Mustafa Al-Mukhtar

Soil erosion is one of the most serious environmental concerns of our time, leading to land degradation, agricultural inefficiency, reservoir siltation, and ecological imbalance. Thus, a thorough assessment of soil erosion is crucial whilst developing and implementing any conservation strategies and measures. In this context, the present research aims to estimate Soil Loss (SL) and Sediment Yield (SY) in the Chami Qeshan Watershed (CQW) between Iraq and Iran using the integrated application of the Revised Universal Soil Loss Equation (RUSLE) and Sediment Delivery Ratio (SDR) techniques. In addition to rain gauge observations, three satellite-derived precipitation datasets were applied for calculating the rainfall–runoff erosivity (R) variable of RUSLE, namely the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS), the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), and the Tropical Rainfall Measuring Mission (TRMM). We also considered four other key factors for predicting soil erosion: soil erodibility (K), slope length and steepness (LS), cover management (C), and conservation practice (P). Ultimately, we used the Pearson correlation to statistically evaluate relationships between input variables and soil erosion outcomes. Results revealed substantial differences among the implemented products, where CHIRPS provided the best reproduction of annual precipitation. Likewise, the CHIRPS-based RUSLE-SDR model reproduced the closest mean soil loss of 12.18 t·ha⁻¹·yr⁻¹ and total outlet sediment yield of 51,826 t·yr⁻¹ to the corresponding gauge-driven model estimates. The correlation analysis found moderate associations between modeled soil loss estimates and LS (r = 0.56–0.57), C (r = 0.50–0.61), and P (r = 0.48–0.63) variables. Given the direct influence of precipitation products on rainfall erosivity and RUSLE–SDR estimates, native-resolution sensitivity analyses and alternative interpolation schemes are recommended to assess result robustness.

The impacts of climate change on future rainfall in northern Iraq at fine temporal and coarse spatial resolutions, covering all areas where rainfed agriculture is located and where large water-harvesting dams are being built, remain unclear. The study aims to investigate spatial-temporal changes in rainfall and their future projections in northern Iraq for the period 2021–2040 under two emission scenarios (i.e., SSP245 and SSP585). The LARS-WG model is calibrated using daily rainfall data from the baseline period (1985 to 2015) at 20 meteorological stations across five governorates, and is integrated with six global circulation models to project future rainfall. Python code, ArcGIS 10.8.2, and IDW were used to visualise the precipitation data. Results proved that rainfall in the southern and southwestern regions is significantly lower than in the northern and north-eastern regions. However, January to March tended to have greater rainfall than the other months from November to May, with a predominant rainfall range of 60–90 mm. These conditions are expected to persist in the future under both scenarios. Projected precipitation under the SSP245 and SSP585 scenarios for the period 2021–2040 shows distinct spatial and temporal variability across the study area. However, reductions in low-rainfall areas and increases in higher precipitation classes were evident, with some areas expected to increase by > 120 mm. For instance, in March, the annual rainfall under SSP585 increased from 1.75% during 2006–2015 to 10.44% during 2031–2040. These findings introduce novel, spatially explicit insights that strengthen sustainable development planning by enabling targeted water resource management, climate adaptation strategies, and risk-informed infrastructure design under future climate scenarios.

The changes in weather and climate patterns have prompted the world to conduct comprehensive and in-depth studies and research due to their direct impact on all aspects of life. Therefore, predicting climate using historical data for a specific area is vital to studying and knowing the level of impact of the resulting change in terms of hydrological, meteorological, and agricultural aspects. In this study, a comprehensive review of the latest and most popular weather generators used in the world was surveyed. A thorough evaluation of 92 papers published between 2000 and 2023 was analyzed and discussed in terms of the geographical locations and climatological conditions, time scale, predictors, and capabilities of weather generators models. Starting in early September 2023, the study made use of the search boxes on Scopus, IEEE Xplore, ScienceDirect, Web of Science, Semantic Scholar, PubMed, and Connected Papers databases. The terms "Weather Generators", "Climate Changes", and “Meteorological Parameters" were mixed with auxiliary words like "Applications", "Program," "Code", and "Software", as well as different variations of the terms "forecasting", "projection", and "prediction", in addition to the main terms. Hence, the reviewed papers provide an insightful tool for researchers to use the weather generator models in similar studies. Ultimately, the study presents a comprehensive and cutting-edge overview of weather generator applications, highlighting the most promising approach for future studies, which helps researchers and those interested in understanding the causes of climate change.

Integrating information on drought incidents into planning and analysis processes can assist land, water, and urban managers to prepare more effectively for water-related hazards. This study aims to assess the spatiotemporal characteristics of drought upstream of the Little Zab River Basin from 2004 to 2018 by integrating satellite-derived data and meteorological indices to address the limitations of gauge measurements. The Coefficient of Variation (CV) was used to analyze precipitation inconsistency on an annual timescale. The Reconnaissance Drought Index (RDI), the second Modified Soil-Adjusted Vegetation Index (MSAVI2), and the Normalized Difference Water Index (NDWI) were adopted as meteorological, agricultural, and hydrological drought indices, respectively. Additionally, the Pearson Correlation Coefficient (PCC) was applied to comprehend the relationship between the implemented variables. Findings exhibited moderate (22.4%–28.5%) CV values in the annual precipitation data. RDIst results identified a significant extreme-to-severe drought event during the hydrological year 2007–2008, persisting with lower intensities into 2008–2009 across most observatories. The NDWI values displayed that the surface area of Dukan Reservoir reached its minimum extents of 133 km2 and 123 km2 in 2008 and 2009, respectively. Although mean MSAVI2 values competently detected the 2008 and 2009 drought incidents, those precipitation deficiencies later harmed the vegetation cover in 2010. There was a significant positive correlation between precipitation, RDIst, NDWI, and mean MSAVI2 values. the study concludes that meteorological drought in the research region instantly leads to hydrological drought, resulting in agricultural drought with a one-year lag.

The purpose of this research is to assess the efficacy of five distinct artificial intelligence model techniques: AdaBoost, Gradient Boosting, Tree, Random Forest, and KNN, to estimate the water quality parameters of dissolved oxygen (DO) and biochemical oxygen demand (BOD). The performance of...

Regions characterized by an arid or semi-arid climate are highly susceptible to prospective climate change impacts worldwide. Therefore, evaluating the effects of global warming on water availability in such regions must be accurately addressed to identify the optimal operation policy of water management facilities. This study used the weather generator model LARS-WG6.0 to forecast possible variations in precipitation and temperature of the Mosul Dam Reservoir in northern Iraq. Future climate change was predicted using three greenhouse gas emission scenarios (i.e., RCP2.6, RCP4.5, and RCP8.5) for four time intervals (2021–2040, 2041–2060, 2061–2080, and 2081–2100) using five Global climate models (GCMs): CSIRO-Mk3.6.0, HadGEM2-ES, CanESM2, BCC-CSM1-1, and NorESM1-M. The model’s calibration and validation were conducted using data from 2001 to 2020 from eight meteorological stations in the study area. The results showed that the weather generator model’s performance was outstanding in predicting daily climate variables. The results also showed that the highest increase in maximum and minimum temperatures was 5.70°C in July and 5.30°C in September, respectively, for the future period 2081–2100 under RCP8.5. The highly forecasted minimum and maximum temperatures were extracted from the CanESM2 and HadGEM2-ES GCM models. It was demonstrated that the study region would experience different patterns of precipitation change during the wet seasons in the evaluated periods. Finally, the variations in precipitation and temperatures in the Mosul dam region would significantly impact the amount of freshwater obtained in these areas due to rising loss rates of evaporation. This could lead to a water shortage and mismanagement of the sustainable operations of the dam.

Understanding sedimentation behavior and its transport capacity in the Tigris River is of significant importance owing to the detrimental consequences caused by it. This study investigates the sediment amounts transported along the reach of the Tigris River in Baghdad. The CCHE2D model which is a common tool developed by the National Center for Computational Hydrological Science and Engineering (NCCHE) was applied to investigate the flow pattern and sediment amounts within 7 km reach. The model was initially calibrated and validated under steady-state conditions at the Sarai gauging station (upstream) and its performance was evaluated around the Abu Nawas water treatment plant (downstream). The result shows that the water surface level and velocity in the Sarai Baghdad gauging station and Abu Nawas raw water station cross-sections are different due to different considerations which are discharged, bank and bed soil types, channel velocity, and section shape. Moreover, the sediment concentrations ranged between 0.240 - 0.350 kg/m3 at the cross-section of the Sarai Baghdad gauging station while the cross-section of Abu Nawas raw water station ranged between (0.040-0.090) kg/m3. Additionally, the study investigated the average suspended sediment concentration at the river reach which ranged from 150 to 300 kg/m3. Finally, the study demonstrated CCHE2D's capacity to simulate river flow and sediment movement.

Evapotranspiration (ETo) is one of the most important processes in the hydrologic cycle, with specific application to sustainable water resource management. As such, this study aims to evaluate the predictive ability of a novel method for monthly ETo estimation, using a hybrid model comprising data pre-processing and an artificial neural network (ANN), integrated with the hybrid particle swarm optimisation–grey wolf optimiser algorithm (PSOGWO). Monthly data from Al-Kut City, Iraq, over the period 1990 to 2020, were used for model training, testing, and validation. The predictive accuracy of the proposed model was compared with other cutting-edge algorithms, including the slime mould algorithm (SMA), the marine predators algorithm (MPA), and the constriction coefficient-based particle swarm optimisation and chaotic gravitational search algorithm (CPSOCGSA). A number of graphical methods and statistical criteria were used to evaluate the models, including root mean squared error (RMSE), Nash–Sutcliffe model efficiency (NSE), coefficient of determination (R²), maximum absolute error (MAE), and normalised mean standard error (NMSE). The results revealed that all the models are efficient, with high simulation levels. The PSOGWO–ANN model is slightly better than the other approaches, with an R² = 0.977, MAE = 0.1445, and RMSE = 0.078. Due to its high predictive accuracy and low error, the proposed hybrid model can be considered a promising technique.

Iraq, including the investigated watershed, has endured destructive floods and drought due to precipitation variability in recent years. Protecting susceptible areas from flooding and ensuring water supply is essential for maintaining basic human needs, agricultural production, and industry development. Therefore, locating and constructing storage structures is a significant initiative to alleviate flooding and conserve excessive surface water for future growth. This study aims to identify suitable locations for Runoff Harvesting (RH) and dam construction in the Hami Qeshan Watershed (HQW), Slemani Governorate, Iraq. We integrated in situ data, remotely sensed images, and Multi-Criteria Decision Analysis (MCDA) approaches for site selection within the Geographical Information Systems (GIS) environment. A total of ten criteria were employed to generate the RH suitability maps, including topographic position index, lithology, slope, precipitation, soil group, stream width, land cover, elevation, distance to faults, and distance to town/city. The weights of the utilized factors were determined via Weighted Linear Combination (WLC) and Analytic Hierarchy Process (AHP). The resulting RH maps were validated through 16 dam sites preselected by the Ministry of Agriculture and Water Resources (MAWR). Findings showed that the WLC method slightly outperformed AHP regarding efficiency and exhibited a higher overall accuracy. WLC achieved a higher average overall accuracy of 69%; consequently, it was chosen to locate new multipurpose dams for runoff harvesting in the study area. The overall accuracy of the 10 suggested locations in HQW ranged between 66% and 87%. Two of these sites align with the 16 locations MAWR has recommended: sites 2 and 5 in the northwest of HQW. It is noteworthy that all MAWR dam sites were situated in medium to excellent RH zones; however, they mostly sat on ineffective geological localities. It is concluded that a careful selection of the predictive factors and their respective weights is far more critical than the applied methods. This research offers decision-makers a practical and cost-effective tool for screening site suitability in data-scarce rugged terrains.