diff --git a/basininflow/__init__.py b/basininflow/__init__.py
index 0a16ae4..fd85096 100644
--- a/basininflow/__init__.py
+++ b/basininflow/__init__.py
@@ -1,4 +1,4 @@
 from basininflow.inflow import create_inflow_file
 
-__version__ = '0.10.0'
+__version__ = '0.11.0'
 __all__ = ['create_inflow_file', ]
diff --git a/basininflow/inflow.py b/basininflow/inflow.py
index 11a79e5..b2c72c4 100644
--- a/basininflow/inflow.py
+++ b/basininflow/inflow.py
@@ -169,23 +169,31 @@ def create_inflow_file(lsm_data: str,
         else:
             raise ValueError(f"Unknown number of dimensions: {ds.ndim}")
 
-    inflow_df = np.nan_to_num(inflow_df, nan=0)
-    inflow_df = inflow_df * weight_df['area_sqm'].values * conversion_factor
     inflow_df = pd.DataFrame(inflow_df, columns=stream_ids, index=datetime_array)
+    inflow_df = inflow_df.replace(np.nan, 0)
+    inflow_df[inflow_df < 0] = 0
+    inflow_df = inflow_df * weight_df['area_sqm'].values * conversion_factor
     inflow_df = inflow_df.groupby(by=stream_ids, axis=1).sum()
+    inflow_df = inflow_df[sorted_rivid_array]
 
-    if cumulative:
-        inflow_df = pd.DataFrame(
-            np.vstack([inflow_df.values[0, :], np.diff(inflow_df.values, axis=0)]),
-            index=inflow_df.index,
-            columns=inflow_df.columns
+    def _cumulative_to_incremental(df) -> pd.DataFrame:
+        return pd.DataFrame(
+            np.vstack([df.values[0, :], np.diff(df.values, axis=0)]),
+            index=df.index,
+            columns=df.columns
         )
 
-    inflow_df[inflow_df < 0] = 0
+    def _incremental_to_cumulative(df) -> pd.DataFrame:
+        return df.cumsum()
+
+    if cumulative:
+        logging.info('Converting to cumulative values')
+        inflow_df = _cumulative_to_incremental(inflow_df)
 
     # Check that all timesteps are the same
     time_diff = np.diff(datetime_array)
     if not np.all(time_diff == datetime_array[1] - datetime_array[0]):
+        logging.warning('Timesteps are not all uniform')
         if timestep is None:
             logging.warning('Timesteps are not all uniform and a target timestep was not provided.')
             timestep = datetime_array[1] - datetime_array[0]
@@ -194,32 +202,21 @@ def create_inflow_file(lsm_data: str,
             # Convert datetime timedelta to timedelta64[ns]
             timestep = np.timedelta64(timestep,'ns')
 
-        # # figure out how many uniform timesteps are represented by each row
-        # timesteps_per_row = np.hstack([np.array(timestep), time_diff]) / timestep
-        # df1 = (
-        #     inflow_df
-        #     .div(timesteps_per_row, axis=0)
-        #     .resample(rule=f'{timestep.astype("timedelta64[s]").astype(int)}S')
-        #     .bfill()
-        # )
-
         # everything is forced to be incremental before this step so we can use cumsum to get the cumulative values
         inflow_df = (
-            inflow_df
-            .cumsum()
+            _incremental_to_cumulative(inflow_df)
             .resample(rule=f'{timestep.astype("timedelta64[s]").astype(int)}S')
             .interpolate(method='linear')
         )
-        inflow_df = pd.DataFrame(
-            np.vstack([inflow_df.values[0, :], np.diff(inflow_df.values, axis=0)]),
-            index=inflow_df.index,
-            columns=inflow_df.columns
-        )
-    datetime_array = inflow_df.index.to_numpy()
+        inflow_df = _cumulative_to_incremental(inflow_df)
 
     # Create output inflow netcdf data
     logging.info("Writing inflows to file")
     os.makedirs(inflow_dir, exist_ok=True)
+
+    inflow_df = inflow_df * .001
+
+    datetime_array = inflow_df.index.to_numpy()
     start_date = datetime.datetime.utcfromtimestamp(datetime_array[0].astype(float) / 1e9).strftime('%Y%m%d')
     end_date = datetime.datetime.utcfromtimestamp(datetime_array[-1].astype(float) / 1e9).strftime('%Y%m%d')
     file_name = f'm3_{vpu_name}_{start_date}_{end_date}.nc'
@@ -237,7 +234,7 @@ def create_inflow_file(lsm_data: str,
         # m3_riv
         # note - nan's and fill values are not supported on netcdf3 files
         m3_riv_var = inflow_nc.createVariable('m3_riv', 'f4', ('time', 'rivid'))
-        m3_riv_var[:] = inflow_df.values
+        m3_riv_var[:] = inflow_df.to_numpy()
         m3_riv_var.long_name = 'accumulated inflow inflow volume in river reach boundaries'
         m3_riv_var.units = 'm3'
         m3_riv_var.coordinates = 'lon lat'