Assessment of the utility of dynamically-downscaled regional reanalysis data to predict streamflow in west central Florida using an integrated hydrologic model

The goal of this study was to evaluate the ability of dynamically downscaled reanalysis data to reproduce local-scale spatiotemporal precipitation and temperature data needed to accurately predict streamflow in the Tampa Bay region of west central Florida. In particular, the Florida State University Center for Ocean-Atmospheric Prediction Studies CLARReS10 data (NCEP DOE 2 reanalysis data (R2) downscaled to 10-km over the Southeast USA using the Regional Spectral Model (RSM) were evaluated against locally available observed precipitation and temperature data and then used to drive an integrated hydrologic model that was previously calibrated for the Tampa Bay region. Resulting streamflow simulations were evaluated against observed data and previously calibrated model results. Results showed that the raw downscaled reanalysis predictions accurately reproduced the seasonal trends of mean daily minimum temperature, maximum temperature and precipitation, but generally overestimated the monthly mean and standard deviation of daily precipitation. Biases in the temporal mean and standard deviation of daily precipitation and temperature predictions were effectively removed using a CDF-mapping approach; however, errors in monthly precipitation totals remained after bias correction. Monthly streamflow simulation error statistics indicated that the accuracy of the streamflow produced by the bias-corrected downscaled reanalysis data was satisfactory (i.e., sufficient for seasonal to decadal planning), but that the accuracy of the streamflow produced by the raw downscaled reanalysis data was unsatisfactory for water resource planning purposes. The findings of this study thus indicate that further improvement in large-scale reanalysis data and regional climate models is needed before dynamically downscaled reanalysis data can be used directly (i.e. without bias correction with local data) to drive hydrologic models. However, bias-corrected dynamically downscaled data show promise for extending local historic climate observation records for hydrologic simulations. Furthermore, results of this study indicate that similarly bias-corrected dynamically downscaled retrospective and future GCM projections should be suitable for assessing potential hydrologic impacts of future climate change in the Tampa Bay region.