R package to aid in the estimation of precipitation amounts from a home-made pluviometer. Building a pluviometer is super-easy, just place a funnel ontop of a cointainer, take note of funnel diameter and, periodically (or after each rain event) note down the volume of water in the container.
The package is not yet on CRAN, to install it use devtools package:
devtools::install_github("mbask/pluviometer")
library(dplyr)
library(lubridate)
library(pluviometer)
library(ggplot2)
library(tidyr)Let's follow a typical workflow to convert readings from home-made rain gauges (pluviometers) into precipitation figures and to estimate the recharge of a water tank connected to a house roof thanks to precipitation on the roof itself.
Having built a couple of rain gauges with different funnel diameters, let's import a couple of readings we gathered during precipitation events:
precipitation_events <- read.table(
text = "
funnel_diameter_cm water_volume_ml date_dmy
11 13 20/06/2015
25 329 24/06/2015
25 100 02/07/2015
25 200 03/07/2015
25 700 18/07/2015",
header = TRUE)funnel_diameter_cm is the diameter of the circular opening where rain is captured, water_volume_ml is the amount of water collected in the rain gauge (either as ml or as g).
The surface area of the house roof and its discharge coefficient:
rainfall_in_tank <- get_roof_runoff_on_same_surface(
precipitation_area = 121,
discharge_coef = 0.8,
first_flush_diverted = 0)Now for the core part: let's compute the amount of precipitation from the rain gauge readings for each precipitation event:
precipitation_events <- precipitation_events %>%
mutate(
date = lubridate::dmy(date_dmy),
rain_mm = get_precipitation_measure(
water_volume_ml,
funnel_diameter_cm %>% get_funnel_area), # ml o l/m2
tank_rain_charge_l = rain_mm %>% rainfall_in_tank # l
)
precipitation_events %>%
select(date, rain_mm, tank_rain_charge_l) %>%
print## date rain_mm tank_rain_charge_l
## 1 2015-06-20 1.367943 132.4169
## 2 2015-06-24 6.702333 648.7858
## 3 2015-07-02 2.037183 197.1993
## 4 2015-07-03 4.074367 394.3987
## 5 2015-07-18 14.260283 1380.3954
ggplot(precipitation_events, aes(x = date, y = rain_mm)) +
geom_bar(stat = "identity") +
scale_y_continuous("Rainfall (mm)") +
scale_x_date("Date") +
theme_light()
Finally a monthly summary:
monthly_precipitation <- precipitation_events %>%
mutate(year = year(date), month = month(date, label = TRUE)) %>%
group_by(month, year) %>%
select(rain_mm, tank_rain_charge_l) %>%
summarise_each(funs(sum))
print(monthly_precipitation)## Source: local data frame [2 x 4]
## Groups: month [?]
##
## month year rain_mm tank_rain_charge_l
## (fctr) (dbl) (dbl) (dbl)
## 1 Jun 2015 8.070276 781.2027
## 2 Jul 2015 20.371833 1971.9934
sprinkler_flow <- 10 # l/min
sprinkler_time_min <- c(rep(10, 3), rep(5, 6)) # minutes/day
sprinkler_daily_vol_l <- Reduce(f = sum, sprinkler_time_min * sprinkler_flow) # l/day
tank_volume_l <- 5000 # lA 5000 litre water tank serves an irrigation system that includes 9 sprinklers that need 10 litre/min of water each on a daily basis.
Let's now simulate 2 months of lawn irrigation (June and July 2015), eg 600 litres of water daily:
water_use_tbl <- data.frame(
date = as.Date("2015/06/01") + 0:60,
daily_water_usage_l = sprinkler_daily_vol_l)We need to know how much rain water went into the tank in the period June-July 2015. Let's join the lawn irrigation table to the precipitation table:
water_use_tbl <- water_use_tbl %>%
left_join(
precipitation_events %>%
select(date, tank_rain_charge_l),
by = "date")When the irrigation season starts (June, 1st) the tank is full of rain water (5000) litres:
water_use_tbl <- within(water_use_tbl, {
water_balance_l <- NA_real_
water_balance_l[1] <- tank_volume_l
is_recharged <- FALSE
rain_lost_l <- 0
})Every time the tank empties it needs to be recharged with water from the well. Let's estimate how much water we have pumped from the well, as opposed to the water we recycled from rain fallen on the house roof, on a daily level.
We will simulate a tank whose water level varies according to sprinkler water needs, water pumped from the well, and rain collected from the roof. Let's walk the water_use_tbl data frame to estimate the water balance on a dayly basis:
for (i in seq(1, nrow(water_use_tbl), by = 1)) {
if (i == 1) {
prior_water_level_l <- water_use_tbl$water_balance_l[i]
} else {
prior_water_level_l <- water_use_tbl$water_balance_l[i - 1]
}
tmp_df <- get_tank_water_level(
tank_level = prior_water_level_l,
water_in = water_use_tbl$tank_rain_charge_l[i],
water_out = water_use_tbl$daily_water_usage_l[i],
tank_volume = tank_volume_l)
water_use_tbl[i,] <- within(
water_use_tbl[i,], {
water_balance_l = tmp_df$tank_level
is_recharged = tmp_df$is_recharged
rain_lost_l = tmp_df$water_lost
tank_rain_charge_l = tmp_df$water_in
})
}The jaw-teeth plot of the daily water balance in the tank reveals the grim role of rainfall in the summer season as far as water budget for irrigation is concerned:
water_use_tbl %>%
select(date, water_balance_l, rain_lost_l) %>%
gather(key = "variable", value = "value", -date) %>%
mutate(value = ifelse(is.na(value), 0, value)) %>%
ggplot(., aes(x = date, y = value, color = variable)) +
geom_line() +
scale_y_continuous("Volume (l)") +
scale_x_date("Date") +
ggtitle("Balance in a water recycling tank") +
theme_light()
It looks like a lot of water is pumped from the well, and occasionally rain water is lost due to the tank having been filled just days before. We also need a much bigger water tank to store rain water...
Finally we can estimate how much water we recycled from rainfall and how much water we pumped from the well:
total_water_budget <- water_use_tbl %>%
mutate(
tank_well_charge_l = is_recharged * tank_volume_l) %>%
select(
tank_rain_charge_l,
tank_well_charge_l,
rain_lost_l) %>%
summarise_each(
funs(sum(., na.rm = TRUE)))
total_water_budget %>% print## tank_rain_charge_l tank_well_charge_l rain_lost_l
## 1 1972.801 30000 780.3954
Hopefully next year won't be as dry as 2015...
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