An Integrated Assessment Metamodel for Developing Adaptation Pathways for Sustainable Water Management in the Lower Rhine Delta
Water management is increasingly challenged by pressures from population growth, sea level rise and potential climate change. Moreover, these developments are highly uncertain and change over time. The challenge is to develop strategies that are either robust insensitive to the future or flexible enough to adapt to changing conditions. Adaptation is not only determined by what is known or anticipated at present, but also by what will be experienced and learned while the future unfolds, and by policy responses to social and water events. As a result, an adaptation pathway emerges. Exploring adaptation pathways into the future will provide indispensable support to decisionmaking in achieving sustainable water management. For a structured exploration of the various adaptation pathways, we are developing a computational model of the Rhine Delta in the Netherlands. With this model, we will simulate an evolvement of the system wherein pressures influence the water system state, and impacts on flood risk and water services such as agriculture and nature, which may then trigger a policy response. The result of this dynamic simulation is an adaptation pathway. The model should run fast enough to calculate many 100 year transient scenarios, representing the dominant processes and natural variability, and be able to implement various policy options. To enable such dynamic modelling we are developing an Integrated Assessment MetaModel (IAMM) using the technique of metamodelling. Metamodels are simple aggregated models that approximate the behaviour of complex and detailed models. The IAMM is simple in terms of process and spatial information, but complex in terms of time series and policy response. The challenge is to capture enough detail and process information ensuring an adequate model behavior for policy analysis. We present the concept and describe for which situations policy options and effects the model can be used to support decisionmaking under uncertainty.