diff --git a/src/analysis/forecast_twl.py b/src/analysis/forecast_twl.py
index 9e6dbc3..4628daa 100644
--- a/src/analysis/forecast_twl.py
+++ b/src/analysis/forecast_twl.py
@@ -51,11 +51,23 @@ def forecast_twl(
             how="inner",
         )
         df_temp["mhw"] = 0.5
+
+        # When calculating mean slope, we go from the dune toe to mhw. However, in some profiles, the dune toe is not
+        # defined. In these cases, we should go to the dune crest
+        df_temp['top_elevation'] = df_temp['dune_toe_z']
+        df_temp.loc[df_temp.dune_toe_z.isnull(), 'top_elevation'] = df_temp.loc[df_temp.dune_toe_z.isnull(),
+                                                                                'dune_crest_z']
+        df_temp['top_x'] = df_temp['dune_toe_x']
+        df_temp.loc[df_temp.dune_toe_x.isnull(), 'top_x'] = df_temp.loc[df_temp.dune_toe_x.isnull(),
+                                                                                'dune_crest_x']
+
         with Pool(processes=n_processes) as pool:
             results = pool.starmap(
                 mean_slope_for_site_id,
-                [(site_id, df_temp, df_profiles, "dune_toe_z", "dune_toe_x", "mhw") for site_id in site_ids],
+                [(site_id, df_temp, df_profiles, "top_elevation", "top_x", "mhw") for site_id in site_ids],
             )
+
+
         df_twl["beta"] = pd.concat(results)
 
     # Estimate runup