Net radiation as forcing?

Check whether sufficient sites have net radiation data.

filnam <- "~/rsofun/data/df_fluxnet.rds"
if (!file.exists(filnam)){
  df_fluxnet <-
    suppressWarnings(
      suppressMessages(
        ingestr::ingest(
          siteinfo  = ingestr::siteinfo_fluxnet2015,
          source    = "fluxnet",
          getvars   = list(netrad = "NETRAD"),
          dir       = "~/data/FLUXNET-2015_Tier1/20191024/DD/",
          timescale = "d"
        )
      )
    )
  saveRDS(df_fluxnet, file = filnam)
} else {
  df_fluxnet <- readRDS(filnam)
}

Missing data across all sites.

df_fluxnet |> 
  tidyr::unnest(data) |> 
  select(netrad) |> 
  visdat::vis_miss(warn_large_data = FALSE, cluster = FALSE)
#> Adding missing grouping variables: `sitename`
#> Warning: `gather_()` was deprecated in tidyr 1.2.0.
#> ℹ Please use `gather()` instead.
#> ℹ The deprecated feature was likely used in the visdat package.
#>   Please report the issue at <]8;;https://github.com/ropensci/visdat/issueshttps://github.com/ropensci/visdat/issues]8;;>.

Get fraction of missing data per site.

calc_f_missing <- function(df, varnam){
  vec <- df |> 
    pull(!!varnam)
  (sum(is.na(vec)) / nrow(df))
}

df_missing <- df_fluxnet |> 
  mutate(f_missing = purrr::map_dbl(data, ~calc_f_missing(., "netrad")))

Distribution of missing data.

df_missing |> 
  ggplot(aes(x = f_missing, y = ..count..)) +
  geom_histogram()
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Available sites with maximum 10% missing.

df_missing |> 
  select(-data) |> 
  filter(f_missing <= 0.1) |> 
  arrange(desc(f_missing)) |> 
  left_join(
    ingestr::siteinfo_fluxnet2015,
    by = "sitename"
  ) |> 
  knitr::kable()
sitename f_missing lon lat elv year_start year_end classid c4 whc koeppen_code igbp_land_use plant_functional_type
AU-Wom 0.0949772 144.0944 -37.4222 705.0 2010 2012 EBF FALSE 189.57100 Cfb Evergreen Broadleaf Forest Evergreen Broadleaf Trees
US-ARM 0.0865753 -97.4888 36.6058 314.0 2003 2012 CRO FALSE 331.86008 Cfa Croplands Cereal crop
US-Ne1 0.0817703 -96.4766 41.1651 361.0 2001 2013 CRO TRUE 344.28632 Dfa Croplands Broadleaf crop
US-Ne3 0.0802950 -96.4397 41.1797 363.0 2001 2013 CRO TRUE 342.51697 Dfa Croplands Broadleaf crop
US-Whs 0.0794521 -110.0522 31.7438 1370.0 2007 2014 OSH FALSE 233.38818 BSk Open Shrublands Shrub
US-Ne2 0.0788198 -96.4701 41.1649 362.0 2001 2013 CRO TRUE 346.08804 Dfa Croplands Broadleaf crop
BE-Lon 0.0784558 4.7461 50.5516 167.0 2004 2014 CRO FALSE 383.65109 Cfb Croplands Cereal crop
CA-Qfo 0.0698630 -74.3421 49.6925 382.0 2003 2010 ENF FALSE 176.82556 Dfc Evergreen Needleleaf Forest Evergreen Needleleaf Trees
US-KS2 0.0698630 -80.6715 28.6086 3.0 2003 2006 CSH FALSE 205.27802 Cfa Woody Savannas Shrub
IT-MBo 0.0674969 11.0458 46.0147 1550.0 2003 2013 GRA FALSE 264.44385 Dfb Grasslands Grass
CN-HaM 0.0657534 101.1800 37.3700 3932.0 2002 2004 GRA FALSE 222.66310 NA NA NA
US-SRG 0.0626223 -110.8277 31.7894 1291.0 2008 2014 GRA FALSE 154.87320 BSk Grasslands Shrub
FR-Gri 0.0605479 1.9519 48.8442 125.0 2004 2013 CRO TRUE 314.90735 Cfb Croplands Cereal crop
US-MMS 0.0594178 -86.4131 39.3232 275.0 1999 2014 DBF FALSE 343.01581 Cfa Deciduous Broadleaf Forest Deciduous Broadleaf Trees
US-Ton 0.0585127 -120.9660 38.4316 177.0 2001 2014 WSA FALSE 304.46140 Csa Woody Savannas Shrub
NL-Loo 0.0561644 5.7436 52.1666 25.0 1996 2013 ENF FALSE 71.05942 Cfb Evergreen Needleleaf Forest Evergreen Needleleaf Trees
AU-Tum 0.0551859 148.1517 -35.6566 932.0 2001 2014 EBF FALSE 248.48276 Cfb Evergreen Broadleaf Forest Evergreen Broadleaf Trees
US-NR1 0.0547945 -105.5464 40.0329 3050.0 1998 2014 ENF FALSE 170.75986 Dfc Evergreen Needleleaf Forest Evergreen Needleleaf Trees
FR-LBr 0.0537408 -0.7693 44.7171 61.0 1996 2008 ENF FALSE 269.57657 Cfb Cropland/Natural Vegetation Mosaic Shrub
US-Me2 0.0522655 -121.5574 44.4523 1253.0 2002 2014 ENF FALSE 244.58331 Csb Evergreen Needleleaf Forest Evergreen Needleleaf Trees
IT-Lav 0.0481735 11.2813 45.9562 1353.0 2003 2014 ENF FALSE 249.79709 Cfb Evergreen Needleleaf Forest Evergreen Needleleaf Trees
DE-Obe 0.0414873 13.7196 50.7836 735.0 2008 2014 ENF FALSE 246.86536 Cfb Evergreen Needleleaf Forest Evergreen Needleleaf Trees
FR-Pue 0.0361644 3.5958 43.7414 270.0 2000 2014 EBF FALSE 239.64929 Csa Mixed Forests Evergreen Needleleaf Trees
FI-Lom 0.0347032 24.2092 67.9972 274.0 2007 2009 WET FALSE 222.68436 Dfc Woody Savannas Grass
US-Wkg 0.0338730 -109.9419 31.7365 1531.0 2004 2014 GRA FALSE 209.49074 BSk Grasslands Grass
US-UMB 0.0325114 -84.7138 45.5598 234.0 2000 2014 DBF FALSE 174.07025 Dfb Deciduous Broadleaf Forest Deciduous Broadleaf Trees
AU-DaS 0.0324853 131.3881 -14.1593 98.0 2008 2014 SAV FALSE 249.25092 Aw Savannas Grass
AT-Neu 0.0229141 11.3175 47.1167 970.0 2002 2012 GRA FALSE 270.08563 Dfc Mixed Forests Evergreen Broadleaf Trees
US-SRM 0.0204234 -110.8661 31.8214 1120.0 2004 2014 WSA FALSE 201.24539 BSk Open Shrublands Shrub
FR-Fon 0.0189041 2.7801 48.4764 103.0 2005 2014 DBF FALSE 335.19290 Cfb Deciduous Broadleaf Forest Deciduous Broadleaf Trees
DE-Tha 0.0184571 13.5669 50.9636 380.0 1996 2014 ENF FALSE 295.66315 Cfb Evergreen Needleleaf Forest Evergreen Needleleaf Trees
DE-Hai 0.0179136 10.4530 51.0792 430.0 2000 2012 DBF FALSE 282.66736 Cfb Mixed Forests Deciduous Broadleaf Trees
DE-Gri 0.0174346 13.5125 50.9495 385.0 2004 2014 GRA FALSE 338.52594 Cfb Mixed Forests Deciduous Broadleaf Trees
GF-Guy 0.0141968 -52.9249 5.2788 48.0 2004 2014 EBF FALSE 230.85413 Af Evergreen Broadleaf Forest Evergreen Broadleaf Trees
AU-Rob 0.0082192 145.6301 -17.1175 772.0 2014 2014 EBF FALSE 265.16736 Csb NA NA
DE-Geb 0.0043053 10.9143 51.1001 161.5 2001 2014 CRO FALSE 332.97009 Cfb Croplands Cereal crop
CN-Cha 0.0000000 128.0958 42.4025 754.0 2003 2005 MF FALSE 320.64484 Dwb Mixed Forests Deciduous Broadleaf Trees
CN-Dan 0.0000000 91.0664 30.4978 4751.0 2004 2005 GRA FALSE 229.43036 ET Grasslands Grass
CN-Din 0.0000000 112.5361 23.1733 261.0 2003 2005 EBF FALSE 274.25833 Cfa Evergreen Broadleaf Forest Evergreen Broadleaf Trees
CN-Ha2 0.0000000 101.3269 37.6086 3217.0 2003 2005 WET FALSE 308.49081 ET Grasslands Grass
CN-Qia 0.0000000 115.0581 26.7414 64.0 2003 2005 ENF FALSE 303.67596 Cfa Woody Savannas Shrub
IT-Isp 0.0000000 8.6336 45.8126 210.0 2013 2014 DBF FALSE 320.68103 Cfb Woody Savannas Deciduous Broadleaf Trees
IT-SR2 0.0000000 10.2910 43.7320 12.0 2013 2014 ENF FALSE 286.22598 Csa Mixed Forests Evergreen Needleleaf Trees

Table of missing data

df_missing |> 
  select(sitename, f_missing) |> 
  knitr::kable()
sitename f_missing
AR-SLu 0.5908676
AR-Vir 0.4363014
AT-Neu 0.0229141
AU-Ade 0.4922374
AU-ASM 0.1712329
AU-Cpr 0.1457534
AU-Cum 0.2666667
AU-DaP 0.1749511
AU-DaS 0.0324853
AU-Dry 0.2986301
AU-Emr 0.1771689
AU-Fog 0.1753425
AU-Gin 0.2979452
AU-GWW 0.4246575
AU-How 0.4448141
AU-Lox 0.5986301
AU-RDF 0.4410959
AU-Rig 0.1315068
AU-Rob 0.0082192
AU-Stp 0.1776908
AU-TTE 0.2712329
AU-Tum 0.0551859
AU-Wac 0.2116438
AU-Whr 0.2321918
AU-Wom 0.0949772
AU-Ync 0.2767123
BE-Bra 0.1900505
BE-Lon 0.0784558
BE-Vie 0.1093006
BR-Sa3 0.4208219
CA-Man 0.1663927
CA-NS1 0.3747945
CA-NS2 0.2668493
CA-NS3 0.2394521
CA-NS4 0.2705479
CA-NS5 0.2032877
CA-NS6 0.2043836
CA-NS7 0.1993151
CA-Qfo 0.0698630
CA-SF1 0.1650685
CA-SF2 0.3764384
CA-SF3 0.2447489
CH-Cha 0.3016438
CH-Dav 0.1885845
CH-Fru 0.3010959
CH-Lae 1.0000000
CH-Oe1 0.2046967
CH-Oe2 1.0000000
CN-Cha 0.0000000
CN-Cng 0.2260274
CN-Dan 0.0000000
CN-Din 0.0000000
CN-Du2 0.1223744
CN-Ha2 0.0000000
CN-HaM 0.0657534
CN-Qia 0.0000000
CN-Sw2 0.6347032
CZ-BK1 0.5194521
CZ-BK2 0.6785388
CZ-wet 0.1296804
DE-Akm 0.2465753
DE-Geb 0.0043053
DE-Gri 0.0174346
DE-Hai 0.0179136
DE-Kli 0.2587796
DE-Lkb 0.2120548
DE-Obe 0.0414873
DE-RuR 0.1328767
DE-RuS 0.3534247
DE-Seh 0.1801370
DE-SfN 0.1963470
DE-Spw 0.1523288
DE-Tha 0.0184571
DK-Fou 0.1835616
DK-NuF 0.6348337
DK-Sor 0.5159337
DK-ZaF 0.9500000
DK-ZaH 0.7391781
ES-LgS 0.2803653
ES-Ln2 1.0000000
FI-Hyy 0.2092286
FI-Jok 0.1931507
FI-Lom 0.0347032
FI-Sod 0.3590998
FR-Fon 0.0189041
FR-Gri 0.0605479
FR-LBr 0.0537408
FR-Pue 0.0361644
GF-Guy 0.0141968
IT-BCi 0.2747198
IT-CA1 0.6616438
IT-CA2 0.6705479
IT-CA3 0.3815068
IT-Col 0.3387167
IT-Cp2 0.4337900
IT-Cpz 0.2421496
IT-Isp 0.0000000
IT-La2 0.5287671
IT-Lav 0.0481735
IT-MBo 0.0674969
IT-Noe 0.1599004
IT-PT1 0.1178082
IT-Ren 0.1398973
IT-Ro1 0.3226788
IT-Ro2 0.2931507
IT-SR2 0.0000000
IT-SRo 0.4620352
IT-Tor 0.1792564
JP-MBF 1.0000000
JP-SMF 1.0000000
NL-Hor 0.1260274
NL-Loo 0.0561644
NO-Adv 0.6815068
NO-Blv 1.0000000
RU-Che 0.6486301
RU-Cok 0.6641553
RU-Fyo 0.1182917
RU-Ha1 0.4520548
SD-Dem 0.1654795
SN-Dhr 0.2390411
US-AR1 0.1157534
US-AR2 0.2232877
US-ARb 0.1767123
US-ARc 0.1739726
US-ARM 0.0865753
US-Blo 0.2313823
US-Cop 0.4160470
US-GBT 0.2106164
US-GLE 0.1185554
US-Ha1 0.3346202
US-KS2 0.0698630
US-Los 0.4213699
US-Me1 0.5465753
US-Me2 0.0522655
US-Me6 0.1616438
US-MMS 0.0594178
US-Myb 0.2482192
US-Ne1 0.0817703
US-Ne2 0.0788198
US-Ne3 0.0802950
US-NR1 0.0547945
US-ORv 0.1205479
US-PFa 1.0000000
US-Prr 0.2609589
US-SRG 0.0626223
US-SRM 0.0204234
US-Syv 0.3982387
US-Ton 0.0585127
US-Tw1 0.1771689
US-Tw2 0.5876712
US-Tw3 0.2726027
US-Tw4 0.4589041
US-Twt 0.1908676
US-UMB 0.0325114
US-UMd 0.1191781
US-Var 0.1693151
US-WCr 0.3688356
US-Whs 0.0794521
US-Wi0 0.4821918
US-Wi3 0.5388128
US-Wi4 0.5780822
US-Wi6 0.7013699
US-Wi9 0.6013699
US-Wkg 0.0338730
ZA-Kru 0.5155666
ZM-Mon 0.8126027