Understanding precipitation dynamics in arid regions such as Iraq is of paramount importance in hydrological and climatological studies, as it is a key approach to water resources management and climate change adaptation. This study aims to develop a mathematical predictive model for rainfall isotopic values using machine learning techniques. Stable isotope data for oxygen (δ¹⁸O) and deuterium (δ²H) in precipitation were collected from 32 meteorological stations distributed across Iraq over a 14-year period (2010–2024). The dataset also included meteorological parameters for these stations, including precipitation amount, air temperature, relative humidity, and calculated station elevation. Several machine learning algorithms (i.e., SVM, GBR, ANN, CatBoost, XGBoost, and RF) were employed to compare predicted isotopic values with actual readings, accounting for rainfall characteristics and patterns. The results demonstrated that the RF model achieved superior predictive performance, with a calibration coefficient (R²) of 0.89 in the testing set, indicating strong predictive capability. This model also recorded the lowest mean absolute error (MAE) of 1.39 and the lowest root mean square error (RMSE) of 3.5 compared to the other algorithms, reflecting improved predictive accuracy. These findings confirm the effectiveness of integrating machine learning, particularly the RF approach, in enhancing the modeling of isotopic signature predictions in environmental studies. Furthermore, they highlight the potential of AI-based models as powerful tools for reconstructing historical isotopic datasets, supporting climate variability assessment and sustainable water resources management in arid and semi-arid regions.

In modern agricultural practices, soils are increasingly exposed to multiple anthropogenic and natural pollutants, with heavy metals playing a significant role. Despite their critical environment and health impact, substantial gaps remain in understanding the levels of these contaminants and their outcomes on soil and water systems. To address the lack of pertinent data, this study assessed the speciation and contamination levels of nickel (Ni) and zinc (Zn) in soil samples collected from Varanasi (n = 9) and Mirzapur (n = 6) districts of eastern Uttar Pradesh, India. The modified Tessier method was used for the sequential extraction procedure to understand heavy metals mobility and availability. Among the geochemical fractions (water soluble, exchangeable, organic matter bound, carbonate bound/specifically sorbed, bound to Fe-oxides (Fe-MnOB)), the residual fraction was predominant. For Ni, principal component analysis revealed the highest positive loading factor for soil pH (0.941), and the residual fraction also demonstrated a significant positive loading value (0.779), indicating their stabilizing influence. In the case of Zn, the highest positive loading factor was observed for diethylenetriaminepentaacetic acid-Cu (0.956), while the highest negative loading factor was associated with soil pH (−0.935). For Zn, negative loadings of pH and CEC contrasted with positive associations of DTPA-Cu and Ni, suggesting differential mobility and source behavior. For Zn speciation, the residual fraction showed the highest positive loading (0.944). The study concluded that the majority of contamination indices for Ni and Zn fell within the slightly to moderately contaminated zone, with geogenic sources playing a dominant role compared to anthropogenic inputs. These results offer a valuable reference point for supervising heavy metal contamination and understanding the potential migration of pollutants within soil, water, and human systems. This study may be the first detailed speciation and index-based assessment of Ni and Zn in eastern UP agricultural soils. Periodic assessment and management of these contaminants are recommended to mitigate their environmental and health impacts.

The long-term effects of integrated nutrient management (INM) on crop performance and soil health—particularly within sub-humid environments—remain insufficiently explored. This research aimed to quantify the relationship between the soil quality index (SQI) and overall system productivity. The SQI represents a numerical indicator of soil functioning and its biological and chemical integrity, while system productivity reflects the economic yield generated by the cropping system. A long-term experiment initiated in 1972 formed the foundation for this study, which was conducted from 2019 to 2021 and included eleven nutrient management treatments. These comprised the following treatments: inorganic fertilizers alone (100% NPK, 150% NPK, 100% NP, 100% N, and 100% NPK without sulfur); combinations of organic and inorganic inputs (50% NPK + FYM and 100% NPK + FYM); lime with inorganic fertilizers (100% NPK + lime); zinc with inorganics (100% NPK + Zn); hand weeding with inorganics (100% NPK + HW); an unfertilized control. The study was implemented in a maize–wheat rotation under the sub-humid climatic conditions of Palampur, Himachal Pradesh, India. System productivity was estimated using wheat grain equivalent yield, and SQI values were generated from selected soil properties. These indicators—along with the sustainable yield index (SYI)—were applied to assess the effectiveness of each treatment. The results showed that the 100% NPK + FYM combination produced the highest SQI, followed by 100% NPK + lime, whereas the 100% N treatment yielded the lowest value. Overall, the findings highlight the crucial role of adopting sustainable nutrient management practices to maintain soil quality and optimize productivity in sub-humid agricultural systems.

Arsenic contamination in groundwater, particularly in developing countries like Pakistan, poses significant health risks, including developmental abnormalities, cardiovascular ailments, and cancer. It also affects aquatic biodiversity, agricultural production, and soil quality, endangering ecosystems and posing a global concern. Various methods like ion exchange, membrane filtration, precipitation, photocatalytic degradation, and adsorption have been explored for arsenic removal, with adsorption proving to be the most effective. This research not only introduces a novel material for water purification but also provides a sustainable and cost-effective alternative to traditional treatment methods. A magnetite/polyaniline-polypyrrole nanocomposite with improved adsorption effectiveness has been created to address the challenge of arsenic removal. By combining inorganic iron oxide with organic conducting polymers, oxidative polymerization of polypyrrole and aniline was achieved in the presence of magnetite (Fe₃O₄). Magnetite is used to facilitate separation from treated water, which lowers the production of secondary waste and increases treatment efficiency overall. The nanocomposite was characterized using FTIR, SEM, XRD, TGA, DSC, Raman spectroscopy, UV–visible, and BET techniques. The colorimetric method confirmed arsenic removal, with further spectrophotometric analysis showing a 92% reduction of arsenic from an aqueous solution using just 0.01 g of the adsorbent at pH 1. The magnetite/polyaniline-polypyrrole nanocomposite offers an effective solution for arsenic removal, warranting further research and optimization for practical applications in arsenic-contaminated regions. This study supports Clean Water and Sanitation that seek to guarantee to access clean and sanitary water for drinking. The results of this study can be used to large-scale water treatment projects, which will help communities whose water supplies are tainted by arsenic.

Iraq has faced escalating water scarcity over the past two decades, driven by climate change, upstream water withdrawals, and prolonged economic instability. These factors have caused deterioration in irrigation systems, inefficient water distribution, and growing social unrest. As per capita water availability falls below critical levels, Iraq is entering a period of acute water stress. This escalating water scarcity directly impacts water and food security, public health, and economic stability. This study aims to develop a general framework combining decision support systems (DSSs) with Integrated Comprehensive Water Management Strategies (ICWMSs) to support water planning, allocation, and response to ongoing water scarcity and reductions in Iraq. Implementing such a system is essential for Iraq to alleviate its continuing severe situation and adequately tackle its worsening water scarcity that has intensified over the years. This integrated approach is fundamental for enhancing planning efficiency, improving operational performance and monitoring, optimizing water allocation, and guiding informed policy decisions under scarcity and uncertainty. The current study highlights various international case studies that show that DSSs integrate real-time data, artificial intelligence, and advanced modeling to provide actionable policies for water management. Implementing such a framework is crucial for Iraq to mitigate this critical situation and effectively address the escalating water scarcity. Furthermore, Iraq’s water management system requires modifications considering present and expected future challenges. This study analyzes the inflows of the Tigris and Euphrates rivers from 1933 to 2022, revealing significant reductions in water flow: a 31% decrease in the Tigris and a 49.5% decline in the Euphrates by 2021. This study highlights the future 7–20% water deficit between 2020 and 2035. Furthermore, this study introduces a flexible, tool-based framework supported by a DSS with the DPSIR model (Driving Forces, Pressures, State, Impacts, and Responses) designed to address and reduce the gap between water availability and increasing demand. This approach proposes a multi-hazard risk matrix to identify and prioritize strategic risks facing Iraq’s water sector. This matrix links each hazard with appropriate DSS-based response measures and supports scenario planning under the ICWMS framework. The proposed framework integrates hydro-meteorological data analysis with hydrological simulation models and long-term investment strategies. It also emphasizes the development of institutional frameworks, the promotion of water diplomacy, and the establishment of transboundary water allocation and operational policy agreements. Efforts to enhance national security and regional stability among riparian countries complement these actions to tackle water scarcity effectively. Simultaneously, this framework offers a practical guideline for water managers to adopt the best management policies without bias or discrimination between stakeholders. By addressing the combined impacts of anthropogenic and climate change, the proposed framework aims to ensure rational water allocation, enhance resilience, and secure Iraq’s water strategies, ensuring sustainability for future generations.

Accurate reservoir storage estimation is fundamental to sustainable water resources management; however, small dam projects are often hindered by the prohibitive costs and time required for high-precision topographic surveys. In this study, a rigorous validation of freely available one-arc-second SRTM DEMs was conducted as an alternative approach for estimating volume-elevation relationships at ten small dams in Iraq. High-precision surveys served as benchmarks, enabling statistical validation of DEM-derived estimates using absolute relative error (ARE), root mean square error (RMSE), mean absolute error, and the coefficient of determination (R²). Reservoir basin morphology was further characterised through planimetric indices, including area-to-volume ratio (AVR), shape factor, and solidity. In parallel, terrain complexity within a 5 km buffer zone was quantified using slope variability, curvature, vector ruggedness measure (VRM), and terrain ruggedness index (TRI). A strong structural agreement was demonstrated (R² > 0.98), although substantial variation in volumetric precision was observed. A global sensitivity analysis using the Morris Method identified the standard deviation of the Terrain Ruggedness Index (TRI) as the dominant predictor of accuracy, with µ* values of 83–86, while all other metrics showed minimal influence (µ* ≈ 0–24). These results establish a clear accuracy threshold for one-arc-second SRTM DEMs: they are sufficiently reliable for preliminary planning (< 20% error) in low-ruggedness terrain (TRI SD < 0.1) but become highly unreliable in rugged landscapes, where errors exceed 150% (TRI SD > 0.1). These findings provide a predictive framework for assessing DEM suitability, supporting the integration of satellite topography into small-scale reservoir planning.

This study assesses the precision of NASA Power meteorological data in Iraq over a 12-year period, utilizing data from 10 meteorological stations. The research focuses on key meteorological parameters, i.e., average daily temperatures, rainfall, wind speed, solar radiation, and relative humidity. Through transparent data analysis and comparison, the validity of NASA Power in local climate monitoring within Iraq is evaluated. Through statistical analysis, the correlation between NASA Power data and meteorological station data is evaluated using Kendall's Tau correlation coefficient test and Mean Bias Error (MBE). The comparison of NASA Power meteorological data with observed data from ten meteorological stations in Iraq revealed significant findings. NASA Power data displayed a high correlation (0.748-0.912) with observed temperatures, indicating accuracy in temperature assessment. The data also showed weak to strong correlations (0.105-0.526) for rainfall and weak to moderate correlations (0.105-0.427) for wind speed, suggesting potential supplementary use, albeit with the need for calibration. For solar radiation, NASA Power data exhibited a strong to very strong correlation (r = 0.636-0.834), making it suitable for solar assessments. For relative humidity, a very strong correlation (r = 0.636-0.834) was demonstrated, indicating the need for further analysis. Despite its reliability as a meteorological data source in Iraq, NASA Power data should undergo validation across various applications and regions to ensure its accuracy and dependability.

تقيم هذه الدراسة دقة بيانات األرصاد الجوية Power NASA في العراق على مدى 12 عا ًما، باستخدام بيانات من 10 محطات أرصاد جوية. يركز البحث على المعلمات الجوية الرئيسية: متوسط درجات الحرارة اليومية، وهطول األمطار، وسرعة الرياح، واإلشعاع الشمسي، والرطوبة النسبية. من خالل تحليل البيانات ومقارنتها بشكل شفاف، يتم تقييم صحة Power NASAفي مراقبة المناخ المحلي داخل العراق. من خالل التحليل اإلحصائي، يتم تقييم االرتباط بين بيانات Power NASAوبيانات محطة األرصاد الجوية باستخدام اختبار معامل ارتباط كيندال تاو وخطأ التحيز المتوسط .(MBE (أظهرت مقارنة بيانات األرصاد الجوية لـ Power NASA مع البيانات المرصودة من عشر محطات أرصاد ًطا عاليًا )0.748 - 0.912( مع درجات الحرارة المرصودة، مما يشير جوية في العراق نتائج مهمة: أظهرت بيانات Power NASA ارتبا( لهطول األمطار وارتبا 0.105 ًط إلى الدقة في تقييم درجة الحرارة، بينما أظهرت ارتبا 0.105 - 0.526 ا ضعيفًا إلى متوسط ) ًطا ضعيفًا إلى قوي )- 0.427( لسرعة الرياح، مما يشير إلى االستخدام التكميلي المحتمل مع الحاجة إلى المعايرة. بالنسبة لإلشعاع الشمسي، أظهرت بيانات NASAارتبا 0.636 - 0.834(، مما يجعلها مناسبة لتقييمات الطا قة الشمسية، وبالنسبة للرطوبة النسبية، فقد أظهرت ًط Power ا قويًا إلى قوي ج ًدا )ارتبا 0.636 - 0.834( لمزيد من التحليالت. وعلى الرغم من موثوقيتها كمصدر للبيانات الجوية في العراق، يجب أن تخضع بيانات ًطا قويًا ج ًدا ) Power NASA للتحقق عبر تطبيقات ومناطق مختلفة لضمان دقتها وموثوقيتها.

The satellite-derived climatic variables offer extensive spatial and temporal coverage for research; however, their inherent biases can subsequently reduce their accuracy for water balance estimate. This study evaluates the effectiveness of bias correction in improving the Tropical Rainfall Measuring Mission (TRMM) rainfall and the Global Land Data Assimilation System (GLDAS) land surface temperature (LST) data and illustrates their long-term (2000–2019) hydrological assessment. The novelty lies in coupling the bias-corrected climate variables with the Thornthwaite–Mather water balance model as well as land use land cover (LULC) for improved predictive hydrological modeling. Bias correction significantly improved the agreement with ground observations, enhancing the R2 value from 0.89 to 0.96 for temperature and from 0.73 to 0.80 for rainfall, making targeted inputs ready to predict hydrological dynamics. LULC mapping showed a predominance of agricultural land (64.5%) in the area followed by settlements (20.0%), forest (7.3%), barren land (6.5%), and water bodies (1.7%), with soils being silt loam, clay loam, and clay. With these improved datasets, the model found seasonal rise in potential evapotranspiration (PET), peaking at 120.7 mm in June, with actual evapotranspiration (AET) following a similar trend. The annual water balance showed a surplus of 523.8 mm and deficit of 121.2 mm, which proves that bias correction not only enhances the reliability of satellite data but also reinforces the credibility of hydrological indicators, with a direct, positive impact on evidence-based irrigation planning and flood mitigation and drought management, especially in data-scarce regions.

Improving the accuracy of reference evapotranspiration (RET) estimation is essential for effective water resource management, irrigation planning, and climate change assessments in agricultural systems. The FAO-56 Penman-Monteith (PM-FAO56) model, a widely endorsed approach for RET estimation, often encounters limitations due to the lack of complete meteorological data. This study evaluates the performance of eight empirical models and four machine learning (ML) models, along with their hybrid counterparts, in estimating daily RET within the Gharb and Loukkos irrigated perimeters in Morocco. The ML models examined include Random Forest (RF), M5 Pruned (M5P), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), with hybrid combinations of RF-M5P, RF-XGBoost, RF-LightGBM, and XGBoost-LightGBM. Six input combinations were created, utilizing Tmax, Tmin, RHmean, Rs, and U2, with the PM-FAO56 model serving as the benchmark. Model performance was assessed using four statistical indicators: Kling-Gupta efficiency index (KGE), coefficient of determination (R2), mean squared error (RMSE), and relative root squared error (RRSE). Results indicate that the Valiantzas 2013 (VAL2013b) model outperformed other empirical models across all stations, achieving high KGE and R2 values (0.95–0.97) and low RMSE (0.32–0.35 mm/day) and RRSE (8.14–10.30%). The XGBoost-LightGBM and RF-LightGBM hybrid models exhibited the highest accuracy (average RMSE of 0.015–0.097 mm/day), underscoring the potential of hybrid ML models for RET estimation in subhumid and semi-arid regions, thereby enhancing water resource management and irrigation scheduling.

Portable hydropower turbines are turbines with a scale below 5 kW and which can be carried from one place to another easily by hand due to their light weight. This study was carried out to evaluate the potential of Archimedes Screw Turbine (AST) as an improved portable hydro-power turbine (PHPT) to address shortcomings in available portable turbines. The design of Archimedes screw hydro-power turbine is mainly concerned with screw geometry, which is determined by a variety of internal and external characteristics, including its length, external and internal diameter, Pitch of blades, and Number of the blades, which were 80 cm, 18 cm, 9.53 cm, 18 cm and two number of blades respectively. The turbine was manufactured from stainless steel material according to design parameters and installed in the laboratory. Experimental testing was performed at different discharges (Q) of 0.3, 0.4, 0.5, 0.6, and 0.7 ft3/s and at the angle of inclination of 22, 30, 45, and 55° of screw shaft to measure power outputs and overall efficiencies. The maximum overall efficiency obtained was 70% at a flow rate of 0.5 ft3/s and at an angle of inclination of 30°. The power output at maximum overall efficiency was 42 watts and hydraulic efficiency was 75.5%. At the flow rate of 0.3 ft3/s and an angle of inclination of 55°, the turbine produced a minimum power output of 22.8 watts and an overall efficiency of 39.4%.Experimentation revealed that the flow rate (Q) and inclination of the turbine shaft affect the turbine Power output (Po) and overall efficiency (ηo). This study helps to manufacture small AST on a large scale, to utilize small flows of water, and to evaluate the possibilities of AST as an appropriate portable hydro-power generation turbine. Further research and experimentation are needed to assess whether 3D printing can be effectively scaled for broader implementation in low-resource areas.