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More intense and frequent rainfalls have increased the number of urban flooding events in recent years, prompting adaptation efforts. Economic optimization is considered an efficient tool to decide on the design level for adaptation. The costs associated with a flooding to the T-year level and the annual capital and operational costs of adapting to this level are described with log-linear relations. The total flooding costs are developed as the expected annual damage of flooding above the T-year level plus the annual capital and operational costs for ensuring no flooding below the T-year level. The value of the return period T that corresponds to the minimum of the sum of these costs will then be the optimal adaptation level. The change in climate, however, is expected to continue in the next century, which calls for expansion of the above model. The change can be expressed in terms of a climate factor (the ratio between the future and the current design level) which is assumed to increase in time. This implies increasing costs of flooding in the future for many places in the world. The optimal adaptation level is found for immediate as well as for delayed adaptation. In these cases, the optimum is determined by considering the net present value of the incurred costs during a sufficiently long time-span. Immediate as well as delayed adaptation is considered.

Changes in extreme precipitation are expected to be one of the most important impacts of climate change in cities. Urban floods are mainly caused by short duration extreme events. Hence, robust information on changes in extreme precipitation at high-temporal resolution is required for the design of climate change adaptation measures. However, the quantification of these changes is challenging and subject to numerous uncertainties. This study assesses the changes and uncertainties in extreme precipitation at hourly scale over Denmark. It explores three statistical downscaling approaches: a delta change method for extreme events, a weather generator combined with a disaggregation method and a climate analogue method. All three methods rely on different assumptions and use different outputs from the regional climate models (RCMs). The results of the three methods point towards an increase in extreme precipitation but the magnitude of the change varies depending on the RCM used and the spatial location. In general, a similar mean change is obtained for the three methods. This adds confidence in the results as each method uses different information from the RCMs. The results of this study highlight the need of using a range of statistical downscaling methods as well as RCMs to assess changes in extreme precipitation.

Outputs from climate models are the primary data source in climate change impact studies. However, their interpretation is not straightforward. In recent years, several methods have been developed in order to quantify the uncertainty in climate projections. One of the common assumptions in almost all these methods is that the climate models are independent. This study addresses the validity of this assumption for two ensembles of regional climate models (RCMs) from the Ensemble-Based Predictions of Climate Changes and their Impacts (ENSEMBLES) project based on the land cells covering Denmark. Daily precipitation indices from an ensemble of RCMs driven by the 40-yr ECMWF Re-Analysis (ERA-40) and an ensemble of the same RCMs driven by different general circulation models (GCMs) are analyzed. Two different methods are used to estimate the amount of independent information in the ensembles. These are based on different statistical properties of a measure of climate model error. Additionally, a hierarchical cluster analysis is carried out. Regardless of the method used, the effective number of RCMs is smaller than the total number of RCMs. The estimated effective number of RCMs varies depending on the method and precipitation index considered. The results also show that the main cause of interdependency in the ensemble is the use of the same RCM driven by different GCMs. This study shows that the precipitation outputs from the RCMs in the ENSEMBLES project cannot be considered independent. If the interdependency between RCMs is not taken into account, the uncertainty in the RCM simulations of current regional climate may be underestimated. This will in turn lead to an underestimation of the uncertainty in future precipitation projections.

Climate change is expected to influence the occurrence and magnitude of rainfall extremes and hence the flood risks in cities. Major impacts of an increased pluvial flood risk are expected to occur at hourly and sub-hourly resolutions. This makes convective storms the dominant rainfall type in relation to urban flooding. The present study focuses on high-resolution regional climate model (RCM) skill in simulating sub-daily rainfall extremes. Temporal and spatial characteristics of output from three different RCM simulations with 25 km resolution are compared to point rainfall extremes estimated from observed data. The applied RCM data sets represent two different models and two different types of forcing. Temporal changes in observed extreme point rainfall are partly reproduced by the RCM RACMO when forced by ERA40 re-analysis data. Two ECHAM forced simulations show similar increases in the occurrence of rainfall extremes of over a 150-year period, but significantly different changes in the magnitudes. The physical processes behind convective rainfall extremes generate a distinctive spatial inter-site correlation structure for extreme events. All analysed RCM rainfall extremes, however, show a clear deviation from this correlation structure for sub-daily rainfalls, partly because RCM output represents areal rainfall intensities and partly due to well-known inadequacies in the convective parameterization of RCMs. The results highlight the problem urban designers are facing when using RCM output. The paper takes the first step towards a methodology by which RCM performance and other downscaling methods can be assessed in relation to the simulation of short-duration rainfall extremes.

Hydrologic models that use components for integrated modelling of surface water and groundwater systems help conveniently simulate the dynamically linked hydrologic and hydraulic processes that govern flow conditions in watersheds. The Soil and Water Assessment Tool (SWAT) is one such model that allows continuous simulations over long time periods in the land phase of the hydrologic cycle by incorporating surface water and groundwater interactions. This study provides a verified structure for the SWAT to evaluate existing flow regimes in a small-sized catchment in Denmark and examines a simple simulation to help quantify the effects of climate change on regional water quantities. SWAT can be regarded among the alternative hydrologic simulation tools applicable for catchments with similar characteristics and of similar sizes in Denmark. However, the modellers would be required to determine a proper set of effective model parameters and agree on a proper balancing among different calibration sites during all parameter optimization trials.

The fourth assessment report of Intergovernmental Panel on Climate Change (IPCC) suggests studies that increase the spatial resolution to solve the scale mismatch between large-scale climatic models and the catchment scale while addressing climate change impacts on aquatic ecosystems. Impacts occur mostly at the local scale. though potential changes in the hydrological cycle and eco-hydrological processes are more difficult to model and analyse at this level. The difficulty is even greater for studies on lowland river systems, which require the modelling of hydrological processes in greater detail. In this Study, the regional impacts of climate change on river flow and environmental flow requirement. which is a negotiated trade-off between water uses, are analysed for a lowland catchment in Denmark through MIKE SHE/MIKE 11 coupling. The Coupled model possesses an important capacity for simulating stream flows and groundwater head levels in a dynamic system. Although the simulation results from different global circulation models (GCMs) indicate different responses in flows to the climate change, there are obvious deviations of the river flows and environmental flow potentials computed for all the scenario cases from the averages of the base period with current conditions. Copyright (C) 2010 John Wile), & Sons. Ltd.

It is crucial for flood management that information about the impacts of climate change on floods and the predictive uncertainties therein becomes available. This has been achieved by using information from different Regional Climate Models for different emission scenarios to assess the uncertainty in climate change for the Meuse River in northwestern Europe. A hydrological model has been used to simulate flows for current and changed climate conditions. The uncertainty in the hydrological model is assumed to be represented by the difference between observed and simulated discharge and incorporated in the uncertainty analysis through the model parameters. Climate change results in an increase of the 100-year flood of about 30%. This increase is primarily caused by an increase of precipitation in winter. The predictive uncertainty in this impact is about 20% resulting from uncertainties in climate change (about 50%) and uncertainties in hydrological model parameters (about 50%).