fromnode and tonode

The attributes fromnode and tonode are used to store a flow network as a edge node topology where every network feature has one and only one node upstream and one and only one node downstream. Nodes are useful if converting a flow network to a graph and are useful in many analysis contexts as there is a single identifier for a confluence or divergence.

divergence

The divergence attribute indicates if a downstream connection is primary (1) or secondary (2). If 0, a connection is not downstream of a divergence. This attribute is useful as it facilitates following a flow network in the “downstream mainstem” direction at every divergence.

return divergence

The return divergence attribute indicates that one or more of the features upstream of a given feature originates from a divergence. If 0, the upstream features are not part of a diversion. If 1, one or more of the upstream features is part of a diversion.

stream calculator

The stream calculator attribute is part of the modified Strahler stream order as implemented in the NHDPlus data model. It indicates if a given feature is part of the downstream mainstem dendritic network or is part of a diverted path. If 0, the path is part of a diversion. Otherwise stream calculator will be equal to stream order. When generating Strahler stream order, if stream calculator is 0 for a given feature, that feature is not considered for incrementing downstream stream order.

summary

As a system, stream calculator, divergence and return divergence support network navigation and processing in the context of diverted paths.

1. A feature at the top of a diversion will have divergence set to 1.
2. All features that are part of a diversion that has not yet recombined with a main path, will have stream calculator set to 0.
3. A feature that is just downstream of where a diversion path recombines with a main path will have return diversion set to 1.

Bringing it all together

The example below shows how we can recreate the non-dendritic attributes and use them in practice.

We’ll start with the small sample watershed that’s included in hydroloom and select only the attributes required to recreate the non-dendritic network.

x <- sf::read_sf(system.file("extdata/new_hope.gpkg", 
                             package = "hydroloom"))

# First we select only an id, a name, and a feature type.
flow_net <- x |>
  select(COMID, GNIS_ID, FTYPE) |>
  sf::st_transform(5070)

# Now we convert the geometric network to an attribute topology
# and convert that to a node topology and join our attributes back
flow_net <- flow_net |>
  make_attribute_topology(min_distance = 5) |>
  hydroloom::make_node_topology(add_div = TRUE) |>
  left_join(sf::st_drop_geometry(flow_net), by = "COMID")

# We only have one outlet so it doesn't matter if it is coastal
# or inland but we have to provide it.
outlets <- filter(flow_net, !tonode %in% fromnode)

# We have these feature types. A larger dataset might include 
# things like canals which would not be considered  "major"
unique(flow_net$FTYPE)

# now we run the add_divergence, add_toids, and add_streamorder
flow_net <- add_divergence(flow_net, 
                           coastal_outlet_ids = c(), 
                           inland_outlet_ids = outlets$COMID, 
                           name_attr = "GNIS_ID", 
                           type_attr = "FTYPE", 
                           major_types = unique(flow_net$FTYPE)) |>
  add_toids() |>
  add_streamorder() |>
  add_return_divergence()

# Make sure we reproduce what came from our source NHDPlus data.
sum(flow_net$divergence == 2)
sum(x$Divergence == 2)
all(flow_net$divergence == x$Divergence)
sum(flow_net$return_divergence == x$RtnDiv)

names(flow_net)

With the above code, we removed all attributes other than an ID, a name and a feature type and recreated both a dendritic (toid) and non-dendritic (fromnode tonode) topology. We added divergence attribute, stream_order, stream_calculator, and return_divergence attributes.