Spatio-temporal drought forecasting within Bayesian networks

Abstract

Spatial variations of future droughts across the Gunnison River Basin in CO, USA, are evaluated in this study, using a recently developed probabilistic forecast model. The Standardized Runoff Index (SRI) is employed to analyze drought status across the spatial extent of the basin. The runoff generated at each spatial unit of the basin is estimated by a distributed-parameter and physically-based hydrologic model. Using the historical runoff at each spatial unit, a statistical forecast model is developed within Bayesian networks. The forecast model applies a family of multivariate distribution functions to forecast future drought conditions given the drought status in the past. Given the runoff in the past (January–June), the forecast model is applied in estimating the runoff across the basin in the forecast period (July–December). The main advantage of the forecast model is its probabilistic features in analyzing future droughts. It develops conditional probabilities of a given forecast variable, and returns the highest probable forecast along with an assessment of the uncertainty around that value. Bayesian networks can also estimate the probability of future droughts with different severities, given the drought status of the predictor period. Moreover, the model can be used to generate maps showing the runoff variation over the basin with the particular chance of occurrence in the future. Our results indicate that the statistical method applied in this study is a useful procedure in probabilistic forecast of future droughts given the spatio-temporal characteristics of droughts in the past. The techniques presented in this manuscript are suitable for probabilistic drought forecasting and have potential to improve drought characterization in different applications.