Study on the Impact of Input Flow Rate on Suspended Sediments in Lakes Using Satellite Imagery: A Case Study

Introduction
The main objective of this research is to prepare a temporal and spatial classification of suspended sediment values using Sentinel-2 satellite imagery and physical methods on the wells in Sistan and Baluchestan province. Specifically, this study aims to produce an accurate classification of suspended sediment values in space and time using satellite data and physical methods. Furthermore, by comparing the suspended sediment values with the input flow rate of the wells, more information about the suspended sediment values was obtained. Finally, a regression-based model was presented to estimate the suspended sediment values based on the input flow rate. By analyzing this information, it is possible to gain a better understanding of the behavior of suspended sediments and the factors affecting them over time. Overall, this research can contribute to a better understanding of the behavior of suspended sediments and the factors affecting them.
Methodology
The aim of this study is to accurately classify suspended sediment values in space and time using Sentinel-2 satellite imagery and physical methods on wells in the Sistan and Baluchestan province. To achieve this goal, the researchers utilized the C2RCC processor for spectral calculations and modeling, which is based on deep learning approaches and simulated water reflectance outputs for high-altitude correction algorithms. The processor allows for the calculation of water reflectance in different spectral bands and the estimation of three main water quality parameters, including the concentration of total suspended solids, chlorophyll-a, and colored dissolved organic matter, using various relationships. After retrieving the maps of the concentration classification of suspended sediment parameters in the reservoirs, the researchers aim to examine the monthly input flow rates to the wells with the estimated concentration of suspended sediments. By comparing the input flow rate values with the concentration of suspended sediments in well 1, the researchers can gain more information about the behavior of suspended sediments and the factors affecting them over time. Finally, a regression model will be developed using the corresponding input flow rate and suspended sediment concentration values, with the monthly input flow rate considered as input and the mean concentration of suspended sediments considered as output. It should be noted that various regression methods, including linear, exponential, GPR, and SVR, have been used to model the relationship between input flow rate and suspended sediment. Each of these methods has unique features and advantages and has been selected based on the type of data and the problem at hand. By combining these methods, a comprehensive and accurate model for predicting the concentration of suspended sediments based on the input flow rate between two semi-wells has been developed, which can contribute to a better understanding of the behavior of suspended sediments and the factors affecting them.
Results and discussion
As the results indicate, the concentration of suspended sediments is low during wet years and increases with the increase in input flow rates into the reservoirs. Eventually, the phenomenon of 120-day winds in early May stabilizes the concentration of suspended sediments. This is due to the fact that the input flow rate from semi-well 1 is higher than other points, resulting in a higher concentration of suspended sediments in this semi-well. This is because the input flow rate directly affects the production and movement of suspended sediments in the lake. With an increase in the input flow rate, the two main factors affecting the production of suspended sediments, namely the water current velocity and the energy of sinusoidal waves, also increase. This increase in water current velocity and energy of sinusoidal waves improves the conditions for the production and accumulation of suspended sediments in this point. Therefore, the concentration of suspended sediments in Chah-Nimeh 1 is generally higher than other points in the lake. This figure shows that the retrieval values of suspended sediments using the physical method based on Sentinel-2 satellite imagery are accurate and reliable. This finding indicates that water turbidity data can be used to validate the retrieval values of suspended sediments from other methods. To investigate the effect of input flow rate on suspended sediments in more detail, a time profile of monthly volume input flow rates in millions of cubic meters versus the average concentration of suspended sediments in Chah-Nimeh 1 in milligrams per cubic meter has been studied. With these figures, it can be observed that the concentration of suspended sediments in the lakes is highly influenced by the input flow rate, and it increases with an increase in input flow rate. Additionally, in other months of the year, the amount of suspended sediments has been somewhat constant and accompanied by slight changes. Therefore, it can be concluded that the amount of suspended sediments in the lakes is strongly influenced by the input flow rate, and it increases with an increase in input flow rate. We developed a model for establishing the relationship between input flow rate and the average concentration of suspended sediments using regression methods and monthly input flow rate and average concentration of suspended sediments in semi-well 1 data. As the results show, the GPR model has achieved acceptable results and has been used as the optimal model.
Conclusion
This study used Sentinel-2 satellite imagery to estimate suspended sediment parameters in lakes and reservoirs. A strong correlation was found between the volume of input flow rates and the average concentration of suspended sediments, particularly in Chah-Nimeh 1. A regression-based model was developed to estimate the overall concentration of suspended sediments. This study can provide helpful information for managing water resources and aquatic ecosystems.