Seasonal Prediction of Regional Reference Evapotranspiration Based on Climate Forecast System Version 2

Abstract

eference evapotranspiration (ETo) is an important hydroclimatic variable for water planning and management. This research explored the potential of using the Climate Forecast System, version 2 (CFSv2), for seasonal predictions of ETo over the states of Alabama, Georgia, and Florida. The 12-km ETo forecasts were produced by downscaling coarse-scale ETo forecasts from the CFSv2 retrospective forecast archive and by downscaling CFSv2 maximum temperature (Tmax), minimum temperature (Tmin), mean temperature (Tmean), solar radiation (Rs), and wind speed (Wind) individually and calculating ETo using those downscaled variables. All the ETo forecasts were calculated using the Penman?Monteith equation. Sensitivity coefficients were evaluated to quantify how and how much does each of the variables influence ETo. Two statistical downscaling methods were tested: 1) spatial disaggregation (SD) and 2) spatial disaggregation with quantile mapping bias correction (SDBC). The downscaled ETo from the coarse-scale ETo showed similar skill to those by first downscaling individual variables and then calculating ETo. The sensitivity coefficients showed Tmax and Rs had the greatest influence on ETo, followed by Tmin and Tmean, and Wind. The downscaled Tmax showed highest predictability, followed by Tmean, Tmin, Rs, and Wind. SDBC had slightly better performance than SD for both probabilistic and deterministic forecasts. The skill was locally and seasonally dependent. The CFSv2-based ETo forecasts showed higher predictability in cold seasons than in warm seasons. The CFSv2 model could better predict ETo in cold seasons during El Niño?Southern Oscillation (ENSO) events only when the forecast initial condition was in either the El Niño or La Niña phase of ENSO.