sharpshootR (version 1.6)

CDECquery: Get water-related data (California only) from the CDEC website.

Description

Get water-related data (California only) from the CDEC website.

Usage

CDECquery(id, sensor, interval = "D", start, end)

Arguments

id

station ID (e.g. 'spw'), single value or vector of station IDs, see details

sensor

the sensor ID, single value or vector of sensor numbers, see details

interval

character, 'D' for daily, 'H' for hourly, 'M' for monthly, 'E' for event: see Details.

start

starting date, in the format 'YYYY-MM-DD'

end

ending date, in the format 'YYYY-MM-DD'

Value

a data.frame object with the following fields: `datetime`, `year`, `month`, `value`.

Details

1.

Station IDs can be found here: http://cdec.water.ca.gov/staInfo.html

2a.

Sensor IDs can be found using this URL: http://cdec.water.ca.gov/dynamicapp/staMeta?station_id=, followed by the station ID.

2b.

Sensor details can be accessed using CDEC_StationInfo with the station ID.

3.

Resevoir capacities can be found here: http://cdec.water.ca.gov/misc/resinfo.html

4.

A new interactive map of CDEC stations can be found here: http://cdec.water.ca.gov

Sensors that report data on an interval other than monthly ('M'), daily ('D'), or hourly ('H') can be queried with an `event` interval ('E'). Soil moisture and temperature sensors are an example of this type of reporting. See examples below.

References

http://cdec.water.ca.gov/queryCSV.html

See Also

CDECsnowQuery, CDEC_StationInfo

Examples

# NOT RUN {
if(requireNamespace("curl") &
   curl::has_internet() &
   require(latticeExtra) &
   require(plyr) &
   require(e1071)) {
  
   # get daily resevoir storage (ac. ft) from Pinecrest, New Melones and Lyons resevoirs
  pinecrest <- CDECquery(id='swb', sensor=15, interval='D', start='2012-09-01', end='2015-01-01')
  new.melones <- CDECquery(id='nml', sensor=15, interval='D', start='2012-09-01', end='2015-01-01')
  lyons <- CDECquery(id='lys', sensor=15, interval='D', start='2012-09-01', end='2015-01-01')
  
  # compute storage capacity
  pinecrest$capacity <- pinecrest$value / 18312 * 100
  new.melones$capacity <- new.melones$value / 2400000 * 100
  lyons$capacity <- lyons$value / 6228 * 100
  
  # combine
  g <- make.groups(new.melones, lyons, pinecrest)
  
  # resonable date scale
  r <- range(g$datetime)
  s.r <- seq(from=r[1], to=r[2], by='1 month')
  
  # better colors
  tps <- list(superpose.line=list(lwd=2, col=brewer.pal(n=3, name='Set1')))
  
  # plot
  xyplot(capacity ~ datetime, groups=which, data=g, type='l', 
         xlab='', ylab='Capacity (%)', ylim=c(-5, 105),
         scales=list(x=list(at=s.r, labels=format(s.r, "%b\n%Y"))), 
         auto.key=list(columns=3, lines=TRUE, points=FALSE),
         par.settings=tps,
         panel=function(...) {
           panel.abline(h=seq(0, 100, by=10), col='grey')
           panel.abline(v=s.r, col='grey')
           panel.xyplot(...)
         })
  
  
  ##
  # New Melones monthly data, retrieve as far back in time as possible 
  new.melones.monthly <- CDECquery(id='nml', sensor=15, interval='M', 
                                   start='1900-01-01', end='2015-01-01')
  
  # convert to pct. capacity
  new.melones.monthly$capacity <- new.melones.monthly$value / 2400000 * 100
  
  
  # make a nice color ramp function
  cols <- colorRampPalette(brewer.pal(9, 'Spectral'), 
                           space='Lab', interpolate='spline')
  
  # plot, each pixel is colored by the total precip by year/month
  levelplot(capacity ~ year * month, data=new.melones.monthly, col.regions=cols, xlab='', 
            ylab='', scales=list(x=list(tick.number=20)), main='New Melones Capacity (%)')
  
  
  ##
  # get daily precip totals from Stan Powerhouse
  x <- CDECquery(id='spw', sensor=45, interval='D', start='1900-01-01', end='2019-01-01')
  
  # compute total precip by year/month
  a <- ddply(x, c('year', 'month'), summarize, s=sum(value, na.rm=TRUE))
  
  # convert monthly precipitation values into Z-scores by month
  a.scaled <- ddply(a, 'month', summarize, year=year, scaled.ppt=scale(s))
  
  # make a nice color ramp function, scaled by the skewness of the underlying distribution
  cols <- colorRampPalette(brewer.pal(9, 'Spectral'),
  space='Lab', interpolate='spline', bias=skewness(a.scaled$scaled.ppt, na.rm=TRUE))
  
  # plot, each pixel is colored by the total precip by year/month
  levelplot(scaled.ppt ~ year * month, data=a.scaled, col.regions=cols, xlab='', 
            ylab='', scales=list(x=list(tick.number=10)), 
            main='Monthly Total Precipitation (as z-score) SPW')
  
  
  ##
  # get pre-aggregated monthly data from Sonora RS
  x <- CDECquery(id='sor', sensor=2, interval='M', start='1900-01-01', end='2019-01-01')
  
  # make a nice color ramp function, scaled by the skewness of the underlying distribution
  cols <- colorRampPalette(brewer.pal(9, 'Spectral'), space='Lab', 
  interpolate='spline', bias=skewness(x$value, na.rm=TRUE))
  
  # plot
  levelplot(value ~ year * month, data=x, col.regions=cols, xlab='', 
  ylab='', scales=list(x=list(tick.number=20)), 
  main='Monthly Total Precipitation (inches) SOR')
  
  
  ### query an 'event' type sensor
  # Bryte test site (BYT)
  # single request: air temperature and soil temperature at depth 1 (25cm)
  # measurement interval is 20 minutes
  x <- CDECquery('BYT', c(4, 194), 'E', '2016-01-01', '2017-01-01')
  
  # data are in long format, check number of records for each sensor
  table(x$sensor_type)
  
  # plot grouped data
  xyplot(value ~ datetime, groups=sensor_type, data=g, type=c('g', 'l'),
         auto.key=list(columns=2, points=FALSE, lines=TRUE))
  
}



# }