diff --git a/docs/source/notebooks/Delineation_workflow.ipynb b/docs/source/notebooks/Delineation_workflow.ipynb
index 7e1c307e..95b530b7 100644
--- a/docs/source/notebooks/Delineation_workflow.ipynb
+++ b/docs/source/notebooks/Delineation_workflow.ipynb
@@ -13,12 +13,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Cookie-cutter template required to connect to the PAVICS-Hydro Raven WPS server\n",
-    "\n",
     "from birdy import WPSClient\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
@@ -28,6 +26,8 @@
     "\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",
+    "\n",
+    "# Connect to the PAVICS-Hydro Raven WPS server\n",
     "wps = WPSClient(url)"
    ]
   },
@@ -42,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -70,9 +70,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6bbe091940>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# df = gpd.read_file(feature_url)\n",
     "df = gpd.GeoDataFrame.from_features([feature])\n",
@@ -88,7 +111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -108,9 +131,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'id': '96929', 'gml_id': 'USGS_HydroBASINS_lake_na_lev12.96929', 'HYBAS_ID': 7120270182, 'NEXT_DOWN': 7120270181, 'NEXT_SINK': 7120034330, 'MAIN_BAS': 7120034330, 'DIST_SINK': 490.9, 'DIST_MAIN': 490.9, 'SUB_AREA': 29.0, 'UP_AREA': 9419.6, 'PFAF_ID': 724089370000, 'SIDE': 'R', 'LAKE': 0, 'ENDO': 0, 'COAST': 0, 'ORDER': 1, 'SORT': 96929, 'area': 28764849.46504176, 'centroid': [-71.3409808346398, 50.478880424555875], 'perimeter': 33017.42666655582, 'gravelius': 1.7366297521025682}\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'area': 28.76484946504176,\n",
+       " 'longitude': -71.3409808346398,\n",
+       " 'latitude': 50.478880424555875,\n",
+       " 'gravelius': 1.7366297521025682,\n",
+       " 'perimeter': 33017.42666655582}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "[properties, ]=resp.get(asobj=True)\n",
     "prop = properties[0]\n",
@@ -137,7 +182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -147,9 +192,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'id': '96929', 'gml_id': 'USGS_HydroBASINS_lake_na_lev12.96929', 'HYBAS_ID': 7120270182, 'NEXT_DOWN': 7120270181, 'NEXT_SINK': 7120034330, 'MAIN_BAS': 7120034330, 'DIST_SINK': 490.9, 'DIST_MAIN': 490.9, 'SUB_AREA': 29.0, 'UP_AREA': 9419.6, 'PFAF_ID': 724089370000, 'SIDE': 'R', 'LAKE': 0, 'ENDO': 0, 'COAST': 0, 'ORDER': 1, 'SORT': 96929, '1': 13532, '2': 335, '5': 4367, '6': 847, '8': 10239, '10': 60, '12': 380, '14': 21, '16': 39, '18': 3248, 'count': 33068, 'nodata': 8.0, 'nan': 0, 'Ocean': 0, 'Forest': 19081, 'Shrubs': 10239, 'Grass': 479, 'Wetland': 21, 'Crops': 0, 'Urban': 0, 'Water': 3248, 'SnowIce': 0} \n",
+      "\n",
+      "\n",
+      "[{'Ocean': 0, 'Forest': 19081, 'Shrubs': 10239, 'Grass': 479, 'Wetland': 21, 'Crops': 0, 'Urban': 0, 'Water': 3248, 'SnowIce': 0}]\n"
+     ]
+    }
+   ],
    "source": [
     "# Note that geojson needs to be installed for this to work. \n",
     "# $ pip install -r requirements_extra.txt\n",
@@ -158,6 +214,26 @@
     "print(statistics)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{\"features\": [{\"geometry\": {\"coordinates\": [[[[1981874.096748, 967399.091191], [1981560.497165, 967640.799616], [1981434.936988, 967882.780767], [1980961.263774, 969007.129417], [1980150.453239, 969613.136897], [1979557.788254, 970982.006618], [1979190.993435, 971263.662311], [1978968.917959, 971674.108713], [1979068.737029, 972221.498664], [1978911.276644, 972753.897266], [1978998.518432, 973199.540058], [1978148.536013, 975486.000658], [1978229.072674, 975929.06883], [1977903.742496, 976905.067086], [1977984.173254, 977348.115496], [1977736.398881, 978051.400822], [1977900.425286, 978054.503503], [1978726.767107, 977503.147592], [1979063.162155, 977488.410214], [1979585.086481, 977194.438111], [1979728.314495, 977383.335657], [1979905.626466, 977961.050647], [1980249.760066, 977888.82216], [1980777.020525, 976821.614008], [1981210.625424, 976808.412368], [1981599.965266, 976584.172313], [1981738.25575, 977013.547429], [1981974.993178, 977251.010851], [1982328.051065, 978903.0378], [1982681.072971, 978846.538719], [1982996.790791, 978315.034465], [1983354.336434, 977981.589513], [1983659.560071, 977324.816337], [1983830.052137, 976894.322922], [1983556.471154, 976787.837664], [1982897.423789, 976458.687678], [1982761.508473, 976042.300741], [1982257.567097, 975700.92043], [1981989.759672, 974591.183905], [1982132.826199, 973944.08598], [1981931.599976, 972702.854607], [1981635.845096, 972309.345787], [1981135.976984, 971194.624495], [1981172.131364, 971160.205611], [1981342.839416, 971032.658661], [1981721.483719, 970283.259585], [1982217.756236, 968683.141738], [1982076.182758, 967974.105719], [1981874.096748, 967399.091191]]]], \"type\": \"MultiPolygon\"}, \"id\": \"0\", \"properties\": {\"1\": 13532, \"10\": 60, \"12\": 380, \"14\": 21, \"16\": 39, \"18\": 3248, \"2\": 335, \"5\": 4367, \"6\": 847, \"8\": 10239, \"COAST\": 0, \"Crops\": 0, \"DIST_MAIN\": 490.9, \"DIST_SINK\": 490.9, \"ENDO\": 0, \"Forest\": 19081, \"Grass\": 479, \"HYBAS_ID\": 7120270182, \"LAKE\": 0, \"MAIN_BAS\": 7120034330, \"NEXT_DOWN\": 7120270181, \"NEXT_SINK\": 7120034330, \"ORDER\": 1, \"Ocean\": 0, \"PFAF_ID\": 724089370000, \"SIDE\": \"R\", \"SORT\": 96929, \"SUB_AREA\": 29.0, \"Shrubs\": 10239, \"SnowIce\": 0, \"UP_AREA\": 9419.6, \"Urban\": 0, \"Water\": 3248, \"Wetland\": 21, \"count\": 33068, \"gml_id\": \"USGS_HydroBASINS_lake_na_lev12.96929\", \"id\": \"96929\", \"nan\": 0, \"nodata\": 8.0}, \"type\": \"Feature\"}], \"type\": \"FeatureCollection\"}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "features"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -167,9 +243,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Ocean': 0.0,\n",
+       " 'Forest': 0.5770231039071005,\n",
+       " 'Shrubs': 0.3096346921495101,\n",
+       " 'Grass': 0.014485303011975323,\n",
+       " 'Wetland': 0.0006350550381033022,\n",
+       " 'Crops': 0.0,\n",
+       " 'Urban': 0.0,\n",
+       " 'Water': 0.09822184589331075,\n",
+       " 'SnowIce': 0.0}"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# total = sum(lu.values())\n",
     "lu = statistics[0]\n",
@@ -188,7 +283,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -204,7 +299,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -226,9 +321,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'area': 28.76484946504176, 'longitude': -71.3409808346398, 'latitude': 50.478880424555875, 'gravelius': 1.7366297521025682, 'perimeter': 33017.42666655582, 'Ocean': 0.0, 'Forest': 0.5770231039071005, 'Shrubs': 0.3096346921495101, 'Grass': 0.014485303011975323, 'Wetland': 0.0006350550381033022, 'Crops': 0.0, 'Urban': 0.0, 'Water': 0.09822184589331075, 'SnowIce': 0.0, 'elevation': 490.04395604395603, 'slope': 3.9660612485567572, 'aspect': 116.79663053081183}\n"
+     ]
+    }
+   ],
    "source": [
     "all_properties={**shapeProperties, **landUse, **terrain_data}\n",
     "print(all_properties)"
@@ -245,9 +348,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# NBVAL_SKIP\n",
     "import cartopy.crs as ccrs\n",
@@ -267,13 +383,6 @@
     "        da.where(da!=-32768).sel(band=1).plot.imshow(ax=ax, transform=crs)\n",
     "        plt.show()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -292,7 +401,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.10"
+   "version": "3.6.7"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/notebooks/Perform_Regionalization.ipynb b/docs/source/notebooks/Perform_Regionalization.ipynb
index 934883c3..ccf84869 100644
--- a/docs/source/notebooks/Perform_Regionalization.ipynb
+++ b/docs/source/notebooks/Perform_Regionalization.ipynb
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -33,9 +33,90 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on method regionalisation in module birdy.client.base:\n",
+      "\n",
+      "regionalisation(ts, latitude, longitude, model_name, properties=None, elevation=None, start_date=datetime.datetime(1, 1, 1, 0, 0), end_date=datetime.datetime(1, 1, 1, 0, 0), name='watershed', ndonors=5, min_nse=0.6, method='SP_IDW', area=0.0) method of birdy.client.base.WPSClient instance\n",
+      "    Compute the hydrograph for an ungauged catchment using a regionalization method.\n",
+      "    \n",
+      "    Parameters\n",
+      "    ----------\n",
+      "    ts : ComplexData:mimetype:`application/x-netcdf`, :mimetype:`application/x-ogc-dods`, :mimetype:`text/plain`, :mimetype:`application/x-zipped-shp`\n",
+      "        Files (text or netCDF) storingdaily liquid precipitation (pr), solid precipitation (prsn), minimum temperature (tasmin), maximum temperature (tasmax), potential evapotranspiration (evspsbl) and observed streamflow (qobs [m3/s]).\n",
+      "    start_date : dateTime\n",
+      "        Start date of the simulation (AAAA-MM-DD). Defaults to the start of the forcing file.\n",
+      "    end_date : dateTime\n",
+      "        End date of the simulation (AAAA-MM-DD). Defaults to the end of the forcing file.\n",
+      "    latitude : float\n",
+      "        Watershed's centroid latitude\n",
+      "    longitude : float\n",
+      "        Watershed's centroid longitude\n",
+      "    name : string\n",
+      "        The name of the watershed the model is run for.\n",
+      "    model_name : {'HMETS', 'GR4JCN', 'MOHYSE'}string\n",
+      "        Hydrological model identifier: {HMETS, GR4JCN, MOHYSE}\n",
+      "    ndonors : integer\n",
+      "        Number of close or similar catchments to use to generate the representative hydrograph at the ungauged site.\n",
+      "    min_nse : float\n",
+      "        Minimum calibration NSE value required to be considered in the regionalization.\n",
+      "    method : {'MLR', 'SP', 'PS', 'SP_IDW', 'PS_IDW', 'SP_IDW_RA', 'PS_IDW_RA'}string\n",
+      "        Regionalisation method to use, one of MLR, SP, PS, SP_IDW,\n",
+      "        PS_IDW, SP_IDW_RA, PS_IDW_RA.\n",
+      "    \n",
+      "        The available regionalization methods are:\n",
+      "    \n",
+      "            Multiple linear regression (MLR)\n",
+      "                Ungauged catchment parameters are estimated individually by a linear regression\n",
+      "                against catchment properties.\n",
+      "    \n",
+      "            Spatial proximity (SP)\n",
+      "                The ungauged hydrograph is an average of the `n` closest catchments' hydrographs.\n",
+      "    \n",
+      "            Physical similarity (PS)\n",
+      "                The ungauged hydrograph is an average of the `n` most similar catchments' hydrographs.\n",
+      "    \n",
+      "            Spatial proximity with inverse distance weighting (SP_IDW)\n",
+      "                The ungauged hydrograph is an average of the `n` closest catchments' hydrographs, but\n",
+      "                the average is weighted using inverse distance weighting\n",
+      "    \n",
+      "            Physical similarity with inverse distance weighting (PS_IDW)\n",
+      "                The ungauged hydrograph is an average of the `n` most similar catchments' hydrographs, but\n",
+      "                the average is weighted using inverse distance weighting\n",
+      "    \n",
+      "            Spatial proximity with IDW and regression-based augmentation (SP_IDW_RA)\n",
+      "                The ungauged hydrograph is an average of the `n` closest catchments' hydrographs, but\n",
+      "                the average is weighted using inverse distance weighting. Furthermore, the method uses the CANOPEX/USGS\n",
+      "                dataset to estimate model parameters using Multiple Linear Regression. Parameters whose regression r-squared\n",
+      "                is higher than 0.5 are replaced by the MLR-estimated value.\n",
+      "    \n",
+      "            Physical Similarity with IDW and regression-based augmentation (PS_IDW_RA)\n",
+      "                The ungauged hydrograph is an average of the `n` most similar catchments' hydrographs, but\n",
+      "                the average is weighted using inverse distance weighting. Furthermore, the method uses the CANOPEX/USGS\n",
+      "                dataset to estimate model parameters using Multiple Linear Regression. Parameters whose regression r-squared\n",
+      "                is higher than 0.5 are replaced by the MLR-estimated value.\n",
+      "    properties : ComplexData:mimetype:`application/json`\n",
+      "        json string storing dictionary of properties. The available properties are: area (km2), longitude (dec.degrees), latitude (dec. degrees), gravelius, perimeter (m), elevation (m), slope(%), aspect, forest (%), grass (%), wetland (%), water (%), urban (%), shrubs (%), crops (%) and snowIce (%).\n",
+      "    area : float\n",
+      "        Watershed area (km2)\n",
+      "    elevation : float\n",
+      "        Watershed's mean elevation (m)\n",
+      "    \n",
+      "    Returns\n",
+      "    -------\n",
+      "    hydrograph : ComplexData:mimetype:`application/x-netcdf`, :mimetype:`application/zip`\n",
+      "        A netCDF file containing the outflow hydrographs (in m3/s) for all subbasins specified as `gauged` in the .rvh file. It reports period-ending time-averaged flows for the preceding time step, as is consistent with most measured stream gauge data (again, the initial flow conditions at the start of the first time step are included). If observed hydrographs are specified, they will be output adjacent to the corresponding modelled  hydrograph.\n",
+      "    ensemble : ComplexData:mimetype:`application/x-netcdf`\n",
+      "        A netCDF file containing the outflow hydrographs (in m3/s) for the basin on which the regionalization method has been applied. The number of outflow hydrographs is equal to the number of donors (ndonors) passed to the method. The average of these hydrographs (either using equal or Inverse-Distance Weights) is the hydrograph generated in \"hydrograph\".\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "# Get the documentation for the method's usage:\n",
     "help(wps.regionalisation)"
@@ -43,7 +124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -73,7 +154,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -92,18 +173,412 @@
   },
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-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
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+       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'q_sim'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 732</li><li><span>nbasins</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-d73f3678-204f-464b-ba1e-ccadd0172f6b' class='xr-array-in' type='checkbox' ><label for='section-d73f3678-204f-464b-ba1e-ccadd0172f6b' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 277.9 539.4 507.8 479.0 452.5 ... 24.36 23.86 23.37 22.92 22.48</span></div><pre class='xr-array-data'>array([[  0.      ],\n",
+       "       [277.916569],\n",
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+       "       ...,\n",
+       "       [ 23.374975],\n",
+       "       [ 22.916863],\n",
+       "       [ 22.479806]])</pre></div></li><li class='xr-section-item'><input id='section-27cb9bf5-de63-482e-8d60-9cabd881d2d5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-27cb9bf5-de63-482e-8d60-9cabd881d2d5' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>basin_name</span></div><div class='xr-var-dims'>(nbasins)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5c0c919f-e07c-4951-825b-371291a82e1b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5c0c919f-e07c-4951-825b-371291a82e1b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f2038ab-581e-4898-8e5f-3d109e8960ba' class='xr-var-data-in' type='checkbox'><label for='data-9f2038ab-581e-4898-8e5f-3d109e8960ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Name/ID of sub-basins with simulated outflows</dd><dt><span>cf_role :</span></dt><dd>timeseries_id</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><pre class='xr-var-data'>array([&#x27;Saumon&#x27;], dtype=object)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-01-01 ... 2002-01-01</div><input id='attrs-d4ad2f1d-25f7-4290-8012-ea39b6e9e4a3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d4ad2f1d-25f7-4290-8012-ea39b6e9e4a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e134bff-83ed-4639-80ed-4b113f8d03d0' class='xr-var-data-in' type='checkbox'><label for='data-3e134bff-83ed-4639-80ed-4b113f8d03d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><pre class='xr-var-data'>array([&#x27;2000-01-01T00:00:00.000000000&#x27;, &#x27;2000-01-02T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2000-01-03T00:00:00.000000000&#x27;, ..., &#x27;2001-12-30T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2001-12-31T00:00:00.000000000&#x27;, &#x27;2002-01-01T00:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-ccd9f157-ec36-46e1-876e-d005bd116785' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ccd9f157-ec36-46e1-876e-d005bd116785' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m**3 s**-1</dd><dt><span>long_name :</span></dt><dd>Simulated outflows</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'q_sim' (time: 732, nbasins: 1)>\n",
+       "array([[  0.      ],\n",
+       "       [277.916569],\n",
+       "       [539.369893],\n",
+       "       ...,\n",
+       "       [ 23.374975],\n",
+       "       [ 22.916863],\n",
+       "       [ 22.479806]])\n",
+       "Coordinates:\n",
+       "    basin_name  (nbasins) object ...\n",
+       "  * time        (time) datetime64[ns] 2000-01-01 2000-01-02 ... 2002-01-01\n",
+       "Dimensions without coordinates: nbasins\n",
+       "Attributes:\n",
+       "    units:      m**3 s**-1\n",
+       "    long_name:  Simulated outflows"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "hydrograph.q_sim"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f66bfc71c18>]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from pandas.plotting import register_matplotlib_converters\n",
     "register_matplotlib_converters()\n",
@@ -113,9 +588,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Max:  <xarray.DataArray 'q_sim' ()>\n",
+      "array(539.36989292)\n",
+      "Mean:  <xarray.DataArray 'q_sim' ()>\n",
+      "array(119.92177053)\n",
+      "Monthly means:  <xarray.DataArray 'q_sim' (month: 12, nbasins: 1)>\n",
+      "array([[160.93082366],\n",
+      "       [ 82.52531876],\n",
+      "       [ 73.72276794],\n",
+      "       [159.31442212],\n",
+      "       [153.70776744],\n",
+      "       [130.69632646],\n",
+      "       [149.09884217],\n",
+      "       [135.01868718],\n",
+      "       [127.69917325],\n",
+      "       [109.88942476],\n",
+      "       [109.48369515],\n",
+      "       [ 44.82918717]])\n",
+      "Coordinates:\n",
+      "    basin_name  (nbasins) object 'Saumon'\n",
+      "  * month       (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\n",
+      "Dimensions without coordinates: nbasins\n"
+     ]
+    }
+   ],
    "source": [
     "print(\"Max: \", hydrograph.q_sim.max())\n",
     "print(\"Mean: \", hydrograph.q_sim.mean())\n",
@@ -131,9 +634,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f66bc4bc208>,\n",
+       " <matplotlib.lines.Line2D at 0x7f66bc483080>,\n",
+       " <matplotlib.lines.Line2D at 0x7f66bc4831d0>,\n",
+       " <matplotlib.lines.Line2D at 0x7f66bc483320>,\n",
+       " <matplotlib.lines.Line2D at 0x7f66bc483470>]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Plot the simulations from the 5 donor parameter sets\n",
     "ensemble.q_sim.isel(nbasins=0).plot.line(hue='realization')"
@@ -141,9 +671,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "http://localhost:9099/outputs/a3317734-8bf3-11ea-99bc-b052162515fb/qsim.nc\n",
+      "http://localhost:9099/outputs/a3317734-8bf3-11ea-99bc-b052162515fb/ensemble.nc\n"
+     ]
+    }
+   ],
    "source": [
     "# You can also obtain the data in netcdf format directly by changing asobj to False:\n",
     "[hydrograph, ensemble] = resp.get(asobj=False)\n",
@@ -168,7 +707,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.10"
+   "version": "3.6.7"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/notebooks/Region_selection.ipynb b/docs/source/notebooks/Region_selection.ipynb
index 81fde406..9e960784 100644
--- a/docs/source/notebooks/Region_selection.ipynb
+++ b/docs/source/notebooks/Region_selection.ipynb
@@ -105,12 +105,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ae337c54fadd42699aae3c132c9a70de",
+       "model_id": "0203199d66544648991c6f814ab87938",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Map(center=[48.63, -74.71], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title', 'zoom_…"
+       "Map(basemap={'url': 'https://{s}.tile.opentopomap.org/{z}/{x}/{y}.png', 'max_zoom': 17, 'attribution': 'Map da…"
       ]
      },
      "metadata": {},
@@ -160,7 +160,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d8b8a69483df42079009b154c35b87bf",
+       "model_id": "b468b5835bea4d8e88bd000bfe694a15",
        "version_major": 2,
        "version_minor": 0
       },
@@ -240,7 +240,61 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.10"
+   "version": "3.6.7"
+  },
+  "nbdime-conflicts": {
+   "local_diff": [
+    {
+     "diff": [
+      {
+       "diff": [
+        {
+         "key": 0,
+         "op": "addrange",
+         "valuelist": [
+          "3.6.7"
+         ]
+        },
+        {
+         "key": 0,
+         "length": 1,
+         "op": "removerange"
+        }
+       ],
+       "key": "version",
+       "op": "patch"
+      }
+     ],
+     "key": "language_info",
+     "op": "patch"
+    }
+   ],
+   "remote_diff": [
+    {
+     "diff": [
+      {
+       "diff": [
+        {
+         "key": 0,
+         "op": "addrange",
+         "valuelist": [
+          "3.6.10"
+         ]
+        },
+        {
+         "key": 0,
+         "length": 1,
+         "op": "removerange"
+        }
+       ],
+       "key": "version",
+       "op": "patch"
+      }
+     ],
+     "key": "language_info",
+     "op": "patch"
+    }
+   ]
   }
  },
  "nbformat": 4,
diff --git a/docs/source/notebooks/Run_Raven_with_Parallel_parameters.ipynb b/docs/source/notebooks/Run_Raven_with_Parallel_parameters.ipynb
index bfe08257..3d0255f4 100644
--- a/docs/source/notebooks/Run_Raven_with_Parallel_parameters.ipynb
+++ b/docs/source/notebooks/Run_Raven_with_Parallel_parameters.ipynb
@@ -75,6 +75,368 @@
    "outputs": [
     {
      "data": {
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+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt, dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-8034efe9-1f21-43bd-844a-2830fbe6d5fd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8034efe9-1f21-43bd-844a-2830fbe6d5fd' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>nbasins</span>: 1</li><li><span>params</span>: 2</li><li><span class='xr-has-index'>time</span>: 732</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1475e935-8893-4d0e-9344-ea411312c965' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1475e935-8893-4d0e-9344-ea411312c965' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>basin_name</span></div><div class='xr-var-dims'>(nbasins)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-36939e17-65d8-4302-8d9a-6bf387f242b7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-36939e17-65d8-4302-8d9a-6bf387f242b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab59eac4-4f2e-4adb-b0fd-fcb5627af87c' class='xr-var-data-in' type='checkbox'><label for='data-ab59eac4-4f2e-4adb-b0fd-fcb5627af87c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Name/ID of sub-basins with simulated outflows</dd><dt><span>cf_role :</span></dt><dd>timeseries_id</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><pre class='xr-var-data'>array([&#x27;Salmon&#x27;], dtype=object)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-01-01 ... 2002-01-01</div><input id='attrs-d689f92b-477d-4ae1-aee4-75ed40d223c7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d689f92b-477d-4ae1-aee4-75ed40d223c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03c709b9-a9ac-4eba-986f-83514be178cc' class='xr-var-data-in' type='checkbox'><label for='data-03c709b9-a9ac-4eba-986f-83514be178cc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><pre class='xr-var-data'>array([&#x27;2000-01-01T00:00:00.000000000&#x27;, &#x27;2000-01-02T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2000-01-03T00:00:00.000000000&#x27;, ..., &#x27;2001-12-30T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2001-12-31T00:00:00.000000000&#x27;, &#x27;2002-01-01T00:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-db728803-a08c-45dc-81b6-ea6e0bec8a19' class='xr-section-summary-in' type='checkbox'  checked><label for='section-db728803-a08c-45dc-81b6-ea6e0bec8a19' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>q_obs</span></div><div class='xr-var-dims'>(time, nbasins)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f7909977-70cb-4fa8-ae02-9bc91de2d01e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f7909977-70cb-4fa8-ae02-9bc91de2d01e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-665627c5-c2e7-4a7b-9094-e72f505d3e43' class='xr-var-data-in' type='checkbox'><label for='data-665627c5-c2e7-4a7b-9094-e72f505d3e43' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m**3 s**-1</dd><dt><span>long_name :</span></dt><dd>Observed outflows</dd></dl></div><pre class='xr-var-data'>array([[ nan],\n",
+       "       [11.1],\n",
+       "       [10.9],\n",
+       "       ...,\n",
+       "       [11.7],\n",
+       "       [12.1],\n",
+       "       [12.3]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>q_in</span></div><div class='xr-var-dims'>(time, nbasins)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-289dcc0d-2667-448f-83eb-60aa81077993' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-289dcc0d-2667-448f-83eb-60aa81077993' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af9bb6eb-5b4e-49da-8750-a7772968338c' class='xr-var-data-in' type='checkbox'><label for='data-af9bb6eb-5b4e-49da-8750-a7772968338c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m**3 s**-1</dd><dt><span>long_name :</span></dt><dd>Observed inflows</dd></dl></div><pre class='xr-var-data'>array([[nan],\n",
+       "       [nan],\n",
+       "       [nan],\n",
+       "       ...,\n",
+       "       [nan],\n",
+       "       [nan],\n",
+       "       [nan]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>precip</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3a637336-18e7-45e2-a6f7-203d118735bf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3a637336-18e7-45e2-a6f7-203d118735bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d30fbb6-553e-4e36-824a-abce4f6e4e65' class='xr-var-data-in' type='checkbox'><label for='data-8d30fbb6-553e-4e36-824a-abce4f6e4e65' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>mm d**-1</dd><dt><span>long_name :</span></dt><dd>Precipitation</dd></dl></div><pre class='xr-var-data'>array([     nan, 2.478706, 0.628235, ..., 0.      , 0.003882, 0.      ])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>q_sim</span></div><div class='xr-var-dims'>(params, time, nbasins)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0c715038-0b43-4448-8ca3-55fadc83cc83' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0c715038-0b43-4448-8ca3-55fadc83cc83' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-01f02488-0730-40cd-8fcb-245189fbcb10' class='xr-var-data-in' type='checkbox'><label for='data-01f02488-0730-40cd-8fcb-245189fbcb10' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m**3 s**-1</dd><dt><span>long_name :</span></dt><dd>Simulated outflows</dd></dl></div><pre class='xr-var-data'>array([[[ 0.      ],\n",
+       "        [ 0.165788],\n",
+       "        ...,\n",
+       "        [13.330653],\n",
+       "        [13.25446 ]],\n",
+       "\n",
+       "       [[ 0.      ],\n",
+       "        [ 0.123831],\n",
+       "        ...,\n",
+       "        [11.191918],\n",
+       "        [11.114937]]])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-7618a864-8e2a-426f-984d-7dfab75ace45' class='xr-section-summary-in' type='checkbox'  ><label for='section-7618a864-8e2a-426f-984d-7dfab75ace45' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>featureType :</span></dt><dd>timeSeries</dd><dt><span>history :</span></dt><dd>Created on 2020-05-01 17:35:52 by Raven</dd><dt><span>description :</span></dt><dd>Standard Output</dd><dt><span>title :</span></dt><dd>Simulated river discharge</dd><dt><span>references :</span></dt><dd>Craig J.R. and the Raven Development Team Raven user&#x27;s and developer&#x27;s manual (Version 2.8) URL: http://raven.uwaterloo.ca/ (2018).</dd><dt><span>comment :</span></dt><dd>Raven Hydrological Framework version 2.9 rev#254</dd><dt><span>model_id :</span></dt><dd>gr4jcn</dd><dt><span>time_frequency :</span></dt><dd>day</dd><dt><span>time_coverage_start :</span></dt><dd>2000-01-01 00:00:00</dd><dt><span>time_coverage_end :</span></dt><dd>2002-01-01 00:00:00</dd></dl></div></li></ul></div></div>"
+      ],
       "text/plain": [
        "<xarray.Dataset>\n",
        "Dimensions:     (nbasins: 1, params: 2, time: 732)\n",
@@ -90,11 +452,11 @@
        "Attributes:\n",
        "    Conventions:          CF-1.6\n",
        "    featureType:          timeSeries\n",
-       "    history:              Created on 2020-03-09 15:55:07 by Raven\n",
+       "    history:              Created on 2020-05-01 17:35:52 by Raven\n",
        "    description:          Standard Output\n",
        "    title:                Simulated river discharge\n",
        "    references:           Craig J.R. and the Raven Development Team Raven use...\n",
-       "    comment:              Raven Hydrological Framework version 2.9 rev#177\n",
+       "    comment:              Raven Hydrological Framework version 2.9 rev#254\n",
        "    model_id:             gr4jcn\n",
        "    time_frequency:       day\n",
        "    time_coverage_start:  2000-01-01 00:00:00\n",
@@ -119,8 +481,8 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fc96e734be0>,\n",
-       " <matplotlib.lines.Line2D at 0x7fc941575710>]"
+       "[<matplotlib.lines.Line2D at 0x7f1c8632c2b0>,\n",
+       " <matplotlib.lines.Line2D at 0x7f1c84e988d0>]"
       ]
      },
      "execution_count": 4,
@@ -129,7 +491,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -157,7 +519,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['observed data series,filename,DIAG_NASH_SUTCLIFFE,DIAG_RMSE,\\nHYDROGRAPH,/tmp/pywps_process_fdil183e/Salmon-River-Near-Prince-George_meteo_daily.nc,-0.0371048,36.562,\\n', 'observed data series,filename,DIAG_NASH_SUTCLIFFE,DIAG_RMSE,\\nHYDROGRAPH,/tmp/pywps_process_fdil183e/Salmon-River-Near-Prince-George_meteo_daily.nc,0.0198906,35.5431,\\n']\n"
+      "['observed data series,filename,DIAG_NASH_SUTCLIFFE,DIAG_RMSE,\\nHYDROGRAPH,/tmp/pywps_process_mv5m3m6u/Salmon-River-Near-Prince-George_meteo_daily.nc,-0.117301,37.9493,\\n', 'observed data series,filename,DIAG_NASH_SUTCLIFFE,DIAG_RMSE,\\nHYDROGRAPH,/tmp/pywps_process_mv5m3m6u/Salmon-River-Near-Prince-George_meteo_daily.nc,-0.0559845,36.8933,\\n']\n"
      ]
     }
    ],
diff --git a/docs/source/notebooks/Running_models_with_multiple_timeseries_files.ipynb b/docs/source/notebooks/Running_models_with_multiple_timeseries_files.ipynb
index 3b760bd6..45586469 100644
--- a/docs/source/notebooks/Running_models_with_multiple_timeseries_files.ipynb
+++ b/docs/source/notebooks/Running_models_with_multiple_timeseries_files.ipynb
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -45,7 +45,400 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<title>Show/Hide data repr</title>\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<title>Show/Hide attributes</title>\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
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+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
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+       "  max-width: 700px;\n",
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+       "  margin-top: 0;\n",
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+       ".xr-array-name {\n",
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+       "  margin-right: 10px;\n",
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+       "  padding-left: 0 !important;\n",
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+       "\n",
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+       "  display: contents;\n",
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+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
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+       ".xr-section-item input:enabled + label {\n",
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+       "  color: var(--xr-font-color2);\n",
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+       "  color: var(--xr-font-color0);\n",
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+       "  grid-column: 1;\n",
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+       "  font-weight: 500;\n",
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+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
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+       "\n",
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+       "\n",
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+       "  color: var(--xr-disabled-color);\n",
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+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
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+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
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+       "\n",
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+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
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+       "  display: none;\n",
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+       "  margin-bottom: 5px;\n",
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+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
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+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
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+       "  color: var(--xr-font-color3);\n",
+       "}\n",
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+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
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+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
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+       "  content: '(';\n",
+       "}\n",
+       "\n",
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+       "  content: ')';\n",
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+       "  content: ',';\n",
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+       "  font-weight: bold;\n",
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+       "\n",
+       ".xr-var-list,\n",
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+       "  display: contents;\n",
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+       "\n",
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+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
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+       "  padding-right: 5px;\n",
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+       "\n",
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+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
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+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
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+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
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+       "  overflow: hidden;\n",
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+       "  padding-right: 10px;\n",
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+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
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+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
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+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt, dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-d1269d0a-6c43-4300-93d8-c1b158cf0133' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d1269d0a-6c43-4300-93d8-c1b158cf0133' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 22280</li><li><span class='xr-has-index'>watershed</span>: 5797</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b68460ef-d13e-4371-ba35-a888b224d60c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b68460ef-d13e-4371-ba35-a888b224d60c' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1950-01-01 ... 2010-12-31</div><input id='attrs-4602fb6b-7d16-4ea1-ae64-dc798d13ff1e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4602fb6b-7d16-4ea1-ae64-dc798d13ff1e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-937f71cd-dfec-42a8-88e2-1d84cf322b29' class='xr-var-data-in' type='checkbox'><label for='data-937f71cd-dfec-42a8-88e2-1d84cf322b29' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>cf_role :</span></dt><dd>timeseries_id</dd><dt><span>_ChunkSizes :</span></dt><dd>22280</dd></dl></div><pre class='xr-var-data'>array([&#x27;1950-01-01T00:00:00.000000000&#x27;, &#x27;1950-01-02T00:00:00.000000000&#x27;,\n",
+       "       &#x27;1950-01-03T00:00:00.000000000&#x27;, ..., &#x27;2010-12-29T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2010-12-30T00:00:00.000000000&#x27;, &#x27;2010-12-31T00:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>watershed</span></div><div class='xr-var-dims'>(watershed)</div><div class='xr-var-dtype'>|S64</div><div class='xr-var-preview xr-preview'>b&#x27;St. John River at Ninemile Bridge, Maine&#x27; ... b&#x27;KETTLE CREEK AT ST. THOMAS&#x27;</div><input id='attrs-7328e3bc-9cb8-42b6-bf6c-e98699305415' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7328e3bc-9cb8-42b6-bf6c-e98699305415' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1c502af-bdc1-4a94-8af8-5166a6ae2059' class='xr-var-data-in' type='checkbox'><label for='data-d1c502af-bdc1-4a94-8af8-5166a6ae2059' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Name of watershed</dd><dt><span>encoding :</span></dt><dd>utf-8</dd><dt><span>standard_name :</span></dt><dd>watershed_name</dd></dl></div><pre class='xr-var-data'>array([b&#x27;St. John River at Ninemile Bridge, Maine&#x27;,\n",
+       "       b&#x27;St. John River at Dickey, Maine&#x27;, b&#x27;Fish River near Fort Kent, Maine&#x27;,\n",
+       "       ..., b&#x27;MIDDLE THAMES RIVER AT THAMESFORD&#x27;,\n",
+       "       b&#x27;BIG OTTER CREEK AT TILLSONBURG&#x27;, b&#x27;KETTLE CREEK AT ST. THOMAS&#x27;],\n",
+       "      dtype=&#x27;|S64&#x27;)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-19bfc366-3227-4bf0-8451-2648fb83222c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-19bfc366-3227-4bf0-8451-2648fb83222c' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>drainage_area</span></div><div class='xr-var-dims'>(watershed)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7c4acf3a-6bfe-4f56-9c40-5f69d9ba42de' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7c4acf3a-6bfe-4f56-9c40-5f69d9ba42de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71f30cc0-7736-467a-b117-2b3c641bb482' class='xr-var-data-in' type='checkbox'><label for='data-71f30cc0-7736-467a-b117-2b3c641bb482' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>drainage_area_at_river_stretch_outlet</dd><dt><span>coverage_content_type :</span></dt><dd>auxiliaryInformation</dd><dt><span>long_name :</span></dt><dd>drainage_area</dd><dt><span>units :</span></dt><dd>km2</dd><dt><span>_ChunkSizes :</span></dt><dd>5797</dd></dl></div><pre class='xr-var-data'>array([3471.689421, 6938.20108 , 2260.093113, ...,  306.      ,  354.1     ,\n",
+       "        330.88    ])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>pr</span></div><div class='xr-var-dims'>(watershed, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6143d64b-63d4-476a-a565-c4b747cbe794' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6143d64b-63d4-476a-a565-c4b747cbe794' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af055a53-c32e-4ae6-9d5c-0c0ae1ae384b' class='xr-var-data-in' type='checkbox'><label for='data-af055a53-c32e-4ae6-9d5c-0c0ae1ae384b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Precipitation</dd><dt><span>standard_name :</span></dt><dd>precipitation_flux</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>_ChunkSizes :</span></dt><dd>[ 363 1393]</dd></dl></div><pre class='xr-var-data'>[129157160 values with dtype=float64]</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>tasmax</span></div><div class='xr-var-dims'>(watershed, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-71697964-f834-4665-a163-ffce8631386a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-71697964-f834-4665-a163-ffce8631386a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66eed30a-28d4-469d-a469-b117c8131e76' class='xr-var-data-in' type='checkbox'><label for='data-66eed30a-28d4-469d-a469-b117c8131e76' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>K</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>long_name :</span></dt><dd>Daily Maximum Near-Surface Air Temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>_ChunkSizes :</span></dt><dd>[ 363 1393]</dd></dl></div><pre class='xr-var-data'>[129157160 values with dtype=float64]</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>tasmin</span></div><div class='xr-var-dims'>(watershed, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7f7a6cc1-466f-4882-9d0e-857a7821f922' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7f7a6cc1-466f-4882-9d0e-857a7821f922' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10e7abeb-0533-4509-a6fb-a8309fad5162' class='xr-var-data-in' type='checkbox'><label for='data-10e7abeb-0533-4509-a6fb-a8309fad5162' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>long_name :</span></dt><dd>Daily Minimum Near-Surface Air Temperature\t</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>_ChunkSizes :</span></dt><dd>[ 363 1393]</dd></dl></div><pre class='xr-var-data'>[129157160 values with dtype=float64]</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>discharge</span></div><div class='xr-var-dims'>(watershed, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d76e0222-8665-425e-a2bd-b57f5348d3d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d76e0222-8665-425e-a2bd-b57f5348d3d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-78fb298c-3c6f-4728-8ca5-8be4d2043c3f' class='xr-var-data-in' type='checkbox'><label for='data-78fb298c-3c6f-4728-8ca5-8be4d2043c3f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>discharge</dd><dt><span>standard_name :</span></dt><dd>water_volume_transport_in_river_channel</dd><dt><span>units :</span></dt><dd>m3 s-1</dd><dt><span>_ChunkSizes :</span></dt><dd>[ 363 1393]</dd></dl></div><pre class='xr-var-data'>[129157160 values with dtype=float64]</pre></li></ul></div></li><li class='xr-section-item'><input id='section-31680162-37f6-4e7c-aa12-92af57bac815' class='xr-section-summary-in' type='checkbox'  ><label for='section-31680162-37f6-4e7c-aa12-92af57bac815' class='xr-section-summary' >Attributes: <span>(15)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>Hydrometeorological data for lumped hydrological modelling of 5797 catchments in North America</dd><dt><span>institute_id :</span></dt><dd>ETS</dd><dt><span>contact :</span></dt><dd>Richard Arsenault: richard.arsenault@etsmtl.ca</dd><dt><span>date_created :</span></dt><dd>2020-08-01</dd><dt><span>source :</span></dt><dd>Hydrometric data from USGS National Water Information Service and ECCC Water Survey Canada. Meteorological data from ECCC stations, NRCan 10km gridded interpolated dataset and Livneh 2014 database. Catchment areas from ECCC HYDAT, HydroSheds and USGS.</dd><dt><span>featureType :</span></dt><dd>timeSeries</dd><dt><span>cdm_data_type :</span></dt><dd>station</dd><dt><span>license :</span></dt><dd>ODC-BY</dd><dt><span>keywords :</span></dt><dd>hydrology, North America, streamflow, hydrometeorological, PAVICS, PAVICS-Hydro, modelling</dd><dt><span>activity :</span></dt><dd>PAVICS_Hydro</dd><dt><span>Conventions :</span></dt><dd>CF-1.6, ACDD-1.3</dd><dt><span>summary :</span></dt><dd>Hydrometeorological database for the PAVICS-Hydro platform, including precipitation, temperature, discharge and catchment area to drive the RAVEN hydrological modelling framework. Provided by the HC3 Laboratory at École de technologie supérieure, Montréal, Canada.</dd><dt><span>institution :</span></dt><dd>ETS (École de technologie supérieure)</dd><dt><span>DODS.strlen :</span></dt><dd>72</dd><dt><span>DODS.dimName :</span></dt><dd>string72</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:        (time: 22280, watershed: 5797)\n",
+       "Coordinates:\n",
+       "  * time           (time) datetime64[ns] 1950-01-01 1950-01-02 ... 2010-12-31\n",
+       "  * watershed      (watershed) |S64 b'St. John River at Ninemile Bridge, Maine' ... b'KETTLE CREEK AT ST. THOMAS'\n",
+       "Data variables:\n",
+       "    drainage_area  (watershed) float64 ...\n",
+       "    pr             (watershed, time) float64 ...\n",
+       "    tasmax         (watershed, time) float64 ...\n",
+       "    tasmin         (watershed, time) float64 ...\n",
+       "    discharge      (watershed, time) float64 ...\n",
+       "Attributes:\n",
+       "    title:          Hydrometeorological data for lumped hydrological modellin...\n",
+       "    institute_id:   ETS\n",
+       "    contact:        Richard Arsenault: richard.arsenault@etsmtl.ca\n",
+       "    date_created:   2020-08-01\n",
+       "    source:         Hydrometric data from USGS National Water Information Ser...\n",
+       "    featureType:    timeSeries\n",
+       "    cdm_data_type:  station\n",
+       "    license:        ODC-BY\n",
+       "    keywords:       hydrology, North America, streamflow, hydrometeorological...\n",
+       "    activity:       PAVICS_Hydro\n",
+       "    Conventions:    CF-1.6, ACDD-1.3\n",
+       "    summary:        Hydrometeorological database for the PAVICS-Hydro platfor...\n",
+       "    institution:    ETS (École de technologie supérieure)\n",
+       "    DODS.strlen:    72\n",
+       "    DODS.dimName:   string72"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Open Canopex dataset using DAP link\n",
+    "ds = xr.open_dataset(CANOPEX_DAP)\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -58,9 +451,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Basin name:  b'WHITEMOUTH RIVER NEAR WHITEMOUTH'\n",
+      "Latitude:  49.51119663557124  °N\n",
+      "Area:  3650.476384548832  km^2\n"
+     ]
+    }
+   ],
    "source": [
     "# With this info, we can gather some properties from the CANOPEX database:\n",
     "tmp=pd.read_csv(TESTDATA['canopex_attributes'])\n",
@@ -75,24 +478,6 @@
     "print(\"Area: \", basin_area, \" km^2\")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Ideally we would be able to pass the DAP link directly to Raven, but there are still some issues \n",
-    "# to fix to be able to do that. For now, we'll download the series at the point of interest. \n",
-    "path = Path(tempfile.mkdtemp()) /  \"ts.nc\"\n",
-    "ts = ds.isel(watershed=watershedID).sel(time=slice(start, stop))\n",
-    "ts.to_netcdf(path)\n",
-    "\n",
-    "# Add precision on time format for Raven\n",
-    "D = nc.Dataset(path, mode=\"a\")\n",
-    "D.variables[\"time\"].units += \" 00:00:00\"\n",
-    "D.close()"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -102,7 +487,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -111,22 +496,15 @@
     "filepathQobs=os.getcwd()+\"/CANOPEX_Qobs.nc\"\n",
     "\n",
     "# Do the extraction for the selected catchment\n",
-    "basindata=xr.open_dataset(CANOPEX_DAP)\n",
-    "newBasin=basindata.isel(watershed=watershedID)\n",
+    "newBasin=ds.isel(watershed=watershedID)\n",
     "\n",
     "# Generate the streamflow time-series netcdf\n",
     "Qobsfile = newBasin['discharge']\n",
     "Qobsfile.to_netcdf(filepathQobs)\n",
-    "D = nc.Dataset(filepathQobs, mode=\"a\")\n",
-    "D.variables[\"time\"].units += \" 00:00:00\"\n",
-    "D.close()\n",
     "\n",
     "# Generate the meteorological time-series netcdf\n",
     "newBasin=newBasin[['drainage_area','pr','tasmax','tasmin']]\n",
-    "newBasin.to_netcdf(filepathMet)\n",
-    "D = nc.Dataset(filepathMet, mode=\"a\")\n",
-    "D.variables[\"time\"].units += \" 00:00:00\"\n",
-    "D.close()"
+    "newBasin.to_netcdf(filepathMet)"
    ]
   },
   {
@@ -138,9 +516,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "observed data series,filename,DIAG_NASH_SUTCLIFFE,DIAG_RMSE,\n",
+      "HYDROGRAPH,/tmp/pywps_process_fufyza3l/CANOPEX_Qobs.nc,-0.12679,30.5267,\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "# The model parameters. We are forcing values here just so the model runs, the models are probably very bad choices!\n",
     "\n",
@@ -183,9 +571,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "observed data series,filename,DIAG_NASH_SUTCLIFFE,DIAG_RMSE,\n",
+      "HYDROGRAPH,/tmp/pywps_process_6r8q6yk0/CANOPEX_Qobs.nc,-0.12679,30.5267,\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "# Test with reversed timeseries files:\n",
     "ts_combined=[str(filepathQobs),str(filepathMet)]\n",
@@ -222,7 +620,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.10"
+   "version": "3.6.7"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/notebooks/canopex.ipynb b/docs/source/notebooks/canopex.ipynb
index ebaa1f52..10a1e4ed 100644
--- a/docs/source/notebooks/canopex.ipynb
+++ b/docs/source/notebooks/canopex.ipynb
@@ -46,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -61,7 +61,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -90,7 +90,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -98,12 +98,7 @@
     "# to fix to be able to do that. For now, we'll download the series at the point of interest. \n",
     "path = Path(tempfile.mkdtemp()) /  \"ts.nc\"\n",
     "ts = ds.isel(watershed=watershedID).sel(time=slice(start, stop))\n",
-    "ts.to_netcdf(path)\n",
-    "\n",
-    "# Add precision on time format for Raven\n",
-    "D = nc.Dataset(path, mode=\"a\")\n",
-    "D.variables[\"time\"].units += \" 00:00:00\"\n",
-    "D.close()"
+    "ts.to_netcdf(path)"
    ]
   },
   {
@@ -115,7 +110,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -168,7 +163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -186,7 +181,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -194,7 +189,7 @@
      "output_type": "stream",
      "text": [
       "observed data series,filename,DIAG_NASH_SUTCLIFFE,DIAG_RMSE,\n",
-      "HYDROGRAPH,/tmp/pywps_process_ki9x4tyz/ts.nc,0.402713,19.5566,\n",
+      "HYDROGRAPH,/tmp/pywps_process_4kf3e_v5/ts.nc,0.402713,19.5566,\n",
       "\n"
      ]
     }
@@ -213,7 +208,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -237,12 +232,12 @@
     ")\n",
     "\n",
     "# Let's call the model with the timeseries, model parameters and other configuration parameters\n",
-    "resp = wps.raven_hmets(ts=str(path), params=calibparams, **config)\n"
+    "resp = wps.raven_hmets(ts=str(path), params=calibparams, nc_index=watershedID, **config)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -261,7 +256,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -269,7 +264,7 @@
      "output_type": "stream",
      "text": [
       "observed data series,filename,DIAG_NASH_SUTCLIFFE,DIAG_RMSE,\n",
-      "HYDROGRAPH,/tmp/pywps_process_wzp4zz4a/ts.nc,0.415524,21.9858,\n",
+      "HYDROGRAPH,/tmp/pywps_process_q2xb9nna/ts.nc,0.415524,21.9858,\n",
       "\n"
      ]
     }
@@ -280,6 +275,25 @@
     "print(diagnostics)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "observed data series,filename,DIAG_NASH_SUTCLIFFE,DIAG_RMSE,\n",
+      "HYDROGRAPH,/tmp/pywps_process_q2xb9nna/ts.nc,0.415524,21.9858,\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(diagnostics)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -289,22 +303,402 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<title>Show/Hide data repr</title>\n",
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+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt, dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'q_sim'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 4748</li><li><span>nbasins</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b8a7f5ce-41b9-4296-a488-49bd2352a3d8' class='xr-array-in' type='checkbox' ><label for='section-b8a7f5ce-41b9-4296-a488-49bd2352a3d8' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 14.08 27.75 26.93 26.14 25.37 ... 13.5 13.22 12.93 12.66 11.95</span></div><pre class='xr-array-data'>array([[ 0.      ],\n",
+       "       [14.083714],\n",
+       "       [27.751569],\n",
+       "       ...,\n",
+       "       [12.934327],\n",
+       "       [12.657255],\n",
+       "       [11.947663]])</pre></div></li><li class='xr-section-item'><input id='section-18ce73f3-b6c9-41fd-b0aa-bdafdf4dddfe' class='xr-section-summary-in' type='checkbox'  checked><label for='section-18ce73f3-b6c9-41fd-b0aa-bdafdf4dddfe' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1998-01-01 ... 2010-12-31</div><input id='attrs-b7fbee86-fb44-454f-aaf5-755a9206a9a2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b7fbee86-fb44-454f-aaf5-755a9206a9a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1fa29662-c0c1-460b-a30a-facfa8cfc9d2' class='xr-var-data-in' type='checkbox'><label for='data-1fa29662-c0c1-460b-a30a-facfa8cfc9d2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><pre class='xr-var-data'>array([&#x27;1998-01-01T00:00:00.000000000&#x27;, &#x27;1998-01-02T00:00:00.000000000&#x27;,\n",
+       "       &#x27;1998-01-03T00:00:00.000000000&#x27;, ..., &#x27;2010-12-29T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2010-12-30T00:00:00.000000000&#x27;, &#x27;2010-12-31T00:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>basin_name</span></div><div class='xr-var-dims'>(nbasins)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ce3fcf66-555a-471d-8854-672ed2117514' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce3fcf66-555a-471d-8854-672ed2117514' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f9ce334-1867-4167-8728-0a5addf2d07e' class='xr-var-data-in' type='checkbox'><label for='data-8f9ce334-1867-4167-8728-0a5addf2d07e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Name/ID of sub-basins with simulated outflows</dd><dt><span>cf_role :</span></dt><dd>timeseries_id</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><pre class='xr-var-data'>array([&#x27;watershed&#x27;], dtype=object)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-51d1b8b4-178b-4d45-925d-f5a9d896ad4e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-51d1b8b4-178b-4d45-925d-f5a9d896ad4e' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m**3 s**-1</dd><dt><span>long_name :</span></dt><dd>Simulated outflows</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'q_sim' (time: 4748, nbasins: 1)>\n",
+       "array([[ 0.      ],\n",
+       "       [14.083714],\n",
+       "       [27.751569],\n",
+       "       ...,\n",
+       "       [12.934327],\n",
+       "       [12.657255],\n",
+       "       [11.947663]])\n",
+       "Coordinates:\n",
+       "  * time        (time) datetime64[ns] 1998-01-01 1998-01-02 ... 2010-12-31\n",
+       "    basin_name  (nbasins) object ...\n",
+       "Dimensions without coordinates: nbasins\n",
+       "Attributes:\n",
+       "    units:      m**3 s**-1\n",
+       "    long_name:  Simulated outflows"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hydrograph.q_sim"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fac1b7c4eb8>]"
+       "[<matplotlib.lines.Line2D at 0x7ff979be5e10>]"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -324,7 +718,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -349,7 +743,7 @@
       "       [29.01578923],\n",
       "       [14.94742054]])\n",
       "Coordinates:\n",
-      "    basin_name  (nbasins) object ...\n",
+      "    basin_name  (nbasins) object 'watershed'\n",
       "  * month       (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "Dimensions without coordinates: nbasins\n"
      ]
@@ -371,18 +765,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "http://localhost:9099/outputs/62fbb5ca-8b13-11ea-a72c-b8ca3a8f5177/test_hmets_NRCAN-0_Hydrographs.nc\n",
-      "http://localhost:9099/outputs/62fbb5ca-8b13-11ea-a72c-b8ca3a8f5177/test_hmets_NRCAN-0_WatershedStorage.nc\n",
-      "http://localhost:9099/outputs/62fbb5ca-8b13-11ea-a72c-b8ca3a8f5177/test_hmets_NRCAN-0_solution.rvc\n",
-      "http://localhost:9099/outputs/62fbb5ca-8b13-11ea-a72c-b8ca3a8f5177/test_hmets_NRCAN-0_Diagnostics.csv\n",
-      "http://localhost:9099/outputs/62fbb5ca-8b13-11ea-a72c-b8ca3a8f5177/rv.zip\n"
+      "http://localhost:9099/outputs/8bdc2c8e-8bf6-11ea-aad1-b052162515fb/test_hmets_NRCAN-0_Hydrographs.nc\n",
+      "http://localhost:9099/outputs/8bdc2c8e-8bf6-11ea-aad1-b052162515fb/test_hmets_NRCAN-0_WatershedStorage.nc\n",
+      "http://localhost:9099/outputs/8bdc2c8e-8bf6-11ea-aad1-b052162515fb/test_hmets_NRCAN-0_solution.rvc\n",
+      "http://localhost:9099/outputs/8bdc2c8e-8bf6-11ea-aad1-b052162515fb/test_hmets_NRCAN-0_Diagnostics.csv\n",
+      "http://localhost:9099/outputs/8bdc2c8e-8bf6-11ea-aad1-b052162515fb/rv.zip\n"
      ]
     }
    ],
@@ -413,7 +807,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.10"
+   "version": "3.6.7"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/notebooks/gridded_data_subset.ipynb b/docs/source/notebooks/gridded_data_subset.ipynb
index e4f82b9f..fa92dbbf 100644
--- a/docs/source/notebooks/gridded_data_subset.ipynb
+++ b/docs/source/notebooks/gridded_data_subset.ipynb
@@ -15,32 +15,23 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import netCDF4 as nc\n",
-    "import numpy as np\n",
-    "import salem\n",
-    "import xarray as xr\n",
     "from birdy import WPSClient\n",
-    "from matplotlib import pyplot as plt\n",
-    "from pyproj import CRS\n",
     "\n",
+    "from example_data import TESTDATA\n",
     "import datetime as dt\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 tempfile\n",
     "from pathlib import Path\n",
-    "from urllib.request import urlretrieve\n",
-    "from zipfile import ZipFile\n",
     "\n",
-    "from example_data import TESTDATA"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
     "# 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",
     "wps = WPSClient(url)\n",
@@ -51,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -74,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -82,7 +73,7 @@
     "ZipFile(vec,'r').extractall(tmp)\n",
     "shp = list(tmp.glob(\"*.shp\"))[0]\n",
     "shdf=salem.read_shapefile(shp)\n",
-    "# shdf.crs=CRS(4326) # This is needed in certain cases!\n",
+    "shdf.crs=salem.wgs84 # This is needed in certain cases!\n",
     "\n",
     "lon_min=shdf['min_x'][0]\n",
     "lon_max=shdf['max_x'][0]\n",
@@ -92,7 +83,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -128,7 +119,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -159,31 +150,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "crs not understood",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-11-1dc0aee27199>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0;31m# 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\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m         \u001b[0mds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massign_coords\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'longitude'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'longitude'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m360\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m         \u001b[0msub\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msalem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mroi\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'latitude'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'longitude'\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mkeep_attrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m         \u001b[0;31m# Define the path to the netcdf file and write to disk (the basin averaged data)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/raven/lib/python3.6/site-packages/salem/sio.py\u001b[0m in \u001b[0;36mroi\u001b[0;34m(self, ds, **kwargs)\u001b[0m\n\u001b[1;32m    503\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'grid'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    504\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 505\u001b[0;31m         \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregion_of_interest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    506\u001b[0m         coords = {self.y_dim: self._obj[self.y_dim].values,\n\u001b[1;32m    507\u001b[0m                   self.x_dim: self._obj[self.x_dim].values}\n",
-      "\u001b[0;32m~/miniconda3/envs/raven/lib/python3.6/site-packages/salem/gis.py\u001b[0m in \u001b[0;36mregion_of_interest\u001b[0;34m(self, shape, geometry, grid, corners, crs, roi, all_touched)\u001b[0m\n\u001b[1;32m   1024\u001b[0m             \u001b[0;31m# corner grid is needed for rasterio\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1025\u001b[0m             shape = transform_geopandas(shape, to_crs=self.corner_grid,\n\u001b[0;32m-> 1026\u001b[0;31m                                         inplace=inplace)\n\u001b[0m\u001b[1;32m   1027\u001b[0m             \u001b[0;32mimport\u001b[0m \u001b[0mrasterio\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1028\u001b[0m             \u001b[0;32mfrom\u001b[0m \u001b[0mrasterio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeatures\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mrasterize\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/raven/lib/python3.6/site-packages/salem/gis.py\u001b[0m in \u001b[0;36mtransform_geopandas\u001b[0;34m(gdf, to_crs, inplace)\u001b[0m\n\u001b[1;32m   1300\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1301\u001b[0m     \u001b[0;31m# Do the job and set the new attributes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1302\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgeometry\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mgeom\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mproject\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgeom\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1303\u001b[0m     \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGeoSeries\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1304\u001b[0m     \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcrs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_crs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/raven/lib/python3.6/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, convert_dtype, args, **kwds)\u001b[0m\n\u001b[1;32m   3846\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3847\u001b[0m                 \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobject\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3848\u001b[0;31m                 \u001b[0mmapped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_infer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconvert_dtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3849\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3850\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32mpandas/_libs/lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.map_infer\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/raven/lib/python3.6/site-packages/salem/gis.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(geom)\u001b[0m\n\u001b[1;32m   1300\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1301\u001b[0m     \u001b[0;31m# Do the job and set the new attributes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1302\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgeometry\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mgeom\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mproject\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgeom\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1303\u001b[0m     \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGeoSeries\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1304\u001b[0m     \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcrs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_crs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/raven/lib/python3.6/site-packages/shapely/ops.py\u001b[0m in \u001b[0;36mtransform\u001b[0;34m(func, geom)\u001b[0m\n\u001b[1;32m    250\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    251\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mgeom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Multi'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mgeom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'GeometryCollection'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 252\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgeom\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpart\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mpart\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgeom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgeoms\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    253\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    254\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Type %r not recognized'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mgeom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/raven/lib/python3.6/site-packages/shapely/ops.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    250\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    251\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mgeom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Multi'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mgeom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'GeometryCollection'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 252\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgeom\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpart\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mpart\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgeom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgeoms\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    253\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    254\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Type %r not recognized'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mgeom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/raven/lib/python3.6/site-packages/shapely/ops.py\u001b[0m in \u001b[0;36mtransform\u001b[0;34m(func, geom)\u001b[0m\n\u001b[1;32m    232\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mgeom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'Polygon'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m                 shell = type(geom.exterior)(\n\u001b[0;32m--> 234\u001b[0;31m                     zip(*func(*izip(*geom.exterior.coords))))\n\u001b[0m\u001b[1;32m    235\u001b[0m                 holes = list(type(ring)(zip(*func(*izip(*ring.coords))))\n\u001b[1;32m    236\u001b[0m                              for ring in geom.interiors)\n",
-      "\u001b[0;32m~/miniconda3/envs/raven/lib/python3.6/site-packages/salem/gis.py\u001b[0m in \u001b[0;36mtransform\u001b[0;34m(self, x, y, z, crs, nearest, maskout)\u001b[0m\n\u001b[1;32m    671\u001b[0m             \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_crs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mij_to_crs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    672\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 673\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'crs not understood'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    674\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    675\u001b[0m         \u001b[0;31m# Then to local grid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mValueError\u001b[0m: crs not understood"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "if dataset=='ERA5':\n",
     "    tsfile=tmp / 'ERA5_ts.nc'\n",
@@ -212,9 +181,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<salem.graphics.Map at 0x7f0b7730f550>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Map of precip snapshot\n",
     "ds.pr.isel(time=2).salem.roi(shape=shdf).salem.quick_map()"
@@ -222,16 +214,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<xarray.DataArray 'pr' (time: 1441)>\n",
+       "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",
+       "Attributes:\n",
+       "    units:       m\n",
+       "    long_name:   Total precipitation\n",
+       "    pyproj_srs:  +proj=longlat +datum=WGS84 +no_defs"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "sub.pr"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -250,7 +261,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -279,7 +290,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -298,9 +309,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "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",
+      ":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",
+      "    :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"
+     ]
+    }
+   ],
    "source": [
     "print(diagnostics)"
    ]
@@ -314,18 +347,118 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "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",
+       "Coordinates:\n",
+       "  * time        (time) datetime64[ns] 2001-01-01 2001-01-02 ... 2001-02-28\n",
+       "    basin_name  (nbasins) object ...\n",
+       "Dimensions without coordinates: nbasins\n",
+       "Attributes:\n",
+       "    units:      m**3 s**-1\n",
+       "    long_name:  Simulated outflows"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "hydrograph.q_sim"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f0b5aaf97b8>]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from pandas.plotting import register_matplotlib_converters\n",
     "register_matplotlib_converters()\n",
@@ -335,9 +468,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Max:  <xarray.DataArray 'q_sim' ()>\n",
+      "array(3571.47403105)\n",
+      "Mean:  <xarray.DataArray 'q_sim' ()>\n",
+      "array(2231.11144315)\n",
+      "Monthly means:  <xarray.DataArray 'q_sim' (month: 2, nbasins: 1)>\n",
+      "array([[2648.69867205],\n",
+      "       [1768.78272545]])\n",
+      "Coordinates:\n",
+      "    basin_name  (nbasins) object ...\n",
+      "  * month       (month) int64 1 2\n",
+      "Dimensions without coordinates: nbasins\n"
+     ]
+    }
+   ],
    "source": [
     "print(\"Max: \", hydrograph.q_sim.max())\n",
     "print(\"Mean: \", hydrograph.q_sim.mean())\n",
@@ -361,7 +512,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.10"
+   "version": "3.6.7"
   }
  },
  "nbformat": 4,
diff --git a/raven/processes/wps_hydrobasins_shape_selection.py b/raven/processes/wps_hydrobasins_shape_selection.py
index 34d765b7..fbd5adf5 100644
--- a/raven/processes/wps_hydrobasins_shape_selection.py
+++ b/raven/processes/wps_hydrobasins_shape_selection.py
@@ -85,7 +85,7 @@ def _handler(self, request, response):
         domain = gis.select_hybas_domain(bbox)
         hybas_gml = gis.get_hydrobasins_location_wfs(bbox, lakes=lakes, level=level, domain=domain)
 
-        if isinstance(shape_url, str):
+        if isinstance(hybas_gml, str):
             write_flags = "w"
         else:
             write_flags = "wb"
diff --git a/raven/processes/wps_raven_gr4j_cemaneige.py b/raven/processes/wps_raven_gr4j_cemaneige.py
index b71cb886..0873b9bf 100644
--- a/raven/processes/wps_raven_gr4j_cemaneige.py
+++ b/raven/processes/wps_raven_gr4j_cemaneige.py
@@ -50,5 +50,5 @@ class RavenGR4JCemaNeigeProcess(RavenProcess):
     tuple_inputs = {'params': GR4JCN.params}
 
     inputs = [wio.ts, wio.nc_spec, params, wio.start_date, wio.end_date, wio.nc_index, wio.duration, wio.run_name,
-              wio.name, wio.area, wio.latitude, wio.longitude, wio.elevation, wio.evaporation,
+              wio.name, wio.area, wio.latitude, wio.longitude, wio.elevation,   wio.evaporation,
               wio.rain_snow_fraction]
diff --git a/setup.cfg b/setup.cfg
index 817ec44d..43b81636 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -3,7 +3,7 @@ current_version = 0.10.0
 commit = False
 tag = False
 parse = (?P<major>\d+)\.(?P<minor>\d+).(?P<patch>\d+)(\-(?P<release>[a-z]+))?
-serialize = 
+serialize =
 	{major}.{minor}.{patch}-{release}
 	{major}.{minor}.{patch}
 
@@ -12,7 +12,7 @@ description-file = README.rst
 
 [bumpversion:part:release]
 optional_value = final
-values = 
+values =
 	beta
 	final
 
@@ -29,19 +29,19 @@ search = Version={current_version}
 replace = {new_version}
 
 [tool:pytest]
-addopts = 
+addopts =
 	--strict
 	--tb=native
 python_files = test_*.py
 norecursedirs = src .git bin
-markers = 
+markers =
 	online: mark test to need internet connection
 	slow: mark test to be slow
 
 [flake8]
 ignore = F401,E402,E401, W503
 max-line-length = 120
-exclude = 
+exclude =
 	.git,
 	__pycache__,
 	docs/source/conf.py,
diff --git a/tests/conftest.py b/tests/conftest.py
index 2a4fab79..36f09af1 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -46,7 +46,7 @@ def era5_hr():
     if not path.exists():
         # Fetch the data and save to disk if the file has not been created yet.
         path.parent.mkdir(exist_ok=True)
-        thredds = URL("https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/birdhouse/ecmwf/era5")
+        thredds = URL("https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/datasets/reanalyses/era5.ncml")
         tas = str(thredds / "tas_era5_reanalysis_hourly_2018.nc")
         pr = str(thredds / "pr_era5_reanalysis_hourly_2018.nc")
 
diff --git a/tests/test_ERA5.py b/tests/test_ERA5.py
index 4eeadcd2..76876230 100644
--- a/tests/test_ERA5.py
+++ b/tests/test_ERA5.py
@@ -10,14 +10,12 @@
 from raven.processes import RavenHMETSProcess
 from raven.models import HMETS
 import json
-
 import matplotlib.pyplot as plt
 
 params = (9.5019, 0.2774, 6.3942, 0.6884, 1.2875, 5.4134, 2.3641, 0.0973, 0.0464, 0.1998, 0.0222, -1.0919,
           2.6851, 0.3740, 1.0000, 0.4739, 0.0114, 0.0243, 0.0069, 310.7211, 916.1947)
 
 
-@pytest.mark.skip(reason="Hanging test due to ncML. Should be removed in a future PR")
 class TestRavenERA5:
     def test_simple(self, era5_hr):
         model = HMETS()
@@ -37,7 +35,6 @@ def test_simple(self, era5_hr):
               )
 
 
-@pytest.mark.skip(reason="Hanging test due to ncML. Should be removed in a future PR")
 class TestRavenERA5Process:
 
     def test_simple(self, era5_hr):
@@ -69,14 +66,13 @@ def test_simple(self, era5_hr):
                     latitude=54.4848,
                     longitude=-123.3659,
                     rain_snow_fraction="RAINSNOW_DINGMAN",
-                    pr=json.dumps({'pr': {'linear_transform': (24000.0,0.0), 'time_shift': -.25}}),
+                    pr=json.dumps({'pr': {'linear_transform': (24000.0, 0.0), 'time_shift': -.25}}),
                     tas=json.dumps({'tas': {'linear_transform': (1.0, -273.15), 'time_shift': -.25}}),
                     )
 
         resp = client.get(
             service='WPS', request='Execute', version='1.0.0', identifier='raven-hmets',
             datainputs=datainputs)
-                
         assert_response_success(resp)
 #        out = get_output(resp.xml)
 #        tmp,_= urlretrieve(out['hydrograph'])