Ireland’s Median Rainfall in January 1850-2014

Introduction:

In the realm of environmental management and agricultural planning, understanding seasonal weather patterns is essential. This blog post delves into an in-depth analysis of rainfall in January across Ireland, a period typically marked by significant precipitation that profoundly affects the region. By examining data collected from various weather stations, this study explores how geographic characteristics, such as elevation and proximity to the coast, influence rainfall distribution. Utilizing advanced GIS tools and statistical methods, this analysis visualizes rainfall variations and examines correlations to offer deeper insights into the climatic influences at play. This comprehensive approach not only enhances our understanding of Ireland’s weather patterns but also informs strategic planning for water resource management and flood prevention strategies.

Data Overview:

The analysis utilizes data from 25 weather stations across Ireland, each station recording environmental metrics with a particular focus on rainfall. For this study, the dataset specifically includes rainfall data for the month of January, collected over several years. Each record in the dataset identifies the station by its unique ID and includes geographic coordinates, allowing for precise mapping. Key attributes such as elevation and distance from the coast are also part of the dataset, providing a basis to explore their influence on rainfall variations. This targeted selection of data supports a focused investigation into spatial patterns and climatic influences across the region.

library(sf)
library(dplyr)
library(tmap)
library(ggplot2)

Data Preparation Code:

setwd("/home/students/24250027")
dt <- load("rainfall (5).RData")

head(stations)

## # A tibble: 6 × 9
##   Station     Elevation Easting Northing   Lat  Long County  Abbreviation Source
##   <chr>           <int>   <dbl>    <dbl> <dbl> <dbl> <chr>   <chr>        <chr> 
## 1 Athboy             87  270400   261700  53.6 -6.93 Meath   AB           Met E…
## 2 Foulksmills        71  284100   118400  52.3 -6.77 Wexford F            Met E…
## 3 Mullingar         112  241780   247765  53.5 -7.37 Westme… M            Met E…
## 4 Portlaw             8  246600   115200  52.3 -7.31 Waterf… P            Met E…
## 5 Rathdrum          131  319700   186000  52.9 -6.22 Wicklow RD           Met E…
## 6 Strokestown        49  194500   279100  53.8 -8.1  Roscom… S            Met E…

head(rain)

## # A tibble: 6 × 4
##    Year Month Rainfall Station
##   <dbl> <fct>    <dbl> <chr>  
## 1  1850 Jan      169   Ardara 
## 2  1851 Jan      236.  Ardara 
## 3  1852 Jan      250.  Ardara 
## 4  1853 Jan      209.  Ardara 
## 5  1854 Jan      188.  Ardara 
## 6  1855 Jan       32.3 Ardara

weather_stations_cleaned <- stations %>%
  inner_join(rain, by = "Station")  
# Filter weather station data for January
rain_jan80 <- filter(rain, Month == 'Jan')

print(names(stations))

## [1] "Station"      "Elevation"    "Easting"      "Northing"     "Lat"         
## [6] "Long"         "County"       "Abbreviation" "Source"

# Summarizing median rainfall
rain_jan80 <- rain_jan80 %>%
  group_by(Station) %>%
  summarize(median_rainfall = median(Rainfall, na.rm = TRUE))

print(rain_jan80)

## # A tibble: 25 × 2
##    Station        median_rainfall
##    <chr>                    <dbl>
##  1 Ardara                   172. 
##  2 Armagh                    75  
##  3 Athboy                    87.1
##  4 Belfast                  102. 
##  5 Birr                      77.5
##  6 Cappoquinn               147. 
##  7 Cork Airport             135. 
##  8 Derry                     97.3
##  9 Drumsna                   99.1
## 10 Dublin Airport            63  
## # ℹ 15 more rows

Data Visualization Techniques:

To elucidate the patterns and relationships in Ireland’s rainfall data, four distinct types of data visualizations were employed:

1. Interactive Maps: Visualize spatial distribution of median rainfall levels, using color-coded symbols for each weather station.
2. Line and Point Graphs: Illustrate trends over time by plotting annual total rainfall with a color gradient indicating the volume of rainfall each year.
3. Scatter Plots: Analyze the relationships between median rainfall and geographical factors like elevation .
4. Bar Plots and Statistical Summary Plots: Detail the comparative rainfall across counties and the trends between rainfall and elevation respectively.

These techniques were selected to comprehensively analyze both individual and relational aspects of the data, enabling informed decisions for environmental management and planning.

Interactive Map:

• Purpose and Visualization Choice: The interactive map highlights spatial rainfall patterns using a color gradient, where darker blue represents higher rainfall. It enables users to quickly identify areas with high or low rainfall and interact with individual weather station data for deeper insights.

• Data Used: The map visualizes median rainfall data from 25 weather stations across Ireland for January. Each station is represented by its geographic location, with rainfall values (in mm) ranging from 63.0 mm at Dublin Airport to 177.7 mm at Killarney. The data captures regional rainfall variations influenced by geography, proximity to the coast, and topography.

# Convert to an sf object
weather_stations_cleaned_sf <- st_as_sf(weather_stations_cleaned, coords = c("Long", "Lat"), crs = 4326)

# Set the interactive map mode
tmap_mode('view')

## tmap mode set to interactive viewing

# Plot the distribution of rainfall
tm_map <- tm_shape(weather_stations_cleaned_sf) +
  tm_dots(col = "Rainfall", size = 0.1, palette = "Blues", style = "quantile")  # Visualization of rainfall at points

# Convert the tmap to leaflet for embedding
library(leaflet)
leaflet_map <- tmap_leaflet(tm_map)

# Show the map
leaflet_map

• Findings: The interactive map reveals clear geographical patterns in rainfall distribution across Ireland, represented using a gradient of blue colors. Darker blue dots indicate higher rainfall levels, as seen in western and coastal areas such as Killarney (177.7 mm) and Ardara (171.6 mm), which experience significant precipitation due to their exposure to Atlantic weather systems. Lighter blue dots signify lower rainfall levels, predominantly in eastern regions like Dublin Airport (63.0 mm) and Phoenix Park (67.6 mm), which are more sheltered from prevailing winds. Coastal proximity strongly influences rainfall, with stations near the coast showing higher values compared to drier inland areas. This color-coded visualization makes it easy to identify regions with contrasting rainfall levels and understand the spatial patterns driven by geographical factors.

• Insights: Regions with high rainfall, such as those along the western and coastal areas, may require robust flood management systems to mitigate the risks associated with excessive precipitation. Conversely, areas with lower rainfall, particularly in the east, would benefit from focused water conservation strategies to ensure sustainable resource management. The stark west-east divide in rainfall distribution underscores the significant influence of Ireland’s Atlantic-driven climate, where proximity to the ocean and prevailing winds play a pivotal role in shaping regional precipitation patterns.

Line and Point Graph:

• Purpose and Visualization Choice: This visualization combines a line graph and color-coded points to display the trend in total annual rainfall over time. The blue line connects yearly totals, providing a clear sense of overall fluctuations, while the color gradient (yellow to red) on the points highlights years with lower (yellow) and higher (red) rainfall. This dual approach was chosen to offer both a continuous view of rainfall trends and a quick visual differentiation of extreme values.

• Data Used: The graph is based on annual rainfall data aggregated from cleaned weather station records. Each data point represents the total rainfall (in millimeters) recorded across all weather stations for a specific year. The rainfall totals range from approximately 20,000 mm to 35,000 mm, showing variations in annual precipitation over the years.

annual_rainfall <- weather_stations_cleaned %>%
  group_by(Year) %>%
  summarize(TotalRainfall = sum(Rainfall, na.rm = TRUE)) %>%
  ungroup()
# Plotting Rainfall vs. Year
ggplot(annual_rainfall, aes(x = Year, y = TotalRainfall)) +
  geom_line(group=1, color="blue") +  
  geom_point(aes(color = TotalRainfall), size = 3) +
  scale_color_gradient(low = "yellow", high = "red") +
  labs(title = "Annual Rainfall Over Years",
       x = "Year",
       y = "Total Rainfall (mm)") +
  theme_minimal()

• Findings: Years with extreme rainfall (above 35,000 mm), represented by red points, stand out as peaks in the dataset, indicating intense precipitation likely linked to significant weather events. Drier years, with rainfall below 25,000 mm, are marked in yellow and tend to cluster sporadically, indicating occasional periods of reduced precipitation. The consistent fluctuations in the graph show no clear upward or downward trend, suggesting that rainfall patterns in Ireland are highly variable but not dominated by a long-term increase or decrease over the period analyzed.

• Insights: High rainfall years, often associated with red points, could correspond to significant meteorological events, such as severe storms or prolonged wet seasons, emphasizing the importance of flood management and disaster preparedness. Drier years (yellow points) reveal potential challenges for water availability, agricultural productivity, and ecosystem resilience, highlighting the need for robust drought mitigation strategies. The persistent variability in annual rainfall indicates the influence of Ireland’s dynamic Atlantic-driven climate, underscoring the need for adaptive approaches to water resource management and long-term infrastructure planning.

Scatter Plot: Rainfall vs. Elevation

• Purpose and Visualization Choice: The scatter plot was created to explore the relationship between elevation and median rainfall at weather stations. Each point represents a weather station, with its position on the plot determined by elevation (x-axis) and median rainfall (y-axis). A color gradient from yellow (low rainfall) to blue (high rainfall) enhances the visualization, making it easier to identify variations in rainfall across stations. A red trend line, fitted using linear regression, highlights the general relationship between the two variables

• Data Used: This scatter plot uses data from weather stations across Ireland, combining elevation (in meters) and median rainfall (in millimeters) for January. The data includes information about station elevation and their corresponding rainfall measurements, providing a basis for understanding how altitude might influence precipitation.

# Preparing data by joining and re-adding geometry
weather_stations_joined <- weather_stations_cleaned %>%
  st_drop_geometry() %>%
  left_join(rain_jan80 %>% st_drop_geometry(), by = "Station")
weather_stations_joined$geometry <- weather_stations_cleaned$geometry

# Scatter Plot of Median Rainfall vs. Elevation
ggplot(weather_stations_joined, aes(x = Elevation, y = median_rainfall)) +
  geom_point(aes(color = median_rainfall)) +
  geom_smooth(method = "lm", se = FALSE, color = "red") +
  scale_color_gradient(low = "yellow", high = "blue") +
  ggtitle("Rainfall vs. Elevation for Weather Stations") +
  xlab("Elevation (m)") +
  ylab("Median Rainfall (mm)")

## `geom_smooth()` using formula = 'y ~ x'

• Findings: The scatter plot shows a weak negative correlation between elevation and median rainfall, as indicated by the downward-sloping red trend line. While some high-elevation stations exhibit higher rainfall levels (e.g., outliers near 150 mm), most stations show decreasing rainfall as elevation increases. The rainfall values cluster predominantly between 90 mm and 130 mm across all elevations, suggesting other factors beyond elevation might significantly influence rainfall distribution.

• Insights: The weak correlation suggests that higher elevations generally experience slightly lower rainfall, though the influence of elevation on rainfall appears limited. Outliers with higher rainfall at elevated locations may result from other factors, such as local topography or regional weather patterns. This analysis highlights the need to consider additional geographic and climatic factors, such as coastal proximity, when assessing rainfall distribution.

Bar Plot and Statistical Summary Plot:

Bar Plot:

• Purpose and Visualization Choice: The bar plot is designed to compare rainfall across counties, highlighting variations in average rainfall levels. Each county is represented as a distinct bar, color-coded for easy identification, while the x-axis labels are rotated for better readability. This format enables clear comparisons between counties and reveals significant differences in rainfall distribution.

• Data Used: This bar plot represents the average median rainfall for January across different counties in Ireland. The data is aggregated by county, calculating the mean of the median rainfall values for all weather stations within each county. Rainfall is measured in millimeters, providing a comprehensive overview of regional precipitation patterns.

# Bar Plot of Average Rainfall by County
avg_rainfall_county <- weather_stations_joined %>%
  group_by(County) %>%
  summarize(Average_Rainfall = mean(median_rainfall, na.rm = TRUE))
ggplot(avg_rainfall_county, aes(x = County, y = Average_Rainfall, fill = County)) +
  geom_bar(stat = "identity") +
  labs(title = "Average Median Rainfall by County in January",
       x = "County",
       y = "Average Rainfall (mm)") +
  theme_minimal() +
   theme(axis.text.x = element_text(angle = 45, hjust = 1))

• Findings: Kerry records the highest average rainfall among counties, exceeding 150 mm, which aligns with its exposure to Atlantic weather systems. Counties like Dublin, Meath and Offaly exhibit significantly lower rainfall levels, with averages below 100 mm, reflecting their inland and sheltered locations.
  • Moderate rainfall levels are observed in counties like Cork and Waterford, highlighting the variability in precipitation even within southern regions.The overall range of average rainfall indicates substantial climatic differences across the country, influenced by geographic and topographic factors.
• Insights: Counties with higher rainfall, such as Kerry and Donegal, are more prone to weather-related challenges like flooding and waterlogging, necessitating robust water management strategies. Lower rainfall regions, including Dublin, Meath and Offaly, may face issues related to water scarcity during drier months, emphasizing the need for conservation efforts. The bar plot highlights the importance of considering geographic and climatic diversity in regional planning and infrastructure development, particularly in areas with extreme rainfall patterns.

Statistical Summary Plot:

• Purpose and Visualization Choice: This statistical summary plot is designed to analyze how elevation influences rainfall at individual weather stations. The use of error bars (showing confidence intervals) provides a visual representation of variability and uncertainty in the rainfall data at each station. Faceted subplots for each station allow for a focused comparison of elevation-rainfall relationships at the station level.

• Data Used: The plot displays the relationship between elevation (in meters) and median rainfall (in millimeters) for individual weather stations across Ireland. The data is grouped by station, with the mean rainfall and its confidence interval (CI) calculated for each elevation level. Each station is represented by a unique color to differentiate their rainfall patterns.

rainfall_summary <- weather_stations_joined %>%
  group_by(Station, Elevation) %>%
  summarize(mean_rainfall = mean(median_rainfall, na.rm = TRUE), .groups = 'drop')

# Statistical summary plot: Median Rainfall vs. Elevation by Station
stat_summary_plot <- ggplot(rainfall_summary, aes(x = Elevation, y = mean_rainfall, color = Station)) +
  stat_summary(fun.data = "mean_cl_normal", geom = "errorbar", width = 0.2, size = 0.5) +
  stat_summary(fun.y = "mean", geom = "point", size = 2) +
  facet_wrap(~ Station) +
  labs(title = "Mean and CI of Median Rainfall vs. Elevation by Station",
       x = "Elevation (m)",
       y = "Median Rainfall (mm)") +
  theme_minimal()

# Print the plot
print(stat_summary_plot)

• Findings: Stations such as Killarney and Ardara, located in higher rainfall regions, exhibit higher mean rainfall values even at moderate elevations. For most stations, rainfall appears to vary minimally with elevation, as points are clustered closely within the same rainfall range across different elevations. Some stations, such as Dublin Airport and Phoenix Park, show relatively low rainfall levels, reflecting their inland and lower-elevation locations. The confidence intervals are narrow for most stations, indicating consistent median rainfall measurements, but a few stations show wider intervals, reflecting higher variability.

• Insights: While elevation can influence rainfall, the data suggests that other factors, such as coastal proximity and regional weather patterns, may play a more dominant role in determining rainfall levels. Stations with wider confidence intervals or higher variability might be subject to localized climatic effects or irregular weather patterns. This plot underscores the complexity of geographic and meteorological interactions, highlighting the need to consider multiple factors when analyzing rainfall distribution.

Interpretation of the Correlation Coefficient:

The correlation analysis shows the relationships between median rainfall and two geographical factors:

Elevation: The correlation coefficient is -0.1135913, indicating a very weak negative relationship. This suggests that elevation has little impact on rainfall distribution in Ireland.

This suggests that in Ireland, as elevation increases, there is a slight tendency for rainfall to decrease, although the impact is minimal.

# Correlation analysis between Median Rainfall and Elevation
correlation_elevation <- cor(weather_stations_joined$Elevation, weather_stations_joined$median_rainfall, use = "complete.obs")
cat("Correlation coefficient between Median Rainfall and Elevation: ", correlation_elevation, "\n")

## Correlation coefficient between Median Rainfall and Elevation:  -0.1135913

Discussion:

The analysis highlights the multifaceted factors influencing rainfall distribution in Ireland, including elevation and regional climatic conditions. The spatial and temporal variability in rainfall patterns is evident through the interactive maps, scatter plots, and statistical summaries, which showcase significant contrasts between coastal and inland areas. While elevation shows only a minimal influence, suggesting that other environmental factors likely contribute significantly to regional rainfall patterns. Understanding this variability is crucial for effective water resource management, flood prevention strategies, and agricultural planning.

Implications:

The insights from this analysis are critical for:

• Developing targeted water management strategies, especially in high rainfall regions prone to flooding.

• Implementing conservation measures in low rainfall areas to ensure sustainable water usage.

• Designing resilient infrastructure and agricultural practices tailored to regional rainfall variability.

• Supporting predictive models for better planning in response to changing weather patterns.

Conclusion:

This blog provides a comprehensive analysis of rainfall patterns across Ireland, leveraging GIS visualizations, statistical analyses, and correlation studies. The findings offer valuable insights into the geographical and climatic factors shaping rainfall distribution. By identifying key trends and relationships, this study supports informed decision-making in environmental planning, resource management, and infrastructure development, contributing to a better understanding of Ireland’s dynamic climatic conditions.