Function to develop a pooling group based on catchment descriptors
Arguments
- CDs
catchment descriptors derived from either GetCDs or CDsXML
- N
minimum Number of total gauged record years for the pooling group
- UrbMax
Maximum URBEXT2015 level with a default of 0.03. Any catchment with URBEXT2015 above this level will be excluded from the pooling group
- DeUrb
logical argument with a default of TRUE. If TRUE, the LCVs of all sites in the pooling group are "De-Urbanised".
- exclude
sites to exclude from the pooling group. Either a single site reference or a vector of site references (numeric). If this is used the next site with the lowest SDM is included such that the total sample of AMAX is at least N.
- include
sites to include that otherwise would not be included by default. For example if it is a subject site that has URBEXT2015 above UrbMax. Or one that has not been selected automatically using the similarity distance measure. Note that one or more sites will be removed to include the user suggested site unless N is increased.
Value
A data.frame of the pooling group with site reference row names and 24 columns, each providing catchment & gauge details for the sites in the pooling group.
Details
A pooling group is created from a CDs object, derived from GetCDs or CDsXML, or specifically with the catchment descriptors (see arguments). To change the default pooling group, one or more sites can be excluded using the 'exclude' option, which requires either a site reference or multiple site references in a vector. If this is done, the site with the next lowest similarity distance measure is added to the group (until the total number of years is at least N). Similarly a site can be included specifically by using the include argument. Sites with URBEXT2015 (urban extent) > 0.03 are excluded from the pooling group by default. This threshold can be adjusted with UrbMax. If DeUrb is set as TRUE (the default), the LCV values for sites in the pooling group are de-urbanised. If the user has more data available for a particular site within the pooling group, the LCV and LSKEW for the site can be updated after the group has been finalised using the LRatioChange function.
The pooling method is as specified by FEH2025. The de-urbanisation functionality assumes that the growth curve associated with an annual maximum flow sample is impacted by urbanisation and that this impact can be modelled as a function of the catchment URBEXT. The method for pooling the catchments together is based on the similarity of AREA, SAAR, FARL, FPEXT, and BFIHOST. These were seen to have the most significant impact on the LCV and LSKEW - and ultimately to provide the lowest 'Pooling Uncertainty Measure' (a statistic for assessing the similarity between pooled and single site gauged estimates).
Examples
# Get some catchment descriptors
cds_73005 <- GetCDs(73005)
# Set up a pooling group object called pool_73005 excluding sites 79005 & 46003
# Then print the group to the console
pool_73005 <- Pool(cds_73005, exclude = c(79005, 46003))
pool_73005
#> AREA SAAR9120 FARL2015 FPEXT BFIHOST19scaled URBEXT2015 Lcv LSkew
#> 73005 212.2 1905 0.9823 0.0739 0.465 0.0261 0.2155752 0.271
#> 73012 183.2 1976 0.9795 0.0712 0.450 0.0116 0.1944398 0.320
#> 63001 170.1 1620 0.9942 0.0471 0.430 0.0039 0.1904754 0.229
#> 72005 219.2 1811 0.9967 0.0484 0.390 0.0029 0.1913552 0.119
#> 13012 130.6 1718 0.9874 0.0414 0.455 0.0001 0.2110135 0.161
#> 72015 140.8 1790 0.9948 0.0549 0.392 0.0038 0.1513681 0.124
#> 56006 184.7 1849 0.9614 0.0365 0.413 0.0030 0.2003848 0.183
#> 60013 243.0 1673 0.9976 0.0336 0.439 0.0023 0.2073053 0.256
#> 60002 298.7 1688 0.9980 0.0315 0.444 0.0026 0.2083468 0.220
#> 48011 167.2 1529 0.9680 0.0349 0.448 0.0054 0.1906585 0.171
#> 15013 173.3 1613 0.9960 0.0308 0.421 0.0014 0.1961759 0.140
#> 76022 249.7 1327 0.9985 0.0781 0.415 0.0068 0.2048908 0.245
#> 60006 131.1 1688 0.9996 0.0296 0.467 0.0071 0.1908663 0.223
#> 76021 223.0 1372 0.9965 0.0471 0.414 0.0039 0.1894729 0.183
#> 49001 209.9 1413 0.9855 0.0338 0.481 0.0226 0.2211798 0.243
#> 79004 142.7 1719 0.9985 0.0319 0.405 0.0008 0.1340687 0.150
#> 73008 127.4 1385 0.9587 0.0931 0.483 0.0126 0.2076698 0.296
#> 54038 241.1 1386 0.9952 0.0382 0.427 0.0031 0.1613201 0.187
#> 47004 135.3 1477 0.9950 0.0339 0.482 0.0128 0.2248365 0.290
#> LKurt QMED N SDM Discordancy Discordant
#> 73005 0.192 165.0 57 0.0000 0.4240 FALSE
#> 73012 0.227 144.0 40 0.1966 1.8300 FALSE
#> 63001 0.191 95.8 64 0.6601 0.0922 FALSE
#> 72005 0.176 272.0 56 0.7344 1.1500 FALSE
#> 13012 0.204 77.7 34 0.7650 1.1300 FALSE
#> 72015 0.195 199.0 46 0.7766 1.9600 FALSE
#> 56006 0.235 164.0 44 0.7774 0.9280 FALSE
#> 60013 0.131 123.0 10 0.7780 1.3200 FALSE
#> 60002 0.195 179.0 64 0.8566 0.1690 FALSE
#> 48011 0.185 50.9 22 0.8591 0.2140 FALSE
#> 15013 0.142 121.0 37 0.8954 1.0000 FALSE
#> 76022 0.145 157.0 28 0.9250 0.7300 FALSE
#> 60006 0.172 86.1 58 0.9392 0.0779 FALSE
#> 76021 0.114 180.0 25 0.9454 0.9850 FALSE
#> 49001 0.233 57.5 55 0.9476 0.6410 FALSE
#> 79004 0.126 148.0 44 0.9594 2.5300 FALSE
#> 73008 0.278 37.4 56 0.9655 2.0000 FALSE
#> 54038 0.131 79.8 52 0.9807 0.9990 FALSE
#> 47004 0.202 46.4 60 0.9916 0.8120 FALSE