diff --git a/src/lisfloodutilities/catchstats/catchstats.py b/src/lisfloodutilities/catchstats/catchstats.py
index 262268b..9b91c2d 100644
--- a/src/lisfloodutilities/catchstats/catchstats.py
+++ b/src/lisfloodutilities/catchstats/catchstats.py
@@ -196,7 +196,7 @@ def catchment_statistics(maps: Union[xr.DataArray, xr.Dataset],
 
         if output is not None:
             fileout = output / f'{ID:04}.nc'
-            if fileout.exists() and  ~overwrite:
+            if fileout.exists() and not overwrite:
                 print(f'Output file {fileout} already exists. Moving forward to the next catchment')
                 continue
         
@@ -252,7 +252,7 @@ def main(argv=sys.argv):
     parser.add_argument("-s", "--statistic", nargs='+', required=True, help='List of statistics to be computed. Possible values: mean, sum, std, var, min, max, median, count')
     parser.add_argument("-o", "--output", required=True, help="Directory where the output NetCDF files will be saved")
     parser.add_argument("-a", "--area", required=False, default=None, help="NetCDF file of pixel area used to weigh the statistics")
-    parser.add_argument("-W", "--overwrite", action="store_true", help="Overwrite existing output files")
+    parser.add_argument("-w", "--overwrite", action="store_true", default=False, help="Overwrite existing output files")
     
     args = parser.parse_args()
 
diff --git a/src/lisfloodutilities/ncextract/ncextract.py b/src/lisfloodutilities/ncextract/ncextract.py
index 2882359..f9d6ba0 100644
--- a/src/lisfloodutilities/ncextract/ncextract.py
+++ b/src/lisfloodutilities/ncextract/ncextract.py
@@ -11,6 +11,7 @@
 """
 
 import argparse
+import numpy as np
 import pandas as pd
 import os
 import sys
@@ -18,10 +19,13 @@
 import xarray as xr
 import cfgrib
 from pathlib import Path
-from typing import Union, Optional
+from typing import Union, Optional, Tuple
+from datetime import datetime
+
+
 
 def read_points(inputcsv: Union[str, Path]) -> xr.Dataset:
-    """It reads a CSV file with coordinates of points: gauging stations, reservoirs...
+    """Reads a CSV file with the coordinates of the points of interest: gauging stations, reservoirs...
 
     Parameters:
     -----------
@@ -35,37 +39,47 @@ def read_points(inputcsv: Union[str, Path]) -> xr.Dataset:
     """
 
     if not os.path.isfile(inputcsv):
-        print(f'ERROR: {inputcsv} is missing!')
-        sys.exit(1)
+        raise FileNotFoundError(f'{inputcsv} does not exist!')
 
     try:
+        # read input CSV
         poi_df = pd.read_csv(inputcsv)
         original_columns = poi_df.columns.copy()
         poi_df.columns = original_columns.str.lower()
+
         # find columns representing coordinates
-        coord_1 = [col for col in poi_df.columns if col.startswith('lon') or col.startswith('x')][0]
-        coord_2 = [col for col in poi_df.columns if col.startswith('lat') or col.startswith('y')][0]
+        x_coord = [col for col in poi_df.columns if col.startswith('lon') or col.startswith('x')][0]
+        y_coord = [col for col in poi_df.columns if col.startswith('lat') or col.startswith('y')][0]
+
         # find the column representing the point ID
-        idx_col = poi_df.columns.difference([coord_1, coord_2])[0]
+        idx_col = poi_df.columns.difference([x_coord, y_coord])[0]
+
         # convert to xarray.Dataset
-        poi_xr = poi_df.set_index(idx_col)[[coord_1, coord_2]].to_xarray()
+        poi_xr = poi_df.set_index(idx_col)[[x_coord, y_coord]].to_xarray()
         rename_dim = {idx_col: col for col in original_columns if col.lower() == idx_col}
-        poi_xr = poi_xr.rename(rename_dim)
+        poi_xr = poi_xr.rename({idx_col: 'id'})
     except:
-        print('ERROR: Please check that the CSV file is formatted correctly!')
-        sys.exit(2)
-
+        raise ValueError(f"Could not read CSV properly. Please check the format.\nDetails: {e}")
+        
     return poi_xr
 
 
 
-def read_inputmaps(directory: Union[str, Path]) -> xr.Dataset:
-    """It extract from a series of input files (NetCDF or GRIB) the time series of a set of points
+def read_inputmaps(
+    directory: Union[str, Path],
+    start: datetime = None,
+    end: datetime = None
+) -> xr.Dataset:
+    """Reads a set of input files (NetCDF or GRIB) and selects the period of interest
 
     Parameters:
     -----------
-   directory: string or pathlib.Path
+    directory: string or pathlib.Path
         the directory containing the input files, which can be either in NetCDF or GRIB format
+    start: datetime
+        Start of the extraction period
+    end: datetime
+        End of the extraction period
 
     Returns:
     --------
@@ -73,81 +87,263 @@ def read_inputmaps(directory: Union[str, Path]) -> xr.Dataset:
         containing the concatenation of all the input maps
     """
 
-    pattern_engine = {'*.nc': 'netcdf4',
-                      '*.grib': 'cfgrib'}
+    pattern_engine = {
+        '*.nc': 'netcdf4',
+        '*.grib': 'cfgrib'
+    }
 
     if not os.path.isdir(directory):
-        print(f'ERROR: {directory} is missing or not a directory!')
-        sys.exit(1)
-    else:
-        directory = Path(directory)
-
+        raise FileNotFoundError(f'{directory} is missing or not a directory!')
+    
+    directory = Path(directory)
     filepaths = []
     for pattern, engine in pattern_engine.items():
         filepaths = list(directory.glob(pattern))
-        if len(filepaths) > 0:
+        if filepaths:
             break
 
     if not filepaths:
-        print(f'ERROR: No NetCDF/GRIB file found in {directory}')
-        sys.exit(2)
+        raise FileNotFoundError(f'No NetCDF/GRIB files found in {directory}')
 
     print(f'{len(filepaths)} input {engine} file(s) found in "{directory}"')
-
-    # chunks is set to auto for general purpose processing
-    # it could be optimized depending on input NetCDF
-    maps = xr.open_mfdataset(filepaths, engine=engine, chunks='auto', parallel=True)
+    
+    try:
+        # load dataset
+        maps = xr.open_mfdataset(filepaths, engine=engine, chunks='auto', parallel=True)
+        # Note: chunks is set to auto for general purpose processing
+        #       it could be optimized depending on input NetCDF
+    except Exception as e:
+        raise RuntimeError(f'Failed to open datasets using engine "{engine}": {str(e)}')
+    
+    # Validate start and end dates
+    if start and not isinstance(start, datetime):
+        raise ValueError(f"The 'start' parameter must be a datetime object, got {type(start)}")
+    if end and not isinstance(end, datetime):
+        raise ValueError(f"The 'end' parameter must be a datetime object, got {type(end)}")
+    
+    if start or end:
+        time_dims = [dim for dim in maps.dims if 'time' in dim.lower()]
+        if not time_dims:
+            print('WARNING: No time dimension found, skipping time filtering')
+            return maps
+        try:
+            maps = maps.sel({time_dims[0]: slice(start, end)})
+        except Exception as e:
+            raise ValueError(f'Failed to apply time filter: {str(e)}')
 
     return maps
 
 
 
-def extract_timeseries(maps: xr.Dataset,
-                       poi: xr.Dataset,
-                       outputfile: Optional[Union[str, Path]] = None
-                      ) -> Optional[xr.Dataset]:
-    """It extract from a series of input files (NetCDF or GRIB) the time series of a set of points
+def read_ldd(
+    file: Union[str, Path],
+) -> xr.DataArray:
+    """Reads the local drainage direction map
+
+    Parameters:
+    -----------
+    file: string or pathlib.Path
+        the NetCDF file of the local drainage direction map
+
+    Returns:
+    --------
+    ldd: xarray.DataArray
+    """
+    
+    return xr.open_dataset(file)['Band1']
+    
+
+def rename_geographic_coords(
+    target: Union[xr.Dataset, xr.DataArray],
+    reference: Union[xr.Dataset, xr.DataArray]
+) -> Union[xr.Dataset, xr.DataArray]:
+    """Renames the geographical coordinates/variables in the target dataset to match those in the reference dataset.
+    
+    Parameters:
+    -----------
+    target: xarray.Dataset or xarray.DataArray
+        Object whose geographical coordinates will be renamed
+    target: xarray.Dataset or xarray.DataArray
+        Reference names of the geographical coordinates
+        
+    Returns:
+    --------
+    A similar object as 'target', but with the names of the geographical coordinates in 'reference'
+    """
+    
+    # names of geographical coordinates in the target dataset
+    try:
+        x_obj = [coord for coord in target.coords if coord.startswith('lon') or coord.startswith('x')][0]
+    except:
+        x_obj = [coord for coord in target.variables if coord.startswith('lon') or coord.startswith('x')][0]
+    try:
+        y_obj = [coord for coord in target.coords if coord.startswith('lat') or coord.startswith('y')][0]
+    except:
+        y_obj = [coord for coord in target.variables if coord.startswith('lat') or coord.startswith('y')][0]
+    
+    # names of geographical coordinates in the reference dataset
+    try:
+        x_ref = [coord for coord in reference.coords if coord.startswith('lon') or coord.startswith('x')][0]
+    except:
+        x_ref = [coord for coord in reference.variables if coord.startswith('lon') or coord.startswith('x')][0]
+    try:
+        y_ref = [coord for coord in reference.coords if coord.startswith('lat') or coord.startswith('y')][0]
+    except:
+        y_ref = [coord for coord in reference.variables if coord.startswith('lat') or coord.startswith('y')][0]
+    
+    return target.rename({x_obj: x_ref, y_obj: y_ref})
+
+
+
+def find_inflow_points(
+    lat: float,
+    lon: float,
+    ldd: xr.DataArray
+) -> xr.Dataset:
+    """This function finds the upstream coordinates of the pixels flowing into the input coordinates
+    
+    Parameteres:
+    ------------
+    lat: float
+        latitude of the input point
+    lon: float
+        longitued of the input point
+    ldd: xarray.DataArray
+        map of local drainage directions
+        
+    Returns:
+    --------
+    points: xarra.Dataset
+        Contains the coordinates of the pixels flowing into the point of interest.
+    """
+    
+    # Determine coordinate system
+    y_coord = [coord for coord in ldd.coords if coord.startswith('lat') or coord.startswith('y')][0]
+    x_coord = [coord for coord in ldd.coords if coord.startswith('lon') or coord.startswith('x')][0]
+
+    # spatial resolution of the input map
+    resolution = np.round(np.mean(np.diff(ldd[x_coord].values)), 4)
+
+    # Define window around the input pixel
+    window = 1.5 * resolution
+    ldd_window = ldd.sel({y_coord: slice(lat + window, lat - window),
+                          x_coord: slice(lon - window, lon + window)})
+    # 2D arrays of the coordinates of the pixels in the window
+    lons, lats = np.meshgrid(ldd_window[x_coord].data, ldd_window[y_coord].data)
+
+    # create a 1D mask of inflow pixels
+    inflow = np.array([[3, 2, 1],
+                       [6, 5, 4],
+                       [9, 8, 7]])
+    mask = (ldd_window == inflow).data.flatten()
+
+    # apply mask
+    lons, lats = lons.flatten()[mask], lats.flatten()[mask]
+    
+    # convert to xarray.Dataset
+    points = pd.DataFrame(data={y_coord: lats, x_coord: lons})
+    points.index.name = 'inflow'
+    points = points.to_xarray()
+
+    return points
+
+
+
+def extract_timeseries(
+    maps: xr.Dataset,
+    poi: xr.Dataset,
+    inflow: bool = False,
+    ldd: Optional[xr.Dataset] = None,
+    output: Optional[Union[str, Path]] = None,
+    overwrite: bool = False
+) -> Optional[xr.Dataset]:
+    """Extracts time series for each point of interest and saves them separately.
 
     Parameters:
     -----------
     maps: xarray.Dataset
         the time stack of input maps from which the time series will be extracted
     poi: xarray.Dataset
-        a Dataset indicating the coordinates of the points of interest. It must have only two variables (the coordinates), and the names of this variables must be dimensions in "maps"
-    ouputfile: optional, string or pathlib.Path
-        the file where the results will be saved. It can be either a CSV or a NetCDF file
+        A Dataset indicating the coordinates of the points of interest. It must have only two variables (the coordinates), and the names of this variables must be dimensions in "maps"
+    inflow: boolean
+        Wheter to extract the value in that pixel (False) or compute the sum of the pixels flowing into it (True)
+    ldd: optional, xarray.Dataset
+        Map of local drainage directions. Only needed if 'inflow' is True
+    output: optional, string or pathlib.Path
+        The directory where the results will be saved. If not provided, returns an xarray.Dataset.
+    overwrite: boolean
+        whether to overwrite or skip points of interest whose output file already exists. By default is False
 
     Returns:
     --------
-    By default, it puts out an xarray.Dataset with the extracted time series. Instead, if "outputfile" is provided, results will be saved to a file (CSV or NetCDF)
+    If 'output' is None, returns an xarray.Dataset with extracted time series.
+    Otherwise, saves results in a set of NetCDF files.
     """
-
+        
+    if "id" not in poi.coords:
+        raise ValueError('ERROR: "poi" must contain an "id" coordinate.')
+        
     coord_1, coord_2 = list(poi)
     if not all(coord in maps.coords for coord in [coord_1, coord_2]):
-        print(f'ERROR: The variables in "poi" (coordinates) are not coordinates in "maps"')
-        sys.exit(1)
-
-    # extract time series
-    maps_poi = maps.sel({coord_1: poi[coord_1], coord_2: poi[coord_2]}, method='nearest')
-
-    if outputfile is None:
-        return maps_poi.compute()
-
+        raise ValueError(f'The variables in "poi" (coordinates) are not coordinates in "maps"')
+    if coord_1.startswith('y') or coord_1.startswith('lat'):
+        coords = [coord_1, coord_2]
     else:
-        outputfile = Path(outputfile)
-        if outputfile.suffix == '.nc':
-            maps_poi.to_netcdf(outputfile)
-        elif outputfile.suffix == '.csv':
-            df = maps_poi.to_dataframe()
-            df_reset = df.reset_index()
-            df_reset.to_csv(outputfile, index=False)
-        else:
-            print('ERROR: the extension of the output file must be either ".nc" or ".csv"')
-            sys.exit(2)
-        print(f'Results exported as {outputfile}')
-
-
+        coords = [coord_2, coord_1]
+        
+    if inflow and ldd is None:
+        raise ValueError('An "ldd" map must be provided if the option "inflow" is enabled.')
+
+    # create output directory
+    if output:
+        output = Path(output)
+        output.mkdir(parents=True, exist_ok=True)
+
+    maps_poi = []
+    for ID in poi.id.data:
+        
+        if output:
+            output_file = output / f'{ID}.nc'
+            if output_file.exists() and not overwrite:
+                print(f'Output file {output_file} already exists. Moving forward to the next point.')
+                continue
+
+        if inflow:
+            # find pixels flowing into the point of interest
+            inflows = find_inflow_points(*[poi.sel(id=ID)[coord].item() for coord in coords], ldd)
+            
+            # extract and sum the time series of the pixels flowing into the point
+            series = maps.sel({coord: inflows[coord] for coord in coords}, method='nearest').sum('inflow')
+            series = series.expand_dims({'id': [ID]})            
+            series = series.assign_coords({coord: ('id',
+                                                   [maps[coord].sel({coord: poi.sel(id=ID)[coord].item()}, method='nearest').item()])
+                                                   # [poi.sel(id=ID)[coord].item()])
+                                           for coord in coords})
+            series.attrs.update(maps.attrs)
 
+        else:
+            # extract time series of the point
+            series = maps.sel({coord: poi.sel(id=ID)[coord].item() for coord in coords}, method='nearest')
+            series = series.expand_dims(dim={'id': [ID]})
+
+        # save time series
+        if output is None:
+            maps_poi.append(series)
+            print('{0} | Time series for point {1} extracted'.format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
+                                                                     ID))
+        else:           
+            series.to_netcdf(output_file)               
+            print('{0} | Time series for point {1} saved in {2}'.format(datetime.now().strftime('%Y-%m-%d %H:%M'),
+                                                                       ID,
+                                                                       output_file))
+    
+    if output is None:
+        return xr.concat(maps_poi, dim='id').compute()
+    
+    return None
+    
+    
+    
 def main(argv=sys.argv):
     prog = os.path.basename(argv[0])
     parser = argparse.ArgumentParser(
@@ -160,32 +356,64 @@ def main(argv=sys.argv):
         """,
         prog=prog,
     )
-    parser.add_argument("-i", "--input", required=True, help="Input CSV file (id, lat, lon)")
-    parser.add_argument("-d", "--directory", required=True, help="Input directory with .nc files")
-    parser.add_argument("-o", "--output", required=True, help="Output file. Two extensions are supported: .csv or .nc")
+    parser.add_argument("-p", "--points", required=True, help="CSV file of points of interest (id, lat, lon)")
+    parser.add_argument("-d", "--dir", required=True, help="Input directory with NetCDF or GRIB files")
+    parser.add_argument("-o", "--output", required=True, help="Output directory for time series")
+    parser.add_argument("-s", "--start", type=str, default=None, help='Start datetime (YYYY-MM-DD) (default: None)')
+    parser.add_argument("-e", "--end", type=str, default=None, help='End datetime (YYYY-MM-DD) (default: None)')
+    parser.add_argument("-i", "--inflow", action="store_true", default=False, help='Extract the aggregation of pixels flowing into the points of interest')
+    parser.add_argument("-l", "--ldd", required=False, help="Map of local drainage directions. Only neccesary if 'inflow' is True")
+    parser.add_argument("-w", "--overwrite", action="store_true", default=False, help="Overwrite existing output files")
 
     args = parser.parse_args()
-
+    
+
+    # parse dates
+    if args.start:
+        try:
+            args.start = datetime.strptime(args.start, "%Y-%m-%d")
+        except ValueError:
+            raise ValueError("Invalid date format in the 'start' argument. Use 'YYYY-MM-DD'.")
+    if args.end:
+        try:
+            args.end = datetime.strptime(args.end, "%Y-%m-%d")
+        except ValueError:
+            raise ValueError("Invalid date format in the 'end' argument. Use 'YYYY-MM-DD'.")
+                    
     try:
-        start_time = time.perf_counter()
 
-        print('Reading input CSV...')
-        points = read_points(args.input)
+        start_time = time.perf_counter()
+        
         print('Reading input maps...')
-        maps = read_inputmaps(args.directory)
+        maps = read_inputmaps(args.dir, start=args.start, end=args.end)
         print(maps)
+        
+        print('Reading input CSV...')
+        points = read_points(args.points)
+        points = rename_geographic_coords(points, maps)
+        
+        if args.inflow:
+            if args.ldd is None:
+                raise ValueError("-ldd must be provided if --inflow is enabled.")
+            else:
+                print('Reading the LDD map...')
+                ldd = read_ldd(args.ldd)
+                args.ldd = rename_geographic_coords(ldd, maps)
+
         print('Processing...')
-        extract_timeseries(maps, points, args.output)
+        extract_timeseries(maps, points, args.inflow, args.ldd, args.output, args.overwrite)
 
         elapsed_time = time.perf_counter() - start_time
         print(f"Time elapsed: {elapsed_time:0.2f} seconds")
 
     except Exception as e:
-        print(f'ERROR: {e}')
+        raise RuntimeError(f'{e}')
         sys.exit(1)
 
+        
+        
 def main_script():
     sys.exit(main())
 
 if __name__ == "__main__":
-    main_script()
+    main_script()
\ No newline at end of file
diff --git a/tests/data/ncextract/datasets/dis24_2018.nc b/tests/data/ncextract/datasets/dis24_2018.nc
new file mode 100644
index 0000000..30fc43b
Binary files /dev/null and b/tests/data/ncextract/datasets/dis24_2018.nc differ
diff --git a/tests/data/ncextract/datasets/dis24_2019.nc b/tests/data/ncextract/datasets/dis24_2019.nc
new file mode 100644
index 0000000..ab6dfa1
Binary files /dev/null and b/tests/data/ncextract/datasets/dis24_2019.nc differ
diff --git a/tests/data/ncextract/datasets/rsfil_2019.nc b/tests/data/ncextract/datasets/rsfil_2019.nc
deleted file mode 100644
index aee75cc..0000000
Binary files a/tests/data/ncextract/datasets/rsfil_2019.nc and /dev/null differ
diff --git a/tests/data/ncextract/datasets/rsfil_2020.nc b/tests/data/ncextract/datasets/rsfil_2020.nc
deleted file mode 100644
index 14602ec..0000000
Binary files a/tests/data/ncextract/datasets/rsfil_2020.nc and /dev/null differ
diff --git a/tests/data/ncextract/expected.csv b/tests/data/ncextract/expected.csv
deleted file mode 100644
index 2275b45..0000000
--- a/tests/data/ncextract/expected.csv
+++ /dev/null
@@ -1,5841 +0,0 @@
-id,time,lon,lat,rsfil
-14,2018-01-02,-109.22500000000001,44.475,0.968578051886524
-14,2018-01-03,-109.22500000000001,44.475,0.9685774981435288
-14,2018-01-04,-109.22500000000001,44.475,0.9685769444005334
-14,2018-01-05,-109.22500000000001,44.475,0.9685763906575382
-14,2018-01-06,-109.22500000000001,44.475,0.9685758369145429
-14,2018-01-07,-109.22500000000001,44.475,0.9685752831715476
-14,2018-01-08,-109.22500000000001,44.475,0.9685747294285523
-14,2018-01-09,-109.22500000000001,44.475,0.968574175685557
-14,2018-01-10,-109.22500000000001,44.475,0.9685736219425618
-14,2018-01-11,-109.22500000000001,44.475,0.9685730681995665
-14,2018-01-12,-109.22500000000001,44.475,0.9685725144565712
-14,2018-01-13,-109.22500000000001,44.475,0.9685719607135759
-14,2018-01-14,-109.22500000000001,44.475,0.9685714069705806
-14,2018-01-15,-109.22500000000001,44.475,0.9685708532275853
-14,2018-01-16,-109.22500000000001,44.475,0.9685702994845901
-14,2018-01-17,-109.22500000000001,44.475,0.9685697457415947
-14,2018-01-18,-109.22500000000001,44.475,0.9685691919985995
-14,2018-01-19,-109.22500000000001,44.475,0.9685686382556041
-14,2018-01-20,-109.22500000000001,44.475,0.9685680845126089
-14,2018-01-21,-109.22500000000001,44.475,0.9685675307696137
-14,2018-01-22,-109.22500000000001,44.475,0.9685669770266183
-14,2018-01-23,-109.22500000000001,44.475,0.9685664232836231
-14,2018-01-24,-109.22500000000001,44.475,0.9685658695406277
-14,2018-01-25,-109.22500000000001,44.475,0.9685653157976325
-14,2018-01-26,-109.22500000000001,44.475,0.9685647620546372
-14,2018-01-27,-109.22500000000001,44.475,0.9685642083116419
-14,2018-01-28,-109.22500000000001,44.475,0.9685636545686466
-14,2018-01-29,-109.22500000000001,44.475,0.9685631008256513
-14,2018-01-30,-109.22500000000001,44.475,0.9685625470826561
-14,2018-01-31,-109.22500000000001,44.475,0.9685619933396608
-14,2018-02-01,-109.22500000000001,44.475,0.9685614395966655
-14,2018-02-02,-109.22500000000001,44.475,0.9685608892338967
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-227,2018-06-05,-107.97500000000001,45.325,1.0
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-227,2018-06-08,-107.97500000000001,45.325,1.0
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-227,2018-06-10,-107.97500000000001,45.325,1.0
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-227,2018-06-17,-107.97500000000001,45.325,1.0
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-227,2018-07-01,-107.97500000000001,45.325,1.0
-227,2018-07-02,-107.97500000000001,45.325,1.0
-227,2018-07-03,-107.97500000000001,45.325,1.0
-227,2018-07-04,-107.97500000000001,45.325,1.0
-227,2018-07-05,-107.97500000000001,45.325,1.0
-227,2018-07-06,-107.97500000000001,45.325,0.9998854966196362
-227,2018-07-07,-107.97500000000001,45.325,0.9992366066925761
-227,2018-07-08,-107.97500000000001,45.325,0.9985269730396262
-227,2018-07-09,-107.97500000000001,45.325,0.9978792296481829
-227,2018-07-10,-107.97500000000001,45.325,0.997261057189504
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-227,2018-07-16,-107.97500000000001,45.325,0.9943591732660454
-227,2018-07-17,-107.97500000000001,45.325,0.9939016226688343
-227,2018-07-18,-107.97500000000001,45.325,0.9934372331224727
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-227,2018-07-20,-107.97500000000001,45.325,0.9929623046028853
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-227,2018-07-28,-107.97500000000001,45.325,0.989936425059555
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-227,2018-08-02,-107.97500000000001,45.325,0.9899684157133282
-227,2018-08-03,-107.97500000000001,45.325,0.9894057022739697
-227,2018-08-04,-107.97500000000001,45.325,0.9883746901255617
-227,2018-08-05,-107.97500000000001,45.325,0.9870999011851848
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-227,2018-08-08,-107.97500000000001,45.325,0.9823493374803486
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-227,2018-08-10,-107.97500000000001,45.325,0.9789237563849573
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-227,2018-08-17,-107.97500000000001,45.325,0.9685792581378674
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-227,2018-08-20,-107.97500000000001,45.325,0.9685737416681421
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-227,2018-08-30,-107.97500000000001,45.325,0.9685635616843923
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-227,2018-09-03,-107.97500000000001,45.325,0.968556621532929
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-227,2018-09-08,-107.97500000000001,45.325,0.9685468571258773
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-227,2018-09-10,-107.97500000000001,45.325,0.9685438544309442
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-227,2018-10-04,-107.97500000000001,45.325,0.968521192023162
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diff --git a/tests/data/ncextract/expected.nc b/tests/data/ncextract/expected.nc
new file mode 100644
index 0000000..3d86019
Binary files /dev/null and b/tests/data/ncextract/expected.nc differ
diff --git a/tests/data/ncextract/ldd.nc b/tests/data/ncextract/ldd.nc
new file mode 100644
index 0000000..8dc1a39
Binary files /dev/null and b/tests/data/ncextract/ldd.nc differ
diff --git a/tests/data/ncextract/reservoirs.csv b/tests/data/ncextract/reservoirs.csv
new file mode 100644
index 0000000..a2a3595
--- /dev/null
+++ b/tests/data/ncextract/reservoirs.csv
@@ -0,0 +1,9 @@
+ResID,lat,lon
+14,44.475,-109.225
+31,48.625,-109.975
+48,44.825,-111.325
+146,43.425,-108.175
+169,48.325,-111.125
+227,45.325,-107.975
+364,46.625,-111.675
+609,47.975,-106.375
diff --git a/tests/data/ncextract/stations.csv b/tests/data/ncextract/stations.csv
deleted file mode 100644
index 7d2b6b4..0000000
--- a/tests/data/ncextract/stations.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-id,lat,lon
-14,44.475000000000001,-109.22499999999999
-31,48.625,-109.97499999999999
-48,44.825000000000003,-111.325
-146,43.424999999999997,-108.175
-169,48.325000000000003,-111.125
-227,45.325000000000003,-107.97499999999999
-364,46.625,-111.675
-609,47.975000000000001,-106.375
diff --git a/tests/test_ncextract.py b/tests/test_ncextract.py
index 465adbe..2d4882b 100644
--- a/tests/test_ncextract.py
+++ b/tests/test_ncextract.py
@@ -1,43 +1,77 @@
 import unittest
 # from lisfloodutilities.compare.nc import NetCDFComparator
-from lisfloodutilities.ncextract import read_points, read_inputmaps, extract_timeseries
-import csv
+from lisfloodutilities.ncextract import read_points, read_inputmaps, read_ldd, extract_timeseries, rename_geographic_coords
+import numpy as np
+import xarray as xr
+from datetime import datetime
+
+
 
 class TestExtract(unittest.TestCase):
-    def compare_csv_files(self, file1, file2):
+
+    def compare_datasets(
+        self,
+        dataset1: xr.Dataset,
+        dataset2: xr.Dataset
+    ) -> bool:
         """
-        Compare the content of two CSV files and return True if they are identical, False otherwise.
+        Compare the content of two xarray.Datasets and return True if they are identical, False otherwise.
         """
-        with open(file1, 'r') as f1, open(file2, 'r') as f2:
-            reader1 = csv.reader(f1)
-            reader2 = csv.reader(f2)
+        # Check if both datasets have the same dimensions
+        if set(dataset1.dims) != set(dataset2.dims):
+            return False
 
-            for row1, row2 in zip(reader1, reader2):
-                if row1 != row2:
-                    return False
+        # Check if both datasets have the same coordinates
+        if not all((dataset1.coords[dim] == dataset2.coords[dim]).all() for dim in dataset1.coords):
+            return False
 
-            # Check if both files have the same number of rows
-            if len(list(reader1)) != len(list(reader2)):
+        # Check if both datasets have the same variables
+        if set(dataset1.data_vars) != set(dataset2.data_vars):
+            return False
+
+        # Check if the variables' values are the same
+        for var in dataset1.data_vars:
+            if not np.allclose(dataset1[var].values, dataset2[var].values):
                 return False
+            
+        # all tests passed
+        return True 
+
+    def test_ncextract(self):
 
-        return True
+        # config
+        inputcsv = 'tests/data/ncextract/reservoirs.csv'
+        data_dir = 'tests/data/ncextract/datasets'
+        ldd_file = 'tests/data/ncextract/ldd.nc'
+        expected_file = 'tests/data/ncextract/expected.nc'
+        start = datetime(2018, 10, 2)
+        end = datetime(2019, 10, 1)
 
-    def test_extract_csv(self):
-        inputcsv = 'tests/data/ncextract/stations.csv'
-        datasets = 'tests/data/ncextract/datasets'
-        outputfile = 'tests/data/output.csv'
-        expected = 'tests/data/ncextract/expected.csv'
+        # read expected results
+        expected = xr.open_dataset(expected_file)
+
+        # read maps
+        maps = read_inputmaps(data_dir, start=start, end=end)['dis24']
+
+        # read points of interest
         poi = read_points(inputcsv)
-        maps = read_inputmaps(datasets)
-        extract_timeseries(maps, poi, outputfile)
-        assert self.compare_csv_files(outputfile, expected)
-
-    # def test_extract_nc(self):
-    #     inputcsv = 'tests/data/ncextract/stations.csv'
-    #     datasets = 'tests/data/ncextract/datasets'
-    #     outputfile = 'tests/data/output.nc'
-    #     expected = 'tests/data/ncextract/expected.nc'
-    #     extract(inputcsv, datasets, outputfile, nc=True)
-    #     comp = NetCDFComparator(None, for_testing=True)
-    #     comp.compare_files(outputfile, expected)
-    #     assert comp.errors == None
+        poi = rename_geographic_coords(poi, maps)
+
+        # read LDD
+        ldd = read_ldd(ldd_file)
+        ldd = rename_geographic_coords(ldd, maps)
+
+        # extract outflow timeseries
+        print('Extracting reservoir outflow...')
+        outflow = extract_timeseries(maps, poi, inflow=False)
+        outflow.name = 'outflow'
+        
+        # extract inflow timeseries
+        print('Extracting reservoir inflow...')
+        inflow = extract_timeseries(maps, poi, inflow=True, ldd=ldd)
+        inflow.name = 'inflow'
+        
+        # merge both extractions
+        output = xr.merge((outflow, inflow))
+
+        self.assertTrue(self.compare_datasets(output, expected))
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