Development and evaluation of a comprehensive drought index. (1st January 2017)

• Record Type: Journal Article
• Authors: Esfahanian, Elaheh; Nejadhashemi, A. Pouyan; Abouali, Mohammad; Adhikari, Umesh; Zhang, Zhen; Daneshvar, Fariborz; Herman, Matthew R.
• Is Part Of: Journal of environmental management. Volume 185(2017)
• Page Start: 31
• Page End: 43
• Publication Date: 2017-01-01
• Subjects: Meteorological drought -- Hydrological drought -- Agricultural drought -- Stream health drought -- Drought monitoring -- Drought predictive model; Environmental policy -- Periodicals; Environmental management -- Periodicals; Environment -- Periodicals; Ecology -- Periodicals; 363.705
• DOI: 10.1016/j.jenvman.2016.10.050
• Languages: English
• ISSNs: 0301-4797

Abstract

Abstract: Droughts are known as the world's costliest natural disasters impacting a variety of sectors. Despite their wide range of impacts, no universal drought definition has been defined. The goal of this study is to define a universal drought index that considers drought impacts on meteorological, agricultural, hydrological, and stream health categories. Additionally, predictive drought models are developed to capture both categorical (meteorological, hydrological, and agricultural) and overall impacts of drought. In order to achieve these goals, thirteen commonly used drought indices were aggregated to develop a universal drought index named MASH. The thirteen drought indices consist of four drought indices from each meteorological, hydrological, and agricultural categories, and one from the stream health category. Cluster analysis was performed to find the three closest indices in each category. Then the closest drought indices were averaged in each category to create the categorical drought score. Finally, the categorical drought scores were simply averaged to develop the MASH drought index. In order to develop predictive drought models for each category and MASH, the ReliefF algorithm was used to rank 90 variables and select the best variable set. Using the best variable set, the adaptive neuro-fuzzy inference system (ANFIS) was used to develop drought predictive models and their accuracy was examined using the 10-fold cross validation technique. The models' predictabilities ranged from R 2 = 0.75 for MASH to R 2 = 0.98 for the hydrological drought model. The results of this study can help managers to better position resources to cope with drought by reducing drought impacts on different sectors. Highlights: This study introduces a new universal drought index named MASH. Thirteen drought indices were calculated for 145 subbasins over 30 years. Meteorological, agricultural, hydrological, & stream heath drought categories were considered. Cluster analysis was performed to create the categorical drought score. Predictive models were developed for categorical drought indices and MASH.