19 Assignment 2 - Evapotranspiration

19.1 📒 instructions

This is where learning happens, not during a lecture. You’ll learn a ton of things by doing them yourself. Much success! 😄

Create a Jupyter Notebook called assignment-02-IDNUMBER, where IDNUMBER is your 9-digit ID. This is the file only file we will check.

19.2 📌 locations and data

Choose two stations with different climates.

Go to NOAA’s Climate Reference Network Data website. The sub-hourly (5-min) data contains information on

air temperature,
precipitation,
global solar radiation,
surface infrared temperature,
relative humidity,
soil moisture and temperature,
wetness, and
1.5 meter wind speed.

There is no data on air pressure, so one needs to use the stations coordinates (lat, lon) to find its height above sea level, and from that infer the air pressure. You can use Google Earth or any other means to find the station’s height.

In the Data Access link, choose a year and a station you would like to analyze. If you are not sure where the stations are, find them using the 2-letter state abbreviation and the station name.

Download the following files: 1. One full year of data for each station. Make sure important data we need to calculate Penman’s ET estimation is available. 2. The headers file 3. The documentation file

Make sure you understand what are the units provided for each measurement (see documentation).

19.3 🛠 tasks

Produce potential ET estimates using Thornthwaite’s equation and Penman’s equation. Produce plots of ET as a function of time for each station, comparing the two methods you used. Also, using Penman’s ET estimates, compare the two stations and discuss about their differences/similarities.

You might find interesting things in the data, such as periods of unusually high/low temperatures, radiation, etc. Discuss how these factors might have affected the ET estimates that you calculated.

You will have two weeks to deliver your assignment. You should not hand in a dry document with only figures and code, I’m expecting text before and after each code/graph cell, explaining what you did, why you did it, and how it fits the story you are telling. Don’t forget to put labels on your plot axes, title, legend, etc.

Your Jupyter Notebook should be fully functional: if we press Kernel > Restart & Run All, all the code must work without any errors.

19.4 🚚 importing the data

Below you can find an example of how to import the data file provided by NOAA’s Climate Reference Network Data website. You might have to make some adjustments to it.

data_file = "CRNS0101-05-2020-CO_Boulder_14_W.txt"
df = pd.read_csv(data_file,
                 header=None,                      # no headers needed, we'll do that later
                 delim_whitespace=True,            # blank spaces separate between columns
                 na_values=["-99.000", "-9999.0"]  # substitute these values for missing (NaN) values
                )
headers = pd.read_csv("HEADERS_sub_hourly.txt",    # load headers file
                      header=1,                    # skip the first [0] line
                      delim_whitespace=True
                     )
df.columns = headers.columns                       # rename df columns with headers columns
# LST = local standard time
df["LST_TIME"] = [f"{x:04d}" for x in df["LST_TIME"]]  # time needs padding of zeros, then convert to string
df['LST_DATE'] = df['LST_DATE'].astype(str)            # convert date into string
df['datetime'] = df['LST_DATE'] + ' ' + df['LST_TIME'] # combine date+time into datetime
df['datetime'] = pd.to_datetime(df['datetime'])        # interpret datetime
df = df.set_index('datetime')                          # make datetime the index
df