diff --git a/docs/CHANGELOG.md b/docs/CHANGELOG.md
index d4f4011f4..15704a4e9 100644
--- a/docs/CHANGELOG.md
+++ b/docs/CHANGELOG.md
@@ -1,6 +1,26 @@
 All notable changes to this project will be documented in this file.
 We follow the [Semantic Versioning 2.0.0](http://semver.org/) format.
 
+## v4.5.x.x - 2024-06-20 - [PR#1196](https://github.com/NOAA-OWP/inundation-mapping/pull/1196)
+
+This PR adds a new tool, which is still in progress. The tool addresses the issue #994 (but is not going to close this issue yet). 
+The tool aims to update the HAND SRC by comparing water surface elevation (WSE) between RAS2FIM and HAND. The algorithm is as below:
+
+- For each RAS cross section, determine the HAND discharge adjustment (Q_Adjust):
+    - HAND minimum WSE is assumed the same as HAND Hydro-conditioned DEM
+    - RAS2FIM minimum WSE is already available from HEC-RAS runs.
+    - Calculate the difference between the minimum WSE values of RAS2FIM and HAND, and call it "WSE_base_error". This difference can address the bathymetry error in HAND SRC.  
+    - Add stage values into RAS2FIM rating curves (by subtracting DEM from WSE), and then interpolate a discharge for the "WSE_base_error". This interpolated discharge is called "Q_Adjust."
+- Calculate the median of "Q_Adjust" values for cross sections within HAND catchments.
+- Add the median "Q_Adjust" values to the flows of HAND SRC
+
+
+
+### Additions
+- tools/adjust_wse_with_ras2fim.py
+
+<br/><br/>
+
 ## v4.5.2.3 - 2024-06-14 - [PR#1169](https://github.com/NOAA-OWP/inundation-mapping/pull/1169)
 
 This tool scans all log directory looking for the word "error" (not case-sensitive). This is primary added to help find errors in the post processing logs such as src_optimization folder (and others).
diff --git a/tools/adjust_wse_with_ras2fim.py b/tools/adjust_wse_with_ras2fim.py
new file mode 100644
index 000000000..ae670737d
--- /dev/null
+++ b/tools/adjust_wse_with_ras2fim.py
@@ -0,0 +1,343 @@
+import os
+from multiprocessing import Pool
+
+import geopandas as gpd
+import matplotlib.lines as mlines
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import rasterio
+from numpy import interp
+
+
+def get_ras2fim_discharge(args):
+    ras_rating_with_stage, this_WSE_base_error, this_point_fid_xs = args
+    this_ras_rating = ras_rating_with_stage[ras_rating_with_stage["fid_xs"] == this_point_fid_xs].sort_values(
+        by="flow"
+    )
+
+    all_flows = this_ras_rating["flow"].values
+    all_stage = this_ras_rating["stage"].values
+
+    if this_WSE_base_error > 0:
+        adjust_value = interp(this_WSE_base_error, all_stage, all_flows)
+    else:
+        adjust_value = 0
+
+    return this_point_fid_xs, adjust_value
+
+
+class adjust_wse_with_ras2fim:
+    '''
+    This code addresses the issue: https://github.com/NOAA-OWP/inundation-mapping/issues/994
+    The results of this work have been used here: https://docs.google.com/presentation/d/1OYqNWQKPgYquWxnJgYcrvuV67iWzwikZ/edit#slide=id.p1
+    '''
+
+    def __init__(self, ras_dir, HAND_dir, hand_discharge_type, output_dir):
+        self.ras_dir = ras_dir
+        self.HAND_dir = HAND_dir
+        self.hand_discharge_type = hand_discharge_type
+        self.output_dir = output_dir
+
+    def get_hydroIds_QAdjust(self, ras_points_with_rc, ras_rating, HAND_SRC):
+        '''
+        For the details and the algorithm, see this issue: https://github.com/NOAA-OWP/inundation-mapping/issues/994
+        '''
+
+        def get_ras2fim_discharge_parallel(ras_points_with_rc, ras_rating_with_stage):
+            args = []
+            for i, row in ras_points_with_rc.iterrows():
+                args.append((ras_rating_with_stage, row.WSE_base_error, row.fid_xs))
+
+            with Pool(7) as pool:
+
+                list_of_returned_results = pool.map(get_ras2fim_discharge, args)
+
+            ras2fim_Qs = pd.DataFrame(list_of_returned_results, columns=["fid_xs", "Q_Adjust_temp"])
+            return ras2fim_Qs
+
+        # the min WSE of ras is needed to compare with HAND min WSE
+        min_flow_idx = ras_rating.groupby('fid_xs')['flow'].idxmin()  # use for min flow
+
+        # Use the row indices to extract the corresponding 'WSE' values.
+        min_flow_wse = ras_rating.loc[min_flow_idx, ['fid_xs', 'flow', 'wse']]
+        min_flow_wse.columns = ['fid_xs', 'ras_min_flow', 'ras_min_wse']
+
+        # merge the results into ras_points_with_rc
+        ras_points_with_rc = ras_points_with_rc.merge(min_flow_wse, on='fid_xs')
+
+        # now start to compute min WSE of HAND
+        # TODO examine using WSE of actual minimum flow instead of assuming the stage to be zero
+        # Note... I tested above and the differenc (improvment in results) ewas insignificant.
+        # get DEM values for ras points
+        DEM_dataset = rasterio.open(os.path.join(self.HAND_dir, 'dem_thalwegCond_0.tif'))
+        ras_points_coords = list(
+            zip(ras_points_with_rc.geometry.centroid.x, ras_points_with_rc.geometry.centroid.y)
+        )
+        ras_points_with_rc["DEM_datum"] = [value[0] for value in DEM_dataset.sample(ras_points_coords)]
+
+        # therefore, HAND minimum WSE is equl to DEM + first stage (this is Ryan method that we call this script after)
+        ras_points_with_rc["HAND_min_wse"] = ras_points_with_rc["DEM_datum"].values + 0.3048
+
+        # compute the base error
+        ras_points_with_rc["WSE_base_error"] = (
+            ras_points_with_rc["HAND_min_wse"] - ras_points_with_rc["ras_min_wse"]
+        )
+
+        # convert ras2fim rating WSE to stage
+        ras_rating_with_stage = ras_rating.merge(ras_points_with_rc[["fid_xs", "DEM_datum"]], on="fid_xs")
+        ras_rating_with_stage["stage"] = ras_rating_with_stage["wse"] - ras_rating_with_stage["DEM_datum"]
+
+        # Interpolate a discharge from ras2fim discharge-stage rating curves for the "WSE_base_error", and call it "Q_Adjust"
+        ras2fim_Qs = get_ras2fim_discharge_parallel(ras_points_with_rc, ras_rating_with_stage)
+        ras_points_with_rc = ras_points_with_rc.merge(ras2fim_Qs, on="fid_xs")
+
+        # Compute the median of "Q_Adjust" of ras2fim points within each HAND HydroID
+        # to do that, first we need to get HydroID for ras points
+        HydroID_tif_dataset = rasterio.open(
+            os.path.join(self.HAND_dir, "gw_catchments_reaches_filtered_addedAttributes_0.tif")
+        )
+        ras_points_with_rc["HydroID"] = [value[0] for value in HydroID_tif_dataset.sample(ras_points_coords)]
+
+        # now retrieve the first available Q from HAND SRC
+        # as a temp dataset needed, remove stage=0 from HAND SRC so we can apply .min to get first discharge
+        # only select HydroIds needed for ras_point
+        HAND_SRC = HAND_SRC[HAND_SRC['HydroID'].isin(ras_points_with_rc["HydroID"].unique())]
+
+        HAND_SRC_temp = HAND_SRC[HAND_SRC['stage'] != 0]
+
+        HAND_first_q = HAND_SRC_temp.groupby("HydroID")[self.hand_discharge_type].min().reset_index()
+        HAND_first_q.rename(columns={self.hand_discharge_type: "HAND_first_q"}, inplace=True)
+
+        ras_points_with_rc = ras_points_with_rc.merge(HAND_first_q, on='HydroID')
+
+        ras_points_with_rc["Q_Adjust"] = (
+            ras_points_with_rc["Q_Adjust_temp"] - ras_points_with_rc["HAND_first_q"]
+        )
+
+        # compute median of baseline error Q for the points within each hydroid
+        HydroIDs_QAdjust = ras_points_with_rc.groupby("HydroID")["Q_Adjust"].median()
+        ras_points_with_rc['Q_HydroId_Median'] = ras_points_with_rc['HydroID'].map(HydroIDs_QAdjust)
+
+        # report the required adusted HAND flows
+        return ras_points_with_rc, ras_points_with_rc[['HydroID', 'Q_HydroId_Median']].drop_duplicates(
+            subset='HydroID'
+        )
+
+    def compare_ras_with_hand(self, ras_points_with_rc, ras_rating, updated_HAND_SRC, flow_intensity):
+        '''
+        This method compute the difference of WSE between HAND and ras2fim for four different ras2fim flow intensities of q1 (first quantile),
+        median, q3 (third quantile) and max.
+        The method assumes the existence of updated HAND SRC created in previous step.
+        '''
+
+        # find selected method of WSE from ras
+        ras_rating["orig_index"] = ras_rating.index.values
+        if flow_intensity == 'max':
+            method_flow = ras_rating.groupby('fid_xs')['flow'].max().reset_index()
+        elif flow_intensity == 'median':
+            method_flow = ras_rating.groupby('fid_xs')['flow'].median().reset_index()
+        elif flow_intensity == 'q1':
+            method_flow = (
+                ras_rating.groupby('fid_xs')['flow'].quantile(0.25, interpolation='nearest').reset_index()
+            )
+        elif flow_intensity == 'q3':
+            method_flow = (
+                ras_rating.groupby('fid_xs')['flow'].quantile(0.75, interpolation='nearest').reset_index()
+            )
+
+        # Merge the flows with the original DataFrame to get the corresponding indices
+        merged_df = ras_rating.merge(method_flow, on=['fid_xs', 'flow'], how='inner')
+
+        # Get the indices of the method flow rows
+        method_flow_idx = merged_df.set_index('fid_xs')['orig_index']
+
+        # Use the row indices to extract the corresponding 'WSE' values. Also get max flow from here and not "max_flow" of gpkg file
+        method_flow_wse = ras_rating.loc[method_flow_idx, ['fid_xs', 'flow', 'wse']]
+        method_flow_wse.columns = ['fid_xs', 'ras_%s_flow' % flow_intensity, 'ras_%s_wse' % flow_intensity]
+
+        # merge the results into ras_points_with_rc
+        ras_points_with_rc = ras_points_with_rc.merge(method_flow_wse, on='fid_xs')
+
+        # now get results at points  using HAND
+        adjusted_HAND_stage = []
+        original_HAND_stage = []
+        for i, row in ras_points_with_rc.iterrows():
+            this_point_method_flow, this_point_HydroID = row["ras_%s_flow" % flow_intensity], row.HydroID
+            this_SRC = updated_HAND_SRC[updated_HAND_SRC["HydroID"] == this_point_HydroID].sort_values(
+                by=self.hand_discharge_type
+            )
+
+            all_flows_adjust = this_SRC[self.hand_discharge_type].values
+            all_flows_orig = this_SRC['original_' + self.hand_discharge_type].values
+            all_stages = this_SRC["stage"].values
+
+            # Perform linear interpolation
+            adjusted_HAND_stage.append(interp(this_point_method_flow, all_flows_adjust, all_stages))
+            original_HAND_stage.append(interp(this_point_method_flow, all_flows_orig, all_stages))
+
+        ras_points_with_rc["Adjusted_HAND_%s_stage" % flow_intensity] = np.array(adjusted_HAND_stage)
+        ras_points_with_rc["Original_HAND_%s_stage" % flow_intensity] = np.array(original_HAND_stage)
+
+        ras_points_with_rc["Adjusted_HAND_%s_wse" % flow_intensity] = (
+            ras_points_with_rc["Adjusted_HAND_%s_stage" % flow_intensity] + ras_points_with_rc["DEM_datum"]
+        )
+        ras_points_with_rc["Original_HAND_%s_wse" % flow_intensity] = (
+            ras_points_with_rc["Original_HAND_%s_stage" % flow_intensity] + ras_points_with_rc["DEM_datum"]
+        )
+
+        ras_points_with_rc["Adjusted_HAND_%s_wse" % flow_intensity] = np.where(
+            ras_points_with_rc["Original_HAND_%s_wse" % flow_intensity].values
+            > ras_points_with_rc['ras_%s_wse' % flow_intensity].values,
+            ras_points_with_rc["Adjusted_HAND_%s_wse" % flow_intensity].values,
+            ras_points_with_rc["Original_HAND_%s_wse" % flow_intensity].values,
+        )
+
+        ras_points_with_rc["Orig_Error"] = (
+            ras_points_with_rc["ras_%s_wse" % flow_intensity]
+            - ras_points_with_rc["Original_HAND_%s_wse" % flow_intensity]
+        )
+        ras_points_with_rc["Adj_Error"] = (
+            ras_points_with_rc["ras_%s_wse" % flow_intensity]
+            - ras_points_with_rc["Adjusted_HAND_%s_wse" % flow_intensity]
+        )
+
+        ras_points_with_rc.drop(
+            columns=["Adjusted_HAND_%s_stage" % flow_intensity, "Original_HAND_%s_stage" % flow_intensity],
+            inplace=True,
+        )
+
+        return ras_points_with_rc
+
+    def comparison_plots(self, ras_points_Adjusted_Q, flow_intensity):
+        '''
+        This method make difference plots using both original HAND SRC and updated HAND SRC.
+        '''
+        if not os.path.exists(os.path.join(self.output_dir, 'plots')):
+            os.makedirs(os.path.join(self.output_dir, 'plots'))
+
+        for error_type in ['Orig', 'Adj']:
+            ras_points_Adjusted_Q["residual_color"] = np.where(
+                ras_points_Adjusted_Q["%s_Error" % error_type].values > 0, "red", "blue"
+            )
+
+            RMSE = np.sqrt(np.mean(ras_points_Adjusted_Q["%s_Error" % error_type].values ** 2))
+            MAE = np.mean(abs(ras_points_Adjusted_Q["%s_Error" % error_type].values))
+            ras_points_Adjusted_Q.plot(color=ras_points_Adjusted_Q["residual_color"].values, markersize=4)
+
+            # Create custom legend markers (two points)
+            legend_marker2 = mlines.Line2D(
+                [], [], color='blue', marker='o', linestyle='', markersize=4, label='HAND > RAS'
+            )
+            legend_marker1 = mlines.Line2D(
+                [], [], color='red', marker='o', linestyle='', markersize=4, label='RAS > HAND'
+            )
+
+            # Add the custom legend markers to the legend
+            plt.legend(handles=[legend_marker1, legend_marker2])
+            plt.title(
+                "WSE comparison between RAS2FIM and %s HAND for %s flows\nRMSE=%.2lfm, MAE=%.2lfm"
+                % (error_type, flow_intensity, RMSE, MAE)
+            )
+            plt.axis('off')
+
+            plt.savefig(
+                os.path.join(self.output_dir, 'plots', 'plot1_%s_%s.png' % (error_type, flow_intensity))
+            )
+            plt.close()
+
+            plt.axhline(y=0, color="blue", linestyle='--')
+            plt.scatter(
+                ras_points_Adjusted_Q["ras_%s_wse" % flow_intensity].values,
+                ras_points_Adjusted_Q["%s_Error" % error_type].values,
+                color="red",
+                s=4,
+            )
+
+            plt.xlabel("RAS2FIM WSE (m)")
+            plt.ylabel("RAS2FIM WSE minus HAND WSE (m)")
+            plt.title("For RAS2FIM %s flows using %s HAND SRC" % (flow_intensity, error_type))
+            plt.savefig(
+                os.path.join(self.output_dir, 'plots', 'plot2_%s_%s.png' % (error_type, flow_intensity))
+            )
+            plt.close()
+
+
+if __name__ == '__main__':
+    # specify required inputs
+    ras_dir = r"../../output_ras2fim_12090301"
+    HAND_dir = r"../../Ali_12090301\12090301\branches\0"
+    output_dir = r"../results/final"
+    hand_discharge_type = 'precalb_discharge_cms'  # options are 'discharge_cms' or 'precalb_discharge_cms'
+    obs = adjust_wse_with_ras2fim(ras_dir, HAND_dir, hand_discharge_type, output_dir)
+
+    # read inputs
+    print('Reading input files')
+    ras_points = gpd.read_file(os.path.join(ras_dir, "reformat_ras_rating_curve_points.gpkg"))
+    ras_points = ras_points[['fid_xs', 'feature_id', 'geometry']]
+
+    # read rating curve info
+    ras_rating = pd.read_csv(
+        os.path.join(ras_dir, 'reformat_ras_rating_curve_table-12090301.csv'),
+        usecols=['fid_xs', 'wse', 'flow'],
+    )
+
+    # remove the points not having info in rating curve file
+    ras_points_with_rc = ras_points[ras_points["fid_xs"].isin(ras_rating["fid_xs"].unique())]
+
+    HAND_SRC = pd.read_csv(
+        os.path.join(HAND_dir, 'hydroTable_0.csv'), usecols=['HydroID', 'stage', hand_discharge_type]
+    )
+
+    # first get the hydroIds Q adjust
+    print('Calculating HAND HydroIDs Q adjust')
+    ras_points_Adjusted_Q, HydroIDs_Adjusted_Q = obs.get_hydroIds_QAdjust(
+        ras_points_with_rc, ras_rating, HAND_SRC
+    )
+    ras_points_Adjusted_Q.to_file(os.path.join(output_dir, "ras_points_Adjusted_Q.gpkg"))
+    HydroIDs_Adjusted_Q.to_csv(os.path.join(output_dir, "HydroIDs_Adjusted_Q.csv"), index=False)
+
+    # step 2...Adjust HAND SRCs
+    print('Updating HAND SRC with Q adjust values')
+    # TODO see why we have negative Q ajust for some hydroIDs...for now make them zero
+    HydroIDs_Adjusted_Q = HydroIDs_Adjusted_Q[HydroIDs_Adjusted_Q['Q_HydroId_Median'] > 0]
+
+    Orig_HAND_SRC = HAND_SRC.copy()
+    Orig_HAND_SRC.rename(columns={hand_discharge_type: 'original_%s' % hand_discharge_type}, inplace=True)
+
+    updated_HAND_SRC = Orig_HAND_SRC.merge(HydroIDs_Adjusted_Q, on='HydroID', how='left')
+
+    # assign zero for the hydroids that do not have any ras points
+    updated_HAND_SRC['Q_HydroId_Median'].fillna(0, inplace=True)
+
+    updated_HAND_SRC[hand_discharge_type] = (
+        updated_HAND_SRC['original_%s' % hand_discharge_type] + updated_HAND_SRC['Q_HydroId_Median']
+    )
+    updated_HAND_SRC.to_csv(os.path.join(output_dir, 'hydroTable_0_updated.csv'), index=False)
+
+    # finally compare RAS with HAND... this prepare difference between HAND and RAS2fim for both original HAND SRC and updated HAND SRC.
+    print('Evaluating the difference between HAND and SRC for different flow intensities:')
+    ras_points_Adjusted_Q = ras_points_Adjusted_Q[
+        ['fid_xs', 'feature_id', 'DEM_datum', 'HydroID', 'Q_HydroId_Median', 'geometry']
+    ]
+
+    MAE_summary = []
+    for flow_intensity in ['q1', 'median', 'q3', 'max']:
+        print('working on %s' % flow_intensity)
+        ras_points_Adjusted_Q = obs.compare_ras_with_hand(
+            ras_points_Adjusted_Q, ras_rating, updated_HAND_SRC, flow_intensity
+        )
+        ras_points_Adjusted_Q.to_file(os.path.join(output_dir, "RAS_HAND_Diff_%s.gpkg" % flow_intensity))
+        obs.comparison_plots(ras_points_Adjusted_Q, flow_intensity)
+
+        # also record MAE
+        MAE_orig_error = np.mean(abs(ras_points_Adjusted_Q['Orig_Error'].values))
+        MAE_Adj_error = np.mean(abs(ras_points_Adjusted_Q['Adj_Error'].values))
+
+        MAE_improve = 100 * (MAE_orig_error - MAE_Adj_error) / MAE_orig_error
+        MAE_summary.append([flow_intensity, MAE_orig_error, MAE_Adj_error, MAE_improve])
+    MAE_summary_DF = pd.DataFrame(
+        MAE_summary,
+        columns=["flow_intensity", 'MAE_difference_original', 'MAE_difference_updated', 'improvment%'],
+    )
+    MAE_summary_DF.to_csv(os.path.join(output_dir, 'MAE_stats.csv'), index=False)