Creating river forcing

In this notebook, we create river forcing from a global river discharge dataset, add BGC tracers, and show how you can customize your river forcing input. We demonstrate two supported discharge datasets: the Dai & Trenberth global river dataset (default) and GloFAS v4.0.

from roms_tools import RiverForcing, Grid

We first create our grid object. In this example, our domain lives in the North Atlantic and surrounds Iceland.

grid = Grid(
    nx=100, ny=100, size_x=800, size_y=800, center_lon=-18, center_lat=65, rot=20
)

Next, we create our river forcing object. We aim to generate river forcing data for the years 1998, 1999, and 2000.

grid.plot()

from datetime import datetime

start_time = datetime(1997, 1, 1)
end_time = datetime(2000, 12, 31)

%%time

river_forcing = RiverForcing(
    grid=grid,
    start_time=start_time,
    end_time=end_time,
    model_reference_date=datetime(2000, 1, 1), # this is the default
)

2026-06-30 21:38:23 - INFO - No river indices provided. Identifying all rivers within the ROMS domain and assigning each to the nearest coastal point.
2026-06-30 21:38:23 - INFO - Compute climatology for river forcing.
2026-06-30 21:38:23 - INFO - Computing river forcing...

CPU times: user 482 ms, sys: 121 ms, total: 603 ms
Wall time: 607 ms

Note

The river forcing dataset includes a parameter called source. If this parameter is not specified by the user, it defaults to the Dai and Trenberth global river dataset (updated in May 2019), which provides monthly time series for approximately 1,000 rivers worldwide, dating back to the year 1900. This default dataset is downloaded internally, meaning the user does not need to provide a file path or filename.

Alternatively, you can use the GloFAS v4.0 dataset, which provides daily discharge for thousands of stations globally at higher spatial resolution. GloFAS must be downloaded manually and requires a path to the file:

source = {
    "name": "GLOFAS",
    "path": '/anvil/projects/x-ees250129/Datasets/Rivers/processed_GloFAS/glofas_v4_rivers_daily.nc',
}

Here the path to the GloFAS data points to a pre-processed version of the dataset that is stored locally and matches the variables (and variable names) required in the Dai and Trenberth river dataset. For more details on the required variables and their names, refer to the documentation here.

If your dataset does not conform to these specifications, please feel free to open a pull request or issue for a feature request.

Regardless of which discharge datasets is used, ROMS-tools will apply a two-step filter to identify relevant rivers:

1. A bounding box filter retains only rivers within the domain extent, controlled by domain_edge_buffer (number of grid cells beyond the domain boundary to include, default 20). You can override this:

RiverForcing(..., domain_edge_buffer=5)

A smaller value is useful for small high-resolution domains where you don’t want rivers just outside the boundary to be included.

2. A coastal snap buffer (coast_snap_buffer_km) excludes rivers too far from any coastal grid cell. The default is 200 km for Dai & Trenberth and 50 km for GloFAS. You can override this:

RiverForcing(..., coast_snap_buffer_km=100)

A smaller value is useful for high-resolution domains where you want to exclude rivers that would snap unrealistically far from their true mouth location.

The river forcing variables are held in an xarray.Dataset object returned by the .ds property.

river_forcing.ds

<xarray.Dataset> Size: 85kB
Dimensions:           (ntracers: 2, river_time: 12, nriver: 6, eta_rho: 102,
                       xi_rho: 102)
Coordinates:
    tracer_name       (ntracers) <U4 32B 'temp' 'salt'
    tracer_unit       (ntracers) <U15 120B 'degrees Celsius' 'PSU'
    tracer_long_name  (ntracers) <U21 168B 'potential temperature' 'salinity'
    month             (river_time) int64 96B 1 2 3 4 5 6 7 8 9 10 11 12
    river_name        (nriver) <U15 360B 'Hvita(Olfusa)' 'Thjorsa' ... 'Svarta'
    abs_time          (river_time) datetime64[ns] 96B 2000-01-16 ... 2000-12-15
  * river_time        (river_time) float64 96B 15.0 45.0 74.0 ... 319.0 349.0
  * nriver            (nriver) int64 48B 1 2 3 4 5 6
Dimensions without coordinates: ntracers, eta_rho, xi_rho
Data variables:
    river_volume      (river_time, nriver) float64 576B 396.0 273.3 ... 8.47
    river_tracer      (ntracers, river_time, nriver) float32 576B 15.62 ... 1.0
    river_index       (eta_rho, xi_rho) float32 42kB 0.0 0.0 0.0 ... 0.0 0.0 0.0
    river_fraction    (eta_rho, xi_rho) float32 42kB 0.0 0.0 0.0 ... 0.0 0.0 0.0
Attributes:
    climatology:  True

xarray.Dataset

• Dimensions:
  • ntracers: 2
  • river_time: 12
  • nriver: 6
  • eta_rho: 102
  • xi_rho: 102
• Coordinates: (8)
  • tracer_name
    (ntracers)
    <U4
    'temp' 'salt'

    long_name :

    Tracer name

    array(['temp', 'salt'], dtype='<U4')

  • tracer_unit
    (ntracers)
    <U15
    'degrees Celsius' 'PSU'

    long_name :

    Tracer concentration unit

    array(['degrees Celsius', 'PSU'], dtype='<U15')

  • tracer_long_name
    (ntracers)
    <U21
    'potential temperature' 'salinity'

    long_name :

    Tracer long name

    array(['potential temperature', 'salinity'], dtype='<U21')

  • month
    (river_time)
    int64
    1 2 3 4 5 6 7 8 9 10 11 12

    long_name :

    Month index (1-12)

    array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])

  • river_name
    (nriver)
    <U15
    'Hvita(Olfusa)' ... 'Svarta'

    long_name :

    River name

    array(['Hvita(Olfusa)', 'Thjorsa', 'JkulsFjll', 'Lagarfljot', 'Bruara',
           'Svarta'], dtype='<U15')

  • abs_time
    (river_time)
    datetime64[ns]
    2000-01-16 ... 2000-12-15

    long_name :

    absolute time

    array(['2000-01-16T00:00:00.000000000', '2000-02-15T00:00:00.000000000',
           '2000-03-15T00:00:00.000000000', '2000-04-15T00:00:00.000000000',
           '2000-05-15T00:00:00.000000000', '2000-06-15T00:00:00.000000000',
           '2000-07-15T00:00:00.000000000', '2000-08-15T00:00:00.000000000',
           '2000-09-15T00:00:00.000000000', '2000-10-15T00:00:00.000000000',
           '2000-11-15T00:00:00.000000000', '2000-12-15T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • river_time
    (river_time)
    float64
    15.0 45.0 74.0 ... 319.0 349.0

    cycle_length :

    365.25

    long_name :

    relative time: days since 2000-01-01 00:00:00

    units :

    days

    array([ 15.,  45.,  74., 105., 135., 166., 196., 227., 258., 288., 319., 349.])

  • nriver
    (nriver)
    int64
    1 2 3 4 5 6

    long_name :

    River ID (1-based Fortran indexing)

    array([1, 2, 3, 4, 5, 6])

• Data variables: (4)
  • river_volume
    (river_time, nriver)
    float64
    396.0 273.3 112.2 ... 38.2 8.47

    long_name :

    River volume flux

    units :

    m^3/s

    array([[396.012146  , 273.31887817, 112.24038696,  51.13676453,
             37.93720627,   8.03249836],
           [407.87805176, 282.79544067, 114.64615631,  52.78458405,
             38.26300049,   8.18966579],
           [396.92681885, 288.7727356 , 121.67308044,  53.98734665,
             37.88405228,   8.50816727],
           [410.82925415, 296.61499023, 149.69039917,  71.63630676,
             38.19176102,  11.15483093],
           [446.65853882, 503.68182373, 218.48077393, 178.76750183,
             39.10426331,  17.1210022 ],
           [442.60714722, 585.10778809, 230.40383911, 311.78033447,
             38.17666245,  15.19416618],
           [384.58535767, 479.68182373, 323.67306519, 209.07264709,
             36.65816498,   9.38349915],
           [384.58535767, 454.57913208, 352.25      , 124.14719391,
             36.63367462,   8.35650063],
           [363.77908325, 346.95455933, 243.37924194, 111.43669891,
             36.79123306,   8.79949951],
           [383.53659058, 300.70455933, 164.69230652, 107.82298279,
             37.49549866,   9.4851675 ],
           [384.17999268, 277.68798828, 134.09616089,  85.8888092 ,
             38.07060623,   8.80366611],
           [380.7371521 , 271.56817627, 119.73376465,  67.66781616,
             38.19752884,   8.47016716]])

  • river_tracer
    (ntracers, river_time, nriver)
    float32
    15.62 15.62 15.62 ... 1.0 1.0 1.0

    long_name :

    River tracer data

    array([[[15.615934, 15.615934, 15.615934, 15.615934, 15.615934,
             15.615934],
            [15.615934, 15.615934, 15.615934, 15.615934, 15.615934,
             15.615934],
            [15.615934, 15.615934, 15.615934, 15.615934, 15.615934,
             15.615934],
            [15.615934, 15.615934, 15.615934, 15.615934, 15.615934,
             15.615934],
            [15.615934, 15.615934, 15.615934, 15.615934, 15.615934,
             15.615934],
            [15.615934, 15.615934, 15.615934, 15.615934, 15.615934,
             15.615934],
            [15.615934, 15.615934, 15.615934, 15.615934, 15.615934,
             15.615934],
            [15.615934, 15.615934, 15.615934, 15.615934, 15.615934,
             15.615934],
            [15.615934, 15.615934, 15.615934, 15.615934, 15.615934,
             15.615934],
            [15.615934, 15.615934, 15.615934, 15.615934, 15.615934,
             15.615934],
    ...
            [ 1.      ,  1.      ,  1.      ,  1.      ,  1.      ,
              1.      ],
            [ 1.      ,  1.      ,  1.      ,  1.      ,  1.      ,
              1.      ],
            [ 1.      ,  1.      ,  1.      ,  1.      ,  1.      ,
              1.      ],
            [ 1.      ,  1.      ,  1.      ,  1.      ,  1.      ,
              1.      ],
            [ 1.      ,  1.      ,  1.      ,  1.      ,  1.      ,
              1.      ],
            [ 1.      ,  1.      ,  1.      ,  1.      ,  1.      ,
              1.      ],
            [ 1.      ,  1.      ,  1.      ,  1.      ,  1.      ,
              1.      ],
            [ 1.      ,  1.      ,  1.      ,  1.      ,  1.      ,
              1.      ],
            [ 1.      ,  1.      ,  1.      ,  1.      ,  1.      ,
              1.      ],
            [ 1.      ,  1.      ,  1.      ,  1.      ,  1.      ,
              1.      ]]], dtype=float32)

  • river_index
    (eta_rho, xi_rho)
    float32
    0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0

    long_name :

    River ID

    units :

    none

    array([[0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           ...,
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.]], shape=(102, 102), dtype=float32)

  • river_fraction
    (eta_rho, xi_rho)
    float32
    0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0

    long_name :

    River volume fraction

    units :

    none

    array([[0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           ...,
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.]], shape=(102, 102), dtype=float32)

• Indexes: (2)
  • river_time
    PandasIndex

    PandasIndex(Index([15.000000000000002,               45.0,               74.0,
                        105.0,              135.0,              166.0,
                        196.0,              227.0,              258.0,
                        288.0,              319.0,              349.0],
          dtype='float64', name='river_time'))

  • nriver
    PandasIndex

    PandasIndex(Index([1, 2, 3, 4, 5, 6], dtype='int64', name='nriver'))

• Attributes: (1)

  climatology :

  True

We will examine these river forcing variables in more detail later.

River locations

Let’s plot the river locations extracted from the global dataset that are relevant to our domain!

The plot shows the river locations extracted from the global dataset, snapped to the nearest coastal grid cell. ROMS requires river locations to be placed on land but adjacent to wet points, so each river mouth is automatically relocated to the nearest qualifying cell.

river_forcing.plot_locations()

river_forcing.plot_locations(river_names=["Hvita(Olfusa)", "Thjorsa", "Svarta"])

The river locations of all rivers are stored in the .indices attribute, a dictionary where each key represents a river name, and the corresponding value is a list of tuples. Each tuple contains two elements: the first represents the eta_rho index, and the second represents the xi_rho index. In this case, each river is associated with a single tuple in the list.

river_forcing.indices

{'Hvita(Olfusa)': [(43, 28)],
 'Thjorsa': [(41, 30)],
 'JkulsFjll': [(62, 63)],
 'Lagarfljot': [(52, 74)],
 'Bruara': [(42, 29)],
 'Svarta': [(63, 46)]}

These updated coastal river locations are now also reflected in the river_index and river_fraction variables.

river_forcing.ds["river_index"]

<xarray.DataArray 'river_index' (eta_rho: 102, xi_rho: 102)> Size: 42kB
array([[0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       ...,
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.]], shape=(102, 102), dtype=float32)
Dimensions without coordinates: eta_rho, xi_rho
Attributes:
    long_name:  River ID
    units:      none

xarray.DataArray

'river_index'

• eta_rho: 102
• xi_rho: 102

• 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

  array([[0., 0., 0., ..., 0., 0., 0.],
         [0., 0., 0., ..., 0., 0., 0.],
         [0., 0., 0., ..., 0., 0., 0.],
         ...,
         [0., 0., 0., ..., 0., 0., 0.],
         [0., 0., 0., ..., 0., 0., 0.],
         [0., 0., 0., ..., 0., 0., 0.]], shape=(102, 102), dtype=float32)

• Coordinates: (0)
• Indexes: (0)
• Attributes: (2)

  long_name :

  River ID

  units :

  none

river_forcing.ds["river_fraction"]

<xarray.DataArray 'river_fraction' (eta_rho: 102, xi_rho: 102)> Size: 42kB
array([[0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       ...,
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.]], shape=(102, 102), dtype=float32)
Dimensions without coordinates: eta_rho, xi_rho
Attributes:
    long_name:  River volume fraction
    units:      none

xarray.DataArray

'river_fraction'

• eta_rho: 102
• xi_rho: 102

• 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

  array([[0., 0., 0., ..., 0., 0., 0.],
         [0., 0., 0., ..., 0., 0., 0.],
         [0., 0., 0., ..., 0., 0., 0.],
         ...,
         [0., 0., 0., ..., 0., 0., 0.],
         [0., 0., 0., ..., 0., 0., 0.],
         [0., 0., 0., ..., 0., 0., 0.]], shape=(102, 102), dtype=float32)

• Coordinates: (0)
• Indexes: (0)
• Attributes: (2)

  long_name :

  River volume fraction

  units :

  none

These arrays contain many zeros! Let’s extract and list only the non-zero values.

for var_name in ["river_index", "river_fraction"]:
    non_zero_values = river_forcing.ds[var_name].values
    non_zero_values = non_zero_values[non_zero_values != 0].tolist()
    print(var_name)
    print(non_zero_values)

river_index
[2.0, 5.0, 1.0, 4.0, 3.0, 6.0]
river_fraction
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0]

Note

The river_index and river_fraction variables are used to represent river locations and their associated fluxes on the grid. These variables are assigned as follows:

• Zero values: river_index and river_fraction are set to zero at grid points where no river is present.

• Non-zero values: When a grid point corresponds to a river, river_index and river_fraction are assigned based on the following rule:

  • river_index: The unique ID of the river.

  • river_fraction: The fraction of the river’s total flux that passes through the grid point.

For example, if River 3 spans two grid points, with half of its total flux passing through each point, the following values would be assigned:

• At each of the two grid points, river_index would be 3 (indicating River 3).

• river_fraction at each grid point would be 0.5, reflecting that half of the river’s flux passes through each point.

This approach ensures that rivers spanning multiple grid points have their flux distributed correctly, allowing the model to accurately represent the river’s volume of flux.

In cases where a river is confined to a single grid point (as typically generated by ROMS-Tools), the river_fraction at that grid point will be 1. For instance, if River 3 is located at a single grid point, the river_fraction at that point will be 1, indicating that the entire flux of River 3 passes through this grid point.

Let’s plot the spatial location of our 6 rivers.

import matplotlib.pyplot as plt
import matplotlib.colors as mcolors

colors = ["gray", "#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd", "#8c564b"]
cmap = mcolors.ListedColormap(colors)

fig, axs = plt.subplots(1, 2, figsize=(13, 5))
river_forcing.ds.river_index.plot(
    ax=axs[0],
    levels=7,
    vmin=0.5,
    vmax=6.5,
    cmap=cmap,
    cbar_kwargs={"ticks": [1, 2, 3, 4, 5, 6]},
)
axs[0].set_title("River ID")

river_forcing.ds.river_fraction.plot(ax=axs[1])
axs[1].set_title("River volume fraction")

Text(0.5, 1.0, 'River volume fraction')

If you squint your eyes, you may notice that the river locations represented in the river_index and river_fraction variables align with the updated river locations shown in the previous plot on the right. The river IDs in the river_index variable are marked with the same colors as in the previous plot.

Monthly versus climatological river forcing

Many river datasets contain missing values. To address this, ROMS-Tools offers the option to replace missing monthly values with climatological averages.

In this first example, we choose the option to never use climatological values, ensuring that the river forcing is derived entirely from the available monthly data.

In this example we’ll also use the GloFAS v4.0 dataset, a river discharge dataset from the Global Flood Awareness System (Grimaldi, et al. 2022. DOI: 10.24381/cds.a4fdd6b9). The version used here has been pre-processed using LDD auxillary data to create a coastal dataset and is stored locally for anvil users.

%%time

river_forcing = RiverForcing(
    grid=grid,
    start_time=start_time,
    end_time=end_time,
    convert_to_climatology="never",  # "never", "always", or "if_any_missing" (default)
    source = {
    "name": "GLOFAS",
    "path": '/anvil/projects/x-ees250129/Datasets/Rivers/processed_GloFAS/glofas_v4_rivers_daily.nc',
        }
)

2026-06-30 21:38:24 - INFO - No river indices provided. Identifying all rivers within the ROMS domain and assigning each to the nearest coastal point.
2026-06-30 21:38:24 - INFO - Computing river forcing...

[########################################] | 100% Completed | 2.56 sms

2026-06-30 21:38:27 - INFO - Creating 212 synthetic river(s) to handle overlapping entries.

CPU times: user 6.55 s, sys: 1.02 s, total: 7.56 s
Wall time: 6.59 s

As we saw earlier in the notebook, the river forcing data is stored in an xarray.Dataset, which can be accessed via the .ds property.

river_forcing.ds

<xarray.Dataset> Size: 42MB
Dimensions:           (nriver: 1194, river_time: 1463, ntracers: 2,
                       eta_rho: 102, xi_rho: 102)
Coordinates:
    river_name        (nriver) <U21 100kB 'GloFAS_63.77N_20.83W' ... 'GloFAS_...
    abs_time          (river_time) datetime64[ns] 12kB 1996-12-31 ... 2001-01-01
  * river_time        (river_time) float64 12kB -1.096e+03 -1.095e+03 ... 366.0
    tracer_name       (ntracers) <U4 32B 'temp' 'salt'
    tracer_unit       (ntracers) <U15 120B 'degrees Celsius' 'PSU'
    tracer_long_name  (ntracers) <U21 168B 'potential temperature' 'salinity'
  * nriver            (nriver) int64 10kB 1 2 3 4 5 ... 1190 1191 1192 1193 1194
Dimensions without coordinates: ntracers, eta_rho, xi_rho
Data variables:
    river_volume      (river_time, nriver) float64 14MB 191.3 161.1 ... 0.0 0.0
    river_tracer      (ntracers, river_time, nriver) float64 28MB 15.62 ... 1.0
    river_index       (eta_rho, xi_rho) float32 42kB 0.0 0.0 0.0 ... 0.0 0.0 0.0
    river_fraction    (eta_rho, xi_rho) float32 42kB 0.0 0.0 0.0 ... 0.0 0.0 0.0

xarray.Dataset

• Dimensions:
  • nriver: 1194
  • river_time: 1463
  • ntracers: 2
  • eta_rho: 102
  • xi_rho: 102
• Coordinates: (7)
  • river_name
    (nriver)
    <U21
    'GloFAS_63.77N_20.83W' ... 'GloF...

    long_name :

    River name

    array(['GloFAS_63.77N_20.83W', 'GloFAS_63.88N_21.22W',
           'overlap_65.62N_14.33W', ..., 'GloFAS_65.38N_21.18W',
           'GloFAS_65.38N_21.03W', 'GloFAS_65.47N_23.97W'],
          shape=(1194,), dtype='<U21')

  • abs_time
    (river_time)
    datetime64[ns]
    1996-12-31 ... 2001-01-01

    long_name :

    absolute time

    array(['1996-12-31T00:00:00.000000000', '1997-01-01T00:00:00.000000000',
           '1997-01-02T00:00:00.000000000', ..., '2000-12-30T00:00:00.000000000',
           '2000-12-31T00:00:00.000000000', '2001-01-01T00:00:00.000000000'],
          shape=(1463,), dtype='datetime64[ns]')

  • river_time
    (river_time)
    float64
    -1.096e+03 -1.095e+03 ... 366.0

    long_name :

    relative time: days since 2000-01-01 00:00:00

    units :

    days

    array([-1096., -1095., -1094., ...,   364.,   365.,   366.], shape=(1463,))

  • tracer_name
    (ntracers)
    <U4
    'temp' 'salt'

    long_name :

    Tracer name

    array(['temp', 'salt'], dtype='<U4')

  • tracer_unit
    (ntracers)
    <U15
    'degrees Celsius' 'PSU'

    long_name :

    Tracer concentration unit

    array(['degrees Celsius', 'PSU'], dtype='<U15')

  • tracer_long_name
    (ntracers)
    <U21
    'potential temperature' 'salinity'

    long_name :

    Tracer long name

    array(['potential temperature', 'salinity'], dtype='<U21')

  • nriver
    (nriver)
    int64
    1 2 3 4 5 ... 1191 1192 1193 1194

    long_name :

    River ID (1-based Fortran indexing)

    array([   1,    2,    3, ..., 1192, 1193, 1194], shape=(1194,))

• Data variables: (4)
  • river_volume
    (river_time, nriver)
    float64
    191.3 161.1 85.53 ... 0.0 0.0 0.0

    long_name :

    River volume flux

    units :

    m^3/s

    array([[191.3125  , 161.125   ,  85.53125 , ...,   0.      ,   0.      ,
              0.      ],
           [192.625   , 165.65625 ,  84.953125, ...,   0.      ,   0.      ,
              0.      ],
           [185.640625, 160.8125  ,  84.453125, ...,   0.      ,   0.      ,
              0.      ],
           ...,
           [148.09375 , 114.03125 , 108.765625, ...,   0.      ,   0.      ,
              0.      ],
           [144.953125, 110.765625, 105.4375  , ...,   0.      ,   0.      ,
              0.      ],
           [143.125   , 107.890625, 102.515625, ...,   0.      ,   0.      ,
              0.      ]], shape=(1463, 1194))

  • river_tracer
    (ntracers, river_time, nriver)
    float64
    15.62 15.62 15.62 ... 1.0 1.0 1.0

    long_name :

    River tracer data

    array([[[15.61593437, 15.61593437, 15.61593437, ..., 15.61593437,
             15.61593437, 15.61593437],
            [15.61593437, 15.61593437, 15.61593437, ..., 15.61593437,
             15.61593437, 15.61593437],
            [15.61593437, 15.61593437, 15.61593437, ..., 15.61593437,
             15.61593437, 15.61593437],
            ...,
            [15.61593437, 15.61593437, 15.61593437, ..., 15.61593437,
             15.61593437, 15.61593437],
            [15.61593437, 15.61593437, 15.61593437, ..., 15.61593437,
             15.61593437, 15.61593437],
            [15.61593437, 15.61593437, 15.61593437, ..., 15.61593437,
             15.61593437, 15.61593437]],
    
           [[ 1.        ,  1.        ,  1.        , ...,  1.        ,
              1.        ,  1.        ],
            [ 1.        ,  1.        ,  1.        , ...,  1.        ,
              1.        ,  1.        ],
            [ 1.        ,  1.        ,  1.        , ...,  1.        ,
              1.        ,  1.        ],
            ...,
            [ 1.        ,  1.        ,  1.        , ...,  1.        ,
              1.        ,  1.        ],
            [ 1.        ,  1.        ,  1.        , ...,  1.        ,
              1.        ,  1.        ],
            [ 1.        ,  1.        ,  1.        , ...,  1.        ,
              1.        ,  1.        ]]], shape=(2, 1463, 1194))

  • river_index
    (eta_rho, xi_rho)
    float32
    0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0

    long_name :

    River ID

    units :

    none

    array([[0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           ...,
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.]], shape=(102, 102), dtype=float32)

  • river_fraction
    (eta_rho, xi_rho)
    float32
    0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0

    long_name :

    River volume fraction

    units :

    none

    array([[0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           ...,
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.]], shape=(102, 102), dtype=float32)

• Indexes: (2)
  • river_time
    PandasIndex

    PandasIndex(Index([-1096.0, -1095.0, -1094.0, -1093.0, -1092.0, -1091.0, -1090.0, -1089.0,
           -1088.0, -1087.0,
           ...
             357.0,   358.0,   359.0,   360.0,   361.0,   362.0,   363.0,   364.0,
             365.0,   366.0],
          dtype='float64', name='river_time', length=1463))

  • nriver
    PandasIndex

    PandasIndex(Index([   1,    2,    3,    4,    5,    6,    7,    8,    9,   10,
           ...
           1185, 1186, 1187, 1188, 1189, 1190, 1191, 1192, 1193, 1194],
          dtype='int64', name='nriver', length=1194))

• Attributes: (0)

As mentioned in the warning message when we created the river forcing, some of the river volume data contained NaN values, which have since been set to zero. Let’s take a closer look at this.

river_forcing.plot("river_volume")

2026-06-30 21:38:31 - WARNING - Only the first 20 rivers will be plotted (received 1194).

[STRIKEOUT:Only the river Lagarfljot (red line) has a complete time series over our period of interest. The river Bruara (purple line) has missing values for roughly the second half of the period, which have been set to zero. All other rivers have missing values for the entire period and have also been set to zero.]

The GloFAS dataset has daily values for all rivers in our domain and you can see that there is significant interannual variability between years.

As before, we can choose to only show the time series for a subset of the rivers.

river_forcing.plot("river_volume", river_names=["GloFAS_63.77N_20.83W", "GloFAS_63.88N_21.22W", "overlap_65.62N_14.33W"])

The river tracer data, consisting of the river temperature and salinity, are not derived from the discharge dataset. Instead, they are always set to constant values: 17°C for temperature and 1 psu for salinity.

We will now select the default option in ROMS-Tools, which converts the river volume data to climatological values when requested using “alwayif any missing values are detected.

%%time

river_forcing = RiverForcing(
    grid=grid,
    start_time=datetime(1998, 1, 1),
    end_time=datetime(2000, 12, 31),
    convert_to_climatology="always",  # "never", "always", or "if_any_missing" (default)
    source = {
    "name": "GLOFAS",
    "path": '/anvil/projects/x-ees250129/Datasets/Rivers/processed_GloFAS/glofas_v4_rivers_daily.nc',
        }
)

2026-06-30 21:38:34 - INFO - No river indices provided. Identifying all rivers within the ROMS domain and assigning each to the nearest coastal point.
2026-06-30 21:38:34 - INFO - Compute climatology for river forcing.
2026-06-30 21:38:34 - INFO - Computing river forcing...

[########################################] | 100% Completed | 2.54 sms

2026-06-30 21:38:37 - INFO - Creating 212 synthetic river(s) to handle overlapping entries.

CPU times: user 6.38 s, sys: 909 ms, total: 7.29 s
Wall time: 6.45 s

The river forcing for all rivers is now represented as a climatology. Note that none of the rivers has a zero river volume flux in any month!

river_forcing.plot("river_volume")

2026-06-30 21:38:40 - WARNING - Only the first 20 rivers will be plotted (received 1194).

Adding BGC river forcing

To run a ROMS-MARBL simulation (i.e., ROMS with biogeochemistry), the model requires not only the standard two tracers (temperature and salinity) but also all 32 MARBL biogeochemical (BGC) tracers. If the river forcing data does not include all 32 BGC tracers, the model will crash.

By setting include_bgc=True, ROMS-Tools adds the required BGC tracers. Choose the dynamic BGC source with bgc_source["name"]:

• ``CONSTANTS`` (default if bgc_source is omitted): all tracers set to recommended scalar concentrations from `river_tracer_defaults.nc <CWorthy-ocean/roms-tools-data>`__ (literature values with references in the file metadata).

• ``RIVR2O``: Snnual mass fluxes for DIC, DOC, DON, DOP, ALK, NO3, PO4 from the global gridded RIVR2O river export product (Fabrice et al. 2025; one annual field per year). ALK_ALT_CO2 and DIC_ALT_CO2 are set as the same value as ALK and DIC as default. ROMS-Tools divides the mass fluxes by the discharge per river to produce tracer concentrations for each river and year.

ROMS-Tools combines dynamic and fill values through ``bgc_source[“fill”]`` (default: {"name": "CONSTANTS"}). Tracers not provided by the dynamic source are filled from the fill source.

First, let us look at an example with constants only.

%%time

river_forcing = RiverForcing(
    grid=grid,
    start_time=start_time,
    end_time=end_time,
    include_bgc=True,
    bgc_source={"name": "CONSTANTS"},
    model_reference_date=datetime(2000, 1, 1),  # optional; this is the default
    source = {
    "name": "GLOFAS",
    "path": '/anvil/projects/x-ees250129/Datasets/Rivers/processed_GloFAS/glofas_v4_rivers_daily.nc',
        }
)

2026-06-30 21:38:41 - INFO - No river indices provided. Identifying all rivers within the ROMS domain and assigning each to the nearest coastal point.
2026-06-30 21:38:43 - INFO - Computing river forcing...

[########################################] | 100% Completed | 2.67 sms

2026-06-30 21:38:46 - INFO - Creating 212 synthetic river(s) to handle overlapping entries.

CPU times: user 14.1 s, sys: 1.63 s, total: 15.7 s
Wall time: 14 s

Now, we can look at the value of a specific tracer. Notice all rivers have the same value for the tracer at every time step.

river_forcing.plot("river_NO3") #non climatology

2026-06-30 21:38:54 - WARNING - Only the first 20 rivers will be plotted (received 1194).

Now let’s look at an example with dynamic BGC from RIVR2O. This requires a path to the annual NetCDF files (freely downloadable from Zenodo). The remaining MARBL tracers (e.g. SiO3, NH4, Fe) are filled from constants via the default fill setting; you can set it explicitly as "fill": {"name": "CONSTANTS"} inside bgc_source. If your simulation period end time is after 2024, values from 2024 are used.

from pathlib import Path
from datetime import datetime
start_time = datetime(2015, 1, 1)
end_time = datetime(2025, 12, 31)

rivr2o_path = Path(
    "/anvil/projects/x-ees250129/Datasets/Rivers/r2o_river_inputs_1901_2024/*.nc"
)

#%%time

river_forcing = RiverForcing(
    grid=grid,
    start_time=start_time,
    end_time=end_time,
    include_bgc=True,
    bgc_source={
        "name": "RIVR2O",
        "path": str(rivr2o_path ),
        "fill": {"name": "CONSTANTS"},
    },
    model_reference_date=datetime(2000, 1, 1),
    source = {
    "name": "GLOFAS",
    "path": '/anvil/projects/x-ees250129/Datasets/Rivers/processed_GloFAS/glofas_v4_rivers_daily.nc',
        }
)

2026-06-30 21:38:56 - INFO - No river indices provided. Identifying all rivers within the ROMS domain and assigning each to the nearest coastal point.
2026-06-30 21:38:56 - WARNING - No records found after the end_time: 2025-12-31 00:00:00.
2026-06-30 21:39:02 - INFO - Computing river forcing...

[########################################] | 100% Completed | 6.24 ss

2026-06-30 21:39:09 - INFO - Creating 212 synthetic river(s) to handle overlapping entries.
2026-06-30 21:39:41 - INFO - Simulation end time is after 2024; using RIVR2O values from 2024.
2026-06-30 21:39:42 - INFO - Partitioning RIVR2O export by discharge among rivers sharing grid cell(s): (307,318)x14, (311,330)x38, (312,327)x42, (308,316)x107, (311,324)x21, (311,323)x42, (311,321)x36, (307,323)x53, (307,319)x9, (311,319)x48, (308,329)x38, (309,331)x48, (310,317)x37, (311,315)x30, (311,316)x55, (312,329)x35, (310,315)x55, (307,320)x18, (312,328)x28, (309,314)x94, (311,314)x46, (310,332)x48, (311,313)x33, (311,312)x103, (312,314)x106, (311,320)x6, (310,318)x4.

Note

Even when the discharge is set to climatology (either from missing data or if convert_to_climatology is set to ‘always’), the BGC tracers are still time-varying such that the climatology discharge is expanded to cover the period of simulation with different BGC tracer data from year to year.

If the simulation period extends beyond the temporal coverage of rivr2o, the last (or first) year of data from rivr2o is used.

The river forcing data has now 34 = (2 + 32) tracers, as reflected by the ntracers dimension.

river_forcing.ds

<xarray.Dataset> Size: 629MB
Dimensions:           (nriver: 1194, river_time: 3655, ntracers: 34,
                       eta_rho: 102, xi_rho: 102)
Coordinates:
    river_name        (nriver) <U21 100kB 'GloFAS_63.77N_20.83W' ... 'GloFAS_...
    abs_time          (river_time) datetime64[ns] 29kB 2014-12-31 ... 2025-01-01
  * river_time        (river_time) float64 29kB 5.478e+03 ... 9.132e+03
    tracer_name       (ntracers) <U11 1kB 'temp' 'salt' ... 'diazP' 'diazFe'
    tracer_unit       (ntracers) <U15 2kB 'degrees Celsius' 'PSU' ... 'mmol/m^3'
    tracer_long_name  (ntracers) <U43 6kB 'potential temperature' ... 'diazot...
  * nriver            (nriver) int64 10kB 1 2 3 4 5 ... 1190 1191 1192 1193 1194
  * ntracers          (ntracers) int64 272B 0 1 2 3 4 5 6 ... 28 29 30 31 32 33
    lat               (nriver) float32 5kB dask.array<chunksize=(1194,), meta=np.ndarray>
    lon               (nriver) float32 5kB dask.array<chunksize=(1194,), meta=np.ndarray>
    calendar_year     (river_time) int64 29kB 2014 2015 2015 ... 2024 2024 2025
Dimensions without coordinates: eta_rho, xi_rho
Data variables:
    river_volume      (river_time, nriver) float64 35MB 244.4 251.9 ... 0.0 0.0
    river_tracer      (ntracers, river_time, nriver) float32 594MB dask.array<chunksize=(1, 12, 1194), meta=np.ndarray>
    river_index       (eta_rho, xi_rho) float32 42kB 0.0 0.0 0.0 ... 0.0 0.0 0.0
    river_fraction    (eta_rho, xi_rho) float32 42kB 0.0 0.0 0.0 ... 0.0 0.0 0.0

xarray.Dataset

• Dimensions:
  • nriver: 1194
  • river_time: 3655
  • ntracers: 34
  • eta_rho: 102
  • xi_rho: 102
• Coordinates: (11)
  • river_name
    (nriver)
    <U21
    'GloFAS_63.77N_20.83W' ... 'GloF...

    long_name :

    River name

    array(['GloFAS_63.77N_20.83W', 'GloFAS_63.88N_21.22W',
           'overlap_65.62N_14.33W', ..., 'GloFAS_65.38N_21.18W',
           'GloFAS_65.38N_21.03W', 'GloFAS_65.47N_23.97W'],
          shape=(1194,), dtype='<U21')

  • abs_time
    (river_time)
    datetime64[ns]
    2014-12-31 ... 2025-01-01

    long_name :

    absolute time

    array(['2014-12-31T00:00:00.000000000', '2015-01-01T00:00:00.000000000',
           '2015-01-02T00:00:00.000000000', ..., '2024-12-30T00:00:00.000000000',
           '2024-12-31T00:00:00.000000000', '2025-01-01T00:00:00.000000000'],
          shape=(3655,), dtype='datetime64[ns]')

  • river_time
    (river_time)
    float64
    5.478e+03 5.479e+03 ... 9.132e+03

    long_name :

    relative time: days since 2000-01-01 00:00:00

    units :

    days

    array([5478., 5479., 5480., ..., 9130., 9131., 9132.], shape=(3655,))

  • tracer_name
    (ntracers)
    <U11
    'temp' 'salt' ... 'diazP' 'diazFe'

    long_name :

    Tracer name

    array(['temp', 'salt', 'PO4', 'NO3', 'SiO3', 'NH4', 'Fe', 'Lig', 'O2',
           'DIC', 'DIC_ALT_CO2', 'ALK', 'ALK_ALT_CO2', 'DOC', 'DON', 'DOP',
           'DOPr', 'DONr', 'DOCr', 'zooC', 'spChl', 'spC', 'spP', 'spFe',
           'spCaCO3', 'diatChl', 'diatC', 'diatP', 'diatFe', 'diatSi',
           'diazChl', 'diazC', 'diazP', 'diazFe'], dtype='<U11')

  • tracer_unit
    (ntracers)
    <U15
    'degrees Celsius' ... 'mmol/m^3'

    long_name :

    Tracer concentration unit

    array(['degrees Celsius', 'PSU', 'mmol/m^3', 'mmol/m^3', 'mmol/m^3',
           'mmol/m^3', 'mmol/m^3', 'mmol/m^3', 'mmol/m^3', 'mmol/m^3',
           'mmol/m^3', 'meq/m^3', 'meq/m^3', 'mmol/m^3', 'mmol/m^3',
           'mmol/m^3', 'mmol/m^3', 'mmol/m^3', 'mmol/m^3', 'mmol/m^3',
           'mg/m^3', 'mmol/m^3', 'mmol/m^3', 'mmol/m^3', 'mmol/m^3', 'mg/m^3',
           'mmol/m^3', 'mmol/m^3', 'mmol/m^3', 'mmol/m^3', 'mg/m^3',
           'mmol/m^3', 'mmol/m^3', 'mmol/m^3'], dtype='<U15')

  • tracer_long_name
    (ntracers)
    <U43
    'potential temperature' ... 'dia...

    long_name :

    Tracer long name

    array(['potential temperature', 'salinity',
           'dissolved inorganic phosphate', 'dissolved inorganic nitrate',
           'dissolved inorganic silicate', 'dissolved ammonia',
           'dissolved inorganic iron', 'iron binding ligand',
           'dissolved oxygen', 'dissolved inorganic carbon',
           'dissolved inorganic carbon, alternative CO2', 'alkalinity',
           'alkalinity, alternative CO2', 'dissolved organic carbon',
           'dissolved organic nitrogen', 'dissolved organic phosphorus',
           'refractory dissolved organic phosphorus',
           'refractory dissolved organic nitrogen',
           'refractory dissolved organic carbon', 'zooplankton carbon',
           'small phytoplankton chlorophyll', 'small phytoplankton carbon',
           'small phytoplankton phosphorous', 'small phytoplankton iron',
           'small phytoplankton CaCO3', 'diatom chloropyll', 'diatom carbon',
           'diatom phosphorus', 'diatom iron', 'diatom silicate',
           'diazotroph chloropyll', 'diazotroph carbon',
           'diazotroph phosphorus', 'diazotroph iron'], dtype='<U43')

  • nriver
    (nriver)
    int64
    1 2 3 4 5 ... 1191 1192 1193 1194

    long_name :

    River ID (1-based Fortran indexing)

    array([   1,    2,    3, ..., 1192, 1193, 1194], shape=(1194,))

  • ntracers
    (ntracers)
    int64
    0 1 2 3 4 5 6 ... 28 29 30 31 32 33

    [34 values with dtype=int64]

  • lat
    (nriver)
    float32
    dask.array<chunksize=(1194,), meta=np.ndarray>

    Array Chunk Bytes 4.66 kiB 4.66 kiB Shape (1194,) (1194,) Dask graph 1 chunks in 1 graph layer Data type float32 numpy.ndarray

  • lon
    (nriver)
    float32
    dask.array<chunksize=(1194,), meta=np.ndarray>

    Array Chunk Bytes 4.66 kiB 4.66 kiB Shape (1194,) (1194,) Dask graph 1 chunks in 1 graph layer Data type float32 numpy.ndarray

  • calendar_year
    (river_time)
    int64
    2014 2015 2015 ... 2024 2024 2025

    long_name :

    absolute time

    array([2014, 2015, 2015, ..., 2024, 2024, 2025], shape=(3655,))

• Data variables: (4)
  • river_volume
    (river_time, nriver)
    float64
    244.4 251.9 133.5 ... 0.0 0.0 0.0

    long_name :

    River volume flux

    units :

    m^3/s

    array([[244.4375   , 251.875    , 133.5      , ...,   0.       ,
              0.       ,   0.       ],
           [303.796875 , 307.03125  , 145.71875  , ...,   0.       ,
              0.       ,   0.       ],
           [323.359375 , 311.3125   , 148.921875 , ...,   0.       ,
              0.       ,   0.       ],
           ...,
           [168.78125  , 140.5859375,  98.4921875, ...,   0.       ,
              0.       ,   0.       ],
           [165.09375  , 135.1796875,  98.015625 , ...,   0.       ,
              0.       ,   0.       ],
           [161.40625  , 129.6875   ,  97.1328125, ...,   0.       ,
              0.       ,   0.       ]], shape=(3655, 1194))

  • river_tracer
    (ntracers, river_time, nriver)
    float32
    dask.array<chunksize=(1, 12, 1194), meta=np.ndarray>

    long_name :

    River volume flux

    units :

    m^3/s

    Array Chunk Bytes 566.02 MiB 55.97 kiB Shape (34, 3655, 1194) (1, 12, 1194) Dask graph 10370 chunks in 159 graph layers Data type float32 numpy.ndarray

  • river_index
    (eta_rho, xi_rho)
    float32
    0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0

    long_name :

    River ID

    units :

    none

    array([[0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           ...,
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.]], shape=(102, 102), dtype=float32)

  • river_fraction
    (eta_rho, xi_rho)
    float32
    0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0

    long_name :

    River volume fraction

    units :

    none

    array([[0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           ...,
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.]], shape=(102, 102), dtype=float32)

• Indexes: (3)
  • river_time
    PandasIndex

    PandasIndex(Index([5478.0, 5479.0, 5480.0, 5481.0, 5482.0, 5483.0, 5484.0, 5485.0, 5486.0,
           5487.0,
           ...
           9123.0, 9124.0, 9125.0, 9126.0, 9127.0, 9128.0, 9129.0, 9130.0, 9131.0,
           9132.0],
          dtype='float64', name='river_time', length=3655))

  • nriver
    PandasIndex

    PandasIndex(Index([   1,    2,    3,    4,    5,    6,    7,    8,    9,   10,
           ...
           1185, 1186, 1187, 1188, 1189, 1190, 1191, 1192, 1193, 1194],
          dtype='int64', name='nriver', length=1194))

  • ntracers
    PandasIndex

    PandasIndex(RangeIndex(start=0, stop=34, step=1, name='ntracers'))

• Attributes: (0)

Here are the tracer names.

river_forcing.ds.tracer_name

<xarray.DataArray 'tracer_name' (ntracers: 34)> Size: 1kB
array(['temp', 'salt', 'PO4', 'NO3', 'SiO3', 'NH4', 'Fe', 'Lig', 'O2',
       'DIC', 'DIC_ALT_CO2', 'ALK', 'ALK_ALT_CO2', 'DOC', 'DON', 'DOP',
       'DOPr', 'DONr', 'DOCr', 'zooC', 'spChl', 'spC', 'spP', 'spFe',
       'spCaCO3', 'diatChl', 'diatC', 'diatP', 'diatFe', 'diatSi',
       'diazChl', 'diazC', 'diazP', 'diazFe'], dtype='<U11')
Coordinates:
    tracer_name       (ntracers) <U11 1kB 'temp' 'salt' ... 'diazP' 'diazFe'
    tracer_unit       (ntracers) <U15 2kB 'degrees Celsius' 'PSU' ... 'mmol/m^3'
    tracer_long_name  (ntracers) <U43 6kB 'potential temperature' ... 'diazot...
  * ntracers          (ntracers) int64 272B 0 1 2 3 4 5 6 ... 28 29 30 31 32 33
Attributes:
    long_name:  Tracer name

xarray.DataArray

'tracer_name'

• ntracers: 34

• 'temp' 'salt' 'PO4' 'NO3' ... 'diazChl' 'diazC' 'diazP' 'diazFe'

  array(['temp', 'salt', 'PO4', 'NO3', 'SiO3', 'NH4', 'Fe', 'Lig', 'O2',
         'DIC', 'DIC_ALT_CO2', 'ALK', 'ALK_ALT_CO2', 'DOC', 'DON', 'DOP',
         'DOPr', 'DONr', 'DOCr', 'zooC', 'spChl', 'spC', 'spP', 'spFe',
         'spCaCO3', 'diatChl', 'diatC', 'diatP', 'diatFe', 'diatSi',
         'diazChl', 'diazC', 'diazP', 'diazFe'], dtype='<U11')

• Coordinates: (4)
  • tracer_name
    (ntracers)
    <U11
    'temp' 'salt' ... 'diazP' 'diazFe'

    long_name :

    Tracer name

    array(['temp', 'salt', 'PO4', 'NO3', 'SiO3', 'NH4', 'Fe', 'Lig', 'O2',
           'DIC', 'DIC_ALT_CO2', 'ALK', 'ALK_ALT_CO2', 'DOC', 'DON', 'DOP',
           'DOPr', 'DONr', 'DOCr', 'zooC', 'spChl', 'spC', 'spP', 'spFe',
           'spCaCO3', 'diatChl', 'diatC', 'diatP', 'diatFe', 'diatSi',
           'diazChl', 'diazC', 'diazP', 'diazFe'], dtype='<U11')

  • tracer_unit
    (ntracers)
    <U15
    'degrees Celsius' ... 'mmol/m^3'

    long_name :

    Tracer concentration unit

    array(['degrees Celsius', 'PSU', 'mmol/m^3', 'mmol/m^3', 'mmol/m^3',
           'mmol/m^3', 'mmol/m^3', 'mmol/m^3', 'mmol/m^3', 'mmol/m^3',
           'mmol/m^3', 'meq/m^3', 'meq/m^3', 'mmol/m^3', 'mmol/m^3',
           'mmol/m^3', 'mmol/m^3', 'mmol/m^3', 'mmol/m^3', 'mmol/m^3',
           'mg/m^3', 'mmol/m^3', 'mmol/m^3', 'mmol/m^3', 'mmol/m^3', 'mg/m^3',
           'mmol/m^3', 'mmol/m^3', 'mmol/m^3', 'mmol/m^3', 'mg/m^3',
           'mmol/m^3', 'mmol/m^3', 'mmol/m^3'], dtype='<U15')

  • tracer_long_name
    (ntracers)
    <U43
    'potential temperature' ... 'dia...

    long_name :

    Tracer long name

    array(['potential temperature', 'salinity',
           'dissolved inorganic phosphate', 'dissolved inorganic nitrate',
           'dissolved inorganic silicate', 'dissolved ammonia',
           'dissolved inorganic iron', 'iron binding ligand',
           'dissolved oxygen', 'dissolved inorganic carbon',
           'dissolved inorganic carbon, alternative CO2', 'alkalinity',
           'alkalinity, alternative CO2', 'dissolved organic carbon',
           'dissolved organic nitrogen', 'dissolved organic phosphorus',
           'refractory dissolved organic phosphorus',
           'refractory dissolved organic nitrogen',
           'refractory dissolved organic carbon', 'zooplankton carbon',
           'small phytoplankton chlorophyll', 'small phytoplankton carbon',
           'small phytoplankton phosphorous', 'small phytoplankton iron',
           'small phytoplankton CaCO3', 'diatom chloropyll', 'diatom carbon',
           'diatom phosphorus', 'diatom iron', 'diatom silicate',
           'diazotroph chloropyll', 'diazotroph carbon',
           'diazotroph phosphorus', 'diazotroph iron'], dtype='<U43')

  • ntracers
    (ntracers)
    int64
    0 1 2 3 4 5 6 ... 28 29 30 31 32 33

    [34 values with dtype=int64]

• Indexes: (1)
  • ntracers
    PandasIndex

    PandasIndex(RangeIndex(start=0, stop=34, step=1, name='ntracers'))

• Attributes: (1)

  long_name :

  Tracer name

As mentioned before, the BGC tracers set by RIVR2O are varying spatially and temporally, such that each river has a different tracer concentration. For example, here we look at DIC and NO3.

river_forcing.plot("river_DIC")

2026-06-30 21:39:45 - WARNING - Only the first 20 rivers will be plotted (received 1194).

river_forcing.plot("river_NO3")

2026-06-30 21:39:57 - WARNING - Only the first 20 rivers will be plotted (received 1194).

River temperature and salinity are still set to the same constant values as before: 15.6°C for temperature and 1 psu for salinity.

river_forcing.plot("river_temp")

2026-06-30 21:40:10 - WARNING - Only the first 20 rivers will be plotted (received 1194).

Saving as NetCDF or YAML file

We can now save our river forcing as a netCDF file via the .save method.

filepath = "/anvil/projects/x-ees250129/x-uheede/my_river_forcing.nc"

We can also export the river forcing parameters to a YAML file.

yaml_filepath = "/anvil/projects/x-ees250129/x-uheede/my_river_forcing.yaml"

river_forcing.save(filepath=filepath)

2026-06-30 21:40:18 - INFO - Writing the following NetCDF files:
/anvil/projects/x-ees250129/x-uheede/my_river_forcing.nc

[PosixPath('/anvil/projects/x-ees250129/x-uheede/my_river_forcing.nc')]

river_forcing.to_yaml(yaml_filepath)

This is the YAML file that was created.

# Open and read the YAML file
with open(yaml_filepath, "r") as file:
    file_contents = file.read()

# Print the contents
print(file_contents)

---
roms_tools_version: 3.6.1.dev24+g32e13b12f
---
Grid:
  nx: 100
  ny: 100
  size_x: 800
  size_y: 800
  center_lon: -18
  center_lat: 65
  rot: 20
  N: 100
  theta_s: 5.0
  theta_b: 2.0
  hc: 300.0
  topography_source:
    name: ETOPO5
  mask_shapefile: null
  close_narrow_channels: false
  hmin: 5.0
  filename: null
RiverForcing:
  start_time: '2015-01-01T00:00:00'
  end_time: '2025-12-31T00:00:00'
  source:
    name: GLOFAS
    path: /anvil/projects/x-ees250129/Datasets/Rivers/processed_GloFAS/glofas_v4_rivers_daily.nc
    climatology: false
  convert_to_climatology: if_any_missing
  include_bgc: true
  bgc_source:
    name: RIVR2O
    fill:
      name: CONSTANTS
    path: /anvil/projects/x-ees250129/Datasets/Rivers/r2o_river_inputs_1901_2024/*.nc
  model_reference_date: '2000-01-01T00:00:00'
  indices:
    GloFAS_63.77N_20.83W:
    - 42, 29
    GloFAS_63.88N_21.22W:
    - 44, 27
    GloFAS_66.12N_16.68W:
    - 62, 63
    GloFAS_63.67N_20.58W:
    - 40, 30
    GloFAS_63.67N_17.78W:
    - 34, 46
    GloFAS_64.17N_15.72W:
    - 36, 60
    GloFAS_64.17N_15.83W:
    - 36, 59
    GloFAS_64.38N_14.78W:
    - 37, 66
    GloFAS_66.02N_22.43W:
    - 73, 33
    GloFAS_63.52N_19.88W:
    - 36, 33
    GloFAS_64.32N_15.38W:
    - 37, 62
    GloFAS_65.17N_22.38W:
    - 62, 28
    GloFAS_65.77N_19.68W:
    - 64, 45
    GloFAS_65.27N_22.22W:
    - 63, 29
    GloFAS_66.02N_19.43W:
    - 67, 48
    GloFAS_65.02N_22.58W:
    - 61, 26
    GloFAS_65.52N_22.03W:
    - 66, 32
    GloFAS_65.72N_14.38W:
    - 53, 73
    GloFAS_64.67N_22.28W:
    - 56, 26
    GloFAS_66.12N_15.18W:
    - 60, 70
    GloFAS_65.97N_20.38W:
    - 68, 43
    GloFAS_65.02N_22.12W:
    - 59, 28
    GloFAS_63.92N_16.43W:
    - 34, 54
    GloFAS_65.97N_19.43W:
    - 66, 48
    GloFAS_64.92N_23.53W:
    - 62, 20
    GloFAS_63.88N_22.33W:
    - 46, 21
    GloFAS_66.02N_18.53W:
    - 65, 52
    GloFAS_65.52N_24.22W:
    - 71, 20
    GloFAS_66.32N_16.43W:
    - 65, 65
    GloFAS_64.82N_23.12W:
    - 60, 22
    GloFAS_63.47N_18.12W:
    - 32, 43
    GloFAS_65.32N_22.12W:
    - 63, 30
    GloFAS_65.42N_23.93W:
    - 69, 21
    GloFAS_65.88N_19.38W:
    - 65, 48
    GloFAS_64.32N_21.88W:
    - 51, 26
    GloFAS_63.42N_18.38W:
    - 32, 41
    GloFAS_63.82N_22.38W:
    - 46, 20
    GloFAS_65.62N_21.43W:
    - 66, 35
    GloFAS_65.62N_22.12W:
    - 67, 31
    GloFAS_65.62N_24.33W:
    - 72, 20
    GloFAS_65.62N_23.58W:
    - 69, 24
    GloFAS_64.92N_13.88W:
    - 42, 74
    GloFAS_65.62N_23.33W:
    - 69, 25
    GloFAS_64.88N_23.97W:
    - 62, 17
    GloFAS_64.82N_23.33W:
    - 61, 21
    GloFAS_64.92N_23.22W:
    - 61, 21
    GloFAS_65.62N_23.78W:
    - 72, 22
    GloFAS_65.67N_18.03W:
    - 63, 53
    GloFAS_64.77N_22.93W:
    - 59, 23
    GloFAS_64.77N_22.58W:
    - 58, 25
    GloFAS_65.62N_23.83W:
    - 72, 22
    GloFAS_65.67N_23.62W:
    - 73, 23
    GloFAS_64.72N_14.43W:
    - 40, 69
    GloFAS_64.72N_14.03W:
    - 40, 71
    GloFAS_64.67N_14.18W:
    - 40, 71
    GloFAS_64.62N_22.38W:
    - 55, 25
    GloFAS_64.57N_22.28W:
    - 54, 25
    GloFAS_64.82N_23.28W:
    - 61, 21
    GloFAS_65.47N_21.68W:
    - 64, 34
    GloFAS_65.47N_13.68W:
    - 49, 76
    GloFAS_65.52N_21.33W:
    - 65, 35
    GloFAS_65.52N_21.47W:
    - 65, 35
    GloFAS_65.47N_21.33W:
    - 63, 35
    GloFAS_65.42N_21.22W:
    - 63, 35
    GloFAS_65.27N_13.83W:
    - 46, 75
    GloFAS_65.27N_13.62W:
    - 46, 76
    GloFAS_65.22N_22.47W:
    - 63, 27
    GloFAS_65.52N_23.38W:
    - 69, 24
    GloFAS_65.62N_21.53W:
    - 66, 35
    GloFAS_65.52N_22.83W:
    - 69, 28
    GloFAS_65.57N_23.93W:
    - 71, 21
    GloFAS_65.17N_22.43W:
    - 63, 27
    GloFAS_65.17N_21.03W:
    - 62, 36
    GloFAS_65.07N_13.62W:
    - 44, 75
    GloFAS_65.07N_13.53W:
    - 43, 75
    GloFAS_65.02N_22.03W:
    - 59, 29
    GloFAS_64.97N_13.93W:
    - 42, 74
    GloFAS_64.97N_23.38W:
    - 62, 22
    GloFAS_65.57N_23.08W:
    - 69, 26
    GloFAS_65.52N_13.72W:
    - 49, 76
    GloFAS_66.17N_16.62W:
    - 63, 64
    GloFAS_66.17N_23.28W:
    - 77, 28
    GloFAS_66.12N_17.22W:
    - 64, 60
    GloFAS_66.12N_20.08W:
    - 69, 44
    GloFAS_66.12N_23.22W:
    - 76, 29
    GloFAS_66.07N_17.28W:
    - 63, 59
    GloFAS_66.02N_20.38W:
    - 69, 43
    GloFAS_66.02N_14.78W:
    - 58, 73
    GloFAS_66.02N_14.62W:
    - 58, 73
    GloFAS_65.97N_23.38W:
    - 75, 26
    GloFAS_66.02N_17.68W:
    - 63, 57
    GloFAS_65.97N_18.58W:
    - 64, 52
    GloFAS_65.97N_21.62W:
    - 71, 37
    GloFAS_65.92N_23.58W:
    - 75, 26
    GloFAS_65.67N_23.93W:
    - 72, 22
    GloFAS_65.92N_19.93W:
    - 66, 44
    GloFAS_65.92N_22.47W:
    - 72, 32
    GloFAS_65.88N_23.38W:
    - 75, 26
    GloFAS_65.82N_21.33W:
    - 69, 37
    GloFAS_65.82N_22.68W:
    - 73, 30
    GloFAS_65.82N_18.08W:
    - 63, 53
    GloFAS_65.82N_23.18W:
    - 74, 29
    GloFAS_65.77N_23.47W:
    - 74, 25
    GloFAS_65.77N_23.93W:
    - 73, 23
    GloFAS_65.72N_21.68W:
    - 68, 36
    GloFAS_65.72N_23.38W:
    - 70, 27
    GloFAS_65.72N_23.68W:
    - 73, 23
    GloFAS_65.67N_23.28W:
    - 70, 27
    GloFAS_65.62N_22.93W:
    - 70, 27
    GloFAS_65.92N_20.33W:
    - 67, 43
    GloFAS_64.52N_22.03W:
    - 53, 25
    GloFAS_64.52N_22.18W:
    - 54, 25
    GloFAS_64.47N_22.22W:
    - 53, 25
    GloFAS_64.47N_21.93W:
    - 52, 26
    GloFAS_64.32N_21.97W:
    - 51, 25
    GloFAS_64.32N_21.78W:
    - 50, 27
    GloFAS_64.27N_15.47W:
    - 36, 61
    GloFAS_64.27N_21.83W:
    - 49, 26
    GloFAS_64.12N_16.03W:
    - 35, 57
    GloFAS_64.07N_21.93W:
    - 47, 24
    GloFAS_64.02N_16.28W:
    - 35, 56
    GloFAS_64.02N_22.68W:
    - 48, 20
    GloFAS_64.02N_22.08W:
    - 47, 23
    GloFAS_63.92N_22.58W:
    - 47, 19
    GloFAS_66.17N_22.03W:
    - 75, 35
    GloFAS_63.88N_22.03W:
    - 45, 22
    GloFAS_63.88N_22.62W:
    - 46, 19
    GloFAS_63.88N_21.08W:
    - 43, 28
    GloFAS_63.88N_21.03W:
    - 43, 28
    GloFAS_63.88N_22.68W:
    - 47, 19
    GloFAS_63.82N_17.08W:
    - 33, 50
    GloFAS_63.77N_16.72W:
    - 33, 52
    GloFAS_63.52N_19.58W:
    - 35, 34
    GloFAS_63.47N_18.62W:
    - 32, 40
    GloFAS_63.42N_18.58W:
    - 32, 40
    GloFAS_66.42N_22.93W:
    - 79, 32
    GloFAS_66.22N_22.18W:
    - 75, 35
    GloFAS_66.22N_22.88W:
    - 77, 31
    GloFAS_66.22N_22.08W:
    - 75, 35
    GloFAS_63.97N_22.08W:
    - 47, 23
    GloFAS_63.52N_20.18W:
    - 37, 31
    GloFAS_63.52N_17.93W:
    - 32, 44
    GloFAS_63.52N_19.68W:
    - 35, 34
    GloFAS_63.52N_17.97W:
    - 32, 44
    GloFAS_63.52N_18.62W:
    - 32, 40
    GloFAS_63.47N_18.18W:
    - 32, 42
    GloFAS_63.47N_19.47W:
    - 34, 35
    GloFAS_63.42N_19.18W:
    - 33, 36
    GloFAS_63.42N_18.83W:
    - 32, 38
    GloFAS_63.42N_19.03W:
    - 33, 37
    GloFAS_63.42N_19.22W:
    - 33, 36
    GloFAS_63.42N_18.97W:
    - 32, 38
    GloFAS_63.42N_20.28W:
    - 37, 31
    GloFAS_63.82N_21.97W:
    - 45, 22
    GloFAS_63.42N_18.62W:
    - 32, 39
    GloFAS_63.38N_18.88W:
    - 32, 38
    GloFAS_63.38N_18.72W:
    - 32, 39
    GloFAS_63.38N_18.68W:
    - 32, 39
    GloFAS_63.38N_18.78W:
    - 32, 38
    GloFAS_66.52N_16.53W:
    - 67, 66
    GloFAS_66.42N_22.68W:
    - 79, 34
    GloFAS_66.42N_23.03W:
    - 80, 31
    GloFAS_66.38N_22.38W:
    - 78, 34
    GloFAS_66.38N_22.97W:
    - 79, 31
    GloFAS_66.38N_15.72W:
    - 64, 69
    GloFAS_66.38N_22.88W:
    - 79, 32
    GloFAS_66.32N_22.33W:
    - 77, 34
    GloFAS_63.42N_18.72W:
    - 32, 39
    GloFAS_63.92N_22.68W:
    - 47, 19
    GloFAS_63.92N_22.72W:
    - 47, 19
    GloFAS_63.92N_22.62W:
    - 47, 19
    GloFAS_63.88N_21.93W:
    - 45, 23
    GloFAS_63.88N_21.83W:
    - 45, 23
    GloFAS_63.88N_21.33W:
    - 44, 26
    GloFAS_63.88N_21.88W:
    - 45, 23
    GloFAS_63.88N_21.78W:
    - 44, 24
    GloFAS_63.88N_16.47W:
    - 33, 53
    GloFAS_63.82N_22.58W:
    - 46, 19
    GloFAS_63.82N_21.53W:
    - 44, 25
    GloFAS_63.82N_22.53W:
    - 46, 19
    GloFAS_63.82N_16.78W:
    - 33, 52
    GloFAS_63.62N_17.83W:
    - 32, 44
    GloFAS_63.82N_21.12W:
    - 42, 28
    GloFAS_63.82N_21.43W:
    - 44, 25
    GloFAS_65.67N_20.68W:
    - 64, 39
    GloFAS_63.82N_22.72W:
    - 46, 19
    GloFAS_63.82N_21.62W:
    - 44, 24
    GloFAS_63.82N_21.93W:
    - 45, 23
    GloFAS_63.82N_22.12W:
    - 45, 22
    GloFAS_63.82N_22.47W:
    - 46, 19
    GloFAS_63.77N_16.78W:
    - 33, 52
    GloFAS_63.77N_17.03W:
    - 33, 50
    GloFAS_63.77N_17.38W:
    - 34, 48
    GloFAS_63.77N_16.83W:
    - 33, 51
    GloFAS_63.77N_20.88W:
    - 42, 28
    GloFAS_63.72N_17.58W:
    - 34, 47
    GloFAS_63.82N_21.03W:
    - 42, 28
    GloFAS_65.97N_22.33W:
    - 72, 32
    GloFAS_65.97N_22.78W:
    - 73, 30
    GloFAS_65.97N_23.03W:
    - 74, 29
    GloFAS_65.92N_14.68W:
    - 57, 73
    GloFAS_65.92N_21.43W:
    - 70, 37
    GloFAS_65.92N_21.47W:
    - 70, 37
    GloFAS_65.92N_21.58W:
    - 70, 37
    GloFAS_65.92N_23.72W:
    - 74, 25
    GloFAS_65.92N_22.62W:
    - 73, 31
    GloFAS_65.92N_19.88W:
    - 66, 44
    GloFAS_65.88N_23.83W:
    - 74, 24
    GloFAS_65.88N_22.43W:
    - 72, 32
    GloFAS_65.88N_23.68W:
    - 74, 25
    GloFAS_66.32N_22.97W:
    - 79, 31
    GloFAS_65.82N_18.18W:
    - 63, 53
    GloFAS_65.82N_23.78W:
    - 74, 24
    GloFAS_65.77N_23.72W:
    - 74, 24
    GloFAS_65.77N_23.53W:
    - 74, 25
    GloFAS_65.77N_14.43W:
    - 54, 73
    GloFAS_65.77N_18.08W:
    - 63, 53
    GloFAS_65.77N_23.33W:
    - 74, 25
    GloFAS_65.77N_21.68W:
    - 68, 36
    GloFAS_65.77N_22.53W:
    - 72, 32
    GloFAS_65.77N_14.47W:
    - 54, 73
    GloFAS_65.72N_21.43W:
    - 67, 36
    GloFAS_65.72N_23.53W:
    - 74, 25
    GloFAS_65.72N_23.83W:
    - 73, 23
    GloFAS_65.72N_21.83W:
    - 68, 36
    GloFAS_65.82N_22.53W:
    - 72, 32
    GloFAS_66.32N_15.03W:
    - 62, 73
    GloFAS_66.27N_15.12W:
    - 62, 72
    GloFAS_66.27N_22.38W:
    - 76, 34
    GloFAS_66.27N_15.28W:
    - 62, 71
    GloFAS_66.22N_21.97W:
    - 75, 35
    GloFAS_66.22N_22.58W:
    - 75, 32
    GloFAS_66.22N_22.68W:
    - 76, 31
    GloFAS_66.17N_22.68W:
    - 75, 32
    GloFAS_66.17N_21.88W:
    - 74, 36
    GloFAS_66.17N_17.03W:
    - 64, 61
    GloFAS_66.17N_18.28W:
    - 66, 55
    GloFAS_66.12N_18.93W:
    - 67, 51
    GloFAS_66.12N_23.47W:
    - 77, 27
    GloFAS_65.97N_23.28W:
    - 75, 29
    GloFAS_66.12N_17.28W:
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    GloFAS_65.72N_14.78W:
    - 54, 72
    GloFAS_65.67N_23.33W:
    - 70, 27
    GloFAS_65.67N_23.97W:
    - 73, 22
    GloFAS_65.67N_23.43W:
    - 69, 25
    GloFAS_65.67N_23.38W:
    - 70, 27
    GloFAS_65.67N_23.58W:
    - 73, 23
    GloFAS_65.67N_21.53W:
    - 66, 35
    GloFAS_65.67N_14.28W:
    - 52, 74
    GloFAS_65.67N_23.83W:
    - 73, 23
    GloFAS_65.67N_20.72W:
    - 64, 39
    GloFAS_65.62N_20.72W:
    - 64, 39
    GloFAS_65.57N_23.43W:
    - 69, 24
    GloFAS_65.62N_20.62W:
    - 64, 39
    GloFAS_65.62N_23.43W:
    - 69, 25
    GloFAS_65.62N_23.93W:
    - 72, 22
    GloFAS_65.62N_22.83W:
    - 69, 28
    GloFAS_65.62N_22.72W:
    - 69, 28
    GloFAS_65.62N_22.58W:
    - 68, 29
    GloFAS_65.62N_20.83W:
    - 64, 38
    GloFAS_65.62N_24.03W:
    - 72, 22
    GloFAS_65.62N_23.47W:
    - 69, 24
    GloFAS_65.62N_22.88W:
    - 69, 28
    GloFAS_65.62N_23.88W:
    - 72, 22
    GloFAS_65.62N_23.97W:
    - 72, 22
    GloFAS_65.57N_20.62W:
    - 63, 39
    GloFAS_65.57N_22.97W:
    - 69, 26
    GloFAS_65.57N_22.22W:
    - 67, 31
    GloFAS_65.62N_24.22W:
    - 72, 20
    GloFAS_65.32N_21.28W:
    - 63, 35
    GloFAS_65.32N_13.78W:
    - 48, 75
    GloFAS_65.32N_13.83W:
    - 48, 75
    GloFAS_65.32N_13.88W:
    - 48, 75
    GloFAS_65.27N_21.08W:
    - 62, 36
    GloFAS_65.27N_21.22W:
    - 62, 36
    GloFAS_65.27N_22.38W:
    - 63, 28
    GloFAS_65.27N_22.43W:
    - 63, 28
    GloFAS_65.27N_13.72W:
    - 46, 75
    GloFAS_65.22N_21.18W:
    - 62, 36
    GloFAS_65.22N_13.83W:
    - 46, 75
    GloFAS_65.22N_21.03W:
    - 62, 36
    GloFAS_65.22N_13.78W:
    - 46, 75
    GloFAS_65.22N_21.08W:
    - 62, 36
    GloFAS_65.52N_22.78W:
    - 69, 28
    GloFAS_65.22N_21.12W:
    - 62, 36
    GloFAS_65.22N_13.88W:
    - 46, 75
    GloFAS_65.22N_13.72W:
    - 46, 75
    GloFAS_65.17N_21.08W:
    - 62, 36
    GloFAS_65.17N_13.78W:
    - 45, 75
    GloFAS_65.17N_13.72W:
    - 45, 75
    GloFAS_65.17N_13.97W:
    - 45, 75
    GloFAS_65.17N_13.83W:
    - 45, 75
    GloFAS_65.17N_22.47W:
    - 63, 27
    GloFAS_65.17N_21.72W:
    - 60, 31
    GloFAS_65.17N_21.12W:
    - 62, 36
    GloFAS_65.17N_13.88W:
    - 45, 75
    GloFAS_65.17N_13.68W:
    - 45, 75
    GloFAS_65.17N_21.88W:
    - 61, 30
    GloFAS_65.17N_13.93W:
    - 45, 75
    GloFAS_65.22N_13.68W:
    - 46, 76
    GloFAS_65.52N_23.22W:
    - 69, 25
    GloFAS_65.52N_23.18W:
    - 69, 25
    GloFAS_65.52N_22.38W:
    - 67, 29
    GloFAS_65.52N_22.43W:
    - 67, 29
    GloFAS_65.47N_23.47W:
    - 69, 24
    GloFAS_65.47N_22.28W:
    - 66, 30
    GloFAS_65.47N_22.12W:
    - 66, 30
    GloFAS_65.47N_20.97W:
    - 63, 37
    GloFAS_65.47N_22.18W:
    - 66, 30
    GloFAS_65.47N_13.62W:
    - 49, 76
    GloFAS_65.47N_22.33W:
    - 66, 30
    GloFAS_65.47N_21.83W:
    - 65, 32
    GloFAS_65.47N_21.38W:
    - 64, 34
    GloFAS_65.47N_23.58W:
    - 69, 23
    GloFAS_65.38N_21.08W:
    - 62, 36
    GloFAS_65.47N_24.03W:
    - 70, 21
    GloFAS_65.42N_21.68W:
    - 64, 34
    GloFAS_65.42N_13.68W:
    - 48, 76
    GloFAS_65.42N_21.78W:
    - 65, 32
    GloFAS_65.42N_22.22W:
    - 66, 30
    GloFAS_65.42N_20.97W:
    - 62, 36
    GloFAS_65.42N_21.72W:
    - 65, 32
    GloFAS_65.42N_20.93W:
    - 62, 36
    GloFAS_65.42N_23.78W:
    - 69, 22
    GloFAS_65.42N_21.08W:
    - 62, 36
    GloFAS_65.42N_21.43W:
    - 64, 34
    GloFAS_65.42N_23.68W:
    - 69, 22
    GloFAS_65.42N_23.72W:
    - 69, 22
    GloFAS_65.38N_21.18W:
    - 62, 36
    GloFAS_65.38N_21.03W:
    - 62, 36
    GloFAS_65.47N_23.97W:
    - 70, 21
    _convention: eta_rho, xi_rho
  coast_snap_buffer_km: null
  domain_edge_buffer: 20

Creating river forcing from an existing YAML file

%time the_same_river_forcing = RiverForcing.from_yaml(yaml_filepath)

2026-06-30 21:40:35 - INFO - Use provided river indices.
2026-06-30 21:40:35 - WARNING - No records found after the end_time: 2025-12-31 00:00:00.
2026-06-30 21:40:43 - INFO - Computing river forcing...

[########################################] | 100% Completed | 6.17 ss

2026-06-30 21:40:49 - INFO - Creating 212 synthetic river(s) to handle overlapping entries.
2026-06-30 21:41:02 - INFO - Simulation end time is after 2024; using RIVR2O values from 2024.
2026-06-30 21:41:03 - INFO - Partitioning RIVR2O export by discharge among rivers sharing grid cell(s): (307,318)x14, (311,330)x38, (312,327)x42, (308,316)x107, (311,324)x21, (311,323)x42, (311,321)x36, (307,323)x53, (307,319)x9, (311,319)x48, (308,329)x38, (309,331)x48, (310,317)x37, (311,315)x30, (311,316)x55, (312,329)x35, (310,315)x55, (307,320)x18, (312,328)x28, (309,314)x94, (311,314)x46, (310,332)x48, (311,313)x33, (311,312)x103, (312,314)x106, (311,320)x6, (310,318)x4.

CPU times: user 29.1 s, sys: 4.48 s, total: 33.5 s
Wall time: 31.8 s

Prescribing the river indices

If you prefer not to use the default behavior of ROMS-Tools, where a river is automatically assigned to the nearest coastal cell, you can manually specify the river indices. This may be useful in scenarios such as:

• A river is mistakenly assigned to the wrong fjord or estuary when multiple of these are located near the original river mouth location.

• You wish to define a river that spans multiple cells.

In these cases, you can provide ROMS-Tools with a custom indices dictionary when creating the RiverForcing.

Specifying valid river indices

To begin, let’s first use the dictionary that ROMS-Tools automatically generated for our river forcing above.

indices = river_forcing.indices
indices

{'GloFAS_63.77N_20.83W': [(42, 29)],
 'GloFAS_63.88N_21.22W': [(44, 27)],
 'overlap_65.62N_14.33W': [(52, 74)],
 'GloFAS_66.12N_16.68W': [(62, 63)],
 'overlap_64.42N_21.97W': [(52, 26)],
 'overlap_65.97N_17.58W': [(63, 57)],
 'overlap_65.67N_18.03W': [(63, 53)],
 'overlap_65.72N_19.43W': [(63, 46)],
 'overlap_63.52N_17.93W': [(32, 44)],
 'overlap_63.72N_17.53W': [(34, 47)],
 'overlap_63.52N_20.08W': [(36, 32)],
 'overlap_63.47N_18.18W': [(32, 42)],
 'GloFAS_63.67N_20.58W': [(40, 30)],
 'overlap_65.97N_17.43W': [(62, 58)],
 'overlap_65.67N_20.28W': [(64, 42)],
 'GloFAS_63.67N_17.78W': [(34, 46)],
 'overlap_63.77N_17.03W': [(33, 50)],
 'overlap_64.27N_14.93W': [(36, 65)],
 'overlap_65.72N_14.83W': [(55, 71)],
 'overlap_64.27N_15.22W': [(36, 63)],
 'overlap_65.62N_20.33W': [(63, 41)],
 'overlap_64.77N_14.03W': [(41, 72)],
 'overlap_65.52N_20.62W': [(63, 39)],
 'overlap_63.77N_16.83W': [(33, 51)],
 'overlap_65.17N_21.03W': [(62, 36)],
 'overlap_63.77N_17.38W': [(34, 48)],
 'overlap_65.82N_14.83W': [(56, 72)],
 'overlap_63.97N_16.33W': [(34, 55)],
 'overlap_63.38N_18.78W': [(32, 38)],
 'overlap_63.77N_17.28W': [(34, 49)],
 'overlap_65.77N_19.38W': [(64, 47)],
 'overlap_64.12N_21.78W': [(48, 25)],
 'overlap_65.77N_22.53W': [(72, 32)],
 'overlap_65.72N_21.62W': [(68, 36)],
 'overlap_64.07N_16.12W': [(35, 57)],
 'overlap_66.12N_15.12W': [(61, 71)],
 'overlap_65.02N_21.78W': [(59, 30)],
 'overlap_65.57N_13.88W': [(51, 75)],
 'overlap_65.92N_18.47W': [(64, 52)],
 'overlap_63.47N_19.38W': [(34, 35)],
 'overlap_64.32N_21.62W': [(50, 27)],
 'overlap_66.22N_15.58W': [(62, 69)],
 'overlap_64.52N_22.18W': [(54, 25)],
 'overlap_64.02N_16.18W': [(35, 56)],
 'overlap_64.62N_14.43W': [(39, 68)],
 'overlap_63.42N_19.18W': [(33, 36)],
 'overlap_66.07N_16.93W': [(63, 62)],
 'overlap_66.17N_16.43W': [(63, 64)],
 'overlap_65.12N_21.08W': [(60, 31)],
 'overlap_66.02N_15.03W': [(59, 71)],
 'GloFAS_64.17N_15.72W': [(36, 60)],
 'overlap_66.07N_17.88W': [(65, 56)],
 'overlap_64.22N_15.38W': [(36, 61)],
 'overlap_64.52N_14.47W': [(38, 68)],
 'overlap_64.57N_22.33W': [(55, 25)],
 'overlap_64.32N_21.97W': [(51, 25)],
 'overlap_63.88N_16.47W': [(33, 53)],
 'overlap_65.52N_22.78W': [(69, 28)],
 'overlap_65.07N_13.62W': [(44, 75)],
 'overlap_64.77N_22.43W': [(58, 25)],
 'overlap_66.07N_21.72W': [(73, 36)],
 'overlap_65.52N_23.08W': [(69, 26)],
 'overlap_64.67N_14.38W': [(40, 69)],
 'GloFAS_64.17N_15.83W': [(36, 59)],
 'overlap_65.72N_23.43W': [(74, 25)],
 'overlap_63.38N_18.68W': [(32, 39)],
 'overlap_63.97N_22.08W': [(47, 23)],
 'overlap_64.12N_15.93W': [(36, 58)],
 'overlap_65.88N_23.28W': [(75, 26)],
 'overlap_64.77N_22.68W': [(59, 24)],
 'GloFAS_64.38N_14.78W': [(37, 66)],
 'overlap_65.62N_22.93W': [(70, 27)],
 'overlap_63.42N_18.47W': [(32, 40)],
 'GloFAS_66.02N_22.43W': [(73, 33)],
 'overlap_63.62N_20.38W': [(38, 31)],
 'overlap_65.62N_21.43W': [(66, 35)],
 'GloFAS_63.52N_19.88W': [(36, 33)],
 'overlap_65.57N_22.43W': [(68, 29)],
 'overlap_65.52N_22.12W': [(67, 31)],
 'overlap_65.82N_23.18W': [(74, 29)],
 'overlap_65.47N_13.62W': [(49, 76)],
 'overlap_63.52N_19.58W': [(35, 34)],
 'overlap_63.97N_22.38W': [(47, 21)],
 'overlap_63.82N_21.97W': [(45, 22)],
 'overlap_65.82N_22.62W': [(73, 30)],
 'overlap_66.07N_18.53W': [(66, 52)],
 'overlap_66.07N_18.97W': [(66, 50)],
 'overlap_66.12N_15.47W': [(61, 69)],
 'overlap_66.38N_15.83W': [(65, 68)],
 'overlap_64.92N_13.72W': [(42, 74)],
 'overlap_66.22N_16.43W': [(64, 65)],
 'overlap_65.88N_19.88W': [(66, 44)],
 'overlap_65.47N_23.47W': [(69, 24)],
 'overlap_65.32N_21.93W': [(64, 31)],
 'overlap_65.12N_21.88W': [(61, 30)],
 'overlap_63.88N_21.33W': [(44, 26)],
 'overlap_65.57N_23.78W': [(72, 22)],
 'overlap_66.17N_22.03W': [(75, 35)],
 'overlap_65.32N_13.78W': [(48, 75)],
 'overlap_65.42N_21.43W': [(64, 34)],
 'overlap_66.22N_22.53W': [(76, 34)],
 'GloFAS_64.32N_15.38W': [(37, 62)],
 'overlap_65.97N_14.88W': [(58, 72)],
 'GloFAS_65.17N_22.38W': [(62, 28)],
 'overlap_66.12N_22.58W': [(75, 32)],
 'overlap_65.88N_22.58W': [(73, 31)],
 'overlap_65.77N_14.43W': [(54, 73)],
 'overlap_66.17N_21.78W': [(74, 36)],
 'GloFAS_65.77N_19.68W': [(64, 45)],
 'overlap_63.82N_21.62W': [(44, 24)],
 'overlap_66.02N_19.28W': [(67, 49)],
 'overlap_65.67N_23.58W': [(73, 23)],
 'overlap_65.32N_21.28W': [(63, 35)],
 'overlap_65.97N_23.68W': [(76, 26)],
 'overlap_65.52N_23.93W': [(71, 21)],
 'overlap_64.77N_22.93W': [(59, 23)],
 'overlap_65.52N_21.33W': [(65, 35)],
 'overlap_66.47N_16.33W': [(67, 66)],
 'overlap_65.92N_18.12W': [(64, 54)],
 'overlap_65.02N_21.88W': [(59, 29)],
 'overlap_64.82N_23.28W': [(61, 21)],
 'overlap_66.12N_18.68W': [(67, 52)],
 'overlap_66.07N_22.38W': [(74, 33)],
 'overlap_65.97N_21.53W': [(71, 37)],
 'overlap_66.27N_22.47W': [(77, 34)],
 'overlap_65.07N_14.22W': [(45, 75)],
 'overlap_65.97N_23.28W': [(75, 29)],
 'overlap_63.42N_19.03W': [(33, 37)],
 'overlap_65.42N_21.72W': [(65, 32)],
 'overlap_65.52N_23.18W': [(69, 25)],
 'overlap_66.22N_15.33W': [(62, 71)],
 'overlap_66.07N_23.38W': [(77, 28)],
 'overlap_66.47N_15.97W': [(66, 68)],
 'overlap_65.82N_21.33W': [(69, 37)],
 'overlap_65.02N_13.62W': [(43, 75)],
 'overlap_65.17N_14.03W': [(47, 74)],
 'overlap_65.62N_20.62W': [(64, 39)],
 'overlap_63.82N_21.43W': [(44, 25)],
 'GloFAS_65.27N_22.22W': [(63, 29)],
 'overlap_65.42N_22.22W': [(66, 30)],
 'overlap_64.97N_22.58W': [(61, 25)],
 'overlap_65.77N_20.28W': [(66, 42)],
 'overlap_65.47N_23.93W': [(70, 21)],
 'overlap_66.02N_20.38W': [(69, 43)],
 'overlap_64.92N_23.08W': [(61, 23)],
 'overlap_66.38N_22.83W': [(79, 32)],
 'overlap_66.07N_23.12W': [(76, 29)],
 'overlap_64.88N_23.62W': [(62, 19)],
 'overlap_65.22N_13.72W': [(46, 75)],
 'overlap_66.38N_22.38W': [(78, 34)],
 'overlap_66.32N_22.97W': [(79, 31)],
 'overlap_64.82N_13.88W': [(41, 73)],
 'overlap_65.02N_22.18W': [(60, 27)],
 'overlap_66.32N_22.58W': [(78, 33)],
 'overlap_66.12N_23.47W': [(77, 27)],
 'overlap_65.47N_23.58W': [(69, 23)],
 'overlap_65.92N_21.43W': [(70, 37)],
 'GloFAS_66.02N_19.43W': [(67, 48)],
 'overlap_66.02N_17.33W': [(63, 59)],
 'overlap_65.97N_19.93W': [(67, 44)],
 'GloFAS_65.02N_22.58W': [(61, 26)],
 'GloFAS_65.52N_22.03W': [(66, 32)],
 'overlap_66.12N_18.88W': [(67, 51)],
 'overlap_63.88N_22.68W': [(47, 19)],
 'GloFAS_65.72N_14.38W': [(53, 73)],
 'overlap_66.17N_18.12W': [(66, 55)],
 'overlap_66.17N_22.78W': [(76, 31)],
 'overlap_66.02N_14.62W': [(58, 73)],
 'overlap_65.77N_23.68W': [(74, 24)],
 'overlap_66.07N_20.08W': [(69, 44)],
 'overlap_63.97N_22.53W': [(48, 20)],
 'overlap_63.77N_20.88W': [(42, 28)],
 'overlap_66.17N_16.97W': [(64, 61)],
 'overlap_64.72N_14.28W': [(40, 70)],
 'overlap_65.12N_22.08W': [(61, 29)],
 'overlap_65.47N_20.97W': [(63, 37)],
 'overlap_66.27N_15.72W': [(63, 68)],
 'overlap_66.47N_16.22W': [(66, 67)],
 'GloFAS_64.67N_22.28W': [(56, 26)],
 'overlap_64.47N_22.08W': [(53, 25)],
 'overlap_66.12N_17.22W': [(64, 60)],
 'overlap_66.27N_14.93W': [(62, 73)],
 'overlap_64.77N_23.62W': [(61, 19)],
 'overlap_64.22N_21.72W': [(49, 26)],
 'GloFAS_66.12N_15.18W': [(60, 70)],
 'overlap_65.42N_23.68W': [(69, 22)],
 'overlap_65.57N_24.33W': [(72, 20)],
 'overlap_64.88N_23.43W': [(61, 20)],
 'overlap_63.97N_22.18W': [(47, 22)],
 'overlap_64.82N_23.97W': [(62, 17)],
 'overlap_63.82N_22.47W': [(46, 19)],
 'overlap_64.42N_14.53W': [(37, 67)],
 'overlap_65.72N_21.38W': [(67, 36)],
 'overlap_65.17N_22.43W': [(63, 27)],
 'overlap_65.38N_13.68W': [(48, 76)],
 'overlap_66.22N_22.88W': [(77, 31)],
 'overlap_64.72N_22.33W': [(57, 26)],
 'GloFAS_65.97N_20.38W': [(68, 43)],
 'overlap_63.82N_21.93W': [(45, 23)],
 'GloFAS_65.02N_22.12W': [(59, 28)],
 'overlap_64.67N_14.18W': [(40, 71)],
 'overlap_65.92N_14.62W': [(57, 73)],
 'overlap_64.72N_23.68W': [(61, 18)],
 'overlap_65.27N_22.33W': [(63, 28)],
 'GloFAS_63.92N_16.43W': [(34, 54)],
 'overlap_64.92N_23.83W': [(62, 18)],
 'overlap_63.88N_21.03W': [(43, 28)],
 'GloFAS_65.97N_19.43W': [(66, 48)],
 'overlap_66.02N_21.68W': [(72, 37)],
 'overlap_64.07N_21.93W': [(47, 24)],
 'overlap_65.52N_24.33W': [(72, 19)],
 'overlap_65.77N_23.97W': [(74, 22)],
 'overlap_65.22N_13.68W': [(46, 76)],
 'overlap_64.97N_23.12W': [(62, 22)],
 'overlap_65.72N_14.68W': [(54, 72)],
 'overlap_66.32N_15.68W': [(64, 69)],
 'overlap_65.88N_20.33W': [(67, 43)],
 'overlap_65.82N_19.68W': [(65, 45)],
 'overlap_66.42N_22.58W': [(79, 34)],
 'GloFAS_64.92N_23.53W': [(62, 20)],
 'GloFAS_63.88N_22.33W': [(46, 21)],
 'overlap_65.67N_23.97W': [(73, 22)],
 'overlap_63.77N_16.72W': [(33, 52)],
 'overlap_65.52N_22.38W': [(67, 29)],
 'GloFAS_66.02N_18.53W': [(65, 52)],
 'overlap_66.42N_23.03W': [(80, 31)],
 'overlap_63.97N_22.62W': [(48, 19)],
 'overlap_64.72N_23.83W': [(61, 17)],
 'GloFAS_65.52N_24.22W': [(71, 20)],
 'overlap_66.17N_14.97W': [(61, 72)],
 'overlap_66.32N_22.78W': [(78, 32)],
 'overlap_63.42N_20.28W': [(37, 31)],
 'overlap_66.07N_18.28W': [(65, 54)],
 'overlap_66.27N_15.03W': [(62, 72)],
 'overlap_65.77N_20.18W': [(65, 43)],
 'overlap_66.38N_16.47W': [(66, 65)],
 'GloFAS_66.32N_16.43W': [(65, 65)],
 'overlap_64.97N_22.88W': [(61, 24)],
 'overlap_66.07N_17.72W': [(64, 57)],
 'overlap_66.02N_19.97W': [(68, 44)],
 'overlap_65.57N_20.93W': [(64, 38)],
 'GloFAS_64.82N_23.12W': [(60, 22)],
 'GloFAS_63.47N_18.12W': [(32, 43)],
 'GloFAS_65.32N_22.12W': [(63, 30)],
 'GloFAS_65.42N_23.93W': [(69, 21)],
 'GloFAS_65.88N_19.38W': [(65, 48)],
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 'GloFAS_64.92N_23.08W': [(61, 23)],
 'GloFAS_64.92N_13.72W': [(42, 74)],
 'GloFAS_64.92N_13.78W': [(42, 74)],
 'GloFAS_64.92N_13.83W': [(42, 74)],
 'GloFAS_64.92N_14.03W': [(41, 72)],
 'GloFAS_64.92N_22.58W': [(59, 24)],
 'GloFAS_64.88N_13.83W': [(41, 73)],
 'GloFAS_64.88N_13.93W': [(41, 73)],
 'GloFAS_65.72N_21.38W': [(67, 36)],
 'GloFAS_64.88N_24.03W': [(62, 17)],
 'GloFAS_64.88N_23.68W': [(62, 19)],
 'GloFAS_64.88N_23.47W': [(61, 20)],
 'GloFAS_64.82N_13.88W': [(41, 73)],
 'GloFAS_64.82N_13.93W': [(41, 73)],
 'GloFAS_64.82N_22.83W': [(59, 23)],
 'GloFAS_64.82N_22.78W': [(59, 24)],
 'GloFAS_64.82N_23.68W': [(61, 19)],
 'GloFAS_64.82N_22.43W': [(58, 25)],
 'GloFAS_64.77N_22.68W': [(59, 24)],
 'GloFAS_64.77N_22.62W': [(58, 25)],
 'GloFAS_64.77N_23.83W': [(61, 18)],
 'GloFAS_64.77N_23.88W': [(61, 17)],
 'GloFAS_64.77N_22.38W': [(57, 26)],
 'GloFAS_64.77N_23.62W': [(61, 19)],
 'GloFAS_64.88N_23.33W': [(61, 21)],
 'GloFAS_65.12N_22.12W': [(61, 29)],
 'GloFAS_65.12N_13.58W': [(44, 75)],
 'GloFAS_65.12N_13.68W': [(44, 75)],
 'GloFAS_65.07N_14.22W': [(45, 75)],
 'GloFAS_65.07N_22.38W': [(60, 27)],
 'GloFAS_65.07N_22.72W': [(61, 25)],
 'GloFAS_65.07N_13.97W': [(44, 75)],
 'GloFAS_65.02N_22.28W': [(60, 27)],
 'GloFAS_65.02N_14.18W': [(42, 74)],
 'GloFAS_65.02N_14.22W': [(41, 72)],
 'GloFAS_65.02N_13.93W': [(42, 74)],
 'GloFAS_65.02N_13.97W': [(42, 74)],
 'GloFAS_65.02N_13.88W': [(42, 74)],
 'GloFAS_65.02N_14.12W': [(42, 74)],
 'GloFAS_64.92N_23.12W': [(61, 23)],
 'GloFAS_65.02N_13.78W': [(43, 75)],
 'GloFAS_65.02N_13.83W': [(43, 75)],
 'GloFAS_65.02N_21.83W': [(59, 30)],
 'GloFAS_65.02N_22.83W': [(61, 24)],
 'GloFAS_65.02N_22.72W': [(61, 25)],
 'GloFAS_65.02N_22.68W': [(61, 25)],
 'GloFAS_64.97N_13.88W': [(42, 74)],
 'GloFAS_64.97N_14.08W': [(42, 74)],
 ...}

Note that the when we use indices to identify rivers, roms-tools will still need to match those indices to the source discharge dataset, if no source is provided, it will default to Dai & Trenberth.

yet_the_same_river_forcing = RiverForcing(
    grid=grid, start_time=start_time, end_time=end_time, indices=indices,
    source = {
    "name": "GLOFAS",
    "path": '/anvil/projects/x-ees250129/Datasets/Rivers/processed_GloFAS/glofas_v4_rivers_daily.nc',
        }
)

2026-06-30 21:41:05 - INFO - Use provided river indices.
2026-06-30 21:41:05 - WARNING - No records found after the end_time: 2025-12-31 00:00:00.
2026-06-30 21:41:11 - INFO - Computing river forcing...

[########################################] | 100% Completed | 5.66 ss

2026-06-30 21:41:17 - INFO - Creating 212 synthetic river(s) to handle overlapping entries.

If the indices are specified, ROMS-Tools will not modify them. This is confirmed by the following plot, where the original river locations align exactly with the updated ones.

yet_the_same_river_forcing.plot_locations()

2026-06-30 21:41:21 - WARNING - Only the first 20 rivers will be plotted (received 1194).

Next, let’s define some multi-cell rivers. The convention for specifying river locations is to use tuples in the format (eta_rho, xi_rho).

multi_cell_indices = {
    "Hvita(Olfusa)": [(43, 28)],
    "Thjorsa": [(41, 30)],
    "JkulsFjll": [(62, 63)],
    "Lagarfljot": [(52, 74)],
    "Bruara": [(42, 29)],
    "Svarta": [(63, 46), (64, 45), (65, 45), (64, 47)],
}

multi_cell_river_forcing = RiverForcing(
    grid=grid, start_time=start_time, end_time=end_time, indices=multi_cell_indices
)

2026-06-30 21:41:21 - INFO - Use provided river indices.
2026-06-30 21:41:22 - WARNING - No records found after the end_time: 2025-12-31 00:00:00.
2026-06-30 21:41:22 - INFO - Compute climatology for river forcing.
2026-06-30 21:41:22 - INFO - Computing river forcing...

multi_cell_river_forcing.plot_locations()

Let’s verify that the rivers are also correctly represented in the river_index and river_fraction variables.

for var_name in ["river_index", "river_fraction"]:
    non_zero_values = multi_cell_river_forcing.ds[var_name].values
    non_zero_values = non_zero_values[non_zero_values != 0].tolist()
    print(var_name)
    print(non_zero_values)

river_index
[2.0, 5.0, 1.0, 4.0, 3.0, 6.0, 6.0, 6.0, 6.0]
river_fraction
[1.0, 1.0, 1.0, 1.0, 1.0, 0.25, 0.25, 0.25, 0.25]

You can see that the river with ID 6 (Svarta, shown in brown in the figure above) is now distributed across four grid cells. Each of these grid cells carries a volume fraction of 0.25, accurately reflecting the distribution of the river’s flux.

We can write and read multi-cell rivers to and from YAML as usual.

multi_cell_yaml_filepath = (
    "/anvil/projects/x-ees250129/x-uheede/my_multi_cell_river_forcing.yaml"
)

multi_cell_river_forcing.to_yaml(multi_cell_yaml_filepath)

# Open and read the YAML file
with open(multi_cell_yaml_filepath, "r") as file:
    file_contents = file.read()

# Print the contents
print(file_contents)

---
roms_tools_version: 3.6.1.dev24+g32e13b12f
---
Grid:
  nx: 100
  ny: 100
  size_x: 800
  size_y: 800
  center_lon: -18
  center_lat: 65
  rot: 20
  N: 100
  theta_s: 5.0
  theta_b: 2.0
  hc: 300.0
  topography_source:
    name: ETOPO5
  mask_shapefile: null
  close_narrow_channels: false
  hmin: 5.0
  filename: null
RiverForcing:
  start_time: '2015-01-01T00:00:00'
  end_time: '2025-12-31T00:00:00'
  source:
    name: DAI
    climatology: false
  convert_to_climatology: if_any_missing
  include_bgc: false
  bgc_source: null
  model_reference_date: '2000-01-01T00:00:00'
  indices:
    Hvita(Olfusa):
    - 43, 28
    Thjorsa:
    - 41, 30
    JkulsFjll:
    - 62, 63
    Lagarfljot:
    - 52, 74
    Bruara:
    - 42, 29
    Svarta:
    - 63, 46
    - 64, 45
    - 65, 45
    - 64, 47
    _convention: eta_rho, xi_rho
  coast_snap_buffer_km: null
  domain_edge_buffer: 20

the_same_multi_cell_river_forcing = RiverForcing.from_yaml(multi_cell_yaml_filepath)

2026-06-30 21:41:24 - INFO - Use provided river indices.
2026-06-30 21:41:24 - WARNING - No records found after the end_time: 2025-12-31 00:00:00.
2026-06-30 21:41:24 - INFO - Compute climatology for river forcing.
2026-06-30 21:41:24 - INFO - Computing river forcing...

Specifying invalid river indices

As discussed above, each river must be located in one or more coastal cells—meaning they must be on land but adjacent to a wet point. If the specified indices do not meet this requirement, attempting to create a RiverForcing object will result in an error, as seen in the next example.

invalid_cell_indices = {
    "Hvita(Olfusa)": [(0, 28)],  # (eta_rho, xi_rho) = (0, 28) is not a coastal point
    "Thjorsa": [(41, 30)],
    "JkulsFjll": [(62, 63)],
    "Lagarfljot": [(52, 74)],
    "Bruara": [(42, 29)],
    "Svarta": [(63, 46), (64, 45), (65, 45), (64, 47)],
}

RiverForcing(
    grid=grid, start_time=start_time, end_time=end_time, indices=invalid_cell_indices
)

2026-06-30 21:41:24 - INFO - Use provided river indices.

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[48], line 1
----> 1 RiverForcing(
      2     grid=grid, start_time=start_time, end_time=end_time, indices=invalid_cell_indices
      3 )

File <string>:14, in __create_fn__.<locals>.__init__(self, grid, start_time, end_time, source, convert_to_climatology, include_bgc, bgc_source, model_reference_date, indices, coast_snap_buffer_km, domain_edge_buffer)

File /anvil/projects/x-ees250129/x-awyatt1/roms-tools/roms_tools/setup/river_forcing.py:315, in RiverForcing.__post_init__(self)
    311     source_indices = {
    312         k: v for k, v in self.indices.items() if not k.startswith("overlap_")
    313     }
    314     self.indices = source_indices
--> 315     check_river_locations_are_along_coast(self.grid.ds.mask_rho, source_indices)
    316     data.extract_named_rivers(source_indices)
    318 ds = self._create_river_forcing(data)

File /anvil/projects/x-ees250129/x-awyatt1/roms-tools/roms_tools/setup/river_forcing.py:1469, in check_river_locations_are_along_coast(mask, indices)
   1467 # Check if the river location is along the coast
   1468 if not coast[eta_rho, xi_rho]:
-> 1469     raise ValueError(
   1470         f"River `{key}` is not located on the coast at grid cell ({eta_rho}, {xi_rho})."
   1471     )

ValueError: River `Hvita(Olfusa)` is not located on the coast at grid cell (0, 28).

Overlapping Rivers

What happens when two or more rivers share the same grid cell, either due to user-specified indices or those automatically inferred by ROMS-Tools?

Note that the river_index and river_fraction variables only allow one river per grid cell.

ROMS-Tools handles this situation gracefully by applying the following logic for each overlapping cell:

• A new synthetic river is created using a volume-weighted sum of the overlapping rivers’ volume and tracer contributions.

• The original rivers are adjusted: their volume contributions are reduced proportionally to account for the removal of the shared cell.

• The new river is appended to the dataset.

These synthesized rivers will appear in the dataset with names like overlap_1, overlap_2, etc.

As an example, we’ll revisit the Iceland domain used above. By coarsening the grid resolution, we force two rivers to occupy the same grid cell, triggering the overlap handling logic.

grid = Grid(nx=50, ny=50, size_x=800, size_y=800, center_lon=-18, center_lat=65, rot=20)

river_forcing_with_overlapping_rivers = RiverForcing(
    grid=grid,
    start_time=start_time,
    end_time=end_time,
)

2026-06-30 21:44:38 - INFO - No river indices provided. Identifying all rivers within the ROMS domain and assigning each to the nearest coastal point.
2026-06-30 21:44:38 - WARNING - No records found after the end_time: 2025-12-31 00:00:00.
2026-06-30 21:44:38 - INFO - Compute climatology for river forcing.
2026-06-30 21:44:39 - INFO - Computing river forcing...
2026-06-30 21:44:39 - INFO - Creating 1 synthetic river(s) to handle overlapping entries.

river_forcing_with_overlapping_rivers.plot_locations()

The rivers Thjorsa (orange) and Bruara (purple) fall onto the same grid cell after Bruara is re-routed to the coast. As a result, ROMS-Tools automatically introduces a new synthetic river, overlap_1 (pink), to represent their combined contribution.

river_forcing_with_overlapping_rivers.ds

<xarray.Dataset> Size: 25kB
Dimensions:           (river_time: 12, nriver: 7, ntracers: 2, eta_rho: 52,
                       xi_rho: 52)
Coordinates:
    month             (river_time) int64 96B 1 2 3 4 5 6 7 8 9 10 11 12
    river_name        (nriver) <U15 420B 'overlap_Bruara' ... 'Thjorsa'
    abs_time          (river_time) datetime64[ns] 96B 2000-01-16 ... 2000-12-15
  * river_time        (river_time) float64 96B 15.0 45.0 74.0 ... 319.0 349.0
    tracer_name       (ntracers) <U4 32B 'temp' 'salt'
    tracer_unit       (ntracers) <U15 120B 'degrees Celsius' 'PSU'
    tracer_long_name  (ntracers) <U21 168B 'potential temperature' 'salinity'
  * nriver            (nriver) int64 56B 1 2 3 4 5 6 7
Dimensions without coordinates: ntracers, eta_rho, xi_rho
Data variables:
    river_volume      (river_time, nriver) float64 672B 311.3 396.0 ... 0.0 0.0
    river_tracer      (ntracers, river_time, nriver) float64 1kB 15.62 ... 1.0
    river_index       (eta_rho, xi_rho) float32 11kB 0.0 0.0 0.0 ... 0.0 0.0 0.0
    river_fraction    (eta_rho, xi_rho) float32 11kB 0.0 0.0 0.0 ... 0.0 0.0 0.0
Attributes:
    climatology:  True

xarray.Dataset

• Dimensions:
  • river_time: 12
  • nriver: 7
  • ntracers: 2
  • eta_rho: 52
  • xi_rho: 52
• Coordinates: (8)
  • month
    (river_time)
    int64
    1 2 3 4 5 6 7 8 9 10 11 12

    long_name :

    Month index (1-12)

    array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])

  • river_name
    (nriver)
    <U15
    'overlap_Bruara' ... 'Thjorsa'

    long_name :

    River name

    array(['overlap_Bruara', 'Hvita(Olfusa)', 'JkulsFjll', 'Lagarfljot',
           'Svarta', 'Bruara', 'Thjorsa'], dtype='<U15')

  • abs_time
    (river_time)
    datetime64[ns]
    2000-01-16 ... 2000-12-15

    long_name :

    absolute time

    array(['2000-01-16T00:00:00.000000000', '2000-02-15T00:00:00.000000000',
           '2000-03-15T00:00:00.000000000', '2000-04-15T00:00:00.000000000',
           '2000-05-15T00:00:00.000000000', '2000-06-15T00:00:00.000000000',
           '2000-07-15T00:00:00.000000000', '2000-08-15T00:00:00.000000000',
           '2000-09-15T00:00:00.000000000', '2000-10-15T00:00:00.000000000',
           '2000-11-15T00:00:00.000000000', '2000-12-15T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • river_time
    (river_time)
    float64
    15.0 45.0 74.0 ... 319.0 349.0

    cycle_length :

    365.25

    long_name :

    relative time: days since 2000-01-01 00:00:00

    units :

    days

    array([ 15.,  45.,  74., 105., 135., 166., 196., 227., 258., 288., 319., 349.])

  • tracer_name
    (ntracers)
    <U4
    'temp' 'salt'

    long_name :

    Tracer name

    array(['temp', 'salt'], dtype='<U4')

  • tracer_unit
    (ntracers)
    <U15
    'degrees Celsius' 'PSU'

    long_name :

    Tracer concentration unit

    array(['degrees Celsius', 'PSU'], dtype='<U15')

  • tracer_long_name
    (ntracers)
    <U21
    'potential temperature' 'salinity'

    long_name :

    Tracer long name

    array(['potential temperature', 'salinity'], dtype='<U21')

  • nriver
    (nriver)
    int64
    1 2 3 4 5 6 7

    long_name :

    River ID (1-based Fortran indexing)

    array([1, 2, 3, 4, 5, 6, 7])

• Data variables: (4)
  • river_volume
    (river_time, nriver)
    float64
    311.3 396.0 112.2 ... 8.47 0.0 0.0

    long_name :

    River volume flux

    units :

    m^3/s

    array([[311.25608444, 396.012146  , 112.24038696,  51.13676453,
              8.03249836,   0.        ,   0.        ],
           [321.05844116, 407.87805176, 114.64615631,  52.78458405,
              8.18966579,   0.        ,   0.        ],
           [326.65678787, 396.92681885, 121.67308044,  53.98734665,
              8.50816727,   0.        ,   0.        ],
           [334.80675125, 410.82925415, 149.69039917,  71.63630676,
             11.15483093,   0.        ,   0.        ],
           [542.78608704, 446.65853882, 218.48077393, 178.76750183,
             17.1210022 ,   0.        ,   0.        ],
           [623.28445053, 442.60714722, 230.40383911, 311.78033447,
             15.19416618,   0.        ,   0.        ],
           [516.33998871, 384.58535767, 323.67306519, 209.07264709,
              9.38349915,   0.        ,   0.        ],
           [491.2128067 , 384.58535767, 352.25      , 124.14719391,
              8.35650063,   0.        ,   0.        ],
           [383.74579239, 363.77908325, 243.37924194, 111.43669891,
              8.79949951,   0.        ,   0.        ],
           [338.20005798, 383.53659058, 164.69230652, 107.82298279,
              9.4851675 ,   0.        ,   0.        ],
           [315.75859451, 384.17999268, 134.09616089,  85.8888092 ,
              8.80366611,   0.        ,   0.        ],
           [309.76570511, 380.7371521 , 119.73376465,  67.66781616,
              8.47016716,   0.        ,   0.        ]])

  • river_tracer
    (ntracers, river_time, nriver)
    float64
    15.62 15.62 15.62 ... 1.0 1.0 1.0

    long_name :

    River tracer data

    array([[[15.61593437, 15.61593437, 15.61593437, 15.61593437,
             15.61593437, 15.61593437, 15.61593437],
            [15.61593437, 15.61593437, 15.61593437, 15.61593437,
             15.61593437, 15.61593437, 15.61593437],
            [15.61593437, 15.61593437, 15.61593437, 15.61593437,
             15.61593437, 15.61593437, 15.61593437],
            [15.61593437, 15.61593437, 15.61593437, 15.61593437,
             15.61593437, 15.61593437, 15.61593437],
            [15.61593437, 15.61593437, 15.61593437, 15.61593437,
             15.61593437, 15.61593437, 15.61593437],
            [15.61593437, 15.61593437, 15.61593437, 15.61593437,
             15.61593437, 15.61593437, 15.61593437],
            [15.61593437, 15.61593437, 15.61593437, 15.61593437,
             15.61593437, 15.61593437, 15.61593437],
            [15.61593437, 15.61593437, 15.61593437, 15.61593437,
             15.61593437, 15.61593437, 15.61593437],
            [15.61593437, 15.61593437, 15.61593437, 15.61593437,
             15.61593437, 15.61593437, 15.61593437],
            [15.61593437, 15.61593437, 15.61593437, 15.61593437,
             15.61593437, 15.61593437, 15.61593437],
    ...
            [ 1.        ,  1.        ,  1.        ,  1.        ,
              1.        ,  1.        ,  1.        ],
            [ 1.        ,  1.        ,  1.        ,  1.        ,
              1.        ,  1.        ,  1.        ],
            [ 1.        ,  1.        ,  1.        ,  1.        ,
              1.        ,  1.        ,  1.        ],
            [ 1.        ,  1.        ,  1.        ,  1.        ,
              1.        ,  1.        ,  1.        ],
            [ 1.        ,  1.        ,  1.        ,  1.        ,
              1.        ,  1.        ,  1.        ],
            [ 1.        ,  1.        ,  1.        ,  1.        ,
              1.        ,  1.        ,  1.        ],
            [ 1.        ,  1.        ,  1.        ,  1.        ,
              1.        ,  1.        ,  1.        ],
            [ 1.        ,  1.        ,  1.        ,  1.        ,
              1.        ,  1.        ,  1.        ],
            [ 1.        ,  1.        ,  1.        ,  1.        ,
              1.        ,  1.        ,  1.        ],
            [ 1.        ,  1.        ,  1.        ,  1.        ,
              1.        ,  1.        ,  1.        ]]])

  • river_index
    (eta_rho, xi_rho)
    float32
    0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0

    long_name :

    River ID

    units :

    none

    array([[0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           ...,
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.]], shape=(52, 52), dtype=float32)

  • river_fraction
    (eta_rho, xi_rho)
    float32
    0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0

    long_name :

    River volume fraction

    units :

    none

    array([[0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           ...,
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.]], shape=(52, 52), dtype=float32)

• Indexes: (2)
  • river_time
    PandasIndex

    PandasIndex(Index([15.000000000000002,               45.0,               74.0,
                        105.0,              135.0,              166.0,
                        196.0,              227.0,              258.0,
                        288.0,              319.0,              349.0],
          dtype='float64', name='river_time'))

  • nriver
    PandasIndex

    PandasIndex(Index([1, 2, 3, 4, 5, 6, 7], dtype='int64', name='nriver'))

• Attributes: (1)

  climatology :

  True

The figure below confirms that the combined volume of Thjorsa and Bruara has been transferred to overlap_1. Meanwhile, the updated volume for both Thjorsa and Bruara is now zero. (Compare this to the corresponding figure above to see the original values!)

river_forcing_with_overlapping_rivers.plot("river_volume")

Note

When writing river forcing data with overlapping rivers to YAML, the overlap_* indices are not included in the YAML file. However, if you recreate the river forcing from that YAML file, the overlapping rivers will be automatically reconstructed. This ensures reproducibility is always maintained.

overlapping_rivers_yaml_filepath = (
    "/anvil/projects/x-ees250129/x-uheede//my_overlapping_river_forcing.yaml"
)

river_forcing_with_overlapping_rivers.to_yaml(overlapping_rivers_yaml_filepath)

# Open and read the YAML file
with open(overlapping_rivers_yaml_filepath, "r") as file:
    file_contents = file.read()

# Print the contents
print(file_contents)

---
roms_tools_version: 3.6.1.dev24+g32e13b12f
---
Grid:
  nx: 50
  ny: 50
  size_x: 800
  size_y: 800
  center_lon: -18
  center_lat: 65
  rot: 20
  N: 100
  theta_s: 5.0
  theta_b: 2.0
  hc: 300.0
  topography_source:
    name: ETOPO5
  mask_shapefile: null
  close_narrow_channels: false
  hmin: 5.0
  filename: null
RiverForcing:
  start_time: '2015-01-01T00:00:00'
  end_time: '2025-12-31T00:00:00'
  source:
    name: DAI
    climatology: false
  convert_to_climatology: if_any_missing
  include_bgc: false
  bgc_source: null
  model_reference_date: '2000-01-01T00:00:00'
  indices:
    Hvita(Olfusa):
    - 22, 14
    JkulsFjll:
    - 31, 32
    Lagarfljot:
    - 26, 37
    Svarta:
    - 32, 23
    Bruara:
    - 21, 15
    Thjorsa:
    - 21, 15
    _convention: eta_rho, xi_rho
  coast_snap_buffer_km: null
  domain_edge_buffer: 20

the_same_river_forcing_with_overlapping_rivers = RiverForcing.from_yaml(
    overlapping_rivers_yaml_filepath
)

2026-06-30 21:44:45 - INFO - Use provided river indices.
2026-06-30 21:44:45 - WARNING - No records found after the end_time: 2025-12-31 00:00:00.
2026-06-30 21:44:45 - INFO - Compute climatology for river forcing.
2026-06-30 21:44:45 - INFO - Computing river forcing...
2026-06-30 21:44:45 - INFO - Creating 1 synthetic river(s) to handle overlapping entries.

the_same_river_forcing_with_overlapping_rivers.ds

<xarray.Dataset> Size: 25kB
Dimensions:           (river_time: 12, nriver: 7, ntracers: 2, eta_rho: 52,
                       xi_rho: 52)
Coordinates:
    month             (river_time) int64 96B 1 2 3 4 5 6 7 8 9 10 11 12
    river_name        (nriver) <U15 420B 'overlap_Bruara' ... 'Thjorsa'
    abs_time          (river_time) datetime64[ns] 96B 2000-01-16 ... 2000-12-15
  * river_time        (river_time) float64 96B 15.0 45.0 74.0 ... 319.0 349.0
    tracer_name       (ntracers) <U4 32B 'temp' 'salt'
    tracer_unit       (ntracers) <U15 120B 'degrees Celsius' 'PSU'
    tracer_long_name  (ntracers) <U21 168B 'potential temperature' 'salinity'
  * nriver            (nriver) int64 56B 1 2 3 4 5 6 7
Dimensions without coordinates: ntracers, eta_rho, xi_rho
Data variables:
    river_volume      (river_time, nriver) float64 672B 311.3 396.0 ... 0.0 0.0
    river_tracer      (ntracers, river_time, nriver) float64 1kB 15.62 ... 1.0
    river_index       (eta_rho, xi_rho) float32 11kB 0.0 0.0 0.0 ... 0.0 0.0 0.0
    river_fraction    (eta_rho, xi_rho) float32 11kB 0.0 0.0 0.0 ... 0.0 0.0 0.0
Attributes:
    climatology:  True

xarray.Dataset

• Dimensions:
  • river_time: 12
  • nriver: 7
  • ntracers: 2
  • eta_rho: 52
  • xi_rho: 52
• Coordinates: (8)
  • month
    (river_time)
    int64
    1 2 3 4 5 6 7 8 9 10 11 12

    long_name :

    Month index (1-12)

    array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])

  • river_name
    (nriver)
    <U15
    'overlap_Bruara' ... 'Thjorsa'

    long_name :

    River name

    array(['overlap_Bruara', 'Hvita(Olfusa)', 'JkulsFjll', 'Lagarfljot',
           'Svarta', 'Bruara', 'Thjorsa'], dtype='<U15')

  • abs_time
    (river_time)
    datetime64[ns]
    2000-01-16 ... 2000-12-15

    long_name :

    absolute time

    array(['2000-01-16T00:00:00.000000000', '2000-02-15T00:00:00.000000000',
           '2000-03-15T00:00:00.000000000', '2000-04-15T00:00:00.000000000',
           '2000-05-15T00:00:00.000000000', '2000-06-15T00:00:00.000000000',
           '2000-07-15T00:00:00.000000000', '2000-08-15T00:00:00.000000000',
           '2000-09-15T00:00:00.000000000', '2000-10-15T00:00:00.000000000',
           '2000-11-15T00:00:00.000000000', '2000-12-15T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • river_time
    (river_time)
    float64
    15.0 45.0 74.0 ... 319.0 349.0

    cycle_length :

    365.25

    long_name :

    relative time: days since 2000-01-01 00:00:00

    units :

    days

    array([ 15.,  45.,  74., 105., 135., 166., 196., 227., 258., 288., 319., 349.])

  • tracer_name
    (ntracers)
    <U4
    'temp' 'salt'

    long_name :

    Tracer name

    array(['temp', 'salt'], dtype='<U4')

  • tracer_unit
    (ntracers)
    <U15
    'degrees Celsius' 'PSU'

    long_name :

    Tracer concentration unit

    array(['degrees Celsius', 'PSU'], dtype='<U15')

  • tracer_long_name
    (ntracers)
    <U21
    'potential temperature' 'salinity'

    long_name :

    Tracer long name

    array(['potential temperature', 'salinity'], dtype='<U21')

  • nriver
    (nriver)
    int64
    1 2 3 4 5 6 7

    long_name :

    River ID (1-based Fortran indexing)

    array([1, 2, 3, 4, 5, 6, 7])

• Data variables: (4)
  • river_volume
    (river_time, nriver)
    float64
    311.3 396.0 112.2 ... 8.47 0.0 0.0

    long_name :

    River volume flux

    units :

    m^3/s

    array([[311.25608444, 396.012146  , 112.24038696,  51.13676453,
              8.03249836,   0.        ,   0.        ],
           [321.05844116, 407.87805176, 114.64615631,  52.78458405,
              8.18966579,   0.        ,   0.        ],
           [326.65678787, 396.92681885, 121.67308044,  53.98734665,
              8.50816727,   0.        ,   0.        ],
           [334.80675125, 410.82925415, 149.69039917,  71.63630676,
             11.15483093,   0.        ,   0.        ],
           [542.78608704, 446.65853882, 218.48077393, 178.76750183,
             17.1210022 ,   0.        ,   0.        ],
           [623.28445053, 442.60714722, 230.40383911, 311.78033447,
             15.19416618,   0.        ,   0.        ],
           [516.33998871, 384.58535767, 323.67306519, 209.07264709,
              9.38349915,   0.        ,   0.        ],
           [491.2128067 , 384.58535767, 352.25      , 124.14719391,
              8.35650063,   0.        ,   0.        ],
           [383.74579239, 363.77908325, 243.37924194, 111.43669891,
              8.79949951,   0.        ,   0.        ],
           [338.20005798, 383.53659058, 164.69230652, 107.82298279,
              9.4851675 ,   0.        ,   0.        ],
           [315.75859451, 384.17999268, 134.09616089,  85.8888092 ,
              8.80366611,   0.        ,   0.        ],
           [309.76570511, 380.7371521 , 119.73376465,  67.66781616,
              8.47016716,   0.        ,   0.        ]])

  • river_tracer
    (ntracers, river_time, nriver)
    float64
    15.62 15.62 15.62 ... 1.0 1.0 1.0

    long_name :

    River tracer data

    array([[[15.61593437, 15.61593437, 15.61593437, 15.61593437,
             15.61593437, 15.61593437, 15.61593437],
            [15.61593437, 15.61593437, 15.61593437, 15.61593437,
             15.61593437, 15.61593437, 15.61593437],
            [15.61593437, 15.61593437, 15.61593437, 15.61593437,
             15.61593437, 15.61593437, 15.61593437],
            [15.61593437, 15.61593437, 15.61593437, 15.61593437,
             15.61593437, 15.61593437, 15.61593437],
            [15.61593437, 15.61593437, 15.61593437, 15.61593437,
             15.61593437, 15.61593437, 15.61593437],
            [15.61593437, 15.61593437, 15.61593437, 15.61593437,
             15.61593437, 15.61593437, 15.61593437],
            [15.61593437, 15.61593437, 15.61593437, 15.61593437,
             15.61593437, 15.61593437, 15.61593437],
            [15.61593437, 15.61593437, 15.61593437, 15.61593437,
             15.61593437, 15.61593437, 15.61593437],
            [15.61593437, 15.61593437, 15.61593437, 15.61593437,
             15.61593437, 15.61593437, 15.61593437],
            [15.61593437, 15.61593437, 15.61593437, 15.61593437,
             15.61593437, 15.61593437, 15.61593437],
    ...
            [ 1.        ,  1.        ,  1.        ,  1.        ,
              1.        ,  1.        ,  1.        ],
            [ 1.        ,  1.        ,  1.        ,  1.        ,
              1.        ,  1.        ,  1.        ],
            [ 1.        ,  1.        ,  1.        ,  1.        ,
              1.        ,  1.        ,  1.        ],
            [ 1.        ,  1.        ,  1.        ,  1.        ,
              1.        ,  1.        ,  1.        ],
            [ 1.        ,  1.        ,  1.        ,  1.        ,
              1.        ,  1.        ,  1.        ],
            [ 1.        ,  1.        ,  1.        ,  1.        ,
              1.        ,  1.        ,  1.        ],
            [ 1.        ,  1.        ,  1.        ,  1.        ,
              1.        ,  1.        ,  1.        ],
            [ 1.        ,  1.        ,  1.        ,  1.        ,
              1.        ,  1.        ,  1.        ],
            [ 1.        ,  1.        ,  1.        ,  1.        ,
              1.        ,  1.        ,  1.        ],
            [ 1.        ,  1.        ,  1.        ,  1.        ,
              1.        ,  1.        ,  1.        ]]])

  • river_index
    (eta_rho, xi_rho)
    float32
    0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0

    long_name :

    River ID

    units :

    none

    array([[0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           ...,
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.]], shape=(52, 52), dtype=float32)

  • river_fraction
    (eta_rho, xi_rho)
    float32
    0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0

    long_name :

    River volume fraction

    units :

    none

    array([[0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           ...,
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.]], shape=(52, 52), dtype=float32)

• Indexes: (2)
  • river_time
    PandasIndex

    PandasIndex(Index([15.000000000000002,               45.0,               74.0,
                        105.0,              135.0,              166.0,
                        196.0,              227.0,              258.0,
                        288.0,              319.0,              349.0],
          dtype='float64', name='river_time'))

  • nriver
    PandasIndex

    PandasIndex(Index([1, 2, 3, 4, 5, 6, 7], dtype='int64', name='nriver'))

• Attributes: (1)

  climatology :

  True