Abstract
Drought is a recurring climate phenomenon that naturally occurs in all climate regions and leads to prolonged periods of water scarcity. The primary cause of water shortages is inadequate precipitation, which can be influenced by meteorological factors such as temperature, humidity, and precipitation patterns. Effective drought mitigation policies necessitate the monitoring and prediction of drought. To determine the severity and impacts of droughts accurately and precisely, probabilistic models have been developed. However, erroneous drought detection with probabilistic models is always possible. As a result, a novel system for meteorological, agricultural, and hydrological droughts based on the Stochastic Process (Markov chain (MC)) has been proposed to address this issue. The proposed method incorporates the Multi-Scalar Seasonally Amalgamated Regional Standardized Precipitation Evapotranspiration Index (MSARSPEI) for timescales 1-48 and employs temporal switched weights. These weights are generated from the Transition Probability Matrix (TPM) of each temporal classification of the drought type in accordance with the MC's fundamental assumption. The developed system was implemented on nine meteorological stations in Pakistan. By leveraging historical data and information, the system enables the categorization of droughts. The resultant classifications can be incorporated into effective drought monitoring systems, which can help in devising specific policies to alleviate the effects of droughts.