Rapid population growth has raised the groundwater resources demand for socio-economic development in the Shatt Al-Arab basin. The sustainable management of groundwater resources requires precise quantitative evaluation, which can be achieved by applying scientific principles and modern techniques. An integrated concept has been used in the current study to identify the groundwater potential zones (GWPZs) in the Shatt Al-Arab basin using remote sensing (RS), geographic information system (GIS), and analytic hierarchy process (AHP). For this purpose, nine groundwater occurrence and movement controlling parameters (i.e., lithology, rainfall, geomorphology, slope, drainage density, soil, land use/land cover, distance to river, and lineament density) were prepared and transformed into raster data using ArcGIS software. These nine parameters (thematic layers) were allocated weights proportional to their importance. Furthermore, the hierarchical ranking was conducted using a pairwise comparison matrix of the AHP in order to estimate the final normalized weights of these layers. We used the overlay weighted sum technique to integrate the layers for the creation of the GWPZs map of the study area. The map has been categorized into five zones (viz., very good, good, moderate, poor, and very poor) representing 4, 51, 35, 9, and 1% of the study area, respectively. Finally, for assessing the effectiveness of the model, the GWPZs map was validated using depth to groundwater data for 99 wells distributed over the basin. The validation results confirm that the applied approach provides significantly solid results that can help in perspective planning and sustainable utilization of the groundwater resources in this water-stressed region.

Severe flood events in the trans-boundary Shatt Al-Arab basin (Iraq-Iran) claim hundreds of human lives and cause damage to the economy and environment. Therefore, developing a flood hazard model to recognize the basin’s susceptible areas to flooding is important for decision makers for comprehensive flood risk management. The flood hazard map was prepared using geographical information systems (GIS) and multi-criteria decision analysis (MCDA) along with the application of analytical hierarchy process (AHP) method to identify the optimal selection of weights for the factors that contribute to flood risk. The flooding causative factors used in this study were rainfall, distance to the river, digital elevation model (DEM), slope, land use/land cover (LULC), drainage density, soils, and lithology. The derived flood hazard map consisted of four distinct categories (zones). These zones that depict high, intermediate, low, and very low flood hazard cover around 20%, 40%, 39%, and 2% of the basin area, respectively. The produced flood hazard map was further verified using a historical flood event in the study area. The results were found to be consistent with the historical data of flood events, revealing the model’s effectiveness in the realistic representation of flood hazard mapping.

Water draining from heavily industrialized basins introduces significant amounts of pollutants to the rivers water and sediments. Heavy industrial activities in the Shatt Al-Arab basin result in increased pollutant loads to the river’s surface sediments. Therefore, it becomes crucial to investigate the influence of anthropogenic activities on both spatial and temporal scales. This study unfolded the extent, sources, and distributions of heavy metals pollution in the sediments of the Shatt Al-Arab River. Extensive samplings were performed during the dry and the wet seasons at 25 stations along the river course for the analysis of 11 heavy metals. The analysis revealed high pollution levels in the river sediments compared to both their historical values and international standards. Statistical analysis techniques such as Principal Component Analysis (PCA) and Factor Analysis (FA) were applied. Statistical analysis showed that all the elements were well represented by four varifactors that explained a cumulative total variance of 74%. PCA/FA indicated that most investigated metals were of anthropogenic origins (i.e., industrial, residential, and agricultural sources). Pollution indices that were applied, such as Contamination Factor (CF) and Nemerow Pollution index (P_N), indicated that sediments were: (i) considerably contaminated with Fe and Mo (ii) moderately contaminated with Cr, Zn, Ni, Cu, Pb and Mn and (iii) not contaminated with Co and V. The P_Nvalues indicated serious pollution in the river sediments in all sites, even though the pollution was not evenly distributed, i.e., the upstream reaches of the river were more polluted compared to the downstream parts. In contrast to many studies that have reported changes in heavy metals concentrations due to seasonal variations, our data showed no significant relationship between metals concentrations and seasonality. This study addresses several of the major limitations of the current knowledge on this river’s pollution sources and analysis, such as the limited number of analyzed pollutants and restricted samplings in the current literature. The findings necessitate the implementation of effective management strategies to control pollution in the river basin.

In the Shatt Al-Arab basin, soil erosion is a major problem due to the steepness of the terrain and the significant difference in altitude between the upstream and downstream parts of the basin. Vast quantities of soil are moved annually, resulting in massive repercussions including soil degradation, structural damage, biodiversity loss, and productivity reduction in the catchment area, huge sediment load, and the pollution of streams and rivers. Consequently, the assessment of soil erosion risk and geographical distribution is essential for constructing a database for developing effective control strategies. Revised Universal Soil Loss Equation (RUSLE) was combined with Remote Sensing (RS) and Geographic Information System (GIS) in the current work to define the soil erosion hazard map in the Shatt Al-Arab basin. The RUSLE model included various characteristics for soil erosion zonation including rainfall erosivity, soil erodibility, slope length and steepness, land cover and management, and conservation support practices. Annual erosion rates in this study in tons per hectare were: extremely high (more than 50); very high (50 to 16.5); high (16.5 to 2.2); medium (2.2 to 1); and low (1 to 0) ton ha⁻¹year⁻¹ representing 16, 4, 13, 7, and 60 % of the basin’s area, respectively. The high soil loss rates are associated with heavy rainfall, loamy soil predominance, elevated terrains/plateau borders with a steep side slope, and intensive farming. Managers and policymakers may use the results of this study to implement adequate conservation programs to prevent soil erosion or recommend soil conservation acts if development projects are to proceed in places with a high soil erosion risk.

Carbon dioxide (CO₂) emissions are a major cause of climate change. However, CO₂ emissions data for 178 countries from 1960 to 2018 revealed inequality in global CO₂ emissions. For example, we found that 50% of the world’s population (ca. 3.75 billion people) was responsible for just 8.9% of the global cumulative carbon emissions. These people are concentrated in low- and middle-income countries. Conversely, 10% of the world’s population (ca. 757 million people), concentrated in high-income countries, were responsible for 46.8% of the global emissions. Furthermore, the literature review disclosed evolution of CO₂ emission inequalities within countries. A significant (p < 0.001) negative (r² = −0.52) correlation was detected between carbon emissions and climate change impacts on national incomes. Such correlation indicated that countries most likely to experience the greatest effects of climate change are also those who make the smallest contributions to its underlying causes. Similar disparities were observed within countries where low-income groups who make the smallest contributions to climate change are subjected to its worst implications. Evaluations of the data from the literature showed that migration could be the result of climate change, though such migration does not happen in isolation. In other words, this kind of migration is frequently linked to other issues such as the fragility and lack of adaptability of the communities. Furthermore, reviews showed that climate change catalyzes instability and conflict. On the other hand, conflict damages the environment and climate in multiple ways. Therefore, it is necessary to collaborate to resolve these two issues concurrently.

Biological processes of rivers are strongly influenced by concentration and fluxes of nitrogen (N) levels. In order to restrain eutrophication, which is typically caused by urbanisation and agricultural expansion, nitrogen levels must be carefully controlled. Data from 2013 to 2017 were gathered from 26 sub-catchments in the Mississippi River basin to assess the effects that catchment size, land cover, and precipitation can have on the discharge and total nitrogen (TN) and how TN yields deviate from a generalised local trend. The findings indicated that land cover and precipitation had a determinative effect on area-weighted discharge (Q_area). More specifically, Q_area had significant positive (directly proportional) relationships with precipitation, forest, and urbanised land cover, and significant negative (inversely proportional) relationships with grassland/pasture and scrub/shrub land covers. Concurrently, the TN concentration significantly increased in the presence of agricultural land cover, but significantly decreased in forest land cover. The TN yield (TN concentration × Q_area) was largely determined by Q_area because the latter was observed to fluctuate more dramatically than concentration levels. Consequently, the TN yield exhibited the same relationships that Q_area had with precipitation and land covers. The TN yield changed significantly (p < 0.05) and positively with instantaneous discharge across all sites. Nevertheless, the rate of TN yield variations with discharge displayed a significant (p < 0.0001) negative (r² = 0.80) relation with the catchment size. Ultimately, this study used discharge readings to facilitate the prediction of TN concentrations and yields across various catchment areas in the Mississippi River basin and provided a robust model for future research in this area.