Climate Data

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Climate Data

[1]:
import matplotlib.pyplot as plt
import pydaymet as daymet
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-01031500:

[2]:
geometry = NLDI().get_basins("01031500").geometry[0]

PyDaymet 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 pydaymet.get_bycoords and for gridded data is called pydaymet.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].

Additionally, we can pass time_scale to get daily, monthly or annual summaries. This flag by default is set to daily. We can pass time_scale as daily, monthly, or annual to get_bygeom or get_bycoords functions to download the respective summaries.

PyDaymet can also compute Potential EvapoTranspiration (PET) at daily time scale using three methods: penman_monteith, priestley_taylor, and hargreaves_samani. Let’s get the data for six days and plot PET.

[3]:
dates = ("2000-01-01", "2000-01-06")
daily = daymet.get_bygeom(geometry, dates, variables="prcp", pet="hargreaves_samani")
[4]:
ax = daily.where(daily.pet > 0).pet.plot(x="x", y="y", row="time", col_wrap=3)
ax.fig.savefig("_static/daymet_grid.png", facecolor="w", bbox_inches="tight")
../_images/notebooks_daymet_6_0.png

Now, let’s get the monthly summaries for six months.

[5]:
var = ["prcp", "tmin"]
dates = ("2000-01-01", "2000-06-30")
monthly = daymet.get_bygeom(geometry, dates, variables=var, time_scale="monthly")
[6]:
ax = monthly.where(monthly.prcp > 0).prcp.plot(x="x", y="y", row="time", col_wrap=3)
../_images/notebooks_daymet_9_0.png

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.

[7]:
coords = (-1431147.7928, 318483.4618)
crs = "epsg:3542"
dates = ("2000-01-01", "2006-12-31")
annual = daymet.get_bycoords(
    coords, dates, variables=["prcp", "tmin"], crs=crs, time_scale="annual"
)
[8]:
fig = plt.figure(figsize=(6, 4), facecolor="w")

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

plt.tight_layout()
fig.savefig("_static/daymet_loc.png", facecolor="w", bbox_inches="tight")
../_images/notebooks_daymet_12_0.png

Next, let’s get annual total precipitation for Hawaii and Puerto Rico for 2010.

[9]:
hi_ext = (-160.3055, 17.9539, -154.7715, 23.5186)
pr_ext = (-67.9927, 16.8443, -64.1195, 19.9381)
hi = daymet.get_bygeom(hi_ext, 2010, variables="prcp", region="hi", time_scale="annual")
pr = daymet.get_bygeom(pr_ext, 2010, variables="prcp", region="pr", time_scale="annual")
[10]:
ax = hi.where(hi.prcp > 0).prcp.plot(size=4)
plt.title("Hawaii, 2010")
ax.figure.savefig("_static/hi.png", facecolor="w", bbox_inches="tight")
../_images/notebooks_daymet_15_0.png
[11]:
ax = pr.where(pr.prcp > 0).prcp.plot(size=4)
plt.title("Puerto Rico, 2010")
ax.figure.savefig("_static/pr.png", facecolor="w", bbox_inches="tight")
../_images/notebooks_daymet_16_0.png