Rainwater harvesting is a promising tool for supplementing surface water and groundwater to overcome the imbalance between water supply and demand under changing climate conditions. Multi-Criteria Evaluation is one of the well-known methods of decision-making. In this study, the geographical information system (GIS)-based Multi-Criteria Evaluation is used to select the optimum rainwater harvesting sites in Maysan province, Iraq. Fuzzy membership is used for standardization of the criteria, and Fuzzy Gamma overlay for a combination of multi-layers using ArcGIS 10.5. Seven criteria layers, including slope, stream order, soil type, precipitation, evaporation, roads, and the Normalized Difference Vegetation Index (NDVI) are derived to identify rainwater-harvesting catchment. The results determined the optimum sites for water storage within the study area. The resultant potential rainwater harvesting catchment map can be used as a reference to enhance the effectiveness of water management, especially in drought-stricken areas that offer significant potential for sustainable agricultural production in the semi-arid region.

Droughts are a major problem in Iraq especially in the Arid and Semi-Arid Lands where they are frequent and causes a great deal of suffering and loss. Drought monitoring and forecasting requires extensive climate and meteorological data which is usually largely missing in developing countries or not available in the required spatial and temporal resolutions. In this study, the drought categories were defined for the years 2000, 2005, 2010, 2015 and 2017 using the TRMM data to map the spatiotemporal meteorological drought, and the Standardized Precipitation Index (SPI) to analyze the meteorological drought at 11 stations located in Western Iraq. The SPI analyses were performed on 12-month datasets for five years. The results showed that the northeast region has the higher rainfall indices and the southwest region has the lowest rainfall. An analysis of the drought and rain conditions showed that the quantity of extreme drought events was higher than that expected in the study area, especially in the south and southwest areas. Therefore, an alternate classification is proposed to describe the drought, which spatially classifies the drought type as mild, moderate, severe and extreme. In conclusion, the integration between TRMM data SPI data proved to be an effective tool to map the spatial distribution and drought assessment in the study area.

Land cover mapping of marshland areas from satellite images data is not a simple process, due to the similarity of the spectral characteristics of the land cover. This leads to challenges being encountered with some land covers classes, especially in wetlands classes. In this study, satellite images from the Sentinel 2B by ESA (European Space Agency) were used to classify the land cover of Al‑Hawizeh marsh/Iraq‑Iran border. Three classification methods were used aimed at comparing their accuracy, using multispectral satellite images with a spatial resolution of 10 m. The classification process was performed using three different algorithms, namely: Maximum Likelihood Classification (MLC), Artificial Neural Networks (ANN), and Support Vector Machine (SVM). The classification algorithms were carried out using ENVI 5.1 software to detect six land cover classes: deep water marsh, shallow water marsh, marsh vegetation (aquatic vegetation), urban area (built‑up area), agriculture area, and barren soil. The results showed that the MLC method applied to Sentinel 2B images provides a higher overall accuracy and the kappa coefficient compared to the ANN and SVM methods. Overall accuracy values for MLC, ANN, and SVM methods were 85.32%, 70.64%, and 77.01% respectively.

Groundwater is among the most important resources of water supply of the worldwide population. The objective of this research is to select the most suitable drilling location of new groundwater wells, with a decision-making tool using geographic information system (GIS). The optimum location was evaluated based on the hydro-geoelectrical parameter derived from vertical electrical sounding (VES) including longitudinal conductance, aquifer thickness, aquifer resistivity, transmissivity, formation factor, and porosity. The analysis of the VES data revealed that the porosity values range from 1.8 to 21%, the formation factor values vary between 0.0355 and 0.14, the transmissivity values range from 3.4 to 6 m²/day, the longitudinal conductance values vary from 3 to 4.6 Ω⁻¹, the aquifer thickness values range from 4.1 to 8.5 m, the aquifer resistivity varies between 0.7 and 40 Ω m. Hydro-geoelectrical data were integrated into GIS to precisely determine the best location for groundwater wells. Fuzzy liner membership was used to standardize the criteria layers; then, these layers were combined using fuzzy gamma overlay to produce the suitability map. The study area was classified into four classes: excluded, low suitability, moderate suitability, and high suitability. It is recommended that this method should be adopted by different governmental agencies involved in water resources management in Nigeria.

The objective of this research is to select the most suitable drilling location of new groundwater exploration wells, with a decision-making tool from Geographic Information System (GIS). The optimum location will be evaluated base on the hydro-geoelectrical parameter derived from Vertical Electrical Sound (VES) including Longitudinal Conductance, the thickness of the aquifer, the apparent resistivity and Transmissivity. From the Geo-electrical method (VES) the finds shows that the aquifers in the study area have Apparent Resistivity ranging from 0.32 to 40.24 Ωm, Thickness between 0.21 m to 15.06 m, Longitudinal Conductance ranging from 0.006 to 10.246Ω-1 and Transmissivity ranging from 0.14 to 10.38m2/day. Hydro-geoelectrical data were integrated into GIS to precisely determine the best location for groundwater borehole. Finally, the study location was classified into three classes; not suitable, moderately suitable and highly suitability with respect to the input factors using the Fuzzy overly combing method. It’s evident that the middle part of the area in study represents the optimum location for the drilling of groundwater boreholes.