How to Generate More Reliable Seasonal Drought Forecasts and Analyze Drought Recovery Under Uncertainties?

Abstract:

Forecasting of drought is vital for resource management and planning. Both societal and agricultural requirements for water weigh heavily on the natural environment, which may become scarce in the event of drought. Although drought forecasts are an important tool for managing water in hydrologic systems, these forecasts are plagued by uncertainties, owing to the complexities of water dynamics and the spatial heterogeneities of pertinent variables. Due to these uncertainties, it is necessary to frame forecasts in a probabilistic manner. While it is common to focus on the meteorological aspects of uncertainty (e.g. precipitation, temperature), this presentation will discuss the impacts of initial condition and model uncertainty on drought, and particularly ways in which these uncertainties may be reduced. In order to reduce these uncertainties, the recently developed Particle Filter with Sequential Bayesian Combination (PF-SBC) algorithm is applied to drought forecasting within the Upper Colorado River Basin (UCRB). It will be shown that this methodology has the potential to increase the reliability of seasonal hydrologic drought forecasts. In addition, the recovery of drought will be examined with initialization of forecasts through the Particle Filter. From these forecasts, it is found that drought recovery is a longer process than suggested in recent literature. Drought in land surface variables (snow, soil moisture) is shown to be persistent up to a year in certain locations, depending on the intensity of the drought. Location within the UCRB appears to be a driving factor in the ability of the land surface to recover from drought, allowing for differentiation between drought prone and drought resistant regions.