LakePy is the pythonic user-centered front-end to the Global Lake Level Database. This package can instantly deliver lake water levels for some 2000+ lakes scattered across the globe. Data comes from three sources (so far!)

• United States Geological Survey National Water Information System
• United States Department of Agriculture: Foreign Agricultural Service's G-REALM Database
• Theia's HydroWeb Database

Funding for this work comes from the Earth Science Information Partners (ESIP) Winter 2020 Grant

See the funded proposal here

Lake level data is incredibly important to federal and local governments, scientists, and citizens. Until now, accessing lake level data involves laborious data-preparation and wrangling. We aim to provide this data quickly and on-demand.

Built with

• Python
  • Pandas
  • PyMySQL
  • Boto3
• Amazon MySQL RDS
• Amazon API Gateway
• Amazon Lambda

• For a full API Reference, please consult LakePy's documentation
• For a list of Lakes with corresponding ID numbers, see the Lake Reference List

pip install lakepy

if you are using conda for package management you can still use pip!

The database can be searched using a name, a source ("grealm", "hydroweb", or "usgs"), or an identification number . The best practice for searching is to first specify a name.

Let's search for Lake Mead instantiating a Lake() object.

import lakepy as lk
my_lake = lk.search("mead")

If there is more than one Lake matching "Mead", the search function will return a RuntimeWarning and display a table.

"Search Result: 'Mead' has more than 1 Result. Showing the 2 most relevant results. Specify 'id_No' or narrow search name."

We will select id_No 138 corresponding to Lake Mead from HydroWeb's database and re-run our search 1 of 2 ways:

• Specify the id_No explicitly as a string

my_lake = lk.search(id_No = "138")

• Specify a name and a source

my_lake = lk.search(name="mead", source="hydroweb", markdown=True)

We highly recommend specifying an id_No whenever possible to avoid issues with similarly named lakes. Either way , the search returns a metadata markdown dataframe

It is important to note that different databases will return different types and amounts of metadata. Currently latitude & longitude are only available from the USGS and HydroWeb databases, but GREALM lakes will soon have them!

The "my_lake" variable is now an object of class Lake() which comes with several attributes

• name
• country
• continent (currently not supported for HydroWeb)
• source
• original_id
• id_No
• observation_period
• latitude (currently not supported for G-REALM)
• longitude (currently not supported for G-REALM)
• misc_data
• dataframe
• data

importantly, my_lake.dataframe and my_lake.data are pandas dataframe instances with associated methods

my_lake.dataframe.describe().to_markdown()

LakePy allows for native time series plotting as well as map-view plots

my_lake.plot_timeseries()

Seaborn/Matplotlib

my_lake.plot_timeseries(how='seaborn')

my_lake.plot_mapview()

Please refer to the LakePy documentation

We would love your help in making this project better. Please refer to our contribution guide to learn how.

Please consider citing us if you use LakePy in your research! The recommended citation is:

James Gearon, & John Franey. (2021, January 4). ESIPFed/LakePy v2.1.0 (Version v2.1.0). Zenodo. http://doi.org/10. 5281/zenodo.4415936

This work is based on funding provided by the ESIP Lab with support from the National Aeronautics and Space Administration (NASA), National Oceanic and Atmospheric Administration (NOAA) and the United States Geologic Survey (USGS). LakePy received additional, generous support in 2021 from Derek Masaki and Farial Shahnaz. Many thanks to them.

MIT © James Hooker Gearon & John Franey