Ensemble bias correction of climate simulations: preserving internal variability.

Climate simulations often need to be adjusted (i.e., corrected) before any climate change impacts studies. However usual bias correction approaches do not differentiate the bias from the different uncertainties of the climate simulations: scenario uncertainty, model uncertainty and internal variability. In particular, in the case of a multi-run ensemble of simulations (i.e., multiple runs of one model), correcting, as usual, each member separately, would mix up the model biases with its internal variability. In this study, two ensemble bias correction approaches preserving the internal variability of the initial ensemble are proposed. These "Ensemble bias correction" (EnsBC) approaches are assessed and compared to the approach where each ensemble member is corrected separately, using precipitation and temperature series at two locations in North America from a multi-member regional climate ensemble. The preservation of the internal variability is assessed in terms of monthly mean and hourly quantiles. Besides, the preservation of the internal variability in a changing climate is evaluated. Results show that, contrary to the usual approach, the proposed ensemble bias correction approaches adequately preserve the internal variability even in changing climate. Moreover, the climate change signal given by the original ensemble is also conserved by both approaches.

Evaluating rain gardens as a method to reduce the impact of sewer overflows in sources of drinking water.

The implications of climate change and changing precipitation patterns need to be investigated to evaluate mitigation measures for source water protection. Potential solutions need first to be evaluated under present climate conditions to determine their utility as climate change adaptation strategies. An urban drainage network receiving both stormwater and wastewater was studied to evaluate potential solutions to reduce the impact of combined sewer overflows (CSOs) in a drinking water source. A detailed hydraulic model was applied to the drainage basin to model the implementation of best management practices at a drainage basin scale. The model was calibrated and validated with field data of CSO flows for seven events from a survey conducted in 2009 and 2010. Rain gardens were evaluated for their reduction of volumes of water entering the drainage network and of CSOs. Scenarios with different levels of implementation were considered and evaluated. Of the total impervious area within the basin directly connected to the sewer system, a maximum of 21% could be alternately directed towards rain gardens. The runoff reductions for the entire catchment ranged from 12.7% to 19.4% depending on the event considered. The maximum discharged volume reduction ranged from 13% to 62% and the maximum peak flow rate reduction ranged from 7% to 56%. Of concern is that in-sewer sediment resuspension is an important process to consider with regard to the efficacy of best management practices aimed at reducing extreme loads and concentrations. Rain gardens were less effective for large events, which are of greater importance for drinking water sources. These practices could increase peak instantaneous loads as a result of greater in-sewer resuspension during large events. Multiple interventions would be required to achieve the objectives of reducing the number, total volumes and peak contaminant loads of overflows upstream of drinking water intakes.