Have a look to this simple explanation:

https://support.minitab.com/en-us/minitab-express/1/help-and-how-to/modeling-statistics/regression/supporting-topics/basics/a-comparison-of-the-pearson-and-spearman-correlation-methods/

Anyway, I doubt that using only this method you can extract much information regarding relationship among variables.

For data with normal distribution for the two groups use Pearson correlation. While for data with abnormal distribution for any group of the two use Spearman Correlation.

At the research level, unless one has substantial statistical expertise, it is always best to have statistical review and help with design by a statistician. Many other factors can enter into deciding how to stratify reservoir data by geology, physiographic area, size of watershed and reservoir, mean depth, water levels and temperature at sampling, number of samples, are all receiving similar rainfall and inflow, land and water uses that contribute to nutrients, pollutants, etc. But if you just want to do some exploring of data, take each water quality parameter for each reservoir, and plot them vs time through the year. Use a different symbol for each reservoir. This is fairly easy in excel. I am not versed in the Spearman and Pearson correlation methods. We used ANOVA an analysis of variance tooL and step wise multiple linear regression at times, from SAS and SPSS statistical programs. Besides discussing with Statistician, a limnologist or hydrologist may also be helpful and also can look over the methods, quality, frequency and timing of data collected through the year. For example, sampling water quality in a reservoir may require more than grabbing a sample at one access point.

The use of correlation coefficients is part of the statistical analysis of the information available in any science. These measure the functional dependence between pairs of attributes and inform about the possibility of finding a mathematical expression by means of which one can be obtained from the other.

Pearson's correlation coefficient is a parametric statistic that offers good results when we work with continuous quantitative variables and with a normal distribution law. Used when the existence of a linear dependency between the attributes is assumed

The Spearman correlation coefficient of correlation is a nonparametric statistic that offers good results when we work with variables without a normal distribution law and where the sample population is small. It can be used when attributes with ordinals, as well as for any type of functional dependency and not only linear.

Pearson correlation coefficient, also known as torque correlation coefficient or zero-order correlation coefficient, was introduced by Sir Carl Pearson. This coefficient is used to determine the extent, type and direction of the relationship between two distance or relative variables or a distance variable and a relative variable. But, Spearman coefficient of correlation shows the relationship between two ordinal variables, in other words, it is a non-parametric correspondence of Pearson correlation coefficient.

Correlation is a statistical method used to assess a possible linear association between two continuous variables. It is simple both to calculate and to interpret.

Pearson's product moment correlation coefficient is used when both variables being studied are normally distributed.

Spearman's rank correlation coefficient is used when one or both variables are skewed or ordinal and is robust when extreme values are present.

For data with normal distribution with the two groups of samples, use Pearson correlation. In contrast, for data with abnormal distribution for any group of the two, use Spearman Correlation for a better result.