气候变化领域集成服务门户

Climate-impact projections are subject to uncertainty arising from climate models, greenhouse gases emission scenarios, bias correction and downscaling methods (BCDS), and the impact models. We studied the effects of hydrological model parameterization and regionalization (HM-P and HM-R) on the cascade of uncertainty. We developed a new, widely-applicable approach that improves our understanding of how HM-P and HM-R along with other uncertainty drivers contribute to the overall uncertainty in climate-impact projections. We analyzed uncertainties arising from general circulation models (GCMs), representative concertation pathways, BCDS, evapotranspiration calculation methods, and specifically HM-P and HM-R. We used the Soil and Water Assessment Tool, a semi-physical process-based hydrologic model with a high capability of parameterization, to project blue and green water resources for historical (1983-2007), near future (2010-2035) and far future (2040-2065) periods in Alberta, a western province of Canada. We developed an Analysis of Variance (ANOVA)-Sequential Uncertainty Fitting Program approach, to decompose the overall uncertainty into contributions of single drivers using the projected blue and green water resources. The monthly analyses of projected water resources showed that HM-P and HM-R contribute 21-51% and 15-55% to the blue water, and 20-48% and 15-50% to the green water overall uncertainty in near future and far future, respectively. Overall, we found that in spring and summer seasons uncertainty arising from HM-P and HM-R dominates other uncertainty sources, e.g. GCMs. We also found that global climate models are another dominant source of uncertainty in future impact projections.