| چکیده انگلیسی مقاله |
Abstract
Background and Objectives
Drought is typically characterized as a temporary deficit in water resources relative to normal regional conditions. This slow-onset phenomenon occurs across all climate zones but exhibits complex structures and region-specific impacts. Extreme droughts are distinguished by their intensity in severe or very severe classes. In drought research employing conventional indices, the identification of extreme droughts depends on analysis across different time scales. The concept of super-drought extends this framework by considering drought occurrences across multiple timescales, providing a broader perspective beyond extreme droughts. Identifying superdroughts involves statistical integration of drought signals at various time scales. International studies on superd-roughts—initiated nearly a decade ago—have significantly advanced our understanding of drought dynamics. This phenomenon is crucial as it reflects the prolonged impact of droughts on regional water and ecological systems. Consequently, this study seeks to explore the potential occurrence of super-droughts, analyze their characteristics, and assess their effects on vegetation, with Khuzestan Province serving as the study region.
Materials and Methods
This research utilizes daily meteorological data—including precipitation, temperature, relative humidity, wind speed, and sunshine hours—from five synoptic stations in Khuzestan Province: Ahvaz, Bandar Mahshahr, Masjed Soleyman, Safiabad Dezful, and Bostan, covering the period 1990–2019. Additionally, NDVI data obtained from the MODIS satellite (Terra sensor, product MOD13A3, spatial resolution of 1 km²) spanning 2000–2019 was analyzed. For each station, an 81-pixel region (9×9 grid) was selected, and the median NDVI value was calculated monthly to serve as the vegetation index. Following quality control and gap filling, the Standardized Precipitation-Evapotranspiration Index (SPEI) was computed across five time scales: 3, 6, 12, 24, and 48 months. Subsequently, the Composite Drought Index (CDI) was derived by applying the Vine copulas technique to these SPEI scales, a method capable of capturing the complex dependence structure among the indices. The relationship between CDI and NDVI was then statistically modeled at each station.
Results
Findings indicated that the Vine copulas method effectively analyzed the dependence across the five SPEI time scales. The resulting CDI enabled the identification of superdrought events within the study area. While some stations experienced similar drought periods, no singular dry spell was common across all five stations. For example, the most intense drought episodes were observed at Masjed Soleyman in December 2001, and at Ahvaz, Bandar Mahshahr, and Safiabad Dezful in various other years.
The correlation between CDI and NDVI was predominantly positive and statistically significant throughout most months, with the highest correlations at Safiabad Dezful between February and April (r > 0.7). At Bostan, this positive relationship persisted nearly year-round. Notably, during the hotter months at Safiabad Dezful, a significant negative correlation emerged, indicating that heightened thermal stress can weaken vegetation even amidst drought conditions.
Conclusion
The study demonstrates that the Vine ccopulas method, by integrating multi-scale SPEI data and accounting for their complex interdependencies, effectively identified superdrought events over a 30-year period at the selected stations. In contrast, a straightforward comparison of SPEI values across scales, without the Vine approach, revealed only a limited number of superdroughts, with Bostan’s station showing none. The CDI showed a strong correlation with NDVI, confirming its utility in detecting drought-related vegetation stress. Generally, increased drought intensity corresponded with decreased vegetation greenness, attributable to water deficits, although during certain hot months, vegetation greenness increased due to thermal stress effects. Future research should consider the sensitivity of CDI to the specific thresholds of drought occurrence, as well as its dependence on the underlying SPEI scales, to refine drought monitoring and ecological impact assessments. |