diff --git a/src/bayesvalidrox/post_processing/post_processing.py b/src/bayesvalidrox/post_processing/post_processing.py
index abafb123d266f7cb95d51ba38be0ab8b66b56415..e61de97c74123a656312b39779f0dd2559c347d8 100644
--- a/src/bayesvalidrox/post_processing/post_processing.py
+++ b/src/bayesvalidrox/post_processing/post_processing.py
@@ -524,19 +524,8 @@ class PostProcessing:
     # -------------------------------------------------------------------------
     def sobol_indices(self, xlabel: str = 'Time [s]', plot_type: str = None, save:bool=True):
         """
-        Provides Sobol indices as a sensitivity measure to infer the importance
-        of the input parameters. See Eq. 27 in [1] for more details. For the
-        case with Principal component analysis refer to [2].
-
-        [1] Global sensitivity analysis: A flexible and efficient framework
-        with an example from stochastic hydrogeology S. Oladyshkin, F.P.
-        de Barros, W. Nowak  https://doi.org/10.1016/j.advwatres.2011.11.001
-
-        [2] Nagel, J.B., Rieckermann, J. and Sudret, B., 2020. Principal
-        component analysis and sparse polynomial chaos expansions for global
-        sensitivity analysis and model calibration: Application to urban
-        drainage simulation. Reliability Engineering & System Safety, 195,
-        p.106737.
+        Visualizes and writes out Sobol' and Total Sobol' indices of the trained metamodel.
+        One file is created for each index and output key.
 
         Parameters
         ----------
@@ -554,13 +543,6 @@ class PostProcessing:
         AttributeError
             MetaModel in given Engine needs to be of type 'pce' or 'apce'.
 
-        Returns
-        -------
-        sobol_cell: dict
-            Sobol indices.
-        total_sobol: dict
-            Total Sobol indices.
-
         """
         # This function currently only supports PCE/aPCE
         metamod = self.engine.MetaModel
diff --git a/src/bayesvalidrox/surrogate_models/polynomial_chaos.py b/src/bayesvalidrox/surrogate_models/polynomial_chaos.py
index 498cb76e6d4fe23c7845dfd0a5aab616aa3cc013..a9f72c2eaae68b36010d357cb8498c0da1331fbb 100644
--- a/src/bayesvalidrox/surrogate_models/polynomial_chaos.py
+++ b/src/bayesvalidrox/surrogate_models/polynomial_chaos.py
@@ -935,8 +935,19 @@ class PCE(MetaModel):
 
     def calculate_sobol(self, save = True):
         """
-        Calculates Sobol' indices of all possible orders and the Total Sobol' indices 
-        based on the coefficients in the PCE.
+        Provides Sobol' indices as a sensitivity measure to infer the importance
+        of the input parameters. See Eq. 27 in [1] for more details. For the
+        case with Principal component analysis refer to [2].
+
+        [1] Global sensitivity analysis: A flexible and efficient framework
+        with an example from stochastic hydrogeology S. Oladyshkin, F.P.
+        de Barros, W. Nowak  https://doi.org/10.1016/j.advwatres.2011.11.001
+
+        [2] Nagel, J.B., Rieckermann, J. and Sudret, B., 2020. Principal
+        component analysis and sparse polynomial chaos expansions for global
+        sensitivity analysis and model calibration: Application to urban
+        drainage simulation. Reliability Engineering & System Safety, 195,
+        p.106737.
 
         Parameters
         ----------
@@ -944,8 +955,10 @@ class PCE(MetaModel):
 
         Returns
         -------
-        sobol
-        totalsobol
+        sobol : dict
+            Sobol' indices of all available orders.
+        totalsobol : dict
+            Total Sobol' indices
 
         """
         # Extract the necessary variables