Keywords
system dynamics; water transport; uncertainty
Start Date
26-6-2018 10:40 AM
End Date
26-6-2018 12:00 PM
Abstract
Inland Waterway Transport (IWT) is an important mode of transportation in many regions of the world, including Central Europe. Natural environmental conditions have substantial impacts on IWT operations. In Central European conditions, the main hydrological hazards for IWT are (in descending order): low stream flows; floods; and river ice. IWT operations are supported by navigation-related hydrological forecasts, including improved probabilistic water-level forecasts that assist shippers in their decision making, most notably under low flow conditions. Based on a participatory modelling approach and stakeholder interviews, we develop a system dynamics (SD) model of forecast-supported transportation of cargos relevant for the energy sector on a waterway subject to low stream flows/ droughts, where adverse impacts on electricity generation should be mitigated. The model is applied to the River Rhine case study, where, in particular, IWT is an important mode of transportation of coal on which electricity generation in Southern Germany still heavily depends (unlike in Northern Germany with its widespread wind farms). Low stream flow events on the River Rhine make operations of IWT difficult and may lead, in particular, to problems with timely delivery of coal, therefore creating delays and supply constraints for electricity generation. With our model, we explore how the improved navigation-related forecasts might increase the sustainability of supply chains dependent on IWT operation. The important component of modelling is the SD description of decision making under uncertainty based on probabilistic information provided by forecasts. We also address the issue of the increasing importance of probabilistic forecasts for IWT operations under conditions of projected climate change that might differ substantially the IWT operations in the case study area.
System dynamics modelling of inland waterway transportation of cargos for energy sector supported by navigation-related probabilistic forecasts
Inland Waterway Transport (IWT) is an important mode of transportation in many regions of the world, including Central Europe. Natural environmental conditions have substantial impacts on IWT operations. In Central European conditions, the main hydrological hazards for IWT are (in descending order): low stream flows; floods; and river ice. IWT operations are supported by navigation-related hydrological forecasts, including improved probabilistic water-level forecasts that assist shippers in their decision making, most notably under low flow conditions. Based on a participatory modelling approach and stakeholder interviews, we develop a system dynamics (SD) model of forecast-supported transportation of cargos relevant for the energy sector on a waterway subject to low stream flows/ droughts, where adverse impacts on electricity generation should be mitigated. The model is applied to the River Rhine case study, where, in particular, IWT is an important mode of transportation of coal on which electricity generation in Southern Germany still heavily depends (unlike in Northern Germany with its widespread wind farms). Low stream flow events on the River Rhine make operations of IWT difficult and may lead, in particular, to problems with timely delivery of coal, therefore creating delays and supply constraints for electricity generation. With our model, we explore how the improved navigation-related forecasts might increase the sustainability of supply chains dependent on IWT operation. The important component of modelling is the SD description of decision making under uncertainty based on probabilistic information provided by forecasts. We also address the issue of the increasing importance of probabilistic forecasts for IWT operations under conditions of projected climate change that might differ substantially the IWT operations in the case study area.
Stream and Session
F3: Modelling and Decision Making Under Uncertainty