sharpshootR (version 1.8)

CDECquery: Easy Access to the CDEC API

Description

A (relatively) simple interface to 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

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.

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

References

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

See Also

CDECsnowQuery CDEC_StationInfo

Examples

# NOT RUN {
# }
# NOT RUN {
if(requireNamespace("curl") &
curl::has_internet() &
  require(latticeExtra) &
  require(plyr) &
  require(e1071)) {
    
    library(RColorBrewer)
    
    # get daily reservoir storage (ac. ft) from 
    # Pinecrest, New Melones and Lyons reservoirs
    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=x, type=c('g', 'l'),
           auto.key=list(columns=2, points=FALSE, lines=TRUE))
    
  }


# }
# NOT RUN {

# }