Regional Drought Analysis Based on Neural Networks

Abstract

The main objective of the research reported herein has been to develop an approach to analyze and quantify the spatial and temporal patterns of meteorological droughts based on annual precipitation data. By using a nonparametric spatial analysis neural network algorithm, the normalized and standardized precipitation data are classified into certain degrees of drought severity (for example, extreme drought, severe drought, mild drought, and nondrought) based on a number of truncation levels corresponding to specified quantiles of the standard normal distribution (the 15%, 35%, and 50% quantiles were used here for illustration). Then posterior probabilities of drought severity at any given point in the region are determined and the point is assigned a Bayesian Drought Severity Index depending on whether the maximum posterior probabilities correspond to extreme, severe, mild, or nondrought. This index may be useful for constructing drought severity maps that display the spatial variability of drought severity for the whole region on a yearly basis. Furthermore, the severity of the drought event for the region as a whole and the sequence and duration of drought episodes through time can be determined. The proposed regional drought analysis approach was applied to analyze and quantify regional droughts for the southwestern region of Colorado. The results were useful for deriving maps of precipitation fields for the entire region, maps of posterior probability of drought severity, and maps of drought severity indices. They were useful for visualizing the spatial pattern of droughts and for deriving other drought properties such as duration. The results obtained suggest that the proposed approach is a viable tool for analyzing and synthesizing droughts on a regional basis.