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Climate Data from GridMET

from pathlib import Path

import matplotlib.pyplot as plt
import pygridmet as gridmet
from pygridmet import GridMET

from pynhd import NLDI

The Daymet database provides climatology data at 1-km resolution. First, we use PyNHD to get the contributing watershed geometry of a NWIS station with the ID of USGS-01318500:

geometry = NLDI().get_basins("01318500").geometry[0]

PyGridMET allows us to get the data for a single pixel or for a region as gridded data. The function to get single pixel is called pygridmet.get_bycoords and for gridded data is called pygridmet.get_bygeom. The arguments of these functions are identical except the first argument where the latter should be polygon and the former should be a coordinate (a tuple of length two as in (x, y)).

The input geometry or coordinate can be in any valid CRS (defaults to EPSG:4326). The date argument can be either a tuple of length two like (start_str, end_str) or a list of years like [2000, 2005].

We can get a dataframe of available variables and their info by calling GridMET().gridmet_table.

GridMET().gridmet_table

	variable	abbr	long_name	units
0	Precipitation	pr	precipitation_amount	mm
1	Maximum Relative Humidity	rmax	daily_maximum_relative_humidity	%
2	Minimum Relative Humidity	rmin	daily_minimum_relative_humidity	%
3	Specific Humidity	sph	daily_mean_specific_humidity	kg/kg
4	Surface Radiation	srad	daily_mean_shortwave_radiation_at_surface	W/m2
5	Wind Direction	th	daily_mean_wind_direction	Degrees clockwise from north
6	Minimum Air Temperature	tmmn	daily_minimum_temperature	K
7	Maximum Air Temperature	tmmx	daily_maximum_temperature	K
8	Wind Speed	vs	daily_mean_wind_speed	m/s
9	Burning Index	bi	daily_mean_burning_index_g	-
10	Fuel Moisture (100-hr)	fm100	dead_fuel_moisture_100hr	%
11	Fuel Moisture (1000-hr)	fm1000	dead_fuel_moisture_1000hr	%
12	Energy Release Component	erc	daily_mean_energy_release_component-g	-
13	Reference Evapotranspiration (Alfalfa)	etr	daily_mean_reference_evapotranspiration_alfalfa	mm
14	Reference Evapotranspiration (Grass)	pet	daily_mean_reference_evapotranspiration_grass	mm
15	Vapor Pressure Deficit	vpd	daily_mean_vapor_pressure_deficit	kPa

dates = ("2000-01-01", "2000-01-06")
daily = gridmet.get_bygeom(geometry, dates, variables=["pr", "pet"])

ax = daily.where(daily.pet > 0).pet.plot(x="lon", y="lat", row="time", col_wrap=3)
ax.fig.savefig(Path("_static", "gridmet_grid.png"), facecolor="w", bbox_inches="tight")

Note that the default CRS is EPSG:4326. If the input geometry (or coordinate) is in a different CRS we can pass it to the function. The gridded data are automatically masked to the input geometry. Now, Let’s get the data for a coordinate in EPSG:3542 CRS.

coords = (-1431147.7928, 318483.4618)
crs = 3542
dates = ("2000-01-01", "2006-12-31")
clm = gridmet.get_bycoords(coords, dates, variables=["pr", "tmmn"], crs=crs)

fig = plt.figure(figsize=(6, 4), facecolor="w")

gs = fig.add_gridspec(1, 2)
axes = gs[:].subgridspec(2, 1, hspace=0).subplots(sharex=True)
clm["tmmn (K)"].plot(ax=axes[0], color="r")
axes[0].set_ylabel(r"$T_{min}$ ($^\circ$C)")
axes[0].xaxis.set_ticks_position("none")
clm["pr (mm)"].plot(ax=axes[1])
axes[1].set_ylabel("$P$ (mm/day)")

plt.tight_layout()
fig.savefig("_static/gridmet_loc.png", facecolor="w", bbox_inches="tight")