Keywords
CMIP3, CMIP5, statistical downscaling, GSFLOW, hydroclimate, simulated uncertainty
Location
Session B1: Research Infrastructures for Integrated Environmental Modeling
Start Date
16-6-2014 2:00 PM
End Date
16-6-2014 3:20 PM
Abstract
In this presentation we show how model uncertainty is transferred from GCMs to hydrologic model results for different downscaling strategies. We use a USGS Groundwater and Surface-water FLOW (GSFLOW) model applied to three small catchments in the northeastern Lake Tahoe basin. A framework is developed for assessing the benefits and difficulties associated with using historical and future climate projections from CMIP3 and CMIP5 datasets for hydrologic investigations. Here we downscale 10 km gridded GCM climate data to a 60m grid using daily values from climate stations and PRISM average monthly climate. Hydrologic model results show that an ensemble/probabilistic station- based downscaling approach provides reasonable downscaled climate data that can be used to evaluate sub-regional scale impacts in hydrologic processes.
Included in
Civil Engineering Commons, Data Storage Systems Commons, Environmental Engineering Commons, Hydraulic Engineering Commons, Other Civil and Environmental Engineering Commons
Uncertainty Transfer in Modeling Layers: From GCM to downscaling to hydrologic surface-groundwater modeling
Session B1: Research Infrastructures for Integrated Environmental Modeling
In this presentation we show how model uncertainty is transferred from GCMs to hydrologic model results for different downscaling strategies. We use a USGS Groundwater and Surface-water FLOW (GSFLOW) model applied to three small catchments in the northeastern Lake Tahoe basin. A framework is developed for assessing the benefits and difficulties associated with using historical and future climate projections from CMIP3 and CMIP5 datasets for hydrologic investigations. Here we downscale 10 km gridded GCM climate data to a 60m grid using daily values from climate stations and PRISM average monthly climate. Hydrologic model results show that an ensemble/probabilistic station- based downscaling approach provides reasonable downscaled climate data that can be used to evaluate sub-regional scale impacts in hydrologic processes.
