Uncertainties are also introduced by propagation within the system: from greenhouse gas emissions and carbon sequestration to the atmospheric concentration of greenhouse gases, and further to climate change (including feedbacks) and its impacts. Since every component in the system contributes a large amount of uncertainty, this is amplified all along the logical chain from emissions to regional and local impacts. The climate model uncertainty (converting greenhouse gas concentrations into climatic variables, such as temperature and precipitation) is already

large. There is a substantial difference between the results obtained using different scenarios and different models. Uncertainties of climate change projections increase with the length of the future time horizon. In the short-term (e.g. the 2020s), climate model uncertainties are dominant. The intra-model uncertainty (for the same model and Enzalutamide supplier different socio-economic and emission scenarios) can be lower than the inter-model uncertainty (for the same scenario and different models), especially for not-too-remote future horizons. Over longer time horizons, uncertainties due to the emission scenarios

become increasingly significant, however. Uncertainty in practical water-related projections is also due to the spatial and temporal scale mismatch between coarse-resolution climate models and the smaller-grid scale, relevant to adaptation, for which information on a much finer scale is required. Further, the time scale

of interest, e.g. for heavy precipitation resulting in flash flooding as the dynamics of flood routing is on a PD0325901 solubility dmso time scale of minutes to hours, differs from the results of available climate model (typically given at daily/monthly intervals). This scale mismatch makes disaggregation necessary, and this is another source of uncertainty. A further portion of the uncertainty is due to hydrological models and deficiencies in observation records available for model validation. Studies based on GCM models envisage a relative sea aminophylline level rise of 45–65 cm by 2100 as well as an increase in the frequency and strength of storm conditions for Poland’s coasts (Pruszak & Zawadzka 2008). Two scenarios used in several studies for the time horizon of 2100 are: a sea-level rise of 30 cm and of 100 cm, which could be respectively called optimistic and pessimistic (Zeidler, 1997 and Pruszak and Zawadzka, 2008). An analysis of the threats of land loss and flood risk was carried out for these two scenarios, and the economic and social costs and losses were assessed. For a 100 cm sea-level rise, more than 2300 km2 and 230 000 people are vulnerable on Polish coasts and the damage due to loss of land could be nearly 30 billion USD plus 18 billion USD at risk of flooding (1995 prices) (Zeidler 1997). A sea-level rise of 1 m plus possible flooding from storm surges (1.5 m) places the maximum inland boundary at 2.5 m AMSL. Zeidler (1997) determined three impact zones between contour lines 0–0.