## 2 Materials and Methods
### Data Preprocessing  
The raw data of Helgoland Roads are characterized by long-term measurements on work-daily frequency, with missing values during weekends and extreme bad weather days. When merged with date of other features such as zooplankton abundance, it ends with approximately 40% of missing data in the time series. To fill the missing data and creating a regular sampled daily time series, a number of imputation methods were tested in sunlight duration, a feature added to the Helgoland Roads from an external source, with no missing values. After creating a synthetic missing values dataset with sunlight duration, we calculated root mean square error (RMSE) and coefficient of determination (R\({}^{2}\)) between the original and interpolated data. Minimum changes in frequency distribution between missing data and interpolated variables, lowest RMSE and highest R\({}^{2}\), were the basis for the decision to use a linear interpolation, supported by [30]. After the interpolation, we have daily datasets of parameters in Table 1 comprising approximately 13 years, from 2 November 2001 to 22 April 2015, and presented in Supplementary Materials, Figure S1.  
In this study, to validate the performance of the ML models, the dataset was split in 80% (n = 3940) for model training, and 20% (n = 980) for model testing, so we could investigate the model generalization ability [31]. To eliminate the dimensional differences of the data and improve the prediction ability of the models, we used the StandardScaler method from the Scikit-Learn package, which standardizes features by removing the mean and scaling to unit variance.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Parameters** & **Units** & **Counts** & **Mean** & **Std** & **Min** & **Max** & **Median** \\ \hline Secchi depth & m & 4920 & 3.70 & 1.80 & 0.20 & 12.00 & 3.67 \\ SST & \({}^{\circ}\)C & 4920 & 10.64 & 5.00 & 1.10 & 20.00 & 10.30 \\ Salinity & – & 4920 & 32.31 & 1.06 & 26.71 & 36.11 & 32.42 \\ SiO\({}_{4}\) & umol L\({}^{-1}\) & 4920 & 6.49 & 5.04 & 0.01 & 37.20 & 5.26 \\ PO\({}_{4}\) & umol L\({}^{-1}\) & 4920 & 0.56 & 0.41 & 0.01 & 3.98 & 0.53 \\ NO\({}_{3}\) & umol L\({}^{-1}\) & 4920 & 10.19 & 9.82 & 0.10 & 77.38 & 7.12 \\ Sunlight & h & 4920 & 4.78 & 4.52 & 0.00 & 16.60 & 3.60 \\ Duration & – & 4920 & \(-\)9.44 & 121.55 & \(-\)564.29 & 351.95 & \(-\)0.52 \\ Wind Direction & Degrees [\({}^{\circ}\)] & 4920 & 203.09 & 74.71 & 20.00 & 353.00 & 212.00 \\ Wind Speed & m s\({}^{-1}\) & 4920 & 8.23 & 3.28 & 1.60 & 20.80 & 7.80 \\ Zooplankton & individuals & & & & & & \\ Abundance & m\({}^{-3}\) & 4920 & 3164.70 & 4450.64 & 5.00 & 75,364.50 & 1676.21 \\ Chlorophyll-a & \(\mu\)g m\({}^{-1}\) & 4920 & 2.40 & 2.86 & 0.00 & 45.45 & 1.48 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical description of parameters used as determinants to predict chlorophyll-a concentration (target) after linear interpolation (std, min and max are standard deviation, minimum and maximum values, respectively).  
Figure 1: Helgoland Roads monitoring site position (black triangle) in the German Bight, between the Helgoland (H) and Dune (D) islands.  
The training dataset, the sample of data used to fit the model, dates from 2 November 2001 to 15 August 2012 (\(\sim\)11 years), while the test set is from 16 August 2012 to 22 April 2015 (\(\sim\)2.5 years) and it is used for model evaluation (Figure 2). For independent validation, we used a linear interpolated time series of HPLC estimated chlorophyll data (5 May 2015 to 27 November 2018, n = 348).