Emerging Technologies for the Management of the Azolvamiento at La Estancilla Dam, Manabí, Ecuador
DOI:
https://doi.org/10.56294/saludcyt2024.1067Keywords:
Siltation, Water Resources Management, Erosion, La Estancilla Dam, SedimentationAbstract
Introduction: this article focused on the management of siltation in the La Estancilla dam in Manabí, Ecuador. It highlights how one of the emerging technologies can help reduce risks to infrastructure and water resources, mitigating siltation.
Objective: the impact of siltation on the dam was analyzed using AutoCAD Civil 3D software, and how emerging technologies contribute to the management and monitoring of sedimentation was also explored.
Methods: an applied research was conducted, adopting a descriptive and quantitative approach. In situ data was collected using GPS and total station, as well as three-dimensional simulations using AutoCAD Civil 3D to model the evolution of siltation. Additionally, a systematic review of the scientific literature was carried out by selecting 20 relevant articles to identify how emerging technologies can improve the monitoring and mitigation process of sedimentation.
Results: the results showed a 45.06% decrease in the active capacity of the reservoir by the end of 2024 and a possible full saturation of more than 100% by 2042, emphasizing the urgent need to implement more advanced technologies. Simulations and the systematic review demonstrated that the use of drones and predictive models can significantly improve accuracy and efficiency in sediment management.
Conclusions: The implementation of emerging technologies could ensure the long-term functionality and sustainability of the reservoir, optimizing siltation mitigation strategies and reducing dependence on conventional methods like dredging
References
1. Obando J, Vera M, Matsumoto I, Dublin D, Natori Y, Calispa A. Garantizar la conservación, la buena gobernanza y los medios de vida sostenibles a través de la gestión del paisaje de los ecosistemas de manglares en Manabí, Ecuador. 2018.
2. Espa P, Batalla R, Brignoli M, Crosa G, Gentili G, Quadroni S. Tackling reservoir siltation by controlled sediment flushing: Impact on downstream fauna and related management issues. PLoS One. 2019;14.
3. Winton R, Calamita E, Wehrli B. Reviews and syntheses: Dams, water quality and tropical reservoir stratification. Biogeosciences. 2019;
4. Majerova M, Neilson B, Schmadel N, Wheaton J, Snow C. Impacts of beaver dams on hydrologic and temperature regimes in a mountain stream. Hydrol Earth Syst Sci. 2015;19:3541–56.
5. Landwehr T, Kantoush S, Pahl‐Wostl C, Sumi T, Irie M. The effect of optimism bias and governmental action on siltation management within Japanese reservoirs surveyed via artificial neural network. Big Earth Data. 2020;4:68–89.
6. Maavara T, Chen Q, Meter KV Van, Brown L, Zhang J, Ni J, et al. River dam impacts on biogeochemical cycling. Nat Rev Earth Environ. 2020;1:103–16.
7. Schälchli U. Basic Equations for Siltation of Riverbeds. Journal of Hydraulic Engineering. 1995 Mar;121(3):274–87.
8. Yang Z, Duan X, Huang J, Dong Y, Zhang X, Liu J, et al. Tracking long-term cascade check dam siltation: implications for debris flow control and landslide stability. Landslides. 2021 Dec 12;18(12):3923–35.
9. Wang Z, Gui L, Zhang J, Li Y. Research on the Influence of Siltation Height of Check Dams the on Discharge Coefficient of Broad-Crested Weirs. Water (Basel). 2023 Jan 27;15(3):510.
10. Kang RS, Chacko E, Kaur D, Viadero R. Silting patterns in the reservoirs of small‐ and medium‐sized earthen check dams in humid subtropical monsoon regions. Earth Surf Process Landf. 2019 Oct 14;44(13):2638–48.
11. Turgeon K, Turpin C, Gregory‐Eaves I. Dams have varying impacts on fish communities across latitudes: A quantitative synthesis. bioRxiv. 2018;
12. Doretto A, Bo T, Bona F, Apostolo M, Bonetto D, Fenoglio S. Effectiveness of artificial floods for benthic community recovery after sediment flushing from a dam. Environ Monit Assess. 2019;191:1–12.
13. Sun W, Zhang P. Analysis of Silt Dam System Grade and Joint Regulation in Typical Small Watershed of Loess Plateau. IOP Conf Ser Earth Environ Sci. 2020 Jun 1;526(1):012030.
14. Yong-Jun L. Integration of flow-sediment mathematical model and visualization system. Hydro-Science and Engineering. 2007;
15. Ji Z, Hamrick JH, Pagenkopf J. Sediment and Metals Modeling in Shallow River. Journal of Environmental Engineering. 2002;128:105–19.
16. Olsen N, Hillebrand G. Long-time 3D CFD modeling of sedimentation with dredging in a hydropower reservoir. J Soils Sediments. 2018;18:3031–40.
17. Hillebrand G, Klassen I, Olsen N. 3D CFD modelling of velocities and sediment transport in the Iffezheim hydropower reservoir. Hydrology Research. 2017;48:147–59.
18. Wu W, Rodi W, Wenka T. 3D NUMERICAL MODELING OF FLOW AND SEDIMENT TRANSPORT IN OPEN CHANNELS. Journal of Hydraulic Engineering. 2000;126:4–15.
19. Kougias I, Aggidis G, Avellan F, Deniz S, Lundin U, Moro A, et al. Analysis of emerging technologies in the hydropower sector. Renewable and Sustainable Energy Reviews. 2019;
20. Mohafid S, Stour L, Agoumi A. Optimization of the methodology for valorization of mud retained by dams: Morocco case study. Global Journal of Engineering and Technology Advances. 2023;
21. Remini B. A NEW MANAGEMENT APPROACH OF DAMS SILTATION. LARHYSS Journal. 2017;51–81.
22. Rashid I, Pandit DrBA. Various Remedial Measures for Dam Siltation. Int J Res Appl Sci Eng Technol. 2023;
23. Kantoush S, Sumi T. The aging of Japan’s dams: Innovative technologies for improving dams water and sediment management. 2016;1030–7.
24. Morris G. Classification of Management Alternatives to Combat Reservoir Sedimentation. Water (Basel). 2020 Mar 19;12(3):861.
25. Rachman LM, Hidayat Y, Tarigan SD, Sitorus SP, Fitri R, Ain AQ. The Effect of Agroforestry System on Reducing Soil Erosion in Upstream Ciliwung Watershed. IOP Conf Ser Earth Environ Sci. 2020 Aug 1;556(1):012010.
26. Lee Y, Yoon T, Shah FA. Economics of integrated watershed management in the presence of a dam. Water Resour Res. 2011 Oct 12;47(10).
27. Perera D, Williams S, Smakhtin V. Present and Future Losses of Storage in Large Reservoirs Due to Sedimentation: A Country-Wise Global Assessment. Sustainability 2023, Vol 15, Page 219 [Internet]. 2022 Sep;15(1):219. Available from: https://www.mdpi.com/2071-1050/15/1/219/htm
28. Pricope N, Bashit MdS. Emerging trends in topobathymetric LiDAR technology and mapping. Int J Remote Sens. 2023;44:7706–31.
29. Szafarczyk A, Toś C. The Use of Green Laser in LiDAR Bathymetry: State of the Art and Recent Advancements. Sensors (Basel). 2022;23.
30. Tishechkin D, Agbor Y. Step into Geological Samples Digital Twins. Day 4 Thu, October 05, 2023. 2023;
31. Evans A, Gardner K, Greenwood S, Still BM. UAV and Structure-From-Motion Photogrammetry Enhance River Restoration Monitoring: A Dam Removal Study. Drones. 2022;
32. Guilinger J, Foufoula‐Georgiou E, Gray A, Randerson J, Smyth P, Barth N, et al. Predicting Postfire Sediment Yields of Small Steep Catchments Using Airborne Lidar Differencing. Geophys Res Lett. 2023;50.
33. Long N, Millescamps B, Guillot B, Pouget F, Bertin X. Monitoring the Topography of a Dynamic Tidal Inlet Using UAV Imagery. Remote Sens. 2016;8:387.
34. Clapuyt F, Vanacker V, Oost K. Reproducibility of UAV-based earth topography reconstructions based on Structure-from-Motion algorithms. Geomorphology. 2016;260:4–15.
35. Chung C, Wang YK. Practical Assessment of Real‐Time Suspended Sediment Load Monitoring Using Time Domain Reflectometry. Water Resour Res. 2022;58.
36. Hsu Y, Wei C, Ting Y, Yuan SY, Chang CL, Chang KC. Capacitive sensing technique for silt suspended sediment concentration monitoring. International Journal of Sediment Research. 2010;25:175–84.
37. Yang L, Wang Y, Zhao Z, Guo Y, Chen S, Zhang W, et al. Particle-Laden Droplet-Driven Triboelectric Nanogenerator for Real-Time Sediment Monitoring Using a Deep Learning Method. ACS Appl Mater Interfaces. 2020;12 34:38192–201.
38. Guo H, Sun Z. Channel and Energy Modeling for Self-Contained Wireless Sensor Networks in Oil Reservoirs. IEEE Trans Wirel Commun. 2014;13:2258–69.
39. Eidam E, Langhorst T, Goldstein E, McLean M. OpenOBS: Open‐source, low‐cost optical backscatter sensors for water quality and sediment‐transport research. Limnol Oceanogr Methods. 2021;20.
40. Lee CJ, Foster G. Assessing the potential of reservoir outflow management to reduce sedimentation using continuous turbidity monitoring and reservoir modelling. Hydrol Process. 2013;27.
41. Reisenbüchler M, Bui M, Rutschmann P. Reservoir Sediment Management Using Artificial Neural Networks: A Case Study of the Lower Section of the Alpine Saalach River. Water (Basel). 2021;
42. Rajaee T, Jafari H. Two decades on the artificial intelligence models advancement for modeling river sediment concentration: State-of-the-art. J Hydrol (Amst). 2020;588:125011.
43. Guo Y, Xu Y, Yu X, Chen H, Gu H, Xie J. AI-based techniques for multi-step streamflow forecasts: Application for multi-objective reservoir operation optimization and performance assessment. Hydrol Earth Syst Sci. 2020;
44. Band SS, Pal SC, Bateni S, Jun C, Saha A, Chowdhuri I, et al. Using computational-intelligence algorithms and remote sensing data to optimize the locations of check dams to control sediment and runoff in Kandolus watershed, Mazandaran, Iran. Geocarto Int. 2022;37:12966–88
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Copyright (c) 2024 Martha Johana Álvarez-Álvarez, Julio Johnny Regalado-Jalca, Julio Cesar Pino Tarragó (Author)
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