf freezing [K] DILE_FC_S1 = 0.0, 0.0, 0.0, 0.0, 0.0, ! coeficient for temperature leaf stress death [1/s] DILE_FC_S2 = 0.0, 0.0, 0.0, 0.0, 0.0, ! One row for each of 8 stages DILE_FC_S3 = 0.0, 0.0, 0.0, 0.0, 0.0, DILE_FC_S4 = 0.0, 0.0, 0.0, 0.0, 0.0, DILE_FC_S5 = 0.5, 0.5, 0.5, 0.5, 0.5, DILE_FC_S6 = 0.5, 0.5, 0.5, 0.5, 0.5, DILE_FC_S7 = 0.0, 0.0, 0.0, 0.0, 0.0, DILE_FC_S8 = 0.0, 0.0, 0.0, 0.0, 0.0, DILE_FW_S1 = 0.0, 0.0, 0.0, 0.0, 0.0, ! coeficient for water leaf stress death [1/s] DILE_FW_S2 = 0.0, 0.0, 0.0, 0.0, 0.0, ! One row for each of 8 stages DILE_FW_S3 = 0.0, 0.0, 0.0, 0.0, 0.0, DILE_FW_S4 = 0.0, 0.0, 0.0, 0.0, 0.0, DILE_FW_S5 = 0.2, 0.2, 0.2, 0.2, 0.2, DILE_FW_S6 = 0.2, 0.2, 0.2, 0.2, 0.2, DILE_FW_S7 = 0.0, 0.0, 0.0, 0.0, 0.0, DILE_FW_S8 = 0.0, 0.0, 0.0, 0.0, 0.0, FRA_GR = 0.2, 0.2, 0.2, 0.2, 0.2, ! fraction of growth respiration LF_OVRC_S1 = 0.0, 0.0, 0.0, 0.0, 0.0, ! fraction of leaf turnover [1/s] LF_OVRC_S2 = 0.0, 0.0, 0.0, 0.0, 0.0, ! One row for each of 8 stages LF_OVRC_S3 = 0.0, 0.0, 0.0, 0.0, 0.0, LF_OVRC_S4 = 0.0, 0.0, 0.0, 0.0, 0.0, LF_OVRC_S5 = 0.2, 0.48, 0.48, 0.48, 0.48, LF_OVRC_S6 = 0.3, 0.48, 0.48, 0.48, 0.48, LF_OVRC_S7 = 0.0, 0.0, 0.0, 0.0, 0.0, LF_OVRC_S8 = 0.0, 0.0, 0.0, 0.0, 0.0, ST_OVRC_S1 = 0.0, 0.0, 0.0, 0.0, 0.0, ! fraction of stem turnover [1/s] ST_OVRC_S2 = 0.0, 0.0, 0.0, 0.0, 0.0, ! One row for each of 8 stages ST_OVRC_S3 = 0.0, 0.0, 0.0, 0.0, 0.0, ST_OVRC_S4 = 0.0, 0.0, 0.0, 0.0, 0.0, ST_OVRC_S5 = 0.12, 0.12, 0.12, 0.12, 0.12, ST_OVRC_S6 = 0.06, 0.06, 0.06, 0.06, 0.06, ST_OVRC_S7 = 0.0, 0.0, 0.0, 0.0, 0.0, ST_OVRC_S8 = 0.0, 0.0, 0.0, 0.0, 0.0, RT_OVRC_S1 = 0.0, 0.0, 0.0, 0.0, 0.0, ! fraction of root tunrover [1/s] RT_OVRC_S2 = 0.0, 0.0, 0.0, 0.0, 0.0, ! One row for each of 8 stages RT_OVRC_S3 = 0.0, 0.0, 0.0, 0.0, 0.0, RT_OVRC_S4 = 0.0, 0.0, 0.0, 0.0, 0.0, RT_OVRC_S5 = 0.12, 0.12, 0.12, 0.12, 0.12, RT_OVRC_S6 = 0.06, 0.06, 0.06, 0.06, 0.06, RT_OVRC_S7 = 0.0, 0.0, 0.0, 0.0, 0.0, RT_OVRC_S8 = 0.0, 0.0, 0.0, 0.0, 0.0, LFMR25 = 1.0, 1.0, 1.0, 1.0, 1.0, ! leaf maintenance respiration at 25C [umol CO2/m**2 /s] STMR25 = 0.05, 0.1, 0.1, 0.1, 0.1, ! stem maintenance respiration at 25C [umol CO2/kg bio/s] RTMR25 = 0.05, 0.0, 0.0, 0.0, 0.0, ! root maintenance respiration at 25C [umol CO2/kg bio/s] GRAINMR25 = 0.0, 0.1, 0.1, 0.1, 0.1, ! grain maintenance respiration at 25C [umol CO2/kg bio/s] LFPT_S1 = 0.0, 0.0, 0.0, 0.0, 0.0, ! fraction of carbohydrate flux to leaf LFPT_S2 = 0.0, 0.0, 0.0, 0.0, 0.0, ! One row for each of 8 stages LFPT_S3 = 0.4, 0.4, 0.4, 0.4, 0.4, LFPT_S4 = 0.2, 0.2, 0.2, 0.2, 0.2, LFPT_S5 = 0.0, 0.0, 0.0, 0.0, 0.0, LFPT_S6 = 0.0, 0.0, 0.0, 0.0, 0.0, LFPT_S7 = 0.0, 0.0, 0.0, 0.0, 0.0, LFPT_S8 = 0.0, 0.0, 0.0, 0.0, 0.0, STPT_S1 = 0.0, 0.0, 0.0, 0.0, 0.0, ! fraction of carbohydrate flux to stem STPT_S2 = 0.0, 0.0, 0.0, 0.0, 0.0, ! One row for each of 8 stages STPT_S3 = 0.2, 0.2, 0.2, 0.2, 0.2, STPT_S4 = 0.5, 0.5, 0.5, 0.5, 0.5, STPT_S5 = 0.0, 0.15, 0.15, 0.15, 0.15, STPT_S6 = 0.0, 0.05, 0.05, 0.05, 0.05, STPT_S7 = 0.0, 0.0, 0.0, 0.0, 0.0, STPT_S8 = 0.0, 0.0, 0.0, 0.0, 0.0, RTPT_S1 = 0.0, 0.0, 0.0, 0.0, 0.0, ! fraction of carbohydrate flux to root RTPT_S2 = 0.0, 0.0, 0.0, 0.0, 0.0, ! One row for each of 8 stages RTPT_S3 = 0.34, 0.4, 0.4, 0.4, 0.4, RTPT_S4 = 0.3, 0.3, 0.3, 0.3, 0.3, RTPT_S5 = 0.05, 0.05, 0.05, 0.05, 0.05, RTPT_S6 = 0.0, 0.05, 0.05, 0.05, 0.05, RTPT_S7 = 0.0, 0.0, 0.0, 0.0, 0.0, RTPT_S8 = 0.0, 0.0, 0.0, 0.0, 0.0, GRAINPT_S1 = 0.0, 0.0, 0.0, 0.0, 0.0, ! fraction of carbohydrate flux to grain GRAINPT_S2 = 0.0, 0.0, 0.0, 0.0, 0.0, ! One row for each of 8 stages GRAINPT_S3 = 0.0, 0.0, 0.0, 0.0, 0.0, GRAINPT_S4 = 0.0, 0.0, 0.0, 0.0, 0.0, GRAINPT_S5 = 0.95, 0.8, 0.8, 0.8, 0.8, GRAINPT_S6 = 1.0, 0.9, 0.9, 0.9, 0.9, GRAINPT_S7 = 0.0, 0.0, 0.0, 0.0, 0.0, GRAINPT_S8 = 0.0, 0.0, 0.0, 0.0, 0.0, BIO2LAI = 0.035, 0.015, 0.015, 0.015, 0.015, ! leaf are per living leaf biomass [m^2/kg] / &noahmp_optional_parameters !------------------------------------------------------------------------------ ! Saxton and Rawls 2006 Pedo-transfer function coefficients !------------------------------------------------------------------------------ sr2006_theta_1500t_a = -0.024 ! sand coefficient sr2006_theta_1500t_b = 0.487 ! clay coefficient sr2006_theta_1500t_c = 0.006 ! orgm coefficient sr2006_theta_1500t_d = 0.005 ! sand*orgm coefficient sr2006_theta_1500t_e = -0.013 ! clay*orgm coefficient sr2006_theta_1500t_f = 0.068 ! sand*clay coefficient sr2006_theta_1500t_g = 0.031 ! constant adjustment sr2006_theta_1500_a = 0.14 ! theta_1500t coefficient sr2006_theta_1500_b = -0.02 ! constant adjustment sr2006_theta_33t_a = -0.251 ! sand coefficient sr2006_theta_33t_b = 0.195 ! clay coefficient sr2006_theta_33t_c = 0.011 ! orgm coefficient sr2006_theta_33t_d = 0.006 ! sand*orgm coefficient sr2006_theta_33t_e = -0.027 ! clay*orgm coefficient sr2006_theta_33t_f = 0.452 ! sand*clay coefficient sr2006_theta_33t_g = 0.299 ! constant adjustment sr2006_theta_33_a = 1.283 ! theta_33t*theta_33t coefficient sr2006_theta_33_b = -0.374 ! theta_33t coefficient sr2006_theta_33_c = -0.015 ! constant adjustment sr2006_theta_s33t_a = 0.278 ! sand coefficient sr2006_theta_s33t_b = 0.034 ! clay coefficient sr2006_theta_s33t_c = 0.022 ! orgm coefficient sr2006_theta_s33t_d = -0.018 ! sand*orgm coefficient sr2006_theta_s33t_e = -0.027 ! clay*orgm coefficient sr2006_theta_s33t_f = -0.584 ! sand*clay coefficient sr2006_theta_s33t_g = 0.078 ! constant adjustment sr2006_theta_s33_a = 0.636 ! theta_s33t coefficient sr2006_theta_s33_b = -0.107 ! constant adjustment sr2006_psi_et_a = -21.67 ! sand coefficient sr2006_psi_et_b = -27.93 ! clay coefficient sr2006_psi_et_c = -81.97 ! theta_s33 coefficient sr2006_psi_et_d = 71.12 ! sand*theta_s33 coefficient sr2006_psi_et_e = 8.29 ! clay*theta_s33 coefficient sr2006_psi_et_f = 14.05 ! sand*clay coefficient sr2006_psi_et_g = 27.16 ! constant adjustment sr2006_psi_e_a = 0.02 ! psi_et*psi_et coefficient sr2006_psi_e_b = -0.113 ! psi_et coefficient sr2006_psi_e_c = -0.7 ! constant adjustment sr2006_smcmax_a = -0.097 ! sand adjustment sr2006_smcmax_b = 0.043 ! constant adjustment /
The SOILPARM.TBL file lists the soil types.
cat ~/wrf-hydro-training/output/lesson2/run_gridded_default/SOILPARM.TBL
Soil Parameters STAS 19,1 'BB DRYSMC F11 MAXSMC REFSMC SATPSI SATDK SATDW WLTSMC QTZ ' 1, 2.79, 0.010, -0.472, 0.339, 0.192, 0.069, 4.66E-5, 2.65E-5, 0.010, 0.92, 'SAND' 2, 4.26, 0.028, -1.044, 0.421, 0.283, 0.036, 1.41E-5, 5.14E-6, 0.028, 0.82, 'LOAMY SAND' 3, 4.74, 0.047, -0.569, 0.434, 0.312, 0.141, 5.23E-6, 8.05E-6, 0.047, 0.60, 'SANDY LOAM' 4, 5.33, 0.084, 0.162, 0.476, 0.360, 0.759, 2.81E-6, 2.39E-5, 0.084, 0.25, 'SILT LOAM' 5, 3.86, 0.061, 0.162, 0.484, 0.347, 0.955, 2.18E-6, 1.66E-5, 0.061, 0.10, 'SILT' 6, 5.25, 0.066, -0.327, 0.439, 0.329, 0.355, 3.38E-6, 1.43E-5, 0.066, 0.40, 'LOAM' 7, 6.77, 0.069, -1.491, 0.404, 0.315, 0.135, 4.45E-6, 1.01E-5, 0.069, 0.60, 'SANDY CLAY LOAM' 8, 8.72, 0.120, -1.118, 0.464, 0.387, 0.617, 2.03E-6, 2.35E-5, 0.120, 0.10, 'SILTY CLAY LOAM' 9, 8.17, 0.103, -1.297, 0.465, 0.382, 0.263, 2.45E-6, 1.13E-5, 0.103, 0.35, 'CLAY LOAM' 10, 10.73, 0.100, -3.209, 0.406, 0.338, 0.098, 7.22E-6, 1.87E-5, 0.100, 0.52, 'SANDY CLAY' 11, 10.39, 0.126, -1.916, 0.468, 0.404, 0.324, 1.34E-6, 9.64E-6, 0.126, 0.10, 'SILTY CLAY' 12, 11.55, 0.138, -2.138, 0.468, 0.412, 0.468, 9.74E-7, 1.12E-5, 0.138, 0.25, 'CLAY' 13, 5.25, 0.066, -0.327, 0.439, 0.329, 0.355, 3.38E-6, 1.43E-5, 0.066, 0.05, 'ORGANIC MATERIAL' 14, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.60, 'WATER' 15, 2.79, 0.006, -1.111, 0.20, 0.17, 0.069, 1.41E-4, 1.36E-4, 0.006, 0.07, 'BEDROCK' 16, 4.26, 0.028, -1.044, 0.421, 0.283, 0.036, 1.41E-5, 5.14E-6, 0.028, 0.25, 'OTHER(land-ice)' 17, 11.55, 0.030, -10.472, 0.468, 0.454, 0.468, 9.74E-7, 1.12E-5, 0.030, 0.60, 'PLAYA' 18, 2.79, 0.006, -0.472, 0.200, 0.17, 0.069, 1.41E-4, 1.36E-4, 0.006, 0.52, 'LAVA' 19, 2.79, 0.01, -0.472, 0.339, 0.192, 0.069, 4.66E-5, 2.65E-5, 0.01, 0.92, 'WHITE SAND' Soil Parameters STAS-RUC 19,1 'BB DRYSMC HC MAXSMC REFSMC SATPSI SATDK SATDW WLTSMC QTZ ' 1, 4.05, 0.002, 1.47, 0.395, 0.174, 0.121, 1.76E-4, 0.608E-6, 0.033, 0.92, 'SAND' 2, 4.38, 0.035, 1.41, 0.410, 0.179, 0.090, 1.56E-4, 0.514E-5, 0.055, 0.82, 'LOAMY SAND' 3, 4.90, 0.041, 1.34, 0.435, 0.249, 0.218, 3.47E-5, 0.805E-5, 0.095, 0.60, 'SANDY LOAM' 4, 5.30, 0.034, 1.27, 0.485, 0.369, 0.786, 7.20E-6, 0.239E-4, 0.143, 0.25, 'SILT LOAM' 5, 5.30, 0.034, 1.27, 0.485, 0.369, 0.786, 7.20E-6, 0.239E-4, 0.143, 0.10, 'SILT' 6, 5.39, 0.050, 1.21, 0.451, 0.314, 0.478, 6.95E-6, 0.143E-4, 0.137, 0.40, 'LOAM' 7, 7.12, 0.068, 1.18, 0.420, 0.299, 0.299, 6.30E-6, 0.990E-5, 0.148, 0.60, 'SANDY CLAY LOAM' 8, 7.75, 0.060, 1.32, 0.477, 0.357, 0.356, 1.70E-6, 0.237E-4, 0.208, 0.10, 'SILTY CLAY LOAM' 9, 8.52, 0.085, 1.23, 0.476, 0.391, 0.630, 2.45E-6, 0.113E-4, 0.230, 0.35, 'CLAY LOAM' 10, 10.40, 0.100, 1.18, 0.426, 0.316, 0.153, 2.17E-6, 0.187E-4, 0.210, 0.52, 'SANDY CLAY' 11, 10.40, 0.070, 1.15, 0.492, 0.409, 0.490, 1.03E-6, 0.964E-5, 0.250, 0.10, 'SILTY CLAY' 12, 11.40, 0.068, 1.09, 0.482, 0.400, 0.405, 1.28E-6, 0.112E-4, 0.268, 0.25, 'CLAY' 13, 5.39, 0.027, 1.21, 0.451, 0.314, 0.478, 6.95E-6, 0.143E-4, 0.117, 0.05, 'ORGANIC MATERIAL' 14, 0.0, 0.0, 4.18, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.00, 'WATER' 15, 4.05, 0.004, 2.03, 0.200, 0.10 , 0.121, 1.41E-4, 0.136E-3, 0.006, 0.60, 'BEDROCK' 16, 4.90, 0.065, 2.10, 0.435, 0.249, 0.218, 3.47E-5, 0.514E-5, 0.114, 0.05, 'OTHER(land-ice)' 17, 11.40, 0.030, 1.41, 0.468, 0.454, 0.468, 9.74E-7, 0.112E-4, 0.030, 0.60, 'PLAYA' 18, 4.05, 0.006, 1.41, 0.200, 0.17, 0.069, 1.41E-4, 0.136E-3, 0.006, 0.52, 'LAVA' 19, 4.05, 0.01, 1.47, 0.339, 0.236, 0.069, 1.76E-4, 0.608E-6, 0.060, 0.92, 'WHITE SAND'
Next we will look at the high-resolution routing domain file, Fulldom_hires.nc
.
Open the Fulldom dataset
# Load a dataset
fulldom = xr.open_dataset('~/wrf-hydro-training/output/lesson2/run_gridded_default/DOMAIN/Fulldom_hires.nc')
Print some info about the dataset
fulldom
<xarray.Dataset> Dimensions: (x: 60, y: 64) Coordinates: * y (y) float64 2.784e+05 2.781e+05 ... 2.629e+05 2.626e+05 * x (x) float64 1.842e+06 1.842e+06 ... 1.857e+06 1.857e+06 Data variables: crs |S1 b'' CHANNELGRID (y, x) int32 0 0 -9999 -9999 ... -9999 -9999 -9999 -9999 FLOWDIRECTION (y, x) int16 16 16 16 16 8 1 4 4 ... 1 1 128 64 64 32 128 64 FLOWACC (y, x) int32 42 39 2 1 1 1 2 1 3 2 1 ... 2 1 1 2 3 1 1 1 1 1 TOPOGRAPHY (y, x) float32 75.404305 75.416466 ... 183.96214 165.79156 RETDEPRTFAC (y, x) float32 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0 1.0 OVROUGHRTFAC (y, x) float32 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0 1.0 STREAMORDER (y, x) int8 2 1 -15 -15 -15 -15 ... -15 -15 -15 -15 -15 -15 frxst_pts (y, x) int32 -9999 -9999 -9999 -9999 ... -9999 -9999 -9999 basn_msk (y, x) int32 -9999 -9999 -9999 -9999 ... -9999 -9999 -9999 LAKEGRID (y, x) int32 -9999 -9999 -9999 -9999 ... -9999 -9999 -9999 landuse (y, x) float32 11.0 11.0 11.0 11.0 ... 11.0 11.0 11.0 11.0 LKSATFAC (y, x) float32 1000.0 1000.0 1000.0 ... 1000.0 1000.0 1000.0 LATITUDE (y, x) float32 41.55638 41.55638 ... 41.385624 41.385624 LONGITUDE (y, x) float32 -73.80026 -73.80026 ... -73.687195 -73.687195 Attributes: Conventions: CF-1.5 GDAL_DataType: Generic Source_Software: WRF-Hydro GIS Pre-processor v5.1 (10/2019) proj4: +proj=lcc +lat_0=41.4710083007812 +lon_0=-97 +lat_1=30... history: Created Thu Oct 29 20:51:34 2020 processing_notes: Created: Thu Oct 29 20:51:34 2020 spatial_ref: PROJCS["unnamed",GEOGCS["GCS_Sphere",DATUM["D_Sphere",... geogrid_used: /home/docker/wrf-hydro-training/example_case/Gridded/D... DX: 250.0 DY: 250.0 MAP_PROJ: 1 corner_lats: [41.42282 41.55638 41.51908 41.385624 41.424145 41.... corner_lons: [-73.85333 -73.80026 -73.63379 -73.687195 -73.85928... TRUELAT1: 30.0 TRUELAT2: 60.0 STAND_LON: -97.0 POLE_LAT: 90.0 POLE_LON: 0.0 MOAD_CEN_LAT: 41.47101 CEN_LAT: 41.47101
array([278370.814357, 278120.814357, 277870.814357, 277620.814357, 277370.814357, 277120.814357, 276870.814357, 276620.814357, 276370.814357, 276120.814357, 275870.814357, 275620.814357, 275370.814357, 275120.814357, 274870.814357, 274620.814357, 274370.814357, 274120.814357, 273870.814357, 273620.814357, 273370.814357, 273120.814357, 272870.814357, 272620.814357, 272370.814357, 272120.814357, 271870.814357, 271620.814357, 271370.814357, 271120.814357, 270870.814357, 270620.814357, 270370.814357, 270120.814357, 269870.814357, 269620.814357, 269370.814357, 269120.814357, 268870.814357, 268620.814357, 268370.814357, 268120.814357, 267870.814357, 267620.814357, 267370.814357, 267120.814357, 266870.814357, 266620.814357, 266370.814357, 266120.814357, 265870.814357, 265620.814357, 265370.814357, 265120.814357, 264870.814357, 264620.814357, 264370.814357, 264120.814357, 263870.814357, 263620.814357, 263370.814357, 263120.814357, 262870.814357, 262620.814357])
array([1842124.01941, 1842374.01941, 1842624.01941, 1842874.01941, 1843124.01941, 1843374.01941, 1843624.01941, 1843874.01941, 1844124.01941, 1844374.01941, 1844624.01941, 1844874.01941, 1845124.01941, 1845374.01941, 1845624.01941, 1845874.01941, 1846124.01941, 1846374.01941, 1846624.01941, 1846874.01941, 1847124.01941, 1847374.01941, 1847624.01941, 1847874.01941, 1848124.01941, 1848374.01941, 1848624.01941, 1848874.01941, 1849124.01941, 1849374.01941, 1849624.01941, 1849874.01941, 1850124.01941, 1850374.01941, 1850624.01941, 1850874.01941, 1851124.01941, 1851374.01941, 1851624.01941, 1851874.01941, 1852124.01941, 1852374.01941, 1852624.01941, 1852874.01941, 1853124.01941, 1853374.01941, 1853624.01941, 1853874.01941, 1854124.01941, 1854374.01941, 1854624.01941, 1854874.01941, 1855124.01941, 1855374.01941, 1855624.01941, 1855874.01941, 1856124.01941, 1856374.01941, 1856624.01941, 1856874.01941])
array(b'', dtype='|S1')
array([[ 0, 0, -9999, ..., -9999, -9999, -9999], [-9999, -9999, 0, ..., -9999, -9999, -9999], [-9999, -9999, 0, ..., -9999, -9999, -9999], ..., [-9999, -9999, 0, ..., -9999, -9999, -9999], [-9999, -9999, 0, ..., -9999, -9999, -9999], [-9999, -9999, 0, ..., -9999, -9999, -9999]], dtype=int32)
array([[ 16, 16, 16, ..., 16, 1, 64], [ 64, 64, 32, ..., 8, 4, 4], [ 64, 64, 64, ..., 4, 4, 4], ..., [ 2, 2, 4, ..., 64, 64, 64], [ 1, 2, 4, ..., 64, 64, 64], [ 1, 1, 4, ..., 32, 128, 64]], dtype=int16)
array([[ 42, 39, 2, ..., 1, 1, 2], [ 2, 2, 34, ..., 1, 1, 1], [ 1, 1, 30, ..., 1, 2, 2], ..., [ 1, 2, 119, ..., 2, 2, 4], [ 1, 3, 123, ..., 1, 1, 3], [ 1, 2, 131, ..., 1, 1, 1]], dtype=int32)
array([[ 75.404305, 75.416466, 76.48548 , ..., 310.60526 , 291.7992 , 251.29752 ], [ 76.27707 , 76.42406 , 76.25327 , ..., 302.14087 , 282.14215 , 255.84447 ], [ 81.26507 , 78.069954, 76.99346 , ..., 255.42345 , 246.44252 , 244.13264 ], ..., [282.48425 , 243.73566 , 206.59193 , ..., 131.777 , 137.93228 , 127.98397 ], [284.58746 , 220.64911 , 186.66727 , ..., 159.66957 , 176.84862 , 156.64742 ], [252.02931 , 198.27661 , 169.94534 , ..., 167.31706 , 183.96214 , 165.79156 ]], dtype=float32)
array([[1., 1., 1., ..., 1., 1., 1.], [1., 1., 1., ..., 1., 1., 1.], [1., 1., 1., ..., 1., 1., 1.], ..., [1., 1., 1., ..., 1., 1., 1.], [1., 1., 1., ..., 1., 1., 1.], [1., 1., 1., ..., 1., 1., 1.]], dtype=float32)
array([[1., 1., 1., ..., 1., 1., 1.], [1., 1., 1., ..., 1., 1., 1.], [1., 1., 1., ..., 1., 1., 1.], ..., [1., 1., 1., ..., 1., 1., 1.], [1., 1., 1., ..., 1., 1., 1.], [1., 1., 1., ..., 1., 1., 1.]], dtype=float32)
array([[ 2, 1, -15, ..., -15, -15, -15], [-15, -15, 1, ..., -15, -15, -15], [-15, -15, 1, ..., -15, -15, -15], ..., [-15, -15, 1, ..., -15, -15, -15], [-15, -15, 1, ..., -15, -15, -15], [-15, -15, 2, ..., -15, -15, -15]], dtype=int8)
array([[-9999, -9999, -9999, ..., -9999, -9999, -9999], [-9999, -9999, -9999, ..., -9999, -9999, -9999], [-9999, -9999, -9999, ..., -9999, -9999, -9999], ..., [-9999, -9999, -9999, ..., -9999, -9999, -9999], [-9999, -9999, -9999, ..., -9999, -9999, -9999], [-9999, -9999, -9999, ..., -9999, -9999, -9999]], dtype=int32)
array([[-9999, -9999, -9999, ..., -9999, -9999, -9999], [-9999, -9999, -9999, ..., -9999, -9999, -9999], [-9999, -9999, -9999, ..., -9999, -9999, -9999], ..., [-9999, -9999, -9999, ..., -9999, -9999, -9999], [-9999, -9999, -9999, ..., -9999, -9999, -9999], [-9999, -9999, -9999, ..., -9999, -9999, -9999]], dtype=int32)
array([[-9999, -9999, -9999, ..., -9999, -9999, -9999], [-9999, -9999, -9999, ..., -9999, -9999, -9999], [-9999, -9999, -9999, ..., -9999, -9999, -9999], ..., [-9999, -9999, -9999, ..., -9999, -9999, -9999], [-9999, -9999, -9999, ..., -9999, -9999, -9999], [-9999, -9999, -9999, ..., -9999, -9999, -9999]], dtype=int32)
array([[11., 11., 11., ..., 11., 11., 11.], [11., 11., 11., ..., 11., 11., 11.], [11., 11., 11., ..., 11., 11., 11.], ..., [11., 11., 11., ..., 11., 11., 11.], [11., 11., 11., ..., 11., 11., 11.], [11., 11., 11., ..., 11., 11., 11.]], dtype=float32)
array([[1000., 1000., 1000., ..., 1000., 1000., 1000.], [1000., 1000., 1000., ..., 1000., 1000., 1000.], [1000., 1000., 1000., ..., 1000., 1000., 1000.], ..., [1000., 1000., 1000., ..., 1000., 1000., 1000.], [1000., 1000., 1000., ..., 1000., 1000., 1000.], [1000., 1000., 1000., ..., 1000., 1000., 1000.]], dtype=float32)
array([[41.55638 , 41.55638 , 41.55605 , ..., 41.519417, 41.51908 , 41.51908 ], [41.55638 , 41.55638 , 41.55605 , ..., 41.519417, 41.51908 , 41.51908 ], [41.555267, 41.555267, 41.554935, ..., 41.518303, 41.517967, 41.517967], ..., [41.423935, 41.423935, 41.423603, ..., 41.387066, 41.386734, 41.386734], [41.42282 , 41.42282 , 41.42249 , ..., 41.385956, 41.385624, 41.385624], [41.42282 , 41.42282 , 41.42249 , ..., 41.385956, 41.385624, 41.385624]], dtype=float32)
array([[-73.80026 , -73.80026 , -73.798775, ..., -73.63527 , -73.63379 , -73.63379 ], [-73.80026 , -73.80026 , -73.798775, ..., -73.63527 , -73.63379 , -73.63379 ], [-73.800705, -73.800705, -73.79922 , ..., -73.63572 , -73.63423 , -73.63423 ], ..., [-73.85289 , -73.85289 , -73.85141 , ..., -73.68823 , -73.68675 , -73.68675 ], [-73.85333 , -73.85333 , -73.85185 , ..., -73.688675, -73.687195, -73.687195], [-73.85333 , -73.85333 , -73.85185 , ..., -73.688675, -73.687195, -73.687195]], dtype=float32)
Plot the TOPOGRAPHY variable, the high-resolution elevation layer
This is the layer that controls much of the terrain routing. You'll notice the higher resolution of this layer compared to the HGT_M field in the geogrid.
fulldom.TOPOGRAPHY.plot(cmap="gist_earth")
<matplotlib.collections.QuadMesh at 0x7f926aaace20>
Plot the CHANNELGRID variable, the location of channel cells on the high-resolution routing grid
fulldom.CHANNELGRID.plot()
<matplotlib.collections.QuadMesh at 0x7f926aa51850>
You should notice an odd gap in the gridded channel network. This is where the lake sits in this particular configuration (gridded routing with a lake).
fulldom.LAKEGRID.plot()
<matplotlib.collections.QuadMesh at 0x7f926a97d820>
Let's also take a look in the NoahMP 2D/3D parameter file, soil_properties.nc
. This is actually a bit of a misnomer, as this file contains parameters related to vegetation, surface, and soil properties. Vegetation and surface properties are in 2D, while soil properties can also (theoretically) vary with depth and are therefore in 3D. All are on the LSM grid.
Open the soil_properties dataset
# Load a dataset
soilprop = xr.open_dataset('~/wrf-hydro-training/output/lesson2/run_gridded_default/DOMAIN/soil_properties.nc')
Print some info about the file
soilprop
<xarray.Dataset> Dimensions: (Time: 1, soil_layers_stag: 4, south_north: 16, west_east: 15) Dimensions without coordinates: Time, soil_layers_stag, south_north, west_east Data variables: bexp (Time, soil_layers_stag, south_north, west_east) float32 4.74 .... cwpvt (Time, south_north, west_east) float32 0.18 0.18 ... 0.18 0.18 dksat (Time, soil_layers_stag, south_north, west_east) float32 5.23e-... dwsat (Time, soil_layers_stag, south_north, west_east) float32 8.05e-... hvt (Time, south_north, west_east) float32 16.0 16.0 ... 16.0 16.0 mfsno (Time, south_north, west_east) float32 2.5 2.5 2.5 ... 2.5 2.5 2.5 mp (Time, south_north, west_east) float32 9.0 9.0 9.0 ... 9.0 9.0 9.0 psisat (Time, soil_layers_stag, south_north, west_east) float32 0.141 ... quartz (Time, soil_layers_stag, south_north, west_east) float32 0.6 ..... refdk (Time, south_north, west_east) float32 2e-06 2e-06 ... 2e-06 2e-06 refkdt (Time, south_north, west_east) float32 3.0 3.0 3.0 ... 3.0 3.0 3.0 rsurfexp (Time, south_north, west_east) float32 5.0 5.0 5.0 ... 5.0 5.0 5.0 slope (Time, south_north, west_east) float32 0.1 0.1 0.1 ... 0.1 0.1 0.1 smcdry (Time, soil_layers_stag, south_north, west_east) float32 0.047 ... smcmax (Time, soil_layers_stag, south_north, west_east) float32 0.434 ... smcref (Time, soil_layers_stag, south_north, west_east) float32 0.312 ... smcwlt (Time, soil_layers_stag, south_north, west_east) float32 0.047 ... vcmx25 (Time, south_north, west_east) float32 60.0 60.0 ... 60.0 60.0 Attributes: TITLE: OUTPUT FROM GEOGRID V4.2 SIMULATION_START_DATE: 0000-00-00_00:00:00 WEST-EAST_GRID_DIMENSION: 16 SOUTH-NORTH_GRID_DIMENSION: 17 BOTTOM-TOP_GRID_DIMENSION: 0 WEST-EAST_PATCH_START_UNSTAG: 1 WEST-EAST_PATCH_END_UNSTAG: 15 WEST-EAST_PATCH_START_STAG: 1 WEST-EAST_PATCH_END_STAG: 16 SOUTH-NORTH_PATCH_START_UNSTAG: 1 SOUTH-NORTH_PATCH_END_UNSTAG: 16 SOUTH-NORTH_PATCH_START_STAG: 1 SOUTH-NORTH_PATCH_END_STAG: 17 GRIDTYPE: C DX: 1000.0 DY: 1000.0 DYN_OPT: 2 CEN_LAT: 41.47101 CEN_LON: -73.74365 TRUELAT1: 30.0 TRUELAT2: 60.0 MOAD_CEN_LAT: 41.47101 STAND_LON: -97.0 POLE_LAT: 90.0 POLE_LON: 0.0 corner_lats: [41.42282 41.55638 41.51908 41.385624... corner_lons: [-73.85333 -73.80026 -73.63379 -73.68... MAP_PROJ: 1 MMINLU: USGS NUM_LAND_CAT: 24 ISWATER: 16 ISLAKE: -1 ISICE: 24 ISURBAN: 1 ISOILWATER: 14 grid_id: 1 parent_id: 1 i_parent_start: 1 j_parent_start: 1 i_parent_end: 16 j_parent_end: 17 parent_grid_ratio: 1 sr_x: 1 sr_y: 1 FLAG_MF_XY: 1 FLAG_LAI12M: 1 history: Thu Oct 29 16:58:54 2020: ncks -O -x -v ... NCO: netCDF Operators version 4.7.9 (Homepage...
array([[[[4.74, ..., 4.74], ..., [4.74, ..., 4.74]], ..., [[4.74, ..., 4.74], ..., [4.74, ..., 4.74]]]], dtype=float32)
array([[[0.18, 0.18, ..., 0.18, 0.18], [0.18, 0.18, ..., 0.18, 0.18], ..., [0.18, 0.18, ..., 0.18, 0.18], [0.18, 0.18, ..., 0.18, 0.18]]], dtype=float32)
array([[[[5.23e-06, ..., 5.23e-06], ..., [5.23e-06, ..., 5.23e-06]], ..., [[5.23e-06, ..., 5.23e-06], ..., [5.23e-06, ..., 5.23e-06]]]], dtype=float32)
array([[[[8.05e-06, ..., 8.05e-06], ..., [8.05e-06, ..., 8.05e-06]], ..., [[8.05e-06, ..., 8.05e-06], ..., [8.05e-06, ..., 8.05e-06]]]], dtype=float32)
array([[[16., 16., ..., 16., 16.], [16., 16., ..., 16., 16.], ..., [16., 16., ..., 16., 16.], [16., 16., ..., 16., 16.]]], dtype=float32)
array([[[2.5, 2.5, ..., 2.5, 2.5], [2.5, 2.5, ..., 2.5, 2.5], ..., [2.5, 2.5, ..., 2.5, 2.5], [2.5, 2.5, ..., 2.5, 2.5]]], dtype=float32)
array([[[9., 9., ..., 9., 9.], [9., 9., ..., 9., 9.], ..., [9., 9., ..., 9., 9.], [9., 9., ..., 9., 9.]]], dtype=float32)
array([[[[0.141, ..., 0.141], ..., [0.141, ..., 0.141]], ..., [[0.141, ..., 0.141], ..., [0.141, ..., 0.141]]]], dtype=float32)
array([[[[0.6, ..., 0.6], ..., [0.6, ..., 0.6]], ..., [[0.6, ..., 0.6], ..., [0.6, ..., 0.6]]]], dtype=float32)
array([[[2.e-06, 2.e-06, ..., 2.e-06, 2.e-06], [2.e-06, 2.e-06, ..., 2.e-06, 2.e-06], ..., [2.e-06, 2.e-06, ..., 2.e-06, 2.e-06], [2.e-06, 2.e-06, ..., 2.e-06, 2.e-06]]], dtype=float32)
array([[[3., 3., ..., 3., 3.], [3., 3., ..., 3., 3.], ..., [3., 3., ..., 3., 3.], [3., 3., ..., 3., 3.]]], dtype=float32)
array([[[5., 5., ..., 5., 5.], [5., 5., ..., 5., 5.], ..., [5., 5., ..., 5., 5.], [5., 5., ..., 5., 5.]]], dtype=float32)
array([[[0.1, 0.1, ..., 0.1, 0.1], [0.1, 0.1, ..., 0.1, 0.1], ..., [0.1, 0.1, ..., 0.1, 0.1], [0.1, 0.1, ..., 0.1, 0.1]]], dtype=float32)
array([[[[0.047, ..., 0.047], ..., [0.047, ..., 0.047]], ..., [[0.047, ..., 0.047], ..., [0.047, ..., 0.047]]]], dtype=float32)
array([[[[0.434, ..., 0.434], ..., [0.434, ..., 0.434]], ..., [[0.434, ..., 0.434], ..., [0.434, ..., 0.434]]]], dtype=float32)
array([[[[0.312, ..., 0.312], ..., [0.312, ..., 0.312]], ..., [[0.312, ..., 0.312], ..., [0.312, ..., 0.312]]]], dtype=float32)
array([[[[0.047, ..., 0.047], ..., [0.047, ..., 0.047]], ..., [[0.047, ..., 0.047], ..., [0.047, ..., 0.047]]]], dtype=float32)
array([[[60., 60., ..., 60., 60.], [60., 60., ..., 60., 60.], ..., [60., 60., ..., 60., 60.], [60., 60., ..., 60., 60.]]], dtype=float32)
Plot the soil porosity (smcmax)
Default parameters by soil texture class are mapped from the SOILPARM.TBL
lookup table to the soil type layer in the geogrid (SCT_DOM
) to create an initial distribution of porosity values.
soilprop.smcmax.sel(soil_layers_stag = 0).plot(vmin=0.4, vmax=0.6, cmap="BuPu")
<matplotlib.collections.QuadMesh at 0x7f926a8f9940>
Plot the vegetation height (hvt)
Similarly, for default configurations, vegetation height values are pulled from MPTABLE.TBL
and mapped via the LU_INDEX
field in the geogrid.
soilprop.hvt.plot(cmap="YlGn")
<matplotlib.collections.QuadMesh at 0x7f926a87cf40>
Now we will plot a timeseries from multiple netcdf files using the open_mfdataset
command. We will plot a hydrograph at a gage point.
Open the chanobs multi-file dataset
We are going to use the *CHANOBS*
files because it will limit outputs only to those from grid cells that we have specified as an observation point. We will discuss more about this and other output files in Lesson 4.
NOTE: open_mfdataset
supports wildcards for pattern matching but requires that the path be absolute with no tilde
We will use wildcards *
to open all files that contain 'CHANOBS' in the name.
**NOTE: Because we are opening multiple files, we need to tell xarray how to concatenate them. Because this is a timeseries with time dimension called 'time' we will specify 'time' as the concatenation dimension.
chanobs = xr.open_mfdataset('/home/docker/wrf-hydro-training/output/lesson2/run_gridded_default/*CHANOBS*',
combine='by_coords')
Print some info about the file
chanobs
<xarray.Dataset> Dimensions: (feature_id: 3, reference_time: 1, time: 168) Coordinates: * time (time) datetime64[ns] 2011-08-26T01:00:00 ... 2011-09-02 * reference_time (reference_time) datetime64[ns] 2011-08-26 * feature_id (feature_id) int32 3 2 1 latitude (feature_id) float32 dask.array<chunksize=(3,), meta=np.ndarray> longitude (feature_id) float32 dask.array<chunksize=(3,), meta=np.ndarray> Data variables: crs (time) |S1 b'' b'' b'' b'' b'' b'' ... b'' b'' b'' b'' b'' order (time, feature_id) int32 dask.array<chunksize=(1, 3), meta=np.ndarray> elevation (time, feature_id) float32 dask.array<chunksize=(1, 3), meta=np.ndarray> streamflow (time, feature_id) float32 dask.array<chunksize=(1, 3), meta=np.ndarray> Attributes: TITLE: OUTPUT FROM WRF-Hydro v5.2.0-beta2 featureType: timeSeries proj4: +proj=lcc +lat_0=41.4710083007812 +lon_0=-97 ... model_initialization_time: 2011-08-26_00:00:00 station_dimension: feature_id model_output_valid_time: 2011-08-26_01:00:00 model_total_valid_times: 168 stream_order_output: 1 cdm_datatype: Station Conventions: CF-1.6 code_version: v5.2.0-beta2 model_output_type: channel_rt model_configuration: retrospective dev_OVRTSWCRT: 1 dev_NOAH_TIMESTEP: 3600 dev_channel_only: 0 dev_channelBucket_only: 0 dev: dev_ prefix indicates development/internal me...
array(['2011-08-26T01:00:00.000000000', '2011-08-26T02:00:00.000000000', '2011-08-26T03:00:00.000000000', '2011-08-26T04:00:00.000000000', '2011-08-26T05:00:00.000000000', '2011-08-26T06:00:00.000000000', '2011-08-26T07:00:00.000000000', '2011-08-26T08:00:00.000000000', '2011-08-26T09:00:00.000000000', '2011-08-26T10:00:00.000000000', '2011-08-26T11:00:00.000000000', '2011-08-26T12:00:00.000000000', '2011-08-26T13:00:00.000000000', '2011-08-26T14:00:00.000000000', '2011-08-26T15:00:00.000000000', '2011-08-26T16:00:00.000000000', '2011-08-26T17:00:00.000000000', '2011-08-26T18:00:00.000000000', '2011-08-26T19:00:00.000000000', '2011-08-26T20:00:00.000000000', '2011-08-26T21:00:00.000000000', '2011-08-26T22:00:00.000000000', '2011-08-26T23:00:00.000000000', '2011-08-27T00:00:00.000000000', '2011-08-27T01:00:00.000000000', '2011-08-27T02:00:00.000000000', '2011-08-27T03:00:00.000000000', '2011-08-27T04:00:00.000000000', '2011-08-27T05:00:00.000000000', '2011-08-27T06:00:00.000000000', '2011-08-27T07:00:00.000000000', '2011-08-27T08:00:00.000000000', '2011-08-27T09:00:00.000000000', '2011-08-27T10:00:00.000000000', '2011-08-27T11:00:00.000000000', '2011-08-27T12:00:00.000000000', '2011-08-27T13:00:00.000000000', '2011-08-27T14:00:00.000000000', '2011-08-27T15:00:00.000000000', '2011-08-27T16:00:00.000000000', '2011-08-27T17:00:00.000000000', '2011-08-27T18:00:00.000000000', '2011-08-27T19:00:00.000000000', '2011-08-27T20:00:00.000000000', '2011-08-27T21:00:00.000000000', '2011-08-27T22:00:00.000000000', '2011-08-27T23:00:00.000000000', '2011-08-28T00:00:00.000000000', '2011-08-28T01:00:00.000000000', '2011-08-28T02:00:00.000000000', '2011-08-28T03:00:00.000000000', '2011-08-28T04:00:00.000000000', '2011-08-28T05:00:00.000000000', '2011-08-28T06:00:00.000000000', '2011-08-28T07:00:00.000000000', '2011-08-28T08:00:00.000000000', '2011-08-28T09:00:00.000000000', '2011-08-28T10:00:00.000000000', '2011-08-28T11:00:00.000000000', '2011-08-28T12:00:00.000000000', '2011-08-28T13:00:00.000000000', '2011-08-28T14:00:00.000000000', '2011-08-28T15:00:00.000000000', '2011-08-28T16:00:00.000000000', '2011-08-28T17:00:00.000000000', '2011-08-28T18:00:00.000000000', '2011-08-28T19:00:00.000000000', '2011-08-28T20:00:00.000000000', '2011-08-28T21:00:00.000000000', '2011-08-28T22:00:00.000000000', '2011-08-28T23:00:00.000000000', '2011-08-29T00:00:00.000000000', '2011-08-29T01:00:00.000000000', '2011-08-29T02:00:00.000000000', '2011-08-29T03:00:00.000000000', '2011-08-29T04:00:00.000000000', '2011-08-29T05:00:00.000000000', '2011-08-29T06:00:00.000000000', '2011-08-29T07:00:00.000000000', '2011-08-29T08:00:00.000000000', '2011-08-29T09:00:00.000000000', '2011-08-29T10:00:00.000000000', '2011-08-29T11:00:00.000000000', '2011-08-29T12:00:00.000000000', '2011-08-29T13:00:00.000000000', '2011-08-29T14:00:00.000000000', '2011-08-29T15:00:00.000000000', '2011-08-29T16:00:00.000000000', '2011-08-29T17:00:00.000000000', '2011-08-29T18:00:00.000000000', '2011-08-29T19:00:00.000000000', '2011-08-29T20:00:00.000000000', '2011-08-29T21:00:00.000000000', '2011-08-29T22:00:00.000000000', '2011-08-29T23:00:00.000000000', '2011-08-30T00:00:00.000000000', '2011-08-30T01:00:00.000000000', '2011-08-30T02:00:00.000000000', '2011-08-30T03:00:00.000000000', '2011-08-30T04:00:00.000000000', '2011-08-30T05:00:00.000000000', '2011-08-30T06:00:00.000000000', '2011-08-30T07:00:00.000000000', '2011-08-30T08:00:00.000000000', '2011-08-30T09:00:00.000000000', '2011-08-30T10:00:00.000000000', '2011-08-30T11:00:00.000000000', '2011-08-30T12:00:00.000000000', '2011-08-30T13:00:00.000000000', '2011-08-30T14:00:00.000000000', '2011-08-30T15:00:00.000000000', '2011-08-30T16:00:00.000000000', '2011-08-30T17:00:00.000000000', '2011-08-30T18:00:00.000000000', '2011-08-30T19:00:00.000000000', '2011-08-30T20:00:00.000000000', '2011-08-30T21:00:00.000000000', '2011-08-30T22:00:00.000000000', '2011-08-30T23:00:00.000000000', '2011-08-31T00:00:00.000000000', '2011-08-31T01:00:00.000000000', '2011-08-31T02:00:00.000000000', '2011-08-31T03:00:00.000000000', '2011-08-31T04:00:00.000000000', '2011-08-31T05:00:00.000000000', '2011-08-31T06:00:00.000000000', '2011-08-31T07:00:00.000000000', '2011-08-31T08:00:00.000000000', '2011-08-31T09:00:00.000000000', '2011-08-31T10:00:00.000000000', '2011-08-31T11:00:00.000000000', '2011-08-31T12:00:00.000000000', '2011-08-31T13:00:00.000000000', '2011-08-31T14:00:00.000000000', '2011-08-31T15:00:00.000000000', '2011-08-31T16:00:00.000000000', '2011-08-31T17:00:00.000000000', '2011-08-31T18:00:00.000000000', '2011-08-31T19:00:00.000000000', '2011-08-31T20:00:00.000000000', '2011-08-31T21:00:00.000000000', '2011-08-31T22:00:00.000000000', '2011-08-31T23:00:00.000000000', '2011-09-01T00:00:00.000000000', '2011-09-01T01:00:00.000000000', '2011-09-01T02:00:00.000000000', '2011-09-01T03:00:00.000000000', '2011-09-01T04:00:00.000000000', '2011-09-01T05:00:00.000000000', '2011-09-01T06:00:00.000000000', '2011-09-01T07:00:00.000000000', '2011-09-01T08:00:00.000000000', '2011-09-01T09:00:00.000000000', '2011-09-01T10:00:00.000000000', '2011-09-01T11:00:00.000000000', '2011-09-01T12:00:00.000000000', '2011-09-01T13:00:00.000000000', '2011-09-01T14:00:00.000000000', '2011-09-01T15:00:00.000000000', '2011-09-01T16:00:00.000000000', '2011-09-01T17:00:00.000000000', '2011-09-01T18:00:00.000000000', '2011-09-01T19:00:00.000000000', '2011-09-01T20:00:00.000000000', '2011-09-01T21:00:00.000000000', '2011-09-01T22:00:00.000000000', '2011-09-01T23:00:00.000000000', '2011-09-02T00:00:00.000000000'], dtype='datetime64[ns]')
array(['2011-08-26T00:00:00.000000000'], dtype='datetime64[ns]')
array([3, 2, 1], dtype=int32)
|
|
array([b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b''], dtype='|S1')
|
|
|
Here we can see that we have a time dimension of length 168 corresponding to the 168 hourly output files from our simulation run_gridded_default
.
Plot a hydrograph for 1 gage point
Now we will select 1 gage from the dataset and plot our streamflow variable. For more information on indexing and selecting data with xarray see the xarray documentation
chanobs.sel(feature_id = 2).streamflow.plot()
[<matplotlib.lines.Line2D at 0x7f9253567f40>]
This concludes lesson 3. In the next lesson we will discuss run-time options and experiment with different options and viewing the effect on the model behavoir using xarray.
IT IS BEST TO EITHER SHUTDOWN THIS LESSON OR CLOSE IT BEFORE PROCEEDING TO THE NEXT LESSON TO AVOID POSSIBLY EXCEEDING ALLOCATED MEMORY. Shutdown the lesson be either closing the browser tab for the lesson or selecting Kernel -> Shut Down Kernel
in JupyterLab.
© UCAR 2020