Methodological advances since the TAR have focused on exploring the effects of different ways of downscaling from the climate model scale to the catchment scale (e.g., Wood et al., 2004), the use of regional climate models to create scenarios or drive hydrological models (e.g., Arnell et al., 2003; Shabalova et al., 2003; Andreasson et al., 2004; Meleshko et al., 2004; Payne et al., 2004; Kay et al., 2006b; Fowler et al., 2007; Graham et al., 2007a, b; Prudhomme and Davies, 2007), ways of applying scenarios to observed climate data (Drogue et al., 2004), and the effect of
hydrological model uncertainty on estimated impacts of climate change (Arnell, 2005).
In general, these studies have shown that different ways of creating scenarios from the same source (a global - scale climate model) can lead to substantial differences in the estimated effect of climate change, but
that hydrological model uncertainty may be smaller than errors in the modelling procedure or differences in climate scenarios (Jha et al., 2004; Arnell, 2005; Wilby, 2005; Kay et al., 2006a, b).
Not exact matches
Uncertainties in the hydrological cycle due to land surface parameterizations can be divided into uncertainties from the spatial distribution of vegetation and from the model para
Uncertainties in the
hydrological cycle due to land surface parameterizations can be divided into
uncertainties from the spatial distribution of vegetation and from the model para
uncertainties from the spatial distribution of vegetation and from the
model parameter values.
On the Relationship Between
Uncertainties in Tropical Divergence and the
Hydrological Cycle in Global
Models.»
``... since
uncertainty is a structural component of climate and
hydrological systems, Anagnostopoulos et al. (2010) found that large
uncertainties and poor skill were shown by GCM predictions without bias correction... it can not be addressed through increased
model complexity....
Contribution from working group I to the fifth assessment report by IPCC TS.5.4.1 Projected Near - term Changes in Climate Projections of near - term climate show small sensitivity to Green House Gas scenarios compared to
model spread, but substantial sensitivity to
uncertainties in aerosol emissions, especially on regional scales and for
hydrological cycle variables.
In his talk, «Statistical Emulation of Streamflow Projections: Application to CMIP3 and CMIP5 Climate Change Projections,» PCIC Lead of
Hydrological Impacts, Markus Schnorbus, explored whether the streamflow projections based on a 23 - member hydrological ensemble are representative of the full range of uncertainty in streamflow projections from all of the models from the third phase of the Coupled Model Intercompari
Hydrological Impacts, Markus Schnorbus, explored whether the streamflow projections based on a 23 - member
hydrological ensemble are representative of the full range of uncertainty in streamflow projections from all of the models from the third phase of the Coupled Model Intercompari
hydrological ensemble are representative of the full range of
uncertainty in streamflow projections from all of the
models from the third phase of the Coupled
Model Intercomparison Project.
In addition, despite our effort to characterize and possibly minimize the climatic
uncertainty, one should be aware of other sources of
uncertainty (e.g., in the
hydrological and hydraulic
modeling, in the space - time discretization, in the impact
model, among others) which affect complex
modeling framework such as the one presented in this work.
Uncertainties in the hydrological cycle due to land surface parameterizations can be divided into uncertainties from the spatial distribution of vegetation and from the model para
Uncertainties in the
hydrological cycle due to land surface parameterizations can be divided into
uncertainties from the spatial distribution of vegetation and from the model para
uncertainties from the spatial distribution of vegetation and from the
model parameter values.