The *streamDAG* package provides indices and tools for analyzing directed acyclic graph (DAG) representations of intermittent stream networks. The DAG framework allows a wide range of analytical approaches for intermittent streams including classic measures from hydrology, ecology, and of course, graph theory. A focus of many *streamDAG* algorithms is the measurement of 1) “local” arc (stream segment) and nodal (inter-arc) characteristics, and 2) network-level complexity and connectivity. While many of these approaches are purely topological, a non-trivial number of DAG indices, particularly weighted approaches, will provide outcomes identical to existing hydrological (non-graph-theoretic) measures for streams. These include Integral Connectivity Scale Length (ICSL) and its variants (Western et al. 2013)
Perennial streams (and even non-stream networks) can be potentially analyzed with *streamDAG* algorithms. However, the major motivator for the package was the development of procedures that consider the spatio-temporal variability of intermittent stream networks. As a result most *streamDAG* algorithms assume that graphs are directed (from upstream to downstream). Thus, these functions may produce errors if directed graphs are used without checking function arguments.

The *streamDAG* package is built under the basic idiom of the *igraph* package (Csardi and Nepusz 2006), and most of its functions utilize *igraph* basis algorithms. Newest (developmental) versions of *streamDAG* can be obtained from the public GitHub repository: https://github.com/moondog1969/streamDAG. The package maintainer is Ken Aho (ahoken@isu.edu). An introduction to the *streamDAG* package can be found in (Aho et al. 2023).

Newest versions of the *streamDAG* package can be installed from the R console via GitHub, after installing the package *devtools*. In particular, use:

```
library(devtools)
install_github("moondog1969/streamDAG")
```

Stable versions of the package will also be housed on the Comprehensive R Archive Network (CRAN) beginning in September 2023. Following this inception (version 1.4-4), *streamDAG* can be installed directly from CRAN mirrors using:

`install.packages("streamDAG")`

After installing *streamDAG*, the package can be loaded into R conventionally:

`library(streamDAG)`

```
Welcome to streamDAG!
For more information on using the package type:
vignette("streamDAG")
```

We begin with an in-depth demonstration of the *streamDAG* package using Murphy Creek, a very simple intermittent stream in the Reynolds Creek experimental watershed in southwestern Idaho (Fig 1). From 6/3/2019 to 10/3/2019, stream presence data were acquired at 15 minute intervals from 25 Murphy Creek nodes, corresponding to 24 stream segment arcs. Bounding nodes were added to encompass the entire length of the network. This resulted in a final Murphy Creek network with 28 nodes and 27 arcs for analysis.

Purely topological analyses can be conducted in *streamDAG* using only an *igraph* codified stream network. Below is a codification of Murphy Creek based on nodes established by Warix et al. (2021). The code `IN_N --+ M1984`

indicates that the stream flows from node `IN_N`

to node `M1984`

, and so on.

```
murphy_spring <- graph_from_literal(IN_N --+ M1984 --+ M1909, IN_S --+ M1993,
M1993 --+ M1951 --+ M1909 --+ M1799 --+ M1719 --+ M1653 --+ M1572 --+ M1452,
M1452 --+ M1377 --+ M1254 --+ M1166 --+ M1121 --+ M1036 --+ M918 --+ M823,
M823 --+ M759 --+ M716 --+ M624 --+ M523 --+ M454 --+ M380 --+ M233 --+ M153,
M153 --+ M91 --+ OUT)
```

This code is contained as an option in the function `streamDAGs`

which also codifies other intermittent stream *igraph* objects.

`streamDAGs("mur_full")`

```
IGRAPH dba9e0e DN-- 28 27 --
+ attr: name (v/c)
+ edges from dba9e0e (vertex names):
[1] IN_N ->M1984 M1984->M1909 M1909->M1799 IN_S ->M1993 M1993->M1951
[6] M1951->M1909 M1799->M1719 M1719->M1653 M1653->M1572 M1572->M1452
[11] M1452->M1377 M1377->M1254 M1254->M1166 M1166->M1121 M1121->M1036
[16] M1036->M918 M918 ->M823 M823 ->M759 M759 ->M716 M716 ->M624
[21] M624 ->M523 M523 ->M454 M454 ->M380 M380 ->M233 M233 ->M153
[26] M153 ->M91 M91 ->OUT
```

Much more flexibility in *streamDAG* functions is possible by defining stream spatial coordinates and graph weighting data, including stream lengths, nutrient loading, and information about stream segment presence (wet) or absence (dry).

The *streamDAG* package contains additional Murphy Creek data, including nodal spatial coordinates (UTMs), stream arc (segment) lengths, and stream arc presence absence data. Instream lengths and distances can be obtained from a number of sources including ARC-GIS. Stream presence can be ascertained using a number of methods, including conductivity and temperature sensors.

```
data(mur_coords) # Node spatial coords
data(mur_lengths) # Arc (stream segment) lengths
data(mur_node_pres_abs) # Node presence / absence data with explicit datetimes
data(mur_arc_pres_abs) # Arc (stream segment) simulated presence / absence data
```

Care should be taken to ensure that the order of relevant rows and columns and elements in ancillary data correspond to the order of nodes and arcs defined in the underlying graph, `G`

with the functions `igraph::V`

(which gives nodes) and `A`

or `igraph::E`

(which give arcs).

Within ancillary datasets, different code identifiers can be used to designate arcs. For instance, for an arc \(z = \vec{uv}\) where \(u\) is the tail of arc \(z\) and \(v\) is the head of \(z\), we could code: `u--+v`

or `u-->v`

or `u --> v`

or `u->v`

, or even `u|v`

. The important thing is that the ordering is consistent with the arcs in the corresponding graph object. For instance, here are the first six arc names for the graph object `murphy_spring`

.

`head(A(murphy_spring))`

```
+ 6/27 edges from dba4d73 (vertex names):
[1] IN_N ->M1984 M1984->M1909 M1909->M1799 IN_S ->M1993 M1993->M1951
[6] M1951->M1909
```

Note that these correspond to the identifiers for the first six stream lengths (in the first six rows) from `mur_lengths`

.

`head(mur_lengths)`

```
Arcs Lengths
1 IN_N -> M1984 20.30
2 M1984 -> M1909 75.00
3 M1909 -> M1799 108.99
4 IN_S -> M1993 68.30
5 M1993 -> M1951 27.60
6 M1951 -> M1909 14.40
```

Naming of nodes should be consistent with the node names in the corresponding graph object. For instance, here are the first six graph node names from `murphy_spring`

.

`head(V(murphy_spring))`

```
+ 6/28 vertices, named, from dba4d73:
[1] IN_N M1984 M1909 IN_S M1993 M1951
```

The naming (and order) correspond to the first six identifiers (column names in this case) for presence absence data from `mur_node_pres_abs`

.

`names(mur_node_pres_abs)[1:7][-1] # ignoring datestamp column 1 `

`[1] "IN_N" "M1984" "M1909" "IN_S" "M1993" "M1951"`

It is easy to depict a spatially explicit stream DAG using the *streamDAG* function `spatial.plot`

. We can make a spatial plot by augmenting graph data with nodal spatial coordinates (Fig 2).

```
x <- mur_coords[,2]; y <- mur_coords[,3]
names = mur_coords[,1]
spatial.plot(murphy_spring, x, y, names, cex.text = .7)
```

ARC-GIS shapefiles can also be used to generate spatial plots with the function `spatial.plot.sf`

. Use of shapefiles requires use of the libraries libraries *ggplot2* and *sf*, and resulting graphs can be customized using *ggplot2* modifiers (Fig 3). Use of shapefiles will eliminate some of the easy to easy-to-use features in `spatial.plot`

including directional arrows indicating flow and the automated deletion of arcs and nodes with presence / absence data (see Section 2.3).

```
library(ggplot2); library(sf); library(ggrepel)
# Note that the directory "shape" also contains required ARC-GIS .shx,.cpg, and .prj files.
mur_sf <- st_read(system.file("shape/Murphy_Creek.shp", package="streamDAG"))
```

```
Reading layer `Murphy_Creek' from data source
`C:\Users\ahoken\AppData\Local\Temp\Rtmpu8HPEX\Rinst45d826d0611\streamDAG\shape\Murphy_Creek.shp'
using driver `ESRI Shapefile'
Simple feature collection with 2 features and 2 fields
Geometry type: LINESTRING
Dimension: XY
Bounding box: xmin: 512864.7 ymin: 4788962 xmax: 514722.6 ymax: 4789265
Projected CRS: NAD83 / UTM zone 11N
```

```
g1 <- spatial.plot.sf(x, y, names, shapefile = mur_sf)
## some ggplot customizations
g1 + expand_limits(y = c(4788562,4789700)) +
theme(plot.margin = margin(t = 0, r = 10, b = 0, l = 0)) +
geom_text_repel(data = mur_coords, aes(x = x, y = y, label = Object.ID), colour = "black",
size = 1.6, box.padding = unit(0.3, "lines"), point.padding =
unit(0.25, "lines"))
```

The activity of stream nodes and/or arcs (segments) can be easily tracked in stream graphs based on STIC or conductivity data using the *streamDAG* functions `delete.arcs.pa`

and `delete.nodes.pa`

.

For instance, the dataset `mur_node_pres_abs`

contains a subset of nodal presence absence data for Murphy Creek in 2019. Below we see rows for time series observations 650 to 655.

`mur_node_pres_abs[650:655,]`

```
Datetime IN_N M1984 M1909 IN_S M1993 M1951 M1799 M1719 M1653 M1572
6491 8/9/2019 22:30 0 0 0 0 0 0 1 0 0 1
6501 8/10/2019 1:00 0 0 0 0 0 0 1 0 0 1
6511 8/10/2019 3:30 0 0 0 0 0 0 1 0 0 1
6521 8/10/2019 6:00 0 0 0 0 0 0 1 0 0 1
6531 8/10/2019 8:30 0 0 0 0 0 0 1 0 0 1
6541 8/10/2019 11:00 0 0 0 0 0 0 1 0 0 1
M1452 M1377 M1254 M1166 M1121 M1036 M918 M823 M759 M716 M624 M523 M454
6491 0 0 1 0 0 1 1 0 0 1 1 1 0
6501 0 0 1 0 0 1 1 0 0 1 1 1 0
6511 0 0 1 1 0 1 1 0 0 1 1 1 0
6521 0 0 1 1 0 1 1 0 0 1 1 1 0
6531 0 0 1 1 0 1 1 0 0 1 1 1 1
6541 0 0 1 1 0 1 1 0 0 1 1 1 1
M380 M233 M153 M91 OUT
6491 0 1 1 1 1
6501 0 1 1 1 1
6511 0 1 1 1 1
6521 0 1 1 1 1
6531 0 1 1 1 1
6541 0 1 1 1 1
```

Modifying `murphy_spring`

based on the nodal observations at 8/9/2019 22:30 we have:

```
npa <- mur_node_pres_abs[650,][,-1]
G1 <- delete.nodes.pa(murphy_spring, npa)
```

The resulting spatial plot is shown as Fig 4. Note that nodes without water are now omitted from the graph. Arcs missing one or more bounding nodes are also omitted.

`spatial.plot(G1, x, y, names, cex.text = .7)`

There are several graphical approaches for distinguishing “dry” and “wet” stream locations. The simplest is to simply show “wet” nodes and arcs bounded by “wet” nodes as in Fig 4. One can also show “dry” node locations by specifying `show.dry = TRUE`

(Fig 5).

`spatial.plot(G1, x, y, names, plot.dry = TRUE, cex.text = .7)`

Finally, one can show “wet” nodes and associated arcs superimposed over the entire network, which includes, potentially, “dry” nodes and arcs. This approach requires generation of `spatial.plot`

object representing the entire network, and specification of this object using the argument `cnw`

i.e., complete network (Fig 6).

```
spc <- spatial.plot(murphy_spring, x, y, names, plot = FALSE)
spatial.plot(G1, x, y, names, plot.dry = TRUE, cex.text = .7, cnw = spc)
```

One can also modify graphs based on arc presence / absence data. The dataframe `mur_arc_pres_abs`

contains simulated multivariate Bernoulli datasets for Murphy Cr. arcs based on 2019 nodal data.

`head(mur_arc_pres_abs) # 1st 6 rows of data`

```
IN_N-->M1984 M1984-->M1909 M1909-->M1799 IN_S-->M1993 M1993-->M1951
1 1 0 1 0 0
2 1 0 1 0 1
3 1 0 1 0 0
4 0 1 1 1 0
5 0 0 1 0 0
6 0 1 0 0 0
M1951-->M1909 M1799-->M1719 M1719-->M1653 M1653-->M1572 M1572-->M1452
1 0 1 0 1 1
2 0 0 1 1 1
3 1 0 1 0 0
4 1 1 1 1 1
5 0 0 0 1 1
6 1 0 0 1 0
M1452-->M1377 M1377-->M1254 M1254-->M1166 M1166-->M1121 M1121-->M1036
1 0 1 1 0 0
2 0 1 0 1 1
3 1 1 1 0 0
4 1 1 0 1 1
5 0 1 0 1 1
6 1 1 1 0 0
M1036-->M918 M918-->M823 M823-->M759 M759-->M716 M716-->M624 M624-->M523
1 1 0 0 1 1 1
2 1 1 1 1 1 1
3 0 1 0 1 1 1
4 1 1 1 1 1 0
5 1 0 0 1 1 1
6 1 0 0 0 1 1
M523-->M454 M454-->M380 M380-->M233 M233-->M153 M153-->M91 M91-->OUT
1 1 0 0 0 1 1
2 1 1 1 0 1 1
3 1 1 0 1 1 1
4 1 1 1 1 1 1
5 1 1 1 1 1 1
6 1 1 0 1 1 1
```

Modifying `murphy_spring`

arcs based on the 6th simulated multivariate Bernoulli dataset of arc presence / absence, we have:

`G2 <- delete.arcs.pa(murphy_spring, mur_arc_pres_abs[6,])`

The resulting spatial plot is shown in Fig 7. Note that all nodes are plotted, but plotted arcs are limited to those with recorded stream activity.

`spatial.plot(G2, x, y, names, cex.text = .7)`

There are many measures useful for describing and distinguishing intermittent stream networks that are based solely on graph topological features (i.e., the presence or absence of nodes and adjoining arcs). These can be separated into local measures that describe the characteristics of individual stream nodes or arcs, and global measures that summarize the characteristics of an entire network, i.e., the entire graph.

A number of local measures are included in the *streamDAG* function `local.summary`

. The function only requires an *igraph* graph object.

```
local <- local.summary(murphy_spring)
round(local, 2)[,1:9]
```

```
IN_N M1984 M1909 IN_S M1993 M1951 M1799 M1719 M1653
alpha.cent 1.00 2.00 6.00 1.00 2.00 3.00 7.00 8.00 9.00
page.rank 0.01 0.01 0.03 0.01 0.01 0.02 0.03 0.04 0.04
imp.closeness.cent 0.00 27.00 90.00 0.00 27.00 40.50 78.75 77.40 78.30
betweenness.cent 0.00 23.00 110.00 0.00 24.00 46.00 126.00 140.00 152.00
n.nodes.in.paths 1.00 1.00 5.00 1.00 1.00 2.00 6.00 7.00 8.00
n.paths 0.00 1.00 5.00 0.00 1.00 2.00 6.00 7.00 8.00
upstream.network.length 0.00 1.00 5.00 0.00 1.00 2.00 6.00 7.00 8.00
path.length.mean 0.00 1.00 1.80 0.00 1.00 1.50 2.50 3.14 3.75
path.length.var NaN 0.00 0.56 NaN 0.00 0.25 0.92 1.55 2.44
path.length.skew NA NA 0.51 NA NA NaN 0.00 -0.35 -0.46
path.length.kurt NA NA -0.61 NA NA NaN -0.25 -0.30 -0.60
path.degree.mean NA 0.00 1.20 NA 0.00 1.00 1.67 1.71 1.75
path.degree.var NaN 0.00 0.16 NaN 0.00 0.00 0.22 0.49 0.44
path.degree.skew NA NA 2.24 NA NA NaN -0.97 0.60 0.40
path.degree.kurt NA NA 5.00 NA NA NaN -1.87 -0.35 -0.23
in.eccentricity 0.00 1.00 3.00 0.00 1.00 2.00 4.00 5.00 6.00
mean.efficiency 0.00 0.04 0.12 0.00 0.04 0.06 0.11 0.11 0.11
```

A graphical summary based only on measures with complete cases and standardized outcomes is shown in Fig 8. Nodes along the x-axis are sorted based on their order in the `murphy_spring`

*igraph* object, which roughly corresponds to their order from sources to sink. In general, nodes increase in information and importance as distance to the sink decreases. Note, however, the “unusual” importance of M1909 due to its location at a confluence (Fig 2).

```
cc <- complete.cases(local)
local.cc <- local[cc,]
s.local <- t(scale(t(local.cc)))
ss.local <- stack(data.frame(s.local))
ss.local$metrics <- rep(row.names(local.cc), 28)
# theme used throughout vignette
theme_facet <- function(){
theme(strip.background = element_blank(),
strip.text.x = element_text(size = 10),
axis.title.y = element_text(margin = margin(r = .2, unit = "in"), size = 11.5),
axis.title.x = element_text(margin = margin(r = .2, unit = "in"), size = 11.5),
panel.background = element_rect(fill = "white", colour = "black",
linewidth = 0.5),
legend.position="none",
panel.grid.major = element_blank(),
panel.spacing = unit(0.2, "lines"),
strip.placement = "inside",
axis.ticks = element_line(colour = "black", linewidth = .2),
panel.grid.minor = element_blank())
}
ggplot(ss.local, aes(x = ind, y = values)) +
geom_bar(stat = "identity") +
facet_wrap(~metrics) +
theme_facet() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, size=4.3)) +
ylab("Standardized local measures") +
xlab("\nNode")
```

A less frequently used, but potentially important tool for measuring nodal importance is the horizontal visibility graph (Luque et al. 2009). Two nodes will be *visible* from each other if, when node data (e.g., degrees) are plotted as horizontal bars along the abscissa axis, and placed along the ordinate based on their location in the stream path, the bars can be connected with a horizontal line (Luque et al. 2009). Note that the importance of M1909 in a visibility analysis based on indegree (Fig 9). Weighted (see below) node visibilities can also be obtained with `multi.path.visibility`

.

```
vis <- multi.path.visibility(murphy_spring, source = c("IN_N","IN_S"),
sink = "OUT", autoprint = F)
barplot(vis$visibility.summary, las = 2, cex.names = .6, ylab = "Visible nodes",
legend.text = c("Downstream", "Upstream", "Both"),
args.legend = list(x = "topright", title = "Direction"))
```