## 3 Materials and Methods  
The data used in the SWAT model were in two different modes: spatial and temporal. The spatial data included DEM, slope, parent material of soil, and LULC (Figure 2). The temporal data consisted of weather data (precipitation, relative humidity, temperature, solar radiation, and wind speed) (Table 1). We extracted the DEM data from EARTHDATA Search [60]. Images with a resolution of 30 m were downloaded from ASTER-DEM. These images were imported to ArcGIS and mosaicked using \"Mosaic to New Raster\", which is a tool in \"Data Management Tools\". The combined map was re-projected after determining the Universal Transverse Mercator (UTM) zone. Next, we used \"DEM Manipulation\" which is a tool under \"Terrain Processing\" which is in turn an option under \"Arc Hydro Tools\" > \"Fill Sinks\". To obtain the flow accumulation, \"Flow Direction\" and \"Flow Accumulation\" were selected from \"Terrain Processing\". The basin's slope was obtained using \"Spatial Analyst Tools\" > \"Surface\" > \"Slope\". We extracted the basin's soil map from the Food  
Figure 1: Study site. Note that the map was drawn based on “GIS-based Multi-Criteria Analysis for Flood-Prone Areas Mapping in the Trans-boundary Shatt Al-Arab Basin, Iraq-Iran” [59]. and Agriculture Organization (FAO) [61]. Next, soil classes were accessed from the SWAT Soil Data [62]. Furthermore, we exported the soil map to ArcGIS 10.4.1 and georeferenced it into the associated UTM coordinate system. The soil map is geocoded for each parent material of the soil (here: soil type) according to the types retrieved from the SWAT Soil Data. LULC data were extracted from the Earth Explorer's land cover data [63]. We selected \"Land Cloud Cover\" and \"Scene Cloud Cover\" of less than 10% to acquire images with minimal or no cloud cover. The ERDAS IMAGINE 2014 was utilized to analyze and improve the acquired images. We used ERDAS IMAGINE supervised classification to classify the LULC categories. Through the supervised classification process, polygons are selected, digitalized, and then placed in an \"Area of Interest\" layer to produce signature files. Numerous polygons for a certain LULC class were made to give this LULC a distinct class. The supervised classification approach is generally more accurate, although it is time-consuming and laborious compared to the unsupervised method [64]. LULC data were also made in the UTM zone (Figure 2). Furthermore, we extracted weather data (precipitation, relative humidity, temperature, solar radiation, and wind speed) from POWER's Data Access Viewer [65]. Figure 2: (**a**) LULC; (**b**) DEM; (**c**) Soil types based on parent material; and (**d**) Slope of the Shat AI-Arab river basin. ArcSWAT in ArcGIS was enabled, and data were added. To obtain the subbasins in the Bhatt Al-Arab basin, we selected Add DEM > Watershed Delineation > Create Streams and Outlets. The subbasins were numbered from the upper parts of the Shat Al-Arab river to the outlet of the basin. Next, slope, soil, and LULC were set using the Hydrological Response Unit (HRU) Analysis tool. The HRU Analysis tool was used to define slope, soil, and LULC resolution (i.e., the number of classes in a unit area). HRU consists of distinct features of the slope, soil, and land use. Within a watershed, geographical variability in terms of slope, soil, and land use class is described using HRUs. For each HRU unit, the model calculates pertinent hydrologic components such as surface runoff, peak runoff, groundwater flow, evapotranspiration, and sediment yield [66,67]. We selected slope class percentage (%) over a slope area of 15%, soil class percentage (%) over a soil area of 15%, and LULC percentage (%) over a subbasin area of 12%. Similarly, weather data were also set by selecting Write Input Tables > Weather Stations > WGEN_CFSR_World. Then we looked at precipitation, relative humidity, temperature, solar radiation, and wind speed. Tables for such data were created, and SWAT data were updated.