Journal of Water Resources and Geosciences

The Current Status of the Indicators of the Sixth Goal of the Sustainable Development Goals in Iraq and Future Prospects

2025-12-17T10:30:15+00:00

Iraq relies heavily on surface water, with most of its resources originating from the Tigris and Euphrates rivers and their tributaries. Sustainable Development Goal 6 focuses on improving water resources and sanitation facilities, and the need for increased investment in the management of water and sanitation ecosystems. This research examines the current state of Goal 6 indicators in Iraq, their achievements, and the primary causes of the water crisis in the country. In addition to exploring the future of the water sector, the research focuses on the question of how much progress has been made in achieving the goals and indicators of the sixth goal? The research utilized a descriptive analytical approach to delineate the current state of the sixth goal indicators in Iraq, aiming to comprehend this reality and subsequently devise future solutions that foster the evolution of these indicators towards the desired outcome. The research yielded significant findings, notably that the proportion of the population benefiting from safely managed drinking water services was 61% in 2022. This percentage is far from reaching the 2030 target if efforts continue at the current pace. The percentage of wastewater flows safely treated in Iraq reached 42% in 2022, while the same percentage in Qatar and Kuwait was 100% and in the UAE was 95%. The study recommended that work should be done to modernize the water infrastructure, replace old water lines and pipes, and activate water quality control systems using modern technology to ensure the availability of clean and stable water, as well as work on the development of sewage networks and wastewater treatment using advanced treatment plants, which leads to reducing pollution and improving public health, as well as investing in recycling technology and the safe use of treated water, which reduces pressure on freshwater resources.

Abu Ghraib Vegetation Forecasting: NDVI Analysis Using Google Earth Engine Landsat 8 Data and MATLAB-Based Machine Learning

2025-12-18T06:04:15+00:00

This study investigates vegetation dynamics and forecasts future vegetation conditions in Abu Ghraib, Iraq, using a monthly Normalized Difference Vegetation Index (NDVI) time series derived from Landsat 8 imagery through the Google Earth Engine (GEE) platform. The study area represents an irrigated, semi-arid agricultural environment affected by seasonal water availability, climatic variability, and land-use pressure. Landsat 8 data from January 2015 to December 2023 were processed to generate monthly NDVI values using spatial and temporal filtering and cloud masking. The resulting time series was used as input for a Random Forest Regression model implemented in MATLAB. A 12-month sliding-window approach transformed the NDVI sequence into supervised learning samples, enabling the model to capture annual seasonality and temporal dependence. The trained model forecast NDVI trends over a five-year horizon. The model produced low absolute prediction errors, with a test RMSE of 0.0149, MAE of 0.0126, and MAPE of 18.5%. However, the negative test R² value of −0.35 indicates limited ability to explain variance in unseen data, despite capturing the general seasonal NDVI pattern. This limitation may be attributed to the relatively short time series and environmental variability. Residual analysis, out-of-bag error behavior, autocorrelation assessment, and feature-importance ranking supported the model’s ability to reproduce the dominant temporal structure. The forecasted outputs can support agricultural planning, irrigation monitoring, environmental assessment, and sustainable resource management in semi-arid irrigated regions.

Assessment of HYDRUS-2D performance in simulating wetting patterns in stratified soil profiles under subsurface drip irrigation

2025-12-15T21:30:32+00:00

Water scarcity in arid and semi-arid regions needs efficient irrigation solutions such as subsurface drip irrigation (SDI). This study investigates soil wetting patterns under SDI in both homogeneous and layered soil profiles using laboratory experiments and numerical simulations via HYDRUS-2D. The experiments examined wetting front advancement in sandy loam and silty clay loam soils using an iron container in the shape of a rectangular open at the top, with the emitter positioned at various depths relative to soil interfaces. Wetting patterns dimensions from laboratory measurements were compared with simulated ones and statistically evaluated using RMSE, MAE, ME and R². The findings revealed good agreement between observed and simulated wetting fronts in homogeneous soils. In layered profiles, good compatibility was noted for horizontal and downward water movement, while upward movement across coarse-to-fine interfaces showed discrepancies, indicating challenges in modeling capillary barriers. Overall, HYDRUS-2D proved to be an effective tool for predicting moisture distribution in complex soil conditions, supporting better SDI design and water management practices.

Climate classification prediction based on compound drought and hot events and AI classification algorithms: Iraq as a case study

2025-12-29T09:21:38+00:00

Climate change is one of the most important challenges facing the world in this century due to its significant role in affecting the way of life on Earth in addition to its significant impact on the world's economies. The increase in extreme hydrological phenomena such as drought and floods has required more studies to analyse these phenomena and develop plans to reduce their impact on society and the economy. In the present research, the climate of Iraq was studied and analysed using the Standardized Precipitation Index (SPI), the Standard Temperature Index (STI), and the analysis of compound hot and dry events. Temperature and precipitation data from four meteorological stations representative of the climate of Iraq were used. Two classification algorithms were used to predict the annual climate classification of Iraq. The results showed that Iraq was affected by climate change, as the percentage of extreme hot and dry events reached 90% during the period 2012-2022, with the annual rainfall rate decreasing by more than 40%, and temperatures were observed to rise at a rate of 0.68 degrees Celsius per decade. The novelty of this research is the application of classification algorithms in machine learning using hot and dry categorical classification as input variables and the study demonstrated the efficiency of the Bayesian network algorithm in predicting the annual and monthly climate classification.

Evaluation of Operation Main Outfall Drain Balance Basins in Dhi Qar and Basra governorates Using Remote Sensing Techniques

2025-12-18T08:06:20+00:00

First, second balance basins and Al- Haddadin basin are considered within a basic design of Main Outfall Drain (MOD) in the provinces of Dhi Qar and Basra. It is a low-lying natural land surrounded by earthen dyke. The purpose of their construction is to contain excess water resulting from the tide in the Shatt al-Basra or to absorb water waves coming MOD pumping station in Nasiriyah in the event that the gates of the Shatt al-Basra regulator are closed until the gates of the regulator are opened in the event of a low tide. The first balancing basin (according to the modern numbering of MOD) extends from (20-36) km and the second from (52-60) km within the borders of Basra Governorate and Al-Haddadin Basin from (128-104) km within the borders of Dhi Qar Governorate. As a rustle to importance of these basins and the difficulty of reaching them to calculate their storage and water level and the lack of studies related to them, this study was to shed light on these basins and to show their operating hydraulics and monthly evapotranspiration rates in addition to deriving equations for knowing the flooding area, storage volume and level in them during their operation using remote sensing models. Mathematical relationships were established between Surface water extent and water level, surface water extent and storage volume. The equations exhibited excellent performance, with coefficients of determination (R²) ranging from 0.998 to 1.000. Specifically, R² values of 0.999 and 0.998 were obtained for the combined first and second balancing basins, while an R² value of 1.000 was achieved for Lake Al-Haddadin.