diff --git a/docs/source/notebooks/gridded_data_subset.ipynb b/docs/source/notebooks/gridded_data_subset.ipynb
index fa92dbbf..f1174287 100644
--- a/docs/source/notebooks/gridded_data_subset.ipynb
+++ b/docs/source/notebooks/gridded_data_subset.ipynb
@@ -15,22 +15,26 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from birdy import WPSClient\n",
-    "\n",
-    "from example_data import TESTDATA\n",
-    "import datetime as dt\n",
+    "from pathlib import Path\n",
     "from urllib.request import urlretrieve\n",
-    "import xarray as xr\n",
-    "import numpy as np\n",
-    "from matplotlib import pyplot as plt\n",
-    "import os\n",
-    "import json\n",
-    "import netCDF4 as nc\n",
-    "import salem\n",
     "from zipfile import ZipFile\n",
     "import glob\n",
+    "import json\n",
+    "import os\n",
+    "import datetime as dt\n",
     "import tempfile\n",
-    "from pathlib import Path\n",
+    "\n",
+    "from birdy import WPSClient\n",
+    "from matplotlib import pyplot as plt\n",
+    "from xclim import subset\n",
+    "import fiona\n",
+    "import netCDF4 as nc\n",
+    "import numpy as np\n",
+    "import rioxarray\n",
+    "import shapely\n",
+    "import xarray as xr\n",
+    "\n",
+    "from example_data import TESTDATA\n",
     "\n",
     "# Set environment variable RAVEN_WPS_URL to \"http://localhost:9099\" to run on the default local server\n",
     "url = os.environ.get(\"RAVEN_WPS_URL\", \"https://pavics.ouranos.ca/twitcher/ows/proxy/raven/wps\")\n",
@@ -72,13 +76,15 @@
     "# We will first need to process the catchment boundaries from the zipped shapefile. \n",
     "ZipFile(vec,'r').extractall(tmp)\n",
     "shp = list(tmp.glob(\"*.shp\"))[0]\n",
-    "shdf=salem.read_shapefile(shp)\n",
-    "shdf.crs=salem.wgs84 # This is needed in certain cases!\n",
+    "vector = fiona.open(shp, \"r\")\n",
     "\n",
-    "lon_min=shdf['min_x'][0]\n",
-    "lon_max=shdf['max_x'][0]\n",
-    "lat_min=shdf['min_y'][0]\n",
-    "lat_max=shdf['max_y'][0]"
+    "lon_min=vector.bounds[0]\n",
+    "lon_max=vector.bounds[2]\n",
+    "lat_min=vector.bounds[1]\n",
+    "lat_max=vector.bounds[3]\n",
+    "\n",
+    "# Get access to the geometry using the fiona API\n",
+    "shdf = [vector.next()[\"geometry\"]]"
    ]
   },
   {
@@ -133,10 +139,15 @@
     "\n",
     "        #Open the dataset file and slice the desired lat/lon (+1°Buffer) and limit to the time simulation duration\n",
     "        ds=xr.open_dataset(NRCAN_url).sel(lat=slice(lat_max+1,lat_min-1), lon=slice(lon_min-1,lon_max+1), time=slice(start, stop))\n",
-    "\n",
-    "        # Now apply the mask of the basin contour and average the values to get a single time series \n",
-    "        sub = ds.salem.roi(shape=shdf).mean(dim={'lat','lon'}, keep_attrs=True)\n",
-    "\n",
+    "        \n",
+    "        # Rioxarray requires CRS definitions for variables\n",
+    "        tas = ds.tas.rio.write_crs(4326)\n",
+    "        pr = ds.pr.rio.write_crs(4326)\n",
+    "        ds = xr.merge([tas, pr])\n",
+    "        \n",
+    "        # Now apply the mask of the basin contour and average the values to get a single time series\n",
+    "        sub = ds.rio.clip(shdf, crs=4326)\n",
+    "        sub = sub.mean(dim={'lat','lon'}, keep_attrs=True)\n",
     "        sub.to_netcdf(tsfile)\n",
     "\n",
     "        # Raven expects to have 00:00:00 at the end of the time vector, so let's add that\n",
@@ -163,12 +174,18 @@
     "\n",
     "        # Special treatment for ERA5 in North America: ECMWF stores ERA5 longitude in 0:360 format rather than -180:180. We need to reassign the longitudes here\n",
     "        ds = ds.assign_coords({'longitude':ds['longitude'].values[:]-360})\n",
-    "        sub = ds.salem.roi(shape=shdf).mean(dim={'latitude','longitude'},keep_attrs=True)\n",
+    "        \n",
+    "        # Rioxarray requires CRS definitions for variables\n",
+    "        tas = ds.tas.rio.write_crs(4326)\n",
+    "        pr = ds.pr.rio.write_crs(4326)\n",
+    "        ds = xr.merge([tas, pr])\n",
+    "        \n",
+    "        sub = ds.rio.clip(shdf, crs=ds.tas.rio.crs)\n",
+    "        sub = sub.mean(dim={'latitude','longitude'}, keep_attrs=True)\n",
     "\n",
     "        # Define the path to the netcdf file and write to disk (the basin averaged data)\n",
-    "\n",
     "        sub.to_netcdf(tsfile)\n",
-    "\n",
+    "        \n",
     "        # Add precision on time format for Raven\n",
     "        D = nc.Dataset(tsfile, mode=\"a\")\n",
     "        D.variables[\"time\"].units += \" 00:00:00\"\n",
@@ -176,7 +193,7 @@
     "    \n",
     "    #Perform the linear transform and time shift\n",
     "    nc_transforms=json.dumps({'tas': {'linear_transform': (1.0, -273.15), 'time_shift': UTCoffset_hours/24}, 'pr': {'linear_transform': (1000, 0.0), 'time_shift': UTCoffset_hours/24}})     \n",
-    "   "
+    "\n"
    ]
   },
   {
@@ -187,7 +204,7 @@
     {
      "data": {
       "text/plain": [
-       "<salem.graphics.Map at 0x7f0b7730f550>"
+       "<matplotlib.collections.QuadMesh at 0x7f4de4454668>"
       ]
      },
      "execution_count": 7,
@@ -196,7 +213,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -209,7 +226,7 @@
    ],
    "source": [
     "# Map of precip snapshot\n",
-    "ds.pr.isel(time=2).salem.roi(shape=shdf).salem.quick_map()"
+    "ds.pr.isel(time=2).rio.clip(shdf, crs=ds.pr.rio.crs).plot()"
    ]
   },
   {
@@ -224,11 +241,13 @@
        "array([1.9725623e-05, 2.7161006e-05, 3.7725804e-05, ..., 1.8322079e-06,\n",
        "       1.3108788e-06, 1.3241714e-06], dtype=float32)\n",
        "Coordinates:\n",
-       "  * time     (time) datetime64[ns] 2000-12-31 2000-12-31T01:00:00 ... 2001-03-01\n",
+       "  * time         (time) datetime64[ns] 2000-12-31 ... 2001-03-01\n",
+       "    spatial_ref  int64 0\n",
        "Attributes:\n",
-       "    units:       m\n",
-       "    long_name:   Total precipitation\n",
-       "    pyproj_srs:  +proj=longlat +datum=WGS84 +no_defs"
+       "    units:         m\n",
+       "    long_name:     Total precipitation\n",
+       "    grid_mapping:  spatial_ref\n",
+       "    transform:     (0.25, 0.0, -74.375, 0.0, -0.25, 51.125)"
       ]
      },
      "execution_count": 8,
@@ -318,16 +337,16 @@
      "text": [
       ":TimeStamp 2001-02-28 00:00:00.00\n",
       ":HRUStateVariableTable\n",
-      "  :Attributes,SURFACE_WATER,ATMOSPHERE,ATMOS_PRECIP,PONDED_WATER,SOIL[0],SOIL[1],SNOW,SNOW_LIQ,CUM_SNOWMELT,CONVOLUTION[0],CONVOLUTION[1],AET,CONV_STOR[0],CONV_STOR[1],CONV_STOR[2],CONV_STOR[3],CONV_STOR[4],CONV_STOR[5],CONV_STOR[6],CONV_STOR[7],CONV_STOR[8],CONV_STOR[9],CONV_STOR[10],CONV_STOR[11],CONV_STOR[12],CONV_STOR[13],CONV_STOR[14],CONV_STOR[15],CONV_STOR[16],CONV_STOR[17],CONV_STOR[18],CONV_STOR[19],CONV_STOR[20],CONV_STOR[21],CONV_STOR[22],CONV_STOR[23],CONV_STOR[24],CONV_STOR[25],CONV_STOR[26],CONV_STOR[27],CONV_STOR[28],CONV_STOR[29],CONV_STOR[30],CONV_STOR[31],CONV_STOR[32],CONV_STOR[33],CONV_STOR[34],CONV_STOR[35],CONV_STOR[36],CONV_STOR[37],CONV_STOR[38],CONV_STOR[39],CONV_STOR[40],CONV_STOR[41],CONV_STOR[42],CONV_STOR[43],CONV_STOR[44],CONV_STOR[45],CONV_STOR[46],CONV_STOR[47],CONV_STOR[48],CONV_STOR[49],CONV_STOR[50],CONV_STOR[51],CONV_STOR[52],CONV_STOR[53],CONV_STOR[54],CONV_STOR[55],CONV_STOR[56],CONV_STOR[57],CONV_STOR[58],CONV_STOR[59],CONV_STOR[60],CONV_STOR[61],CONV_STOR[62],CONV_STOR[63],CONV_STOR[64],CONV_STOR[65],CONV_STOR[66],CONV_STOR[67],CONV_STOR[68],CONV_STOR[69],CONV_STOR[70],CONV_STOR[71],CONV_STOR[72],CONV_STOR[73],CONV_STOR[74],CONV_STOR[75],CONV_STOR[76],CONV_STOR[77],CONV_STOR[78],CONV_STOR[79],CONV_STOR[80],CONV_STOR[81],CONV_STOR[82],CONV_STOR[83],CONV_STOR[84],CONV_STOR[85],CONV_STOR[86],CONV_STOR[87],CONV_STOR[88],CONV_STOR[89],CONV_STOR[90],CONV_STOR[91],CONV_STOR[92],CONV_STOR[93],CONV_STOR[94],CONV_STOR[95],CONV_STOR[96],CONV_STOR[97],CONV_STOR[98],CONV_STOR[99]\n",
-      "  :Units,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm\n",
-      "  1,0.00000,0.00000,-3.91667,0.00000,19.18146,339.42372,3.91667,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000\n",
+      "  :Attributes,SURFACE_WATER,ATMOSPHERE,ATMOS_PRECIP,PONDED_WATER,SOIL[0],SOIL[1],SOIL[2],SNOW_LIQ,SNOW,CANOPY,AET,CANOPY_SNOW,GLACIER,GLACIER_ICE\n",
+      "  :Units,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm\n",
+      "  1,0.00000,0.48141,-4.01176,0.00000,0.00000,0.00000,0.01160,0.00000,3.53035,0.00000,0.00078,0.00000,0.00000,0.00000\n",
       ":EndHRUStateVariableTable\n",
       ":BasinStateVariables\n",
       "  :BasinIndex 1,watershed\n",
-      "    :ChannelStorage, 0.00000\n",
-      "    :RivuletStorage, 63759773.48786\n",
-      "    :Qout,1,1475.92068,1492.85062\n",
-      "    :Qlat,3,1475.92068,1492.85062,1510.14332,1475.92068\n",
+      "    :ChannelStorage, -43200.00000\n",
+      "    :RivuletStorage, 40914.37846\n",
+      "    :Qout,1,0.42306,0.45152\n",
+      "    :Qlat,6,0.40543,0.43271,0.46182,0.49289,0.52605,0.56145,0.42306\n",
       "    :Qin ,20,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000\n",
       ":EndBasinStateVariables\n",
       "\n"
@@ -354,65 +373,65 @@
      "data": {
       "text/plain": [
        "<xarray.DataArray 'q_sim' (time: 59, nbasins: 1)>\n",
-       "array([[   0.      ],\n",
-       "       [1804.451682],\n",
-       "       [3571.474031],\n",
-       "       [3497.758444],\n",
-       "       [3426.289786],\n",
-       "       [3356.990978],\n",
-       "       [3289.787652],\n",
-       "       [3224.608061],\n",
-       "       [3161.382979],\n",
-       "       [3100.045617],\n",
-       "       [3040.531534],\n",
-       "       [2982.778556],\n",
-       "       [2926.726694],\n",
-       "       [2872.318067],\n",
-       "       [2819.496829],\n",
-       "       [2768.209095],\n",
-       "       [2718.402874],\n",
-       "       [2670.027999],\n",
-       "       [2623.036064],\n",
-       "       [2577.380364],\n",
-       "       [2533.01583 ],\n",
-       "       [2489.898976],\n",
-       "       [2447.98784 ],\n",
-       "       [2407.24193 ],\n",
-       "       [2367.622175],\n",
-       "       [2329.090872],\n",
-       "       [2291.611637],\n",
-       "       [2255.149362],\n",
-       "       [2219.670167],\n",
-       "       [2185.141357],\n",
-       "       [2151.53138 ],\n",
-       "       [2118.809788],\n",
-       "       [2086.947198],\n",
-       "       [2055.915251],\n",
-       "       [2025.68658 ],\n",
-       "       [1996.234772],\n",
-       "       [1967.534337],\n",
-       "       [1939.560674],\n",
-       "       [1912.290037],\n",
-       "       [1885.69951 ],\n",
-       "       [1859.766975],\n",
-       "       [1834.471081],\n",
-       "       [1809.791224],\n",
-       "       [1785.707513],\n",
-       "       [1762.20075 ],\n",
-       "       [1739.252403],\n",
-       "       [1716.844583],\n",
-       "       [1694.960024],\n",
-       "       [1673.582056],\n",
-       "       [1652.694588],\n",
-       "       [1632.282085],\n",
-       "       [1612.329551],\n",
-       "       [1592.822507],\n",
-       "       [1573.746977],\n",
-       "       [1555.089464],\n",
-       "       [1536.83694 ],\n",
-       "       [1518.976824],\n",
-       "       [1501.49697 ],\n",
-       "       [1484.38565 ]])\n",
+       "array([[ 1.      ],\n",
+       "       [ 3.696874],\n",
+       "       [11.041824],\n",
+       "       [15.442052],\n",
+       "       [14.715303],\n",
+       "       [13.787689],\n",
+       "       [12.918549],\n",
+       "       [12.104198],\n",
+       "       [11.341181],\n",
+       "       [10.626263],\n",
+       "       [ 9.956411],\n",
+       "       [ 9.328785],\n",
+       "       [ 8.740723],\n",
+       "       [ 8.18973 ],\n",
+       "       [ 7.673471],\n",
+       "       [ 7.189756],\n",
+       "       [ 6.736532],\n",
+       "       [ 6.311879],\n",
+       "       [ 5.913995],\n",
+       "       [ 5.541192],\n",
+       "       [ 5.19189 ],\n",
+       "       [ 4.864607],\n",
+       "       [ 4.557955],\n",
+       "       [ 4.270633],\n",
+       "       [ 4.001424],\n",
+       "       [ 3.749184],\n",
+       "       [ 3.512845],\n",
+       "       [ 3.291405],\n",
+       "       [ 3.083923],\n",
+       "       [ 2.889521],\n",
+       "       [ 2.707373],\n",
+       "       [ 2.536707],\n",
+       "       [ 2.3768  ],\n",
+       "       [ 2.226973],\n",
+       "       [ 2.08659 ],\n",
+       "       [ 1.955057],\n",
+       "       [ 1.831815],\n",
+       "       [ 1.716343],\n",
+       "       [ 1.608149],\n",
+       "       [ 1.506775],\n",
+       "       [ 1.411792],\n",
+       "       [ 1.322796],\n",
+       "       [ 1.239411],\n",
+       "       [ 1.161282],\n",
+       "       [ 1.088077],\n",
+       "       [ 1.019488],\n",
+       "       [ 0.955222],\n",
+       "       [ 0.895007],\n",
+       "       [ 0.838588],\n",
+       "       [ 0.785726],\n",
+       "       [ 0.736196],\n",
+       "       [ 0.689788],\n",
+       "       [ 0.646306],\n",
+       "       [ 0.605564],\n",
+       "       [ 0.567391],\n",
+       "       [ 0.531624],\n",
+       "       [ 0.498112],\n",
+       "       [ 0.466712],\n",
+       "       [ 0.437292]])\n",
        "Coordinates:\n",
        "  * time        (time) datetime64[ns] 2001-01-01 2001-01-02 ... 2001-02-28\n",
        "    basin_name  (nbasins) object ...\n",
@@ -439,7 +458,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f0b5aaf97b8>]"
+       "[<matplotlib.lines.Line2D at 0x7f4de4350c50>]"
       ]
      },
      "execution_count": 14,
@@ -448,7 +467,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -476,12 +495,12 @@
      "output_type": "stream",
      "text": [
       "Max:  <xarray.DataArray 'q_sim' ()>\n",
-      "array(3571.47403105)\n",
+      "array(15.44205159)\n",
       "Mean:  <xarray.DataArray 'q_sim' ()>\n",
-      "array(2231.11144315)\n",
+      "array(4.37489408)\n",
       "Monthly means:  <xarray.DataArray 'q_sim' (month: 2, nbasins: 1)>\n",
-      "array([[2648.69867205],\n",
-      "       [1768.78272545]])\n",
+      "array([[7.23797308],\n",
+      "       [1.20505662]])\n",
       "Coordinates:\n",
       "    basin_name  (nbasins) object ...\n",
       "  * month       (month) int64 1 2\n",
@@ -494,6 +513,13 @@
     "print(\"Mean: \", hydrograph.q_sim.mean())\n",
     "print(\"Monthly means: \", hydrograph.q_sim.groupby(hydrograph.time.dt.month).mean(dim='time'))"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -512,7 +538,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.7"
+   "version": "3.6.10"
   }
  },
  "nbformat": 4,
diff --git a/environment.yml b/environment.yml
index 56c263d5..5e3354bb 100644
--- a/environment.yml
+++ b/environment.yml
@@ -17,6 +17,7 @@ dependencies:
 - scipy
 - xarray==0.13
 - xclim==0.12.2
+- rioxarray
 - matplotlib
 - dask
 - pandas
@@ -44,4 +45,3 @@ dependencies:
 - pip:
   - spotpy
   - urlpath
-  - salem
diff --git a/raven/utilities/gis.py b/raven/utilities/gis.py
index 49e8b03e..b73b14d8 100644
--- a/raven/utilities/gis.py
+++ b/raven/utilities/gis.py
@@ -7,7 +7,7 @@
 from typing import Union
 
 import pandas as pd
-from raven.utils import crs_sniffer, single_file_check, archive_sniffer
+from raven.utils import crs_sniffer, single_file_check
 from shapely.geometry import shape, Point
 
 GEO_URL = "http://boreas.ouranos.ca/geoserver/wfs"