Allocation Assessment • tbeploads

library(tbeploads)

The anlz_aa() function compares mean annual total nitrogen (TN) loads against the allowable allocations established by the Tampa Bay Nitrogen Management Consortium (TBNMC) as part of the 5-year Reasonable Assurance (RA) assessment. Allocations are defined for four source categories: non-point source / MS4 stormwater (NPS), industrial point source (IPS), domestic point source (DPS), and material losses (ML). Each category uses a different method for computing mean loads and, where applicable, applies hydrologic normalization.

Required inputs

anlz_aa() takes six arguments: the year range, pre-computed load data for each source, and the tbbase spatial lookup table:

anlz_aa(yrrng, dps_data, ips_data, ml_data, nps_data, tbbase)

yrrng is an integer vector of length 2 giving the first and last years of the assessment period. The function internally expands this to a full sequence using seq(min, max). For example, assessing 2022 to 2024:

yrrng <- c(2022, 2024)

The four load inputs are computed using the corresponding anlz_* functions described in the individual loading vignettes. Each is briefly described below.

DPS loads

DPS loads come from anlz_dps_facility(). The function requires a vector of file paths to entity data files provided by TBNMC partners. See the DPS vignette for full details on the required file format and processing steps.

fls_dps <- list.files(system.file("extdata/", package = "tbeploads"),
  pattern = "ps_dom_", full.names = TRUE)
dps <- anlz_dps_facility(fls_dps)
head(dps)

IPS loads

IPS loads come from anlz_ips_facility(). The function requires a vector of file paths to industrial facility discharge files. See the IPS vignette for full details.

fls_ips <- list.files(system.file("extdata/", package = "tbeploads"),
  pattern = "ps_ind_", full.names = TRUE)
ips <- anlz_ips_facility(fls_ips)
head(ips)

ML loads

Material loss loads come from anlz_ml_facility(). The function requires a vector of file paths to material loss data files. See the ML vignette for full details.

fls_ml <- list.files(system.file("extdata/", package = "tbeploads"),
  pattern = "ps_indml", full.names = TRUE)
ml <- anlz_ml_facility(fls_ml)
head(ml)

NPS loads

NPS loads come from anlz_nps() called with summ = "basin" and summtime = "year". This produces annual basin-level TN and hydrologic loads that anlz_aa() disaggregates to individual MS4 entities. See the NPS vignette for full details on the required inputs.

data(tbbase)
data(rain)
data(allwq)
data(allflo)

nps <- anlz_nps(
  yrrng    = c("2022-01-01", "2024-12-31"),
  tbbase   = tbbase,
  rain     = rain,
  allwq    = allwq,
  allflo   = allflo,
  vernafl  = system.file("extdata/verna-raw.csv", package = "tbeploads"),
  summ     = "basin",
  summtime = "year"
)
head(nps)

The NPS loads passed to anlz_aa() are not pre-corrected for point-source loads upstream of stream gauges (unlike anlz_nps_psremove()); for gaged basins, anlz_aa() removes the upstream point-source contribution internally before disaggregating loads to MS4 entities (see below).

Running the assessment

With all inputs prepared, the assessment is run as:

data(tbbase)
result <- anlz_aa(c(2022, 2024), dps, ips, ml, nps, tbbase)
head(result)

The function returns one row per entity (NPS/MS4) or entity/facility (IPS, DPS, ML) per bay segment, with the following columns:

Column	Description
`bay_seg`	Integer bay segment (1 = OTB, 2 = HB, 3 = MTB, 4 = LTB, 55 = RALTB)
`segment`	Bay segment name
`entity`	MS4 entity name or facility operator
`entity_full`	Full entity name (NPS rows only)
`facname`	Facility name (IPS, DPS, ML rows only)
`permit`	NPDES permit number (IPS rows only)
`source`	Allocation type (`"MS4"`, `"Nonpoint Source/MS4"`, `"IPS"`, `"DPS - end of pipe"`, `"DPS - reuse"`, `"ML"`); `NA` for unmatched entities
`alloc_pct`	Fractional TN allocation (0-1)
`alloc_tons`	Allowable TN load (tons/yr); for `ishared` rows, the group’s collective allocation, repeated on every member row, not an individual facility allocation
`eff_load_tons`	Mean hydrologically-normalized TN load (tons/yr); equals `load_tons` for DPS and ML, and for IPS facilities not flagged `hydro_affected` in `ps_allocations`
`load_tons`	Mean annual TN load without normalization (tons/yr); always the facility’s own individual load, even for `ishared` rows
`ishared`	`TRUE` when the facility is jointly assessed against a collective allocation shared with other facilities; `FALSE` for NPS/MS4 and DPS rows
`group_id`	Character identifier for the shared group a row belongs to when `ishared` is `TRUE` (`NA` otherwise)

Entities present in the computed loads but absent from the allocation tables are retained in the output with NA allocation fields so that unmatched entries are visible for troubleshooting, with one exception: unmatched NPS/MS4 entities with a mean annual load under 0.01 tons/yr are dropped, since these are negligible land-use polygon artifacts (e.g. land in tbbase not attributed to any jurisdiction, or a jurisdiction’s boundary crossing into an adjacent basin/segment where it has no allocation) rather than real troubleshooting signal. Set verbose = TRUE to print a message reporting what was dropped and why.

Source-specific methodology

NPS/MS4

Gaged-basin TN loads are gauge-measured totals and so include any industrial or domestic point-source discharge upstream of the gauge. Before disaggregation, anlz_aa() subtracts the basin’s IPS and DPS TN loads from gaged-basin totals so that only the true non-point-source contribution is assigned to MS4 entities. Ungaged-basin TN loads are already non-point-source only, since the modeled estimate never includes point-source discharge, and are left unchanged.

The NPS path disaggregates basin-level loads (after the point-source removal above) to individual MS4 jurisdictions using land use and runoff coefficient data from tbbase. The core disaggregation uses two factors computed internally by util_aa_npsfactors():

factor_tn: each CLUCSID’s share of basin TN load, proportional to EMC, area, and runoff coefficient relative to the basin total.
factor_rc: each entity’s share of a CLUCSID’s load within a basin, proportional to entity area times runoff coefficient relative to the CLUCSID total.

A conservation land correction is applied before summing across CLUCSIDs, using the true underlying MS4 jurisdiction (this ordering matters, see below). The conserv_correction dataset provides entity- and CLUCSID-specific fractions of area times runoff coefficient attributable to conservation land. These fractions are derived from a legacy land cover file that includes a binary conservation land flag while retaining the original MS4 jurisdiction for each spatial unit. Each entity’s disaggregated TN load for a given basin and CLUCSID is scaled by (1 - conserv_frac) to remove the conservation land contribution. The tbbase file herein does not include a conservation land overlay because the original spatial layer is unavailable for routine GIS updates, so conserv_correction is stored as a separate stable dataset and applied as a backend correction.

Only after this correction are two groups of entities aggregated regardless of their underlying MS4 jurisdiction: agricultural land use (category "Agriculture") is attributed to entity "All", and land under a Municipal Separate storm sewer Generic Permit (entities "MSGP COT" and "MSGP PINELLAS" in tbbase, plus "PORT MANATEE", whose footprint spans both Middle Tampa Bay and Lower Tampa Bay) is attributed to entity "Non-MS4/Ag NPS", consistent with the loading tables generated for each Reasonable Assurance period. Lower Tampa Bay has no other MSGP entities, so Port Manatee is its sole contributor. Conservation land correction must precede both relabeling steps since conserv_correction is keyed by the true entity and would never match either aggregate label.

After disaggregation, entity loads and 1992-1994 baseline water volumes from hydro_baseline are summed across basins to the bay segment level. Corrections from aa_corrections (representing atmospheric deposition and approved project TN adjustments) are subtracted before hydrologic normalization:

$\text{eff\_tn} = (\text{tn\_entity} - \text{corr\_tons}) \times \frac{\text{mean\_h2o\_9294}}{\text{total\_h2o}}$

total_h2o is the annual total basin water load for that basin, gated by whether the basin is gaged (per dbasing$gagetype): for gaged basins, NPS water is estimated from a stream gauge and so already reflects any upstream IPS + DPS discharge, so total_h2o is the NPS water alone (adding IPS/DPS water again would double-count it); for ungaged basins, the modeled NPS-only water excludes point-source discharge entirely, so IPS and DPS water are added to it to reconstruct the true total. mean_h2o_9294 is the 1992-1994 baseline total basin water volume from hydro_baseline. Effective loads are averaged over yrrng as sum / length(yrrng), so missing years contribute zero rather than being excluded.

Bay segments Terra Ceia Bay (6) and Manatee River (7) are merged into segment 55 (Remaining Lower Tampa Bay) after disaggregation, consistent with hydro_baseline and TBNMC reporting. Boca Ciega Bay (segment 5) is excluded from the allocation framework.

IPS

Raw facility loads are joined to facility metadata on entity + facility name rather than coastal segment (coastco), since several distinct NPDES permits share a single coastco. Monthly loads are summed to annual totals per permit and bay segment, averaged over yrrng, and compared against ps_allocations.

Hydrologic normalization is applied only to permits flagged hydro_affected in ps_allocations (mostly Mosaic mining operations); every other IPS facility uses its raw (unnormalized) load. For flagged facilities:

$\text{eff\_tn} = \text{tn\_load} \times \frac{\text{mean\_h2o\_9294}}{\text{basin\_total\_h2o}}$

where basin_total_h2o is the same gaged/ungaged-gated basin water quantity described in the NPS path above.

Permits flagged ishared = TRUE in ps_allocations carry their group’s collective allocation in alloc_tons rather than an individual one, while load_tons/eff_load_tons remain each permit’s own load. ishared and hydro_affected are independent flags — a permit can be jointly assessed without being hydrologically normalized, or vice versa. group_id identifies which shared group a permit belongs to ("ips_mosaic_hb" or "ips_kinder_morgan"; NA when ishared is FALSE).

DPS

Domestic point source loads require no hydrologic normalization. Monthly loads from dps_data are summed to annual totals per facility and averaged over yrrng. Boca Ciega Bay (bay_seg = 5) is excluded and bay segments 6 and 7 are remapped to 55 before joining against dps_allocations for the Remainder Lower Tampa Bay segment. The join key is entity + facility name + bay segment + source type (end of pipe or reuse).

ML

Material loss loads require no hydrologic normalization. Monthly loads are summed to annual totals per facility and averaged over yrrng, then joined to ml_allocations on entity + facname + bay segment — one output row per facility, always. Facilities with ishared = TRUE in ml_allocations carry their group’s collective allocation in alloc_tons rather than an individual one, while load_tons/eff_load_tons remain each facility’s own load. group_id identifies which shared group a facility belongs to ("ml_mosaic_hb"; NA when ishared is FALSE).

Reporting tables

Two functions build formatted flextable reports directly from anlz_aa() output. First, show_aaassess() reports allocation percentages, allocated tons, and effective loads by entity and facility:

result <- anlz_aa(c(2022, 2024), dps, ips, ml, nps, tbbase)
show_aaassess(result, bay_seg = 2)

Second, show_aaloads() reports annual TN loads by source, including atmospheric deposition and groundwater/springs/conservation contributions, plus a hydrologically-normalized total. It requires annavg = FALSE output from anlz_aa(), along with anlz_gw(), anlz_spr(), and anlz_ad() results:

result_yr <- anlz_aa(c(2022, 2024), dps, ips, ml, nps, tbbase, annavg = FALSE)
gw <- anlz_gw(contdry, contwet, yrrng = c(2022, 2024), summtime = "year")
spr <- anlz_spr(tbwxlpth, wqpth, yrrng = c(2022, 2024), summ = "segment", summtime = "year")
ad <- anlz_ad(rain, vernafl, summ = "segment", summtime = "year")
show_aaloads(result_yr, bay_seg = 2, gw, spr, ad)

Supporting datasets

The following package datasets support the allocation assessment and are used internally by anlz_aa():

Dataset	Description
`nps_allocations`	NPS/MS4 allowable allocations by entity and bay segment
`ps_allocations`	IPS allowable allocations by NPDES permit
`dps_allocations`	DPS allowable allocations by facility, bay segment, and source type
`ml_allocations`	ML allowable allocations by facility and bay segment
`hydro_baseline`	1992-1994 mean annual basin water volumes (million m3/yr)
`aa_corrections`	AD and approved project TN corrections per entity and bay segment
`conserv_correction`	Conservation land area fractions per entity, basin, and CLUCSID