Wavelets have proven to be highly effective at frequency signal depend on from data. Wavelet transform and analysis are computationally efficient. Their efficiency is generally important frequency properties in terms of a multi-scale wavelet series. This research is tried a new method for modelling of water quality parameters. It is related with signal process by using of wavelet model. Wavelet models have specific properties for signal processing depend on data. Time series analyses are helpful tools to investigate daily, monthly and seasonal ecological observations. Wavelet tools present the picture of data in time and frequency domain. In this study, samples of different surface water resources were tested between 2016-2017 to determine temporal and spatial variations of BOD5 and COD values. A 12-month data series was examined by using wavelets methodology and compared with results evaluated from laboratory analysis. Water samples were obtained in fresh waters during all years. Temporal variation in these parameters reflects variation of precipitation. There is a sufficient evidence of the linear correlation between BOD5 and COD concentrations with total rainfall rate. Wavelet analyses of extreme events show the role of seasonal oscillations, and small-, meso- and large- scale effects on water quality parameters. Wavelet analysis helps us to interpret both frequency and temporal variations due to regional and large scale factors. Based on this fact, contents of parameters were determined for suitability for different activities. BOD5 and COD values were discussed in terms of irrigation water quality for agricultural cultivation.