Long Range Experimental Hydrologic Forecasting for the Eastern U.s

We explore a strategy for long-range hydrologic forecasting that uses ensemble climate model forecasts as input to a macroscale hydrologic model to produce runoff and streamflow forecasts at spatial and temporal scales appropriate for water management. Coarse-scale monthly ensemble climate model forecasts produced by the NCEP/CPC Global Spectral Model (GSM) are bias corrected, downscaled to 1/8 degree horizontal resolution, and disaggregated to a daily time step for input to the Variable Infiltration Capacity (VIC) hydrologic model. Bias-correction is effected by evaluating the GSM ensemble forecast variables as percentiles relative to the GSM model climatology, and then extracting the percentiles’ associated variable values instead from the observed climatology. The monthly meteorological forecasts are then interpolated to the finer hydrologic model scale, and a daily signal that preserves the forecast anomaly is imposed through resampling of the historic record. With the resulting monthly runoff and streamflow forecasts for the East Coast and Ohio River basin, we evaluate the bias correction and resampling approaches during the southeastern U.S. drought from May August 2000, and also for the El Nino conditions of December 1997 February 1998. For the summer 2000 study period, the weak direction of the climate model forecasts is balanced by a degree of skill derived from persistence in the land surface hydrologic states. In contrast, the El Nino-condition hydrologic forecasts derive more skill from the climate model forecast signal than from persistence in the antecedent hydrologic model state. The hydrologic forecasting strategy appears successful in translating climate forecast signals to hydrologic variables of interest for water management.