diff --git a/Makefile b/Makefile
index 4fea72d..0816220 100644
--- a/Makefile
+++ b/Makefile
@@ -67,11 +67,12 @@ impacts: ./data/interim/impacts_forecasted_foreshore_slope_sto06.csv ./data/inte
 	--profiles-output-file "./data/interim/profiles.csv" \
 	--sites-output-file "./data/interim/sites.csv"
 
-./data/interim/waves.csv: ./data/interim/sites.csv ./data/raw/processed_shorelines/waves.mat
+./data/interim/waves.csv: ./data/interim/sites.csv ./data/raw/processed_shorelines/waves.mat ./data/raw/processed_shorelines/sites_waves.mat
 	$(PYTHON_CLI) create-waves-csv \
 	--waves-mat "./data/raw/processed_shorelines/waves.mat" \
 	--sites-csv "./data/interim/sites.csv" \
-	--output-file "./data/interim/waves.csv"
+	--waves-output-file "./data/interim/waves.csv" \
+	--sites-waves-output-file "./data/interim/sites_waves.csv"
 
 ./data/interim/tides.csv: ./data/interim/sites.csv ./data/raw/processed_shorelines/tides.mat
 	$(PYTHON_CLI) create-tides-csv \
diff --git a/src/data/parse_mat.py b/src/data/parse_mat.py
index ffec095..2afa87a 100644
--- a/src/data/parse_mat.py
+++ b/src/data/parse_mat.py
@@ -168,6 +168,10 @@ def parse_waves(waves_mat):
                     "P": mat_data["P"][i][j][0],
                     "Exs": mat_data["Exs"][i][j][0],
                     "Pxs": mat_data["Pxs"][i][j][0],
+                    "Ecum": mat_data["Ecum"][i],
+                    "Exscum": mat_data["Exscum"][i],
+                    "Pcum": mat_data["Pxscum"][i],
+                    "Pxscum": mat_data["Pxscum"][i],
                 }
             )
 
@@ -432,21 +436,41 @@ def replace_unique_sites(df, df_sites):
     return df
 
 
+def split_site_wave_params(df_waves):
+    """
+    When we parse the waves.mat file, the cumulative wave energy and power properties are given for each time step.
+    This is unnecessary, so let's extract them out of our dataframe and put them in their own seperate dataframe.
+    :param df_waves:
+    :return:
+    """
+    cols_to_extract = ["Ecum", "Exscum", "Pcum", "Pxscum"]
+
+    df_sites_waves = df_waves.loc[:, cols_to_extract].groupby(["site_id"]).first()
+    df_waves = df_waves.drop(columns=cols_to_extract, errors="ignore")
+    return df_waves, df_sites_waves
+
+
 @click.command(short_help="create waves.csv")
 @click.option("--waves-mat", required=True, help=".mat file containing wave records")
 @click.option(
     "--sites-csv", required=True, help=".csv file description of cross section sites"
 )
-@click.option("--output-file", required=True, help="where to save waves.csv")
-def create_waves_csv(waves_mat, sites_csv, output_file):
-    logger.info("Creating %s", output_file)
+@click.option("--waves-output-file", required=True, help="where to save waves.csv")
+@click.option(
+    "--sites-waves-output-file", required=True, help="where to save sites_waves.csv"
+)
+def create_waves_csv(waves_mat, sites_csv, waves_output_file, sites_waves_output_file):
+    logger.info("Creating %s", waves_output_file)
     df_waves = parse_waves(waves_mat=waves_mat)
     df_sites = pd.read_csv(sites_csv, index_col=[0])
     df_waves = replace_unique_sites(df_waves, df_sites)
     df_waves.set_index(["site_id", "datetime"], inplace=True)
     df_waves.sort_index(inplace=True)
-    df_waves.to_csv(output_file)
-    logger.info("Created %s", output_file)
+    df_waves, df_sites_waves = split_site_wave_params(df_waves)
+    df_waves.to_csv(waves_output_file)
+    df_sites_waves.to_csv(sites_waves_output_file)
+    logger.info("Created %s", waves_output_file)
+    logger.info("Created %s", sites_waves_output_file)
 
 
 # @click.command(short_help="create profile_features.csv")