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    Analysis of drought intensity, frequency and trends using the spei in Turkey
    (Springer, 2023) Serkendiz, Hıdır; Tatlı, Hasan; Kılıç, Ayşegül; Çetin, Mahmut; Sungur, Ali
    This study addresses into the critical issue of drought as a natural disaster, especially in regions characterized by arid and semi-arid climates like Turkey. The primary aim of this study is to investigate the historical occurrences of meteorological drought events in Turkey, focusing on their past frequency, intensity, and spatial distribution. The study employs the Standardized Precipitation Evapotranspiration Index (SPEI) method and utilizes 50 years of monthly temperature and precipitation data collected from 222 meteorological stations across the country. Drought severity is assessed using the run theory method, and trends in drought patterns are analyzed through the Mann–Kendall trend test. Additionally, the text explores the connection between elevation and the geographical distribution of drought events. The study’s findings reveal a noticeable increase in the occurrence of drought periods over time. Among the selected periods, the most widespread drought event was observed in the year 2001. The Bozcaada meteorology station exhibited the highest frequency of drought with a value of 223, while the Ispir meteorology station recorded the lowest frequency with a value of 151. Over the course of the 50-year analysis, no significant correlation was found between drought and elevation, although a gradual increase was noted in the last 10 years. The results also indicate a gradual north-to-south increase in drought intensity in Turkey. The study identifies four distinct drought hotspots in the country: the Western Anatolia Region, Central and Southern Anatolia Region, Southeastern Anatolia Region, and Eastern Anatolia Region.
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    Machine learning and geographic information systems-based framework for multidimensional analysis of cascading drought impacts using remote sensing and in-situ data
    (Elsevier B.V., 2025) Serkendiz, Hıdır; Tatli, Hasan; Özelkan, Emre; Çetin, Mahmut
    This study proposes a multidimensional conceptual framework to assess the cascading impacts of drought on the agricultural sector. The framework consists of four interconnected components: the triggering hazard, biophysical drivers, socio-ecological impacts, and socio-economic outcomes. To demonstrate its applicability, the framework was applied to the Konya Closed Basin, a drought-sensitive agricultural region in central Türkiye. The study integrates remote sensing indicators (NDVI, NDWI, LST, and land cover), ground-based observations (precipitation, temperature, groundwater levels), and statistical trend analyses (Mann-Kendall) to characterize drought dynamics and land use transitions. Machine learning algorithms were used to model land use change: drought-related indicators such as NDVI, NDWI, LST, and Palmer Drought Severity Index (PDSI) served as input variables, while Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Random Forests (RF) were applied as classification tools to predict land cover changes over time. Between 1990 and 2018, approximately 510,000 ha of irrigated land, including rice fields, were converted into non-irrigated areas. Despite this trend, the production of water-intensive crops such as maize and sugar beet continued to rise, indicating a maladaptive trajectory in agricultural practices. This mismatch between environmental constraints and production patterns highlights unsustainable water use and signals potential long-term risks to both water and food security. The proposed framework not only enhances understanding of cascading drought impacts but also offers critical insights for adaptive agricultural and water governance, supporting evidence-based policymaking in climate-vulnerable regions. © 2025 Elsevier B.V.
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    Multidimensional assessment of agricultural drought vulnerability based on socioeconomic and biophysical indicators
    (Elsevier, 2023) Serkendiz, Hıdır; Tatlı, Hasan; Özcan, Hasan; Çetin, Mahmut; Sungur, Ali
    Agricultural drought threatens Turkiye's food security and economy, revealing a lack of multidimensional provincial vulnerability studies. Therefore, the primary objective of this study is to evaluate Turkiye's vulnerability to drought utilizing a multidimensional strategy incorporating biophysical and socio-economic indicators. In this context, an initial step involved the development of a conceptual framework for drought vulnerability, drawing upon the Intergovernmental Panel on Climate Change recommended elements of exposure, sensitivity, and adaptive capacity. Subsequently, this conceptual framework was applied at the provincial level, encompassing parameters such as temperature, precipitation, soil characteristics, water resources, agricultural earnings, demographic traits, educational levels, innovation, insurance coverage, and road density. The Aridity Index was used as an indicator for drought exposure, representing the physical dimension of drought. Under the sensitivity component, a total of 14 variables were defined under the indicators of soil, water and land resources, economy, and population, while under the adaptive capacity component, a total of five variables were identified. The weights of these indicators and variables were determined based on expert opinions using the Analytic Hierarchy Process method. As a result, five different indices were obtained by combining the indicators: Exposure Index, Sensitivity Index, Adaptation Capacity Index, Potential Drought Impact Index, and Agricultural Drought Vulnerability Index. In addition, spatial distribution models of these indices were also generated. Research results showed that the climatic regions most exposed to drought are also the most vulnerable. The Central, Southeastern, and Eastern Anatolian regions constitute the regions where the most vulnerable provinces are concentrated spatially.