Seasonal Theil-Sen estimates with incomplete data - r

Question

I'm new to time series, but am trying to do some simple analyses for monthly water quality values across multiple lakes. I have 20 water bodies, and monthly Dissolved Oxygen values for 2 years and 7 months. I am able to successfully run a nonparametric Mann-Kendall and Theil-Sen estimates for each water body across the time period, but am struggling to figure out how to run similar tests while taking into account seasonality.
Seasonal Mann-Kendalls seem to be working, but seasonal Theil-Sen estimates seem to fail when using sea.sens.slope() from the trend package. I am receiving the error "number of items to replace is not a multiple of replacement length". I THINK i'm getting this because I only have 7 months of data on the final year and not a full 12.
Eg:
library(trend)
library(Kendall)
library(dplyr)
#Sample Dataframe with 20 columns (waterbodies) and 31 months
df <- as.data.frame(matrix(rnorm(31*20,10,1),ncol=20))
#Creating a function to run MK and sen's Slopes on each water body
series.func <- function(x) {
c(mk = MannKendall(x), ss = sens.slope(x))
}
#running the function on each column
results <- df %>% ts(start = c(2019, 01),
end = c(2022, 07),
frequency = 12) %>% lapply(., series.func)
This gives me a list with MK and Theil Sen results for each water body, however when I attempt to do the same thing with seasonal Mann-Kendall and seasonal sen's slopes I get the error I mentioned
#Same function but with seasonal MK and Theil Sen
series.func <- function(x) {
c(smk = smk.test(x), seass = sea.sens.slope(x)
)
}
#running the function on each column
results <- df %>% ts(start = c(2019, 01),
end = c(2022, 07),
frequency = 12) %>% lapply(., series.func)
My questions are:
Is it possible to run sea.sens.slope() when I only have 7 months of data for my third year?
If it is possible, what exactly determines what a "season" is? My data is tropical and there aren't 4 distinct "seasons"
Thank you!