برآورد نقشه‌های فرسایندگی و بارش در مناطقی با ایستگاه باران‎سنجی محدود (مطالعه موردی: استان سمنان)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه بیابانزدایی، دانشکده کویرشناسی، دانشگاه سمنان، سمنان، ایران

2 گروه مدیریت مناطق خشک، دانشکده کویرشناسی، دانشگاه سمنان، سمنان، ایران

چکیده

برای برآورد فرسایش آبی معادلات تجربی زیادی ارائه شده که معادله جهانی فرسایش خاک اصلاح شده (RUSLE) یکی از پرکاربردترین این معادلات بود. یکی از فاکتورها این معادله، فرسایندگی باران (R) می‌باشد. برای محاسبه مستقیم R نیاز به داده‎های دقیقه‌ای بارش است که در تعداد محدودی از ایستگاه‌های سینوپتیک وجود دارد و دسترسی به این داده‎ها با مشکلاتی همراه است. در این پژوهش با استفاده از داده‎های در دسترس مانند متوسط بارش سالانه، مقدار فرسایندگی باران برآورد شد. استان سمنان با وسعتی برابر با 96816 کیلومتر مربع، دارای تعداد محدودی ایستگاه سینوپتیک و باران‎سنجی است، که برآورد فرسایندگی باران را در این استان، دشوار می‎کند. در این مطالعه به منظور برطرف ساختن کمبود ایستگاه‌های باران‎سنجی و سینوپتیک، از متغیرهای کمکی شامل ارتفاع  (DEM)، پوشش گیاهی نرمال شده (NDVI)، دمای سطح زمین (LST) و داده‎های شبکه‎ای جهانی بارش ""Open Land Map Precipitation(LMP) که بیشترین ارتباط را با بارش داشتند، استفاده شد. برای این منظور ابتدا با استفاده از داده‎های کمکی و مدل غیرخطی جنگل تصادفی (RF) نقشه بارش استان تهیه شد. در ایستگاه‎های سینوپتیک مقدار فرسایندگی بر اساس شاخص EI30 و متوسط بارندگی سالانه ایستگاه‎ها تعیین و ارتباط بین بارش و فرسایندگی باران با استفاده از رگرسیون غیرخطی تعیین شد. نتایج نشان داد مقدار مجذور میانگین مربعات خطا RMSE)) و ضریب همبستگی (r) مدل RF در برآورد بارش ایستگاه‌های مورد توجه برابر با 9/16 میلیمتر و (p<0.01) 98/0 بود، که نشان از دقت بالای این مدل در برآورد بارش استان می‌باشد. نقشه بارش استان نشان داد که میزان بارش سالانه مناطق مختلف استان  بین 420-70 میلیمتر متغیر می‌باشد. نقشه‎های طبقه‎بندی بارش نشان داد که نیمی از استان دارای بارش کمتر از 100 میلیمتری می‎باشد. 30 درصد استان بارشی بین 100 تا 150 میلیمتری دارند و تنها حدود 17 درصد از سطح استان بارشی بیش از 150 میلیمتر را دارا است. بررسی رگرسیون‌های خطی و غیرخطی نشان داد که تابع توانی به خوبی قادر به برآورد فرسایندگی باران با استفاده از داده‌های متوسط بارش سالانه بود. به‌طوری که ضریب همبستگی معادله‌ی که فرسایندگی را به عنوان تابعی از بارش برآورد می‌کند، برابر با **96/0 بدست آمد. بیشترین و کمترین مقادیر فرسایندگی باران به ترتیب برابر با  380 و 39 MJha-1mm h-1year-1 در مناطق شمالی و جنوبی استان  بدست آمد. نتایج این مطالعه نشان داد که استفاده از شیوه‌های نوین داده‎کاوی جهت تهیه نقشه بارش و مدل‎سازی و پردازش در محیط برنامه‎نویسی در مناطق-ی با تعداد معدود ایستگاه‌های باران‌سنجی، تهیه نقشه‌های دقیق بارش و فرسایندگی باران را ممکن می‌سازد.

کلیدواژه‌ها

عنوان مقاله [English]

Estimation of Rainfall Erosivity Map in Areas with Limited Number of Rainfall Station (Case study: Semnan Province)

نویسندگان [English]

  • Elham Amini 1
  • Ali Zolfaghari 2
  • Hasan Kaboli 2
  • Mohammad Rahimi 1
1 Department of desertification, Faculty of Desert Science, Semnan University, Semnan, Iran.
2 Department of Arid lands management, Faculty of Desert Science; Semnan University. Iran.
چکیده [English]

Water erosion is one of the most important challenges of agriculture and watershed management in the world and it has been considered by many researchers. To estimate water erosion, many experimental models have been proposed, of which the Revised Universal Soil Loss Equation (RUSLE) is one of the most widely used models for estimation of soil erosion. Rainfall erosivity (R) is one of the factors in this model. Direct calculation of R required meteorological gauge stations which are available at a limited number of synoptic stations. In this study, the attempt was to estimate rainfall erosivity using available data such as annual rainfall. Semnan province, with an area of 96816 km2, has a limited number of synoptic and rain gauge stations, makes it difficult to estimate rain erosivity in this province. In this study the auxiliary variables including digital elevation model (DEM), normalized vegetation index (NDVI), land surface temperature (LST) and global precipitation network data "Open Land Map Precipitation" (LMP) were used for spatial prediction of annual rainfall. The rainfall map of the study area was prepared using auxiliary data and using random forest (RF) model. Also in synoptic stations, the amount of erosivity was determined based on the EI30 index and average annual rainfall. Finally, the relation between rainfall and erosivity and annual rainfall was determined using nonlinear regression. Root mean square error (RMSE) and correlation coefficient (r) of RF model for prediction of annual rainfall were 16.9 mm and 0.98, respectively. The results of the rainfall map in the study area showed that the rainfall varied between 70-420 mm year-1.  Rainfall classification maps showed that near the half of the study area has annual rainfall less than 100 mm, 30% of the province has annual rainfall of between 100 and 150 mm and only about 17% of the province has annual rainfall more than 150 mm year-1. The maximum and minimum values of erosivity were 380 and 39 MJha-1mm h-1year-1 in the northern and southern part of the study area, respectively. Our results indicated using new method of data mining, it is possible to spatial prediction of rainfall and erosivity, especially in areas with small number of synoptic stations.  

کلیدواژه‌ها [English]

  • Auxiliary variables
  • Network precipitation data
  • Random Forest model

مراجع

 
Akbarzadeh, M., Moghadam, M. R., Jalili, A., Jafari, M., & Arzani, H. (2007). Effect of precipitation on cover and production of rangeland plants in Polour. Iranian Journal of Natural Resources Research, 60, 307-322. (In Persian)
Amundson, R., Berhe, A. A., Hopmans, J. W., Olson, C., Sztein, A. E., & Sparks, D. L. (2015). Soil and human security in the 21st century. Science, 348(6235), 1261071. ‏
Cheng, J., Liang, S., Wang, J., & Li, X. (2009). A stepwise refining algorithm of temperature and emissivity separation for hyperspectral thermal infrared data. IEEE Transactions on Geoscience and Remote Sensing, 48(3), 1588-1597. ‏
Chen, G., Sha, W., Iwasaki, T., & Ueno, K. (2012). Diurnal variation of rainfall in the Yangtze River Valley during the spring‐summer transition from TRMM measurements. Journal of Geophysical Research: Atmospheres, 117(D6).
Cheshmberah, F., Zolfaghari, A. A., Taghizadeh-Mehrjardi, R., & Scholten, T. (2022). Evaluation of mathematical models for predicting particle size distribution using digital soil mapping in semiarid agricultural lands. Geocarto International, just accepted.  doi.org/10.1080/10106049.2022.2076911
Chu, D., Lu, L., & Zhang, T. (2007). Sensitivity of normalized difference vegetation index (NDVI) to seasonal and interannual climate conditions in the Lhasa area, Tibetan plateau, China. Arctic, Antarctic, and Alpine Research, 39(4), 635-641. ‏
De Kauwe, M. G., Taylor, C. M., Harris, P. P., Weedon, G. P., & Ellis, R. J. (2013). Quantifying land surface temperature variability for two Sahelian mesoscale regions during the wet season. Journal of Hydrometeorology, 14(5), 1605-1619. ‏
Doulabian, S., Toosi, A. S., Calbimonte, G. H., Tousi, E. G., & Alaghmand, S. (2021). Projected climate change impacts on soil erosion over Iran. Journal of Hydrology, 598, 126432. ‏
Gazanfari Moqhadam M, Alizadeh A, Mousavi Baygi M, Farid Hosseini A, Banayan Avail M. 2011. In order to compare interpolation methods used in forecasting models PERSIANN with daily rainfall data (Case study: Northern Khorasan). Journal of Soil and Water. 25(1): 215–207. (In Persian).
Ghebrezgabher, M. G., Yang, T., Yang, X., & Sereke, T. E. (2020). Assessment of NDVI variations in responses to climate change in the Horn of Africa. The Egyptian Journal of Remote Sensing and Space Science, 23(3), 249-261.‏
Grillakis, M. G., Polykretis, C., & Alexakis, D. D. (2020). Past and projected climate change impacts on rainfall erosivity: Advancing our knowledge for the eastern Mediterranean island of Crete. Catena, 193, 104625. ‏
Hakim Khani, Sh.; Hakim Khani, A. 2010. Prohibition of rain erosion map for Lorestan province. Quarterly Journal of Watershed Management Research (Research and Construction). 89: 62-72. (In Persian)
Hengl T. (2018). Monthly precipitation in mm at 1 km resolution based on SM2RAIN-ASCAT 2007-2018, IMERGE, CHELSA Climate and WorldClim.
Huffman, G. J., Bolvin, D. T., Nelkin, E. J., Wolff, D. B., Adler, R. F., Gu, G.,... & Stocker, E. F. (2007). The TRMM multisatellite precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. Journal of hydrometeorology, 8(1), 38-55. ‏
Jing, W., Zhang, P., Jiang, H., & Zhao, X. (2017). Reconstructing satellite-based monthly precipitation over Northeast China using machine learning algorithms. Remote Sensing, 9(8), 781.
Kavian, A.; Jafarian, Z.; Jahanshahi, M. )2016(. Mapping of rain erosion in Kerman province by geostatistical methods. 2016, Natural Geography Research, Volume 48, Number 1, pp. 51-68. (In Persian)
Kim, J.B., Saunders, P., Finn, J.T., )2005(. Rapid assessment of soil erosion in the Rio Lempa Basin, Central America, using the universal soil loss equation and geographic information systems. Environ. Manage. 36 (6), 872–885.
Kimani, M. W., Hoedjes, J. C., & Su, Z. (2017). An assessment of satellite-derived rainfall products relative to ground observations over East Africa. Remote sensing, 9(5), 430.
Khosravi, M., Zolfaghari, A., Kaboli, S. H., & Ghafari, H. (2022). Application of Digital Soil Mapping in Soil Particle Size Zonation and Estimation of Saturated Soil Hydraulic Conductivity for Optimal Management of Watersheds (Case Study: Damghanrood Watershed). Iranian Journal of Soil and Water Research, 53(2), 245-261. (In Persian)
Kühnlein, M., Appelhans, T., Thies, B., & Nauss, T. (2014). Improving the accuracy of rainfall rates from optical satellite sensors with machine learning—A random forests-based approach applied to MSG SEVIRI. Remote Sensing of Environment, 141, 129-143. ‏
Lal, R., & Elliot, W. (1994). Erodibility and erosivity. Soil Erosion Research Methods. Soil and Water Conservation Society. Ankeny, 181-20.
Lee, J. H., & Heo, J. H. (2011). Evaluation of estimation methods for rainfall erosivity based on annual precipitation in Korea. Journal of Hydrology, 409(1-2), 30-48. ‏
Lee, M. H., & Lin, H. H. (2015). Evaluation of annual rainfall erosivity index based on daily, monthly, and annual precipitation data of rainfall station network in Southern Taiwan. International Journal of Distributed Sensor Networks, 11(6), 214708. ‏
Liaw, A. (2002). Wiener M. Classification and regression by randomForest. R News, 2(3), 18-22. ‏
Liu, Y., Zhao, W., Liu, Y., & Pereira, P. (2020). Global rainfall erosivity changes between 1980 and 2017 based on an erosivity model using daily precipitation data. Catena, 194, 104768. ‏
McBratney, A. B., Santos, M. M., & Minasny, B. (2003). On digital soil mapping. Geoderma, 117(1-2), 3-52. ‏
Moradi, HR.; Behzadfar, M.; Sadeghi, S.H.R. )2006(. Investigating the relationship between rainfall parameters and rain erosion factor in Khuzestan province. Scientific Journal of Agriculture, 29 pp. 69-83. (In Persian)
Nearing, M. A., Yin, S. Q., Borrelli, P., & Polyakov, V. O. (2017). Rainfall erosivity: An historical review. Catena, 157, 357-362.
‏ Nik Kami, D.; Mehdian, M, H. 2014. Preparation of a suitable rainfall index map of the country. Journal of Watershed Engineering and Management. pp. 364-376. (In Persian)
Panagos, P., Borrelli, P., Meusburger, K., Yu, B., Klik, A., Jae Lim, K.,... & Ballabio, C. (2017). Global       rainfall erosivity assessment based on high-temporal resolution rainfall records. Scientific reports, 7(1), 1-12.
‏ Petkovšek, G., & Mikoš, M. (2004). Estimating the R factor from daily rainfall data in the sub-Mediterranean climate of southwest Slovenia/Estimation du facteur R à partir de données journalières de pluie dans le climat sub-méditerranéen du Sud-Ouest de la Slovénie. Hydrological sciences journal, 49(5). ‏
Renard, K.G., Freimund, J.R. )1994(. Using monthly precipitation data to estimate the R factor in the revised USLE.
Richard, Y., & Poccard, I. J. I. J. O. R. S. (1998). A statistical study of NDVI sensitivity to seasonal and interannual rainfall variations in Southern Africa. International Journal of Remote Sensing, 19(15), 2907-2920. ‏
Rutebuka, J., De Taeye, S., Kagabo, D., & Verdoodt, A. (2020). Calibration and validation of rainfall erosivity estimators for application in Rwanda. Catena, 190, 104538. ‏
Salarvand, J., Ghasemi Aghbash, F., & Asodolahi, Z. (2019). Mapping rainfall erosivity in Lorestan province using Kriging geostatistic technique. Journal of Climate Research, 1397(36), 57-72.
Satgé, F., Xavier, A., Pillco Zolá, R., Hussain, Y., Timouk, F., Garnier, J., & Bonnet, M. P. (2017). Comparative assessments of the latest GPM mission’s spatially enhanced satellite rainfall products over the main Bolivian watersheds. Remote Sensing, 9(4), 369.
Shahhosein, T., Nazarnejad, H., & Asadzadeh, F. (2022). Rainfall Erosivity Mapping for West Azerbaijan Province. Applied Soil Research, 9(4), 49-61.
‏ Sharifan, H.) 2007(. Investigating the relationships between erosion coefficient and different rainfall parameters in Gorgan region. Journal of Agricultural Sciences and Natural Resources, p.207. (In Persian)
Sharma, M., Bangotra, P., Gautam, A. S., & Gautam, S. (2021). Sensitivity of normalized difference vegetation index (NDVI) to land surface temperature, soil moisture and precipitation over district Gautam Buddh Nagar, UP, India. Stochastic Environmental Research and Risk Assessment, 1-11. ‏
Spracklen, D. V., Arnold, S. R., & Taylor, C. M. (2012). Observations of increased tropical rainfall preceded by air passage over forests. Nature, 489(7415), 282-285. ‏
Sokol, Z., & Bližňák, V. (2009). Areal distribution and precipitation–altitude relationship of heavy short-term precipitation in the Czech Republic in the warm part of the year. Atmospheric Research, 94(4), 652-662.‏
Teng, H., Ma, Z., Chappell, A., Shi, Z., Liang, Z., & Yu, W. (2017). Improving rainfall erosivity estimates using merged TRMM and gauge data. Remote Sensing, 9(11), 1134. ‏
Uddin, K., Abdul Matin, M., Maharjan, S. )2018(. Assessment of land cover change and its impact on changes in soil erosion risk in Nepal. Sustainability 10 (12), 4715.
Wang, J., Rich, P. M., & Price, K. P. (2003). Temporal responses of NDVI to precipitation and temperature in the central Great Plains, USA. International journal of remote sensing, 24(11), 2345-2364.
Wilding, L., 1985. Spatial variability: its documentation, accomodation and implication to soil surveys, Soil spatial variability, Las Vegas NV, 30 November-1 December 1984. pp. 166-194.
‏Wischmeier, W. H., & Smith, D. D. (1978). Predicting rainfall erosion losses: a guide to conservation planning (No. 537). Department of Agriculture, Science and Education Administration. ‏
Yin, S. Q., Xie, Y., Liu, B., & Nearing, M. A. (2015). Rainfall erosivity estimation based on rainfall data collected over a range of temporal resolutions. Hydrology and Earth System Sciences, 19(10), 4113-4126.
Yosefikebriya,A.,  Nadi, M &  sheikhi arjanki, sh. (2019). Increase The accuracy of monthly and annual precipitation maps using covariates In Mazandaran province. Iranian water research journal. 14(3), 107-114. (In Persian)
‏Zanjani, B., Seyed Kaboli, H., & Rashidian, M. (2019). Downscaling TRMM satellite-based precipitation data using non-stationary relationships between precipitation and land surface characteristics. Journal of RS and GIS for Natural Resources, 10(2), 85-101. (In Persian)
Zareh, S, Soltanei, S., & Taze, M. (2019). Assessment spatial variability of rainfall erosivity factor (Case study: Fars Province). Geography and Planning, 23(68), 157-177.
Zarekia, S., Zare, N., Ehsani, A., Jafari, F., & Yeganeh, H. (2012). Relationship between rainfall and annual forage production of important range species (case study: Khoshkerood-Saveh). Iranian Journal of Range and Desert Research, 19(4), 614-623. (In Persian)
Zhang, X., Friedl, M. A., Schaaf, C. B., Strahler, A. H., & Liu, Z. (2005). Monitoring the response of vegetation phenology to precipitation in Africa by coupling MODIS and TRMM instruments. Journal of Geophysical Research: Atmospheres, 110(D12).
‏ Zhong, L., Ma, Y., Salama, M., & Su, Z. (2010). Assessment of vegetation dynamics and their response to variations in precipitation and temperature in the Tibetan Plateau. Climatic change, 103(3), 519-535.
تحقیقات آب و خاک ایران
دوره 53، شماره 9
آذر 1401
صفحه 2027-2044

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