.. _gen_ma_2025_cog:

COG files for Ma et al. 2025 random forest water table depth and uncertainty at the 1 arcsec resolution
^^^^^^^^^^^^^^^^^^

A copy of the ma_2025 30m dataset stored as a COG (Cloud Optimized Geotiff). You can download the full WTD COG file using `hf_hydrodata.get_raw_file() <https://hf-hydrodata.readthedocs.io/en/latest/hf_hydrodata.gridded.html#hf_hydrodata.gridded.get_raw_file>`_. See the linked Python API Reference for example syntax.


**Dataset Name**: ma_2025_cog

**Data Source**: ma_2025

**Citations:**

Please refer to the following citations for more information on this dataset and cite them if you use the data

* https://doi.org/10.1038/s43247-025-03094-3

**Extent and Resolution**:

* Available Date Range: 

* Grid: conus2_wtd.30

  - Spacial Resolution:  24.14076631 meters

  - XY Grid Spacial Extent:  246056 x 144287

  - LatLon Spacial Exent:  -126.88755692881833, 21.8170599154073,  -64.7677149695924, 53.20274381640737

  - Origin (meters): -2848561.29, -1724573.11

  - Projection: +proj=lcc +lat_1=30 +lat_2=60 +lon_0=-97.0 +lat_0=40.0000076294444 +a=6370000.0 +b=6370000

Variables
^^^^^^^^^^^^^^^^^^

This describes the available variables of the dataset.
Use the dataset, variables and temporal_resolution in python access functions as described in the Working with Gridded Data, and Working with Point Observations.

.. list-table:: Subsurface Variables
    :widths: 25 60 30 20 20 20
    :header-rows: 1

    * - variable
      - description
      - temporal_resolution
      - units
      - grid
      - 3D
    * - wtd_uncertainty
      - Uncertainty in water table depth estimation from a random forest model
      - static
      - m
      - conus2_wtd.30
      - no
    * - water_table_depth
      - Water table depth
      - static
      - m
      - conus2_wtd.30
      - no