Reservoirs are active reactors for the biogeochemical cycling of carbon (C) and nutrients (nitrogen: N, phosphorus: P, and silica: Si), however, our in-depth understanding of C and nutrient cycling in reservoirs is still limited by the fact that it involves a variety of closely linked and coupled biogeochemical and hydrological processes. In this study, the updated process-based Barman model was applied to three reservoirs of the Seine Basin during 2019-2020, considering the variations of carbon dioxide (CO₂) concentrations and key water quality variables. The model simulations captured well the observed seasonal variations of water quality variables, although discrepancies remained for some variables. According to the model, we found that: (1) the three reservoirs are autotrophic ecosystems and showed high removal efficiency of dissolved inorganic carbon and nutrients during 2019-2020; (2) phytoplankton assimilation, benthic denitrification, precipitation and dissolution of calcium carbonate, and gas exchange at the water-air interface are the dominant processes for water quality variations in reservoirs; (3) based on scenarios results, trophic state and mean water depth of reservoir would impact the biogeochemical processes and the retention efficiency of nitrate and dissolved silicate. Finally, we expect that the successful application of Barman model in the reservoirs of the Seine Basin could provide a useful tool for simulating reservoir water quality changes and thus evaluating the impacts of reservoirs on downstream water quality.

Modeling water and solute transport in the vadose zone for groundwater resource management requires an accurate determination of soil hydraulic parameters. Estimating these parameters by inverse modeling using in situ observations is very common. However, little attention has been given to the potential of pore water isotope information to parameterize soil water transport models. By conducting a Morris and Sobol sensitivity analysis, we highlight the interest of combining water content and pore water isotope composition data in a multi-objective calibration approach to constrain soil hydraulic property parameterization. We then investigate the effect of the sampling frequency of the observed data used for model calibration on a synthetic case. When modeling is employed in order to estimate the annual groundwater recharge of a sandy aquifer, it is possible to calibrate the model without continuous monitoring data, using only water content and pore water isotopic composition profiles from a single sampling time. However, even if not continuous, multi-temporal data improve model calibration, especially pore water isotope data. The proposed calibration method was validated with field data. For groundwater recharge estimate studies, these results imply a significant reduction in the time and effort required, by avoiding long-term monitoring, since only one sampling campaign is needed to extract soil samples.