## 2 Methodology
### Selection of Optimization Methods  
In order to be able to propose optimization procedures, it is necessary to understand the available methods, their analysis and their degree of difficulty. We strived to do so for several optimization methods in a previous literature review [8]. Based on new monitoring and optimization objectives, it was then possible to select an appropriate optimization method [16]. The optimization objective of the W1 WQMP was to Verify whether reducing the number of sampling stations (and points in depth) and sampling frequency have an impact on the spatio-temporal portrait (loss of information) of the cyanobacteria species (Figure 2). For this optimization objective, we chose to apply the method proposed by [24] which consists of visualizing species (in this case cyanobacteria) distribution through factorial correspondence analysis and then comparing a series of years (in this case 2011 to 2016) to be able to decide whether the sampling strategy will serve to obtain a spatio-temporal distribution and whether it is possible to reduce the sampling frequency and the number of sampling sites. For the W2 WQMPs of the case studies, we chose to apply the optimization approaches proposed by [PERSON] et al. [13] and [PERSON] et al. [19] (Figure 3). [PERSON] et al. (2012) propose a combination of multivariate analysis / principal component analysis (NMDS/PCA) and Kriging and Moran's index to quantify information redundancy for neighboring sampling sites in a lake or river station network in order to reduce the number of sampling sites. We chose this method to respond to the first optimization  
Figure 6: Onboarding the data of a WQMP: Step 1: Setting (e.g., sampling objectives; sampling site justification; protocols; tools; probes; probe calibration protocols; laboratories; laboratory specificities; field observations; parameters and measuring units. Step 2: Importing shapefiles of watersheds and water bodies, metadata on shapefiles; waterbody descriptions (e.g., bathymetry, length, fetch, volume, perimeter, etc.). Step 3: Creating sampling sites (e.g., geographical location, justification, waterbody (from drop-down lists created during steps 1 and 2. Step 4: Entering measurement contexts – fieldwork (e.g., field personnel, time frame spent in the field, sampling protocols, sampling tools; probes and laboratories used, field measurements are taken (e.g., transparency), field observations (e.g., rainfall and wind), etc.). Step 5: Importing data from Excel and CSV files from probes and laboratories automatically connected to the contexts. Step 6: Validating import reports and corrections if necessary. objective of the W2 WQMP: Verify whether a reduction of the number of sampling sites was possible based on criteria such as information redundancy between sampling stations. [PERSON] et al. (2014) propose a general linear regression model to assess the increase in uncertainty for reduced sampling frequency and evaluate statistical confidence in trend detection. We applied this model to the W2 WQMP to respond to the optimization objective: Verify whether sampling frequency was sufficient and whether a different type of sampling frequency should be adopted. We also applied the Kruskal-Wallis test to the data of W2 to verify whether the fecal coliform (FC), total phosphorous (TP) and total suspended solid (TSS) concentrations were different depending on the observed precipitation classes of W2 [25]. The objective was to verify whether sufficient data were collected in wet weather conditions, as this information would have an incidence on changing from a fixed sampling calendar as is presently the case in W2, to a flexible sampling calendar in order to obtain more data during wet weather conditions. The objective of sampling in wet weather conditions was to obtain water quality data following run-off. For the W2 watershed, only two hydrological stations are available and river flow is not measured during water quality sampling.