Integrating Climate, Hydrologic and Decision-support Models for Regional Assessments

Interpretation of the impact of climate change or climate variability on water resources management, requires information at scales much smaller than the current resolution of regional or global climate models. The small-scale variability of rainfall is (typically) resolved by nested models or statistical downscaling. The latter framework is attractive in ensemble prediction due to its computational efficiency. The work presented here focusses on the application of a statistical space-time downscaling model to resolve subgrid scale (hydrologic) variability of precipitation from the output of a regional model. In addition, this downscaled output is used in a hydrologic model to assess its impact on regional water management decisions.