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The Earth's future depends on how we manage the manifold risks of climate change (CC). It is state-of-the-art to assume that risk reduction requires participatory management involving a broad range of stakeholders and scientists. However, there is still little knowledge about the optimal design of participatory climate change risk management processes (PRMPs), in particular with respect to considering the multitude of substantial uncertainties that are relevant for PRMPs. To support the many local to regional PRMPs that are necessary for a successful global-scale reduction of CC risks, we present a roadmap for designing such transdisciplinary knowledge integration processes. The roadmap suggests ways in which uncertainties can be comprehensively addressed within a PRMP. We discuss the concept of CC risks and their management and propose an uncertainty framework that distinguishes epistemic, ontological, and linguistic uncertainty as well as ambiguity. Uncertainties relevant for CC risk management are identified. Communicative and modeling methods that support social learning as well as the development of risk management strategies are proposed for each of six phases of a PRMP. Finally, we recommend how to evaluate PRMPs as such evaluations and their publication are paramount for achieving a reduction of CC risks.
Evaluation of radiation components in a global freshwater model with station-based observations
(2016)
In many hydrological models, the amount of evapotranspired water is calculated using the potential evapotranspiration (PET) approach. The main driver of several PET approaches is net radiation, whose downward components are usually obtained from meteorological input data, whereas the upward components are calculated by the model itself. Thus, uncertainties can be large due to both the input data and model assumptions. In this study, we compare the radiation components of the WaterGAP Global Hydrology Model, driven by two meteorological input datasets and two radiation setups from ERA-Interim reanalysis. We assess the performance with respect to monthly observations provided by the Baseline Surface Radiation Network (BSRN) and the Global Energy Balance Archive (GEBA). The assessment is done for the global land area and specifically for energy/water limited regions. The results indicate that there is no optimal radiation input throughout the model variants, but standard meteorological input datasets perform better than those directly obtained by ERA-Interim reanalysis for the key variable net radiation. The low number of observations for some radiation components, as well as the scale mismatch between station observations and 0.5° × 0.5° grid cell size, limits the assessment.
Understanding how temperature affects cod (Gadus morhua) ecology is important for forecasting how populations will develop as climate changes in future. The effects of spawning-season temperature and habitat size on cod recruitment dynamics have been investigated across the North Atlantic. Ricker and Beverton and Holt stock–recruitment (SR) models were extended by applying hierarchical methods, mixed-effects models, and Bayesian inference to incorporate the influence of these ecosystem factors on model parameters representing cod maximum reproductive rate and carrying capacity. We identified the pattern of temperature effects on cod productivity at the species level and estimated SR model parameters with increased precision. Temperature impacts vary geographically, being positive in areas where temperatures are <5°C, and negative for higher temperatures. Using the relationship derived, it is possible to predict expected changes in population-specific reproductive rates and carrying capacities resulting from temperature increases. Further, carrying capacity covaries with available habitat size, explaining at least half its variability across stocks. These patterns improve our understanding of environmental impacts on key population parameters, which is required for an ecosystem approach to cod management, particularly under ocean-warming scenarios. Key words: carrying capacity , cod , hierarchical models , North Atlantic , temperature , uncertainty