| تعداد نشریات | 282 |
| تعداد نشریات سازمان | 80 |
| تعداد شمارهها | 3,219 |
| تعداد مقالات | 35,690 |
| تعداد مشاهده مقاله | 53,561,195 |
| تعداد دریافت فایل اصل مقاله | 92,841,444 |
بررسی کارآیی ساختارهای مختلف مدل ترکیبی در روش انگشتنگاری رسوب برای تعیین سهم منابع مختلف رسوب تهنشینشده در مخزن سد خاکی لاور فین، استان هرمزگان | ||
| پژوهش های آبخیزداری | ||
| مقاله 7، دوره 32، شماره 2 - شماره پیاپی 123، تیر 1398، صفحه 79-94 اصل مقاله (844.81 K) | ||
| نوع مقاله: پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/wmej.2019.124151.1170 | ||
| نویسندگان | ||
| سمانه حبیبی1؛ حمید غلامی* 2؛ ابوالحسن فتح ابادی3؛ دسموند والینگ4 | ||
| 1دانش آموختهی دکترای مهندسی آبخیزداری، گروه مهندسی منابع طبیعی، دانشکده کشاورزی و منابع طبیعی، دانشگاه هرمزگان، بندرعباس، ایران | ||
| 2عضو هیات علمی گروه مهندسی منابع طبیعی، دانشکده کشاورزی و منابع طبیعی، دانشگاه هرمزگان، بندرعباس، ایران | ||
| 3عضو هیات علمی گروه مرتع و آبخیزداری، دانشکده کشاورزی و منابع طبیعی، دانشگاه گنبد کاووس، گنبد، ایران | ||
| 4استاد گروه جغرافیای طبیعی، دانشگاه اکستر، اکستر، انگلستان | ||
| چکیده | ||
| برای مهارکردن فرسایش و کاهشدادن رسوب خروجی هر آبخیز باید قسمتهایی از آنکه سهمشان در رسوب خروجی بیشتر است شناسایی گشته، و اقدامهای حفاظتی بر آنها متمرکز شود. یکی از متداولترین روشهایی که در سالهای اخیر در تعیین سهم منابع مختلف بهکار رفته روش انگشتنگاری است. تاکنون ساختارهای مختلفی از این روش داده شده که لازم است کارآیی آنها نسبت به یکدیگر مقایسه، تا ضعفها و قوتهای آنها شناسایی شود. برای کمّیکردن سهم منابع در تولید رسوبهای تهنشینشده در مخزن سد لاور فین در استان هرمزگان، کارآیی هشت مدل ترکیبی کولینز، هیوز، موتا، اسلاتری، لندور، لندور اصلاحشده، مدل بیزی با تبدیل CLR، مدل بیزی با توزیع دریکله بررسی و مقایسه گردیدند. پس از جمعآوری اطلاعات اولیه و تهیهی نقشههای پایه در بازدید میدانی، 23 نمونهی سطحی از سه زیرحوضه و نه نمونه از رسوبهای تهنشینشده در مخزن سد جمعآوری، و برای هر نمونه 56 ردیاب اندازهگیری شد. ترکیب بهینهی ردیاب با استفاده از روشهای آماری شناسایی، و مدلهای مختلف ترکیبی اجرا شدند. نتایج نشان دادند که چهار عنصر Mn، La، Nd وTh ترکیب بهینهی ردیابها بود. نتایج مدلهای ترکیبی نشان داد که در حالتی که اندازهی ردیابهای نمونههای رسوب در دامنهی اندازهی متوسط ردیابهای منابع باشد عملکرد تمام مدلها شبیه است. در حالتی که اندازهی ردیابهای نمونههای رسوب خارج از اندازهی متوسط ردیابهای منابع باشد، بسته به نوع تابع، بهینهسازی عملکرد مدلهای مختلف متفاوت، و عملکرد مدلها با تابع هدف مشابه یکسان بود، بهطوری که در این پژوهش مدلهای کولینز با هیوز و بیزی با تبدیل CLR، موتا با اسلتری و لندور با لندور اصلاحشده شبیه عمل کردند. بهطور کلی، کارآیی مدلهای ترکیبی مختلف در انگشتنگاری متفاوت است، و خروجی آنها به نوع تابع هدف، که در بهینهسازی کمینه میشود، بستگی دارد. | ||
| کلیدواژهها | ||
| تحلیل تابع تشخیص؛ ترکیب بهینه ردیابها؛ مدل ترکیبی؛ منشاءیابی رسوب | ||
| عنوان مقاله [English] | ||
| Assessment of the Applicability of the Mixing ModelsUsed in the Sediment Fingerprinting of Different SourcesDeposited in the Lavar Fin Reservoir, the Province of Hormozgan | ||
| نویسندگان [English] | ||
| Samaneh Habibi1؛ Hamid Gholami2؛ Abolhassan Fathabadi3؛ Desmond Walling4 | ||
| 1Ph.D. of Watershed Management, Department of Natural Resources Engineering, University of Hormozgan, Bandar-Abbas, Hormozgan, Iran | ||
| 2Department of Natural Resources Engineering, University of Hormozgan, Bandar-Abbas, Hormozgan, Iran | ||
| 3Department of Rangeland and Watershed Management Engineering, University of Gonbad, Gonbad, Iran | ||
| 4Professor of Physical Geography, University of Exeter, Exeter, UK | ||
| چکیده [English] | ||
| Identification of the erosion-pron parts of a watershed is of at most importance if the soil conservation activities are to be implemented on it to mitigate sedimentation into the flood-receiving reservoir. Sediment fingerprinting is one of the most common methods used for quantifying source contributions of the suspended load. As the mixing models with different structures of sediment fingerprinting method are implemented, their advantagesand disadvantages should be identified. The applicability of eight mixing models,namely: Collins, Hughes, Motha, Slattery, Landwher, Modified Landwher, and Bayesian with the CLR transformation and the Dirichlet distribution were investigated in order to quantify source contributions of sediment deposited in the Lavar Reservoir, the Province of Hormozgan. Twenty-three soil samples were collected from the contributing watersheds, 9 sediments samples were extracted from the reservoir, and concentration of 56 elements were measured in each of the samples. The optimum composite fingerprints were identified by statistical methods and the mixing models were executed. Based on the results, four geochemical properties,namely Mn, La, Nd and Th were selected as optimum fingerprints. The results obtained by all of the mixing models were similar when the values of tracer concentrations in the sediment samples fall inside of those ranges in the source samples. When the values of tracers in the sediment samples fall outside of those values in the source samples, the mixing models with the same objective functions presented similar results. The results of Collins̛, model were similar to those of Hughes, and the results of Bayesian models were similar to those of Hughes the with the CLR transformation;the results calculated by the Motha were similar to those presented by Slattery, and results of Landwher were similar to the modified Landwher. Generally, applicability of the various mixing models in fingerprinting are different, as their outputs are dependent on the target functions, which are minimized in optimization. | ||
| کلیدواژهها [English] | ||
| Discriminant function analysis, mixing model, optimum composite fingerprints, sediment sourcing | ||
| مراجع | ||
|
Brosinsky A, Foerster S, Segl K, Kaufmann H. 2014. Spectral fingerprinting:sediment source discrimination and contribution modelling of artificial mixtures based on VNIR-SWIR spectral properties. J. Soil Sediments 14: 1949–1964. Collins AL, Pulley S, Foster IDL, Gellis A, Porto P, Horowitz AJ. 2017. Sediment source fingerprinting as an aid to catchment management: A review of the current state of knowledge and a methodological decision-tree for end-users. Journal of Environment Management, 194: 86–108. https://doi.org/10.1016/j.jenvman. 2016.09.075 Collins AL, Walling DE, Leeks GJL. 1997. Fingerprinting the origin of fluvial suspended sediment in larger river basins: combining assessment of spatial provenance and source type. Geografiska Annaler. 79: 239–254.
Collins AL, Walling DE. 2004. Documenting catchment suspended sedimentsources: problems, approaches and prospects. Prog. Phys. Geogr. 28: 159–196. CollinsAL, Walling DE. 2007. The storage and provenance of fine sediment on thechannel bed of two contrasting lowland permeable catchments, UK. River Res.Appl. 23: 429–450. Collins AL, Walling DE, Sichingabula HM, Leeks GJL. 2001. Using 137Cs measurements to quantify soil erosion and redistribution rates for areas under different land use in the Upper Kaleya River basin, southern Zambia. Geoderma. 104: 299–323. Collins AL, Zhang Y, Walling DE, Grenfell SE, Smith P, Grischeff J, Brogden D. 2012. Quantifying fine-grained sediment sources in the River Axe Catchment, southwest England: Application of a Monte-Carlo numerical modelling framework incorporating local and genetic algorithm optimisation. Hydrological Processes. 26 (13): 1962–1983. doi:10.1002/hyp.8283. Collins AL, Zhang YS, Duethmann D, Walling DE, Black KS. 2013. Using a novel tracing-tracking framework to source fine-grained sediment loss to watercourses at sub-catchment scale. Hydrological Processes. 27 (6): 959–974. doi:10.1002/hyp.9652. Cooper RJ, Krueger T, Hiscock KM, Rawlins BG. 2015. High-temporal resolution fluvial sediment source fingerprinting with uncertainty: A Bayesian approach. Earth Surface Processes and Landforms. 40(1): 78–92. doi:10.1002/esp.3621. Dahmardeh Behrooz R, Gholami H, Telfer MW, Jansen JD, Fathabadi A. 2019. Uisng GLUE to pull apart the provenance of atmospheric dust. Aeolian Research. 37: 1–13. https://doi.org/10.1016/j.aeolia. 2018.12.001. Davies J, Olley J, Hawker D, McBroom J. 2018. Application of the Bayesian approach to sediment fingerprinting and source attribution. Hydrological Processes. 32(26): 3978–3995. Devereux OH, Prestegaard KL, Needelman BA, Gellis AC. 2010. Suspended-sediment sources in an urban watershed, Northeast Branch Anacostia River, Maryland. Hydrological Processes. 24: 1391–1403.doi. 10.1002/hyp.7604. Franks SW, Rowan JS. 2000. Multi-parameter fingerprinting of sediment sources: Uncertainty estimation and tracer selection. Comput. Methods Water Resour. 13:1067–1074. Gellis AC, Hupp CR, Pavich MJ, Landwehr JM, Banks WSL, Hubbard BE, Langland MJ, Ritchie JC, Reuter JM. 2009. Sources, transport, and storage of sediment in the Chesapeake Bay Watershed. U.S. Geological Survey ScientificInvestigations Report. 2008–5186.pp.95: 10.3133/sir20085186 Gholami, H., Jafari TakhtiNajad, E., Collins, A.L. and Fathabadi, A. 2019. Monte Carlo fingerprinting of the terrestrial sources of differentparticle size fractions of coastal sediment deposits using geochemicaltracers: some lessons for the user community. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-019-05443-0 Gholami H, Telfer MW, Blake WH, Fathabadi A. 2017. Aeolian sediment fingerprinting using a Bayesian mixing model. Earth Surf. Process. Landforms. 42: 2365–2376. doi: 10.1002/esp.4189. Habibi S, Gholami H, FathabadiA, Jansen J. 2019. Fingerprinting sources of reservoir sediment via two modelling approaches. Science of the Total Environment,663- 78-96. https://doi.org/10.1016/j.scitotenv. 2019.01.327. Haddachi A, Olley J, Pietsch T. 2015. Quantifying sources of suspended sediment in three size fractions. J. Soils Sediments. 15: 2086–2100. Haddadchi A, Olley J, Laceby P. 2014. Accuracy of mixing models in predicting sediment source contributions.Sci. Total Environ,Nov. 1: 497–498:139–52. Haddadchi A, Ryder D, Evrard O, Olley J. 2013. Sediment fingerprinting in fluvial systems: Review of tracers, sediment sources and mixing models. International Journal of Sediment Research. 28: 560–578. doi.org/10.1016/S1001-6279(14)60013-5. Hughes AO, Olley JM, Croke JC, McKergow LA. 2009. Sediment source changes over the last 250 years in a dry-tropical catchment, central Queensland, Australia. Geomorphology. 104: 262–275.doi.org/10.1016/j.geomorph.2008.09.003. Jafari TakhtiNajad, E, Gholami H, Collins AL. and Fathabadi, A. 2019. Identifying and quantifying the terrestrial sediment source contributions to coastal dunes for targeting wind erosion mitigation in Jagin Wateershed, Hormozgan province, Iran. Watershed Management Research, vol 32, no.1, Ser.No:122, Springer, 3-18. Doi:10.22092/wmej.2019.123109.1142. KlagesMG, HsiehYP. 1975. Suspended solids carried by the GallatinRiver of southwestern Montana: II. Using minerology for inferringsources. J Environ Qual. 4:68–73. Koiter AJ, Owens PN, Petticrew EL, Lobb DA. 2013. The behavioural characteristics of sediment properties and their implications for sediment fingerprinting as an approach for identifying sediment sources in river basins. Earth-Science Reviews. 125: 24–42. Laceby JP, Olley J, Pietsch TJ, Sheldon F, Bunn SE.2015. Identifying subsoil sediment sources with carbon and nitrogen stable isotope ratios. Hydrol. Process. 29(8): 1956–1971. doi: 10.1002/hyp.10311. Lees JA. 1997. Mineral magnetic properties of mixtures of environmental andsynthetic materials: linear additivity and interaction effects. Geophys. J. Int. 131:335–346. Miller J, Macklin G, Orbock Miller SM. 2015. Application of geochemical tracersto fluvial sediment. Springer Publishing. Motha JA, Wallbrink PJ, Hairsine PB, Grayson RB. 2003. Determining the sources of suspended sediment in a forested catchment in southeastern Australia. Water Resources. 39 (3):1056. doi:10.1029/2001wr000794. Mukundan R, Walling DE, Gellis AC, Slattery MC, Radcliffe DE. 2012. Sediment source fingerprinting: transforming from a research tool to a management tool. J. Am. Water Resour. Assoc. 48: 1241–1257. Owens PN, Blake WH, Gaspar L, Gateuille D, Koiter AJ, Lobb DA, Petticrew EL, Reiffarth DG, Smith HG, Woodward JC. 2017. Fingerprinting and tracing the sources of soils and sediments: earth and ocean science, geoarchaeological, forensic, and human health applications. Earth Sci. Rev. 162:1–23. https://doi.org/10.1016/j. earscirev.2016.08.012. Palazón L, Gaspar L, Latorre B, Blake WH, Navas A. 2015. Identifying sediment sources by applying Peart MR, Walling DE. 1986. Fingerprinting sediment source: the example of a drainage basin in Devon, UK. In: Drainage Basin Sediment Delivery. IAHS Publ. 159, IAHS Press, Wallingford, UK. pp 41–55. Porto P, Walling DE, Cogliandro V, Gallegari G. 2016. Validating a mass balance accounting approach to using 7Be measurements to estimate event‐based erosion rates over an extended period at the catchment scale. Water Resource Research. 52(7): 5285–5300. Slattery M, Walden J, Burt TP. 2000. Fingerprinting suspended sediment sources using mineral magnetic measurements- A quantitative approach. Tracers in geomorphology, John Wiley and Sons: pp. 309–322. Smith HG, Blake WH. 2014. Sediment fingerprinting in agricultural catchments: A critical re-examination of source discrimination and data corrections. Geomorphology. 204: 177–191. Vale SS, Fuller IC, Procter JN, Basher LR, Smith I E. 2016. Characterization and quantification of suspended sediment sources to the Manawatu River, New Zealand. Science of the Total Environment. 543: 171–186. Wall GJ, Wilding LP. 1975. Minerology and related parameters of fluvialsuspended sediments in Northwestern Ohio. J Environ Qual. 5:168–173. Walling DE. 2005. Tracing suspended sediment sources in catchments and river systems. Science of the Total Environment. 344(1–3): 159–184. doi:10.1016/j.scitotenv.2005.02.011. WallingDE, Woodward JC, Nicholas AP. 1993. A multi-parameterapproachto fingerprinting suspended-sedimentsources.In: Tracers in hydrology.IAHS Publ. 215, IAHS Press, Wallingford, UK. pp 313–318. Wood PA. 1978. Fine-sediment mineralogy of source rocks andsuspended sediment, rother catchment, West Sussex.EarthSurfProcess. 3:255–263. | ||
|
آمار تعداد مشاهده مقاله: 704 تعداد دریافت فایل اصل مقاله: 463 |
||