# burdekin_streamflow <- read.csv(file.choose())
burdekin <- read.csv("burdekin streamflow.csv")
burdekin$DJF <- burdekin$Dec + burdekin$Jan[-1] + burdekin$Feb[-1]
burdekin$DJF[53] = NA
# print(burdekin)
plot(DJF~Year, data=burdekin, type = "b", lty = 2, pch = 16, col = "blue", xlab = "Year", ylab = "DJF streamfloww (ml)", main = "Burdekin Summer Streamflow")
There is no trend, as there is no overall increase or decrease over time, and there is large variation between years, as seen by the two large peaks being many orders of magnitude above the lows of the graph.
summary(burdekin$DJF)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 121130 855907 2921023 5559435 6649214 47290102 1
burdekin$anomaly <- ifelse(burdekin$DJF > 2921023, "Above", "Below")
NOAA_ONI <- read.csv("NOAA_ONI_1950_to_2010.csv")
burdekin$SON.ONI <- NOAA_ONI$SON[2:54]
burdekin$phase <- ifelse(burdekin$SON.ONI < -0.5, "La Nina",
ifelse(burdekin$SON.ONI > 0.5, "El Nino", "Neutral"))
tabulation <- table(burdekin$anomaly, burdekin$phase)
tabulation
##
## El Nino La Nina Neutral
## Above 4 11 11
## Below 11 6 9
mosaicplot(t(tabulation), cex.axis = 1.1, xlab = "SOI phase", ylab = "Proportion of years", col = c("blue", "red"), main = "Effect of ONI on yield outcome/streamflow")
Using the mosaic plot, we can see that La Nina conditions tend to be favourable for yield outcome as streamflow is greater, so if La Nina (or neutral) is predicted to be in the next summer, it would be reasonable to be more liberal with water usage. However, we also see in the DJF streamflow over time graph from a. that there is no pattern to streamflow, due to a plethora of other potential climate/weather factors influencing it.
So, using the mosaic plot would be of use during the planning process, as long as it is used in conjunction with other predictive models for other factors influencing the hydrology of the Burdekin river. Planning based on a single model will always lead to unexpected results, and the only way to get a more holistic understanding of the streamflow is to use multiple models.