Development and evaluation of a comprehensive drought index.

Area of Science:

• Environmental Science
• Hydrology
• Agricultural Science

Background:

• Droughts are globally significant natural disasters with wide-ranging sectoral impacts.
• A lack of a universal drought definition hinders effective management and response.
• Existing indices often focus on single impact categories, neglecting a holistic view.

Purpose of the Study:

• To develop a universal drought index (MASH) encompassing meteorological, agricultural, hydrological, and stream health impacts.
• To create predictive drought models for individual categories and the overall MASH index.
• To enhance drought impact assessment and resource allocation strategies.

Main Methods:

• Aggregation of thirteen common drought indices into the MASH index.
• Utilized cluster analysis to select the most relevant indices per category.
• Employed the ReliefF algorithm and adaptive neuro-fuzzy inference system (ANFIS) for predictive modeling.

Main Results:

• Developed the MASH index by averaging categorical drought scores.
• Predictive models achieved high accuracy, with R² values ranging from 0.75 (MASH) to 0.98 (hydrological).
• Identified key variables for drought prediction using the ReliefF algorithm.

Conclusions:

• The MASH index provides a comprehensive approach to defining and assessing drought.
• Accurate predictive models can significantly improve drought preparedness and response.
• This research offers valuable tools for managers to mitigate drought's multifaceted impacts.