“…Moreover, because long-term records of environmental variables show often long-range memory, some other tools are usually applied. The fractionally integrated moving average models [ARFIMA (p,d,q)] have been widely used in the literature to describe meteorological variables (Yaya and Fashae, 2015;Bowers and Tung, 2018), pollutants and soil gas (Pan and Chen, 2008;Donner et al, 2015;Belbute and Pereira, 2017;Reisen et al, 2018), and hydrological time series (Montanari et al, 1997;Wang et al, 2007). This class of models is used when the longterm correlations in the data decay more slowly than an exponential form, that is, a typical shape of autocorrelation in the autoregressive moving average [ARMA(p,q)] processes (Box et al, 2015).…”