River water-quality time series often exhibit fractal scaling, which here refers to autocorrelation that decays as a power law over some range of scales. This paper provides a comprehensive overview of the various approaches for quantifying fractal scaling in irregularly sampled data and provides new understanding and quantification of the methods’ performances. More generally, the findings and approaches may be broadly applicable to irregularly sampled data in other scientific disciplines.