Analysis of meteorological drought characteristics in Iran using high-resolution TerraClimate data and the runs theory

Extended Abstract

Introduction

Drought is one of the most significant natural hazards in arid and semi-arid regions, affecting water resources, agriculture, vegetation, and human health. Iran, located in the arid belt of the world, frequently experiences severe and prolonged droughts, which have intensified in recent decades due to climate change and precipitation variability. Assessing drought characteristics and monitoring is essential for effective water resource management and risk reduction. Drought can be classified as meteorological, agricultural, or hydrological, depending on the component of the hydrological cycle affected. Traditional drought monitoring relies on sparse ground-based station data, which often has limited coverage and spatial resolution. High-resolution gridded climate datasets, such as TerraClimate, provide long-term monthly data on precipitation, temperature, evapotranspiration, and other hydrological variables, overcoming the limitations of sparse station networks. The Standardized Precipitation Evapotranspiration Index (SPEI), a widely used meteorological drought index, integrates precipitation and potential evapotranspiration to quantify drought intensity and duration more realistically, particularly under changing climatic conditions. Event-based approaches, such as the Runs Theory, enable the identification and characterization of drought episodes, including their duration, intensity, magnitude (severity), and interevent intervals. This study applies SPEI and the runs theory to high-resolution TerraClimate data (1985–2024) to assess drought characteristics across Iran. At the national scale, this framework enables detailed spatiotemporal analysis of short-, medium-, and long-term droughts, providing valuable information for water management, agricultural planning, and climate adaptation strategies.



Materials and Methods

TerraClimate gridded data (1985–2024), comprising monthly precipitation and potential evapotranspiration at a spatial resolution of 1/24° (approximately 4 km), were employed. The SPEI at pixel level was computed at 3-, 9-, and 12-month timescales to evaluate short-, medium-, and long-term drought events. Calculation involved: (1) derivation of the monthly climatic water balance (precipitation minus potential evapotranspiration); and (2) standardisation using a three-parameter log-logistic probability distribution and transferring the probability value to a normal distribution. Drought events were delineated using the run theory, with monthly percentile thresholds applied to account for seasonal variability and consecutive drought periods. Principal drought characteristics included duration, magnitude or severity, intensity, inter-event intervals, and event frequency over the period 1985–2024. Trend analysis used the modified Mann-Kendall test to identify significant spatiotemporal changes in SPEI, with serial correlation adjusted for. All analyses were performed in Python, using a raster-based dataset to ensure comprehensive spatial coverage and to detect localized patterns that are often undetected by station networks. This integrated approach—combining multi-timescale drought assessment, event-based characterisation, and trend detection—provides a thorough evaluation of drought risk and dynamics across Iran's arid, semi-arid, and relatively humid regions.



Results and Discussion

Drought conditions in Iran intensified in duration, severity, and spatial extent from 1985 to 2024, exhibiting considerable regional heterogeneity. Seasonal or short-term (3-month) droughts occurred frequently in northern Iran, whereas the central, eastern, and southeastern arid regions experienced longer and more intense droughts. At the 9-month timescale, droughts extended regionally, revealing persistent water deficits in the central and eastern areas. Annual or long-term (12-month) droughts affected nearly the entire country, sparing only narrow northern coastal zones, and underscoring widespread hydrological stress. Analyses of cumulative severity and intensity indicated disproportionate impacts in central and southeastern Iran, aligning with prior reports of elevated drought risk in these zones. Event frequency revealed that arid regions experienced fewer but more severe and persistent droughts, suggesting delayed recovery and accumulated hydrological deficits. The modified Mann-Kendall test detected significant negative trends in SPEI across more than 95% of the country at the 3-month timescale, over 99% at the 9-month timescale, and nationwide at the 12-month scale. These trends reflect a progression from localized seasonal droughts to pervasive national-scale phenomena, extending even to historically wetter northern areas. High-resolution gridded datasets demonstrated clear advantages over traditional station-based methods in resolving fine-scale and regional drought patterns. The combination of SPEI and run theory provides a robust framework for characterising drought properties, temporal evolution, and spatial variability, offering essential insights for water resource management and climate adaptation.



Conclusion

This analysis provides a comprehensive evaluation of drought characteristics and trends in Iran from 1985 to 2024, based on high-resolution TerraClimate data, SPEI, and run theory. Results show increased drought duration, intensity, and spatial extent, particularly in central, eastern, and southeastern regions, with a shift from seasonal events to persistent nationwide hydrological stress. Run theory enabled precise quantification of duration, severity, intensity, and inter-event intervals, highlighting limitations of station-based monitoring in resolving fine-scale dynamics. Nationwide significant negative SPEI trends underscore escalating hydrological drought and the need for multi-timescale, data-informed management approaches. The framework serves as an operational tool for early warning, climate adaptation, agricultural planning, and water allocation, with potential application to other arid and semi-arid regions worldwide. Integration of high-resolution gridded data, multi-timescale indices, and event-based analysis enhances resilience to climate variability and supports evidence-based policy for sustainable water and agricultural management. This transferable methodology facilitates broader national and regional drought risk assessment.