Pedro Coelho’s research while affiliated with University of Lisbon and other places

This study set out to assess the performance of the state-of-the-art CE-QUAL-W2 v4.5 sediment diagenesis model. The model was applied to a reservoir in Portugal using observed sediment particulate organic carbon values corresponding to a six-year period (2016–2021). The model was calibrated by comparing its results with 35 observed dissolved oxygen and water temperature profiles, as well as annual total nitrogen, total phosphorus, biochemical oxygen demand, and chlorophyll-a measurements corresponding to three different depths. In addition to model calibration, a sensitivity analysis was also conducted by varying the input particulate organic carbon values and applying a user-specified sediment oxygen model (zero-order model). The results demonstrated the overall effectiveness of the sediment diagenesis model, which accurately simulated dissolved oxygen profiles, nutrient concentrations, and organic matter levels (Dissolved oxygen profiles: NSE = 0.41 ± 0.67; RMSE = 1.73 mg/L ± 0.69), highlighting its potential as an effective tool for simulating lakes and reservoirs and supporting water management processes. The study further suggests that the zero-order model is able to serve as an effective starting point for implementing the sediment diagenesis model, providing an initial estimate for mean reservoir sediment oxygen demand (SOD) values.

This study evaluates the reliability of ERA5 and ERA5‐Land reanalysis datasets in describing the mean daily air temperature of four climate domains in mainland Portugal. The reanalysis datasets were compared with ground observations from 94 meteorological stations (1980–2021). Overall, the results demonstrated a good degree of correlation between the observed and reanalysis data on both a daily and seasonal scale. Both the latitudinal distribution of the air temperature and the moderating effect of the Atlantic Ocean are well described. However, in the case of Portugal, the ERA5‐Land was shown to be considerably more effective at describing the mean daily air temperature than ERA5. The results also indicated that, in general, the reanalysis methodologies perform better when applied to air temperature simulation in flatter regions as opposed to regions with high‐altitude and complex terrain. The study further suggests that ERA5 and ERA5‐Land reanalysis should be used with caution in the case of short‐term environmental studies. In fact, relevant differences were shown to exist between the reanalyses and the observed daily mean air temperature datasets for certain specific years. Overall, considering the RMSE between the ERA5‐Land reanalysis datasets for mean daily air temperature and the observed datasets there is a 28% probability of locally having a mean RMSE <1.5°C, 52% probability of having a mean RMSE >1.5°C and <2.0°C, and 16% probability of having a RMSE >2.0°C and <3.0°C. These conclusions will hopefully contribute to improving our understanding of the uncertainty sources in relation to ERA5 and ERA5‐Land reanalysis data for different climate domains.