DOC: clarify hsu soiling ratio minimum value in docstring

docs/sphinx/source/whatsnew/v0.15.2.rst

@@ -22,6 +22,9 @@ Enhancements
 
 Documentation
 ~~~~~~~~~~~~~
+* Clarified that :py:func:`pvlib.soiling.hsu` has an implicit minimum
+  soiling ratio of approximately 0.6563 due to the mathematical form
+  of the model. (:issue:`2534`, :pull:`2743`)
 
 
 Testing
@@ -42,4 +45,4 @@ Maintenance
 
 Contributors
 ~~~~~~~~~~~~
-
+* :ghuser:`Omesh37`

pvlib/soiling.py

@@ -16,9 +16,11 @@ def hsu(rainfall, cleaning_threshold, surface_tilt, pm2_5, pm10,
     Calculates soiling ratio given particulate and rain data using the
     Fixed Velocity model from Humboldt State University (HSU).
 
-    The HSU soiling model [1]_ returns the soiling ratio, a value between zero
-    and one which is equivalent to (1 - transmission loss). Therefore a soiling
-    ratio of 1.0 is equivalent to zero transmission loss.
+    The HSU soiling model [1]_ returns the soiling ratio, a value between
+    zero and one which is equivalent to (1 - transmission loss).
+    Therefore a soiling ratio of 1.0 is equivalent to zero transmission loss.
+    Due to the mathematical form of the HSU model, the soiling ratio has a
+    minimum of approximately 0.6563. See ``Notes`` for details.
 
     Parameters
     ----------
@@ -54,6 +56,17 @@ def hsu(rainfall, cleaning_threshold, surface_tilt, pm2_5, pm10,
     soiling_ratio : Series
         Values between 0 and 1. Equal to 1 - transmission loss.
 
+    Notes
+    -------
+    Due to the mathematical form of the HSU model
+    (``SR = 1 - 0.3437 * erf(0.17 * ω^0.8473)``),
+    the soiling ratio has a minimum value of approximately 0.6563
+    (i.e., maximum transmission loss of ~34.37%), regardless of
+    the accumulated particulate mass. The HSU model is developed
+    (validated) for accumulated mass densities up to 10 g/m²,
+    corresponding to a soiling ratio of approximately 0.6875.
+    See [1]_ for details.
+
     References
     -----------
     .. [1] M. Coello and L. Boyle, "Simple Model For Predicting Time Series