Watershed Model Summary

[rburghol]

Checklist for completing the Rmd.

• [x] Pull in all of the pre-generated metrics from VAHydro into a table
  • [x] Revise sqldf statement in the Rmd to display human-readable names for metric names
• [x] Pull in all of the pre-generated plots
  • [x] Display high-quality images side-by-side
• [x] Pull in csv from the file path given as an argument
• [x] Match the range of the time series
• [x] Descriptive annotations and text
• [x] QA stuff:
  • [x] Warm-up period
  • [x] FTABLE river cross-section diagram/table
• [x] Plots that rely on the full data run file _hydrd_wy.csv
  • [x] l90 bar plot by year
  • [x] l30 bar plot by year
  • [x] l07 bar plot by year
  • [x] l01 bar plot by year
  • [x] Model Performance Scatterplots
  • [x] %-Error Box Plots
• [ ] Basin-level summary plots
  • [ ] Using the rseg.hydrocode supplied to the Rmd, we have some existing routines that will find all of the river segments above that segment. We can then plot the data for all of those tributary segments
  • [ ] Box plot, see example of data retrieval with vahydro_metric_grid and oxplot: https://github.com/HARPgroup/vahydro/blob/master/R/examples/metric_grid-p6.1.R
  • [ ] RWB locate watershed tracing code
  • [ ] Generate map of all these segments (JK to find code for generating map)
• [x] Descriptive text (not relevant right now, since new models have no descriptive text within them)
  • [x] Stored on the models as properties like we do for VWP
• [ ] Document Formatting:
  • [ ] Adding figure captions for the ftable (all!) figures
  • [x] Editing the output width/height to see if we can fit 2 figures per page (ftables & barplots)

  • [ ] Setting the model comparison scatter plots to a 1:1 ratio - while displaying the entire figure well (labels, readability)

I want to think of how we'd like to present model results to characterize the validation of the new hsp2 system as well as general model error analysis. Last WSP we did a basic error analysis in an appendix, based on error between models and usgs gages but got feedback from public comment period that it would benefit from more context, graphics, etc. We also did appendices with "minor basin" summaries, tho these were heavy on narrative and light on data (just summary flow stats).

So I see this as an opportunity to work on a flexible watershed summary that includes usgs error analysis. Something that is usable in both wsp and permit analysis, something that benefits the public understanding but also can benefit us modelers, and also be used for model validation (it should be also able to compare model to model).

This will ultimately form the "final report deliverable" for the harp project.

Retrieving JSON objects with all model results in them

• all model scenario results for a single model can be found here

  
  # json_obj_url is set in config.R

obj_url <- paste(json_obj_url, model$pid, sep="/") model_info <- ds$auth_read(obj_url, "text/json", "") model_info <- fromJSON(model_info ) elid <- model_info [[1]]$om_element_connection$value

----------------
### Running the RMD
#### Compare 2 hsp2/hspf models
   - Rmd: https://github.com/HARPgroup/HARParchive/blob/master/HARP-2022-Summer/AutomatedScripts/ws_model_summary.Rmd 
   - Example arguments: 
```r
  rmarkdown::render('C:/Users/VT_SA/Documents/GitHub/HARParchive/HARP-2022-Summer/AutomatedScripts/ws_model_summary.Rmd',
                                output_file = 'C:/Users/VT_SA/Documents/HARP/MarkdownSummaryTest',
                                params = list( doc_title = ("Test HSP2 Model Summary"),
                                rseg.file.path = c("/media/model/p6/out/river/hsp2_2022/hydr/JA4_7280_7340_hydrd_wy.csv", "/media/model/p6/out/river/subsheds/hydr/JA4_7280_7340_hydrd_wy.csv" ),
                                rseg.hydrocode = c("JA4_7280_7340","vahydrosw_wshed_JA4_7280_7340"),
                                rseg.ftype = c("cbp60","vahydro"),
                                rseg.model.version = c("cbp-6.0","cbp-6.1"),
                                runid.list = c("hsp2_2022","subsheds"),
                                rseg.metric.list = c("7q10", "l90_Qout", "l30_year")
                                )
                  )

3 models Including OM, hspf and hsp2

rmarkdown::render(
   '/usr/local/home/git/HARParchive/HARP-2022-2023/AutomatedScripts/ws_model_summary.Rmd',
   output_file = '/WorkSpace/modeling/projects/rappahannock/cia/calib_rapidan.docx',
   params = list( 
      doc_title = ("Test HSP2 Model Summary"),
      rseg.file.path = c("http://deq1.bse.vt.edu:81/data/proj3/out/runlog400.257087.log", "http://deq1.bse.vt.edu:81/p6/out/river/hsp2_2022/hydr/RU2_6090_6220_hydrd_wy.csv", "http://deq1.bse.vt.edu:81/p6/out/river/subsheds/hydr/RU2_6090_6220_hydrd_wy.csv" ),
      rseg.hydrocode = c("vahydrosw_wshed_RU2_6090_6220", "RU2_6090_6220","vahydrosw_wshed_RU2_6090_6220"),
      rseg.ftype = c("vahydro", "cbp60","vahydro"),
      ftable.submod = c("p6", "p6","p6"),
      ftable.id = c("vahydro_2022", "vahydro_2022","vahydro_2022"),
      rseg.model.version = c("vahydro-1.0","cbp-6.0","cbp-6.1"),
      seg = c("RU2_6090_6220", "RU2_6090_6220","RU2_6090_6220"),
      runid.list = c("runid_400", "hsp2_2022","subsheds"),
      rseg.metric.list = c("7q10", "l90_Qout", "l30_year", "l30_Qout", "l7_Qout")
   )
)

---

Resulting Document Layout:

1. Title
2. Table of Contents
3. Summarizing table of the models' parameters
4. Displaying VAHydro pulled figures & Ftables
5. Generating model comparison figures from the data
6. Summary table of wanted metrics from VAHydro

[jdkleiner]

Ideas of items to include in summary:

Maps

• https://github.com/HARPgroup/HARParchive/issues/749

Products developed for 2020 State Plan minor basin summaries:

• Link to State Plan: https://www.deq.virginia.gov/home/showpublisheddocument/16134/637991887436000000

Items in State Plan Appendix Model Error and Predictive Uncertainty:

Analyses developed for VWP modelling projects:

• USGS Gage vs VAHydro Model - R Markdown: https://github.com/HARPgroup/vahydro/issues/752

HARP 2018-2019 Dashboard PDFs (DEQ model vs USGS gage comparisons):

• https://github.com/HARPgroup/HARParchive/tree/master/HARP-2018-2019
• Example: james river.pdf

[juliabruneau]

In order for the Analysts to help summarize the watershed model summary, we have started on a document that outlines our work from the summer and fall. This document will hopefully give background information about the switch from hspf to hsp2, and then more specific information on the steps that we have taken to adapt data from h5's, analyzing it, and how data is in VAHydro. In the appendix we will provide example figures/graphs on examples about the analysis we have done. Finally, a summary on key statistics of the analysts' work and next steps in the project for the spring.

Table of contents for now:

1. Introduction
2. Watershed Modeling by the Virginia Department of Environmental Quality
3. The Hydrological Simulation Program (HSP) 3.1 HSP Model Run and VAHydro Data Processing Workflow 3.1.1 Data from HSP Model Runs 3.1.2 Organizing Data 3.1.3 Summarizing Data 3.1.4 Data in VAHydro (ftables??)
4. Statistics on HARP Analysts’ Work
5. Next Steps in the Project References Appendix A. Example Figures B. List of Scripts and Their Uses

Does this sound like a useful piece of documentation for the final report from the harp project? Is there anything that we should add? We can start working on this, and then at some point in January provide a draft for review.

[durelles]

Yes - I almost capitalized and bolder:). Thanks, S

On Dec 16, 2022, at 4:32 PM, juliabruneau @.***> wrote:

In order for the Analysts to help summarize the watershed model summary, we have started on a document that outlines our work from the summer and fall. This document will hopefully give background information about the switch from hspf to hsp2, and then more specific information on the steps that we have taken to adapt data from h5's, analyzing it, and how data is in VAHydro. In the appendix we will provide example figures/graphs on examples about the analysis we have done. Finally, a summary on key statistics of the analysts' work and next steps in the project for the spring.

Table of contents for now:

Introduction Watershed Modeling by the Virginia Department of Environmental Quality The Hydrological Simulation Program (HSP) 3.1 HSP Model Run and VAHydro Data Processing Workflow 3.1.1. Data from HSP Model Runs 3.1.2. Organizing Data 3.1.3. Summarizing Data 3.1.4. Data in VAHydro (ftables??) Statistics on HARP Analysts’ Work Next Steps in the Project References Appendix A. Example Figures B. List of Scripts and Their Uses Does this sound like a useful piece of documentation for the final report from the harp project? Is there anything that we should add? We can start working on this, and then at some point in January provide a draft for review.

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[jdkleiner]

@juliabruneau Yes, I think this list looks great and will form some excellent documentation of your work on this project!

For the watershed model summaries we've started outlining here, I believe the idea is to come up with a way to produce a dynamic R Markdown, capable of receiving inputs for watershed of interest and model runs of interest, then the script will produce a tidy document characterizing model validation and error analysis comparing scenario runs of HSPF to HSP2, or HSP2 to HSP2, or HSP2 to usgs gage and so on. The R Markdown document would contain maps, figures, tables and metric comparisons.

[jdkleiner]

See latest mapping script: https://github.com/HARPgroup/HARParchive/issues/749

[juliabruneau]

New Metric Generation

The scenario_vs_scenario.R file is now renamed in HARParchive/HARP-2022-Summer/AutomatedScripts as ws_model_summary.R (as discussed in initial meeting).

File: https://github.com/HARPgroup/HARParchive/blob/master/HARP-2022-Summer/AutomatedScripts/ws_model_summary.Rmd

I have included these stats in addition to the pre-existing l90, l30, l7 & l1 stats:

• 7q10
• August low flow (ml8)
• September lowest 10% (mne9_10)
• Qout

All of these stats are the ones we have previously calculated and pushed to VAHydro (that rely solely on Qout).

I used the pre-existing riverseg: PM2_4860_4670 and gageid: 01643700 from the rmd file, since I'm not 100% sure how to match gages with river segments.

[rburghol]

Automated testing: https://github.com/HARPgroup/vahydro/issues/309

[juliabruneau]

json error

Ran the chunk:

rmarkdown::render('C:/Users/VT_SA/Documents/GitHub/HARParchive/HARP-2022-Summer/AutomatedScripts/ws_model_summary.Rmd',
+                   output_file = 'C:/Users/VT_SA/Documents/HARP/MarkdownSummaryTest',
+                   params = list( doc_title = ("Test HSP2 Model Summary"),
+                                  rseg.file.path = c( "/media/model/p6/out/river/hsp2_2022/hydr/JA4_7280_7340_hydrd_wy.csv", "path/to/flow/data" ),
+                                  rseg.hydrocode = c("JA4_7280_7340","vahydrosw_wshed_JA4_7280_7340"),
+                                  rseg.ftype = c("cbp60","vahydro"),
+                                  rseg.model.version = c("cbp-6.0","cbp-6.1"),
+                                  runid.list = c("hsp2_2022","subshed"),
+                                  rseg.metric.list = c("7q10", "l90_Qout", "l30_year")
+                   )
+ )

Received this error:

Quitting from lines 55-115 (ws_model_summary.Rmd) 
Error in fromJSON(rseg_model_info.r) : 
  json_str must be a character string

This is the source of the error (line 4):

if (exists("json_obj_url")) {
  rseg_obj_url.r <- paste(json_obj_url, rseg.model.r$pid, sep="/")
  rseg_model_info.r <- ds$auth_read(rseg_obj_url.r, "text/json", "")
  rseg_model_info.r <- fromJSON(rseg_model_info.r)

rseg_obj_url.r
[1] "http://deq1.bse.vt.edu:81/d.dh/node/62/7042044"
>   rseg_model_info.r <- ds$auth_read(rseg_obj_url.r, "text/json", "")
> rseg_model_info.r
[1] FALSE

[glenncampagna]

Hydr files for hsp2 and hspf for segment JL1_6770_6850 should be ready for comparison

These parameters are for running the rmd:

rseg.file.path = c( "/media/model/p6/out/river/hsp2_2022/hydr/JL1_6770_6850_hydrd_wy.csv", "/media/model/p6/out/river/vadeq_2021/hydr/JL1_6770_6850_hydrd_wy.csv" ),
rseg.hydrocode = c("vahydrosw_wshed_JL1_6770_6850","vahydrosw_wshed_JL1_6770_6850"),
rseg.ftype = c("vahydro","vahydro"),
rseg.model.version = c("cbp-6.0","cbp-6.0”),
runid.list = c("hsp2_2022”, ”vadeq_2021”),

[juliabruneau]

Work In Progress 4/10 & 4/12

• [x] How can we make them span the width automatically; not at a set width (0.5 right now)?
• [x] Make the image quality better
• [x] Reduce the space around the images

Currently using grid.arrange to display images side-by-side, problems:

• Can be only used with "grobs" = grid graphical objects
  • Using ggplot generates grobs, but the quality of the images are bad, since ggraw() downloads a screengrab of the png
  • Using ![ ](url) generates high-quality images, but they are not grob objects
• Investigating further how high quality images could be plotted side-by-side

Quality with GRID.ARRANGE(): VS Quality with ![ ](url) for example

Matching the time series with match():

• I am successful in matching the time series with 2 data series - when they are not in a loop

• Current loop gives us NA for the sdate and edate

  • I don't fully understand how match() works as a function / the output

    
    for (r in 1:length(rseg.model.version)) {
    rseg.data.r <- data.table::fread(rseg.file.path[r])
    assign(paste0('rseg.data.',r),rseg.data.r)
    rseg.cols.r <- names(rseg.data.r)

  if (r == 1){ data.match <- rseg.data.r$date sdate <- as.Date(min(data.match)) edate <- as.Date(max(data.match)) } else { data.match <- match(data.match, rseg.data.r) sdate <- as.Date(min(data.match)) edate <- as.Date(max(data.match)) } }

for (r in 1:length(rseg.model.version)) { rseg.flow.r <- zoo(as.numeric(as.character(rseg.data.r$Qout)), order.by = rseg.data.r$index) rseg.flow.r <- window(rseg.flow.r, start = sdate, end = edate) mode(rseg.flow.r) <- 'numeric'

assign(paste0('rseg.flow.',r),rseg.flow.r) }

- [x] Investigate `match()` further / another solution?

[rburghol]

This is excellent progress @juliabruneau -- I have one suggestion for the width parameter:

• col_width <- 1.0 / num_models
• And we should put our efforts into high quality images.
• match seems intriguing, will this just automatically create an overlapping timeseries?

[juliabruneau]

Matching datasets and Bar Plots

The code to use match() for having identical date ranges for comparison seems to work:

> head(rseg.flow.1)
1984-10-01 11:30:00 1984-10-02 11:30:00 1984-10-03 11:30:00 1984-10-04 11:30:00
           178.3537            235.1851            225.2305            199.9935
1984-10-05 11:30:00 1984-10-06 11:30:00
           175.5761            156.1415
> head(rseg.flow.2)
1984-10-01 11:30:00 1984-10-02 11:30:00 1984-10-03 11:30:00 1984-10-04 11:30:00
           178.3537            235.1851            225.2305            199.9935
1984-10-05 11:30:00 1984-10-06 11:30:00
           175.5761            156.1415

> tail(rseg.flow.1)
2005-12-26 11:30:00 2005-12-27 11:30:00 2005-12-28 11:30:00 2005-12-29 11:30:00
          1005.2903            835.9024            664.5346            643.3333
2005-12-30 11:30:00 2005-12-31 11:30:00
           610.4067            546.9785
> tail(rseg.flow.2)
2005-12-26 11:30:00 2005-12-27 11:30:00 2005-12-28 11:30:00 2005-12-29 11:30:00
          1005.2903            835.9024            664.5346            643.3333
2005-12-30 11:30:00 2005-12-31 11:30:00
           610.4067            546.9785

I took a random 3rd dataset; it kept the same start date, and changed the end date of all the datasets to 1991:

> tail(rseg.flow.1)
1991-03-29 11:30:00 1991-03-30 11:30:00 1991-03-31 11:30:00 1991-04-01 11:30:00
           803.8933            738.3517            470.9966            338.3706
1991-04-02 11:30:00 1991-04-03 11:30:00
           270.7234            233.8215
> tail(rseg.flow.2)
1991-03-29 11:30:00 1991-03-30 11:30:00 1991-03-31 11:30:00 1991-04-01 11:30:00
           803.8933            738.3517            470.9966            338.3706
1991-04-02 11:30:00 1991-04-03 11:30:00
           270.7234            233.8215
> tail(rseg.flow.3)
1991-03-29 11:30:00 1991-03-30 11:30:00 1991-03-31 11:30:00 1991-04-01 11:30:00
           803.8933            738.3517            470.9966            338.3706
1991-04-02 11:30:00 1991-04-03 11:30:00
           270.7234

The 90-day low flow bar plot generated using the IHA group2() function (as it is in the output document):

[juliabruneau]

Model Performance Scatterplots & %-Diff Boxplot

I was able to generate scatterplots for all different low flow scenarios. Right now, the output figure height should still be edited so that the figures aren't as squished. But this is what it looks like right now:

Here's a closer look of just one plot of each:

Update: Created a %-Diff Boxplot:

• Don't know if we should include 7q10 as well

[juliabruneau]

Loop bug

This loop is not generating unique datasets for some reason:

for (r in 1:length(rseg.model.version)) {
  rseg.flow.r <- zoo(as.numeric(as.character(rseg.data.r$Qout)), order.by = rseg.data.r$index)
  rseg.flow.r <- window(rseg.flow.r, start = sdate, end = edate)
  mode(rseg.flow.r) <- 'numeric'

  assign(paste0('rseg.flow.',r),rseg.flow.r)
}

Result - named uniquely but the data is identical from model2:

> head(rseg.flow.1)
1984-10-01 11:30:00 1984-10-02 11:30:00 1984-10-03 11:30:00 1984-10-04 11:30:00 1984-10-05 11:30:00 1984-10-06 11:30:00 
           178.3537            235.1851            225.2305            199.9935            175.5761            156.1415 
> head(rseg.flow.2)
1984-10-01 11:30:00 1984-10-02 11:30:00 1984-10-03 11:30:00 1984-10-04 11:30:00 1984-10-05 11:30:00 1984-10-06 11:30:00 
           178.3537            235.1851            225.2305            199.9935            175.5761            156.1415

input data in the zoo function:

> head(rseg.data.1$Qout)
[1] 180.3891 235.8186 224.8300 199.4006 175.1088 155.8118
> head(rseg.data.2$Qout)
[1] 178.3537 235.1851 225.2305 199.9935 175.5761 156.1415

> head(rseg.data.1$index)
[1] "1984-10-01 11:30:00" "1984-10-02 11:30:00" "1984-10-03 11:30:00" "1984-10-04 11:30:00" "1984-10-05 11:30:00"
[6] "1984-10-06 11:30:00"
> head(rseg.data.2$index)
[1] "1984-10-01 11:30:00" "1984-10-02 11:30:00" "1984-10-03 11:30:00" "1984-10-04 11:30:00" "1984-10-05 11:30:00"
[6] "1984-10-06 11:30:00"

[juliabruneau]

Model Comparison with 3 Datasets

Scatterplots

• The first if loop runs if the length() > 2, so that Model 1 and Model 2 are compared
  • EX: output stored as plot
• The second if loop runs if the legth() == 3, and thus the comparison between Model 1 and Model 3 are added onto the previously generated plots from the first loop
  • EX: plot <- plot + ggline() ...
• Then those plots are displayed in a grid

Future Work

• As of now, all l90, l30, l7 and l1 plots are being plotted one by one, with the only difference being the variable/column names and datasets
  • 4 functions could easily be done where the inputs are:
  • dataset,x , y
  • There would be 2 functions to generate the primary plots, and 2 functions in the second loop to add on to the primary ones

Creating functions would significantly shorten the markdown, since we do have to have many lines within these ggplot figure generation codes (different labels and axis names & a legend if 3 models).

• I am prioritizing getting all the necessary coding done, but this could be future work when fine tuning the code. (@rburghol, @jdkleiner, @glenncampagna )

Example JUST to generate l90 figures with 3 Models:

First Loop:

l90.scatter <- ggplot(l90.data, aes(x = get(names(l90.data)[2]), y = get(names(l90.data)[3]), 
                                    color = "Model 2")) +
  geom_point() +
  geom_smooth(formula = y ~ x, method=lm) +
  theme_light() +
  theme(axis.title.x = element_text(size = 9),
        axis.title.y = element_text(size = 9),
        legend.position = "none") +
  ylab(colnames(l90.data[2])) +
  xlab(colnames(l90.data[3])) +
  geom_abline(intercept = 0, slope = 1, col = 'lightgray')

  l90_diff <- unlist(l90.data[2] - l90.data[3]) # has to unlist in order to plot with ggplot!
  l90_diff_pct <- 100.0 * (l90_diff) / l90.data[3]

  l90.diff <- ggplot(l90.data, aes(x = get(names(l90.data)[1]), y = l90_diff,
                                   color = "Model 2")) +
    geom_point() +
    geom_smooth(formula = y ~ x, method=lm) +
    theme_light() +
    theme(axis.title.x = element_text(size = 9),
          axis.title.y = element_text(size = 9),
          legend.position = "none") +
    ylab(paste("Difference: Model 1 - Model 2")) +
    xlab(paste("Year"))

Second Loop:

legend_colors <- c("Model 2" = "blue", "Model 3" = "red") 

  l90.scatter <- l90.scatter +
    geom_point(l90.data, mapping = aes(x = get(names(l90.data)[2]), y = get(names(l90.data)[4]), 
                                      color = "Model 3")) + 
    geom_smooth(formula = y ~ x, method=lm) +
    ylab(paste("Model 2 and 3")) +
    xlab(paste("Model 1")) +
    labs(color = "Model 1 vs:") + 
    scale_color_manual(values = legend_colors) +
    theme(legend.title = element_text(size=10),
          legend.text = element_text(size=10),
          legend.position = "top",
          legend.key.size = unit(0.25,"cm"))

  l90_diff.2 <- unlist(l90.data[2] - l90.data[4])
  l90_diff_pct.2 <- 100.0 * (l90_diff) / l90.data[4]

  l90.diff <- l90.diff +
    geom_point(l90.data, mapping = aes(x =get(names(l90.data)[1]), y = l90_diff.2, 
                                      color = "Model 3")) +
    geom_smooth(formula = y ~ x, method=lm) +
    ylab(paste("Model Difference")) +
    xlab(paste("Year")) +
    labs(color = "Model 1 vs:") + 
    scale_color_manual(values = legend_colors) +
    theme(legend.title = element_text(size=10),
          legend.text = element_text(size=10),
          legend.position = "top",
          legend.key.size = unit(0.25,"cm"))

[glenncampagna]

gregexec() error preventing knitting -- only analyst w/ this problem

I've been getting this error from rendering and chunk-running the rmd:

Error in gregexec(pattern = what, x$txt, ...) : 
  could not find function "gregexec"

and it shows up from this line: model.info <- model.comparison %>% as.data.frame() %>% add_rownames() %>% qflextable() I believe originating from the qflextable() function From research it seems like the function is in base R, but maybe called by qflextable?

[glenncampagna]

Pulling length with additional params arguments

Additional variables now need to be set within the render command which are used to locate and read in the length needed by @megpritch these are ftable.submod , ftable.id , and seg more to be added after testing tmrw

[glenncampagna]

Current use & example using render() to knit

Use: rmarkdown::render([path to rmd], output_file = [output path], params = list( doc_title = ("Test HSP2 Model Summary"), rseg.file.path = c([hydr filepath1] , [hydr filepath2] ), rseg.hydrocode = c([hydrocode1],[hydrocode ]), rseg.ftype = c([ftype1],[ftype2]), rseg.model.version = c([version1],[version2]), runid.list = c([id1],[id2]), rseg.metric.list = c([list of metrics]), ftable.submod = c([submod1],[submod2]), ftable.id = c([fid1],[fid2]), seg = c([seg1],[seg2])))

Examples: (glenn's)

rmarkdown::render('~/Desktop/GitHub/HARParchive/HARP-2022-Summer/AutomatedScripts/ws_model_summary.Rmd', output_file = '~/Desktop/HARPteam22/ws_summ_doc1', params = list( doc_title = ("Test HSP2 Model Summary"), rseg.file.path = c("/media/model/p6/out/river/hsp2_2022/hydr/JA4_7280_7340_hydrd_wy.csv","/media/model/p6/out/river/subsheds/hydr/JA4_7280_7340_hydrd_wy.csv" ), rseg.hydrocode = c("JA4_7280_7340","vahydrosw_wshed_JA4_7280_7340"), rseg.ftype = c("cbp60","vahydro"), rseg.model.version = c("cbp-6.0","cbp-6.1"), runid.list = c("hsp2_2022","subsheds"), rseg.metric.list = c("Qout","l90_Qout","l30_Qout","l07_Qout","l01_Qout","consumptive_use_frac","wd_cumulative_mgd","ps_cumulative_mgd","wd_mgd","ps_mgd"), ftable.submod = c("p6", "p6"), ftable.id = c("vahydro_2022", "vahydro_2022"), seg = c("JA4_7280_7340", "JA4_7280_7340")))

(megan's)

rmarkdown::render('C:/aa_HARP/aa_GitHub/HARParchive/HARP-2022-Summer/AutomatedScripts/ws_model_summary.Rmd',
                  output_file = 'C:/aa_HARP/MarkdownFTABLEtest', params = list( doc_title = ("Test HSP2 Model Summary"), rseg.file.path = c("/media/model/p6/out/river/hsp2_2022/hydr/JA4_7280_7340_hydrd_wy.csv","/media/model/p6/out/river/subsheds/hydr/JA4_7280_7340_hydrd_wy.csv" ), rseg.hydrocode = c("JA4_7280_7340","vahydrosw_wshed_JA4_7280_7340"), rseg.ftype = c("cbp60","vahydro"), rseg.model.version = c("cbp-6.0","cbp-6.1"), runid.list = c("hsp2_2022","subsheds"), rseg.metric.list = c("Qout","l90_Qout","l30_Qout","l07_Qout","l01_Qout","consumptive_use_frac","wd_cumulative_mgd","ps_cumulative_mgd","wd_mgd","ps_mgd"), ftable.submod = c("p6", "p6"), ftable.id = c("vahydro_2022", "vahydro_2022"), seg = c("JA4_7280_7340", "JA4_7280_7340")))

• [x] modify rmd to iterate through lengths, not pulling from vahydro
• [x] read in f-tables by iteration, creating paths from esite and new params
• [x] locate ftable & length file paths to do full rmd test -- used workflow in https://github.com/HARPgroup/HARParchive/issues/817#issuecomment-1555000467

[megpritch]

Less FTABLE Clutter

Still some undesirable overlap on the y-axis with the channel length, but the depths are now more readable. We also removed the extra FTABLE plots showing the channel from different angles.

[jdkleiner]

@megpritch Nice work!

[glenncampagna]

Experimenting w/ % difference for metrics

• The % difference for low metrics between scenarios (2020, 2040, climate change) was used throughout the 2020 state plan and will likely be incorporated into summary documents for the 2023 project

• % difference columns were added to the metrics table at the end of the rmd and compare metric values to the first model supplied in the params (probably 2020 demand scenario -- runid_11)

• Examples for 2 and 3-model comparisons

• This may be better put in a separate table, and the % difference can be removed entirely if not useful

[rburghol]

Test CIA routine on Rappahannock. Looks tight:

rmarkdown::render('/usr/local/home/git/HARParchive/HARP-2022-Summer/AutomatedScripts/ws_model_summary.Rmd',
                             output_file = '/WorkSpace/modeling/projects/rappahannock/cia/cia_hsp2022_subsheds.docx',
                             params = list( doc_title = ("Test HSP2 Model Summary"),
                             rseg.file.path = c("http://deq1.bse.vt.edu:81/p6/out/river/hsp2_2022/hydr/RU5_6030_0001_hydrd_wy.csv", "http://deq1.bse.vt.edu:81/p6/out/river/subsheds/hydr/RU5_6030_0001_hydrd_wy.csv" ),
                             rseg.hydrocode = c("RU5_6030_0001","vahydrosw_wshed_RU5_6030_0001"),
                             rseg.ftype = c("cbp60","vahydro"),
                             rseg.model.version = c("cbp-6.0","cbp-6.1"),
                             runid.list = c("hsp2_2022","subsheds"),
                             rseg.metric.list = c("7q10", "l90_Qout", "l30_year")
                            )
               )

[rburghol]

Testd beaver creek (subwatershed):

rmarkdown::render('/usr/local/home/git/vahydro/R/OWS_summaries/ws_model_summary.Rmd',
output_file = '/WorkSpace/modeling/projects/james_river/rivanna/beaver_hsp2/cia_vahydro_subsheds.docx',
params = list( doc_title = ("Test HSP2 Model Summary"),
rseg.file.path = c("http://deq1.bse.vt.edu:81/data/proj3/out/runlog600.351963.log", "http://deq1.bse.vt.edu:81/p6/out/river/subsheds/hydr/JL1_6562_6560_hydrd_wy.csv" ),
rseg.hydrocode = c("vahydrosw_wshed_JL1_6562_6560","vahydrosw_wshed_JL1_6562_6560"),
rseg.ftype = c("vahydro","vahydro"),
rseg.model.version = c("vahydro-1.0","cbp-6.1"),
runid.list = c("runid_600","subsheds"),
rseg.metric.list = c("7q10", "l90_Qout", "l30_year")
)
)