Setting up data using headwaters package

Load headwaters package

library(devtools)

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install_github(repo = "headwaters", username = "anarosner")

## Installing github repo headwaters/master from anarosner
## Downloading master.zip from https://github.com/anarosner/headwaters/archive/master.zip
## Installing package from C:\Users\arosner\AppData\Local\Temp\1\Rtmpy4eE90/master.zip
## Installing headwaters
## "C:/R/R-31~1.1/bin/x64/R" --vanilla CMD INSTALL  \
##   "C:\Users\arosner\AppData\Local\Temp\1\Rtmpy4eE90\devtools1b9855463978\headwaters-master"  \
##   --library="C:/R/R-3.1.1/library" --install-tests

library(headwaters)

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## Differential Evolution algorithm in R
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Temporarily, load datat that will later on be contained in package data

#Everything in the package's data dir *should* load using load_all, or explicitly using load_data.  
#But, that's not working yet... this is a temp fix
setwd("C:/ALR/Models/headwaters")
for (i in list.files(path = "./data"))
     load(file.path("data",i), verbose = T )

## Loading objects:
##   states.spatial
## Loading objects:
##   weather.grid.coords
## Loading objects:
##   weather.grid.poly

Specify file locations

model.dir<-"C:/ALR/Models/headwaters_sdoah"
buffer.file<-"park_coast_buffer"
# buffer<-readShapePoly(file.path(model.dir,"sdoah_inputs",buffer.file))
catchment.file<-"C:/ALR/Data/StreamData/NHDplus/NHDPlusCatchment/sdoah/Catchment"

Load flow gages

#Retrieve gages
g.spatial<-gage.retrieve(buffer.file = file.path(model.dir,"sdoah_inputs",buffer.file),temp.dir = file.path(model.dir,"temp"),log.dir = file.path(model.dir,"logs") )

## Warning: EOF within quoted string
## Warning: Missing geographic coordinates:
##  7 sites do not have lat coordinates and are being ignored
##  7 sites do not have long coordinates and are being ignored

## Gage retrieval complete
920 gages identified
Drainage area >0 and <=50 sq km)

#Plot gages
plot(g.spatial,col="red",pch=16)
# plot(buffer,add=T,border="red")
plot(states.spatial,add=T)

#Place gages into NHDplus catchments
g.spatial<-gage.place.nhdplus(gages.spatial = g.spatial, catchment.file = catchment.file)

## Starting to load catchments...
    (this part could take a while)    
Completed loading catchments...
Starting to plot gages to catchments
Completed plotting gages to catchments

## Warning: 37 gages did not map to a NHDplus catchment:
## Warning: 02045200; 02046500; 02050500; 02053400; 02053510; 0205356401; 02055100; 02063000; 02076500; 02077240; 0208273070; 02082731; 02084903; 02084904; 02084905; 02084908; 02084909; 0208524090; 02086000; 0208650112; 0209330990; 0209331325; 0209437850; 0211351575; 0211397263; 03465000; 03466228; 03473500; 03477500; 03480500; 03486311; 03486500; 03487501; 03488445; 03489850; 03491544; 03492006

#Trace dams upstream
#   (not using basin characteristics impoundments, because don't have that processed for sdoah)
g.spatial<-gage.trace.dams(gages.spatial = g.spatial)

## Loading dam information...
Starting to trace network upstream for 346 gages... 
     ...  1...  2...  3...  4...  5...  6...  7...  8...  9...  10...  11...  12...  13...  14...  15...  16...  17...  18...  19...  20...  21...  22...  23...  24...  25...  26...  27...  28...  29...  30...  31...  32...  33...  34...  35...  36...  37...  38...  39...  40...  41...  42...  43...  44...  45...  46...  47...  48...  49...  50...  51...  52...  53...  54...  55...  56...  57...  58...  59...  60...  61...  62...  63...  64...  65...  66...  67...  68...  69...  70...  71...  72...  73...  74...  75...  76...  77...  78...  79...  80...  81...  82...  83...  84...  85...  86...  87...  88...  89...  90...  91...  92...  93...  94...  95...  96...  97...  98...  99...  100...  101...  102...  103...  104...  105...  106...  107...  108...  109...  110...  111...  112...  113...  114...  115...  116...  117...  118...  119...  120...  121...  122...  123...  124...  125...  126...  127...  128...  129...  130...  131...  132...  133...  134...  135...  136...  137...  138...  139...  140...  141...  142...  143...  144...  145...  146...  147...  148...  149...  150...  151...  152...  153...  154...  155...  156...  157...  158...  159...  160...  161...  162...  163...  164...  165...  166...  167...  168...  169...  170...  171...  172...  173...  174...  175...  176...  177...  178...  179...  180...  181...  182...  183...  184...  185...  186...  187...  188...  189...  190...  191...  192...  193...  194...  195...  196...  197...  198...  199...  200...  201...  202...  203...  204...  205...  206...  207...  208...  209...  210...  211...  212...  213...  214...  215...  216...  217...  218...  219...  220...  221...  222...  223...  224...  225...  226...  227...  228...  229...  230...  231...  232...  233...  234...  235...  236...  237...  238...  239...  240...  241...  242...  243...  244...  245...  246...  247...  248...  249...  250...  251...  252...  253...  254...  255...  256...  257...  258...  259...  260...  261...  262...  263...  264...  265...  266...  267...  268...  269...  270...  271...  272...  273...  274...  275...  276...  277...  278...  279...  280...  281...  282...  283...  284...  285...  286...  287...  288...  289...  290...  291...  292...  293...  294...  295...  296...  297...  298...  299...  300...  301...  302...  303...  304...  305...  306...  307...  308...  309...  310...  311...  312...  313...  314...  315...  316...  317...  318...  319...  320...  321...  322...  323...  324...  325...  326...  327...  328...  329...  330...  331...  332...  333...  334...  335...  336...  337...  338...  339...  340...  341...  342...  343...  344...  345...  346

nrow(g.spatial) #number including dams

## [1] 409

g.spatial<-gage.filter.dams(gages.spatial = g.spatial)
nrow(g.spatial) #number excluding dams

## [1] 298

plot(g.spatial,col="blue",pch=16, add=T)

#Load basin characteristics
g.spatial<-gage.load.char(gages.spatial = g.spatial, basin.char.file = file.path(model.dir,"sdoah_inputs","shenandoahUpstreamStats2.RData"))

## Warning: 101 records in y cannot be matched to x

#Place gages into weather grid polygons 
g.spatial<-gage.place.weather.grid( gages.spatial=g.spatial, plot=T )

## Mapping gages to weather grid cells...
Completed mapping gages to weather grid cells...
194 unique weather files to be used for 298 flow gages
Drawing plot...

#View sample of gage info
head(g.spatial@data[!is.na(g.spatial$forest),])

##      FEATUREID agency_cd  site_no                             station_nm
## 890    5908591      USGS 01628150             DEEP RUN NEAR GROTTOES, VA
## 1037   8466033      USGS 01662500           RUSH RIVER AT WASHINGTON, VA
## 1124   8566663      USGS 02031500 N F MOORMANS RIVER NEAR WHITE HALL, VA
##      dec_lat_va dec_long_va coord_acy_cd dec_coord_datum_cd   huc_cd
## 890       38.27      -78.76            U              NAD83 02070005
## 1037      38.71      -78.15            U              NAD83 02080103
## 1124      38.14      -78.75            U              NAD83 02080204
##      drain_area_va sv_begin_date sv_end_date sv_count_nu da_sqkm large
## 890           1.18    1979-11-01  1982-09-27          30   3.056     0
## 1037         14.60    1982-06-02  2006-08-15          21  37.814     0
## 1124         11.20    1942-05-04  1984-11-01         183  29.008     0
##      small   X ArcSite SiteID Latitude Longitude TotDASqKM LengthKM
## 890      0 264     222  3F265    38.28    -78.76     5.399    6.947
## 1037     0  43      28  1F223    38.75    -78.22    60.239   16.211
## 1124     0 209     178  3F081    38.14    -78.75     2.954    2.263
##      ReachSlope ReachElev Calc_TotDASqKM tminann_brkt tmaxann_brkt
## 890       4.034     452.0          5.399        5.829        17.92
## 1037      1.320     233.4         60.239        5.804        17.98
## 1124     19.522     521.9          2.954        5.520        17.19
##      so4dep_brkt prcpwin_brkt prcpann_brkt no3dep_brkt prcpnov_brkt
## 890        7.438        212.1         1097       6.616        87.15
## 1037       8.498        218.5         1189       7.404        98.82
## 1124      11.000        275.6         1362       9.993       112.19
##      elev_nalcc slope_deg wtlnd_or_wtr  wetland   water undev_forest
## 890       526.7     15.31      0.02216 0.000000 0.02216        94.80
## 1037      366.7     11.14      0.39267 0.007893 0.38478        71.64
## 1124      714.4     21.10      0.00000 0.000000 0.00000       100.00
##      impervious herbacious herb_or_ag forest developed develnotopen
## 890       26.04   0.000000      5.075  92.89     2.017       0.4211
## 1037      46.58   0.007893     26.785  68.63     4.201       0.8268
## 1124      10.41   0.000000      0.000  96.95     3.050       0.0000
##      conus_wetland conus_opnwtr agriculture DA_Discrepancy
## 890        0.37677      0.02216       5.075              0
## 1037       0.02565      0.34532      26.777              0
## 1124       0.00000      0.00000       0.000              0
##           weather.filename region
## 890  data_38.3125_-78.8125   east
## 1037 data_38.6875_-78.1875   east
## 1124 data_38.1875_-78.8125   east

Load flow observations

#Limit to gages that have basin data for sdoah
#  as it turns out, only 3 gages have basin info, so it should make for a nice, quick demonstration
g.spatial<-g.spatial[!is.na(g.spatial$forest),]


#Load/calculate/aggregate flow data for seasonal and annual timesteps
q.matrices<-flow.retrieve( gages.spatial = g.spatial, 
                          periods = c("seasonal","annual"), log.dir = file.path(model.dir,"logs") )

## Begin loading and aggregating stream flow observations...
  --  Loading/aggregating gage 1 of 3   --          Gage 01628150, loaded 1096 rows
  --  Loading/aggregating gage 2 of 3   --          Gage 01662500, loaded 8766 rows
  --  Loading/aggregating gage 3 of 3   --          Gage 02031500, loaded 11871 rows

#View sample of flow data
q.matrices[["seasonal"]][30:40,3,]

##              mean   max min    low records.period records.period.rolling
## 1956-05-28 12.672  66.0 2.8 2.9571             92                     92
## 1956-08-28  3.842  32.0 0.5 0.7857             92                     92
## 1956-11-28 23.051 384.0 0.7 0.9000             91                     91
## 1957-02-28 20.679 260.0 3.0 3.4000             90                     90
## 1957-05-28 23.287 115.0 5.2 6.0714             92                     92
## 1957-08-28  3.111  30.0 0.1 0.1000             92                     92
## 1957-11-28  4.171  28.0 0.1 0.1000             89                     83
## 1958-02-28 26.286 122.0 5.9 6.9714             90                     90
## 1958-05-28 48.450 250.0 6.8 7.7143             92                     92
## 1958-08-28  2.729   8.5 0.6 0.7429             92                     92
## 1958-11-28  0.967   8.0 0.2 0.2000             91                     91

q.matrices[["seasonal"]][30:40,,1]

##            01628150 01662500 02031500
## 1956-05-28       NA   12.987   12.672
## 1956-08-28       NA   12.123    3.842
## 1956-11-28       NA   10.409   23.051
## 1957-02-28       NA   15.962   20.679
## 1957-05-28       NA   21.961   23.287
## 1957-08-28       NA    3.580    3.111
## 1957-11-28       NA    3.591    4.171
## 1958-02-28       NA   26.671   26.286
## 1958-05-28       NA   37.346   48.450
## 1958-08-28       NA    8.248    2.729
## 1958-11-28       NA    1.933    0.967

q.matrices[["seasonal"]][130:140,,1]

##            01628150 01662500 02031500
## 1981-05-28  0.53946       NA       NA
## 1981-08-28  0.55696       NA       NA
## 1981-11-28  0.05637       NA       NA
## 1982-02-28  1.00711       NA       NA
## 1982-05-28  2.11033       NA       NA
## 1982-08-28  1.35457       NA       NA
## 1982-11-28       NA       NA    4.264
## 1983-02-28       NA       NA   24.229
## 1983-05-28       NA       NA   72.500
## 1983-08-28       NA       NA    3.286
## 1983-11-28       NA       NA   21.637

q.matrices[["annual"]][25:40,,1]

##            01628150 01662500 02031500
## 1973-09-30       NA    25.51       NA
## 1974-09-30       NA    15.01       NA
## 1975-09-30       NA    21.08       NA
## 1976-09-30       NA    15.07       NA
## 1977-09-30       NA    12.08       NA
## 1978-09-30       NA       NA       NA
## 1979-09-30       NA       NA       NA
## 1980-09-30   1.7551       NA       NA
## 1981-09-30   0.3487       NA       NA
## 1982-09-30   1.1439       NA       NA
## 1983-09-30       NA       NA    26.08
## 1984-09-30       NA       NA    30.91
## 1985-09-30       NA       NA       NA
## 1986-09-30       NA       NA       NA
## 1987-09-30       NA       NA       NA
## 1988-09-30       NA       NA       NA

Create weather grid polygons from file contents

#Will only need to do this very occassionally, when adding a new geographic region,
#    and it takes a while
#    so I'm going to skip it for this markdown
# weather.grid.poly <- weather.grid.create( regions=c("east","ohio") )

Load weather observations

#Load/calculate/aggregate weather data for seasonal and annual timesteps
w.matrices <- weather.retrieve(gages.spatial=g.spatial, periods=c("seasonal","annual"))

## Begin reading 3 met files...
  --  loading file 1 of 3   --  
  --  loading file 2 of 3   --  
  --  loading file 3 of 3   --  

tail(w.matrices[["seasonal"]][,1,])

##            precip.total precip.e precip.e.lag1 precip.e.lag2 precip.e.lag3
## 2009-11-28        271.4    271.4         249.0         282.7         162.5
## 2010-02-28        322.1    278.8         271.4         249.0         282.7
## 2010-05-28        203.7    247.0         278.8         271.4         249.0
## 2010-08-28        156.1    156.1         247.0         278.8         271.4
## 2010-11-28        207.4    207.4         156.1         247.0         278.8
## 2011-02-28           NA       NA         207.4         156.1         247.0
##              tmin   tmax   tavg     pet pet.lag1 pet.lag2    gdd gdd.lag1
## 2009-11-28  7.606 18.484 13.045 0.20853  0.55048  0.34868  364.5   1118.6
## 2010-02-28 -5.706  3.623 -1.042 0.07014  0.20853  0.55048    0.0    364.5
## 2010-05-28  6.723 19.985 13.354 0.37579  0.07014  0.20853  413.2      0.0
## 2010-08-28 18.166 30.773 24.469 0.58804  0.37579  0.07014 1331.2    413.2
## 2010-11-28  6.992 21.034 14.013 0.23894  0.58804  0.37579  463.7   1331.2
## 2011-02-28     NA     NA     NA      NA  0.23894  0.58804     NA    463.7
##            gdd.lag2 frozen melt.doy melt.doy.lag1 melt.doy.lag2
## 2009-11-28    335.7      5        0             0            62
## 2010-02-28   1118.6     83       59             0             0
## 2010-05-28    364.5     10       62            59             0
## 2010-08-28      0.0      0        0            62            59
## 2010-11-28    413.2      9        0             0            62
## 2011-02-28   1331.2     NA       NA             0             0

tail(w.matrices[["seasonal"]][,,1])

##            data_38.3125_-78.8125 data_38.6875_-78.1875
## 2009-11-28                 271.4                 270.9
## 2010-02-28                 322.1                 239.3
## 2010-05-28                 203.7                 276.0
## 2010-08-28                 156.1                 230.6
## 2010-11-28                 207.4                 319.2
## 2011-02-28                    NA                    NA
##            data_38.1875_-78.8125
## 2009-11-28                 337.0
## 2010-02-28                 401.3
## 2010-05-28                 245.5
## 2010-08-28                 183.0
## 2010-11-28                 248.9
## 2011-02-28                    NA

tail(w.matrices[["annual"]][,,1])

##            data_38.3125_-78.8125 data_38.6875_-78.1875
## 2006-09-30                1137.1                1159.5
## 2007-09-30                1051.0                 931.5
## 2008-09-30                 921.9                1090.7
## 2009-09-30                 874.0                 904.0
## 2010-09-30                 955.2                1137.1
## 2011-09-30                    NA                    NA
##            data_38.1875_-78.8125
## 2006-09-30                1306.6
## 2007-09-30                1167.6
## 2008-09-30                1061.9
## 2009-09-30                 979.9
## 2010-09-30                1162.5
## 2011-09-30                    NA

Merge gage (basin char and drainage area), flow, and weather

#Next and last thing for data prep to add to package...

Footnotes, take a look at r version and packages used

print(sessionInfo()) 

## R version 3.1.1 (2014-07-10)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## 
## locale:
## [1] LC_COLLATE=English_United States.1252 
## [2] LC_CTYPE=English_United States.1252   
## [3] LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C                          
## [5] LC_TIME=English_United States.1252    
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] headwaters_0.1      zoo_1.7-11          xtable_1.7-3       
##  [4] waterData_1.0.3     latticeExtra_0.6-26 RColorBrewer_1.0-5 
##  [7] lattice_0.20-29     USGSwsStats_0.6     boot_1.3-11        
## [10] USGSwsGraphs_0.8    akima_0.5-11        KernSmooth_2.23-12 
## [13] leaps_2.9           USGSwsBase_0.8.0    digest_0.6.4       
## [16] memoise_0.2.1       deldir_0.1-6        rgeos_0.3-6        
## [19] rgdal_0.8-16        plyr_1.8.1          nlme_3.1-117       
## [22] MASS_7.3-33         maptools_0.8-30     sp_1.0-15          
## [25] lubridate_1.3.3     lme4_1.1-7          Rcpp_0.11.2        
## [28] Matrix_1.1-4        foreign_0.8-61      EcoHydRology_0.4.12
## [31] XML_3.98-1.1        DEoptim_2.2-2       topmodel_0.7.2-2   
## [34] operators_0.1-6     dplyr_0.2           DataCombine_0.1.26 
## [37] ggplot2_1.0.0       reshape2_1.4        devtools_1.5       
## 
## loaded via a namespace (and not attached):
##  [1] assertthat_0.1   car_2.0-20       codetools_0.2-8  colorspace_1.2-4
##  [5] data.table_1.9.2 evaluate_0.5.5   formatR_0.10     grid_3.1.1      
##  [9] gtable_0.1.2     htmltools_0.2.4  httr_0.3         knitr_1.6       
## [13] magrittr_1.0.1   minqa_1.2.3      munsell_0.4.2    nloptr_1.0.0    
## [17] nnet_7.3-8       parallel_3.1.1   proto_0.3-10     RCurl_1.95-4.1  
## [21] rmarkdown_0.2.50 scales_0.2.4     splines_3.1.1    stringr_0.6.2   
## [25] tools_3.1.1      whisker_0.3-2    yaml_2.1.13

Footnotes, look at inventory of objects created

ls.objects()

##                   Name                     Type       Size   Rows Columns
## 1           catchments SpatialPolygonsDataFrame  1.207e+09 217271       4
## 2    weather.grid.poly SpatialPolygonsDataFrame 33,668,200  10972       4
## 3          catch.match                  integer 18,642,184 296330      NA
## 4  weather.grid.coords               data.frame  1,757,616  10972       4
## 5                g.all               data.frame    509,080   1316      13
## 6               g.bkup   SpatialPointsDataFrame    492,432    792      13
## 7                    g               data.frame    432,280    792      13
## 8           q.matrices                     list    397,480      5      NA
## 9       states.spatial SpatialPolygonsDataFrame    369,024     74       9
## 10           g.spatial   SpatialPointsDataFrame    358,800      3      51
## 11          w.matrices                     list    158,936      4      NA
## 12              buffer SpatialPolygonsDataFrame    122,592      1       2
## 13           x.spatial   SpatialPointsDataFrame    115,272    279      34
## 14                   x               data.frame     92,056    279      34
## 15           get.gages                 function     50,912     NA      NA
## 16          ls.objects                 function     44,752     NA      NA
## 17               match               data.frame     31,648    393       4
## 18          summary.na                 function     23,008     NA      NA
## 19   plot.gages.buffer                 function     22,136     NA      NA
## 20  plot.gages.nhdplus                 function     15,112     NA      NA
## 21          plot.gages                 function     10,600     NA      NA
## 22          capitalize                 function      8,536     NA      NA
## 23            metadata                     list      1,352      1      NA
## 24      catchment.file                character        152      1      NA
## 25           proj4.NHD                character        152      1      NA
## 26            base.dir                character        120      1      NA
## 27         buffer.file                character        120      1      NA
## 28                   i                character        120      1      NA
## 29           model.dir                character        120      1      NA