نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشیار گروه مهندسی آب دانشکده آب و خاک دانشگاه علوم کشاورزی و منابع طبیعی گرگان
2 دانشجوی کارشناسی ارشد علوم و مهندسی آب، دانشگاه علوم کشاورزی و منابع طبیعی گرگان
3 دانشیار گروه مهندسی آب، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران
چکیده
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
reservoirs are very important for storing rainwater and floods, and water shortage management. In nearly all reservoirs, storage capacity is steadily lost due to trapping and accumulation of sediment. Sediment deposition in water reservoirs has major implications for storage capacity, reservoir lifetime and water quality. The present study aimed to evaluate the temporal dynamics of water stored and sedimentation rate in a reservoir using remote sensing data. For this purpose, the study was carried out in O. H. Ivie reservoir located in the America country. The techniques used to carry out this study have been pre-processing of Landsat 8 images, modeling and identifying water pixels using MNDWI index, evaluating reservoir capacity, and compression of results with recent bathymetric survey data to assessment sedimentation rate. According to the results, the average errors of computing the volume of water stored in the reservoir was about 9%. Based on this, the storage capacity of O. H. Ivie reservoir has decreased from 695 million cubic meters at the beginning of operation (1991) to 472 million cubic meters in 2019. The results showed that the lost storage capacity of the reservoir due to sedimentation is about 32% of the original volume and the annual sedimentation rate is 1.4%. Also, by evaluating the obtained results, the average height of sediment deposited in the reservoir between 2004 and 2019 was estimated to be about 9 meters. This research confirmed that remote sensing can estimate storage capacity and sedimentation rate in the reservoir with minimal cost and time.
کلیدواژه‌ها [English]
Estimation of sedimentation rate and storage capacity of reservoir dams using satellite imagery
EXTENDED ABSTRACT
For thousands of years humans have relied on reservoirs—regulated natural lakes and human made ones, for water supply, irrigation, and more recently hydropower generation. Reservoirs created by impounding sediment-laden streams infill over time, reducing storage capacity and altering water quality. sedimentation rates are poorly understood due to sparse bathymetry survey data and challenges in modeling sedimentation sequestration. The loss of reservoir capacity especially in developing countries brings with it adverse environmental, social and economic problems to people relying on these dams Sediment deposition in water reservoirs has major implications for storage capacity, reservoir lifetime, and water quality. Changes in rainfall patterns and land use will consequently alter the rate of erosion and therefore have a direct effect on sedimentation rates. Therefore, it is imperative that reservoir capacity re-assessment studies are regularly carried out. current methods of sediment analysis being employed in many countries are resource demanding in a context of financial and material resources scarcity. Reservoir capacity estimation and sedimentation analysis have commonly been conducted through the use of either direct or indirect methods of sediment quantification. direct methods refer to hydrographic survey techniques, which measure the actual sediments in the reservoir. Indirect methods refers to sediment sampling and soil loss models which can be used to indirectly quantify expected sediment flow into a reservoir without conducting any direct measurements in the reservoir. we proposed a novel approach to estimate reservoir sedimentation rates and storage capacity losses using Landsat-8 OLI satellites and daily in situ water levels.
The study sought to monitor sedimentation of O. H. Ivie reservoir in the state of Texas in USA. Six Landsat 8 OLI datasets for the period 2016 to 2019 were used. Remotely sensed sedimentation data was analyzed using the MNDWI index method. The study employed a longitudinal survey design. Longitudinal survey design allowed the use of data collected overtime between years 1991 and 2019. Dam level and remotely sensed data was used in the study and the Modified Normalized Difference Water Index (MNDWI) method was employed for data analysis. After computing the water surface areas, the reservoir capacity between two consecutive reservoir water levels was computed by the Prismoidal method. Finally, we estimated the sediment volume and sedimentation rate based on the difference between the near-present storage capacity and the original maximum storage in design-shown. DAHITI and TWDB database were used to validate the data.
By comparing the results of estimating the volume of water stored in the O. H. Ivie reservoir with the data of DAHITI database, the result shown, the average error of calculating the volume of water stored was found to be about 9%. Based on TWDB database and the results, the storage capacity of O. H. Ivie reservoir has decreased from 695 million cubic meters at the beginning of operation (1991) to 472 million cubic meters in 2019. The results showed that the lost storage capacity of the reservoir due to sedimentation is about 32% of the original volume and the annual sedimentation rate is 1.4%. Also, the lost storage volume of the reservoir between 2004 and 2019 was about 198 million cubic meters. by evaluating the obtained results, the average height of sediment deposited in the reservoir between 2004 and 2019 was estimated to be about 9 meters.
The study has indicated that the use of GIS and remote sensing techniques enabled a fast and reasonably accurate estimation of live storage capacity losses due to sedimentation. The approach has also been found to be cost-effective and convenient approaches to estimate the elevation–area–capacity curves for the reservoirs. The results have also indicated that this approach for sedimentation surveys can be carried out at smaller
intervals and longer periods than Conventional methods, Remote sensing and GIS can be used to a large extent, to overcome the difficulty in the collection, transfer, and sharing of a large amount of bathymetric data. Moreover, the proposed methodology can be used largely, to overcome information scarcity problems when the field survey data and physically based models are unavailable. The results of the present study can assist in developing effective management strategies and providing realistic options to policymakers for managing soil erosion hazards most efficiently for prioritizing different regions of the reservoirs for remedial treatments.
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