Ahmed Elbeltagi

Drought assessment in mountainous, hydroclimatically complex basins remains challenging due to limitations in single-index evaluations that commonly misrepresent plant-available moisture under variable atmospheric demand. The Aras–Kura Basin in Türkiye exemplifies this complexity, with seasonal dynamics of precipitation, snowfall, and potential evapotranspiration (PET) influencing agricultural and ecological drought risks. Existing studies predominantly rely on indices like the De Martonne Aridity Index (DMAI), which neglect PET, leading to oversimplified moisture classifications. This research addresses critical gaps by systematically comparing DMAI and the Thornthwaite Moisture Index (TMI), the latter incorporating PET, using high-resolution ERA5 reanalysis data spanning 1981–2020. Findings reveal significant discrepancies between DMAI and TMI, with DMAI frequently classifying nearly 35% of the basin as “humid” during summer while TMI identifies those areas as “arid” or “semiarid” due to elevated PET pressures. Seasonal analysis uncovers strong spring–summer humidity correlations ( r > 0.3, p < 0.05) in sub-basins (Tekman and Köprüköy), where spring precipitation compensates for winter drought. However, rapid snowmelt in April–May combined with spring PET increases rapidly depletes soil moisture, causing ephemeral drought episodes reflected in TMI but masked by DMAI. This dual-index framework improves detection of “hidden drought” conditions, supporting effective agricultural planning and adaptive water allocation. The novelty lies in fine-scale, temporal–spatial integration of DMAI and TMI, revealing limitations of traditional indices and guiding nuanced drought policy for the present study site. This work establishes a foundation for coupling with crop models, remote sensing, and socioeconomic assessments in the Aras–Kura Basin and comparable hydroclimatic regions worldwide.

Efficient water management is critical for sustainable chrysanthemum production under protected cultivation was conducted at Indian Agricultural Research Institute (IARI), New Delhi, India. While HYDRUS-based models are widely used for simulating soil water dynamics under drip irrigation, their application in floriculture crops remains limited, particularly for deriving crop-specific irrigation strategies. This study integrates field experimentation (2020–2023) with HYDRUS-2D simulations to evaluate soil water distribution under drip fertigation across multiple soil types. The effects of emitter discharge, irrigation scheduling, and soil hydraulic properties were analyzed to determine optimal root-zone moisture conditions. Results showed that soil moisture remained within the optimal range for up to 48 h after irrigation under appropriate scheduling. Among soil types, moisture retention followed the order: silt ≈ silty clay loam > loam > sandy clay loam > sandy loam. Model validation demonstrated high accuracy (R ² = 0.90–0.96; RMSE = 0.011–0.012; Ceff = 0.90–0.92), confirming the reliability of HYDRUS-2D. Simulation results indicated that an emitter discharge of 1.0 lph combined with a 48-hour irrigation interval maintained optimal root-zone moisture while minimizing deep percolation losses. The study highlights the potential of simulation-based approaches for optimizing irrigation design and improving water-use efficiency in greenhouse floriculture systems.

Water stress is a global challenge that severely impacts crop production by hindering essential physiological processes. To address this issue, proximal sensing has emerged as a promising technique for the early identification of stress in vegetables, enabling timely management interventions and optimizing yield. This study aimed to use RGB image indices and an artificial neural network (ANN) model to quantify the responses of various plant traits, such as fresh biomass (FB) weight, dry biomass (DB) weight, canopy water content (CWC), relative chlorophyll content (SPAD), soil moisture content (SMC), and tomato yield across different irrigation levels. Field experiments were conducted during the 2022 and 2023 growing seasons, capturing digital RGB images and measuring plant traits at the flowering and fruit-ripening stages. The results revealed that a reduced irrigation level led to a decrease in various plant traits. The study also revealed significant differences in RGB image indices between different irrigation levels, with strong positive relationships identified for the majority of RGB image indices incorporating green components (G) and R ² reaching 0.99 for various plant traits. However, the red-blue simple ratio (RB) index, which does not consider the G, did not significantly correlate with any of the plant traits. The ANN models achieved high prediction accuracy, with high R ² values reaching 0.99 for various plant traits and yields. These findings underscore the practicality and reliability of employing RGB imaging indices in conjunction with ANN models for effectively managing tomato crop growth and production, particularly under limited water conditions.

Climate change and population growth are intensifying water scarcity in arid regions, yet previous analyses focusing on a single driver may not fully capture the compounded effects of climatic and anthropogenic factors. This study integrates water-balance analysis, trend analysis, and correlation-based statistical analysis to examine the combined effects of hydroclimatic anomalies and socioeconomic activities on water resource dynamics in ecologically vulnerable Northwest China. Our results show that despite increasing precipitation, warming-associated increases in evapotranspiration, together with irrigation-based water use accounting for 89.8% of total consumption, have offset the potential runoff gains, suggesting that agricultural water use is a major anthropogenic contributor to regional water stress. Based on these findings and a comparative review of representative arid-region practices in Israel, Australia, and Saudi Arabia, we propose a technology-market-institution tripartite governance framework for Northwest China. This framework is intended to support more proactive adaptation in regional water management and to provide a context-specific reference for advancing SDG 6 and SDG 13 in dryland regions.

Accurate estimation of reference evapotranspiration (ETo) is critical for effective water resource management, irrigation planning, and climate adaptation, particularly in semi‐arid regions where water scarcity and climatic variability are pronounced. This study assessed the performance and temporal stability of four machine learning (ML) algorithms [Multilayer Perceptron (MLP), Random SubSpace (RSS), M5P model tree (M5P), and Random Forest (RF)] in estimating daily ET in the Iğdır Basin, one of eastern Türkiye's most productive yet hydrologically vulnerable agricultural zones. Using ERA5‐Land reanalysis data, the models were trained and validated across two distinct periods (1950–1999 and 2000–2024) to evaluate both historical accuracy and robustness under changing climatic conditions. Solar radiation (SR) was identified as the primary driver of ETo, highlighting the dominant role of radiative energy in modulating evapotranspiration variability, even under the predominantly water‐limited conditions of the basin's semi‐arid environment. A near‐perfect correlation between ETo and SR confirmed the dominant influence of solar energy on evaporative demand. Among the tested models, M5P and MLP achieved the highest predictive accuracy and maintained consistent performance across both periods, indicating strong generalizability and resistance to climatic non‐stationarity. In contrast, RS and RF exhibited higher error rates and reduced sensitivity to nonlinear interactions, limiting their applicability in dynamic environmental conditions. These results emphasise the ecological and hydrological relevance of energy‐based modelling approaches for drought‐sensitive regions. The integration of ERA5‐Land data with machine learning provides a scalable and data‐efficient alternative to conventional ETo estimation methods, particularly where in situ measurements are limited. The proven accuracy and stability of the M5P and MLP models offer a reliable foundation for long‐term hydrological forecasting, adaptive irrigation planning, and climate resilience efforts in eastern Türkiye's agricultural landscapes.

Traditional methods for estimating water footprints for rice production are often time-consuming and resource-intensive, highlighting the need for efficient and accurate predictive models. This study addresses this gap by evaluating the performance of seven machine learning models—Linear Regression (LR), M5P, Multi-layer Perceptron (MLP), Sequential Minimal Optimization – Support Vector Machine (SMO-SVM), Random SubSpace (RSS), Random Forest (RF), and Random Tree (RT)—in predicting the green and blue water footprints of rice in Punjab, India. Best subset regression and correlation matrix indicate that humidity, wind speed, sunshine hours, solar radiation, and total rainfall are optimal inputs for green water footprint prediction, while maximum temperature, humidity, wind speed, sunshine hours, and solar radiation are best for blue water footprint prediction. The RT model outperformed others in that, for green water footprint prediction, it achieved a correlation coefficient (CC) of 0.9991, mean absolute error (MAE) of 0.1314, root mean square error (RMSE) of 0.4553, relative absolute error (RAE) of 0.0477, and root relative squared error (RRSE) of 0.1283 during the training stage. However, during the testing stage, the RF model performed better (CC = 0.79, MAE = 154.2732, RMSE = 192.3973, RAE = 55.5602, and RRSE = 58.6433). For blue water footprint prediction, the RT model remained the best performer in both stages (training: CC = 0.9991; testing: CC = 0.9981, MAE = 0.7920, RMSE = 0.8583, RAE = 0.4440, and RRSE = 0.8290). These results suggest that machine learning can effectively support water management strategies by providing quick and reliable estimates of water footprints, which is crucial for sustainable rice production. By utilizing these models, policymakers can make informed decisions to optimize water usage and ensure sustainable agricultural practices.