diff --git a/notebooks/Network_comparison.ipynb b/notebooks/Network_comparison.ipynb
new file mode 100644
index 000000000..a71aa199d
--- /dev/null
+++ b/notebooks/Network_comparison.ipynb
@@ -0,0 +1,558 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e91978fd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import modelskill as ms\n",
+    "import mikeio1d as m1d\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c2cc9f94",
+   "metadata": {},
+   "source": [
+    "## Current workflow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c88c8a87",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# - Loading model_results\n",
+    "path_to_res1d = '../tests/testdata/network.res1d'\n",
+    "nt = m1d.open(path_to_res1d)\n",
+    "\n",
+    "# Loading observations\n",
+    "# - We fabricate an observation set to try the current workflow\n",
+    "np.random.seed(42)\n",
+    "\n",
+    "observations = nt.read()\n",
+    "observations = observations + np.random.normal(0, 10, observations.shape)\n",
+    "# Arbitrarily selecting 2 columns of WaterLevel that will be used as observations\n",
+    "relevant_columns = [col for col in observations.columns if \"WaterLevel\" in col][8:10]\n",
+    "observations = observations.loc[:, relevant_columns].rename(columns=lambda x: \"sensor_\" + x.split(\":\")[1])\n",
+    "observations = observations.resample(\"1min\").mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "568f6f18",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>n</th>\n",
+       "      <th>bias</th>\n",
+       "      <th>rmse</th>\n",
+       "      <th>urmse</th>\n",
+       "      <th>mae</th>\n",
+       "      <th>cc</th>\n",
+       "      <th>si</th>\n",
+       "      <th>r2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>observation</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>sensor_9</th>\n",
+       "      <td>110</td>\n",
+       "      <td>0.743778</td>\n",
+       "      <td>9.167965</td>\n",
+       "      <td>9.137744</td>\n",
+       "      <td>7.701389</td>\n",
+       "      <td>0.181837</td>\n",
+       "      <td>0.047257</td>\n",
+       "      <td>0.008068</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sensor_10</th>\n",
+       "      <td>110</td>\n",
+       "      <td>0.535862</td>\n",
+       "      <td>9.336934</td>\n",
+       "      <td>9.321544</td>\n",
+       "      <td>7.807391</td>\n",
+       "      <td>0.017057</td>\n",
+       "      <td>0.048156</td>\n",
+       "      <td>-0.003818</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               n      bias      rmse     urmse       mae        cc        si  \\\n",
+       "observation                                                                    \n",
+       "sensor_9     110  0.743778  9.167965  9.137744  7.701389  0.181837  0.047257   \n",
+       "sensor_10    110  0.535862  9.336934  9.321544  7.807391  0.017057  0.048156   \n",
+       "\n",
+       "                   r2  \n",
+       "observation            \n",
+       "sensor_9     0.008068  \n",
+       "sensor_10   -0.003818  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "obs_9 = ms.PointObservation(observations, item=\"sensor_9\")\n",
+    "obs_10 = ms.PointObservation(observations, item=\"sensor_10\")\n",
+    "\n",
+    "res1d_item = \"WaterLevel:7\"\n",
+    "nt = m1d.open(path_to_res1d)\n",
+    "df = nt.to_dataframe()\n",
+    "modres = ms.PointModelResult(df, item=res1d_item)\n",
+    "\n",
+    "comparer_1 = ms.match(obs_9, modres)\n",
+    "comparer_2 = ms.match(obs_10, modres)\n",
+    "\n",
+    "ccol0 = ms.ComparerCollection([comparer_1, comparer_2])\n",
+    "ccol0.skill()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "393b840c",
+   "metadata": {},
+   "source": [
+    "- We can simplify further the workflow and not initialize `PointObservation` and `PointModelResult`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35f13324",
+   "metadata": {},
+   "source": [
+    "The challenge is the 1d matching (?)\n",
+    "\n",
+    "- [Design 1](https://github.com/DHI/modelskill/pull/536) assumes the user knows the sensor location in `mikeio1d` _coordinates_ (reach, chainage, node, catchment).\n",
+    "- [Design 2](https://github.com/DHI/modelskill/pull/536) suggests a more general approach where we would need to define a __shared__ coordinate system that represents a network architecture. We would need a plugin for each potential model data source (`mikeio1d`, `EPANET`, surrogates)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8730b218",
+   "metadata": {},
+   "source": [
+    "### Design 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7607223b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\japr\\Repos\\modelskill\\src\\modelskill\\quantity.py:190: UserWarning: unit='meter' was automatically set for type_name='Water Level'\n",
+      "  warnings.warn(f\"{unit=} was automatically set for {type_name=}\")\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>n</th>\n",
+       "      <th>bias</th>\n",
+       "      <th>rmse</th>\n",
+       "      <th>urmse</th>\n",
+       "      <th>mae</th>\n",
+       "      <th>cc</th>\n",
+       "      <th>si</th>\n",
+       "      <th>r2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>observation</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>sensor_9</th>\n",
+       "      <td>110</td>\n",
+       "      <td>0.743778</td>\n",
+       "      <td>9.167965</td>\n",
+       "      <td>9.137744</td>\n",
+       "      <td>7.701389</td>\n",
+       "      <td>0.181837</td>\n",
+       "      <td>0.047257</td>\n",
+       "      <td>0.008068</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               n      bias      rmse     urmse       mae        cc        si  \\\n",
+       "observation                                                                    \n",
+       "sensor_9     110  0.743778  9.167965  9.137744  7.701389  0.181837  0.047257   \n",
+       "\n",
+       "                   r2  \n",
+       "observation            \n",
+       "sensor_9     0.008068  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res = ms.NetworkModelResult(path_to_res1d, \"Water Level\", node=7)\n",
+    "obs_1 = ms.PointObservation(observations, item=\"sensor_9\")\n",
+    "\n",
+    "comparer_1 = ms.match(obs_1, res)\n",
+    "comparer_1.skill()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a59a1d87",
+   "metadata": {},
+   "source": [
+    "- Multiple observations with one result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "80271095",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>n</th>\n",
+       "      <th>bias</th>\n",
+       "      <th>rmse</th>\n",
+       "      <th>urmse</th>\n",
+       "      <th>mae</th>\n",
+       "      <th>cc</th>\n",
+       "      <th>si</th>\n",
+       "      <th>r2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>observation</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>sensor_9</th>\n",
+       "      <td>110</td>\n",
+       "      <td>0.743778</td>\n",
+       "      <td>9.167965</td>\n",
+       "      <td>9.137744</td>\n",
+       "      <td>7.701389</td>\n",
+       "      <td>0.181837</td>\n",
+       "      <td>0.047257</td>\n",
+       "      <td>0.008068</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sensor_10</th>\n",
+       "      <td>110</td>\n",
+       "      <td>0.535862</td>\n",
+       "      <td>9.336934</td>\n",
+       "      <td>9.321544</td>\n",
+       "      <td>7.807391</td>\n",
+       "      <td>0.017057</td>\n",
+       "      <td>0.048156</td>\n",
+       "      <td>-0.003818</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               n      bias      rmse     urmse       mae        cc        si  \\\n",
+       "observation                                                                    \n",
+       "sensor_9     110  0.743778  9.167965  9.137744  7.701389  0.181837  0.047257   \n",
+       "sensor_10    110  0.535862  9.336934  9.321544  7.807391  0.017057  0.048156   \n",
+       "\n",
+       "                   r2  \n",
+       "observation            \n",
+       "sensor_9     0.008068  \n",
+       "sensor_10   -0.003818  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "quantity = ms.Quantity(\"Water Level\", unit=\"m3_per_sec\")\n",
+    "mod_item = ms.NetworkModelResult(path_to_res1d, quantity, node=7)\n",
+    "obs1 = ms.PointObservation(observations, quantity=quantity, item=\"sensor_9\")\n",
+    "obs2 = ms.PointObservation(observations, quantity=quantity, item=\"sensor_10\")\n",
+    "\n",
+    "comparer_1 = ms.match(obs1, mod_item)\n",
+    "comparer_2 = ms.match(obs2, mod_item)\n",
+    "\n",
+    "ccol = ms.ComparerCollection([comparer_1, comparer_2])\n",
+    "ccol.skill()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55f42b62",
+   "metadata": {},
+   "source": [
+    "- Multiple models with one observation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9ee90ffe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>n</th>\n",
+       "      <th>bias</th>\n",
+       "      <th>rmse</th>\n",
+       "      <th>urmse</th>\n",
+       "      <th>mae</th>\n",
+       "      <th>cc</th>\n",
+       "      <th>si</th>\n",
+       "      <th>r2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>model</th>\n",
+       "      <th>observation</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>model 1</th>\n",
+       "      <th>sensor_9</th>\n",
+       "      <td>110</td>\n",
+       "      <td>0.743778</td>\n",
+       "      <td>9.167965</td>\n",
+       "      <td>9.137744</td>\n",
+       "      <td>7.701389</td>\n",
+       "      <td>0.181837</td>\n",
+       "      <td>0.047257</td>\n",
+       "      <td>0.008068</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>model 2</th>\n",
+       "      <th>sensor_9</th>\n",
+       "      <td>110</td>\n",
+       "      <td>2.248587</td>\n",
+       "      <td>9.408963</td>\n",
+       "      <td>9.136325</td>\n",
+       "      <td>7.882746</td>\n",
+       "      <td>0.205237</td>\n",
+       "      <td>0.047249</td>\n",
+       "      <td>-0.044767</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>model 3</th>\n",
+       "      <th>sensor_9</th>\n",
+       "      <td>110</td>\n",
+       "      <td>0.808813</td>\n",
+       "      <td>9.240242</td>\n",
+       "      <td>9.204776</td>\n",
+       "      <td>7.772271</td>\n",
+       "      <td>0.212348</td>\n",
+       "      <td>0.047603</td>\n",
+       "      <td>-0.007633</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>model 4</th>\n",
+       "      <th>sensor_9</th>\n",
+       "      <td>110</td>\n",
+       "      <td>0.808813</td>\n",
+       "      <td>9.240242</td>\n",
+       "      <td>9.204776</td>\n",
+       "      <td>7.772271</td>\n",
+       "      <td>0.212348</td>\n",
+       "      <td>0.047603</td>\n",
+       "      <td>-0.007633</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       n      bias      rmse     urmse       mae        cc  \\\n",
+       "model   observation                                                          \n",
+       "model 1 sensor_9     110  0.743778  9.167965  9.137744  7.701389  0.181837   \n",
+       "model 2 sensor_9     110  2.248587  9.408963  9.136325  7.882746  0.205237   \n",
+       "model 3 sensor_9     110  0.808813  9.240242  9.204776  7.772271  0.212348   \n",
+       "model 4 sensor_9     110  0.808813  9.240242  9.204776  7.772271  0.212348   \n",
+       "\n",
+       "                           si        r2  \n",
+       "model   observation                      \n",
+       "model 1 sensor_9     0.047257  0.008068  \n",
+       "model 2 sensor_9     0.047249 -0.044767  \n",
+       "model 3 sensor_9     0.047603 -0.007633  \n",
+       "model 4 sensor_9     0.047603 -0.007633  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "quantity = ms.Quantity(\"Water Level\", unit=\"m3_per_sec\")\n",
+    "models = [\n",
+    "    ms.NetworkModelResult(path_to_res1d, quantity, node=7, name=\"model 1\"),\n",
+    "    ms.NetworkModelResult(path_to_res1d, quantity, reach=\"100l1\", gridpoint=\"start\", name=\"model 2\"),\n",
+    "    ms.NetworkModelResult(path_to_res1d, quantity, reach=\"54l1\", gridpoint=0, name=\"model 3\"),\n",
+    "    ms.NetworkModelResult(path_to_res1d, reach=\"54l1\", gridpoint=0, name=\"model 4\")\n",
+    "]\n",
+    "\n",
+    "obs1 = ms.PointObservation(observations, quantity=quantity, item=\"sensor_9\")\n",
+    "\n",
+    "comparer_1 = ms.match(obs1, models)\n",
+    "comparer_1.skill()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "modelskill",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/src/modelskill/__init__.py b/src/modelskill/__init__.py
index 4a362ffb2..a1590fb33 100644
--- a/src/modelskill/__init__.py
+++ b/src/modelskill/__init__.py
@@ -39,8 +39,13 @@
     GridModelResult,
     DfsuModelResult,
     DummyModelResult,
+    NetworkModelResult,
+)
+from .obs import (
+    observation,
+    PointObservation,
+    TrackObservation,
 )
-from .obs import observation, PointObservation, TrackObservation
 from .matching import from_matched, match
 from .configuration import from_config
 from .settings import options, get_option, set_option, reset_option, load_style
@@ -90,6 +95,7 @@ def load(filename: Union[str, Path]) -> Comparer | ComparerCollection:
     "GridModelResult",
     "DfsuModelResult",
     "DummyModelResult",
+    "NetworkModelResult",
     "observation",
     "PointObservation",
     "TrackObservation",
diff --git a/src/modelskill/matching.py b/src/modelskill/matching.py
index f8b15aa4d..5b5d66613 100644
--- a/src/modelskill/matching.py
+++ b/src/modelskill/matching.py
@@ -28,7 +28,13 @@
 from .model.dummy import DummyModelResult
 from .model.grid import GridModelResult
 from .model.track import TrackModelResult
-from .obs import Observation, PointObservation, TrackObservation, observation
+from .model.network import NetworkModelResult
+from .obs import (
+    Observation,
+    PointObservation,
+    TrackObservation,
+    observation,
+)
 from .timeseries import TimeSeries
 from .types import Period
 
@@ -51,6 +57,7 @@
     DfsuModelResult,
     TrackModelResult,
     DummyModelResult,
+    NetworkModelResult,
 ]
 ObsInputType = Union[
     str,
@@ -452,6 +459,7 @@ def _parse_single_model(
     | GridModelResult
     | DfsuModelResult
     | DummyModelResult
+    | NetworkModelResult
 ):
     if isinstance(
         mod,
@@ -484,6 +492,7 @@ def _parse_single_model(
                 GridModelResult,
                 DfsuModelResult,
                 DummyModelResult,
+                NetworkModelResult,
             ),
         )
         return mod
diff --git a/src/modelskill/model/__init__.py b/src/modelskill/model/__init__.py
index b0ba468ae..c03a8e62e 100644
--- a/src/modelskill/model/__init__.py
+++ b/src/modelskill/model/__init__.py
@@ -21,6 +21,7 @@
 from .dfsu import DfsuModelResult
 from .grid import GridModelResult
 from .dummy import DummyModelResult
+from .network import NetworkModelResult
 
 __all__ = [
     "PointModelResult",
@@ -29,4 +30,5 @@
     "GridModelResult",
     "model_result",
     "DummyModelResult",
+    "NetworkModelResult",
 ]
diff --git a/src/modelskill/model/factory.py b/src/modelskill/model/factory.py
index 4ee2cb7f3..26ec9ffbc 100644
--- a/src/modelskill/model/factory.py
+++ b/src/modelskill/model/factory.py
@@ -9,6 +9,7 @@
 from .track import TrackModelResult
 from .dfsu import DfsuModelResult
 from .grid import GridModelResult
+from .network import NetworkModelResult
 
 
 from ..types import GeometryType, DataInputType
@@ -27,7 +28,13 @@ def model_result(
     aux_items: Optional[list[int | str]] = None,
     gtype: Optional[Literal["point", "track", "unstructured", "grid"]] = None,
     **kwargs: Any,
-) -> PointModelResult | TrackModelResult | DfsuModelResult | GridModelResult:
+) -> (
+    PointModelResult
+    | TrackModelResult
+    | DfsuModelResult
+    | GridModelResult
+    | NetworkModelResult
+):
     """A factory function for creating an appropriate object based on the data input.
 
     Parameters
diff --git a/src/modelskill/model/network.py b/src/modelskill/model/network.py
new file mode 100644
index 000000000..d481c252f
--- /dev/null
+++ b/src/modelskill/model/network.py
@@ -0,0 +1,62 @@
+from __future__ import annotations
+from typing import Optional, Sequence, Literal
+
+from mikeio1d import Res1D
+
+from ..timeseries import _parse_network_input
+from ..quantity import Quantity
+from .point import PointModelResult
+
+
+class NetworkModelResult(PointModelResult):
+    """Model result for a network location.
+
+    Construct a NetworkModelResult from a res1d data source.
+
+    Parameters
+    ----------
+    data : str, Path or mikeio1d.Res1D
+        filename (.res1d) or object with the data
+    quantity : str
+        The name of the model result,
+        by default None (will be set to file name or item name)
+    reach : str, optional
+        Reach id in the network
+    node : int, optional
+        Node id in the network
+    chainage : float, optional
+        Chainage number in its respective reach
+    gridpoint : int, optional
+        Index associated to the gridpoints in the reach
+    name : Optional[str], optional
+        The name of the model result,
+        by default None (will be set to file name or item name)
+    aux_items : Optional[list[int | str]], optional
+        Auxiliary items, by default None
+    """
+
+    def __init__(
+        self,
+        data: Res1D | str,
+        quantity: Optional[str | Quantity] = None,
+        *,
+        reach: Optional[str] = None,
+        node: Optional[int] = None,
+        chainage: Optional[float] = None,
+        gridpoint: Optional[int | Literal["start", "end"]] = None,
+        name: Optional[str] = None,
+        aux_items: Optional[Sequence[int | str]] = None,
+    ) -> None:
+        if isinstance(quantity, str):
+            quantity = Quantity.from_mikeio_eum_name(quantity)
+
+        variable = quantity.name if isinstance(quantity, Quantity) else None
+        data = _parse_network_input(
+            data,
+            variable=variable,
+            reach=reach,
+            node=node,
+            chainage=chainage,
+            gridpoint=gridpoint,
+        )
+        super().__init__(data=data, name=name, quantity=quantity, aux_items=aux_items)
diff --git a/src/modelskill/timeseries/__init__.py b/src/modelskill/timeseries/__init__.py
index 08fa449a4..1884b8910 100644
--- a/src/modelskill/timeseries/__init__.py
+++ b/src/modelskill/timeseries/__init__.py
@@ -1,9 +1,10 @@
 from ._timeseries import TimeSeries
-from ._point import _parse_point_input
+from ._point import _parse_point_input, _parse_network_input
 from ._track import _parse_track_input
 
 __all__ = [
     "TimeSeries",
     "_parse_point_input",
     "_parse_track_input",
+    "_parse_network_input",
 ]
diff --git a/src/modelskill/timeseries/_point.py b/src/modelskill/timeseries/_point.py
index 426d0c9d7..b5aca4149 100644
--- a/src/modelskill/timeseries/_point.py
+++ b/src/modelskill/timeseries/_point.py
@@ -2,12 +2,14 @@
 from collections.abc import Hashable
 from dataclasses import dataclass
 from pathlib import Path
-from typing import Sequence, get_args, List, Optional
+from typing import Literal, Sequence, get_args, List, Optional
 import numpy as np
 import pandas as pd
 import xarray as xr
 
+import warnings
 import mikeio
+import mikeio1d
 
 from ..types import GeometryType, PointType
 from ..quantity import Quantity
@@ -76,9 +78,14 @@ def _parse_point_input(
             stem = Path(data).stem
             data = xr.open_dataset(data)
             name = name or data.attrs.get("name") or stem
+        elif suffix == ".res1d":
+            name = name or Path(data).stem
+            data = mikeio1d.open(data)
+
     elif isinstance(data, mikeio.Dfs0):
         data = data.read()  # now mikeio.Dataset
-
+    elif isinstance(data, mikeio1d.Res1D):
+        data = data.read()  # now mikeio1d.Res1D
     # parse items
     if isinstance(data, (mikeio.DataArray, pd.Series, xr.DataArray)):
         item_name = data.name if data.name is not None else "PointModelResult"
@@ -177,3 +184,73 @@ def _parse_point_input(
 
     assert isinstance(ds, xr.Dataset)
     return ds
+
+
+def _parse_network_input(
+    data: mikeio1d.Res1D | str,
+    variable: Optional[str] = None,
+    *,
+    node: Optional[int] = None,
+    reach: Optional[str] = None,
+    chainage: Optional[str | float] = None,
+    gridpoint: Optional[int | Literal["start", "end"]] = None,
+) -> pd.Series:
+    def variable_name_to_res1d(name: str) -> str:
+        return name.replace(" ", "").replace("_", "")
+
+    if isinstance(data, (str, Path)):
+        if Path(data).suffix == ".res1d":
+            data = mikeio1d.open(data)
+        else:
+            raise ValueError("Input data must have '.res1d' file extension.")
+
+    by_node = node is not None
+    by_reach = reach is not None
+    with_chainage = chainage is not None
+    with_index = gridpoint is not None
+
+    if by_node and not by_reach:
+        location = data.nodes[str(node)]
+        if with_chainage or with_index:
+            warnings.warn(
+                "'chainage' or 'gridpoint' are only relevant when passed with 'reach' but they were passed with 'node', so they will be ignored."
+            )
+
+    elif by_reach and not by_node:
+        location = data.reaches[reach]
+        if with_index == with_chainage:
+            raise ValueError(
+                "Locations accessed by chainage must be specified either by chainage or by index, not both."
+            )
+
+        if with_index and not with_chainage:
+            gridpoint = 0 if gridpoint == "start" else gridpoint
+            gridpoint = -1 if gridpoint == "end" else gridpoint
+            chainage = location.chainages[gridpoint]
+
+        location = location[chainage]
+
+    else:
+        raise ValueError(
+            "A network location must be specified either by node or by reach."
+        )
+
+    if variable is None:
+        if len(location.quantities) != 1:
+            raise ValueError(
+                f"The network location does not have a unique quantity: {location.quantities}, in such case 'variable' argument cannot be None"
+            )
+        res1d_name = location.quantities[0]
+    else:
+        # After filtering by node or by reach and chainage, a location will only
+        # have unique quantities
+        res1d_name = variable_name_to_res1d(variable)
+    df = location.to_dataframe()
+    if df.shape[1] == 1:
+        colname = df.columns[0]
+        if res1d_name not in colname:
+            raise ValueError(f"Column name '{colname}' does not contain '{res1d_name}'")
+
+        return df.rename(columns={colname: res1d_name})[res1d_name].copy()
+    else:
+        raise ValueError(f"Multiple matching quantites found at location: {df.columns}")
diff --git a/src/modelskill/types.py b/src/modelskill/types.py
index 0ba3dcd63..77aef9a77 100644
--- a/src/modelskill/types.py
+++ b/src/modelskill/types.py
@@ -5,6 +5,7 @@
 import pandas as pd
 import xarray as xr
 import mikeio
+import mikeio1d
 
 
 class GeometryType(Enum):
@@ -85,6 +86,7 @@ def from_string(s: str) -> "GeometryType":
     mikeio.DataArray,
     xr.Dataset,
     xr.DataArray,
+    mikeio1d.Res1D,
 ]
 TrackType = Union[str, Path, pd.DataFrame, mikeio.Dfs0, mikeio.Dataset, xr.Dataset]
 
diff --git a/tests/model/test_network.py b/tests/model/test_network.py
new file mode 100644
index 000000000..92284c800
--- /dev/null
+++ b/tests/model/test_network.py
@@ -0,0 +1,52 @@
+import pytest
+import mikeio1d
+
+import numpy as np
+import pandas as pd
+import modelskill as ms
+
+parse_network = ms.timeseries._parse_network_input
+
+
+@pytest.fixture
+def res1d_datapath() -> str:
+    return "tests/testdata/network.res1d"
+
+
+@pytest.fixture
+def res1d_object(res1d_datapath) -> mikeio1d.Res1D:
+    return mikeio1d.open(res1d_datapath)
+
+
+def test_read_quantity_by_node(res1d_object):
+    series = parse_network(res1d_object, variable="Water Level", node=3)
+    df = res1d_object.read()
+    assert isinstance(series, pd.Series)
+    assert series.name == "WaterLevel"
+    np.testing.assert_allclose(df["WaterLevel:3"].values, series.values)
+
+
+@pytest.mark.parametrize(
+    "network_kwargs",
+    [
+        dict(gridpoint="end"),
+        dict(gridpoint=2),
+        dict(chainage=47.683),
+        dict(chainage="47.683"),
+    ],
+)
+def test_read_quantity_by_reach(res1d_object, network_kwargs):
+    series = parse_network(
+        res1d_object, variable="Water Level", reach="100l1", **network_kwargs
+    )
+    df = res1d_object.read()
+    assert isinstance(series, pd.Series)
+    assert series.name == "WaterLevel"
+    np.testing.assert_allclose(df["WaterLevel:100l1:47.6827"].values, series.values)
+
+
+def test_node_and_reach_as_arguments(res1d_object):
+    with pytest.raises(
+        ValueError, match="Item can only be specified either by node or by reach"
+    ):
+        parse_network(res1d_object, variable="Water Level", reach="100l1", node=2)
diff --git a/tests/testdata/network.res1d b/tests/testdata/network.res1d
new file mode 100644
index 000000000..45a661f6e
Binary files /dev/null and b/tests/testdata/network.res1d differ