Getting Started

The goal of the smodex package is to ensure the seamless computation of soil moisture anomalies from climate datasets. Hence, the main steps involved in this computation have been simplified to:

  1. Data Loading,

  2. Climatology computation,

  3. Anomalies computation,

  4. Data visualization, and

  5. Data sharing

In this page, we walk you through how to utilize the main functionalities of the smodex package to perform these actions. Ensure you have the smodex package, you can consult the installation guide for the different ways of installing the package in your development environment.

1. Data Loading

The smodex downloader module provides functionalities that enables you access datasets from the ERA5 Climate Data Store by specifying the details of the requested datasets in a JSON file and providing all the neccesary information for downloading the datasets you need. To do this, follow the following steps:

  1. Step 1. Ensure you have the ERA5 CDS API installed in your development environment. This can be done by:

pip install cdsapi

This provides all the neccesary functionalities and backends for accessing the CDS datasets.

Next to this, create a configuration file (JSON file) that contains all the data specification for the data you would like to request, example:

{
    "product_type": "reanalysis",
    "variable": [
        "volumetric_soil_water_layer_1",
        "volumetric_soil_water_layer_2",
        "volumetric_soil_water_layer_3",
        "volumetric_soil_water_layer_4"
        ],
    "year": 1981,
    "month": [ "01", "02", "03"],
    "day": ["01", "08", "16", "24", "30"],
    "time": ["00:00", "06:00", "12:00", "18:00"],
    "area": [47.148, 10.255, 46.297, 12.542],
    "format": "netcdf"
    }

This JSON file in general should contain the information on your Area of Interest and other specific information on the datasets you would like to download.

  1. Step 2. Specify the time range (start date and end date) and download your data to the specified path:

from smodex.downloader import cds_downloader
start_date = '2010-01-01'
end_date = '2020-12-31'
conf_path = 'download.json'
download_path = 'moisture_data/'

cds_downloader(start_date = start_date,
               end_date = end_date,
               conf_path = conf_path,
               download_path = download_path
               )

  1. Download data files

  2. Compute climatology based on reference period

  3. Compute anomaly based on specified time-scale

2. Climatology computation

This steps simply computes the average and standard deviation of soil moisture climatology using one of a weekly, dekadal, or monthly rolling means. The expected file input is an annual netcdf file that contains daily volumetric soil water layers (see the previous step for direction on how to access this data from the Climate Data Store)

from smodex.sm_climatology import SMClimatology
input_path = "path/to/raw/soil_moisture_files/"
clim_path = "path/to/to/be/computed/climatological/files/"
reference_dates = ('1991-01-01', '2020-12-31') # climatology reference period

sm_climate = SMClimatology(in_path,
                           climatology_path,
                           reference)
sm_climate.compute_climatology()

3. Anomalies computation

With the previous steps, we are now ready to compute the soil moisture anomalies for our area of interest using the compute_anomalies() function.

in_path = "path/to/raw/soil_moisture_files/"
out_path = "path/to/store/anomalies/files"
clim_path = "path/to/computed/climatological/files/"
reference=("1991-01-01", "2020-12-31")
c_year="2012" # year to compute anomalies for
out_format="netcdf" # output format
area=(3.685, 42.991, 17.162, 50.565)
interp_method="linear"
espg_code="3035"


def compute_anomalies(
    in_path,
    out_path,
    clim_path,
    reference=("1991-01-01", "2020-12-31"),
    c_year="2019",
    out_format="netcdf",
    area=(3.685, 42.991, 17.162, 50.565),
    interp_method="linear",
    espg_code="3035"
    )

4. Data visualization

Coming soon!

5. Data sharing

Coming soon!