## 1 Introduction  
Drought is a natural disaster characterized by insufficient water resources in a given region to meet demand, typically associated with periods of low precipitation [1; 2]. In addition to the widely accepted simple definition, drought can also be described as a complex process involving trends of soil moisture depletion, reduced streamflow, and declining groundwater levels, often accompanied by increasing temperatures [3]. This situation leads to negative impacts on ecosystems, agriculture, water resources, and human life [4]. Drought can be classified in several ways: meteorological drought as a result of periods of below-average precipitation [5]; hydrological drought, which occurs due to reduced surface water flows [6]; agricultural drought, caused by insufficient soil moisture to support normal crop development [7]; and socioeconomic drought, which affects human lives and leads to economic disruption [8]. These are conventional classifications, but today the terms flash drought, characterized by rapid onset and assessed on a 5-day basis [9], and ecological drought, characterized by reduced water availability leading to reduced vegetation activity (with consequent disruption of ecosystem services) [10; 11], are also commonly used. Each of these types (except socioeconomic) is defined and assessed based on specific hydroclimatic variables [12], while socioeconomic drought is related to local water supply and monitors water demand through socioeconomic systems [13]. As a natural disaster with increasing costs each year, drought has a significant and widespread impact on many economic activities and people at any given time [14; 15]. Due to its complex and nonlinear nature, the mathematical characterization of drought events remains a substantial challenge. Dynamic drought processes are often simplified by defining drought indices, which are simplified formulae derived by analyzing long-term trends of variables like precipitation, evaporation, and temperature using statistical methods [16]. Drought indices, which can characterize drought in terms of duration, severity, and intensity [17], are indispensable in drought assessment as they facilitate drought risk analysis and monitoring by comparing current and future drought characteristics with past events [18]. Some of the drought indices commonly used in the literature include the Standardized Precipitation Index (SPI) [19], Standardized Precipitation Evaporation Index (SPEI) [20], Palmer Drought Severity Index (PDSI) [21], Soil Moisture Anomaly (SMA) [22], Evaportranspiration Deficit Index (ETDI) [23], Streamflow Drought Index (SDI) [24], Enhanced Vegetation Index (EVI) [25], Normalized Difference Vegetation Index (NDVI), Drought Reconnaissance Index (DRI) [26], Evaporative Demand Drought Index (EDDI) [27], and Effective Drought Index (EDI) [28], all of which were designed with different drought impacts in mind. These indices can represent a single class of drought, while some can represent multiple classes of drought. For instance, SPI is commonly used to define meteorological drought, SDI for hydrological drought, SMA for agricultural drought, and PDSI for both meteorological and agricultural drought [29]. These indices may be effective for describing particular drought features, but were not necessarily designed to incorporate all drought impacts. Drought indices can be classified as single, multivariate, or combined based on the number of parameters they use. While single drought indices are easier to compute, their success in reflecting drought conditions may be less than that of multivariate and combined indices [30]. Multivariate indices are derived by combining different variables. For example, the SPEI uses both precipitation and evapotranspiration values, while the PDSI includes precipitation, temperature, and water-holding capacity of soil.