تحلیل فضایی مخاطرات محیطی

تحلیل فضایی مخاطرات محیطی

بررسی تاثیر تغییرات پوشش و کاربری زمین در قابلیت فرسایش خاک – مطالعه موردی حوضه قره‌سو گرگانرود

نویسندگان
امیر صفاری 1
عباسعلی نوری 1
جلال کرمی 2
1 دانشگاه خوارزمی
2 دانشگاه تربیت مدرس
10.29252/jsaeh.5.1.83
چکیده
فرسایش خاک یکی از ریسک‌های اصلی تهدیدکننده منابع آب و خاک در ایران است که رابطه‌ای قوی با نوع پوشش و کاربری زمین دارد. در این پژوهش بوسیله مدل RUSLE با بهره‌گیری از تصاویر سنجنده‌هایTM ، ETM و OLI ماهواره لندست در یک بازه 30 ساله برای سه سال 1985 ، 2000و 2015 تاثیر تغییرات پوشش زمین بر پتانسیل فرسایش خاک در حوضه آبخیز قره‌سو مورد بررسی قرار گرفت. نتایج تغییرات پوشش زمین نشان‌دهنده کاهش پوشش‌های جنگل متراکم، جنگل با تراکم کم و باغ و مرتع در برابر افزایش سطوح کشاورزی، زمین‌های بدون پوشش و سکونتگاه‌های انسانی در طی بازه سی ساله است. همچنین نتایج مدل RUSLE سیر افزایشی پتانسیل فرسایش خاک درحوضه آبخیز قره‌سو را نشان می‌دهد، هرچند که در نواحی جلگه‌ای با کاربری کشاورزی روندی معکوس با روند کلی در نتیجه اصلاح و تغییر الگوی کشت و رشد کشاورزی آبی دیده می‌شود. میانگین پتانسیل فرسایش خاک برآورد شده درحوضه قره‌سو برای 1985 ، 2000و 2015 به ترتیب 102.02، 103.11و 103.76 تن در هکتار در سال است. همچنین در کلاس‌های بیش از 100 (تن در سال در هکتار) یا طبقات با پتانسیل خیلی زیاد و بحرانی این مقدار از 43.8 درصد به 45.5 درصد از مساحت حوضه در سال 2015 افزایش یافته است. این روند افزایشی در سطح زیر حوضه ها نیز مورد آزمون قرار گرفت و در اکثر آنها پتانسیل فرسایش خاک بر اساس روند تغییرات کاربری زمین رو به افزایش است.
کلیدواژه‌ها
مدل RUSLE
فرسایش خاک
قره‌سو
سنجش از دور
پوشش زمین

عنوان مقاله English

Investigation about the influence of land-cover and land use changes on soil erodibility potential, case study: Gharesou, Gorganrood

نویسندگان English

amir saffari 1
amir saffari 1
jalal karami 2
1 Kharazmi University
2 Tarbiat Modares University
چکیده English

Investigation about the influence of land-cover and land use changes on soil erodibility potential, case study: Gharesou, Gorganrood

Land use and land cover (LUC) change associated with climatic and geomorphologic conditions of the area have an accelerating impact on the land degradation. Natural as well as human-induced land use land cover change (LUCC) has significant impacts on regional soil degradation, including soil erosion, soil acidification, nutrient leaching, and organic matter depletion. Since the last century, soil erosion accelerated by human activities has become a serious environmental problem. It has a manifold environmental impact by negatively affecting water supply, reservoir storage capacity, agricultural productivity, and freshwater ecology of the region. In recent years, many researchers have highlighted the environmental consequences of soil erosion.

Soil erosion estimation at a regional scale is influenced by the complexity of the soil erosion process and the availability of data describing the soil erosion factors. In the last decade, regional and national level assessments of soil erosion were carried out using different approaches, ranging from indicator or factor-based approaches to process-based models. However, the revised universal soil loss (RUSLE) and its modifications are still widely used because of its simplicity and a greater availability of input parameters.

Gharesou basin is one of the sub-basins of Gharesou, it suffered from severe erosion in some areas over the past years. This erosion has occurred for different reasons and one of them is land use change and weak management of water and soil resources. The purpose of this research is to investigate the effects of land-cover changes on the potential of soil erosion in Gharesou Basin, a sub-basin of Gorganrood, in Golestan province. For this, we have employed RUSLE Model and used landsat satellite images from the sensors of TM, ETM, and OLI for 1985, 2000, and 2015. The potential soil erosion in this study was estimated using RUSLE model, which can be described using following equation:

A = R × K × LS × C × P

where A is amount of soil erosion calculated in tons per hectare per year, R is rainfall factor , K is soil erodibility factor , L is slope length factor, S is slope steepness factor, C is cover and management factor, and P is erosion control practice factor. To run the RUSLE model in GIS, first, rainfall raster layer, soil, slope, Digital Elevation Model, and also layers of soil protection range were created. Each of the involved factors was calculated in separate units in the basin level. In this research, Gharesou basin was analyzed based on raster network data with 30 meters cell size, because, from one hand it's small

enough to show heterogeneity of the basin and on the other hand, it matches pixel dimensions of landsat satellite images.

The results of land-cover changes have revealed a decrease in dense forest areas, low forest areas and the mixture of orchard, forest and pastures in a thirty years period. According to the results of RUSLE, changes of the classes indicate a general trend to the soil loss in the basin. Therefore, Gharesou basin is a basin with increasing soil erosion potential. In the plain and coastal plain areas of the basin, that is the mainly cultivated area, the amount of erosion is different from the other areas, and soil loss process is decreasing. It's due to the changes of cultivation method from traditional to modern, increase of irrigated farming area, choosing more environmentally friendly plants, and also, increase in the area of cities and villages from 7.14 percent to 29.04 percent during 30 years. In the study classes, for output of RUSLE model, in every 3 years of study, the maximum area relates to the classes of 100 to 200 Ton per year that is more seen in the mountainous regions. In these regions, all factors except vegetation are toward soil loss. Also, during 30 years, the amount of dense vegetation decreased from 34.56 to 31.55. In fact the only factor in protecting soil in (prone to erosion) areas has given its place to less effective vegetation, so, the area of this region has increased and Gharesou basin is in danger of soil loss in mountainous and forest parts. Also, areas with more than 200 Ton in hectare, with the lowest amount, have had a tangible increase during 30 year of study and its amount has increased from 11.74 to 12.50. These areas are usually located in mountainous parts with no vegetation. Also, the average of soil erosion potential estimated in Gharesou basin for 1985, 2000 and 2015 is 102.02, 103.11, and 103.76 (ton per hectare per year). This amount was found in the sub-basins too and except the sub-basin 4 located in coastal plain areas of the basin, with farming use, the amount of other sub-basins is increasing. According to the results of study, mountainous parts of Gharesou basin, has the most damage due to the accumulation of involved factors in the potential increase of soil loss. So, the necessity of watershed management is observed. Also modification of cultivation pattern and soil conservation training in farming lands of foothills and hillsides are required.

Keywords: RUSLE Model, soil erosion, Gharesou, Remote Sensing, land-cover changes

کلیدواژه‌ها English

RUSLE Model
soil erosion
Gharesou
Remote Sensing
land-cover changes
Ahmed, T. 2000. Fuzzy class membership approach to soil modeling, Agricultural Systems,63: 97-110.
Alkharabsheh, M. M., Alexandeidis, T. K., Bilas, G., Misopolinos, N., & Silleos, N. 2013. The Impact of Land Cover Change on soil erosin hazard in northen Jordan using remote sensing and GIS. Procedia Environmental Sciences, 19: 912-921. [DOI:10.1016/j.proenv.2013.06.101]
Bahadur, K. C. Krishna. 2009. Mapping soil erosion susceptibility using remote sensing and GIS: a case of the Upper Nam Wa Watershed, Nan Province, Thailand. Environ Geol, 57:695–705. DOI 10.1007/s00254-008-1348-3
Bartsch, KP. Van Miegroet, H., Boettinger, J., Dobrwolski, JP. 2002. Using empirical erosion models and GIS to determine erosion risk at Camp Williams, Journal of Soil and Water Conservation, 57: 29–37.
Benkobi, L., Trlica, M.J., and Smith, J.L. 1994. Evaluation of a refined surface cover subfactor for use in RUSLE. J. Range Manage. 47: 74-78. [DOI:10.2307/4002845]
Biesemans, J., Meirvenne, M.V., and Gabriels, D. 2000. Extending the RUSLE with the Monte Carlo error propagation technique to predict long-term average off-site sediment accumulation. J. Soil Water Conservation, 55: 35-42.
Boardman J., Ligneau L., De Roo A.P.J., and Vandaele K. 1994. Flooding of property by run off from agricultural land in northwestern Europe. Geomorph, 10: 183-196. [DOI:10.1016/b978-0-444-82012-9.50017-7]
Bruce, R.R., Langdale, G.W., East, L.J., and Miller, W.P. 1995. Surface soil degradation and soil productivity restoration and maintanace. Soil Sci. Soc. Am. J. 59: 654-660. doi:10.2136/sssaj1995.03615995005900030003x
Chakroun, H., Bonn, F., Fortin, J.P., 1993, Combination of single storm erosion and hydrological models into a geographic information system, Farm Land Erosion: In Temperate Plains Environment and Hills, 261–270. [DOI:10.1016/b978-0-444-81466-1.50027-7]
Clark R.D. 1980. Erosion condition classification system. Bureau of Land Management, Denver Service Center, Denver CO.
Deore, S.J. 2006. Prioritization of Micro-watersheds of Upper Bhama Basin on the Basis of Soil Erosion Risk Using Remote Sensing and GIS Technology. Ph.D. Thesis. Department of Geography. University of Pune.
Fox, A.D., Desholm, M., Kahlert, J., Christensen, T.K., and Petersen, K. 2006. Information needs to support environmental impact assessment of the effects of European marine offshore wind farms on birds. Special Issue: Wind, Fire and Water: Renewable Energy and Birds. 148: 129-144. [DOI:10.1111/j.1474-919x.2006.00510.x]
Garcia-ruiz, j. m. 2010. The effects of land uses on soil erosion in Spain: A review. Catena, 81(1), 1-11. [DOI:10.1016/j.catena.2010.01.001]
Haan, C.T., Barfield, B.J., and Hayes, J.C. 1994. Design hydrology and sedimentology for small catchments. Academic Press, San Diego.
Haen HD. 1991. Environmental consequences of agricultural growth In: Vosti SA, Reardon T, Winfried Von Urff (eds) Agricultural sustainability, growth and poverty alleviation and policies, Feldafing.
Laflen, J.M., Lane, L.J., Foster, G.R. 1991. WEPP: a new generation of erosion prediction technology, Journal of Soil and Water Conservation, 46, No. 1, PP. 34–38.
McCool, D.K., Brown, L.C., and Foster, G.R. 1987. Revised slope steepness factor for the universal soil loss equation. Trans. Am. Soc. Agric. Eng. 30: 1387-1396. [DOI:10.13031/2013.30576]
Millward, A.A., and Mersey, J.E. 1999. Adapting the RUSLE to model soil erosion potential in a mountainous tropical watershed. Catena, 3: 109-129. [DOI:10.1016/s0341-8162(99)00067-3]
Monchareon L .1982. Application of soil maps and report for soil and water conservation. Department of land development, Bangkok.
Moore, I., Burch, G., 1986, Physical basis of the length-slope factor in the universal soil loss equation, Soil Science Society of America Journal, 50: 1294–1298. [DOI:10.2136/sssaj1986.03615995005000050042x]
Pacheco F.A.L., Varandas S.G.P., Fernandes L.S., Junior R.V. 2014. Soil losses in rural watersheds with environmental land use conflicts. Sci. Total Environ. 485: 110 - 120
Pimental D., Harvey C., Resosudarmo P., Sinclair K., Kurz D., McNair M., Crist S., Shpritz L., Saffouri R., and Blair R. 1995. Environmental costs of soil erosion and conservation benefits. Science, 267: 1117-1123.
Renard, K. G., & Freimund, J.R. 1994. Using monthly precipitation data to estimate the R factor in the revised USLE, J. Hydrol, 157: 287-306. [DOI:10.1016/0022-1694(94)90110-4]
Renard, K.G., Foster, G.R., Weesies, G.A. & Porter, J.P., 1991, RUSLE: revised universal soil loss equation. Journal of Soil and Water Conservation, 46: 30-33.
Van der Knijff, J.M., Jones, R.J.A., Montanarella, L. 2000. Soil Erosion Risk Assessment in Europe, EUR 19044 EN. Office for Official Publications of the European Communities, Luxembourg.
Veihe, A. 2002. The spatial variability of erodibility and its relation to soil types: a study from northern Ghana, Geoderma, 106: 101-120. [DOI:10.1016/s0016-7061(01)00120-3]
Wang, G., Gertner, G., Fang, S., and Anderson, AB. 2003. Mapping multiple variables for predicting soil loss by geostatistical methods with TM images and a slope map. Photogrammetric Engineering and Remote Sensing, 69: 889-898. [DOI:10.14358/pers.69.8.889]
Wijitkosum S. 2012, Impacts of land use changes on soil erosion in Pa Deng sub-district, adjacent area of Kaeng Krachan National Park, Thailand. Soil and Water Research, 7(1): 10-17.
Williams JR. 1975. Sediment routing for agricultural watersheds. Water Resour Bull 11: 965–974. [DOI:10.1111/j.1752-1688.1975.tb01817.x]
Wischmeier, W.H., and Smith, D.D. 1978. Predicting rainfall erosion. Losses: a guide to conservation planning. Agriculture Handbook, Vol. 537. US Department of Agriculture, Washington, DC.
Ahmed, T. 2000. Fuzzy class membership approach to soil modeling, Agricultural Systems,63: 97-110.
Alkharabsheh, M. M., Alexandeidis, T. K., Bilas, G., Misopolinos, N., & Silleos, N. 2013. The Impact of Land Cover Change on soil erosin hazard in northen Jordan using remote sensing and GIS. Procedia Environmental Sciences, 19: 912-921. [DOI:10.1016/j.proenv.2013.06.101]
Bahadur, K. C. Krishna. 2009. Mapping soil erosion susceptibility using remote sensing and GIS: a case of the Upper Nam Wa Watershed, Nan Province, Thailand. Environ Geol, 57:695–705. DOI 10.1007/s00254-008-1348-3
Bartsch, KP. Van Miegroet, H., Boettinger, J., Dobrwolski, JP. 2002. Using empirical erosion models and GIS to determine erosion risk at Camp Williams, Journal of Soil and Water Conservation, 57: 29–37.
Benkobi, L., Trlica, M.J., and Smith, J.L. 1994. Evaluation of a refined surface cover subfactor for use in RUSLE. J. Range Manage. 47: 74-78. [DOI:10.2307/4002845]
Biesemans, J., Meirvenne, M.V., and Gabriels, D. 2000. Extending the RUSLE with the Monte Carlo error propagation technique to predict long-term average off-site sediment accumulation. J. Soil Water Conservation, 55: 35-42.
Boardman J., Ligneau L., De Roo A.P.J., and Vandaele K. 1994. Flooding of property by run off from agricultural land in northwestern Europe. Geomorph, 10: 183-196. [DOI:10.1016/b978-0-444-82012-9.50017-7]
Bruce, R.R., Langdale, G.W., East, L.J., and Miller, W.P. 1995. Surface soil degradation and soil productivity restoration and maintanace. Soil Sci. Soc. Am. J. 59: 654-660. doi:10.2136/sssaj1995.03615995005900030003x
Chakroun, H., Bonn, F., Fortin, J.P., 1993, Combination of single storm erosion and hydrological models into a geographic information system, Farm Land Erosion: In Temperate Plains Environment and Hills, 261–270. [DOI:10.1016/b978-0-444-81466-1.50027-7]
Clark R.D. 1980. Erosion condition classification system. Bureau of Land Management, Denver Service Center, Denver CO.
Deore, S.J. 2006. Prioritization of Micro-watersheds of Upper Bhama Basin on the Basis of Soil Erosion Risk Using Remote Sensing and GIS Technology. Ph.D. Thesis. Department of Geography. University of Pune.
Fox, A.D., Desholm, M., Kahlert, J., Christensen, T.K., and Petersen, K. 2006. Information needs to support environmental impact assessment of the effects of European marine offshore wind farms on birds. Special Issue: Wind, Fire and Water: Renewable Energy and Birds. 148: 129-144. [DOI:10.1111/j.1474-919x.2006.00510.x]
Garcia-ruiz, j. m. 2010. The effects of land uses on soil erosion in Spain: A review. Catena, 81(1), 1-11. [DOI:10.1016/j.catena.2010.01.001]
Haan, C.T., Barfield, B.J., and Hayes, J.C. 1994. Design hydrology and sedimentology for small catchments. Academic Press, San Diego.
Haen HD. 1991. Environmental consequences of agricultural growth In: Vosti SA, Reardon T, Winfried Von Urff (eds) Agricultural sustainability, growth and poverty alleviation and policies, Feldafing.
Laflen, J.M., Lane, L.J., Foster, G.R. 1991. WEPP: a new generation of erosion prediction technology, Journal of Soil and Water Conservation, 46, No. 1, PP. 34–38.
McCool, D.K., Brown, L.C., and Foster, G.R. 1987. Revised slope steepness factor for the universal soil loss equation. Trans. Am. Soc. Agric. Eng. 30: 1387-1396. [DOI:10.13031/2013.30576]
Millward, A.A., and Mersey, J.E. 1999. Adapting the RUSLE to model soil erosion potential in a mountainous tropical watershed. Catena, 3: 109-129. [DOI:10.1016/s0341-8162(99)00067-3]
Monchareon L .1982. Application of soil maps and report for soil and water conservation. Department of land development, Bangkok.
Moore, I., Burch, G., 1986, Physical basis of the length-slope factor in the universal soil loss equation, Soil Science Society of America Journal, 50: 1294–1298. [DOI:10.2136/sssaj1986.03615995005000050042x]
Pacheco F.A.L., Varandas S.G.P., Fernandes L.S., Junior R.V. 2014. Soil losses in rural watersheds with environmental land use conflicts. Sci. Total Environ. 485: 110 - 120
Pimental D., Harvey C., Resosudarmo P., Sinclair K., Kurz D., McNair M., Crist S., Shpritz L., Saffouri R., and Blair R. 1995. Environmental costs of soil erosion and conservation benefits. Science, 267: 1117-1123.
Renard, K. G., & Freimund, J.R. 1994. Using monthly precipitation data to estimate the R factor in the revised USLE, J. Hydrol, 157: 287-306. [DOI:10.1016/0022-1694(94)90110-4]
Renard, K.G., Foster, G.R., Weesies, G.A. & Porter, J.P., 1991, RUSLE: revised universal soil loss equation. Journal of Soil and Water Conservation, 46: 30-33.
Van der Knijff, J.M., Jones, R.J.A., Montanarella, L. 2000. Soil Erosion Risk Assessment in Europe, EUR 19044 EN. Office for Official Publications of the European Communities, Luxembourg.
Veihe, A. 2002. The spatial variability of erodibility and its relation to soil types: a study from northern Ghana, Geoderma, 106: 101-120. [DOI:10.1016/s0016-7061(01)00120-3]
Wang, G., Gertner, G., Fang, S., and Anderson, AB. 2003. Mapping multiple variables for predicting soil loss by geostatistical methods with TM images and a slope map. Photogrammetric Engineering and Remote Sensing, 69: 889-898. [DOI:10.14358/pers.69.8.889]
Wijitkosum S. 2012, Impacts of land use changes on soil erosion in Pa Deng sub-district, adjacent area of Kaeng Krachan National Park, Thailand. Soil and Water Research, 7(1): 10-17.
Williams JR. 1975. Sediment routing for agricultural watersheds. Water Resour Bull 11: 965–974. [DOI:10.1111/j.1752-1688.1975.tb01817.x]
Wischmeier, W.H., and Smith, D.D. 1978. Predicting rainfall erosion. Losses: a guide to conservation planning. Agriculture Handbook, Vol. 537. US Department of Agriculture, Washington, DC.