Assessing Hydrologic Impact of Climate Change with Uncertainty Estimates: Bayesian Neural Network Approach

Hydrologic drought prediction under climate change: Uncertainty modeling with Dempster–Shafer and Bayesian approaches

a r t i c l e i n f o Representation and quantification of uncertainty in climate change impact studies are a difficult task. Several sources of uncertainty arise in studies of hydrologic impacts of climate change, such as those due to choice of general circulation models (GCMs), scenarios and downscaling methods. Recently, much work has focused on uncertainty quantification and modeling in reg...

Incorporating Climate Uncertainty into Estimates of Climate Change Impacts

Quantitative estimates of the impacts of climate change on economic outcomes are important for public policy. We show that the vast majority of estimates fail to account for well-established uncertainty in future temperature and rainfall changes, leading to potentially misleading projections. We reexamine seven well-cited studies and show that accounting for climate uncertainty leads to a much ...

Modeling the Impact of Organizational Change: A Bayesian Network Approach

Uncertainty Investigation of Precipitation and Temperature Scenarios for the Sira Basin under Climate Change Impact

Results of assessment of the future climate change impacts is associated with some uncertainties. Considering the range of uncertainties increases reliability of the results. In this study, climate change impacts on daily precipitation, maximum and minimum temperature of Sira basin are assessed using LARS-WG model, for 2036-65 period. Accordingly, uncertainty of new emissions scenarios (RCP2.6،...

Climate change impact assessment: Uncertainty modeling with imprecise probability

[1] Hydrologic impacts of climate change are usually assessed by downscaling the General Circulation Model (GCM) output of large-scale climate variables to local-scale hydrologic variables. Such an assessment is characterized by uncertainty resulting from the ensembles of projections generated with multiple GCMs, which is known as intermodel or GCM uncertainty. Ensemble averaging with the assig...