Document Type : Research Paper
Author
Assistant Professor, Department of Geography, Faculty of Letters and Humanities, Ferdowsi University of Mashhad, Mashhad, Iran
Abstract
Keywords
Main Subjects
Anthropogenic climate warming has profoundly disrupted the Earth’s climate system, driving unprecedented shifts across the global hydrological cycle. Perhaps the most striking manifestation of these disturbances is the escalating frequency, intensity, and duration of hydroclimatic extremes most notably, precipitation events. Classical paradigms long held that arid regions were destined for relentless desiccation. Yet, recent empirical observations contradict this assumption, revealing a sharp upward trajectory in daily precipitation extremes within these very environments. Such erratic climate behavior poses a severe threat to sustainable societal development. When these anomalous events strike, they often inflict irreversible damage upon ecological networks, public health frameworks, and critical economic infrastructure.
Encompassing a vast expanse of the Middle East’s arid and semi-arid belt, Iranian watersheds harbor a hydrological that is exquisitely sensitive to climate perturbations. Driven by an accelerated water cycle and shifting rainfall patterns, these geographic zones now confront an escalating risk of catastrophic flooding. It is a fundamental tenet of hydro climatology that watersheds possess an inherent vulnerability to extreme, torrential downpours. However, systematically assessing this vulnerability across Iran is notoriously difficult. The country’s vast territorial extent and formidable topographic complexity are further compounded by the absence of a dense, spatially continuous network of ground-based meteorological stations.
To navigate these structural uncertainties, the scientific community must pivot. Relying on subjective methodologies is no longer tenable; transitioning toward fully objective, data-driven frameworks has emerged as an absolute necessity. Motivated by this critical research gap, the present study seeks to elucidate the spatial patterns and magnitude of watershed vulnerability across Iran in response to extreme precipitation. We deployed the Shannon Entropy approach to operationalize this objective. By mathematically evaluating the internal dispersion and information content of the datasets, this technique eliminates subjective human judgment, thereby ensuring rigorous and entirely unbiased weighting. In this study, precipitation extremes were processed and modeled over a twenty-year (2001–2020) utilizing GPM-IMERG datasets. Ultimately, this approach yields a comprehensive, integrated spatial representation of watershed vulnerability one that is firmly grounded in the actual physical behavior of regional climate systems.
In this study, a long-term two decades (2001–2020) was selected to analyze extreme precipitation events in Iranian watersheds, utilizing GPM-IMERG satellite data. The evaluation of satellite-derived datasets like GPM-IMERG critically depends on their consistency with a standardized ground-based monitoring network. The reference data employed comprises 128 synoptic meteorological stations distributed across Iran’s catchment areas, ensuring comprehensive spatial coverage. The core aim of this research is to quantitatively and spatially assess the vulnerability of Iran’s watersheds to extreme precipitation events, grounded within a data-driven, multi-criteria framework. This approach encompasses three principal stages: (1) extraction and processing of precipitation extreme event indicators, (2) objective weighting of each indicator based on information theory, and (3) integration of these layers to generate a final vulnerability map. The spatial vulnerability analysis hinges predominantly on high-resolution precipitation data. Guided by recommendations from the Expert Team on Climate Change Detection and Indices (ETCCDI), four key indices were selected. The first, Rx1day, measures the maximum daily precipitation within the period, reflecting the intensity of extreme precipitation events. The second, R10mm, counts the number of days with heavy precipitation (≥10 mm), while R20mm tallies days with very heavy precipitation (≥20 mm). Finally, the Simple Daily Intensity Index (SDII), calculated as the total precipitation on rainy days divided by the number of rainy days, indicates the average daily precipitation intensity during precipitation events.
Between 2001-2020, the spatial distribution of annual precipitation across Iran exhibited a pronounced gradient, with values ranging dramatically from 58.9 to over 1100 mm. Such stark heterogeneity is primarily driven by the complex interplay of latitudinal positioning, orographic forcing, and proximity to moisture sources. The absolute precipitation maxima are heavily concentrated along the Caspian coastal belt. This humid zone encompasses the Talesh-Anzali Wetland, Haraz, and gharesoo basins, along with specific reaches of the Greater Sefidrud. Moving westward and into the southwest, a secondary moisture core emerges. Here, the Karkheh, Karun, and Lake Urmia basins consistently record annual totals between 400 and 650 mm. Within these western catchments specifically Urmia, Karkheh, and Karun satellite-derived GPM-IMERG estimates demonstrate robust alignment with ground-based observations. This statistical fidelity is evidenced by Willmott’s index of agreement exceeding 0.8, and Kling-Gupta Efficiency (KGE) scores ranging from 0.66 to 0.92.
A defining hydrodynamic signature of Iran’s western and southwestern margins is the compounding effect of frequent and highly intense extreme precipitation events. The epicenters of this spatial clustering are localized within the Karkheh, Karun, and Hendijan-Jarrahi watersheds. According to the GPM-IMERG dataset, these zones experience heavy (R10mm) and very heavy (R20mm) precipitation on 20 to 25 days and 8 to 12 days annually, respectively. Such elevated occurrence rates coincide with maxima in standard intensity metrics. For instance, maximum one-day precipitation (Rx1day) yields figures spanning 65 to 82.5mm while the Simple Daily Intensity Index (SDII) easily surpasses the 9 to 11.9 mm/day threshold. To rigorously assess catchment vulnerability to these hydro-climate extremes, Shannon entropy was applied to the GPM-IMERG data as an objective weighting operator. This approach yields a highly resolved delineation of regional risk profiles. Because Shannon entropy fundamentally roots its weight allocation in the spatial heterogeneity of extreme indices, it effectively models vulnerability as a continuously distributed spatial variable. Consequently, regions characterized by the steepest spatial gradients naturally receive the highest computational weights. Analytical outputs from this procedure indicate that low and moderate vulnerability classifications account for 46.5% and 30% of the national territory, respectively. Conversely, the most acute risk zones encompassing 23.5 % of Iran’s total landmass are predominantly anchored along the western foothills and the southern coastal strips.
Defining the Core Task
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Refining Language Style
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Analyzing the Persian Source
Examining the Source Material
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Mapping Precipitation Patterns
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Analyzing Agreement Results
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Localizing Spatial Clustering
Quantifying Precipitation Frequency
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Assessing Extreme Occurrence
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Modeling Spatial Vulnerability
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Interpreting Vulnerability Data
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Defining Geographic Distribution
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The results of this research resonate with the existing international literature across several distinct dimensions. When evaluating the fidelity of global precipitation datasets, the performance of GPM-IMERG within Iran strongly corroborates the findings of Herold et al. (2016) and Wang et al. (2021). Our observations confirm the acute sensitivity of precipitation intensity and duration metrics to the specific product utilized, its spatial resolution, and the localized physical dynamics of the passing weather systems. Shifting the focus to climate change impacts, we noted a marked intensification of single-day precipitation events across several of the country’s more arid, southern basins. Such localized strengthening mirrors the broader conclusions drawn by Yao et al. (2020) and Breugem et al. (2020), who documented the escalation of extreme precipitation driven by global warming. Furthermore, an examination of the nexus between extreme precipitation and hydrological hazards reveals a striking spatial overlap between highly vulnerable zones and historical flood occurrences. This spatial coherence effectively replicates the paradigm introduced by Rodrigues et al. (2020) within the Iranian context. It underscores a crucial methodological takeaway: translating extreme climate indices into actionable risk metrics provides a formidable tool for engineering vulnerability reduction strategies at the basin scale. Ultimately, this analytical approach holds significant practical utility. When aligned with the framework that Keller et al. (2021) characterize as iterative and integrated climate risk management, our methodology can actively direct regional adaptation efforts. It establishes a robust foundation for advancing early warning systems, reassessing the design parameters of hydraulic infrastructure, and optimizing land-use planning across the nation’s watersheds. By bridging the gap between theory and practice, the climatological insights derived from extreme event analysis can directly inform evidence-based policymaking, fostering both proactive hazard mitigation and the sustainable stewardship of water resources.
Research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
Declaration of Generative AI and AI-assisted technologies in the writing process
No artificial intelligence tools were used by the author(s) during the preparation of this work.
This declaration does not apply to the use of basic tools for checking grammar, spelling, references, etc. If there is nothing to disclose, there is no need to add a statement.
The data for the present study are publicly and freely available at https://gpm.nasa.gov/data/imerg.
The authors would like to thank anonymous referees for their constructive comments.
This study did not involve human participants or animals and therefore did not require ethical approval. The authors confirm that all ethical standards of research, including avoidance of data fabrication, falsification, and plagiarism, were fully observed.
The authors declare that they have no conflict of interest.
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