Yampa River Freshwater Decarbonization

Gray or Green Infrastructure

The Yampa River in northwest Colorado is one of the few free-flowing rivers in the western United States and a major tributary of the Colorado River. Virridy is monitoring water quality and applying our patent data-science technologies along a 40 mile stretch of the river, measuring how the organic loading in the river is impacted by agriculture, ranches, towns and wastewater utilities.

Virridy is working with several utilities along this stretch of river to identify how nature-based watershed restoration projects could improve river water quality, and avoid the construction of expensive, greenhouse gas emitting wastewater treatment plant upgrades, while generating carbon credits to subsidize the watershed solutions.

In-Situ Sensor Data

ML Semi Quantification Model

  selected_sensor selected_lab
1       Turbidity    Turbidity

Registered S3 method overwritten by 'quantmod':
  method            from
  as.zoo.data.frame zoo 

Confusion Matrix and Statistics

          Reference
Prediction [-Inf,6) [6, Inf]
  [-Inf,6)       31        6
  [6, Inf]        4       21
                                          
               Accuracy : 0.8387          
                 95% CI : (0.7233, 0.9198)
    No Information Rate : 0.5645          
    P-Value [Acc > NIR] : 0.000004221     
                                          
                  Kappa : 0.6692          
                                          
 Mcnemar's Test P-Value : 0.7518          
                                          
            Sensitivity : 0.8857          
            Specificity : 0.7778          
         Pos Pred Value : 0.8378          
         Neg Pred Value : 0.8400          
             Prevalence : 0.5645          
         Detection Rate : 0.5000          
   Detection Prevalence : 0.5968          
      Balanced Accuracy : 0.8317          
                                          
       'Positive' Class : [-Inf,6)        
                                          

Confusion Matrix and Statistics

          Reference
Prediction  0  1
         0 29  9
         1  1 23
                                          
               Accuracy : 0.8387          
                 95% CI : (0.7233, 0.9198)
    No Information Rate : 0.5161          
    P-Value [Acc > NIR] : 0.0000001089    
                                          
                  Kappa : 0.6798          
                                          
 Mcnemar's Test P-Value : 0.02686         
                                          
            Sensitivity : 0.7188          
            Specificity : 0.9667          
         Pos Pred Value : 0.9583          
         Neg Pred Value : 0.7632          
             Prevalence : 0.5161          
         Detection Rate : 0.3710          
   Detection Prevalence : 0.3871          
      Balanced Accuracy : 0.8427          
                                          
       'Positive' Class : 1               
                                          

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

  selected_sensor selected_lab
2    Conductivity Conductivity

Confusion Matrix and Statistics

            Reference
Prediction   [-Inf,200) [200, Inf]
  [-Inf,200)         20          2
  [200, Inf]          5         85
                                          
               Accuracy : 0.9375          
                 95% CI : (0.8755, 0.9745)
    No Information Rate : 0.7768          
    P-Value [Acc > NIR] : 0.00000389      
                                          
                  Kappa : 0.8117          
                                          
 Mcnemar's Test P-Value : 0.4497          
                                          
            Sensitivity : 0.8000          
            Specificity : 0.9770          
         Pos Pred Value : 0.9091          
         Neg Pred Value : 0.9444          
             Prevalence : 0.2232          
         Detection Rate : 0.1786          
   Detection Prevalence : 0.1964          
      Balanced Accuracy : 0.8885          
                                          
       'Positive' Class : [-Inf,200)      
                                          

Confusion Matrix and Statistics

          Reference
Prediction  0  1
         0 23  5
         1  2 82
                                          
               Accuracy : 0.9375          
                 95% CI : (0.8755, 0.9745)
    No Information Rate : 0.7768          
    P-Value [Acc > NIR] : 0.00000389      
                                          
                  Kappa : 0.8272          
                                          
 Mcnemar's Test P-Value : 0.4497          
                                          
            Sensitivity : 0.9425          
            Specificity : 0.9200          
         Pos Pred Value : 0.9762          
         Neg Pred Value : 0.8214          
             Prevalence : 0.7768          
         Detection Rate : 0.7321          
   Detection Prevalence : 0.7500          
      Balanced Accuracy : 0.9313          
                                          
       'Positive' Class : 1               
                                          

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

  selected_sensor selected_lab
3            FDOM          TOC

Confusion Matrix and Statistics

          Reference
Prediction [-Inf,5) [5, Inf]
  [-Inf,5)       30        6
  [5, Inf]       11       83
                                          
               Accuracy : 0.8692          
                 95% CI : (0.7989, 0.9219)
    No Information Rate : 0.6846          
    P-Value [Acc > NIR] : 0.0000009134    
                                          
                  Kappa : 0.6869          
                                          
 Mcnemar's Test P-Value : 0.332           
                                          
            Sensitivity : 0.7317          
            Specificity : 0.9326          
         Pos Pred Value : 0.8333          
         Neg Pred Value : 0.8830          
             Prevalence : 0.3154          
         Detection Rate : 0.2308          
   Detection Prevalence : 0.2769          
      Balanced Accuracy : 0.8321          
                                          
       'Positive' Class : [-Inf,5)        
                                          

Confusion Matrix and Statistics

          Reference
Prediction  0  1
         0 41 16
         1  0 73
                                         
               Accuracy : 0.8769         
                 95% CI : (0.8078, 0.928)
    No Information Rate : 0.6846         
    P-Value [Acc > NIR] : 0.000000287    
                                         
                  Kappa : 0.7421         
                                         
 Mcnemar's Test P-Value : 0.0001768      
                                         
            Sensitivity : 0.8202         
            Specificity : 1.0000         
         Pos Pred Value : 1.0000         
         Neg Pred Value : 0.7193         
             Prevalence : 0.6846         
         Detection Rate : 0.5615         
   Detection Prevalence : 0.5615         
      Balanced Accuracy : 0.9101         
                                         
       'Positive' Class : 1              
                                         

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

  selected_sensor selected_lab
4           Chl-a     Kjeldahl

Confusion Matrix and Statistics

            Reference
Prediction   [-Inf,0.3) [0.3, Inf]
  [-Inf,0.3)         88         13
  [0.3, Inf]          5         24
                                        
               Accuracy : 0.8615        
                 95% CI : (0.79, 0.9158)
    No Information Rate : 0.7154        
    P-Value [Acc > NIR] : 0.00006388    
                                        
                  Kappa : 0.6363        
                                        
 Mcnemar's Test P-Value : 0.09896       
                                        
            Sensitivity : 0.9462        
            Specificity : 0.6486        
         Pos Pred Value : 0.8713        
         Neg Pred Value : 0.8276        
             Prevalence : 0.7154        
         Detection Rate : 0.6769        
   Detection Prevalence : 0.7769        
      Balanced Accuracy : 0.7974        
                                        
       'Positive' Class : [-Inf,0.3)    
                                        

Confusion Matrix and Statistics

          Reference
Prediction  0  1
         0 86  9
         1  7 28
                                         
               Accuracy : 0.8769         
                 95% CI : (0.8078, 0.928)
    No Information Rate : 0.7154         
    P-Value [Acc > NIR] : 0.000008898    
                                         
                  Kappa : 0.6928         
                                         
 Mcnemar's Test P-Value : 0.8026         
                                         
            Sensitivity : 0.7568         
            Specificity : 0.9247         
         Pos Pred Value : 0.8000         
         Neg Pred Value : 0.9053         
             Prevalence : 0.2846         
         Detection Rate : 0.2154         
   Detection Prevalence : 0.2692         
      Balanced Accuracy : 0.8407         
                                         
       'Positive' Class : 1              
                                         

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

USGS Stream Temperature Data

These are the existing USGS stream temperature monitoring stations.

Yampa River Steamboat Springs Reach

IDW Interpolation

IDW Shiny App

Urban Sky Land Cover Image Analysis

You have loaded FedData v4.
As of FedData v4 we have retired
dependencies on the `sp` and `raster` packages.
All functions in FedData v4 return `terra` (raster)
or `sf` (vector) objects by default, and there may be
other breaking changes.

The legacy packages maptools, rgdal, and rgeos, underpinning the sp package,
which was just loaded, will retire in October 2023.
Please refer to R-spatial evolution reports for details, especially
https://r-spatial.org/r/2023/05/15/evolution4.html.
It may be desirable to make the sf package available;
package maintainers should consider adding sf to Suggests:.
The sp package is now running under evolution status 2
     (status 2 uses the sf package in place of rgdal)

Attaching package: 'rasterVis'

The following object is masked from 'package:gam':

    gplot

Loading required package: sp

Please note that rgdal will be retired during 2023,
plan transition to sf/stars/terra functions using GDAL and PROJ
at your earliest convenience.
See https://r-spatial.org/r/2022/04/12/evolution.html and https://github.com/r-spatial/evolution
rgdal: version: 1.6-5, (SVN revision 1199)
Geospatial Data Abstraction Library extensions to R successfully loaded
Loaded GDAL runtime: GDAL 3.5.3, released 2022/10/21
Path to GDAL shared files: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/rgdal/gdal
 GDAL does not use iconv for recoding strings.
GDAL binary built with GEOS: TRUE 
Loaded PROJ runtime: Rel. 9.1.0, September 1st, 2022, [PJ_VERSION: 910]
Path to PROJ shared files: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/rgdal/proj
PROJ CDN enabled: FALSE
Linking to sp version:1.6-0
To mute warnings of possible GDAL/OSR exportToProj4() degradation,
use options("rgdal_show_exportToProj4_warnings"="none") before loading sp or rgdal.

Attaching package: 'reshape2'

The following object is masked from 'package:tidyr':

    smiths

The following objects are masked from 'package:data.table':

    dcast, melt

terra 1.7.39

Attaching package: 'terra'

The following object is masked from 'package:scales':

    rescale

The following object is masked from 'package:rgdal':

    project

The following object is masked from 'package:tidyr':

    extract

The following object is masked from 'package:ggpubr':

    rotate

The following object is masked from 'package:data.table':

    shift

class      : RasterLayer 
dimensions : 4311, 2513, 10833543  (nrow, ncol, ncell)
resolution : 30.1, 29.6  (x, y)
extent     : 289422, 365063.3, 4417034, 4544639  (xmin, xmax, ymin, ymax)
crs        : +proj=utm +zone=13 +datum=WGS84 +units=m +no_defs 
source     : nlcd_1.tif 
names      : nlcd_1 
values     : 11, 95  (min, max)

# A tibble: 20 × 4
      ID Class                        Color   Description                       
   <dbl> <chr>                        <chr>   <chr>                             
 1    11 Open Water                   #5475A8 Areas of open water, generally wi…
 2    12 Perennial Ice/Snow           #FFFFFF Areas characterized by a perennia…
 3    21 Developed, Open Space        #E8D1D1 Areas with a mixture of some cons…
 4    22 Developed, Low Intensity     #E29E8C Areas with a mixture of construct…
 5    23 Developed, Medium Intensity  #ff0000 Areas with a mixture of construct…
 6    24 Developed High Intensity     #B50000 Highly developed areas where peop…
 7    31 Barren Land (Rock/Sand/Clay) #D2CDC0 Areas of bedrock, desert pavement…
 8    41 Deciduous Forest             #85C77E Areas dominated by trees generall…
 9    42 Evergreen Forest             #38814E Areas dominated by trees generall…
10    43 Mixed Forest                 #D4E7B0 Areas dominated by trees generall…
11    51 Dwarf Scrub                  #AF963C Alaska only areas dominated by sh…
12    52 Shrub/Scrub                  #DCCA8F Areas dominated by shrubs; less t…
13    71 Grassland/Herbaceous         #FDE9AA Areas dominated by gramanoid or h…
14    72 Sedge/Herbaceous             #D1D182 Alaska only areas dominated by se…
15    73 Lichens                      #A3CC51 Alaska only areas dominated by fr…
16    74 Moss                         #82BA9E Alaska only areas dominated by mo…
17    81 Pasture/Hay                  #FBF65D Areas of grasses, legumes, or gra…
18    82 Cultivated Crops             #CA9146 Areas used for the production of …
19    90 Woody Wetlands               #C8E6F8 Areas where forest or shrubland v…
20    95 Emergent Herbaceous Wetlands #64B3D5 Areas where perennial herbaceous …

Output at: szoom.gif

[1] TRUE

NTT

#Coordinate with Garrett

Streamflow Analysis