تهیه نقشه فرسایندگی باران استان لرستان با استفاده از روش زمین‌آماری کریجینگ

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی مقطع کارشناسی ارشد جنگلداری، دانشکده منابع طبیعی و محیط زیست، دانشگاه ملایر

2 گروه مرتع و آبخیزداری دانشکده منابع طبیعی دانشگاه ملایر

3 استادیار گروه محیط زیست، دانشکده کشاورزی و منابع طبیعی، دانشگاه لرستان

چکیده

فرسایندگی باران به صورت قدرت تراکمی باران در بروز فرسایش تعریف می‌شود. اگر سایر خصوصیات موثر بر فرسایش ثابت در نظر گرفته شود، میزان هدررفت خاک مستقیماً متناسب با میزان فرسایندگی باران خواهد بود. در پژوهش حاضر با هدف تهیه نقشه فرسایندگی باران در استان لرستان، در ابتدا میزان فرسایندگی باران در 53 ایستگاه­ هواشناسی در سطح استان با شاخص اصلاح شده فورنیه (MF) محاسبه شد. در ادامه برای نشان دادن همبستگی مکانی میان داده­های فرسایندگی باران از ترسیم واریوگرام در محیط نرم­افزارGS+5.1.1 استفاده شد. در نهایت با بکارگیری روش زمین آماری کریجینگ در نرم­افزار ArcGIS10.3، توزیع مکانی فرسایندگی باران نقشه­سازی شد و به منظور انتخاب روش مناسب­تر، دو روش کریجینگ معمولی و کریجینگ ساده با یکدیگر مقایسه شدند. بعد از انتخاب مدل واریوگرام و درون­یابی انجام شده توسط روش­های مورد بررسی در این تحقیق، در ادامه صحت درون­یابی با روش ارزیابی متقابل مورد ارزیابی قرار گرفت. براساس نتایج بدست آمده روش کریجینگ معمولی بدلیل بالاتر بودن مقدار R2 و پایین­تر بودن مقادیر میانگین خطا (ME)، میانگین استاندارد شده خطا (MSE)، ریشه دوم میانگین استاندارد شده خطا (RMSE) دقت بالاتری را در مقایسه با روش کریجینگ ساده نشان داد. روش کریجینگ معمولی با  میزان میانگین خطای برآورد پایین­تر (06/0) و مربع میانگین ریشه خطا (4/0) مناسب­ترین روش برای درون­یابی در این پژوهش ارزیابی شد. همچنین میزان همبستگی(R2) مقادیر برآوردی و مشاهده­ای فرسایندگی باران با روش­ کریجینگ معمولی  68/0 برآورد شد. در نهایت با استفاده از واریوگرام نمایی و روش کریجینگ معمولی نقشه فرسایندگی باران در سطح استان ترسیم شد که  براساس آن مناطق واقع در امتداد شمالی-جنوبی و مرکزی استان لرستان دارای فرسایندگی زیاد و مناطق غربی و شرقی دارای فرسایندگی کمتری هستند. نقشه فرسایندگی باران تولید شده در این پژوهش می­تواند به منظور برآورد میزان هدررفت خاک با مدل USLE و شناسایی مناطق با پتانسیل فرسایش زیاد مورد استفاده قرار گیرد.

کلیدواژه‌ها


عنوان مقاله [English]

Mapping rainfall erosivity in Lorestan province using Kriging geostatistic technique

نویسندگان [English]

  • javad Salarvand 1
  • Farhad Ghasemi Aghbash 2
  • Zahra Asodolahi 3
1 M. SC of Forestry, Faculty of Natural Resource and Environment, Malayer University
2 Range and watershed management, Faculty of Natural resources, Malayer university
3 Assistant Professor, Environment department, Faculty of Agriculture and Natural Resource, Lorestan University
چکیده [English]

Extended Abstract
Introduction
Rainfall erosivity is defined as the aggregative power of the rain. If other effective features on soil erosion be considered constant then soil loss could be directly connected to rainfall erosivity. Rain erosion term was proposed by Wichmeier and Smith in 1978 to consider the effect of climate on raw erosion. Measurements of meteorological parameters by the traditional methods require a dense rain gauge network. But, due to the topography and cost problems, it is not possible to create such a network in practice. Given the significant change in rainfall in time and space on the one hand and low rain-gauge stations to record rainfall on the other hand, the necessity of using geostatistical methods for rainfall erosivity mapping is inevitable. Geostatistical methods use the spatial correlation between observations in the estimation processes. In these cases the spatial distribution pattern of rainfall erosivity can be produced using different methods of interpolation.
Materials and Methods
Study area
Lorestan province is located in southwest of Iran and covers an area of 28249 square kilometers. It is located between the latitudes 32º 37' and 34º 22' N and the longitudes 46˚51ʹ and 50˚30ʹE. The main objective of this research were: (1) analyze the spatial distribution of rainfall erosivity using two different interpolation methods namely ordinary Kriging and simple Kriging; (2) put forward the best interpolation method through cross-validation, construct the high resolution grid data of rainfall erosivity and provide the reliable information for relevant researches
 
Monthly rainfall erosivity model
 In the first step, the precipitation data collected from 53 precipitation stations and Modified Fournier Index (MF) calculated based on Eq. (1)
                                                                                                (1)
 
Where MF is the modified Fournier index value (mm), p < sub>i  is averagemonthly precipitation (mm) and P is average annual precipitation (mm). Then Eq. (2) and Eq. (3) were used to estimate rainfall erosivity or R-factor values (MJ mm ha -1 h -1 year -1).
 





                                 (2)




             (3)   
 





It is suggested that Eq. (2) be applied for locations with MF values less than 55 mm and Eq. (3) be used for locations with MF values greater than 55 mm.
 
Geostatistical Methods
In this article two interpolation techniques namely simple and ordinary Kriging were compared in GS+5.1.1 and ArcGIS10.3 software’s in order to determine which one describe better the spatial distribution of rainfall erosivity. Kriging methods assume that the spatial variation of a continuous climatic variable is too irregular to be modeled by a continuous mathematical function, and its spatial variation could be better predicted by a probabilistic surface. The predictions of Kriging-based methods are currently a weighted average of the data available at neighboring sampling points (weather stations). The weighting is chosen so that the calculation is not biased and the variance is minimal. A function that relates the spatial variance of the variable is determined using a semi-variogram model which indicates the semi variance between the climatic values at different spatial distances.
 
Validation and techniques comparison
The resulting maps from interpolation were compared by using a set of validation statistics include Mean error (ME), Mean Standardized Error (MSE) and the root mean square error (RMSE) by Eq. (4), (5) and (6)
 





(4)


   




(5)  

 



(6)

 


 
 
 




 
Results and Discussion
Based on the results, rainfall erosivity values varied from 11.1 to 749.5 MJmmha−1 h−1 y−1. Differences between the simple and ordinary Kriging models regarding the validation statistics were narrow, but allowed for a comparison. The obtained results showed that ordinary Kriging with higher R2 and lower ME, MSE and RMSE had better precision in mapping rainfall erosivity. The spatial distribution of rainfall erosivity showed the areas along north-south of Lorestan province and central regions had higher values while lower rainfall erosivity was seen in the western and eastern areas of the study area.
 
 
Conclusion
The availability of high-quality environmental maps is a key issue for agricultural and hydrological management in many regions of the world.  Produced rainfall erosivity map in this research can be used for estimation of soil loss by USLE model. Rainfall erosivity maps also can be suitable as guidance for soil conservation practices and identifying areas with the high potential of soil retention as an ecosystem service. Further research may be directed to find reliable erosivity indices which can be computed from daily precipitation data.

کلیدواژه‌ها [English]

  • Erosion
  • Kriging
  • Variogram
  • Interpolation
  1.  

    1. Ballabio, C., Borrell, P., Spinoni, J., Meusburger, K., Michaelides, S., Beguería, S., Klik, A., Petan, S., Janeček, M., Olsen, P., Aalto, J., Lakatos, M., Rymszewicz, A., Dumitrescu, A., Perčec Tadić, M., Diodato, N., Kostalova, J., Rousseva, S., Banasik, K., Alewel, C and Panagos, P, Mapping monthly rainfall erosivity in Europe, Science of the Total Environment, 579: 1298-1315.
    2. Black, C. A. 1986. Methods of Soil Analysis. Part 1, PP: 545-566. Ser. No. 9., ASA. Madison, WI.
    3. Cressie, N.A.C., 1993. Statistics for spatial data. John Willy and Sons, Inc., New York. 900 p.
    4. Faridi, P., Rezaei, P., Ghorbani, P and Kazemi, M., Application of GIS in Modeling rainfall erosivity factor (Case Study: Gabric Watershed- Hormozgan province East South), Quarterly journal of Environmental Erosion Research, 3(10): 39-51.
    5. Fathizad, H., Karimi, H and Tazeh, M., 2014, Reviews different geostatistical algorithms for mapping annual rainfall in Ilam, Applied Geographical Sciences Research, 14(35): 140-154.
    6. Ferro, V., Giordano, G. and Iovino, M., 1991, Isoerosivity and erosion risk map for Sicily, Hydrological sciences journal, 36(6), 549-564.
    7. Gharoudi Tali, M., 2005. GIS in a 3D environment, Jahad daneshgahi press, Teacher Training Unit, No 49, 173 pages.
    8. Gholami, h., Fathizhad, H., Safari, A and Binisz, M., 2016, Evaluation erosivity factor of rain using Geostatistical algorithms (Case Study: Ilam province, Iran), Quarterly journal of Environmental Erosion Research, 4(20): 1-16.
    9. Hakim Khani, S., Mahdian, M.H., Aran Khedri, V and Ghorban por, d., 2008, Rainfall erosivity mapping, The third national conference on erosion and sediment, Tehran, Soil Conservation and Watershed Management Research Center, 281-288.

    10. Hassani Pak, A., 2010, Geostatistics, Tehran university press, 380 pages.

    11. Hutchinson, M. F. 1998, Interpolation of rainfall data with thin plate smoothing splines: II analysis of topographic dependence, Journal of Geographic Information and Decision Analysis, 2(2), 168-185.

    12. Lal, R., 1990, Soil Erosion in the Tropics, Principles and Management, McGraw-Hill, New York, 580 pages.

    13. Lashani Zand, M., 2004. A Study on the Climate of Iran's Droughts and its Coping Strategies, Case Study: Six Areas in the West and Northwest of Iran, PhD thesis, Faculty of Literature and Humanities University of Isfahan,

    14. Kamarei, R., 2010, Changes in production site, density and percent canopy cover (Nitraria schoberi L.) using geostatistical methods in the desert Meighan, Master's thesis Range Management Engineering, Tarbiat Modarres unversity, 250 pages.

    15. Khosravi, Y and Abbasi, A, 2016, Spatial analysis of environmental data with geostatistics, Azar Kelk press, 280 pages.

    16. Mirmosavi, S.H., Mazidi, A and Khosravi, Y., 2010, Determine the best methods of statistics to estimate the distribution of rainfall using GIS (Case Study: Isfahan Province), geographical space, 10(30): 105-120.

    17. Morgan, R. P. C., 1995, Soil Erosion and Conservation. Addison- Wesley, London, 198 pages.

    18. Naoum, S., and Tsanis,  I. K., 2003, Temporal and spatial variation of annual rainfall on the island of Crete, Greece, Hydrol. Process, 17: 1899–1922.

    19. Oliveira, P.T.S., Wendland, E and Nearing, M.A., 2013, Rainfall erosivity in Brazil: a review, Catena, 100:139–147.

    20. Price, T. D., McKenney, D. W., Nalderc, I. A., Hutchinson, M. F., and Kesteven, J. F., 2000, A comparison of two statistical methods for spatial interpolation of Canadian monthly mean climate data, Agricultural and Forest Meteorology, 101: 81–94.

    21. Qin, W., Guo, Q., Zuo, C., Shan, Z., Ma, L. and Sun, G., 2016. Spatial distribution and temporal trends of rainfall erosivity in mainland China for 1951–2010. Catena, 147,177-186.

    22. Quine, T. A. and  Zhang, Y., 2002. An investigation of spatial variation in soil erosion, soil properties and crop production within an agricultural field in Devon,U.K. J. Soil and Water Conserv. 57: 50-60

    23. Renard, K.G. and Freimund, J.R. 1994, Using monthly precipitation data to estimate the R-factor in the revised USLE, Journal of hydrology, 157(1): 287-306.

    24. Renard, K. G., Foster, G. R., Weesies, G.A., McCool, D.K. and Yoder, D.C. 1997. Predicting soil erosion by water: a guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE) (Vol. 703). Washington: US Department of Agriculture, Agricultural Research Service.

    25. Sanchez-Moreno, J and Mannaerts, C and Jetten, V, 2014, Rainfall erosivity mapping for Santiago Island, Cape Verde, Geoderma, 217-218: 74-82.

    26. Shabani, M, 2011, Evaluation of geostatistic al methods for rainfall erosivity mapping, Fars province, Journal of Watershed Engineering and Management, 3(3): 168-176.

    27. Shabani, A., Shahbakhti, M and Abbaspour, R.A, 2011, Modeling the distribution of atmospheric pollutants in Tehran using statistical and geostatistical methods, Geomatics conference, National mapping agency, 10 pages.

    28. Saghafian, B., 2012, Manual methods of spatial distribution of climatic factors using data  point, Publication No. 585, Deputy Strategic and office engineering and technical standards for water and Alpha Energy Department.

    29. Statistical Yearbook of Lorestan province, 2014, Lorestan Governor, 638 pages.

    30. Tazeh, M and Khosravi, Y., 2008. Drought zoning based on the Transeau index using GIS statistics (Case study of the western part of Isfahan province), International Conference on Tree Botany and Climate Change in Caspian Ecosystems, Sari Caspian Ecosystem Institute.

    31. Wang, G., Gertner, G., Singh, V., Shinkareva, S., Parysow, P and Anderson, A., 2002, Spatial and temporal prediction and uncertainty of soil loss using the revised universal soil loss equation: a case study of the rainfall-runoff erosivity R factor. Ecological Modeling, 153:143–155.

    32. Wichmeier, W.H., and Smith, D. D., 1978, Predicting rainfall losses: a guide to conservation planning, Agriculture Handbook No. 537, US Department of Agriculture, Washington, DC.

    33. Yu, B. and Rosewell, C.J. 1996, Technical Notes: A Robust Estimator of the R-factor for the Universal Soil Loss Equation, Transactions of the ASAE, 39(2): 559-561.