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Over 50 percent of the world's population live in urban areas and the percentage is steadily increasing. Oslo, the capital city of Norway, is projected to grow by 20 percent by 2030 according to Statistics Norway. Urbanization has strong impacts on quality and quantity of local runoff as well as erosion and sediment transport. The urban hydrological system is very complex due to interaction of overland runoff and groundwater with the sewer system and man-made changes of the land surface. The water balance in urban catchments is significantly changed due to human activities. Here we study the water balance in an urban catchment in Oslo by three independent methods, data analysis, MIKE Urban and Storm Water Management Model (SWMM). By using the three methods, we quantify each water balance component, i.e. precipitation, evapotranspiration, base flow from sewage system, base flow from groundwater and runoff from precipitation. The study contributes not only to the understanding of urban water system, but also to the ongoing project at the Oslo Municipality to implement low impact development (LID) infrastructure, such as green roof and rain gardens, to reduce and delay overland flow. The catchment studied is a suburban residential area that drains into the Akerselva River, which is the largest river in the Oslo.Acknowledgements: This work is supported by the Research Council of Norway (New Water Ways: 270742). [ABSTRACT FROM AUTHOR]

Humans are "urban species". In Norway, more than 80 percent of the population are living in cities and towns. Urbanization has strong impacts on quality and quantity of local runoff as well as erosion and sediment transport. Flooding of urban areas poses great challenges to human safety and sustained economic growth. Together with urban development, increase of intensive rainfall in terms of magnitude and frequency due to climate change makes the problem of urban floods more challenging. Oslo, the capital of Norway is continually developing rapidly, uses the Stormwater tree step approach (S3SA) for an overland flood protection strategy. The two first steps are infiltration and detention of rain. The third step is to use safe floodways to drain water. Maps of floodways are crucial in urban flood mitigation and city planning. In this study, we use four methods to identify floodways. These methods are accumulation analysis based on single flow direction multiple flow direction algorithm, SIMulated Water Erosion (SIMWE) and fully dynamic surface flow modelling. The study compares the differences between the methods and highlights their advantages and disadvantages for various applications.Acknowledgements: This work is supported by the Research Council of Norway (New Water Ways: 270742). [ABSTRACT FROM AUTHOR]

Quantifying hydrologic uncertainty associated with the nonlinear and heteroscedastic dependence structure of multi-step-ahead deterministic flood forecasts is fundamentally challenging. A hydrologic uncertainty processor (HUP) can effectively measure hydrologic uncertainty raised in deterministic forecast models under the hypothesis that there is no input uncertainty. This study explored a HUP embedded with a copula function for configuring the probabilistic flood forecast model. The outputs of the HUP formed a posterior distribution of the process, conditional upon the deterministic forecasts. The main merit of the copula-based HUP lies in capturing the features of the nonlinear and heteroscedastic dependence structure among hydrologic variables as well as in alleviating the uncertainty encountered in operational flood forecasts. To demonstrate reliability and effectiveness of the proposed method, the copula-based HUP was implemented to generate multi-step-ahead forecasts from horizons t+1 up to t+4 at a time scale of 6-hours for a flood-induced inflow series of the Three Gorges Reservoir (TGR) in China. The back propagation neural network and the nonlinear autoregressive with exogenous inputs neural network were investigated to offer a better deterministic forecast result for further probabilistic forecasting. For comparison purpose, the meta-Gaussian HUP was also conducted. The results indicated that the proposed method not only outperformed the comparative HUP but also significantly enhanced the accuracy of probabilistic flood forecasts for the TGR inflows, where effective quantification of hydrologic uncertainty was achieved. We demonstrated that the copula-based HUP could suitably extract the complex nonlinear and heteroscedastic dependence structure relating to the outputs of the deterministic forecast model. Consequently, hydrologic uncertainty could be alleviated and model reliability as well as forecast accuracy for future horizons could be significantly improved.Acknowledgements: This work is supported by the Research Council of Norway (FRINATEK Project 274310). [ABSTRACT FROM AUTHOR]