#made some changes to accomodate the new structure of the MESH_v2. Catchment 25-31 were merged into one single catchment (25). The rain and ET data is untouched but are cropped down to 25 catchments in the preprocessing code.

07_Modelling/01_Input/BCGeneration/hunter_rma_preprocessing.py

@@ -147,7 +147,7 @@ if S['include_boundary_flows'].lower() == 'yes':
         df = df.drop(['Q[ML/d]', key], axis = 1)
         #increase river temperature by Delta
         if S['Increase_riv_temp'] == 'yes':
-            df['Temperature'] = df['Temperature'] + S['Riv_temp_increase']
+            df['Temperature'] = df['Temperature'] + float(S['Riv_temp_increase'])
         wq_timeseries[key] =  df
         
         
@@ -174,7 +174,7 @@ if S['include_CC_wq'].lower() == 'yes':
         df.index =  df.index + (start_date - pres_start_date)
         #increase temperature by Delta
         if S['Increase_SST_temp'] == 'yes':
-            df['Temperature'] = df['Temperature'] + S['SST_increase']
+            df['Temperature'] = df['Temperature'] + float(S['SST_increase'])
         CC_timeseries[key] = df.copy()
 
 # Read WWTP data from setup file
@@ -209,9 +209,13 @@ if S['include_wwtp_flows'].lower() == 'yes':
 
         # Convert from ML/day to m3/s
         df[key] = df[['Q[ML/d]']] * 1000 / 24 / 3600
+        
+        #Shift the water quality time series data frame by 
+        df.index =  df.index + (start_date - pres_start_date)
 
         # Add to inflow time series dataframes
         inflow_timeseries = inflow_timeseries.join(df[[key]])
+        
 
         # Add to water quality timeseries
         wq_timeseries[key] = df.drop(['Q[ML/d]', key], axis = 1)
@@ -224,7 +228,10 @@ rain_master = pd.read_csv(
 
 # Trim climate data to current date range
 eto_master = eto_master[start_date:end_date]
+eto_master = eto_master.iloc[:,0:25]
 rain_master = rain_master[start_date:end_date]
+rain_master = rain_master.iloc[:,0:25]
+
 #inflow_timeseries.index.difference(rain_master.index)
 
 
@@ -593,7 +600,7 @@ if S['include_WQ'].lower() == 'yes':
         wq.index = np.tile(i, wq.shape[0])
         wq_df = wq_df.append(wq)
     #Shift the water quality time series data frame by 
-    wq_df.index =  wq_df.index + (start_date - pres_start_date) 
+    #wq_df.index =  wq_df.index + (start_date - pres_start_date) 
 
 # Write element inflows for RMA
 # Consolidate inflow elements in RMA mesh (only include those with inflows)
@@ -644,9 +651,8 @@ for current_year in range(start_date.year, end_date.year + 1):
 
             if S['include_WQ'].lower() == 'yes':
                 # Get water quality values for current day
-                #wq = wq_df.loc[index, :].set_index('constituent')
+                wq = wq_df.loc[index, :].set_index('constituent')
-                index + 100
+                #wq = wq_df[wq_df.index == index].set_index('constituent')  #TH I changed this since the constituent part did not work here. 
-                wq = wq_df[wq_df.index == index].set_index('constituent')  #TH I changed this since the constituent part did not work here. 
                 # Get names of WWTP, catchment, and boundaries at current element
                 try:
                     w_names = [x for x in q_names if x in wwtp_data.keys()]