NH13C-1943
Watershed Dynamics, with focus on connectivity index and management of water related impacts on road infrastructure

Monday, 14 December 2015
Poster Hall (Moscone South)
Zahra Kalantari, Stockholm University, Stockholm, Sweden
Abstract:
In Sweden, spatially explicit approaches have been applied in various disciplines such as landslide modelling based on soil type data and flood risk modelling for large rivers. Regarding flood mapping, most previous studies have focused on complex hydrological modelling on a small scale whereas just a few studies have used a robust GIS-based approach integrating most physical catchment descriptor (PCD) aspects on a larger scale. This study was built on a conceptual framework for looking at SedInConnect model, topography, land use, soil data and other PCDs and climate change in an integrated way to pave the way for more integrated policy making. The aim of the present study was to develop methodology for predicting the spatial probability of flooding on a general large scale. This framework can provide a region with an effective tool to inform a broad range of watershed planning activities within a region. Regional planners, decision-makers, etc. can utilize this tool to identify the most vulnerable points in a watershed and along roads to plan for interventions and actions to alter impacts of high flows and other extreme weather events on roads construction. The application of the model over a large scale can give a realistic spatial characterization of sediment connectivity for the optimal management of debris flow to road structures. The ability of the model to capture flooding probability was determined for different watersheds in central Sweden. Using data from this initial investigation, a method to subtract spatial data for multiple catchments and to produce soft data for statistical analysis was developed. It allowed flood probability to be predicted from spatially sparse data without compromising the significant hydrological features on the landscape. This in turn allowed objective quantification of the probability of floods at the field scale for future model development and watershed management.