Physicochemical data statistical analysis?

Recurrence Quanrtification Analysis is a non-linear time series analysis tool particularly suited for your problem because is free of any stationary hypothesis and is able to deal with short series (for ecological application you can see: https://www.sciencedirect.com/science/article/abs/pii/S0304380006006223,

while for a geeneral presentation of the method and a vast literature archive I suggest: http://www.recurrence-plot.tk/

The R routines for recurrence are: https://www.frontiersin.org/articles/10.3389/fpsyg.2018.02232/full

where you can find also how dealing with multivariate data.

Last but not least, RQA is particularly suited to detect transitions that can be very important in ecology modeling:

https://www.sciencedirect.com/science/article/abs/pii/S0375960196007414

https://www.sciencedirect.com/science/article/abs/pii/S1896112618303316

https://www.sciencedirect.com/science/article/abs/pii/S0165027008005712

Article Defect detection and localization in polymer composites base...

Alessandro Giuliani

Attached the ecological RQA, such kind of paper can be surely published on a good journl, moreover if you have some external variable to correlate with the observed dynamics (e.g. period of the year, level of water, rain intensity, episoded of pollution, exceptional algal blooms) it should be perfect.

Suganth Velumani

There are several statistical analyses that could be suitable for the analysis of 2-year data on the physicochemical parameters of a lake. Some options might include:

Descriptive statistics: Descriptive statistics, such as mean, median, mode, and standard deviation, can be used to summarize the overall trends and patterns in the data. These statistics can help you to understand the distribution and variability of the different physicochemical parameters over time.

Correlation analysis: Correlation analysis can be used to identify any relationships between the different physicochemical parameters and to understand how changes in one parameter may affect other parameters. This can be done using techniques such as Pearson's correlation coefficient or Spearman's rank correlation coefficient.

Time series analysis: Time series analysis can be used to examine trends and patterns in the data over time. This can include techniques such as linear regression, autocorrelation, and spectral analysis.

To make the dataset more likely to be published in a good journal, you may want to consider including additional data that helps to contextualize or interpret the physicochemical parameters. For example, you could include data on factors that may be influencing the lake's water quality, such as land use, agricultural practices, or industrial activity. You could also include data on the presence of specific contaminants or pollutants, or on the abundance of different aquatic species. This additional data could help to provide a more complete picture of the lake's overall health and to identify potential sources of stress or degradation.

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