# Tips for Creating Watershed Maps in R

There have been a few posts on this blog on creating watershed maps (here, here, and here), but this post is going to be focused on some of my recent experiences on creating watershed maps in R with files that may be missing attributes, are in the wrong projection, and contain data that need to be clipped to a specific boundary shapefile. There are lots of packages that exist to do one or more of these things, but anyone who has ever tried to create watershed maps in R knows that there isn’t one package that does it all. My main goal for this post is to outline the most efficient workflow and use of packages that also allow for the most compatibility when plotting shapefiles and raster files in one figure.

In this post, we are going to be creating a map of the Tuolumne River Basin boundary and plot elevation data within the basin. All the data are found here. First we will read in the Tuolumne boundary shape file (.shp) and the elevation raster file (.asc which is an ASCII file) using the appropriate functions and do some preliminary plotting to see what we have.

#Import libraries

library(rgdal)
library(ggplot2)
library(raster)

elevation.raster = raster("doi_10.6071_M3FH3D__v5/tuolumne_merced_data_2009-2015/Merced_Tuolumne_Dataset_SpatialData/SpatialData/merced_tuolumne_100mdem_utm.asc")

#Plot the files

ggplot() +  geom_polygon(data = tuolumne.basin, aes(x = long, y = lat, group = group), colour = "dark red", fill = NA)
plot(elevation.raster)


So we have the pieces that we need to build the map, but notice that the latitude and longitude are in the wrong projection. We can use the following command to check what projection the shapefile is in:

proj4string(tuolumne.basin)


We see that the output is: “+proj=utm +zone=11 +datum=NAD83 +units=m +no_defs”. So we are in the Universal Transverse Mercator coordinate system, but we should change to WGS 84. We can do this using the function “spTransform” which will swap out the projection by adjusting the CRS (Coordinate Reference System) attribute of the shapefile. You can use “proj4string” to verify that the transformation took place.

 tuolumne.basin.transformed <- spTransform(tuolumne.basin, CRS("+proj=longlat +ellps=WGS84 +datum=WGS84"))


Now we need to transform the raster files coordinates. Note that the raster file doesn’t have an associated coordinate reference system listed. If you try to change the projection at this point, you will get an error. This is a minor inconvenience since we know that the coordinate system should match that of the raw Tuolumne shapefile and we can just insert the original coordinate system as a string under the projargs attribute. Then we can transform it to match the coordinate system of the transformed shapefile using “projectRaster”.

elevation.raster@crs@projargs <- "+proj=utm +zone=11 +datum=NAD83 +units=m +no_defs"

elevation.raster.transformed <- projectRaster(elevation.raster,crs=crs(tuolumne.basin.transformed))


Great, now we have all the data in the right projection. Now we have to clip the raster layer to show only the data in the bounds of our shapefile. We first use the “crop” function in the raster library to clip the layer based on the extent of the shapefile boundary as well as the mask function. It is important to do both otherwise the clip will not work!

elevation.raster.transformed.cropped <- crop(elevation.raster.transformed, extent(tuolumne.basin.transformed))


Now we need to get the appropriate elevation values and coordinates from the raster object so that we can plot it using ggplot.When we use ggplot here, notice that we only need to use geom_raster and elevation data since the clipped data will perfectly follow the shapefile boundary.

#Isolate elevation values from the raster file

val <- getValues(elevation.raster.transformed.cropped)
xy <-as.data.frame(xyFromCell(elevation.raster.transformed.cropped,1:ncell(elevation.raster.transformed.cropped)))
xy <- cbind(xy,val)

#Plot it!

ggplot()+geom_raster(data=xy, aes(x=x, y=y, fill=val))+ scale_fill_viridis_c()+theme_bw()


It’s almost perfect aside from that gray box that results from clipping and masking. After we clip, we are converting all values outside the boundary of the shapefile to NAs, which falls out of the bounds of our color scale. To fix this, we simply insert an additional argument to scale_fill_viridis_c() and we also make some additional aesthetic changes to the theme.

#Final plot function

ggplot()+geom_raster(data=xy, aes(x=x, y=y, fill=val))+ scale_fill_viridis_c(na.value=NA,name = "Elevation (m)")+theme_bw()+ggtitle("Tuolumne River Basin Elevation (m)")+xlab("Longitude") + ylab("Latitude")+theme(text = element_text(size = 20))


# MORDM Basics VI: Processing the output and reevaluating for robustness

In the previous post, we conducted a basic WaterPaths tutorial in which we ran a simulation-optimization of the North Carolina Research Triangle test case (Trindade et al, 2019); across 1000 possible futures, or states of the world (SOWs). To briefly recap, the Research Triangle test case consists of three water utilities in Cary (C), Durham (D) and Raleigh (R). Cary is the main supplier, having a water treatment plant of its own. Durham and Raleigh purchase water from Cary via treated transfers.

Having obtained the .set file containing the Pareto-optimal decision variables and their respective performance objective values, we will now walk through the .set file processing and visualize the decision variables and performance objective space.

## Understanding the .set file

First, it is important that the .set file makes sense. The NC_output_MS_S3_N1000.set file should have between 30-80 rows, and a total of 35 columns. The first 20 columns contain values of the decision variables. Note that only Durham and Raleigh have water transfer triggers as they purchase treated water from Cary.

1. Restriction trigger, RT (C, D, R)
2. Transfer trigger, TT (D, R)
3. Jordan Lake allocation, JLA (C, D, R)
4. Reserve fund contribution as a percentage of total annual revenue, RC (C, D, R)
5. Insurance trigger, IT (C, D, R)
6. Insurance payments as a percentage of total annual revenue, IP (C, D, R)
7. Infrastructure trigger, INF (C, D, R)

The last 15 columns contain the objective values for the following performance objectives of all three utilities:

1. Reliability (REL) to be maximized
2. Restriction frequency (RF) to be minimized
3. Infrastructure net present cost (INF_NPC) to be minimized
4. Peak financial cost of drought mitigation actions (PFC) to be minimized
5. Worst-case financial cost of drought mitigation actions (WCC) to be minimized

This reference set needs to be processed to output a .csv file to enable reevaluation for robustness analysis. To do so, run the post_processing.py file found in this GitHub repository in the command line:

python post_processing.py

In addition to post-processing the optimization output files, this file also conduct regional minimax operation, where each regional performance objective is taken to be the objective value of the worst-performing utility (Gold et al, 2019).

This should output two files:

1. NC_refset.csv No header row. This is the file that will be used to run the re-evaluation for robustness analysis in the next blog post.
2. NC_dvs_objs.csv Contains a header row. This file that contains the labeled decision variables and objectives, including the minimax regional performance objectives. Will be used for visualizing the reference set’s decision variables and performance objectives.

## Visualizing the reference set

Due to the higher number of decision variables, we utilize parallel axis plots to identify how varying the decision variables can potentially affect certain performance objectives. Here, we use the regional reliability performance objective, REL, as an example. Figure 1 below demonstrates how all decision variables vary with regional reliability.

From Figure 1, most solutions found via the optimization conducted in the previous blog post seem to have relatively high reliability across the full range of decision variable values. It is unclear as to how each decision variable might affect regional reliability. It is thus more helpful to identify specific sets of decision variables (or policies) that enable the achievement of reliability beyond a certain threshold.

With this in mind, we assume that all members of the Triangle require that their collective reliability be at least 98%. This results in the following figure:

Figure 2 has one clear takeaway: Pareto-optimality does not indicate satisfactory performance. In addition, setting this threshold make the effects of each decision variable clearer. It can be seen that regional reliability is significantly affected by both Durham and Raleigh’s infrastructure trigger (INF). Desirable levels of regional reliability can be achieved when Durham sets a high INF value. Conversely, Raleigh can set lower INF values to benefit from satisfactory reliability. Figure 2 also shows having Durham set a high insurance trigger (IT) may benefit the regional in terms of reliability.

However, there are caveats. Higher INF and IT values for Durham implies that the financial burden of investment and insurance payments are being borne by Raleigh and Cary, as Durham is able to withstand more risk without having to trigger an investment or infrastructure payment. This may affect how each member utility perceives their individual risks and benefits by forming a cooperative contract.

The code to plot these figures can be found under ‘refset_parallel.py’ in the repository.

## Robustness analysis and what’s next

So how is setting a threshold value of regional reliability significant?

Within the MORDM framework, robustness is defined using a multivariate satisficing metric (Gold et al, 2019). Depending on the requirements of the stakeholders, a set of criteria is defined that will then be used distinguish between success (all criteria are met) and failure (at least one criteria is not met). Using these criteria, the rest of Pareto-optimal policies are simulated across a number of uncertain SOWs. Each policy’s robustness is then represented by the percent of SOWs in which it meets the minimum performance criteria that has been set.

In this post, we processed the reference set and visualized its decision variable space with respect to each variable’s effect on the reliability performance objective. A similar process can be repeated across all utilities for all performance objectives.

Using the processed reference set, we will conduct multi-criterion robustness analysis using two criteria:

1. Regional reliability should be at least 98%
2. Regional restriction frequency should be less than or equal to 20%

We will also conduct sensitivity analysis to identify the the decision variables that most impact regional robustness. Finally, we will conduct scenario discovery to identify SOWs that may cause the policies to fail.

## References

Gold, D. F., Reed, P. M., Trindade, B. C., & Characklis, G. W. (2019). Identifying actionable compromises: Navigating multi‐city robustness conflicts to discover cooperative safe operating spaces for regional water supply portfolios. Water Resources Research, 55(11), 9024–9050. https://doi.org/10.1029/2019wr025462

Trindade, B. C., Reed, P. M., & Characklis, G. W. (2019). DEEPLY UNCERTAIN PATHWAYS: Integrated multi-city Regional Water Supply Infrastructure Investment and portfolio management. Advances in Water Resources, 134, 103442. https://doi.org/10.1016/j.advwatres.2019.103442

# Make LaTeX easier with custom commands

LaTeX is a powerful tool for creating professional looking documents. Its ability to easily format mathematical equations, citations and complex figures makes LaTeX especially useful for developing peer-reviewed journal articles and scientific reports. LaTeX is highly customizable, which allows you to create documents that are not carbon copies of generic Microsoft Word templates.

Using LaTeX does have it’s drawbacks- instead of simply typing on a page, you construct the document by writing LaTeX code. Once you’ve written your code, a compiler translates it into a finished and formatted document. This can sometimes result in high overhead time for fixing bugs and managing format. But coding a document also has advantages, in addition to the vast array of existing LaTeX libraries and commands, you can create your own custom commands that speed up the writing and formatting process. Below I’ll overview the basics of creating custom LaTeX commands and provide some illustrative examples.

## Commands with no arguments

If you have an equation or a complex sequence of text that you know you’ll be repeating, you can create a custom command to produce it. For example, if I’m constantly referencing the equation for an Ordinary Least Squares (OLS) estimator, I can make a new command that produces it:

\newcommand{\OLS}{$\hat{\beta}=(X^TX)^{-1}X^Ty$}


There are three parts to defining this command, as shown in the figure below:

1. Tell LaTex you are defining a new command by specifying “\newcommand”
2. name the command (make sure to include the backslash)
3. Specify the output of the new command

Example LaTex code that calls the OLS command:

I can store complex terms using a predefined command: \OLS


Compiled output:

## Commands with basic arguments

### Single argument

You can also define commands that accept arguments. For example, if you want to make commands to assist tracking changes in a document, you can create a command that formats a section of added text so it has the color blue and is bolded:

\newcommand{\addtxt}[1]{{\color{blue} \textbf{#1}}} % Highlight text that has been added



The command defined above accepts one argument (shown as the “[1]”) and calls that argument using “#1”, as highlighted in the figure below:

Example Latex code using my command:

Demonstrating my custom commands with arguments:\addtxt{This text has be inserted into this sentence}


Example compiled output:

### Multiple arguments

You can also define commands with multiple arguments, for example, you can create a template sentence that provides an update the timing of a project:

\newcommand{\projReportA}[2]{The project was planned to finish on \textbf{#1}after reviewing current progress we have determined that it will likely finish on \textbf{#2}} % insert a date for when a project was planned to be completed and when a project is likely to be completed


Here, argument #1 is the date when the project was planned on being completed, and argument #2 is the date that the project will likely be completed.

Example use of this function:

Another way you can use an argument:\\ \\
\projReportA{September $9^{th}$}{October $1^{st}$}


Example compiled output:

## Commands with optional arguments

The project report command above can be modified to accept a default completion date with an option to include an updated date.

\newcommand{\progReportB}[2][September 9th]{The project was planned to finish on \textbf{#2}, after reviewing current progress, we have determined that it will likely finish on \textbf{#1}}


To create an optional argument, specify the default value of the first argument in a new set of brackets. Note that for basic Latex this only works for a single default argument, for more defaults you can use a package such as xparse.

Here’s an example using this new command with the default argument:

Here I'll will use the command without the optional argument, so it will print the default: \\
\\
\progReportB{September $9^{th}$}


This will compile to:

Here’s an example with the optional argument specified

Now I'll add the optional argument, which will be added in place of the default: \\ \\
\progReportB[October $1^{st}$]{September $9^{th}$}


This will compile to:

### Concluding thoughts

These simple examples only scratch the surface of what you can do with LaTex commands. I should also note that while custom commands are useful, LaTex also contains a large suite of packages with predefined commands that can be easily imported into your document.