Estimates of climate change impacts are often characterized by large uncertainties that reflect ignorance of many physical, biological, and socio-economic processes, and which hamper efforts to anticipate and adapt to climate change. A key to reducing these uncertainties is improved understanding of the relative contributions of individual factors. We evaluated uncertainties for projections of ...

in this research, data fusion (df) method was applied to simulate the hydrological process of taleghan reservoir daily inflow. two different df algorithms were proposed and assessed based on k-nearest neighbors (knn) algorithm. four artificial neural network models and two hammerstein-wiener (hw) models were used as the individual simulation models. comparison of the results between individual ...

Various aspects of the role of uncertainty in greenhouse gas emission reduction policy are analyzed with the integrated assessment model FUND. FUND couples simple models of economy, climate, climate impacts, and emission abatement. Probability distribution functions are assumed for all major parameters in the model. Monte Carlo analyses are used to study the e!ects of parametric uncertainties. ...

The relationship between climate and forest productivity is an intensively studied subject in forest science. This thesis is embedded within the general framework of future forest growth under climate change and its implications for the ongoing forest conversion of pure pine forests into mixed oak-pine forests. My objective is to investigate future forest productivity at different spatial scale...

Climate change is predicted to be one of the greatest drivers of ecological change in the coming century. Increases in temperature over the last century have clearly been linked to shifts in species distributions. Given the magnitude of projected future climatic changes, we can expect even larger range shifts in the coming century. These changes will, in turn, alter ecological communities and t...

Recent studies show that multimodel combinations improve hydroclimatic predictions by reducing model uncertainty. Given that climate forecasts are available from multiple climate models, which could be ingested with multiple watershed models, what is the best strategy to reduce the uncertainty in streamflow forecasts? To address this question, we consider three possible strategies: (1) reduce t...

To increase public trust in the results of climate science, we recommend that the uncertainties inherent in climate forecasts be made explicit. In cases where these uncertainties can be legitimately expressed as probabilities, we suggest that use of a dual verbal/numerical format for expressing uncertainty (e.g., ‘likely (65-80%)’) will enhance communicative effectiveness. We also propose use o...