Estimate of the Surface Evaporation from the Tharthar Lake (Buhayrat Tharthar) in Iraq, Based on Remote Sensing Data and Techniques of Geographic Information Systems GIS

Abstract

Tharthar is the largest water reservoir in Iraq. It is Giving the circumstances of drought and water crisis in Iraq, which is considered a semi-arid region, along with significantly feverish temperatures, especially in the summer. It is expected that evaporation rates will increase. Therefore, it was necessary to determine the water loss due to evaporation from Lake Tharthar. Due to the lack of ground measurement stations providing such data, the study relied on remote sensing data, specifically satellite imagery from Landsat 8 and Landsat 9. These images were used to extract temperature values for Lake Tharthar for the hydrological year extending from October 2022 to September 2023. This was done in conjunction with geographic information system (GIS) software and using ArcMap to calculate the annual evaporation rate and volume of the lake. Spatial modeling of evaporation was conducted for each month using the modified Lambert equation, adapted from the Penman-Cridle equation, which is most suitable for the climatic conditions of Iraq. Using this methodology saved time, effort, and cost, and yielded satisfactory results compared to field measurements of nearby water bodies. Temperature and evaporation rates were calculated at the level of each image pixel (30×30 meters). The research concluded many important findings, including that the total annual evaporation from the lake is estimated at 1881.16 millimeters (2.930 billion cubic meters per year). Accordingly, the annual loss per square kilometer from Lake Tharthar is estimated at 188 million cubic meters. This means that when Lake Tharthar reaches its maximum capacity, expanding the area to 2710 square kilometers, the evaporation loss will increase to 5.089 billion cubic meters per year. This necessitates relevant authorities to take necessary measures to reduce the annual evaporation loss from the lake surface. The study recommends adopting this method in cases where data is not available for locations lacking weather stations or when there is a data shortage.