Climate change is likely to cause higher temperatures and alterations in precipitation patterns, with potential impacts on water resources. One important issue in this respect is inflow to drinking water reservoirs. Moreover, deteriorating infrastructures cause leakage in water distribution systems and urbanization augments water demand in cities. In this paper, a framework for assessing the combined impacts of multiple trends on water availability is proposed. The approach is focused on treating uncertainty in local climate projections in order to be of practical use to water suppliers and decision makers. An index for water availability (WAI) is introduced to quantify impacts of climate change, population growth, and ageing infrastructure, as well as the effects of implementing counteractive measures, and has been applied to the city of Bergen, Norway. Results of the study emphasize the importance of considering a range of climate scenarios due to the wide spread in global projections. For the specific case of Bergen, substantial alterations in the hydrological cycle were projected, leading to stronger seasonal variations and a more unpredictable water availability. By sensitivity analysis of the WAI, it was demonstrated how two adaptive measures, increased storage capacity and leakage reduction, can help counteract the impacts of climate change.

Low-impact development (LID) structures are combined with traditional measures to manage stormwater and cope with increased runoff rates originating from heavy urbanization and climate change. As the use of LIDs for climate adaptation increases, practitioners need more knowledge on LID performance in future climates for successful planning and implementation. In this study, temporal downscaling of regional climate projections for three cities in Norway is performed, using the concept of scale invariance to downscale the distribution of extreme precipitation from daily to sub-daily timescales. From this, local-scale intensity-duration-frequency (IDF) curves for future precipitation were obtained. Using climate projections of daily temporal resolution as input to water balance models and the obtained IDF relationships as input to event-based models allowed for assessing the retention capacity, peak flow reduction potential and pollution control of three different types of LIDs: green roofs, bioretention cells, and detention basins. The downscaling resulted in large local variations in presumed increase of both precipitation amount and intensity, contradicting current design recommendations in Norway. Countrywide, a decrease in the overall LID performance was found, although some positive effects of temperature rises were detected. The study illustrated the importance of evapotranspiration- and infiltration-based processes in future stormwater management and how coupling of LID structures in series can significantly reduce required detention volumes.

Climate change is expected to lead to higher precipitation amounts and intensities causing an increase of the risk for flooding and combined sewer overflows in urban areas. To cope with these changes, water managers are requesting practical tools that can facilitate adaptive planning. This study was carried out to investigate how recent developments in downscaling techniques can be used to assess the effects of adaptive measures. A combined spatial-temporal downscaling methodology using the Statistical DownScaling Model-Decision Centric and the Generalized Extreme Value distribution was applied to project future precipitation in the city of Bergen, Norway. A raingarden was considered a potential adaptive measure and its performance was assessed using the RECARGA simulation tool. The benefits and limitations of using the proposed method has been demonstrated and compared to current design practices in Norway. Large differences in the raingarden9s performance with respect to percentage overflow and lag-time reduction was found for varying projections. This highlights the need for working with a range of possible futures. Further, it was found that K_sat was the determining factor for peak-flow reduction and that different values of K_sat had different benefits. Engineering flexible solutions by combining measures holding different characteristics will induce robust adaptation.