R help - Jul 2014 - Checking whether a time series is stationary with irregular spaced data

I attempting to model the relationship between water temperature and air
temperature. The seasonal component of the temperature time series has been
modeled using a sinusoidal function, leaving the air and water temperature
residuals. I want to model the relationship with

M5<- gls(Water ~ Air +Air1 +Air2, correlation = corCAR1(form =~ Date))

Where Water is the water temperature residual, Air is the air temperature
residuals at 1 and 2 day lags. I have included an autocorrelation structure that
takes into account the fact that the water temperature were taken at irregular
spaced intervals.

I would like to test whether the time series is stationary, I found a blog post
that used the following graphical methods and tests (Cent_Water is the water
temperature centered by subtracting the mean value)

Acf(Cent_Water)
Pacf(Cent_Water)
Box.test(Cent_Water, lag=20, type="Ljung-Box")
adf.test(Cent_Water, alternative="stationary")
kpss.test(Cent_Water)

Are these methods useable with irregular spaced data as I believe it is not
possible to use Acf?

Any suggestions would be greatly appreciated

Best wishes
Tom