Hydrologic implications of errors in bias-corrected regional reanalysis data for west central Florida

Abstract

This study investigated the limitations associated with using dynamically-downscaled, bias-corrected reanalysis data (i.e. regional reanalysis data) to predict hydrologic behavior of low-relief rainfall driven systems using an integrated surface/groundwater model. Four different sets of global reanalysis data (NCEP/NCAR-R1, NCEP-DOE-R2, ERA40, and 20CR) that were previously downscaled using two RCMs (MM5 and RSM) were obtained, bias-corrected on a daily basis using the CDF-mapping approach, and used to drive an integrated hydrologic model (INTB) that was previously calibrated and verified for the Tampa Bay region. All raw dynamically-downscaled reanalysis datasets accurately estimated the annual cycle of daily maximum and minimum temperature, except the NCEP/NCAR R1+MM5 data which consistently underestimated daily maximum temperature. All raw regional reanalysis precipitation data significantly overestimated precipitation, particularly for the dry season. Bias-correction using the CDF-mapping approach effectively removed biases in the temporal mean and standard deviation of both the daily precipitation and temperature predictions. Biases in the mean monthly and mean annual precipitation totals were removed by CDF-mapping on a daily basis, but the standard deviation of the monthly and annual precipitation totals were not accurately reproduced. Furthermore inaccuracies in actual daily precipitation time series aggregated into monthly and annual rainfall total time series that showed significant and temporally persistent errors. Precipitation timing errors produced by regional reanalysis data were propagated and enhanced by non-linear streamflow generation, groundwater flow and storage processes in the hydrologic model and produced significant errors in both actual and mean daily, monthly and annual streamflow and groundwater level predictions. These results show that improvement in large-scale reanalysis products and regional climate models may be required before dynamically downscaled bias-corrected reanalysis data can be used as a surrogate for observational data in hydrologic model applications for low-relief, rainfall driven systems. (C) 2014 Published by Elsevier B.V.