From 96af6e0bcc795d9c0d58b79ce6241429a9b8209b Mon Sep 17 00:00:00 2001 From: Beaudette Date: Wed, 18 Sep 2024 11:30:22 -0700 Subject: [PATCH] new metadata, better maps --- AQP/soilDB/fetchSCAN-demo.Rmd | 54 +++++--- AQP/soilDB/fetchSCAN-demo.html | 223 ++++++++++++++------------------- 2 files changed, 128 insertions(+), 149 deletions(-) diff --git a/AQP/soilDB/fetchSCAN-demo.Rmd b/AQP/soilDB/fetchSCAN-demo.Rmd index af41c03..89c9c18 100644 --- a/AQP/soilDB/fetchSCAN-demo.Rmd +++ b/AQP/soilDB/fetchSCAN-demo.Rmd @@ -62,41 +62,57 @@ library(sharpshootR) library(reshape2) library(latticeExtra) library(tactile) -library(maps) +library(terra) +library(spData) -# get basic sensor metadata for several SCAN/SNOTEL sites -m <- SCAN_site_metadata(site.code=c(2072,356,2148,2187)) +# get basic station metadata for several SCAN/SNOTEL sites +m <- SCAN_site_metadata(site.code = c(2072, 356, 2148, 2187)) -knitr::kable(m, row.names = FALSE) +# select columns from station metadata +knitr::kable(m[, c('Site', 'Name', 'Network', 'HUC', 'upedonid')], row.names = FALSE) # result is a list -x <- fetchSCAN(site.code=c(574), year=c(2016)) +x <- fetchSCAN(site.code = c(574), year = c(2016)) -# print a list of sensor types -names(x[-1]) +# print a list of sensor types and associated metadata +names(x) ``` ## Metadata for all Stations -```{r, fig.width=8, fig.height=7} +```{r} data('SCAN_SNOTEL_metadata', package = 'soilDB') -map('state', lwd=2) -title(main='SCAN / SNOTEL Sites') -points(Latitude ~ Longitude, data=SCAN_SNOTEL_metadata, subset=Network == 'SCAN', bg='DarkRed', pch=21) -points(Latitude ~ Longitude, data=SCAN_SNOTEL_metadata, subset=Network == 'SNOTEL', bg='RoyalBlue', pch=21) +# number of stations by network +table(SCAN_SNOTEL_metadata$Network) +``` + +```{r, fig.width=8, fig.height=7} +data("us_states") +us_states <- vect(us_states) +us_states <- project(us_states, 'epsg:5070') -legend('bottomleft', legend=c('SCAN', 'SNOTEL'), pch=21, pt.bg=c('DarkRed', 'RoyalBlue'), bty='n') +s <- vect(SCAN_SNOTEL_metadata, geom = c('Longitude', 'Latitude'), crs = 'epsg:4269') +s <- project(s, 'epsg:5070') ``` + +```{r, fig.width=8, fig.height=7} +plot(us_states, axes = FALSE, main = 'SCAN, SNOTEL, CSCAN, SNOWLITE Stations') +points(s[s$Network %in% c('SCAN', 'CSCAN')], col = 'firebrick') +points(s[s$Network %in% c('SNTL', 'SNTLT')], col = 'royalblue') + +legend(x = -2029093, y = 801043.2, legend = c('SCAN / CSCAN', 'SNOTEL / SNOWLITE'), pch = 21, pt.bg = c('firebrick', 'royalblue'), bty = 'n') +``` + + ```{r, fig.width=8, fig.height=7} -map('state', lwd=2) -title(main='SCAN / SNOTEL Sites') -points(Latitude ~ Longitude, data=SCAN_SNOTEL_metadata, bg='RoyalBlue', pch=21, cex=0.85) -points(Latitude ~ Longitude, data=SCAN_SNOTEL_metadata, subset=!is.na(pedlabsampnum), bg='DarkRed', pch=22) +plot(us_states, axes = FALSE, main = 'SCAN, SNOTEL, CSCAN, SNOWLITE Stations') +points(s, col = 'royalblue') +points(s[!is.na(s$pedlabsampnum), ], col = 'firebrick') -legend('bottomleft', legend=c('All Stations', 'Linked to Lab Data'), pch=c(21, 22), pt.bg=c('RoyalBlue', 'DarkRed'), bty='n') +legend(x = -2029093, y = 801043.2, legend = c('All Stations', 'Linked to Lab Data'), pch = 21, pt.bg = c('royalblue', 'firebrick'), bty = 'n') ``` @@ -457,7 +473,7 @@ Get KSSL data for this site, via user pedon ID. This will include estimated para Previously the VG parameters had to be obtained manually from [these reports](https://ncsslabdatamart.sc.egov.usda.gov/rptExecute.aspx?p=34942&r=6&submit1=Get+Report). ```{r} # get KSSL data -s <- fetchKSSL(pedon_id = 'S08NV003003') +s <- fetchLDM(x = 'S08NV003003', what = 'upedonid') # VG parameters now returned by fetchKSSL() as of 2016-11-17 knitr::kable(horizons(s)[, c('hzn_desgn', 'hzn_top', 'hzn_bot', 'theta_r', 'theta_s', 'alpha', 'npar')]) diff --git a/AQP/soilDB/fetchSCAN-demo.html b/AQP/soilDB/fetchSCAN-demo.html index ac8737e..64a9b01 100644 --- a/AQP/soilDB/fetchSCAN-demo.html +++ b/AQP/soilDB/fetchSCAN-demo.html @@ -11,7 +11,7 @@ - + A Unified Interface to SCAN/SNOTEL Data @@ -354,7 +354,7 @@

A Unified Interface to SCAN/SNOTEL Data

D.E. Beaudette and J. Skovlin

-

2023-09-28

+

2024-09-18

@@ -402,128 +402,90 @@

Metadata for Select Stations

library(reshape2) library(latticeExtra) library(tactile) -library(maps) +library(terra) +library(spData) -# get basic sensor metadata for several SCAN/SNOTEL sites -m <- SCAN_site_metadata(site.code=c(2072,356,2148,2187)) +# get basic station metadata for several SCAN/SNOTEL sites +m <- SCAN_site_metadata(site.code = c(2072, 356, 2148, 2187)) -knitr::kable(m, row.names = FALSE) +# select columns from station metadata +knitr::kable(m[, c('Site', 'Name', 'Network', 'HUC', 'upedonid')], row.names = FALSE) -------------- + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + - - - - - - - - + + + + + + + + - - - - - - - - + + + + + + +
Site NameSiteState NetworkCountyElevation_ftLatitudeLongitude HUCclimstanm upedonidpedlabsampnum
Blue Lakes356CaliforniaSNOTELAlpine806738.60800-119.92437180400120101NANANA
Deep Springs2187CaliforniaSCANInyo539937.37222-117.97383180902010102Deep SpringsS2012CA02700213N97485
2072 Eros Data Center2072South Dakota SCANMinnehaha160243.73835-96.61455 101702031402Eros Data Center S2003SD09900103N0688
356Blue LakesSNTL180400120101NA
2148 Jordan Valley Cwma2148Idaho SCANOwyhee450842.94863-117.01060 170501080405 NANANA
2187Deep SpringsSCAN180902010102S2012CA027002 
# result is a list
-x <- fetchSCAN(site.code=c(574), year=c(2016))
+x <- fetchSCAN(site.code = c(574), year = c(2016))
 
-# print a list of sensor types
-names(x[-1])
-
##  [1] "STO"      "SAL"      "TAVG"     "TMIN"     "TMAX"     "PRCP"     "PREC"     "SNWD"    
-##  [9] "WTEQ"     "WDIRV"    "WSPDV"    "LRADT"    "metadata"
+# print a list of sensor types and associated metadata +names(x) +
##  [1] "SMS"      "STO"      "SAL"      "TAVG"     "TMIN"     "TMAX"     "PRCP"     "PREC"    
+##  [9] "SNWD"     "WTEQ"     "WDIRV"    "WSPDV"    "LRADT"    "metadata"

Metadata for all Stations

data('SCAN_SNOTEL_metadata', package = 'soilDB')
 
-map('state', lwd=2)
-title(main='SCAN / SNOTEL Sites')
-points(Latitude ~ Longitude, data=SCAN_SNOTEL_metadata, subset=Network == 'SCAN', bg='DarkRed', pch=21)
-points(Latitude ~ Longitude, data=SCAN_SNOTEL_metadata, subset=Network == 'SNOTEL', bg='RoyalBlue', pch=21)
-
-legend('bottomleft', legend=c('SCAN', 'SNOTEL'), pch=21, pt.bg=c('DarkRed', 'RoyalBlue'), bty='n')
-

-
map('state', lwd=2)
-title(main='SCAN / SNOTEL Sites')
-points(Latitude ~ Longitude, data=SCAN_SNOTEL_metadata, bg='RoyalBlue', pch=21, cex=0.85)
-points(Latitude ~ Longitude, data=SCAN_SNOTEL_metadata, subset=!is.na(pedlabsampnum), bg='DarkRed', pch=22)
-
-legend('bottomleft', legend=c('All Stations', 'Linked to Lab Data'), pch=c(21, 22), pt.bg=c('RoyalBlue', 'DarkRed'), bty='n')
-

+# number of stations by network +table(SCAN_SNOTEL_metadata$Network) +
## 
+## CSCAN  SCAN  SNTL SNTLT 
+##    21   210   910    45
+
data("us_states")
+us_states <- vect(us_states)
+us_states <- project(us_states, 'epsg:5070')
+
+s <- vect(SCAN_SNOTEL_metadata, geom = c('Longitude', 'Latitude'), crs = 'epsg:4269')
+s <- project(s, 'epsg:5070')
+
plot(us_states, axes = FALSE, main = 'SCAN, SNOTEL, CSCAN, SNOWLITE Stations')
+points(s[s$Network %in% c('SCAN', 'CSCAN')], col = 'firebrick')
+points(s[s$Network %in% c('SNTL', 'SNTLT')], col = 'royalblue')
+
+legend(x = -2029093, y = 801043.2, legend = c('SCAN / CSCAN', 'SNOTEL / SNOWLITE'), pch = 21, pt.bg = c('firebrick', 'royalblue'), bty = 'n')
+

+
plot(us_states, axes = FALSE, main = 'SCAN, SNOTEL, CSCAN, SNOWLITE Stations')
+points(s, col = 'royalblue')
+points(s[!is.na(s$pedlabsampnum), ], col = 'firebrick')
+
+legend(x = -2029093, y = 801043.2, legend = c('All Stations', 'Linked to Lab Data'), pch = 21, pt.bg = c('royalblue', 'firebrick'), bty = 'n')
+

@@ -540,20 +502,20 @@

Sensor and Site Data for a Single Station

# check the results str(x, 1) 
## List of 14
-##  $ SMS     :'data.frame':    6656 obs. of  8 variables:
-##  $ STO     :'data.frame':    7296 obs. of  8 variables:
+##  $ SMS     :'data.frame':    6656 obs. of  9 variables:
+##  $ STO     :'data.frame':    7296 obs. of  9 variables:
 ##  $ SAL     :'data.frame':    0 obs. of  0 variables
-##  $ TAVG    :'data.frame':    1459 obs. of  8 variables:
-##  $ TMIN    :'data.frame':    1459 obs. of  8 variables:
-##  $ TMAX    :'data.frame':    1459 obs. of  8 variables:
-##  $ PRCP    :'data.frame':    1319 obs. of  8 variables:
-##  $ PREC    :'data.frame':    1323 obs. of  8 variables:
+##  $ TAVG    :'data.frame':    1459 obs. of  9 variables:
+##  $ TMIN    :'data.frame':    1459 obs. of  9 variables:
+##  $ TMAX    :'data.frame':    1459 obs. of  9 variables:
+##  $ PRCP    :'data.frame':    1319 obs. of  9 variables:
+##  $ PREC    :'data.frame':    1323 obs. of  9 variables:
 ##  $ SNWD    :'data.frame':    0 obs. of  0 variables
 ##  $ WTEQ    :'data.frame':    0 obs. of  0 variables
 ##  $ WDIRV   :'data.frame':    0 obs. of  0 variables
 ##  $ WSPDV   :'data.frame':    0 obs. of  0 variables
 ##  $ LRADT   :'data.frame':    0 obs. of  0 variables
-##  $ metadata:'data.frame':    1 obs. of  12 variables:
+## $ metadata:'data.frame': 1 obs. of 19 variables: 
# tabulate number of soil moisture measurements per depth (cm)
 table(x$SMS$depth)
## 
@@ -627,23 +589,23 @@ 
Getting Data from Multiple Stations

 # same format as before, sensor data are "stacked" within each list element
 str(x, 1)
## List of 14
-##  $ SMS     :'data.frame':    5752 obs. of  8 variables:
-##  $ STO     :'data.frame':    5843 obs. of  8 variables:
+##  $ SMS     :'data.frame':    5752 obs. of  9 variables:
+##  $ STO     :'data.frame':    5843 obs. of  9 variables:
 ##  $ SAL     :'data.frame':    0 obs. of  0 variables
-##  $ TAVG    :'data.frame':    1460 obs. of  8 variables:
-##  $ TMIN    :'data.frame':    1460 obs. of  8 variables:
-##  $ TMAX    :'data.frame':    1460 obs. of  8 variables:
-##  $ PRCP    :'data.frame':    581 obs. of  8 variables:
-##  $ PREC    :'data.frame':    1316 obs. of  8 variables:
-##  $ SNWD    :'data.frame':    731 obs. of  8 variables:
-##  $ WTEQ    :'data.frame':    713 obs. of  8 variables:
+##  $ TAVG    :'data.frame':    1460 obs. of  9 variables:
+##  $ TMIN    :'data.frame':    1460 obs. of  9 variables:
+##  $ TMAX    :'data.frame':    1460 obs. of  9 variables:
+##  $ PRCP    :'data.frame':    581 obs. of  9 variables:
+##  $ PREC    :'data.frame':    1316 obs. of  9 variables:
+##  $ SNWD    :'data.frame':    731 obs. of  9 variables:
+##  $ WTEQ    :'data.frame':    713 obs. of  9 variables:
 ##  $ WDIRV   :'data.frame':    0 obs. of  0 variables
 ##  $ WSPDV   :'data.frame':    0 obs. of  0 variables
 ##  $ LRADT   :'data.frame':    0 obs. of  0 variables
-##  $ metadata:'data.frame':    2 obs. of  12 variables:
+## $ metadata:'data.frame': 2 obs. of 19 variables: 
# deeper look
 str(x$SMS)
-
## 'data.frame':    5752 obs. of  8 variables:
+
## 'data.frame':    5752 obs. of  9 variables:
 ##  $ Site      : int  356 356 356 356 356 356 356 356 356 356 ...
 ##  $ Date      : Date, format: "2015-01-01" "2015-01-02" "2015-01-03" ...
 ##  $ Time      : chr  "12:00" "12:00" "12:00" "12:00" ...
@@ -651,7 +613,8 @@ 
Getting Data from Multiple Stations

 ##  $ water_day : int  93 94 95 96 97 98 99 100 101 102 ...
 ##  $ value     : num  0 0 0 0 0 0 0 0 0 0 ...
 ##  $ depth     : num  5 5 5 5 5 5 5 5 5 5 ...
-##  $ sensor.id : Factor w/ 5 levels "SMS.I_2","SMS.I_8",..: 1 1 1 1 1 1 1 1 1 1 ...

+##  $ sensor.id : Factor w/ 5 levels "SMS.I_2","SMS.I_8",..: 1 1 1 1 1 1 1 1 1 1 ...
+##  $ datetime  : POSIXct, format: "2015-01-01 12:00:00" "2015-01-02 12:00:00" "2015-01-03 12:00:00" ...
# get unique set of soil moisture sensor depths
 sensor.depths <- unique(x$SMS$depth)
 
@@ -863,20 +826,20 @@ 
Case Study: Pine Nut SCAN Site

 # check available data
 str(x, 1)
## List of 14
-##  $ SMS     :'data.frame':    14259 obs. of  8 variables:
-##  $ STO     :'data.frame':    27933 obs. of  8 variables:
+##  $ SMS     :'data.frame':    14259 obs. of  9 variables:
+##  $ STO     :'data.frame':    27933 obs. of  9 variables:
 ##  $ SAL     :'data.frame':    0 obs. of  0 variables
-##  $ TAVG    :'data.frame':    2918 obs. of  8 variables:
-##  $ TMIN    :'data.frame':    2917 obs. of  8 variables:
-##  $ TMAX    :'data.frame':    2919 obs. of  8 variables:
-##  $ PRCP    :'data.frame':    2918 obs. of  8 variables:
-##  $ PREC    :'data.frame':    2927 obs. of  8 variables:
-##  $ SNWD    :'data.frame':    1407 obs. of  8 variables:
+##  $ TAVG    :'data.frame':    2918 obs. of  9 variables:
+##  $ TMIN    :'data.frame':    2917 obs. of  9 variables:
+##  $ TMAX    :'data.frame':    2919 obs. of  9 variables:
+##  $ PRCP    :'data.frame':    2918 obs. of  9 variables:
+##  $ PREC    :'data.frame':    2927 obs. of  9 variables:
+##  $ SNWD    :'data.frame':    1407 obs. of  9 variables:
 ##  $ WTEQ    :'data.frame':    0 obs. of  0 variables
 ##  $ WDIRV   :'data.frame':    0 obs. of  0 variables
 ##  $ WSPDV   :'data.frame':    0 obs. of  0 variables
 ##  $ LRADT   :'data.frame':    0 obs. of  0 variables
-##  $ metadata:'data.frame':    1 obs. of  12 variables:
+## $ metadata:'data.frame': 1 obs. of 19 variables: 
## still working on a solution for this issue: https://github.com/ncss-tech/soilDB/issues/14
 # ! multiple soil temperature sensors installed at each depth
 table(sensor.id = x$STO$sensor.id, sensor.depth = x$STO$depth)
@@ -904,13 +867,13 @@

Case Study: Pine Nut SCAN Site

# check: head(sms) -
##   Site       Date  Time water_year water_day value depth sensor.id year doy
-## 1 2144 2008-01-01 12:00       2008        93   2.1     5   SMS.I_2 2008   1
-## 2 2144 2008-01-02 12:00       2008        94   1.8     5   SMS.I_2 2008   2
-## 3 2144 2008-01-03 12:00       2008        95   2.6     5   SMS.I_2 2008   3
-## 4 2144 2008-01-04 12:00       2008        96   3.3     5   SMS.I_2 2008   4
-## 5 2144 2008-01-05 12:00       2008        97   5.1     5   SMS.I_2 2008   5
-## 6 2144 2008-01-06 12:00       2008        98  12.1     5   SMS.I_2 2008   6
+
##   Site       Date  Time water_year water_day value depth sensor.id            datetime year doy
+## 1 2144 2008-01-01 12:00       2008        93   2.1     5   SMS.I_2 2008-01-01 12:00:00 2008   1
+## 2 2144 2008-01-02 12:00       2008        94   1.8     5   SMS.I_2 2008-01-02 12:00:00 2008   2
+## 3 2144 2008-01-03 12:00       2008        95   2.6     5   SMS.I_2 2008-01-03 12:00:00 2008   3
+## 4 2144 2008-01-04 12:00       2008        96   3.3     5   SMS.I_2 2008-01-04 12:00:00 2008   4
+## 5 2144 2008-01-05 12:00       2008        97   5.1     5   SMS.I_2 2008-01-05 12:00:00 2008   5
+## 6 2144 2008-01-06 12:00       2008        98  12.1     5   SMS.I_2 2008-01-06 12:00:00 2008   6
head(sms.wide)
##   Site       Date    5   10   20  51 102
 ## 1 2144 2008-01-01  2.1  4.4  8.8 9.2 2.0
@@ -968,7 +931,7 @@ 
Water Retention Curve Development

 
Previously the VG parameters had to be obtained manually from these
 reports.

 
# get KSSL data
-s <- fetchKSSL(pedon_id = 'S08NV003003')
+s <- fetchLDM(x = 'S08NV003003', what = 'upedonid')
 
 # VG parameters now returned by fetchKSSL() as of 2016-11-17
 knitr::kable(horizons(s)[, c('hzn_desgn', 'hzn_top', 'hzn_bot', 'theta_r', 'theta_s', 'alpha', 'npar')])

@@ -1245,8 +1208,8 @@ 
Conversion of VWC to Matric Potential: All Depths

           xlab='Julian Day', ylab='Year')

-

This document is based on aqp version 2.0.2 and -soilDB version 2.7.8.

+

This document is based on aqp version 2.0.4 and +soilDB version 2.8.5.