بررسی شوری آب و خاک و ارتباط آن با پستی و بلندی های سطح زمین با استفاده از مدل فازی در محیط GIS - مطالعه موردی: حوضه آبخیز سیاخ دارنگون در غرب شیراز

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار ژئومورفولوژی، دانشکده کشاورزی و منابع طبیعی داراب، دانشگاه شیراز

2 استادیار ژئومورفولوژی گروه جغرافیا- دانشگاه شیراز

10.22131/sepehr.2018.31482

چکیده

افزایش جمعیت، کاهش منابع آب، کاهش منابع غذایی، خشکسالی و آلودگی آب و خاک در بسیاری از مناطق کره زمین منجر به مشکلات فراوانی شده است. با توجه به اهمیت کیفیت آب زیر زمینی و خاک در حوضههای آبخیز مختلف از جمله شمال غرب استان فارس، هدف این مطالعه بررسی کیفیت آب و خاک از نظر هدایت الکتریکی (شوری)برای کشت گیاه گندم با استفاده از روش فازی و مقایسه آن با لندفرمها در محیط GIS میباشد. برای 40 نقطه نمونه آب (چاه) و 70 نمونه خاک (پروفیل در 100 سانتی متر اولیه خاک) با استفاده از روش میانگین عکس فاصله (IDW)نقشه پهنه بندی آب و خاک تهیه شد. سپس به منظور همگن کردن میزان شوری آب و خاک و بررسی ارتباط آنها با لندفرمهای منطقه مورد مطالعه، قوانین فازی و استفاده از استانداردهای کیفیت آب و خاک به کار گرفته شد. در روش فازی مقادیر شوری بین 0 و 1 قرار گرفتند. نتایج حاصل از نقشه فازی شوری خاک منطقه نشان داد که 31/24 درصد از منطقه در کلاس ضعیف (نامناسب)، 78/11 درصد در کلاس متوسط، 74/25 درصد در کلاس خوب و 16/38 درصد از منطقه در کلاس خیلی خوب قرار گرفته اند. درحالیکه برای شوری آب مشخص شد که 6/36 درصد در کلاس متوسط، 69/31 درصد در کلاس خوب و 65/31 درصد از منطقه در کلاس خیلی خوب قرار گرفته اند. در پایان ارتباط بین نقشه لندفرم و نقشه شوری آب و خاک منطقه مورد مطالعه تعیین شد. نتایج نشان داد که حداقل شوری خاک و آب در دشتها واقع شده است. در مطالعه حاضر جنس سازندهای منطقه و عدم شوری آنها در دشتها باعث شده که این مناطق از شوری کمتری نسبت به سایر قسمتها برخوردار باشند. در نتیجه برای مناطقی که از نظر زمین شناسی و پستی و بلندی مشابه منطقه مورد مطالعه هستند، بدون نمونه برداری و تجزیه و تحلیل در آزمایشگاه میتوان مشخص نمود که مناطق واقع در ارتفاعات کمتر (دشت ها) دارای شوری کم میباشند. در واقع به کمک نقشههای زمین شناسی (جنس سازند) و نقشههای لندفرم (پستی و بلندی ها) میتوان میزان شوری را تخمین زد.

کلیدواژه‌ها


عنوان مقاله [English]

Investigating the salinity of water and soil and its relation with rough terrain using Fuzzy Model in GIS Environment - Case study: Syakh Darngun Watershed, West of Shiraz

نویسندگان [English]

  • Marzieh Mokarram 1
  • Saeed Negahban 2
1 Assistant Professor of Shiraz University
2 Assistant Professor of Shiraz University
چکیده [English]

Extended Abstract
Introduction
Investigating the spatial andTranslation errorInvestigatingiiiii temporal variations of soil salinity plays a major role in managing the watershed and preventing the development of salinity (Mohammadi, 2007). Also, the study of groundwater salinity due to the complexity of hydrological processes, characteristics of the aquifer, and their variability is a difficult task. However, these problems exacerbate by external factors such as atmospheric conditions and human activities affecting the permeability and hydrological processes (Mirzaee and Hassan-Nia, 2013). Because of the costly nature of experiments involving salinity sampling, as well as the computational models not being calibrated and the complexity of these models in order to overcome these limitations and to determine salinity in the depths of the soil, determination of models consistent with natural behaviors and the use of existing models, Increase day by day. On the other hand, considering the fact that many lands are under cultivation in the northwest of Fars province, it is important to study the chemical properties of the soil and water in the region, including salinity.
There are various methods for studying the salinity of water and soil, for example, Syringes et al. (2006) predicted the salinity of soil profile and the drainage outlet in a research using neural networks in an experimental area in India. Arfin et al. (2003) used an artificial neural network model and linear regression model to predict the soil and water salinity. Topographic index is a measure of the extent of flow accumulation at the given point of the topographic surface. As catchment area increases and slope gradient decreases, topographic index increases. Like other combined morphometric variables, topographic index can be derived from a digital elevation model (DEM) by the sequential application of methods for local and nonlocal morphometric characteristics, followed by an arithmetic combination of the results of these calculations.
 
Materials & Methods
The studied watershed is located in the west of Shiraz, between the cities of Shiraz and Kazeroon. The most important urban center in this basin is the city of Bayza. The geo-location of the studied area is N 29° 12´to 29° 48´and E 52° 06´ to 52° 36´ (Figure 1). The area of the study region is 623.63 KM2. The highest and lowest altitudes in the study area are 1630 and 3083 meters respectively. The average temperature in the region is 16.8 degrees varying from 4.7 to 29.2. The study area is very rich for cultivating crops. It is also a very rich in terms of topography, geology and biodiversity. Regarding the presence of agricultural lands in this region as well as the significance of irrigation water quality and the type of soil in terms of electrical conductivity (EC), the study of the soil and water characteristics of the region is very important in terms of salinity.
The data used in this research include electrical conductivity of water and soil samples provided by Fars Agricultural Jihad Organization (2013). This region was selected considering the importance of the study region for agriculture. The zoning maps for each of them were prepared in the ArcGIS environment with the help of these sample points which were selected randomly. Then, the EC data of water and soil was homogenized and ranged from 0 to 1 with the help of membership functions. Finally, the relationship of the amount of water and soil salinity with the watershed rough terrain was investigated.
 
Discussion and Results
According to the interpolation maps, it was determined that the lowest and the highest values for water salinity in the study area were 0.42 and 3.07 respectively, while for soil salinity were 0.87 and 8.75 respectively. According to the salinity zoning map prepared for soil samples in the study area, it is determined that the highest soil salinity is in the southwest of the study area, while the north and center of the study area have lower soil salinity. Also, the results of water salinity obtained by IDW method showed that the highest salinity of water is in the north of the region, while the lowest salinity of water is observed in parts of the south of the study area. The fuzzy map values of the study area are between 0.08 to 0.99, that except for a very small part of the study area located in the southeast, the rest of the area contain saline water. Also, the results of soil salinity fuzzy map of the studied area showed that the soil salinity values were between 0.61 and 0.92. In fact, the soil in the study area has a lot of salinity.  
 
Conclusion
After finalizing the fuzzy map of water and soil salinity by fuzzy method, the final salinity map was classified into four classes. Values less than 0.25, between 0.25 and 0.5, 0.5 to 0.75 and more than 0.75 were classified into inappropriate, moderate, good and very good grades, respectively. (The low values: < 0.25 (inappropriate for drinking), moderate: 0.25 – 0.50, high: 0.50 – 0.75, very high: > 0.75 (a-ppropriate for drinking)). Using fuzzy method for soil salinity, it was determined that 24.31% of the area was in poor class (inappropriate), 11.78 in the moderate class, 25.74 in the good class and 38.16% of the area was in the very good class, while for water salinity, it was found that 36.6% was in the moderate class, 31.69% in the good class and 31.65% was in the very good class. At the end, the relationship between the Landform map and the salinity map of the soil and water in the study area was determined. The results showed that salinity of the water in the valleys is very high, while soil salinity in the upstream drainage has shown the highest values. The results also showed that the minimum salinity of the soil and water are in the plains.

کلیدواژه‌ها [English]

  • Water Salinity
  • Soil Salinity
  • Inverse Distance Weighting (IDW)
  • Fuzzy Method
  • West of Shiraz
1. اژیرابی، کامکار، عبدی؛ رحیم، بهنام، امید؛ 1394.  مقایسه شاخص‌های مختلف استخراج شده از تصاویر ماهواره لندست برای پهنه بندی شوری خاک در مزرعه نمونه ارتش گرگان .  نشریه مدیریت خاک و تولید پایدار، شماره 11. ص 173.
2. حق وردی، ا. 1386 . تخمین شوری پروفیل خاک در شبکه آبیاری و زهکشی دشت تبریز با استفاده از مدل‌های شبکه عصبی مصنوعی و مدل کامپیوتری saltmod . پایان نامه کارشناسی ارشد. مهندسی کشاورزی-آبیاری و زهکشی، دانشکده کشاورزی، دانشگاه بوعلی سینا.
3. محمدی، ج. 1387 . مطالعه تغییرات مکانی شوری خاک در منطقه رامهرمز با استفاده از نظریه ژئو استاتیستیک (کوکریجینگ).مجله علوم و فنون کشاورزی و منابع طبیعی، جلد 3، شماره 1، صفحه 6-1.
4. محمودی، جعفری، کریم زاده، رمضانی؛ فرید، رضا، حمیدرضا، نفیسه 1394. پهنه‌بندی شوری خاک‌های منطقه جنوب شرق استان اصفهان با استفاده از داده‌های زمینی و سنجنده TM ماهواره ای.  فصلنامه علوم و فنون کشاورزی و منابع طبیعی، علوم آب و خاک، شماره 71، صص 31-45.
5. میرزایی، ع.ا.، حسن نیا، ر.د. 1392. مقایسه روش‌های شبکه عصبی مصنوعی، فازی- عصبی تطبیقی و منحنی سنجه شوری در برآورد شوری آب زیر زمینی (مطالعه موردی: اراضی پایاب سد حاجیلر). نشریه آبیاری و زهکشی ایران. شماره 1. جلذ 7. ص 58-49.
6. نورزاده، م.، هاشمی، م.، .ملکوتی، م.ج. 1390. پهنه‌بندی پیوسته هدایت الکتریکی- اسیدیته خاک بر اساس خوشه‌بندی فازی برای دشت قم. نشریه علوم آب و خاک. جلد 15 شماره 57 صفحات 199-207.
 
7. Ali El-Keblawy, Mahmoud Ali Abdelfattah , A. Khedr. 2015. Relationships between landforms, soil characteristics and dominant xerophytes in the hyper-arid northern United Arab Emirates. Journal of Arid Environments 117 (2015) 28e36.
8. Ali M, Thiem VD, Park JK, Ochiai RL, Canh DG, Danovaro-Holliday MC, Kaljee LM, Clemens JD, Acosta CJ. Geographic analysis of vaccine uptake in a cluster randomized controlled trial in Hue, Vietnam. Health and Place. 2007;13:577–578.
9. Ali R.R. and , F.S. Moghanm. 2013. Variation of soil properties over the landforms around Idku lake, Egypt. The Egyptian Journal of Remote Sensing and Space Sciences (2013) 16, 91–101.
10. Ariffin J, Abdul Ghani A, Zakaria N and Shukri Yahya A, 2003. Sediment prediction regression approach. 1st International Conference on Managing rivers in the 21st2.
Burrough PA, McDonnell RA (1998) Principles of geographical information systems. Spatial Information System and Geostatistics.
11.  Dahiya S. Analysis of groundwater quality using fuzzy synthetic evaluation. Journal of Hazardous Materials. 2007; (147): 938-946.
12. E. Bijanzadeh, M. Mokarram, R. Naderi. 2014. Applying Spatial Geostatistical Analysis Models for Evaluating Variability of Soil Properties in Eastern Shiraz, Iran. Iran Agricultural Research, Vol. 33, No. 2, 2014.
13.  Henderson, B.L., E.N. Bui, C.J. Moran, D.A.P. Simon, 2005. Australia-wide predictions of soil properties using decision trees. Geoderma, 124: 383-398.
14. Kumar, A., Bohra, C., Singh, L.K., 2003. Environment, Pollution and Management. APH Publishing, 2003 - Environmental management - 604 pages. ISBN: 81- 7648- 419-9.
15. Madyaka, M. 2008. Spatial modeling and prediction of soil salinization using SaltMod in a GIS environment. J. ITC., thesis in Geoinformation science and earth observation.
16.  McBratney, A. B., Odeh, I. O. A. (1997). “Application of fuzzy sets in soil science: fuzzy logic, fuzzy measurements and fuzzy decisions”.Geoderma. 77, PP. 85–113.
17. Meier A, Schindler G, Werro N (2008) Fuzzy classification on relational databases. In: Galindo M (ed) Handbook of research on fuzzy information processing in databases (Bd. II, S. 586–614). Information Science Reference, Hershey Oxford University Press, New York.
18.  Mini, V., P.L. Patil and G.S. Dasog, 2007. A Remote Sensing Approach for Establishing the Soil Physiographic Relationship in the Coastal Agro Eco system of North Karnataka. Karnataka J. Agric. Sci., 20(3): 524-530.
19. Mokarram M., K. Rangzan, A. Moezzi, J. Baninemeh. 2010. Land suitability evaluation for wheat cultivation by fuzzy theory approache as compared with parametric method. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 38, Part II.
20. Muhammetoglu A, Yardimci A, A Fuzzy Logic Approach to Assess Groundwater Pollution Levels Below Agricultural Fields. Environmental Monitoring and Assessment. 2006; 118:337-354.
21. Park, S.J., T.P. Burt, 2002. Identification and characterization of pedo-geomorphological processes on a hillslope. Soil Sci. Soc. Am. J., 66: 1897-1910.
22. Sanchez, J. F. (2007). Applicability of knowledgebased and Fuzzy theory-oriented approaches to land suitability for upland rice and rubber. M.Sc. Thesis, ITC, the Netherland.
23. Sarangi A., Singh M., Bhattacharya A.K., and Singh A.K. 2006 .Subsurface drainage performance study using SALTMOD and ANN models, Agricultural Water Management, 4: 240-248.
24. Shobha G.,JayavardhanaGubbi,KrishnaSRaghavan,LakshmikanthK Kaushik,M.Palaniswami. 2014. A novel fuzzy rule based systemforassessment ofgroundwater potability: AcasestudyinSouth India. IOSRJournalofComputerEngineering(IOSR-JCE). Volume 15, Issue 2(Nov. -Dec. 2013), PP35-41.
25. Srinivasulu, A., Sujanirao, CH., Lakshmi, G.V., Satyanarayana, T.V., and Boonstra, J. 2004. Model studies on salt and water balances at Konanki pilot area, Andhra Pradesh ,India. Irrigation and Drainage systems., 18: 1-
26. Tsoukalas, L.H., and Uhrig, R.E., 1997, Fuzzy and neural approaches in engineering: New York, John Wiley and Sons, Inc., 587 p.
27. Zadeh LH. 1965. Fuzzy sets. Information and Control 8, 338–353.