3.3.1. PCOMS Validation

The PCOMS model application was forced with each land boundary condition scenario. The results were analysed in order to better understand the response of the numerical model's different land forcings. The analysis focused on the first quarter of 2018, corresponding to a wet season period, and includes an extreme rain event in late March. The obtained modelling results were compared with in-situ operational observations, remote sensing products, and state-of-the-art regional and global operational ocean models for the same study area.

Results from each land boundary scenario were compared with salinity and temperature values recorded by the Silleiro buoy. This buoy, belonging to the Puertos del Estado monitoring network, is located 50 km offshore of northwestern Iberia (coordinates: 42.119◦ N, 9.440◦ W; Figure 3). When high river discharges coincide with upwelling prevailing wind conditions, northern winds for this coastal stretch, the WIBP signature can be detected in the salinity and temperature records [23].

Observed surface salinity records remained almost constant in January 2018 at the Silleiro buoy. However, direct discharge scenarios such as Observed and LAMBDA exhibited a salinity decrease around 8 January. This was not reproduced by the scenarios using the estuarine proxy: Complete Observations and Complete LAMBDA (Figure 7). The complete scenarios receive land boundary salinity values that have been previously mixed in the estuarine proxy, while direct discharge scenarios are forced using zero salinity conditions (Figure 6). Moreover, estuarine fluxes have positive and negative fluxes while the direct discharge scenarios are releasing continuously lower salinity discharges.

To compare the present methodology results with state-of-the-art operational models, timeseries from CMEMS-Global and IBI-MFC were added to this work scenarios (Figure 7). The IBI-MFC is the regional ocean model of the European Commission Copernicus Programme based on a NEMO model application run at a horizontal resolution of 0.028◦ (≈2.40 km). This model implementation, operated by Puertos del Estado and Mercator-Océan, includes high frequency processes (i.e., tidal forcing, surges and high frequency atmospheric forcing, freshwater river discharge, etc.). CMEMS models use land boundary conditions drawn from monthly river climatology, conditions that are directly discharged at the surface layer.

In late February, another salinity decrease was observed but overestimated, mainly in direct discharge scenarios. The Climatology scenario presents lower salinity than the Observed scenario, due to the Douro River climatology flow for February; 1140 m<sup>3</sup> s<sup>−</sup><sup>1</sup> is much larger than observe values for February 2018, which only reached 264 m<sup>3</sup> s<sup>−</sup>1.

During the extreme rain event in late March, salinity values decreased by more than 2.5 salinity units and its signal lasted more than 10 days with its main peak on 22 March. The LAMBDA scenario is the direct discharge scenario that best represented this event, as the excess of freshwater estimated by the watershed model for the Douro River, two times more than observed, compensates the lack of other river sources. From this result, we concluded that it is essential to have the right freshwater budget draining in the study area to represent accurately this type of event. In the LAMBDA scenario, only two rivers, Minho and Douro, are discharging close to the buoy area, while in reality there are many other smaller watersheds that can contribute to generate this large salinity signature. CMEMS-Global results clearly underestimate the largest freshwater event, while IBI-MFC obtains good results for the extreme event. It does however overestimates other earlier events.

When the estuarine proxy is used, additional river contributions are needed to achieve similar salinity changes. In the scenarios using the proxy, Complete Observations and Complete LAMBDA, a constant salinity of 25 salinity units for the other rivers was considered as a compromise to the average salinity from different types of estuaries (Table 2). Nevertheless, this approach still seems very conservative, and salinity did not decrease as observed. It is probable that estuaries in the area have a geomorphology similar to the Douro estuary and may contribute to lower salinity values. Further analyses and experiments are needed to evaluate how minor rivers could be included and parameterised.

A possible solution to explore in future works is to implement estuarine proxies for the smaller rivers.

**Figure 7.** Sea surface salinity observed (black dots) and modelled at Silleiro buoy during the first quarter of 2018. PCOMS modelling scenarios include: no discharge simulations (Reference, Blue line), with direct discharge (Climatology (red line), Observed (purple line), and LAMBDA (yellow line)) as well as with the proxy for the main six estuaries plus 45 direct discharges (Complete Observations (green line) and Complete LAMBDA (pink line)). Surface salinity from CMEMS-Global (dashed red line) and IBI-MFC (dashed blue line) regional product are also included. The analysed was divided into three sections (**A**) (January), (**B**) (February and first week of March), and (**C**) (rest of March and first week of April) for clarity. See Section 3.3 for more configuration details.

Regarding sea surface temperature (SST), modelling results tend to represent quite accurately the main trends during the analysed period (Figure 8). As with salinity, January temperature drops a grea<sup>t</sup> deal in the direct discharge scenarios (Observed, LAMBDA and Climatology). However, during the extreme event, the scenarios using LAMBDA-simulated temperature were the only scenarios capable of reproducing the temperature drop. The statistical analysis of the surface temperature at this location (Table 3) showed that any scenario including rivers improved the estimation of SST with the only exception being the Climatology scenario.

**Figure 8.** Sea surface temperature (black dots) and modelled at the Silleiro buoy during the first quarter of 2018. PCOMS modelling scenarios include: with no discharge (Reference, Blue line), with direct discharge (Climatology (red line), Observed (purple line), and LAMBDA (yellow line)) and with the proxy for the main six estuaries plus 45 direct discharges (Complete Observations (green line) and Complete LAMBDA (pink line)). Surface salinity from CMEMS-Global (dashed red line) and IBI-MFC (dashed blue line) regional product are also included. The analysed was divided into three sections (**A**) (January), (**B**) (February and first week of March) and (**C**) (rest of March and first week of April) for clarity. See Section 3.3 for more configuration details.


**Table 3.** Surface salinity and temperature coefficient of determination (R2), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) between the observations of Silleiro buoy and each PCOMS configuration using LAMBDA boundary conditions scenario for the period of 1 January–4 January 2018. The Complete scenarios are the only ones using the proxy.

To evaluate the possible impact of river discharges on the coastal hydrodynamics, the meridional velocity in the vicinity of A Guarda (Figure 3), located in the Minho estuary mouth and around 80 km north of the Douro mouth, was obtained for the Reference scenario, with no river inputs, and the Complete Observations scenario. The Complete Observations scenario achieved a meridional velocity up to three times that of the Reference scenario. Figure 9 shows that during the highest peak of river discharges (i.e., Douro River reached values around 3200 m<sup>3</sup> s<sup>−</sup>1), the Reference scenario presented values around 0.5 m s<sup>−</sup><sup>1</sup> while more realistic land boundary scenarios can reach maximum values around 1.5 m s<sup>−</sup>1.

**Figure 9.** Surface modelled meridional velocity at the A Guarda station for the PCOMS reference scenario (no river discharge) and the Complete Observations scenario boundary conditions for the first quarter of 2018. Douro River observed flow (source: Portuguese Environmental Agency −APA) is represented in the right axis.

Earth Observations (EO) products can play an important role for model validation. They provide a spatial and temporal coverage that yields complete and verified information obtained by the numerical models. The remote sensing product chosen to evaluate the sea-surface temperature (SST) of the PCOMS modelling results is the Multi-scale Ultra-high Resolution (MUR). The MUR version 4.1 used in this study (http://dx.doi.org/10.5067/GHGMR-4FJ04, accessed on 15 April 2022) provides daily SST estimates on a global 0.01◦ × 0.01◦ grid and features the 1-km resolution MODIS retrievals, which are fused with AVHRR GAC, microwave, and in-situ SST data by applying internal correction for relative biases among the data sets [27]. In Figure 10, SST- and PCOMS-model results forced with LAMBDA Complete Observations

scenario display similar structures and values for western Iberia during the extreme rain event peak. In the image, the colder temperature signature of the Douro, Tagus, and Guadalquivir rivers' individual plumes as well as other smaller rivers can be easily identified.

**Figure 10.** Sea surface temperature for 21 March 2018 from the Multi-scale Ultra-high Resolution (MUR; Left) and PCOMS with LAMBDA Complete Observations boundary conditions. Basic comparison statistics: Average PCOMS SST: 14.287; Average MUR SST: 13.992; Coef. Correlation: 0.929; Bias: 0.295 and RMSE: 0.537.

Finally, modelling results were compared with Earth Observed (EO) salinity-derived products developed in the context of the LAMBDA project [28]. This new SMOS SSS global product was developed with the main goal of capturing the coastal processes. This L4 SMOS SSS product allows downscaling the salinity by using as a template the Sea Surface Temperature (SST) at 0.05◦ from OSTIA. EO and numerical modelling results for the extreme event of late March 2018 present similar spatial structures and intensities (Figure 11). Though using satellite-derived SSS still has many drawbacks, i.e., low horizontal resolution to capture some coastal variability, this comparison indicates a promising evolution of these kinds of products for data analysis and model validation for coastal areas.

**Figure 11.** *Cont*.

**Figure 11.** Daily Sea surface salinity for 24 March 2018 (**a**) and 26 March 2018 (**b**) from the BEC-SMOS Global L4 V2 (Left) and surface layer of PCOMS model with LAMBDA Complete Observations scenario (Right). Basic comparison statistics for (**a**) are: Average PCOMS SSS: 35.936; Average SMOS SSS: 35.892; Coef. Correlation; 0.702; Bias: 0.045 and RMSE: 0.218. Basic comparison statistics for (**b**) are: Average PCOMS SSS: 35.929; Average SMOS SSS: 35.884; Coef. Correlation; 0.676; Bias: 0.045 and RMSE: 0.275.

## 3.3.2. Scenario Analysis

Each of the Ocean scenarios was evaluated in terms of WIBP low salinity extension for two periods: (i) considering average values for a typical wet month (February 2018; Figure 12) and during the peak of an extreme rain event (21 March 2018; Figure 13).

During February 2018 (Figure 12), the Climatology, LAMBDA, and Complete LAMBDA scenarios exhibited the greatest extension of the West Iberia Buoyant Plume (WIBP), while scenarios using river observations (Observed and Complete Observations) had their plumes more confined near the coastal area. The latter group also exhibited a relatively reduced Tagus estuary plume (latitude 38.6). The scenarios using the estuarine proxy (Complete scenarios) reduced the extension of the plume relative to the direct discharges' scenarios, even when these included an additional 45 rivers with a constant value of 25 salinity units.

A similar pattern of results is observed during the extreme rain event for each modelling scenario. Figure 13 represents the mean SSS for the 21 March 2018, a day after the salinity minimum was registered at the Silleiro buoy. Though the Climatology scenario appears to have a similar spatial distribution during the event, it does not reach the minimum temperature and salinity values displayed in Figure 7. In addition, the Tagus estuary plume appears to be overestimated by the Climatology and LAMBDA scenarios when compared to the Observed scenario. The effect of the proxy in the Tagus estuary is clearly noticed in the Complete Observed scenarios where its impact is reduced.

**Figure 12.** Surface salinity difference during February 2018 between the reference scenario with each LAMBDA land boundary scenarios (**a**) Observed; (**b**) LAMBDA; (**c**) Climatology; (**d**) Complete Observations; (**e**) Complete LAMBDA. Salinity difference between 0 and −0.5 is not represented.

**Figure 13.** Surface salinity difference during the 21 March 2018 between the reference scenario with each LAMBDA land boundary scenarios (**a**) Observed; (**b**) LAMBDA; (**c**) Climatology; (**d**) Complete Observations; (**e**) Complete LAMBDA. Salinity difference between 0 and −0.5 is not represented.
