diff --git a/dataretrieval/waterdata/api.py b/dataretrieval/waterdata/api.py
index fc88f9d4..a6f14723 100644
--- a/dataretrieval/waterdata/api.py
+++ b/dataretrieval/waterdata/api.py
@@ -165,8 +165,7 @@ def get_daily(
         if your internet connection is spotty. The default (NA) will set the
         limit to the maximum allowable limit for the service.
     convert_type : boolean, optional
-        If True, the function will convert the data to dates and qualifier to
-        string vector
+        If True, converts columns to appropriate types.
 
     Returns
     -------
@@ -475,6 +474,8 @@ def get_monitoring_locations(
         The returning object will be a data frame with no spatial information.
         Note that the USGS Water Data APIs use camelCase "skipGeometry" in
         CQL2 queries.
+    convert_type : boolean, optional
+        If True, converts columns to appropriate types.
 
     Returns
     -------
@@ -666,8 +667,7 @@ def get_time_series_metadata(
         if your internet connection is spotty. The default (None) will set the
         limit to the maximum allowable limit for the service.
     convert_type : boolean, optional
-        If True, the function will convert the data to dates and qualifier to
-        string vector
+        If True, converts columns to appropriate types.
 
     Returns
     -------
@@ -842,8 +842,7 @@ def get_latest_continuous(
         if your internet connection is spotty. The default (None) will set the
         limit to the maximum allowable limit for the service.
     convert_type : boolean, optional
-        If True, the function will convert the data to dates and qualifier to
-        string vector
+        If True, converts columns to appropriate types.
 
     Returns
     -------
@@ -1017,8 +1016,7 @@ def get_latest_daily(
         if your internet connection is spotty. The default (None) will set the
         limit to the maximum allowable limit for the service.
     convert_type : boolean, optional
-        If True, the function will convert the data to dates and qualifier to
-        string vector
+        If True, converts columns to appropriate types.
 
     Returns
     -------
@@ -1183,8 +1181,7 @@ def get_field_measurements(
         if your internet connection is spotty. The default (None) will set the
         limit to the maximum allowable limit for the service.
     convert_type : boolean, optional
-        If True, the function will convert the data to dates and qualifier to
-        string vector
+        If True, converts columns to appropriate types.
 
     Returns
     -------
diff --git a/dataretrieval/waterdata/utils.py b/dataretrieval/waterdata/utils.py
index 68ae9e13..0f6e8bd9 100644
--- a/dataretrieval/waterdata/utils.py
+++ b/dataretrieval/waterdata/utils.py
@@ -668,32 +668,48 @@ def _arrange_cols(
         return df.rename(columns={"id": output_id})
 
 
-def _cleanup_cols(df: pd.DataFrame, service: str = "daily") -> pd.DataFrame:
+def _type_cols(df: pd.DataFrame) -> pd.DataFrame:
     """
-    Cleans and standardizes columns in a pandas DataFrame for water data endpoints.
+    Casts columns into appropriate types.
 
     Parameters
     ----------
     df : pd.DataFrame
         The input DataFrame containing water data.
-    service : str, optional
-        The type of water data service (default is "daily").
 
     Returns
     -------
     pd.DataFrame
-        The cleaned DataFrame with standardized columns.
+        The DataFrame with columns cast to appropriate types.
 
-    Notes
-    -----
-    - If the 'time' column exists and service is "daily", it is converted to date objects.
-    - The 'value' and 'contributing_drainage_area' columns are coerced to numeric types.
     """
-    if "time" in df.columns and service == "daily":
-        df["time"] = pd.to_datetime(df["time"]).dt.date
-    for col in ["value", "contributing_drainage_area"]:
-        if col in df.columns:
-            df[col] = pd.to_numeric(df[col], errors="coerce")
+    cols = set(df.columns)
+    numerical_cols = [
+        "altitude",
+        "altitude_accuracy",
+        "contributing_drainage_area",
+        "drainage_area",
+        "hole_constructed_depth",
+        "value",
+        "well_constructed_depth",
+        ]
+    time_cols = [
+        "begin",
+        "begin_utc",
+        "construction_date",
+        "end",
+        "end_utc",
+        "datetime", # unused
+        "last_modified",
+        "time",
+        ]
+
+    for col in cols.intersection(time_cols):
+        df[col] = pd.to_datetime(df[col], errors="coerce")
+
+    for col in cols.intersection(numerical_cols):
+        df[col] = pd.to_numeric(df[col], errors="coerce")
+
     return df
 
 
@@ -749,8 +765,10 @@ def get_ogc_data(
     )
     # Manage some aspects of the returned dataset
     return_list = _deal_with_empty(return_list, properties, service)
+
     if convert_type:
-        return_list = _cleanup_cols(return_list, service=service)
+        return_list = _type_cols(return_list)
+
     return_list = _arrange_cols(return_list, properties, output_id)
     # Create metadata object from response
     metadata = BaseMetadata(response)