Locations

Locations are where model results exist in the Network. The main location types are nodes, reaches, gridpoints, and catchments.

Data structures

There are two main data structures for locations: location collections and single locations.

Location collections

Access location collections from a Res1D object. Each collection shows available quantities and location IDs.

res.nodes
<ResultNodes> (119)
Quantities (1)
  • Water level (m)
Derived Quantities (3)
  • NodeFlooding
  • NodeWaterDepth
  • NodeWaterLevelAboveCritical
res.reaches
<ResultReaches> (118)
Quantities (2)
  • Water level (m)
  • Discharge (m^3/s)
Derived Quantities (6)
  • ReachAbsoluteDischarge
  • ReachFilling
  • ReachFlooding
  • ReachQQManning
  • ReachWaterDepth
  • ReachWaterLevelAboveCritical
res_catchments.catchments
<ResultCatchments> (31)
Quantities (5)
  • Total Runoff (m^3/s)
  • Actual Rainfall (m/s)
  • Zink, Load, RR (kg/s)
  • Zink, Mass, Accumulated, RR (kg)
  • Zink, RR (mg/l)
Derived Quantities (0)
    Note

    Gridpoints only exist as single locations on a reach, and have no collection.

    Single locations

    Access a single location by indexing its respective collection with its unique ID. Each location shows available quantities and static properties.

    res.nodes['1']
    <Manhole: 1>
    Attributes (8)
    • id: 1
    • type: Manhole
    • xcoord: -687934.6000976562
    • ycoord: -1056500.69921875
    • ground_level: 197.07000732421875
    • bottom_level: 195.0500030517578
    • critical_level: inf
    • diameter: 1.0
    Quantities (1)
    • Water level (m)
    Derived Quantities (3)
    • NodeFlooding
    • NodeWaterDepth
    • NodeWaterLevelAboveCritical
    res.reaches['100l1']
    <Reach: 100l1>
    Attributes (9)
    • name: 100l1
    • length: 47.6827148432828
    • start_chainage: 0.0
    • end_chainage: 47.6827148432828
    • n_gridpoints: 3
    • start_node: 100
    • end_node: 99
    • height: 0.30000001192092896
    • full_flow_discharge: 0.12058743359507902
    Quantities (2)
    • Water level (m)
    • Discharge (m^3/s)
    Derived Quantities (6)
    • ReachAbsoluteDischarge
    • ReachFilling
    • ReachFlooding
    • ReachQQManning
    • ReachWaterDepth
    • ReachWaterLevelAboveCritical
    # gridpoint on reach 100l1 at chainage 23.841
res.reaches['100l1']['23.841']
    <ResultGridPoint>
    Attributes (5)
    • reach_name: 100l1
    • chainage: 23.8413574216414
    • xcoord: -687897.8000488281
    • ycoord: -1056390.4503479004
    • bottom_level: 195.0500030517578
    Quantities (1)
    • Discharge (m^3/s)
    Derived Quantities (0)
      Tip

      Gridpoints can also be indexed by number instead of chainage. For example:

      res.reaches['100l1'][0]  # first gridpoint
res.reaches['100l1'][-1] # last gridpoint
      res_catchments.catchments['100_16_16']
      <Catchment: 100_16_16>
      Attributes (3)
      • id: 100_16_16
      • area: 22800.0
      • type: Kinematic Wave
      Quantities (5)
      • Total Runoff (m^3/s)
      • Actual Rainfall (m/s)
      • Zink, Load, RR (kg/s)
      • Zink, Mass, Accumulated, RR (kg)
      • Zink, RR (mg/l)
      Derived Quantities (0)

        Quantities

        Quantities are the actual model results. Each single location or location collection has associated quantities.

        res.nodes.WaterLevel
        <QuantityCollection (119): Water level (m)>
        res.nodes['1'].WaterLevel
        <Quantity: Water level (m)>

        The Network structure is generic and applies across different domains (e.g. collection systems, water distribution, rivers). Sometimes this can be challenging to find a particular result. Here are some examples of result types mapped onto this structure.

        Location Example quantities
        Nodes Water level (e.g. manhole, basin, outlet, junction)
        Pump discharge in structure
        Reaches Discharge (e.g. pipes, pumps, weirs)
        Water level (e.g. at specific chainges)
        Catchments Catchment discharge
        Total runoff
        Global Water balance
        User defined variable types

        Refer to the Quantities page for more information on how to read and plot the returned quantities.

        Static attributes

        Each location has a set of static attributes.

        res.nodes['1']._static_attributes
        ['id',
 'type',
 'xcoord',
 'ycoord',
 'ground_level',
 'bottom_level',
 'critical_level',
 'diameter']
        res.nodes['1'].ground_level
        197.07000732421875

        Reading data

        All result data for a single location or location collection can be read into a pandas DataFrame.

        df = res.reaches['100l1'].read()
df.head()
        WaterLevel:100l1:0 WaterLevel:100l1:47.6827 Discharge:100l1:23.8414
        1994-08-07 16:35:00.000 195.441498 194.661499 0.000006
        1994-08-07 16:36:01.870 195.441498 194.661621 0.000006
        1994-08-07 16:37:07.560 195.441498 194.661728 0.000006
        1994-08-07 16:38:55.828 195.441498 194.661804 0.000006
        1994-08-07 16:39:55.828 195.441498 194.661972 0.000006 
        df = res.reaches.read()
df.head()
        WaterLevel:100l1:0 WaterLevel:100l1:47.6827 WaterLevel:101l1:0 WaterLevel:101l1:66.4361 WaterLevel:102l1:0 WaterLevel:102l1:10.9366 WaterLevel:103l1:0 WaterLevel:103l1:26.0653 WaterLevel:104l1:0 WaterLevel:104l1:34.4131 ... Discharge:93l1:24.5832 Discharge:94l1:21.2852 Discharge:95l1:21.9487 Discharge:96l1:14.9257 Discharge:97l1:5.71207 Discharge:98l1:8.00489 Discharge:99l1:22.2508 Discharge:9l1:5 Discharge:Weir:119w1:0.5 Discharge:Pump:115p1:41.214
        1994-08-07 16:35:00.000 195.441498 194.661499 195.931503 195.441498 193.550003 193.550003 195.801498 195.701508 197.072006 196.962006 ... 0.000004 0.000003 0.000001 0.000005 0.000013 0.000003 0.000002 0.000031 0.0 0.0
        1994-08-07 16:36:01.870 195.441498 194.661621 195.931503 195.441605 193.550140 193.550064 195.801498 195.703171 197.072006 196.962051 ... 0.000004 0.000003 0.000001 0.000005 0.000010 0.000003 0.000002 0.000031 0.0 0.0
        1994-08-07 16:37:07.560 195.441498 194.661728 195.931503 195.441620 193.550232 193.550156 195.801498 195.703400 197.072006 196.962082 ... 0.000004 0.000003 0.000001 0.000005 0.000010 0.000003 0.000002 0.000033 0.0 0.0
        1994-08-07 16:38:55.828 195.441498 194.661804 195.931503 195.441605 193.550369 193.550308 195.801498 195.703690 197.072006 196.962112 ... 0.000004 0.000003 0.000001 0.000005 0.000009 0.000003 0.000002 0.000037 0.0 0.0
        1994-08-07 16:39:55.828 195.441498 194.661972 195.931503 195.441605 193.550430 193.550369 195.801498 195.703827 197.072006 196.962128 ... 0.000004 0.000003 0.000001 0.000005 0.000009 0.000003 0.000002 0.000039 0.0 0.0

        5 rows × 376 columns

        GeoDataFrames

        Locations collections can be extracted into a GeoDataFrame, both with and without quantities.

        gdf = res.reaches.to_geopandas()
gdf.plot()

        gdf = res.reaches.to_geopandas(agg='max')
gdf.plot(column='max_Discharge', linewidth=3, cmap='RdYlGn_r', legend=True)

        Examples

        Tip

        There are also several notebook examples available on our GitHub repositoryhttps://github.com/DHI/mikeio1d/tree/main/notebooks.