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.

This review was conducted to highlight the most influential factors and specify the trends reducing uncertainty and increasing the accuracy of soil and water assessment tool (SWAT)-based hydrological models. Although the resolution of input data on the results of SWAT-based hydrological models has been extensively determined. There is still a gap in providing comprehensive review framework to be emerged for identifying the impact of the data resolution and accuracy. The factors taken into consideration in this study were the impact of digital elevation model (DEM) resolution, soil data resolution, land use and land cover (LULC) resolution, and the impact of weather data resolution. Identifying the best DEM resolution depends on the watershed response and hydrological processes. However, for sediment yield estimation, more attention should be paid to the accuracy of soil data. Furthermore, the impact of LULC resolution on the accuracy of streamflow is still not sufficiently understood, whereas fine resolution is required for an accurate simulation of the sediment yield. Sub-daily precipitation data is essential for an accurate estimation of streamflow. Despite the fact that climate forecast system reanalysis (CFSR) and tropical rainfall measuring mission (TRMM) are the most widely used climate products, climate hazards group infrared precipitation with station data (CHIRPS) produces an adequate estimation for streamflow when there is insufficient gauged data. However, other aspects have not been deeply taken into consideration, including the interactive and complementary impacts of these factors. Thus, more attention and focus should be given to these issues. This review and evaluation can be a significant guide for selecting the suitable input data to implement efficient SWAT-based watershed models.

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.

In this study, the catchment area’s runoff of Hemren dam reservoir within middle River Diyala reach beyond Derbendi-Khan dam at the northeast of Iraq is simulated using the Soil Water Assessment Tool (ArcSWAT-2012) model integrated with remote sensing and geographical information systems. The model was calibrated in monthly time step for the period extended from 1981 to 2000 with 2 years of warm-up period and validated with observed stream flow for years between 2001 and 2008. The model calibration and parameters sensitivity analysis were conducted using automatic calibration method within the SWAT-CUP program. The model performance for the studied catchment area was evaluated with many statistical criteria. In general, the performance of the model was good. Results of sensitivity analysis showed that there are seven parameters that mostly effect on the hydrological processes in the studied catchment; these were ALPHA_BF, CH_K2, CN2, CH_N2, SOL_AWC, REVAPMN, and GW_REVAP. Results showed that an effective runoff happens at wet seasons, and there is not continuous effective base flow from the studied catchment, and the average annual inflow volume to Hemren dam reservoir during the simulation period was 0.871 BCM, i.e., 17.42% of the overall inflow volume to Hemren dam reservoir. Furthermore, it was founded that the use of climate forecast system reanalysis of global weather station data is possible in the studied catchment area. Finally, a simple direct regression formula is determined by correlating the monthly runoff volume with monthly rainfall depth.

Agro-climatology is highly worthy in the identification of agro-climates with the climate change conditions to introduce new agricultural crops. Agro-Climatic Zones permits recognizing areas with different potential products according to their climate change environmental conditions. This study aims to produce Agro-Climatic Zones map using satellite-based climate data in Iraq. Climate data based on satellite data were downloaded and processed with a spatial resolution of 0.25º, then interpolated using the spline method. The annual precipitation was downloaded from TRMM_3B43 v7 satellite data from 1998 to 2018 and the average annual air temperature for the period 2000 to 2018 was downloaded from the GLDAS Model (NOAH025_M v2.1). The GIS-based approach was implemented to create the necessary datasets and to develop an Agro-Climatic Zones map in the Iraq Republic. The results have mapped Iraq republic Agro-Climatic Zones into five zones, which are arid, cool winter, very warm summer (ACZ 1 represent about 48.8 % of the total area); arid, mild winter, very warm summer (ACZ 2 about 36.8 % of the total area); semi-arid, cool winter, very warm summer (ACZ 3 about 8.8 % of the total area); semi-humid to Mediterranean, cool winter, very warm summer (ACZ 4 about 4.2 % of the total area); and very humid to humid, cold to cool winter, warm summer (ACZ 5 about 1.4 % of the total area).