Assessing the robustness of raingardens under climate change using SDSM and temporal downscaling
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Climate change is expected to lead to higher precipitation amounts and intensities. This study was carried out to (1) estimate the future precipitation extremes in Bergen (Norway) and (2) assess the robustness of raingardens as stormwater peak flow measures. A combined spatial temporal downscaling method using the Statistical DownScaling Model-Decision Centric (SDSM-DC) and the Generalized Extreme Value (GEV) distribution was applied to estimate future precipitation. Raingarden performance was simulated with the modelling tool RECARGA. The method gave results similar to multiplying with a climate factor as recommended by Norsk klimaservicesenter (2016). Uncertainties were found to be higher from temporal rather than spatial downscaling. The method is best suited as a tool for demonstrating possible climate change scenarios, and stress testing systems of interest. The robustness of raingardens as stormwater peak flow measures was found to be highly dependent on saturated hydraulic conductivity (Ksat). The results obtained indicate that a higher Ksat is beneficial for reducing overflow and increasing lag time. However, a lower Ksat value achieves the highest peak flow reductions. According to the research, a higher Ksat than what is earlier recommended for cold climates is needed to make raingardens robust under climate change.