diff --git a/probabilistic-analysis/probabilistic_assessment.py b/probabilistic-analysis/probabilistic_assessment.py
index ac67dbe..ee6858d 100644
--- a/probabilistic-analysis/probabilistic_assessment.py
+++ b/probabilistic-analysis/probabilistic_assessment.py
@@ -329,7 +329,7 @@ def get_storm_demand_volume(ref_aep, ref_vol, n, mode='fit'):
 
 
 def process(beach_name, beach_scenario, n_runs, start_year, end_year,
-            output_years, output_ep, zsa_profile_file, zfc_profile_file,
+            output_years, output_ep, zsa_profile_file, zrfc_profile_file,
             output_folder, figure_folder, sea_level_rise, bruun_factor,
             underlying_recession, storm_demand, plot_stats, omit_from_shp,
             min_chainage, segment_gaps, insert_points, append_points):
@@ -345,7 +345,7 @@ def process(beach_name, beach_scenario, n_runs, start_year, end_year,
         output_years      (list): years to save profiles
         output_ep        (list): EP values for saved profiles
         zsa_profile_file (str): path to storm demand vs chainge data (ZSA)
-        zfc_profile_file (str): path to storm demand vs chainge data (ZFC)
+        zrfc_profile_file (str): path to storm demand vs chainge data (ZRFC)
         output_folder      (str): where to save profiles
         sea_level_rise (dict):
             'year' (list): years
@@ -420,14 +420,14 @@ def process(beach_name, beach_scenario, n_runs, start_year, end_year,
                                            np.array(ref_vol)[sort_idx])
 
     # Load profile data for current beach
-    pbar_profiles = tqdm(['ZSA', 'ZFC'], leave=False)
+    pbar_profiles = tqdm(['ZSA', 'ZRFC'], leave=False)
     for profile_type in pbar_profiles:
         pbar_profiles.set_description('{}'.format(profile_type))
 
         if profile_type == 'ZSA':
             df_in = pd.read_csv(zsa_profile_file)
-        if profile_type == 'ZFC':
-            df_in = pd.read_csv(zfc_profile_file)
+        if profile_type == 'ZRFC':
+            df_in = pd.read_csv(zrfc_profile_file)
 
         col_names = [c for c in df_in.columns if c.isdigit()]