کاربرد نقشه‌برداری رقومی در پهنه‌بندی ذرات اولیه و برآورد هدایت هیدرولیکی اشباع خاک به‌منظور مدیریت بهینه حوزه‌های آبخیز (مطالعه موردی: حوزه آبخیز دامغان‎رود)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، گروه مناطق خشک، دانشکده کویرشناسی، دانشگاه سمنان، ایران

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

3 استادیار،گروه مدیریت مناطق خشک، دانشکده کویرشناسی، دانشگاه سمنان، ایران

4 استادیار، گروه علوم و مهندسی خاک، دانشکده کشاورزی، دانشگاه شهید چمران، اهواز. ایران

چکیده

توزیع اندازه ذرات اولیه خاک یکی از مهم‌ترین خصوصیات خاک بوده که بر بسیاری از خصوصیات هیدرولیکی خاک از جمله هدایت هیدرولیکی اشباع، مؤثر است؛ لذا دانش دقیق از نحوه پراکنش اندازه ذرات خاک در حوزه آبخیز بر مدیریت بهینه حوزه آبخیز بسیار تأثیرگذار است. در این مطالعه تغییرات مکانی ذرات شن، سیلت و رس خاک در سطح حوزه آبخیز دامغان‎رود با قدرت تفکیک مکانی 30 متر در عمق 30-0 سانتی‎متری، 60-30 سانتی‎متری پیش‎بینی شد. به این منظور 110 نقطه نمونه‎برداری با استفاده از روش مکعب لاتین تعیین شد و نمونه‎برداری در دو عمق انجام گرفت. متغیرهای محیطی از تصاویر ماهواره لندست و مدل رقومی ارتفاع (DEM) استخراج شدند. جهت ارتباط بین ذرات خاک و متغیرهـای محیطی از مدل RF استفاده شد. نتایج نشان داد که ضریب تبیین مدل RF در عمق 30-0 سانتی‎متری برای ذرات رس، شن و سیلت با دامنه‎ای به ترتیب برابر با 6/0، 52/0 و 71/0 و در عمق 60-30 سانتی‎متری به ترتیب برابر با 69/0، 67/0 و 49/0 به دست آمد. در لایه سطحی، متغیرهای کمکی مستخرج از داده­های سنجش‌ازدور و در لایه عمقی، متغیرهای مستخرج از DEM بیشترین ارتباط را با داده‎های ذرات خاک داشتند. مقادیر هدایت هیدرولیکی اشباع خاک (Ks) برآورد شده با استفاده از توابع انتقالی بین 08/0 تا 1 متر در روز متغیر بود، که کمترین مقدار Ks در اراضی با رخنمون­های سنگی و خاک‎های مارنی مشاهده شد. نتایج نشان داد که پراکنش مکانی هدایت هیدرولیکی اشباع خاک (Ks) مشتق شده از داده­های شن و رس، به‌خوبی با واقعیت منطقه همخوانی داشت. به‌طوری‌که کمترین مقادیر Ks در مناطق با رخنمون سنگی و در خاک­های مارنی مشاهده شد. 

کلیدواژه‌ها


عنوان مقاله [English]

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)

نویسندگان [English]

  • mahin khosravi 1
  • Ali. Asghar Zolfaghari 2
  • Seyed Hasan Kaboli 3
  • Heidar Ghafari 4
1 Ph D. Student, Management of Arid Areas Department, Faculty of Desertification University of Semnan, Iran
2 Associate Professor, Dep. of Arid lands management, Faculty of Desert Science; Semnan University. Iran.
3 Assistant Professor, Dep. of Arid lands management, Faculty of Desert Science; Semnan University. Iran.
4 Soil science Department, Faculty of Agriculture, Shahid Chamran University of Ahvaz, .Iran.
چکیده [English]

The soil particle size distribution is one of the most important of soil properties that effect on the soil hydraulic properties, including saturated hydraulic conductivity. Therefore, accurate knowledge of spatial distributon of soil particle size in the watershed is very effective on the optimal management of the watershed. In this study, the spatial distribution of sand, silt and clay particles were predicted in the Damghanrood watershed with a spatial resolution of 30 m at the depths of 0-30, 30-60 cm. For this purpose, 110 soil sampling points were determined using conditional Latin hypercube sampling (cLHS) method. Environmental variables were extracted from Landsat 8 Operational Land Imager (OLI) satellite and digital elevation model (DEM). The random forest (RF) model was used for determined the relationship between soil particles and environmental variables. The results showed that the coefficient of determination (R2) of the RF model at a depth of 0-30 cm for clay, sand and silt particles with a range of 0.6, 0.52 and 0.71, respectively, and at a depth of 30-60 cm, respectively. It was obtained with 0.69, 0.67 and 0.49. In the surface layer, the auxiliary variables extracted from the remote sensing data and in the deep layer, the variables extracted from the most part were related to the soil particle data. The results showed that the coefficient of determination (R2) of the RF model for prediction clay, sand and silt fractions at depth of 0-30 cm was of 0.6, 0.52 and 0.71, respectively, and at a depth of 30-60 cm, for prediction of these fraction the R2 value was 0.69, 0.67 and 0.49, respectively. In the surface layer, the auxiliary variables extracted from the remote sensing data were more important variables for prediction of particle fraction but in deep layer, the terrain attributes were the most important variables in prediction of particle size fractions. The values of saturated hydraulic conductivity (Ks) estimated using pedotransfer functions varied between 0.08 to 1 m / day. The lowest amount of Ks was observed in lands with rock outcrops and marl soils. The results showed that the spatial distribution of Ks derived from sand and clay data was well overly with the reality of the region. So that the lowest values of Ks were observed in areas with rock outcrops and in marly soils.

کلیدواژه‌ها [English]

  • "Environmental covariates"
  • "Random Forest model"
  • "Pedotransfer function"
Adhikari, K., Kheir, R.B., Greve, M.B., Bocher, P.K., Malone, B.P., Minasny, B., McBratney, A.B. and Greve, M.H., (2013). High-resolution 3-D mapping of soil texture in Denmark. Soil Sci. Soc. Am. J. 77, 860–876.
Adamchuk, V. and Viscarra Rossel, R. (2011). Precision agriculture: proximal soil sensing Arrouays, D., Lagacherie, P., Hartemink, A.E., (2017). Digital soil mapping across the globe. Geoderma Regional 9, 1–4.
Amirian Chakan, A, Z. Taghizadeh Mehrjerdi, R. Sarmadian, F and Heidari, A. )2016(. Preparation of three-dimensional maps of the final particle size distribution of soil (soil texture) using depth equations and artificial neural networks. Iranian Soil and Water Research, 84(1), 113-123.
Amirian-Chakan, A., Minasny, B., Taghizadeh-Mehrjardi, R., Akbarifazli, R., Darvishpasand, Z. and Khordehbin, S. (2019). Some practical aspects of predicting texture data in digital soil mapping. Soil and Tillage Research, 194, 104289. ‏
Akpa, S. I. C., Odeh, I. O. A. and Bishop, T. F. A. (2014). Digital mapping of soil particle-size fractions for Nigeria. Soil Science Society of America Journal, 78, 1953-1966.
Akumu, C. E., Johnson, J. A., Etheridge, D., Uhlig, P., Woods, M., Pitt, D. G. and McMurray, S. (2015). GIS-fuzzy logic based approach in modeling soil texture: Using parts of the Clay Belt and Hornepayne region in Ontario Canada as a case study. Geoderma, 239-240, 13-24.
Arrouays, D., Grundy, M. G., Hartemink, A. E., Hempel, J. W., Heuvelink, G. B., Hong, S. Y., ...& Zhang, G. L. (2014). GlobalSoilMap: Toward a fine-resolution global grid of soil properties. Advances in agronomy, 125, 93-134.
Azadmard, B., Mosaddeghi, M. R., Ayoubi, S., Chavoshi, E. and Raoof, M. (2018). Spatial variability of near-saturated soil hydraulic properties in Moghan plain, North-Western Iran. Arabian Journal of Geosciences, 11(16), 1-1.
‏ Babaei, F., Zolfaghari, A. A., Yazdani, M. R., & Sadeghipour, A. (2018). Spatial analysis of infiltration in agricultural lands in arid areas of Iran. Catena, 170, 25-35.‏
Becker, R., Gebremichael, M., & Märker, M. (2018). Impact of soil surface and subsurface properties on soil saturated hydraulic conductivity in the semi-arid Walnut Gulch Experimental Watershed, Arizona, USA. Geoderma322, 112-120
Bogunovic, I., Mesic, M., Zgorelec, Z., Jurisic, A. and Bilandzija, D. (2014). Spatial variation of soil nutrients on sandy-loam soil. Soil and tillage research, 144, 174-183
Breiman, L. (2001). Random forests. Mach. Learning 45 (1), 5–32. https://doi.org/10. 1023/A:1010933404324.
Brungard, C.W., Boettinger, J.L., Duniway, M.C., Wills, S.A. and Edwards, Jr.T.C., (2015). Machine learning for predicting soil classes in three semi-arid landscapes. Geoderma 239–240, 68–83.
Centeno, L. N., Hu, W., Timm, L. C., She, D., da Silva Ferreira, A., Barros, W. S., ... and Caldeira, T. L. (2020). Dominant control of macroporosity on saturated soil hydraulic conductivity at multiple scales and locations revealed by wavelet analyses. Journal of Soil Science and Plant Nutrition, 20, 1686-1702
Chen, F., Dudhia, J., (2001). Coupling an advanced land surface-hydrology model with the Penn state–NCAR MM5 modeling system. Part I: model implementation and sensitivity. Monthly Weather Review.
Cosby, B. J., Hornberger, G. M., Clapp, R. B., and Ginn, T. (1984). A statistical exploration of the relationships of soil moisture characteristics to the physical properties of soils. Water resources research, 20(6), 682-690.
dos Santos, R. C. V., Vargas, M. M., Timm, L. C., Beskow, S., Siqueira, T. M., Mello, C. R., ... & Reichardt, K. (2021). Examining the implications of spatial variability of saturated soil hydraulic conductivity on direct surface runoff hydrographs. CATENA, 207, 105693
Dai, Y., Zeng, X., Dickinson, R.E., Baker, I., Bonan, G.B., Bosilovich, M.G., Denning, A.S. Dirmeyer, P.A., Houser, P.R., Niu, G., Oleson, K.W., Schlosser, C.A., Yang, Z.L., (2003). The common land model. Bull. Am. Meteorol. Soc. 84, 1013–1024.
Diaz-Uriarte, R., de Andres, S.A. (2006). Gene selection and classification of microarray data using random forest. BMC Bioinf. 7. https://doi.org/10.1186/1471-2105-7-3.
Farhan, A. A., and Abed, B. S. (2021). Estimation of Surface Runoff to Bahr AL-Najaf. Journal of Engineering, 27(9), 51-63.‏
Fu, T., Gao, H., Liang, H., & Liu, J. (2021). Controlling factors of soil saturated hydraulic conductivity in Taihang Mountain Region, northern China. Geoderma Regional, 26, e00417.‏
Gee, G.W., and Bauder, J.W. )1986(. Particle size analysis. P 404-407 In: Klute, A. (Ed). Methods of Soil Analysis. Part 1. 2nd edition. Agron. Monogr. 9. ASA and SSSA Madison WI.
Godoy, V. A., Zuquette, L. V. and Gómez-Hernández, J. J. (2019). Spatial variability of hydraulic conductivity and solute transport parameters and their spatial correlations to soil properties. Geoderma, 339, 59-69 Godoy, V. A., Zuquette, L. V. and Gómez-Hernández, J. J. (2019). Spatial variability of hydraulic conductivity and solute transport parameters and their spatial correlations to soil properties. Geoderma, 339, 59-69.
Gülser, C. and Candemir, F. (2008). Prediction of saturated hydraulic conductivity using some moisture constants and soil physical properties. Proceeding Balwois.
Greve, M. H., Kheir, R. B., Greve, M. B. and Bocher, P. K. (2012a). Quantifying the ability of environmental parameters to predict soil texture fractions using regression-tree model with GIS and LIDAR data: The case study of Denmark. Ecological Indicators, 18, 1-10.
Greve, M. H., Kheir, R. B., Greve, M. B. and BØcher, P. K. (2012b). Using digital elevation models as an environmental predictor for soil clay contents. Soil Science Society of America Journal, 76, 2116-2127.
Grimm, R., Behrens, T., Marker, M. and Elsenbeer, H. (2008). Soil organic carbon concentrations and stocks on Barro Colorado Island-digital soil mapping using random forests analysis. Geoderma, 146, 102–113.
Gutmann, E.D. and Small, E.E. (2007). A comparison of land surface model soil hydraulic properties estimated by inverse modeling and pedotransfer functions. Water Resources Research, 43, W05418. http://dx.doi.org/10.1029/2006WR005135.
Hartemink, A. E. and McBratney, A. B. (2008). A soil science renaissance. Geoderma, 148, 123-129.
Hengl, T., de Jesus, J.M., MacMillan, R.A., Batjes, N.H., Heuvelink, G.B.M., Ribeiro, E., Samuel-Rosa, A., Kempen, B., Leenaars, J.G.B., Walsh, M.G.and Gonzalez, M.R. (2014). SoilGrids1km−global soil information based on automated mapping. PLoS ONE 9(8), e105992.
Hengl, T., Heuvelink, G.B., Kempen, B., Leenaars, J.G., Walsh, M.G., Shepherd, K.D., Sila, A., MacMillan, R.A., de Jesus, J.M., Tamene, L. and Tondoh, J.E. (2015). Mapping soil properties of Africa at 250 m resolution: random forests significantly improve current predictions. PLoS ONE 10, e0125814.
Honarbakhsh, A., Tahmoures, M., Afzali, S. F., Khajehzadeh, M., & Ali, M. S. (2022). Remote sensing and relief data to predict soil saturated hydraulic conductivity in a calcareous watershed, Iran. catena212, 106046
Javadian, R, & Nemati. (2018). Investigation of thermal comfort in architectural adaptation to climatic conditions in Semnan. Application of Remote Sensing and GIS in Planning, 9 (1), 74-90.
Ju, L., Zhang, J., Meng, L., Wu, L., Zeng, L., 2018. An adaptive Gaussian process-based iterative ensemble smoother for data assimilation. Advances in Water Resources, 115, 125–13.
Kempen, B., Heuvelink, G.B.M., Brus, D. and Walvoort, D. (2014). Towards globalsoilmap.net products for the Netherlands. In: Arrouays, D., McKenzie, N., Hempel, J., de Forges, A.R., McBratney, A.B. (Eds.), GlobalSoilMap–Basis of the Global Spatial Soil Information System. CRC Press, pp. 85–90.
Kowalczyk, E.A., Wang, Y.P., Law, R.M., Davies, H.L., McGregor, J.L., Abramowitz, G., (2006). The CSIRO Atmosphere Biosphere Land Exchange (CABLE) model for use in climate models and as an offline model. CSIRO Marine. Atmospheric Research, 13, 42.
 
Li, H.Y., Shi, Z., Webster, R. and Triantafilis, J. (2013). Mapping the three-dimensional variation of soil salinity in a rice-paddy soil. Geoderma 195–196, 31–41.
Liu, F., Zhang, G. L., Song, X., Li, D., Zhao, Y., Yang, J and Yang, F. (2020). High-resolution and three-dimensional mapping of soil texture of China. Geoderma, 361, 114061.
Liaw, A. and Wiener, M., (2002). Classification and regression by randomforest. R News 2 (3), 18–22.
McBratney, A.B., Mendonca-Santos, L. and Minasny, B. (2003). On digital soil mapping. Geoderma 117, 3–52.
Minasny, B., McBratney, A.B., Mendonca-Santos, M.L., Odeh, I.O.A. and Guyon, B. (2006). Prediction and digital mapping of soil carbon storage in the Lower Namoi Valley. Soil research. 44, 233–244.
Montanarella, L. and Vargas, R. (2012). Global governance of soil resources as a necessary condition for sustainable development. Curr. Opin. Env Sust. 4 (5), 559–564.
More, S. B., Deka, P. C., Patil, A. P., & Naganna, S. R. (2022). Machine learning-based modeling of saturated hydraulic conductivity in soils of tropical semi-arid zone of India. Sādhanā, 47(1), 1-15
Mousavi, S., Sarmadian, F., Omid, M., Bogaert, P. (2021). Digital Modeling of Three-Dimensional Soil Salinity Variation Using Machine Learning Algorithms in Arid and Semi-Arid lands of Qazvin Plain. Iranian Journal of Soil and Water Research, 52(7), 1915-1929.
Mulder, V.L., Lacoste, M., de Forges, A.R. and Arrouays, D. (2016). Globalsoilmap France: high-resolution spatial modelling the soils of France up to two-meter depth. Science of the Total Environment, 573, 1352–1369.
Niu, G.Y., Yang, Z.L., Mitchell, K.E., Chen, F., Ek, M.B., Barlage, M., Kumar, A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., Xia, Y., (2011). The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements. Journal of Geophysical Research: Atmospheres, 116, 1–19.
Nussbaum, M., Spiess, K., Baltensweiler, A., Grob, U., Keller, A., Greiner, L., Schaepman, M.E. and Papritz, A. (2018). Evaluation of digital soil mapping approaches with large sets of environmental covariates. Soil, 4 (1), 1–22.
Oleson, K., Lawrence, D.M., Bonan, G.B., Drewniak, B., Huang, M., Koven, C.D., Levis, S., Li, F., Riley, W.J., Subin, Z.M., Swenson, S., Thornton, P.E., Bozbiyik, A., Fisher, R., Heald, C.L., Kluzek, E., Lamarque, J.-F., Lawrence, P.J., Leung, L.R., Lipscomb, W., Muszala, S.P., Ricciuto, D.M., Sacks, W.J., Sun, Y., Tang, J., Yang, Z.-L., (2013). Technical description of version 4.5 of the Community Land Model (CLM), NCAR Technical Note NCAR/TN-503+STR (422 pp).
Picciafuoco, T., Morbidelli, R., Flammini, A., Saltalippi, C., Corradini, C., Strauss, P., & Blöschl, G. (2019). A Pedotransfer Function for Field‐Scale Saturated Hydraulic Conductivity of a Small Watershed. Vadose Zone Journal, 18(1), 1-15.‏
Pahlavan-Rad, M. R. and Akbarimoghaddam, A. (2018). Spatial variability of soil texture fractions and pH in a flood plain (case study from eastern Iran). Catena, 160, 275-281. ‏
Phillips, J.D. (2016). Identifying sources of soil landscape complexity with spatialadjacency graphs. Geoderma 267, 58–64.
Qiu, Z., Hall, C., Drewes, D., Messinger, G., Prato, T., Hale, K., & VanAbs, D. (2014). Hydrologically sensitive areas, land use controls, and protection of healthy watersheds. Journal of Water Resources Planning and Management, 140(7), 1–10.
Raeesi, M., Zolfaghari, A. A., Yazdani, M. R., Gorji, M., & Sabetizade, M. (2019). Prediction of soil organic matter using an inexpensive colour sensor in arid and semiarid areas of Iran. Soil Research, 57(3), 276-286.‏
Ramcharan, A., Hengl, T., Nauman, T., Brungard, C., Waltman, S., Wills, S. and Thompson, J. (2018). Soil property and class maps of the conterminous United States at 100-meter spatial resolution. Soil Science Society of America Journal, 82, 186–201.
Rezaei Tavabh, K. (2015). Limnological study and determination of biological value of Damghanrood river in Semnan province. The first annual conference of Iranian agricultural research, Kharazmi Higher Institute of Science and Technology, Shiraz.
Rossel, R. V., Chen, C., Grundy, M. J., Searle, R., Clifford, D., and Campbell, P. H. (2015). The Australian three-dimensional soil grid: Australia’s contribution to the GlobalSoilMap project. Soil Research, 53(8), 845-864.‏
Sanchez, P.A., Ahamed, S., Carre, F., Hartemink, A.E., Hempel, J., Huising, J., Lagacherie, P., McBratney, A.B., McKenzie, N.J., Mendonca-Santos, M.d.L., Minasny Budiman, Montanarella, L., Okoth, P., Palm, C.A., Sachs, J.D., Shepherd, K.D., Vagen, T., Vanlauwe, B., Walsh, M.G., Winowiecki, L.A. and Zhang, G.L. (2009). Digital soil map of the world. Science 325, 680–681.
Soet, M. and Stricker, J.N.M. (2003). Functional behaviour of pedotransfer functions in soil water flow simulation. Hydrological Processes, 17, 1659–1670.
Stockmann, U., Adams, M.A., Crawford, J.W., Field, D.J., Henakaarchchi, N., Jenkins, M., Minasny, B., McBratney, A.B., de Courcelles, V.D.R., Singh, K., Wheeler, I., Abbott, L., Angers, D.A., Baldock, J., Bird, M., Brookes, P.C., Chenu, C., Jastrow, J.D., Lal, R., Lehmann, J., O'Donnellk, A.G., Parton, W.J., Whitehead, D. and Zimmermann, M. (2013). The knowns, known unknowns and unknowns of sequestration of soil organic carbon. Agric. Agriculture, Ecosystems & Environment, 164, 80–99.
Taghizadeh-Mehrjardi, R., Minasny, B., Sarmadian, F. and Malone, B. (2014). Digital mapping of soil salinity in Ardakan region, central Iran. Geoderma ,213, 15–28.
Taghizadeh-Mehrjardi, R., Nabiollahi, K. and Kerry, R. (2016). Digital mapping of soil organic carbon at multiple depths using different data mining techniques in Baneh region, Iran. Geoderma, 266, 98–110.
Ungaro F., Ragazzi F., Cappellin R., and Giandon P. (2008). Arsenic concentration in the soils of the Brenta Plain
(Northern Italy): Mapping the probability of exceeding contamination thresholds. J. Geo. Explo, 96: 117-131.
Villagra-Mendoza, K. and Horn, R. (2018). Effect of biochar on the unsaturated hydraulic conductivity of two amended soils. International Agrophysics, 32(3).
Yu, D., Yang, J., Shi, L., Zhang, Q., Huang, K., Fang, Y., Zha, Y., 2018. On the uncertainty of initial condition and initialization approaches in variably saturated flow modeling. Hydrology and Earth System Sciences, 1–42. https://doi.org/10.5194/hess-2018-557
Zhao, Y., Peth, S., Horn, R., Krümmelbein, J., Ketzer, B., Gao, Y., ... and Peng, X. (2010). Modeling grazing effects on coupled water and heat fluxes in Inner Mongolia grassland. Soil and Tillage Research, 109(2), 75-86. ‏
Zeraatpisheh, M., Jafari, A., Bodaghabadi, M. B., Ayoubi, S., Taghizadeh-Mehrjardi, R., Toomanian, N., ... and Xu, M. (2020). Conventional and digital soil mapping in Iran: Past, present, and future. Catena, 188, 104424
Zhang, Y., Schaap, M. G., and Wei, Z. (2019). Hierarchical multimodel ensemble estimates of soil water retention with global coverage. arXiv preprint arXiv:1906.03182.
‏Zhang, X., Wendroth, O., Matocha, C., Zhu, J. and Reyes, J. (2020). Assessing field-scale variability of soil hydraulic at and near saturation. Catena, 187, 104335.
‏ Ziaee, D & Khwaja al-Din, S,J. 2014. Preparation of texture map and percentage of surface soil saturation moisture with the help of remote sensing (Case study in Isfahan). Iranian Range and Desert Research, 20 (4), 795-808.
Zolfaghari, A. A., Yazdani, M. R., Khosravi. M. and Mahmoudi S. M. (2010). Comparison of different data mining methods for digital mapping of primary soil particles in Semnan plain lands. Iranian Journal of Soil and Water Research, 51(2), 375-385.