پیش بینی اثر تغییر اقلیم بر خطر فرسایش خاک در حوزه آبخیز ناورود

نوع مقاله: مقاله پژوهشی

نویسندگان

1 گروه علوم خاک، دانشگاه تهران

2 دانشگاه گیلان

3 دانشگاه کرمان

چکیده

سابقه و هدف: تغییر اقلیم می‌تواند با تغییر الگوی بارش فرسایش خاک را به عنوان مهم‌ترین عامل تخریب اراضی جهان، تحت-تاثیر قرار دهد. بنابراین ارزیابی خطر فرسایش خاک و ارزیابی اثر تغییرات اقلیمی بر آن امری ضروری به نظر می‌رسد. هدف از این تحقیق که در حوزه آبخیز ناورود در استان گیلان انجام شد، بررسی اثر تغییر اقلیم در آینده بر خطر فرسایش و تلفات خاک می‌باشد.
مواد و روش‌ها: در تحقیق حاضر، روند تغییر اقلیم در استان گیلان با استفاده از برخی متغیرهای موثر اقلیمی با استفاده از نرم‌افزار XLSTAT بر مبنای آمار دو ایستگاه رشت و بندرانزلی بررسی شد. سپس خطر فرسایش خاک با تلفیق نسخه تجدیدنظر شده معادله جهانی هدررفت خاک، سامانه اطلاعات جغرافیایی و سنجش از دور در حال حاضر و دو دوره 20 ساله آینده، در حوزه آبخیز ناورود مورد ارزیابی قرار گرفت. لایه‌های اطلاعاتی مربوط به عامل‌های K، LS، C و P معادله جهانی تجدیدنظر شده هدررفت خاک از پژوهش قبلی اخذ شد. مدل گردش عمومی جو و سه سناریوی A1B، A2 و B1 به منظور بررسی تغییر اقلیم استفاده شد. بر مبنای خروجی این مدل و با استفاده از آمار روزانه بارش در دوره پایه 2007-2002 و مدل LARS-WG، بارش روزانه دو دوره 20 ساله 2065-2046 و 2099-2080 برای سه ایستگاه خرجگیل، خلیان و ناو که در درون حوزه واقع شده‌اند، شبیه‌سازی شد.
یافته‌ها: نتایج نشان داد که بارندگی در آینده در دو ایستگاه خلیان و ناو، کاهش و در ایستگاه خرجگیل، افزایش می‌یابد. با این وجود، به دلیل افزایش شدت بارندگی‌ها، در تمامی حالت‌ها میزان عامل فرسایندگی باران در آینده بیش‌تر از دوره پایه می‌باشد. بر اساس نتایج به‌دشت آمده، خطر فرسایش در دوره پایه بین صفر تا بیش از 77 تن در هکتار در سال، برای دوره 2065-2046، بین صفر تا بیش از 115 تن در هکتار در سال و در دوره 2099-2080 بین صفر تا بیش از 98 تن در هکتار در سال متغیر است.
نتیجه‌گیری: نتایج نشان داد طی دوره‌های آینده، میزان فرسایندگی به علت افزایش شدت بارندگی افزایش می‌یابد. بیش‌تر سطح حوزه دارای خطر فرسایش کم، و نواحی جنوب غرب حوزه و بخش‌های میانی شمال آن عمدتا دارای خطر فرسایش زیاد هستند. هم‌‌چنین بررسی نتایج نشان می‌دهد با وجود این‌که فرسایندگی باران در تعدادی از ایستگاه‌ها بیش‌ترین میزان است، اما مقدار فرسایش آن‌ها زیاد نیست، که می‌تواند به علت تاثیر پوشش گیاهی باشد. افزایش تراکم پوشش گیاهی به‌ویژه اگر از نوع پوشش متراکم جنگلی باشد،
می‌تواند تاثیر فرسایندگی باران را کاهش داده و در نتیجه خطر فرسایش کم شود.

کلیدواژه‌ها

عنوان مقاله [English]

Forecasting the effect of climate change on soil erosion hazard in Navrood watershed

چکیده [English]

Background and Objectives: Climate Change and its consequence changes in precipitation patterns can affect soil erosion, as the most important global problem of land degradation. Therefore, it is essential to assess soil erosion risk under climate change condition. The aim of this study was to evaluate the impacts of future climate change on soil erosion risk in Navrood watershed, located in west of Guilan province, North of Iran.
Materials and Methods: In this study, the trend of climate change was evaluated through effective climatic parameters by XLSTAT software based on the data obtained from Rasht and Bandar Anzali stations. Also the soil erosion risk was predicted using RUSLE in combination with geographic information system and remote sensing, in Navrood watershed. The data of previous research were used to calculate the K, LS, C and P factors for the RUSLE model. The atmospheric general circulation model NCCCSM and three scenarios A1B, A2 and B1 were used to study climate change. The daily rainfall pattern were simulated for two 20-year periods of 2046-2065 and 2080-2099 for Kharajgil, Khalian and NAV stations located inside the watershed, based on the outputs of NCCCSM, daily rainfall values of the base period 2002-2007, and the LARS-WG model.
Results: The results showed that a decrease will occur in rainfall at the Nav and Khalian stations; while there will be an increase for Kharajgil station. In contrast, the rainfall erosivity will increased for all scenarios and stations in future in compare with the base period due to increase of rainfall intensity. Based on the obtained results, soil erosion risk changes from zero to more than 77 tons per hectare per year, between zero and over 115 tons per hectare per year, and from zero to more than 98 ton per hectare per year across the watershed at the base period (2002-2007), and 2046-2065 and 2080-2099 periods, respectively.
Conclusion: The results showed that rainfall erosivity will increased due to increase of rainfall intensity. Most of the watershed area is faced with low erosion risk, but the south-western and middle north parts of the watershed are experiencing high erosion. Additionally, although rainfall erosivity is at its highest level at some stations, but the erosion rate is low because of the positive impact of plant coverage in reducing soil erosion. Higher the density of plant coverage, particularly forest type, reduces the negative impacts of rainfall erosivity, resulted in lower soil erosion risk.

کلیدواژه‌ها [English]

  • Climate change scenario
  • LARS-WG Model
  • Rainfall erosivity
  • RUSLE
1.Arnell, N.W., and Reynard, N.S. 1996. The effects of climate change due to global warming
on river flows in Great Britain. J. Hydrol. 183: 397-424.
2.Arnoldus, H.M.J. 1980. An approximation of the rainfall factor in the Universal Soil
Loss Equation. In: M. DeBoodt, D. Gabriels, (Eds.), Assessment of Erosion. Chichester,
New York. Pp: 127-132.
3.Asadi, H., Honarmand, M., Vazifedoust, M., and Mousavi, A. 2017. Assessment of Changes in
Soil Erosion Risk Using RUSLE in Navrood Watershed, Iran. J. Agric. Sci. Tech. 19: 231-244.
4.Babaeian, I., Najafi Nik, Z., Zabol Abasi, F., Habibi Nokhandan, M., Adab, H., and Malbusi, Sh.
2009. Iranian climatic changes between 2010 and 2039 using small scale measurements of the
general circulation model data on atmosphere (ECHO-G). J. Geograph. Dev. 16: 135-152.
(In Persian)
5.Booij, M.J. 2005. Impact of climate change on river flooding assessed with different spatial
model resolutions. J. Hydrol. 303: 176-198.
6.Chmura, D.J., Anderson, P.D., Howe, G.T., Harrington, C.A., Halofsky, J.E., Peterson, D.L.,
Shaw, D.C., and Clair, J.B. 2011. Forest responses to climate change in the northwestern
United States: Ecophysiological foundations for adaptive management. Forest Ecology and
Management. 261: 7. 1121-1142.
7.Church, J.A., Gregory, J.M., Huybrechts, P., Kuhn, M., Lambeck, K., Nhuan, M.T., Qin, D.,
and Woodworth, P.L. 2001. Changes in sea level. In: Houghton J.T., Ding Y., Griggs, D.J.,
Noguer, M., van der Linden, P.J., Xiaosu, D. (Eds.), Climate Change 2001. The Scientific
Basis. Cambridge University Press, Cambridge, Pp: 639-693.
8.Diodato, N. 2004. Local models for rainstorm induced hazard analysis on Mediterranean river
torrential geomorphological systems. Nat. Hazards Earth Syst. Sci. 4: 389-397.
9.Fantappiè, M., Priori, S., and Costantini, E.A.C. 2015. Soil erosion risk, Sicilian region
(1:250,000 scale). J. Maps. 11: 2. 323-341.
10.Fatolazadeh, T. 2015. Examine the types and severity of erosion in the sub-basins watershed
Navrood. J. Physic. Geograph. 8: 27. 25-38. (In Persian)
11.Gaatib, R., and Larabi, A. 2014. Integrated evaluation of soil erosion hazard and risk
management in the Oued Beht watershed using remote sensing and GIS techniques: Impacts
on El Kansra Dam Siltation (Morocco). J. Geogr. Inf. Syst. 6: 677-689.
12.Gholami, A., Shahedi, K., Habib-Nejad-Roshan, M., Vafakhah, M., and Soleimani, K. 2017.
Forecasting and comparison of future climate change by using of GCM models under
different scenarios in Talar watershed of Mazandaran province. J. Range Water. Manage.
70: 1. 181-196. (In Persian)
13.Haas, L. 2002. Mediterranean water resource planning and climate change adaptation. Water,
wetlands and climate change, Building linkages for their integrated management.
Mediterranean Regional Roundtable. Athens, Greece, December 10-11 Draft for Discussion,
62p.
14.Hadinia, H. 2013. Impact of climatic change on rice water demand in Rasht. M.Sc. Thesis,
the Faculty of Agricultural Sciences, University of Guilan. 95p. (In Persian)
15.Hasanpour Kashani, M., Ghorbani, M.A., Dinpazhouh, Y., and Shahmorad, S. 2015.
Rainfall-runoff simulation in the Navrood river basin using Truncated Volterra model and
artificial neural networks. J. Water. Manage. Res. 6: 12. 1-10. (In Persian)
16.Honarmand, M. 2010. Assessment and mapping of soil erosion hazard using revised
universal soil loss equation (RUSLE), geographic information system (GIS) and remote
sensing (RS) in Navrood watershed (Guilan province). M.Sc. Thesis, Faculty of Agricultural
Sciences, University of Guilan. 105p. (In Persian)
17.Katirayee, P.S., Hejam, S., and Iran Nejad, P. 2006. The role of frequency variation and
daily rainfall intensity in shaping rainfall patterns during 1960-2001 in Iran. J. Earth Space
Physic. 33: 67-83. (In Persian)
18.Kebede, W., Habitamu, T., Efrem, G., and Fantaw, Y. 2015. Soil erosion risk assessment in
the Chaleleka wetland watershed, Central Rift Valley of Ethiopia. Environmental Systems
Research 4:5, DOI 10.1186/s40068-015-0030-5.
19.Lu, D., Li, G., Valladares, G.S., and Batistella, M. 2004. Mapping soil erosion risk in
Rondonia, Barzilian Amazonia using RUSLE, remote sensing and GIS. Land Degradation
and Development, 15: 499-512.
20.Masoom Pour, F. 2005. Examination of the efficiency of MPSIAC model for estimating
erosion and sediment in Navrood watershed. M.Sc. Thesis, Faculty of Natural Resources.
The University of Mazandaran, Iran. 78p. (In Persian)
21.Massah Bovani, A., and Morid, S. 2005. Effects of climatic change on Zayandeh Rood water
flow in Isfahan. J. Natur. Resour. Agric. Sci. 9: 4. 12-27. (In Persian)
22.Mohammadi, B. 2011. Analysis of annual precipitation trends in Iran. J. Geograph. Environ.
Program. 22/43: 3. 95-106. (In Persian)
23.Nasiri, B., and Yarmoradi, Z. 2017. Predict changes in climate parameters Lorestan province
in 50 years by using HADCM3. Scientific Research Quarterly of Geographical Data.
26: 101. 143-154. (In Persian)
24.Nunes, J., and Nearing, M. 2011. Modelling impacts of climatic change: Case studies using
the new generation of erosion models. Wiley- Blackwell, Oxford, Pp: 289-312.
25.O’Neal, M.R., Nearing, M.A., Vining, Z.C., Southworth, J., and Pfeifer, R.A. 2005. Climate
change impacts on soil erosion in Midwest United States with changes in crop management.
Catena. 61: 165-184.
26.Paroissien, J.B., Darboux, F., Couturier, A., Devillers, B., Mouillot, F., Raclot, D., and
Le Bissonnais, Y. 2015. A method for modeling the effects of climate and land use changes
on erosion and sustainability of soil in a Mediterranean watershed (Languedoc, France).
J. Environ. Manage. 150: 57-68.
27.Prasannakumar, V., Shiny, R., Geetha, N., and Vijith, H. 2011. Spatial prediction of soil
erosion risk by remote sensing, GIS and RUSLE approach: A case study of Siruvani river
watershed in Attapady valley, Kerala, India. Environ. Earth Sci. 64: 965-972.
28.Prasannakumar, V., Vijith, H., Abinod, S., and Geetha, N. 2012. Estimation of soil erosion
risk within a small mountainous sub-watershed in Kerala, India, using Revised Universal
Soil Loss Equation (RUSLE) and geo-information technology. Geoscience Frontiers.
3: 2. 209-215.
29.Prasuhn, V., Liniger, H.P., Herweg, K., Candinas, A., and Clement, J.P. 2013. A
high-resolution soil erosion risk map of Switzerland as strategic policy support system. Land
Use Policy. 32: 281-291.
30.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). Agriculture Handbook No. 703, USDA, Washington, DC, USA, 404p.
31.Routschek, A., Schmidt, J., and Kreienkamp, F. 2014. Impact of climate change on soil
erosion: A- high-resolution projection on catchment scale until 2100 in Saxony/Germany.
Catena 121: 99-109.
32.Sabz Gostar Consultation Engineering Co. 2003. Multi-purpose Comprehensive Scheme at
the Watershed 7 Nav, Asalem. Natural Resources Administration of Guilan Province, Iran.
Ministry of Agriculture. (In Persian)
33.Salahie, B., Ali Jahan, M., Eini, S., and Derakhshi, J. 2017. Prediction of initiation and ends
dates of moderat and severe frosts in Kermanshah province selected on the outputs of some
climate models. J. Geograph. Plan. 21: 59. 175-195.
34.Sereda, J., Bogard, M., Hudson, J., Helps, D., and Dessouki, T. 2011. Climate warming and
the onset of salinization: Rapid changes in the limnology of two Northern Plains lakes.
Limnologica. 41: 1-9.
35.Serpa, D., Nunes, J.P., Santos, J., Sampaio, E., Jacinto, R., Veiga, S., Lima, J.C., Moreira,
M., Keizer, J.J., Abrantes, N., and Corte, J. 2015. Impacts of climate and land use changes
on the hydrological and erosion processes of two contrasting Mediterranean catchments.
Science of the Total Environment. 538: 64-77.
36.Sobhani, B., Eslahi, M., and Babaeian, I. 2015. The functionality of fine patterns of
statistical downscaling model (SDSM) and LARS-WG patterns in simulation of
meteorological variables at Orumiyeh lake watershed. The Quarterly of Investigations on
Natural Geography. 47: 4. 499-516. (In Persian)
37.Zhang, X.C., Nearing, M.A., Garbrecht, J.D., and Steiner, J.L. 2004. Downscaling monthly
forecasts to simulate impacts of climate change on soil erosion and wheat production. Soil
Sci. Soc. Amer. J. 68: 1376-1385.