NOAA-OWP · ZahraGhahremani · Sep 11, 2025 · Sep 11, 2025 · Sep 12, 2025 · Sep 22, 2025
diff --git a/config/params_template.env b/config/params_template.env
@@ -21,6 +21,7 @@ export stage_min_meters=0
 export stage_interval_meters=0.3048
 export stage_max_meters=25
 export slope_min=0.001
+export slope_scale=1000000000
 export min_catchment_area=0.25
 export min_stream_length=0.5
 

diff --git a/docs/CHANGELOG.md b/docs/CHANGELOG.md
@@ -1,6 +1,13 @@
 All notable changes to this project will be documented in this file.
 We follow the [Semantic Versioning 2.0.0](http://semver.org/) format.
 
+## v4.8.x.3x - 2025-09-11 - [PR#1654](https://github.com/NOAA-OWP/inundation-mapping/pull/1654)
+
+Adjusted the script to source columns from `src_base` instead of `src_full` . 
+
+
+### Changes
+`dev-fix-update-hydrotable`: as described. 
 ## v4.8.14.2 - 2025-10-17 - [PR#1677](https://github.com/NOAA-OWP/inundation-mapping/pull/1677)
 
 During a merge of the recent PR for mprunner fixes, one critical line was dropped. The line takes the return value from the child multi-proc function and adds it to a list collection of return results. 

diff --git a/src/add_crosswalk.py b/src/add_crosswalk.py
@@ -3,6 +3,7 @@
 import argparse
 import json
 import sys
+import os
 
 import geopandas as gpd
 import numpy as np
@@ -18,6 +19,7 @@
 # Define acceptable slope range
 SLOPE_MIN = 9.999e-7
 SLOPE_MAX = 0.5
+SLOPE_SCALE = float(os.getenv("slope_scale"))
 
 
 def add_crosswalk(
@@ -284,11 +286,18 @@ def add_crosswalk(
     # hfab_mask = (input_src_base['SLOPE_HFAB'] >= SLOPE_MIN) & (input_src_base['SLOPE_HFAB'] <= SLOPE_MAX)
 
     # Apply masks to filter out invalid slope values
-    sword_slope = input_src_base['SLOPE_IRIS_SWORD'].where(sword_mask)
+    sword_slope = input_src_base['SLOPE_IRIS_SWORD'].where(sword_mask).astype(float)
     # hfab_slope = input_src_base['SLOPE_HFAB'].where(hfab_mask)
 
     # Assign SLOPE values with priority: IRIS_SWORD then RISE_RUN
-    input_src_base['SLOPE'] = sword_slope.combine_first(input_src_base['SLOPE_RISE_RUN'])
+    input_src_base['SLOPE'] = (
+        sword_slope.combine_first(input_src_base['SLOPE_RISE_RUN']).astype(float)
+    )
+
+    #now to preserve slope precisions, we will record slope values as an integer by multiplying by a scale
+    #later in the code, when we need to use slope values, we will need to divide the values by the same scale
+    input_src_base['SLOPE'] = ((SLOPE_SCALE * input_src_base['SLOPE']).round()).astype('int64')
+
 
     input_src_base = input_src_base.rename(columns=lambda x: x.strip(" "))
     input_src_base = input_src_base.apply(pd.to_numeric, **{'errors': 'coerce'})
@@ -302,7 +311,7 @@ def add_crosswalk(
     input_src_base['Discharge (m3s-1)'] = (
         input_src_base['WetArea (m2)']
         * pow(input_src_base['HydraulicRadius (m)'], 2.0 / 3)
-        * pow(input_src_base['SLOPE'], 0.5)
+        * pow(input_src_base['SLOPE']/SLOPE_SCALE, 0.5)
         / input_src_base['ManningN']
     )
 
@@ -367,6 +376,10 @@ def add_crosswalk(
 
     output_src = output_src.merge(crosswalk[['HydroID', 'feature_id']], on='HydroID')
 
+    #also add default manning and slope for reseting hydrotable and src full later
+    output_src['default_SLOPE']=output_src['SLOPE']
+    output_src['default_ManningN']=output_src['ManningN']
+
     del crosswalk
 
     output_crosswalk = output_src[['HydroID', 'feature_id']]

diff --git a/src/bathy_rc_adjust.py b/src/bathy_rc_adjust.py
@@ -294,7 +294,7 @@ def bathy_rc_lookup(
         modified_src_base['Discharge (m3s-1)'] = (
             modified_src_base['WetArea (m2)_bathy_adj']
             * pow(modified_src_base['HydraulicRadius (m)_bathy_adj'], 2.0 / 3)
-            * pow(modified_src_base['SLOPE'], 0.5)
+            * pow(modified_src_base['SLOPE'], 0.5) #note that slope here is from src base, which is raw data (unscaled data)
             / modified_src_base['ManningN']
         )
         ## mask discharge values for stage = 0 rows in SRC (replace with 0) --> do we need SRC to start at 0??

diff --git a/src/bathymetric_adjustment.py b/src/bathymetric_adjustment.py
@@ -12,6 +12,8 @@
 import geopandas as gpd
 import pandas as pd
 
+SLOPE_SCALE = float(os.getenv("slope_scale"))
+
 
 # -------------------------------------------------------
 # Function to use RFC Bathymetry where available over eHydro Data
@@ -135,7 +137,7 @@ def correct_rating_for_ehydro_bathymetry(fim_dir, huc, bathy_file_ehydro, verbos
         src_df['Discharge (m3s-1)'] = (
             src_df['WetArea (m2)']
             * src_df['HydraulicRadius (m)'] ** (2.0 / 3)
-            * src_df['SLOPE'] ** 0.5
+            * (src_df['SLOPE']/SLOPE_SCALE) ** 0.5
             / src_df['ManningN']
         )
         # Force zero stage to have zero discharge
@@ -281,7 +283,7 @@ def correct_rating_for_ai_bathymetry(fim_dir, huc, strm_order, bathy_file_aibase
             src_df['Discharge (m3s-1)'] = (
                 src_df['WetArea (m2)']
                 * src_df['HydraulicRadius (m)'] ** (2.0 / 3)
-                * src_df['SLOPE'] ** 0.5
+                * (src_df['SLOPE']/SLOPE_SCALE) ** 0.5
                 / src_df['ManningN']
             )
             # Force zero stage to have zero discharge
@@ -302,7 +304,6 @@ def correct_rating_for_ai_bathymetry(fim_dir, huc, strm_order, bathy_file_aibase
             src_df2 = src_df.copy()
             discharge_bathymetry = src_df2['Discharge (m3s-1)']
             src_df['Discharge (m3s-1)_bathymetryAdjusted'] = discharge_bathymetry
-
             src_df.to_csv(src, index=False)
 
         else:
@@ -362,7 +363,7 @@ def correct_rating_for_ai_bathymetry(fim_dir, huc, strm_order, bathy_file_aibase
             discharge_cms = (
                 src_df['WetArea (m2)']
                 * src_df['HydraulicRadius (m)'] ** (2.0 / 3)
-                * src_df['SLOPE'] ** 0.5
+                * (src_df['SLOPE']/SLOPE_SCALE) ** 0.5
                 / src_df['ManningN']
             )
             src_df.loc[src_df["Bathymetry_source"] == "AI_Based", "Discharge (m3s-1)"] = discharge_cms

diff --git a/src/longitudinal_flow_adjustment.py b/src/longitudinal_flow_adjustment.py
@@ -15,6 +15,8 @@
 import scipy
 from scipy.ndimage import generic_filter
 
+SLOPE_SCALE = float(os.getenv("slope_scale"))
+
 
 # -------------------------------------------------------
 def extract_longitudinal_variables(src_df, hydroid, stage):
@@ -147,6 +149,27 @@ def filter_longitudinal_discharge_jitters(fim_dir, huc):
     log_text = f'Filtering Longitudinal Flow Fluctuation for HUC8: {huc}\n'
     fim_huc_dir = join(fim_dir, huc)
 
+    # if int(branch) == 0:
+    src_full_0 = join(fim_huc_dir, 'branches', str(0), 'src_full_crosswalked_0.csv')
+    ht_0_path = join(fim_huc_dir, 'branches', str(0), 'hydroTable_0.csv')
+
+    if os.path.isfile(src_full_0) and os.path.isfile(ht_0_path):
+        src_0_df = pd.read_csv(src_full_0, low_memory=False)
+        ht_0_df = pd.read_csv(ht_0_path, low_memory=False)
+
+        src_0_df.loc[src_0_df['Bathymetry_source'] == str(0), 'Bathymetry_source'] = 'No Bathymetry Applied'
+        src_0_df.loc[src_0_df['Bathymetry_source'] == 0, 'Bathymetry_source'] = 'No Bathymetry Applied'
+        src_0_df['Bathymetry_source'] = src_0_df['Bathymetry_source'].fillna('No Bathymetry Applied')
+        ht_0_df['Bathymetry_source'] = src_0_df['Bathymetry_source']
+
+        # Save updated branch 0 ht and src tables
+        src_0_df = src_0_df.drop_duplicates(subset=['HydroID', 'Stage'], keep='first').reset_index(drop=True)
+        src_0_df.to_csv(src_full_0, index=False)
+        ht_0_df = ht_0_df.drop_duplicates(subset=['HydroID', 'stage'], keep='first').reset_index(drop=True)
+        ht_0_df.to_csv(ht_0_path, index=False)
+    else:
+        print("Files do not exist: src_full_crosswalked_0.csv and hydroTable_0.csv")
+
     # Get src_full, hydrotable and catchment from each branch
     src_all_branches_path = []
     cathment_gpkg_path = []
@@ -309,7 +332,7 @@ def filter_longitudinal_discharge_jitters(fim_dir, huc):
             src_df['Discharge (m3s-1)'] = (
                 src_df['WetArea (m2)']
                 * pow(src_df['HydraulicRadius (m)'], 2.0 / 3)
-                * pow(src_df['SLOPE'], 0.5)
+                * pow(src_df['SLOPE']/SLOPE_SCALE, 0.5)
                 / src_df['ManningN']
             )
             # Refining Discharge for lake hydroIDs with the original Q

diff --git a/src/nonmonotonic_src_adjustment.py b/src/nonmonotonic_src_adjustment.py
@@ -15,6 +15,8 @@
 import numpy as np
 import pandas as pd
 
+SLOPE_SCALE = float(os.getenv("slope_scale"))
+
 
 # -------------------------------------------------------
 # Analysing each HydroID SRC for nonmonotonic SRC
@@ -67,7 +69,7 @@ def analyze_nonmonotonic_src(srcs_df, strm_order):  # , thalweg_hydroids
         srcs_df.loc[row_slice, 'Discharge (m3s-1)'] = (
             wet_area
             * (srcs_df.loc[row_slice, 'HydraulicRadius (m)'] ** (2.0 / 3))
-            * (srcs_df.loc[row_slice, 'SLOPE'] ** 0.5)
+            * ((srcs_df.loc[row_slice, 'SLOPE']/SLOPE_SCALE) ** 0.5)
             / srcs_df.loc[row_slice, 'channel_n']
         )
         # srcs_df['Discharge (m3s-1)_subdiv'] = np.where(
@@ -200,7 +202,6 @@ def correct_nonmonotonic_src(fim_dir, huc, strm_order):  # , bankfull_flows_file
         src_df['subdiv_applied'] = src_df.groupby('HydroID')['subdiv_applied'].ffill()
         src_df['channel_n'] = src_df.groupby('HydroID')['channel_n'].ffill()
         src_df['overbank_n'] = src_df.groupby('HydroID')['overbank_n'].ffill()
-
         src_df22 = src_df.copy()
         discharge_nonmono = src_df22['Discharge (m3s-1)']
         src_df22['Discharge (m3s-1)_nonmonotonicAdjusted'] = discharge_nonmono
@@ -214,6 +215,7 @@ def correct_nonmonotonic_src(fim_dir, huc, strm_order):  # , bankfull_flows_file
         src_df = src_df5.copy()
         src_df.to_csv(src, index=False)
 
+
         # Adjusting hydro tables for nonmonotonic SRC
         # Reporting in the log file
         log_text += f'Adjusting Nonmonotonic hydroTable for HUC {huc} Branch {branch}'

diff --git a/src/subdiv_chan_obank_src.py b/src/subdiv_chan_obank_src.py
@@ -22,6 +22,7 @@
 
 sns.set_theme(style="whitegrid")
 warnings.simplefilter(action='ignore', category=FutureWarning)
+SLOPE_SCALE = float(os.getenv("slope_scale"))
 
 """
     Compute channel geomety and Manning's equation using subdivision method (separate in-channel vs. overbank)
@@ -267,7 +268,7 @@ def subdiv_mannings_eq(df_src):
     df_src['Discharge_chan (m3s-1)'] = (
         df_src['WetArea_chan (m2)']
         * pow(df_src['HydraulicRadius_chan (m)'], 2.0 / 3)
-        * pow(df_src['SLOPE'], 0.5)
+        * pow(df_src['SLOPE']/SLOPE_SCALE, 0.5)
         / df_src['channel_n']
     )
     df_src['Velocity_chan (m/s)'] = df_src['Discharge_chan (m3s-1)'] / df_src['WetArea_chan (m2)']
@@ -291,7 +292,7 @@ def subdiv_mannings_eq(df_src):
     df_src['Discharge_obank (m3s-1)'] = (
         df_src['WetArea_obank (m2)']
         * pow(df_src['HydraulicRadius_obank (m)'], 2.0 / 3)
-        * pow(df_src['SLOPE'], 0.5)
+        * pow(df_src['SLOPE']/SLOPE_SCALE, 0.5)
         / df_src['overbank_n']
     )
     df_src['Velocity_obank (m/s)'] = df_src['Discharge_obank (m3s-1)'] / df_src['WetArea_obank (m2)']