Abstract/Summary

Climate-induced changes in precipitation will lead to greater frequency of high and low-flow events, causing further phosphorus losses due to increased mobilisation and delivery and decreased dilution. The uncertainty associated with climate-induced changes to water quality is rarely represented in water quality models. Bayesian Belief Networks (BBNs) are probabilistic graphical models incorporating uncertainty, making them useful frameworks for communicating risk. This study presents a set of catchment-specific BBNs to simulate total reactive phosphorus (TRP) concentrations in four agricultural catchments under projected climate change. Downscaled discharge time series from six climate models (five models plus their mean), for two Representative Concentration Pathways (RCP 4.5 and 8.5) and three time periods (the 2020s, the 2040s, and the 2080s), were used to create discharge scenarios for the catchment-specific BBNs. The BBN-simulated monthly mean TRP concentrations showed no obvious trends over time or differences between the RCP scenarios, with the ensemble-driven future TRP essentially replicating the results obtained for the baseline period. We found that in four small (7-12 km2) catchments farmed for livestock or arable crops with one or no wastewater treatment plants, the ensemble-projected effects of climate change alone were not a significant driver of monthly TRP concentrations. However, the single-model TRP concentration distributions simulated with the HadGEM2-ES model, showed differences from the baseline in the drier months. This difference occurred because the catchment-specific BBNs were sensitive to changes in the mean monthly discharge simulated using in the HadGEM2-ES projections but not by the other ensemble members.