| تعداد نشریات | 286 |
| تعداد نشریات سازمان | 84 |
| تعداد شماره‌ها | 2,846 |
| تعداد مقالات | 32,300 |
| تعداد مشاهده مقاله | 41,387,207 |
| تعداد دریافت فایل اصل مقاله | 18,562,202 |
تغییر مکانی و زمانی تراز ایست آبی زیرزمینی در آبخوان دشت مشگین، استان اردبیل | ||
| پژوهش های آبخیزداری | ||
| مقاله 5، دوره 35، شماره 2 - شماره پیاپی 135، تیر 1401، صفحه 45-59 اصل مقاله (1.85 M) | ||
| نوع مقاله: پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/wmrj.2021.356299.1437 | ||
| نویسندگان | ||
| زینب حزباوی* 1؛ مرتضی قراچورلو2 | ||
| 1استادیار گروه مرتع و آبخیزداری، پژوهشکده مدیریت آب، دانشکده کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی، اردبیل | ||
| 2دکترای ژئومورفولوژی، دانشگاه محقق اردبیلی، اردبیل | ||
| چکیده | ||
| این پژوهش با هدف آگاهی از تغییر زمانی و مکانی تراز آب زیرزمینی در آبخوان دشت مشگین در استان اردبیل بر پایه‌ی تحلیل‌‌های آمار فضایی و وایازی (رگرسیون) زمانی و با دادههای ژرفای آب زیرزمینی در مقیاس سالانه در 20 چاه نمونه در دهه‌ی 1395-1386 انجام شد. برای دست‌یابی به روش بهینه‌ی پهنه‌بندیِ تغییرهای مکانی تراز آب زیرزمینی از مقایسه‌ی روشهای گوناگون درون‌یابی مانند تابع‌های شعاعی پایه، وزن‌دهی معکوس فاصله و کریجینگ بر پایه‌ی اعتبارسنجی متقاطع بهره‌گیری شد. رابطه‌های وایازی میان تراز آب زیرزمینی و سال‌‌های بررسی، برای شناخت شیوه‌ی افزایش و کاهش مجموعه‌ی زمانی داده‌‌ها برازش داده شدند. نتیجه‌ی مقایسه‌ی روشهای درون‌یابی بر پایه‌ی اعتبارسنجی متقاطع نشان داد که ریشه‌ی دوم میانگین مربع‌های خطا به‌دست‌آمده از کاربرد روشهای تابع‌های شعاعی پایه، وزن‌دهی معکوس فاصله و کریجینگ به‌ترتیب 21/98، 22/85 و 22/57 در 1386، و 23/45، 27/88 و 26/25 در 1395 بود. بنابراین، به‌دلیل توان به‌کار برده‌شده بر دادههای پراکنده، و کم بودن جا‌های نمونه‌برداری در هر دو سال، خطای برآورد روش تابع شعاعی پایه کم‌ترین بود، و روش بهینه‌ی برآورد تراز آب زیرزمینی در آبخوان دشت مشگین دانسته شد. میانگین وزنی افت در کل آبخوان در این دهه نُه متر محاسبه شد. افزون بر این، نقشه‌ی هم‌افت تراز آب زیرزمینی نشان‌گر کاهش تراز آب زیرزمینی در نیمه‌ی غربی، و افزایش آن در نیمه‌ی شرقی آبخوان بود، که با عامل‌های انسانی (برداشت آب از چاه‌ها در زمین‌های کشاورزی) مرتبط است. در مجموع به‌نظر می‌رسد که دقت و کارآیی روشهای درون‌یابی به ویژگی‌های منطقه و دادههای بررسی‌شده بستگی دارد. در آبخوان دشت مشگین، بیش‌تر بودن تخلیه از تغذیه در افت‌وخیزهای سالانه‌ی تراز آب زیرزمینی مؤثر بوده است. بنابراین، برقرار کردن توازن میان الگوی کشت و آبیاری، و الگوی تغییر زمانی-مکانی تراز آب زیرزمینی در طرح‌های مدیریت آبخوان دشت مشگین ضروری است. | ||
| کلیدواژه‌ها | ||
| تغییرپذیری؛ توزیع مکانی؛ دشت مشگین؛ روشهای درون‌یابی؛ منابع آب زیرزمینی | ||
| عنوان مقاله [English] | ||
| Spatio-Temporal Variations of Groundwater Level in Meshgin Plain Aquifer, Ardabil Province | ||
| نویسندگان [English] | ||
| Zeinab Hazbavi1؛ Morteza Gherachorlo2 | ||
| 1Assistant Professor, Department of Watershed Management, Water Management Research Center, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran | ||
| 2Ph.D., Geomorphology, University of Mohaghegh Ardabili, Ardabil, Iran | ||
| چکیده [English] | ||
| This study was conducted with the aim of understanding the temporal and spatial variations of groundwater levels in the Meshgin Plain Aquifer, located in Ardabil Province, with the application of spatial statistics and temporal regression and using groundwater depth data on an annual scale from 2007 to 2016. In order to achieve the optimal method of zoning spatial changes in groundwater level, a comparison of different interpolation methods including Radial Basis Functions (RBFs), Inverse Distance Weighted (IDW), and Kriging (KRG) based on cross-validation was used. In addition, regression relationships between groundwater level and the study years were fitted to determine how the data series ascended or descended. A comparison of interpolation methods based on cross-validation showed that the root mean square error (RMSE) of RBF, IDW, and KRG methods were 21.98, 22.85, 22.57 in 2007; and 23.45, 27.88, 26.25 in 2016, respectively. Therefore, the RBF method with the lowest estimation error due to the power applied to scattered data and low sampling points in both years was selected as the optimal method for estimation of groundwater level in the aquifer. The weighted average of groundwater table loss was calculated as equal to nine meters. Furthermore, the iso-decline map of groundwater level was indicative of a decrease in groundwater level in the western half and its increase in the eastern half of the aquifer which is related to anthropogenic factors (withdrawal of groundwater in agricultural lands). In general, it can be stated that the accuracy and efficiency of interpolation methods depend on the region and data characteristics. In the case of Meshgin Plain Aquifer, the discharge factor has been more effective than the recharge factor in annual fluctuations of groundwater level. Accordingly, it is necessary to establish a balance between the pattern of cultivation and irrigation and the pattern of spatio-temporal variations of groundwater level in managerial plans for Meshgin Plain Aquifer. | ||
| کلیدواژه‌ها [English] | ||
| Groundwater resources, interpolation methods, Meshghin Plain, regression, spatial distribution, variability | ||
| مراجع | ||
|
Ahadoghli M. 2020. Investigating the tourism potential of Mesghinshahr district using pralong method. M.Sc. Thesis in Geomorphology. Faculty of Social Sciences. University of Mohaghegh Ardabili, 111 p. (In Persian). Ahmadian M, Chavoshian MS, Darvish M. 2015. Determination of groundwater level fluctuations as an indicator of semi-arid land resources degradation using geostatistical methods (Case study: Kaboudrahang-Famenin Plain). Iranian Journal of Range and Desert Research, 22(1): 109–120. (In Persian). Arkoc O. 2021. Modeling of spatiotemporal variations of groundwater levels using different interpolation methods with the aid of GIS, Case study from Ergene Basin, Turkey. Modeling Earth Systems and Environment, pp. 1–10. Asghari Sarasekanrood S, Ghale E, Ebadi E. 2021. Investigation of land use changes and its relationship with groundwater level (Case study: Ardabil Plain). Journal of Remote Sensing and GIS for Natural Resources, 12(1): 86–106. (In Persian). Biswas B, Jain S, Rawat S. 2018. Spatio-temporal analysis of groundwater levels and projection of future trend of Agra City, Uttar Pradesh, India. Arabian Journal of Geosciences, 11(11): 1–18. Carrard N, Foster T, Willetts J. 2019. Groundwater as a source of drinking water in Southeast Asia and the Pacific: A multi-country review of current reliance and resource concerns. Water, 11(8): 20 p. Cheraghi S, Najafi B, Shajari S, Javan M. 2020. The trend of changes in groundwater quantity and quality in the Sarvestan plain of Fars province. Watershed Management Researches (Pajouhesh-Va-Sazandegi), 33(2): 82–96. (In Persian). Chowdhury A. 2016. Assessment of spatial groundwater level variations using geostatistics and GIS in Haringhata Block, Nadia District, West Bengal. International Journal of Research in Engineering and Technology, 5(5): 276–280. Daneshvar Vousoughi F, Dinpashoh Y, Aalami M. 2012. Effect of drought on groundwater level in the past two decades (Case study: Ardebil plain). Water and Soil Science (Agricultural Science), 21(4): 165–179. (In Persian). Dehghani R, Noorali A. 2016. Comparison of geo-statistical methods and artificial neural network in estimating groundwater level (Case study: Nourabad plain, Lorestan). Journal of Environmental Science and Technology, 18(1): 31–43. (In Persian). Delbari M, Boostanian M, Afrasiab P. 2016. Spatio-temporal variability analysis and mapping of groundwater level in Kuhpayeh-Sagzi aquifer (Esfahan Province) using geostatistical methods. Geographic Space, 15(52): 305–324. (In Persian). Development and planning assembly of Ardabil Province. 2012. Logistic plan studies of Ardabil Province. Management and Planning Organization. Ardebil, 63 p. (In Persian). Di Piazza F, Lo Conti LV, Noto F, Viola G. 2011. Comparative analysis of different techniques for spatial interpolation of rainfall data to create a serially complete monthly time series of precipitation for Sicily, Italy. International Journal of Applied Earth Observation and Geoinformation, 13(3): 396–408. Ebadi Y, Javdan J, Rezaei Moghaddam M. 2019. Estimating accuracy of artificial neural networks (ANN) and geo statistical methods in interpolating groundwater levels (Case study: Shabestar-Sufian Plain. Geographical Data, 28(110): 133–145. (In Persian). Esfandiari F, Alijahan M, Rahimi M. 2015. Assessment of algebraic and geo-statistical models for estimation of groundwater table in Ardabil plain. Quantitative Geomorphological Research, 3(2): 44–62. (In Persian). Esfandiari F, Gharachorlu M, Ebay E. 2018 Assessment and estimation the spatial variation of groundwater level by various interpolation methods in Sarab plain. Geography and Development Iranian Journal, 16(51): 65–80. (In Persian). Esfandiari F, Ghorbani Filabadi R, Nasiri Khiavi A, Mostafazadeh R. 2019. Assessing the accuracy of algebraic and geostatistical techniques to determine the spatial variations of groundwater quality in Boroojen Plain. Journal of Natural Environmental Hazards, 8(20): 115–130. (In Persian). Esfandyari F, Mostafazadeh R, Ebadi E, Saadati R. 2020. Modelling the spatial distribution and depletion of groundwater level in Tabriz Plain. Geographical Engineering of Territory, 3(6): 1–16. (In Persian). Ghabelnezam E. 2021. Modelling and assessing the ecosystem services of Khiav Chay Watershed with an emphasis on erosion and runoff yield. M.Sc. Thesis, Department of Natural Resources, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, 124 p. (In Persian). Ghafari S, Moradi H, Modares R. 2018. Comparison of temporal and spatial changes of groundwater level in Isfahan-Borkhar, Najafabad and Chadegan Plains. Physical Geography Research Quarterly, 50(1): 22–24. (In Persian). Ghaffari M, Chavoshbashi AA, Eslami A, Hatami MH, Pourakbar M, Hashemi M. 2021. Spatial and temporal variation of groundwater quality around a volcanic mountain in northwest of Iran. Groundwater for Sustainable Development, 14: p.100627. Isaaks EH, Srivastava RM. 1989. An introduction to applied geostatistics. Oxford University Press. New York. 561 p Jie C, Hanting Z, Hui Q, Jianhua W, Xuedi Z. 2013. Selecting proper method for groundwater interpolation based on spatial correlation. Digital Manufacturing and Automation (ICDMA), Fourth International Conference on Qingdao, pp. 1192–1195. Khazaz L, Oulidi HJ, El Moutaki S, Ghafiri A. 2015. Comparing and evaluating probabilistic and deterministic spatial interpolation methods for groundwater level of Haouz in Morocco. Geographic Information System, 7(6): 631–642. Momeni M, Ghayoumi AF. 2010. Statistical Analysis with SPSS. Moalef, Tehran, 344 p. (In Persian). Maghami Moghim G, Taghipour A. 2019. Investigating the effective factors on changing groundwater levels of Safi Abad Plain of Esfarayneh. Desert Ecosystem Engineering Journal, 8(22): 27–42. (In Persian). Norallahi S, Asghari Moghaddam A, Elham F, Barzegar R. 2020. Assessing the effective factors on groundwater quality of Meshgin-Shahr plain aquifer (Ardebil province) with emphasis on possible origin of some heavy metals. Scientific Quarterly Journal of Geosciences, 29(114): 143–152. Rahab FKJ, Ghabayen SM, Salha AA. 2011. Effect of GIS techniques on the accuracy of the spatial representation of groundwater monitoring data in Gaza Strip. Environmental Science and Technology, 4(6): 579–589. Rahnama MB, Sohrabi N, Barani GhA. 2020. Investigation of groundwater level variations in Shahdad aquifer using groundwater modeling system (GMS) software. Iranian Journal of Irrigation and Drainage, 5(14): 1650–1663. (In Persian). Saghafian B, Daneshkar arasteh P, Rahimi Bandarabadi S, Fattahi E, Mohammadzadeh M. 2012. Guideline of spatial distribution of climatological factors using point data. Office of Deputy for Strategic Supervision, Ministry of Energy, Bulletin No. 585, 134 p. (In Persian). Salehnia N, Bastani M. 2017. Considering virtual water trade strategy of crops and horticultural products in Iran. Iranian Journal of Irrigation & Drainage, 11(5): 750–762. (In Persian). Salha AA. 2010. Assessment of spatial representation of groundwater monitoring and meteorological data in Gaza Strip. M.Sc. Thesis- Water Resources Engineering, Islamic University of Gaza, Palestin, 197 p. Shabani M, Hadi Ghanbari M, Atania H, Behyari A. 2019. Study the cold and mountainous climate (Case study Meshghinshahr District). Current Researches in Sciences and Researches, 1(6): 1–9. (In Persian). Sun YA, Shaozhongkang A, Zhang L. 2009. Comparison of interpolation methods for depth to groundwater and its temporal and spatial variations in the Minqin oasis of northwest China. Environmental Modeling & Software, 24(10): 1163–1170. Torabi Azad M, Siahsaraei S, Eftekhari R. 2010. Comprehensive training of Geo-statistic analysis in GIS. Geographic Organization of Armed Forces. Tehran, 488 p. (In Persian). Vahedi F. 2016. Prediction of groundwater level using artificial intelligence methods (Case study: Meshghinshahr Aquifer). Ms.c. Thesis. Faculty of Natural Sciences. Tabriz University, 161 p. (In Persian). Yilmaz AG, Shanableh A, Al-Ruzouq RI, Kayemah N. 2020. Spatio-temporal trend analysis of groundwater levels in Sharjah, UAE. International Journal of Environmental Science and Development, 11(1):9–14. Zareian MJ, Eslamian S. 2021. Groundwater withdrawal adjustment based on changes in groundwater balance components (a case study: an arid region in central Iran). Arabian Journal of Geosciences, 14(18):1–13. | ||
|
آمار تعداد مشاهده مقاله: 390 تعداد دریافت فایل اصل مقاله: 692 |
||