This blog is run by Jason Jon Benedict and Doug Beare to share insights and developments on open source software that can be used to analyze patterns and trends in in all types of data from the natural world. Jason currently works as a geospatial professional based in Malaysia. Doug lives in the United Kingdom and is currently Director of Globefish Consultancy Services which provides scientific advice to organisations that currently include the STECF [Scientific, Technical and Economic Committee for Fisheries, https://stecf.jrc.europe.eu/] and ICCAT, https://www.iccat.int/en/

Saturday, 8 November 2014

Come hell with high water - Sea level rises at two stations in Bangladesh

Key points of post
  • Bangladesh is low-lying
  • Many people live along its river and coasts
  • These communities are vulnerable to rises in sea-level which is predicted to occur as a consequence of climate change

Coastal Bangladesh is extremely low-lying and its coastal communities in particular are vulnerable from any future rises in sea level which can cause flooding. Jason and I wondered, therefore, if there were any time-series on sea-level for Bangladesh available, and what information they might contain.

Via Google we discovered the University of Hawaii Sea Level Centre, which monitors seawater levels at stations throughout the world, exploiting its network and relationships with a range of local partners.  In this case it’s the Bangladesh Inland Water Transport Authority who provided the original data.


Here we plot average daily sea level available on the UHSLC site for two stations (please map below) at Charchanga (at the mouth of the Meghna River) and Hiron Point (in the Sundarbans). [Note there are data for 5 other stations in Bangladesh but the time-series data available were, unfortunately, too short a duration]. The data are also color-coded to correspond to the six seasons of Bangladesh (winter, spring, summer, monsoon, autumn, & late autumn). 

MapID1f246f024be6
Data: stations.google • Chart ID: MapID1f246f024be6googleVis-0.5.6
R version 3.1.2 (2014-10-31) • Google Terms of UseDocumentation and Data Policy

The first interesting feature, noted at both locations, is the pronounced seasonality with peaks in sea-level being recorded in the  monsoon, autumn and late autumn periods.  Winter and spring correspond to lowest average daily water levels at both locations.  The seasonal variation in sea-level is due primarily to the effect of the Monsoon; ie. the south-westerly winds tend to cause the seas to ‘pile-up’ along the coast of Bangladesh, while the flow from the major rivers pouring through the delta into the Bay of Bengal increases substantially also as a result of the Monsoon. 

The most worrying aspect of these data is the substantial change in long-term trend summarized here with a simple linear model (red line).  At Charchanga average daily sea-levels recorded rose by approximately 7 mm's per year between 1980 and 2000 while at Hiron Point the figure was approximately 5 mm's per year (1977 to 2003). Unfortunately more recent data were unavailable from the Sea Level Center and we are currently unsure if these trends are continuing. But please if you know of more data for Bangladesh let us know.

Certain aspects of the series are, however, rather curious and it’s not a straightforward story of increasing trends.  At Hiron Point, for example, extreme sea-level events (say above 2600 mm's) were relatively frequently between 1980 and 1995 whereas, since 1996 they have been observed less often. Contrast this with Charchanga where there were some exceptional peaks recorded in 1996, ’97, and ’98. 

All such changes could have profound implications for the livelihoods of coastal Bangladeshis.

Details of how to obtain the data and plot the graphs here in R are demonstrated below.  

As mentioned earlier in the post, the data was obtained from the University of Hawaii Sea Level Centre and we have used the research quality dataset which can be found at this link - http://uhslc.soest.hawaii.edu/data/download/rq

You can download the dataset in various formats which include csv and NetCDF and the associated metadata for each station is also there for you to check of any inconsistencies in the data collection or station relocation and instrumentation issues. The data can also be downloaded at two different time scales, which is hourly and daily. 

# Load required libraries
 
library(ggplot2)
library(scales)
library(grid)
library(plyr)
library(lubridate)
library(zoo)
 
# Set working directory
 
setwd("D:/ClimData/SeaLevel")
 
# Read csv file
 
sl<-read.csv("rqd0138a.csv",header=FALSE)
 
# Rename columns
 
colnames(sl)<-c("year","month","day","sl_mm")
 
# Format date columns
 
sl$date <- as.Date(paste(sl$year,sl$month,sl$day),format="%Y%m%d")
sl$month <- as.numeric(format(sl$date,"%m"))
sl$year <- as.numeric(format(sl$date,"%Y"))
sl$monthf <- factor(sl$month,levels=as.character(1:12),labels=c("Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"),ordered=TRUE)
sl$mday <- strptime(sl$date, "%Y-%m-%d")$mday
sl$jday <- strptime(sl$date, "%Y-%m-%d")$yday+1
sl$daymth <- as.character(paste(sl$month,sl$day,sep="-"))
sl$daymth <-as.Date(sl$daymth,format="%m-%d")
 
# Classify data into seasons
 
sl$season <- "Season"
 
sl$season[sl$month == 1 & sl$mday >= 1 |  sl$month == 2 & sl$mday <= 13| sl$month == 1]<-'Winter'
sl$season[sl$month == 2 & sl$mday >= 14 |  sl$month == 4 & sl$mday <= 14 | sl$month == 3]<-'Spring'
sl$season[sl$month == 4 & sl$mday >= 15 |  sl$month == 6 & sl$mday <= 14 | sl$month == 5]<-'Summer'
sl$season[sl$month == 6 & sl$mday >= 15 |  sl$month == 8 & sl$mday <= 17 | sl$month == 7]<-'Monsoon'
sl$season[sl$month == 8 & sl$mday >= 18 |  sl$month == 10 & sl$mday <= 18| sl$month == 9]<-'Autumn'
sl$season[sl$month == 10 & sl$mday >= 19 |  sl$month == 12 & sl$mday <= 16| sl$month == 11]<-'Late Autumn'
sl$season[sl$month == 12 & sl$mday >= 17 |  sl$month == 12 & sl$mday <= 31| sl$month == 1]<-'Winter'
 
sl$season = factor(sl$season, c("Winter", "Spring", "Summer", "Monsoon","Autumn","Late Autumn"))
 
## Plot Sea Level
 
hp_sl <-   ggplot(sl, aes(date, sl_mm,colour=season))+
           #geom_line(size=0.5)+
           geom_point(shape=5,size=1)+
           geom_smooth(method="lm",size=0.5,col="red")+
           scale_x_date(name="\n\n\n Source: University of Hawaii Sea Level Centre / Bangladesh Inland Water Transport Authority (BIWTA) - 2014",labels=date_format("%Y"),breaks = date_breaks("2 years"))+
           ylab("Milimetres (mm)\n")+
           xlab("\nYear")+
           theme_bw()+
           ggtitle("Sea Level at Charchanga - Bangladesh (1980-2000)\n")+
           theme(plot.title = element_text(lineheight=1.2, face="bold",size = 14, colour = "grey20"),
           panel.border = element_rect(colour = "black",fill=F,size=1),
           panel.grid.major = element_line(colour = "grey",size=0.25,linetype='longdash'),
           panel.grid.minor = element_blank(),
           axis.title.y=element_text(size=11,colour="grey20"),
           axis.title.x=element_text(size=9,colour="grey20"),
           panel.background = element_rect(fill = NA,colour = "black"))
 
hp_sl
 
# Get gradient and add to plot
 
m <- lm(sl_mm~year, data=sl )
ms <- summary(m)
 
slope <- coef(m)[2]
lg <- list(slope = format(slope, digits=3))
eq <- substitute(italic(Gradient)==slope,lg)
eqstr <-as.character(paste(as.expression(eq),"/year"))
hp_sl <- hp_sl + annotate(geom="text",as.Date(-Inf, origin = '1970-01-01'), y = Inf, 
         hjust = -0.1, vjust = 2, label = eqstr,parse = TRUE,size=3)
 
hp_sl
 
# Save plot to png
 
ggsave(hp_sl, file="Charchanga_SeaLevel_Plot_Seasons.png", width=10, height=6,dpi=400,unit="in",type="cairo")
 
# Code to produce html code of embedded sea level stations map using googleVis
 
# Load libraries
 
library(RCurl)
library(XML)
library(leafletR)
library(googleVis)
 
# Convert html table into data frame
 
theurl <- "http://uhslc.soest.hawaii.edu/data/download/rq"
tables <- readHTMLTable(theurl)
n.rows <- unlist(lapply(tables, function(t) dim(t)[1]))
 
tbl <- tables[[which.max(n.rows)]]
 
bgd.tbl <- subset(tbl, Country =="Bangladesh")
 
bgd.tbl$Latitude <- as.numeric(levels(bgd.tbl$Latitude)[bgd.tbl$Latitude])
bgd.tbl$Longitude <- as.numeric(levels(bgd.tbl$Longitude)[bgd.tbl$Longitude])
 
google.location <- paste(bgd.tbl$Latitude, bgd.tbl$Longitude, sep = ":")
stations.google <- data.frame(bgd.tbl, google.location)
 
# Plot map
 
map <- gvisMap(data = stations.google, locationvar = "google.location",tipvar = "Location",
       options=list(showTip=TRUE, enableScrollWheel=TRUE,mapType='terrain', useMapTypeControl=TRUE,width=100,height=400,
       icons=paste0("{","'default': {'normal': 'http://i.imgur.com/f3q6Oaj.gif',\n",
       "'selected': 'http://i.imgur.com/f3q6Oaj.gif'","}}")))
 
plot(map)
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