2.2 Trend analysis
Different statistical procedures can be used to detect a trend ofSSL . In this study, we applied a parametric approach used in Rets
et al. (2020) to compare SSL trends with their findings on water
discharge (Q , m3·s−1) trends.
Since this method is not presented clearly in the source paper, we
explain it below. In this approach, the slope estimator is a coefficient\(\beta\) from a linear model (estimated using Ordinary Least Squares
(OLS)) fitted to predict SSL with a year (yr ). It can be
calculated as follows:
\(\hat{\text{SSL}}=\alpha+\beta yr\), (1)
\(\overline{\text{SSL}}=\frac{\sum_{i=1}^{n}{\text{SS}L_{i}}}{n}\),
(2)
\(\text{slop}e_{\text{OLS}}=100\frac{\beta}{\overline{\text{SSL}}}\),
(3)
Where: \(\alpha\) is a model intercept; \(\hat{\text{SSL}}\) is a
predicted suspended sediment load; \(\text{SS}L_{i}\) is suspended
sediment load at year i ; n is a total length of the
observation period in years; \(\overline{\text{SSL}}\) is a mean
suspended sediment load for n years. Therefore\(\text{slop}e_{\text{OLS}}\) express the trend in percent per year.