Abdullah Muratoglu

Artan nüfus, değişen tüketim alışkanlıkları ve iklim değişikliği su yönetimini küresel ölçekte kritik hale getirmiştir. Uluslararası ticaret yoluyla üretimde kullanılan su ülkeler arasında dolaylı olarak transfer edilmekte, bu durum özellikle su stresi yaşayan ülkeler açısından stratejik önem taşımaktadır. Bu çalışmada, 2015–2024 döneminde Türkiye’nin 29 stratejik tarımsal ve hayvansal ürün bazında uluslararası mavi ve yeşil sanal su akışları analiz edilmiştir. Bulgular, Türkiye’nin yıllık ortalama 20 milyar m³ sanal su ihracatı ve 35,6 milyar m³ sanal su ithalatı ile net sanal su ithalatçısı olduğunu göstermiştir. İhracatın %86’sı yeşil sudan oluşurken, net dengenin %32’sini mavi su oluşturmaktadır. Bu durum, Türkiye’nin ihracatını büyük ölçüde yağışa dayalı üretimle gerçekleştirdiğini, sulama suyu gereksinimi yüksek ürünleri ise ithalat yoluyla karşıladığını göstermektedir. Sanal su ihracatının %29’u Irak’a yönelirken, ithalatın %42’si Rusya’dan yapılmaktadır. Ürün bazında buğday unu ve ayçiçeği yağı toplam sanal su ihracatının %50’sini oluştururken, pamuk lifi mavi su ithalatının %60’ından fazlasını tek başına karşılamaktadır. Sonuçlar, Türkiye’nin yeşil su ağırlıklı ticaret yapısı sayesinde avantaj sağladığını; ancak mavi su yoğun ürünler, tedarik bağımlılığı ve yıllar arası dalgalanmalar nedeniyle su ve arz güvenliği açısından potansiyel riskler barındırdığını göstermektedir. Sanal su yönetiminin dış ticaret ve tarım politikalarıyla bütüncül ele alınması gerekmektedir.

Accurate estimation of reference evapotranspiration (ET ₀ ) and crop coefficients (K _c ) is critical for irrigation planning, particularly in data‐limited regions where agriculture dominates freshwater consumption. Although machine learning (ML) methods have been widely applied to ET ₀ and K _c estimation, most studies address these parameters separately or focus on a single seasonal K _c value rather than stage‐specific coefficients. This study presents an integrated machine learning framework that simultaneously estimates monthly ET ₀ and wheat stage‐specific crop coefficients (K _{c_ini} , K _{c_mid} , K _{c_end} ) using long‐term district‐level meteorological data from Türkiye. A total of 19 algorithms, including linear models, tree‐based ensembles, boosting methods, support vector machines (SVR) and deep learning models, were evaluated using 10‐fold cross‐validation and standard performance metrics. The results showed that linear models provided the most consistent performance for monthly and annual ET ₀ estimation ( R ² ≈0.85–0.87), while nonlinear ensemble models achieved superior accuracy for K _c prediction ( R ² > 0.95). Feature importance analysis identified solar radiation and temperature as dominant ET ₀ drivers, whereas elevation and relative humidity were key controls for stage‐specific K _c . The proposed framework provides a scalable approach for regional agricultural water management and supports model selection under data‐limited conditions.

Climate change and anthropogenic pressures significantly affect Mediterranean water resources, necessitating robust trend analysis methods for effective water management. This study presents a comprehensive assessment of hydrometeorological trends in Türkiye’s largest freshwater lake system, the Beysehir Basin (1981–2016), using a multi-method analytical framework. Traditional approaches (Mann–Kendall test and Sen’s slope estimator) were integrated with advanced techniques (Innovative Trend Analysis, Innovative Polygon Trend Analysis, IV-ITA, and Star-IPTA) to evaluate trends in precipitation, temperature, streamflow, lake water level, and groundwater level. The results reveal statistically significant warming trends at both stations, particularly during the summer months. The strongest increase was observed in summer at Çumra (τ = 0.565, p < 0.0001) and Beyşehir (τ = 0.470, p < 0.0001), with additional significant increases in spring, autumn, and annual temperature series ( p < 0.05); However, winter trends were generally not significant. Precipitation trends exhibited considerable spatial variability at the Beyşehir station, September precipitation showed a significant increasing trend under the Yue and Wang (2004) approach (τ = 0.231, p = 0.007), while spring precipitation at Çumra demonstrated a significant decreasing trend (τ = −0.205, p < 0.05). Annual precipitation trends were not consistently significant across stations. Lake water levels displayed consistent declining trends across multiple temporal scales, and groundwater levels indicated systematic depletion (Sen’s slope: 0.382–0.435 m/yr; Mann–Kendall τ: 0.778–0.86; ITA: 0.459–0.511). A comparative evaluation demonstrated that the innovative methods provided enhanced sensitivity in detecting subtle hydrometeorological changes compared with traditional approaches. Regular polygon patterns in temperature and groundwater analyses contrasted with more irregular precipitation patterns, reflecting distinct environmental response mechanisms. Overall, these findings highlight the increasing vulnerability to water scarcity in the Beyşehir Basin and underscore the urgent need for adaptive water management strategies in Mediterranean watershed systems. The proposed methodological framework provides a replicable template for comprehensive trend assessment in regions experiencing similar climatic and anthropogenic pressures.

This study explores the concept of “virtual water” as a new dimension in the sustainable management of global water resources and highlights its strategic significance. Amid the growing impacts of climate change and increasing water scarcity, the virtual water approach—which accounts not only for the physical presence of water but also its indirect use in production processes—has gained prominence. The study examines the conceptual framework of the water footprint methodology, detailing its blue, green, and grey water components, and analyzes the role of virtual water in globalization and international trade. While assessing the economic and geopolitical aspects of virtual water transfers, the paper also addresses Türkiye’s virtual water flows, opportunities for water savings, and risks associated with external dependency. Furthermore, it investigates the integration of virtual water management with circular economy principles and underscores the strategic importance of water resources within the goals of the European Union’s Green Deal. In this context, the study offers concrete policy recommendations for integrating the virtual water approach into national policies, promoting water-efficient agricultural production, incorporating water footprint assessments into decision-making processes, and enhancing intersectoral coordination. In conclusion, the virtual water approach provides a strategic framework that goes beyond traditional water management by evaluating water-related interactions across sectors and nations through a holistic lens.

Agricultural activities account for the majority of global freshwater consumption, prompting extensive research into strategies for reducing water demand in the sector. Crop pattern optimization has emerged as a key strategy for achieving significant water savings. However, most existing studies prioritize yield maximization over minimizing water consumption. Additionally, water-based optimization efforts often rely on low temporal resolution data and rarely offer practical recommendations for crop pattern design. This study aims to evaluate water-saving potential in agriculture through crop pattern optimization based on water footprint (WF) assessments, focusing on the Ceyhan River Basin—an important hotspot for diverse crop production in Türkiye. The method effectively balances blue, green, and grey water components by integrating high-resolution spatial WF data from the Soil and Water Assessment Tool (SWAT) hydrological model into a multi-objective optimization framework. We developed six scenarios: Scenario S1 prioritizes total WF reduction, while Scenario S6 focuses solely on blue WF, with intermediate scenarios representing linear blends of these priorities. The results show that Scenario S1 achieves a 23.4% reduction in total WF (614 million m³)—comparable to the annual wheat consumption of 1.8 million people—whereas Scenario S6 attains a 47.9% decrease in blue WF (464 million m³), potentially meeting the daily water needs of 5.6 million individuals. Notably, an intermediate scenario (S5) provides a balanced reduction across WF components with a more uniform crop distribution, enhancing its practical appeal. Recognizing that farmer practices and economic concerns may hinder implementation, we recommend establishing targeted incentive mechanisms—such as financial support for farmers who adopt optimized cropping patterns—to facilitate the transition toward water-efficient practices. Overall, this research offers a robust decision-support tool for policymakers and water resource managers, providing clear guidance for aligning agricultural practices with sustainable water management and enhancing food and water security in water-scarce regions.

The intensifying effects of climate change on soil hydrology and agricultural water use require comprehensive understanding for sustainable maize production, a key crop in Türkiye's agricultural system. This study combines empirical trend analysis with AquaCrop model simulations under the RCP 4.5 climate change scenario to investigate the complex interactions between climate change, soil-water dynamics, and agricultural water footprint (WF) of maize cultivation from 2004 to 2022 in a major agricultural region (Akşehir) of Türkiye, providing critical insights for irrigation management and food security.Through Mann-Kendall tests and Sen's slope estimators, coupled with AquaCrop model simulations, we identified considerable climate change-induced shifts in hydroclimatic parameters affecting soil-water relationships. Reference evapotranspiration (ET₀) showed a significant decreasing trend (τ = -0.43) with a decline of 2.7 mm/year, while crop evapotranspiration (ETc) exhibited an even stronger declining pattern (τ = -0.58) with a decrease of 2.8 mm/year. These trends occurred against a backdrop of significantly increasing atmospheric CO₂ concentration (τ = 1.000) with an annual increase of 2.1 ppm/year.The analysis of WF components revealed promising trends for sustainable water management under changing climate conditions. The unit blue WF showed a significant decreasing trend (τ = -0.35) with an annual reduction of 4.27 m³/ton, indicating improved irrigation efficiency, while the unit total WF demonstrated a strong declining trend (τ = -0.58) with a decrease of 2.7 m³/ton/year. Although the unit green WF showed a slight increasing trend (τ = 0.17) with an annual increase of 1.17 m³/ton, this shift from blue to green water use suggests a positive transition toward more sustainable water management practices in a changing climate. This favorable redistribution of water sources, combined with improved irrigation efficiency, has supported agricultural productivity, as evidenced by the marginally significant increasing trend in maize production (τ = 0.39).While these findings demonstrate successful adaptation of maize cultivation systems to changing climatic conditions in our study region under the RCP 4.5 scenario, broader country-level and global analyses are essential to understand geographic variations in water productivity and potential shifts in agricultural suitability under climate change. These spatially explicit insights would be valuable for developing targeted adaptation strategies and ensuring sustainable food production across different agro-ecological zones. Our results highlight the importance of regional-scale studies in understanding climate-water-crop interactions and emphasize the need for integrated approaches to enhance agricultural water productivity while maintaining environmental sustainability in the face of accelerating climate change.*Key Words:* Climate change, water footprint, AquaCrop model, soil hydrology, maize production

The agricultural sector plays a vital role in food security and water resource management. Climate change impacts, combined with growing population and increasing food demand, have led to higher plant water consumption, making effective water management crucial in agriculture. This study examines how climate change affects agricultural water footprint (WF) in Türkiye from 1990 to 2019, along with local climate parameters, production quantities, and yield data.Our research shows distinct climate change patterns in Türkiye: slight decreases in average wind speed and solar radiation, a significant decline in relative humidity, and a clear upward trend in maximum and minimum temperatures. While reduced wind speed and solar radiation may slightly decrease plant water consumption, the higher temperatures and lower humidity likely have more substantial negative effects on evapotranspiration. Importantly, we found that crop yield is the main factor influencing agricultural WF variations in Türkiye. Despite climate challenges, technological advances and better farming practices led to around 60% increase in crop yield. This improvement reduced virtual water content (VWC) of crops by 35% and decreased the country's total agricultural WF by around 10%. However, the relatively small reduction in WF compared to the significant improvements in yield and VWC indicates the strong influence of climate change and changing crop patterns.Although the national agricultural WF exhibits a modest declining trend over the 30-year period, indicating improvements in water resources, climate change continues to pose significant challenges. Since such substantial yield increases are unlikely to continue, climate change impacts on WF are expected to worsen. These findings highlight the critical need for comprehensive water management strategies to address climate change effects and maintain sustainable water resources in Türkiye.