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It is expected that the port of Rotterdam will experience effects of global climate change. The port of Rotterdam is located at the estuaries of the Meuse and Rhine rivers with a connection to the North Sea that is open, but can in theory be closed by the maeslantkering during extreme storm events. Nevertheless, it is expected that, with current climate change projections, water levels will rise because of both sea level rise and from an increase in water flow from the rivers due to increased precipitation inland (PBL, 2005).

Climate change scenarios that make predictions for water elevation are based foremost on projected CO2 emission scenarios. From these scenarios, temperature rise is projected, which will impact global water cycles. Expected effects of temperature rise are glacier meltdown, water expansion and changes in

precipitation patterns, all leading to rising water levels. Such scenarios have been created on an international level, most notably by the Intergovernmental Panel on Climate Change (IPCC). The IPCC’s Assessment Reports present recent knowledge on the calculated impacts of climate change, giving global and regional predictions (IPCC, 2007). These reports are in the scale of continents and are not useful for making predictions of local water level rise in the port of Rotterdam, due to the complexity of the global climate system. For making calculations smaller in geographical scale specifically for The Netherlands, climate change scenarios have been created by the Royal Dutch Meteorological Institute (KNMI) and the Dutch Environmental Assessment Agency (PBL). The KNMI has developed four different scenarios of climate change and focus on the target years of 2050 and 2100 (KNMI, 2006), whereas the PBL mainly focuses on social consequences of climate change (PBL, 2005). Climate change scenarios for target year 2100 have also been developed by Commission Veerman, also known as the Deltacommission, a national board of advice charged by the Dutch government to provide advice on climate change adaptation in The Netherlands. To translate the projected climate change scenarios in the Netherlands to projected water level rise in the port of Rotterdam, studies have been performed by the Dutch National Research Programme Knowledge for Climate (Huizinga, 2010).

Table 7

Climate change scenarios used in the Knowledge for Climate study (Huizinga, 2010).

The Knowledge for Climate programme has performed a study in which flood depth and flood extent are calculated for the Rotterdam port area based on climate change scenarios taken from the KNMI and Commission Veerman (Huizinga, 2010). The Knowledge for Climate calculations are based on the G+ scenario of KNMI for 2050 and a scenario of the Dutch national Commission Veerman for 2100 (see Table 7). For both target years, 2050 and 2100, an alternative scenario has been developed by Commission Veerman in which the port of Rotterdam has an

adaptation strategy in place, consisting of additional envisioned water barriers that can lock the port area during an emergency (the ‚lockable/open‛ scenarios, Figure 27). In total, five climate scenarios were developed: 2010, 2050, 2050 lockable/open, 2100, 2100 lockable/open. Figure 28 shows average flooding depth for the port of Rotterdam for each scenario and return period. Floodings with a return period of 1/10 per year are predicted to be 30 cm higher in 2050 compared to the current situation and 60 cm higher in 2100. Interestingly, the scenarios for the adaptation strategy ‚lockable/open‛ show unexplained greater average flooding depth.

Figure 27

The lockable/open adaptation strategy developed by Commission Veerman envisions multiple barriers that can close during an

emergency (Huizinga, 2010).

Figure 28

Area averaged flooding depth (cm) for various return periods, years and measures – logarithmic scaling (Huizinga, 2010).

For these scenarios, the consequences are calculated for flood events of different magnitudes. Flood events are considered by their return periods (see Figure 28), meaning the chance that a flood event of a certain magnitude occurs in a certain year. As water levels rise due to climate change, the magnitude of flood events per return period increases. For this study, the five knowledge for Climate programme scenarios are taken as the most recent and specific predictions of flood depth and flood extent for the port of Rotterdam, and for each scenario the return periods of 100, 1,000, 4,000 and 10,000 years are considered, in total producing 20 flood damage maps. A return period of 10,000 years may seem extremely slim in happening, but it corresponds with the safety standards of the dykes of the province of South-Holland, where Rotterdam is located.

7.5

For follow-up analyses for policy considerations, damage maps are created for the port of Rotterdam, using the flood damage method as described in Figure 3. In order to produce a damage map that shows direct economic damage per location (in a 5 by 5 m grid), the following steps were performed.

1. A land use map was made that adds land use categories to locations in the port area. A cadastral map of plots is used from the Dutch Land Registry Office and numbers are allotted to the plots of the port of Rotterdam, representing the land use classes of Table 6. See Figure 29. The port area selected for this study does not incorporate the entire port due to time considerations, but focuses instead on the areas identified by Huizinga (2010) as most vulnerable due to their low elevations. Open spaces, including shipping lanes and unused land, have not received values. 2. An inundation map must be selected from the calculations of the

Knowledge for Climate programme, representing a climate change scenario and showing the depth of inundation per location in the Port of Rotterdam.

3. Both maps are transformed into a raster map with a 5 by 5 m grid. Then, for every grid location, inundation damage is calculated by multiplying the replacement value of the grid location with the damage factor appropriate for the inundation depth at that location, as expressed by the damage curve representative of the land use category of that location.