"International Journal of Geography, Geology and Environment"

Surface storage dams are their most important source of water. They store water from mountains and valleys on the Earth's surface, constituting a vital resource.

Some recent studies have shown that sedimentation can significantly reduce dam efficiency. Unless addressed scientifically, the rapid flow of water from mountainous areas carries with it dirt and small gravel. These materials settle in the lake over time, causing a decrease in the volume of water storage. This problem is observed in many dams in Iraq, especially in the northern regions (Al-Mamooriet al., 2019) ^[2] and has been studied by Iraqi researchers.

This study aims to examine the amount of sediment accumulating in the lake to develop solutions that help preserve water storage for the longest possible period (Hassan, 2018) ^[1]. Volumetric analysis of sediments within the lake area This study is the first to classify lake sediments

The percentages of the volume fractions of clastic sediments (sand, silt, and clay) were extracted using the methods mentioned above. It was noted that silt and clay represented the dominant fractions in the studied samples, while sand represented the secondary fraction, as shown in Table (2). The percentage of clay ranged between (5.1% - 48.4%), with an average of (22.26%), while the percentage of silt ranged between (40% - 62.3%), with an average of (51.96%), and the percentage of sand ranged between (4.7% - 53.4%), with an average of (25.08%).

It is clear from the results using mathematical relationships to classify sediments that models

We find that most of the samples fall within the quiet water range, indicating that the samples do not move long distances and settle quickly due to the calm water. Samples (9 and 10) also do not follow a specific pattern, indicating that they are deposited by different processes or indicate instability in water movement. Coarse samples (9, 10) have the best sorting and the lowest standard deviation. Fine samples (7, 1, 2) have the worst sorting and the highest standard deviation. This pattern is consistent with sediment transport theories, where long-distance transport processes lead to better sample sorting, and thus the softest and worst-sorted samples.