Process-guided deep learning water temperature predictions: 3a Lake Mendota inputs

Description

This dataset includes model inputs that describe local weather conditions for Lake Mendota, WI. Weather data comes from two sources: locally measured (2009-2017) and gridded estimates (all other time periods). There are two comma-delimited files, one for weather data (one row per model timestep) and one for ice-flags, which are used by the process-guided deep learning model to determine whether to apply the energy conservation constraint (the constraint is not applied when the lake is presumed to be ice-covered). The ice-cover flag is a modeled output and therefore not a true measurement (see "Predictions" and "pb0" model type for the source of this prediction). This dataset is part of a larger data release of lake temperature model inputs and outputs for 68 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9AQPIVD).

Resources

Name Format Description Link
55 The metadata original source https://data.doi.gov/harvest/object/2e51be34-c76d-43d8-b682-deb699c84aeb
0 http://dx.doi.org/10.1029/2003JD003823
0 CSDGM IMPORT ERROR: No digtinfo/formcont http://dx.doi.org/10.5066/P9AQPIVD
0 https://doi.org/10.5194/gmd-12-473-2019
0 http://dx.doi.org/10.1029/2019WR024922

Tags

  • reservoirs
  • hybrid-modeling
  • modeling
  • temperate-lakes
  • deep-learning
  • united-states
  • thermal-profiles
  • us
  • climate-change
  • usgs-5d98e0c4e4b0c4f70d1186f1
  • water
  • temperature
  • machine-learning

Topics

Categories