sharpshootR: A Soil Survey Toolkit

library(latticeExtra)
library(plyr)
library(e1071)
library(RColorBrewer)

CDEC_StationInfo('HHM')



# 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))