Extended abstract
Introduction
In recent decades, droughts have intensified globally, affecting diverse climatic regions with varying characteristics. Rising global temperatures alter climatic components like precipitation, evapotranspiration, and soil moisture, impacting river regimes and exacerbating water scarcity. Droughts are categorized into meteorological, agricultural, hydrological, and socio-economic types, with interconnected impacts on groundwater depletion, agricultural productivity, drinking water access, and socio-political stability. In arid/semi-arid regions like Iran, groundwater is critical for drinking, agriculture, and economic development, necessitating advanced management strategies to address spatial-temporal variability. Studies emphasize analyzing hydrological and climatic trends using parametric/non-parametric methods. The non-parametric Mann-Kendall test is widely used for trend detection in non-normal data, while Discrete Wavelet Transform (DWT) decomposes time-series data to identify non-stationary patterns. Global Land Data Assimilation System (GLDAS) data (25 km resolution), developed by NASA and NOAA, enables large-scale analysis of land surface parameters (e.g., soil moisture, evaporation) using models like CLM and Noah. This study employs GLDAS data, Mann-Kendall tests, and 1D wavelet analysis to assess hydroclimatic trends across Iran’s climates and major basins. By integrating statistical and signal-processing methods, it aims to decode drought frequency oscillations in meteorological, hydrological, and groundwater indices, offering insights for policymakers to optimize water management amid climate change. The research distinguishes itself through national-scale analysis, frequency-domain insights from satellite data (vs. station-based studies), and simultaneous evaluation of broad trends and granular fluctuations, aiding climate adaptation and drought forecasting.

Methodology
In this study, data were initially obtained from the Google Earth Engine platform. Annual precipitation data for a 20-year period from 2003 to 2022 were extracted using CHIRPS, which offers a temporal resolution of five days. CHIRPS data are specifically designed for climate and drought management studies, covering the period from 1981 to present. Due to the absence of precipitation data before 2003 in GLDAS, CHIRPS was selected. Additionally, GLDAS data for groundwater level, soil moisture, and surface runoff were acquired for the same period at a 25 km spatial resolution. These variables are directly linked to the water cycle and are crucial for water resource planning, drought management, climate change prediction, and policy-making. Daily and five-day data were aggregated into monthly scales using MATLAB and then processed in ENVI software for the entire study period. The Mann-Kendall non-parametric test was employed to detect trends and non-stationarity in the hydroclimatic time series. To analyze the nature and patterns of changes, one-dimensional discrete wavelet transform (DWT) was applied. Wavelets are powerful mathematical tools for decomposing signals and time series, enabling identification of short- and long-term trends and fluctuations. Daubechies wavelet, well-suited for precipitation data, was chosen as the mother wavelet. DWT decomposed the 12-month meteorological, hydrological, and groundwater drought indices of Iran’s climates into eight levels of detail and approximation components. This approach facilitated detailed trend analysis of drought indices over the study period, providing valuable insights into hydroclimatic variability and drought dynamics.

Results
The trend analysis using the Mann-Kendall test revealed that precipitation exhibited a decreasing, though statistically insignificant, trend across most regions. Similarly, actual evapotranspiration, soil moisture, and groundwater levels showed significant decreasing trends, consistent with the decline in precipitation. Overall, the results indicate that precipitation, or meteorological drought, is the primary hydrometeorological component, acting as the starting point for other types of droughts. Wavelet analysis of drought trends across different climates in Iran showed that the trend of the Approximation frequency, reflecting low-frequency changes in the original signal, was similar across all climates. This suggests a common change pattern nationwide. The Approximation frequency displayed an increasing trend for all drought types, indicating that drought frequency has intensified in recent years and is likely to continue increasing. The Detail frequency analysis revealed that fluctuations in the SPI (Standardized Precipitation Index) and SSI (Standardized Soil Moisture Index) were significantly greater in wetter climates than in drier regions. In dry areas, fewer details were observed in the trends of these indices, likely due to greater rainfall variability, larger water resources, and complex hydrological structures in wetter areas. Changes in groundwater levels were less pronounced compared to other indices, resulting in fewer details in both high- and low-frequency trends. This can be attributed to the delayed response of groundwater to rainfall or meteorological drought. In contrast, hydrological drought, specific to surface water resources, showed substantial fluctuations due to the strong dependence of surface water on precipitation in most regions.

Discussion and Conclusion
Iran’s climate is influenced by the subtropical high-pressure system, a dynamic warm air system that has intensified in strength and extent due to global warming. This expansion reduces precipitation in affected areas because these high-pressure systems induce descending air currents, which inhibit cloud formation and rainfall while increasing evapotranspiration. Although global warming has raised temperatures in Iran, actual evapotranspiration has decreased over the past 20 years due to declining precipitation. Groundwater levels have also declined, primarily driven by reduced rainfall alongside factors like over-extraction, illegal wells, and industrial use in low-precipitation regions. Overall, precipitation or meteorological drought is the primary hydroclimatic factor influencing other drought types. Wavelet analysis of drought indices (SPI and SSI) revealed that fluctuations are more pronounced in humid climates compared to arid ones, likely due to greater rainfall variability, more abundant water resources, and complex hydrology in wetter regions. In contrast, arid areas show fewer fluctuations and details due to limited water sources. Groundwater changes exhibited minimal variability, reflecting delayed responses to drought and greater stability across climates. Surface water drought showed significant variability, influenced by its strong dependence on rainfall and sensitivity to short-term events like floods or dry spells. This dynamic nature causes surface water to respond rapidly to climatic changes. The study’s findings help identify critical regions vulnerable to reduced rainfall, increased evapotranspiration, groundwater depletion, and surface water fluctuations. Despite the lower resolution of satellite data compared to ground observations, this research provides valuable insights for water resource management, drought and flood risk planning, agricultural development, and land-use planning at a national scale.