diff --git a/docs/howto/authentication.rst b/docs/howto/authentication.rst
index c65563f8..17e6c04a 100644
--- a/docs/howto/authentication.rst
+++ b/docs/howto/authentication.rst
@@ -38,7 +38,20 @@ authentication methods:
# The credentials and project_id arguments can be omitted.
df = pandas_gbq.read_gbq("SELECT my_col FROM `my_dataset.my_table`")
-2. Application Default Credentials via the :func:`google.auth.default`
+2. If running on `Google Colab `_,
+ pandas-gbq attempts to authenticate with the
+ ``google.colab.auth.authenticate_user()`` method. See the `Getting started
+ with BigQuery on Colab notebook
+ `_ for an
+ example of using this authentication method with other libraries that use
+ Google BigQuery.
+
+ .. note::
+
+ To use Colab authentication, install version 1.8.0 or later of the
+ ``pydata-google-auth`` package.
+
+3. Application Default Credentials via the :func:`google.auth.default`
function.
.. note::
@@ -48,10 +61,11 @@ authentication methods:
user account credentials.
A common problem with default credentials when running on Google
- Compute Engine is that the VM does not have sufficient scopes to query
- BigQuery.
+ Compute Engine is that the VM does not have sufficient `access scopes
+ `_
+ to query BigQuery.
-3. User account credentials.
+4. User account credentials.
pandas-gbq loads cached credentials from a hidden user folder on the
operating system.
@@ -214,5 +228,5 @@ more of the following circumstances:
(or similar) notebook.
If the conditions above apply to you, your needs may be better served
-by the content in the `Authentication (Highly Constrained Development Environment)
+by the content in the `Authentication (Highly Constrained Development Environment)
`_ section.