| تعداد نشریات | 282 |
| تعداد نشریات سازمان | 80 |
| تعداد شمارهها | 3,196 |
| تعداد مقالات | 35,395 |
| تعداد مشاهده مقاله | 52,098,417 |
| تعداد دریافت فایل اصل مقاله | 91,668,130 |
ارزیابی خسارت خشکسالی با استفاده از شاخصهای ماهوارهای خشکسالی و فاکتورهای آسیبپذیری: مطالعه موردی: استان ایلام | ||
| تحقیقات مرتع و بیابان ایران | ||
| مقاله 6، دوره 29، شماره 4 - شماره پیاپی 89، دی 1401، صفحه 542-561 اصل مقاله (1.35 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/ijrdr.2022.128077 | ||
| نویسندگان | ||
| زاهده حیدری زادی1؛ مجید اونق* 2؛ چوقی بایرام کمکی3 | ||
| 1دانشجوی دکتری بیابانزدایی، دانشکده آبخیزداری و مدیریت مناطق بیابانی، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران | ||
| 2استاد، دانشکده آبخیزداری و مدیریت مناطق بیابانی، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران | ||
| 3استادیار، دانشکده آبخیزداری و مدیریت مناطق بیابانی، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران | ||
| چکیده | ||
| خشکسالی یک پدیده اقلیمی ناخوشایند است که بهطور مستقیم بر ابعاد مختلف جوامع انسانی تأثیر میگذارد. بهمنظور آگاهی و انتخاب تصمیم مدیریتی مناسب، طراحی و توسعه یک رویکرد یکپارچه برای کنترل موثرتر این پدیده و ارائه هشدارهای اولیه ضروری است. در این مطالعه، دوازده شاخص مختلف سنجش از دور از ماهواره مودیس (MODIS) و مدل رقومی ارتفاعی (DEM) برای پایش خشکسالی در طول فصل رشد برای سالهای 2018-2000 مورد استفاده قرار گرفت. شاخص استاندارد شده بارش (SPI) با مقیاس زمانی یک تا 12 ماه بهعنوان داده مرجع استفاده شد. سپس روابط بین 13 شاخص و SPI با مقیاسهای زمانی مختلف با استفاده از رویکرد یادگیری ماشین و تکنیک جنگل تصادفی مدلسازی استفاده شد. از دادههای رطوبت نسبی خاک، شاخص بارش-تبخیر و تعرق استانداردشده (SPEI) و دادههای عملکرد محصول بهمنظور اعتبارسنجی مدل استفاده شد. نتایج نشان داد که جنگل تصادفی کارکرد خوبی (R2=88/0) برای شبیهسازی SPI دارد. در مرحله بعد با استفاده از مدل خشکسالی که در مرحله قبل ساخته شد، شاخص خطر خشکسالی (DHI) بر اساس احتمال وقوع خشکسالی محاسبه شد. شاخص آسیبپذیری خشکسالی (DVI) نیز با استفاده از هفت شاخص اجتماعی و اقتصادی محاسبه شد. در نهایت، شاخص خسارت خشکسالی (DRI) با تلفیق شاخص خطر خشکسالی و شاخص آسیبپذیری خشکسالی برای استان ایلام بهدست آمد. نتایج نقشه خسارت نشان داد که دو شهرستان در معرض خسارت خشکسالی خیلیشدید، چهار شهرستان در معرض خسارت زیاد و چهار شهرستان در معرض خطر خشکسالی متوسط و کم قرار دارند. بهطور کلی، نتایج این مطالعه یک روش جامع برای ارزیابی خشکسالی منطقهای ارائه میدهد. همچنین بر اساس این مدل، میتوان شهرستانهای با آسیبپذیری بالا را شناسایی کرد تا با ارائه برنامههای مدیریتی بهموقع به بهبود وضعیت کمک کند. | ||
| کلیدواژهها | ||
| استان ایلام؛ جنگل تصادفی؛ خسارت خشکسالی؛ SPI | ||
| عنوان مقاله [English] | ||
| Assessment of Drought risk using multi-sensor drought indices and vulnerability factors: A case study of Illam of Province | ||
| نویسندگان [English] | ||
| Zahedeh Heidarizadi1؛ Majid Ownegh2؛ chooghibiram komaki3 | ||
| 1Ph. D student of combat Desertification, Faculty of Rangeland and watershed Management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran | ||
| 2Professors, Faculty of Rangeland and watershed Management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran | ||
| 3Assistant prof, Fisheries and Environmental Sciences, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran, | ||
| چکیده [English] | ||
| Drought is an unpleasant climatic phenomenon that directly affects different dimensions of human societies. In order to know and choose the right management decision, it is necessary to design and develop an integrated approach to more effectively control this phenomenon and provide early warnings.In this study, twelve various remotely sensed indices of the Moderate Resolution Imaging Spectroradiometer (MODIS) and digital elevation model (DEM) were used to monitor drought during 2000–2018 growing season. Standardized Precipitation Index (SPI) with time scales of 1 to 12 months was used as reference data. The relations between thirteen indices and SPI with different time scales were modulated using machine learning approach. The random forest technique was used to construct a comprehensive drought monitoring model in Ilam Province. Validation data were provided based on relative soil moisture, Standardized Precipitation Evapotranspiration Index (SPEI), and crop yield data. It was observed that random forest produced good applicability (R2 = 0.88) for SPI prediction. In the next step, the Drought Hazard Index (DHI) was generated based on the probability occurrences of drought using the comprehensive drought model which was made in the previous step. The Drought Vulnerability Index (DVI) was calculated by using 7 socioeconomic indices. Finally, the Drought Risk Index (DRI) was obtained by multiplying DHI and DVI for Ilam province. The result of the DRI map showed that 2 Counties are at very high risk of drought, 4 Counties are at high risk and 4 Counties are at moderate and low risk of drought. Overall, the result of our study provides a comprehensive method for assessment of regional drought. Also based on this model, Counties with high vulnerability can be identified to provide timely management programs to help improve the situation. | ||
| کلیدواژهها [English] | ||
| Ilam province, random forest, risk management, spi | ||
| مراجع | ||
|
AghaKouchak, A., Farahmand, A., Melton, F.S., Teixeira, J., Anderson, M.C., Wardlow, B.D. and Hain, C.R., 2015. Remote sensing of drought: progress, challenges and opportunities. Reviews of Geophysics, 53 (2): 452–480. (In Persian with English summary).
Azizi, Q. and Safarkhani, E., 2010. Evaluation of drought and its effect on rainfed wheat yield in Ilam province with emphasis on recent droughts (2000-2001). Space planning and arrangement, 6 (2): 77-61. (In Persian with English summary).
Dabanli, I.J.N.H., 2018. Discussions, E.S.S. Drought Risk Assessment by Using Drought Hazard and Vulnerability Indexes. Natural Hazards and Earth System Sciences Discussions. 1–15.
Dai, A., 2011. Erratum: drought under global warming: a review. Wiley Interdisciplinary Reviews: Climate Change. 2 (1): 45–65.
Dutra, E., Giuseppe, F. D., Wetterhall, F. and Pappenberger, F., 2013. Seasonal forecasts of droughts in African basins using the Standardized Precipitation Index. Hydrology and Earth System Sciences. 17(6): 2359-2373. doi:10.5194/hess-17-2359-2013.
Gao, B.-C., 1996. NDWI—a normalized difference water index for remote sensing of vegetation liquid water from space. Remote sensing of environment. 58 (3): 257–266.
Gessner, U., Naeimi, V., Klein, I., Kuenzer, C., Klein, D. and Dech, S., 2013. The relationship between precipitation anomalies and satellite-derived vegetation activity in Central Asia. Global and Planetary Change. 110: 74-87. https://doi.org/10.1016/j.gloplacha.2012.09.007.
Gu, Y., Brown, J.F., Verdin, J.P. and Wardlow, B., 2007. A five-year analysis of MODIS NDVI and NDWI for grassland drought assessment over the central Great Plains of the United States. Geophysical research letters. 34 (6).
Hayes, M., Svoboda, M., Le Comte, D., Redmond, K. T. and Pasteris, P., 2005. Drought monitoring: New tools for the 21st century. Drought and water crises: Science, technology, and management issues. 53, 69.
Huete, A.R., Post, D.F., and Jackson, R.D., 1984. Soil Spectral effects and 4-space vegetation discrimination. Journal of Remote sensing of Environment. 15 (2):155-165.
Karanjadi, A., and Pourqasmi, H., 2018. Landslide susceptibility assessment using data mining models, case study: Chelchai watershed. Watershed. journal Engineering and Management. 11(1): 28-42. (In Persian with English summary).
Khoshnazar, A., Corzo Perez, G.A. and Diaz, V., 2021. Spatiotemporal Drought Risk Assessment Considering Resilience and Heterogeneous Vulnerability Factors: Lempa Transboundary River Basin in The Central American Dry Corridor. Journal of Marine Science and Engineering. 9(4):386. https://doi.org/10.3390/jmse9040386. (In Persian with English summary).
Khoshnazar, A., Nasrabadi, T. and Abbasi Maedeh, P., 2012. Evaluating the efficiency of artificial neural network in prediction of Electrical conductivity of Zarrinehroud River. Human and Environment. 10(22): 1-16. (In Persian with English summary).
Kim, H., Park, J., Yoo J, and Kim, T.W., 2015. Assessment of drought hazard, vulnerability, and risk: a case study for administrative districts in South Korea. Journal of Hydro-environment Research. 9(1):28–35. doi:https://digitalcommons.unl.edu/droughtnetnews/80
Kogan, F.N., 1993. United States droughts of late 1980's as seen by NOAA polar orbiting satellites. International Geoscience and Remote Sensing Symposium. 1:197-199.
Liaw, A. and Wiener, M., 2002. “Classification and Regression by randomForest.” R News. 2(3): 18-22. https://CRAN.R-project.org/doc/Rnews/.
Lin, M.L., Chu, C.M. and Tsai, B.W., 2011. Drought risk assessment in western Inner-Mongolia. International Journal of Environmental Research. 5 (1): 139-148.
Jia, H. and Wang, D. P. J., 2016. Risk mapping of integrated natural disasters in China. Natural Hazards. 80(3): 2023– 2035. http://doi.org/10.1007/s11069-015-2057-3.
Lin, Y.-C., Kuo, E.-D. and Chi, W.-J., 2021. Analysis of Meteorological Drought Resilience and Risk Assessment of Groundwater Using Signal Analysis Method. Water Resources Management. 35: 179–197.
Luetkemeier, R., Stein, L., Drees, L. and Liehr, S., 2017. Blended Drought Index: Integrated Drought Hazard Assessment in the Cuvelai-Basin. Climate. 5 (3): 51. https://doi.org/10.3390/cli5030051.
Madadi, G., Mostafavi, A. and Khosravani, F., 2016. Estimation of soil moisture changes in agriculture using GLDAS data, a case study: Ilam Province, 7th National Conference on Sustainable Agriculture and Natural Resources. Tehran, 7 July. 2016. https://civilica.com /doc/636155. (In Persian with English summary).
McKee, T. B., Doesken, N. J. and Kleist, J., 1993. The relationship of drought frequency and duration to time scales. In Proceedings of the 8th Conference on Applied Climatology. 17 (22): 179-183.
Mizzell, H., 2008. Improving Drought Detection in the Carolinas: Evaluation of Local, State, and Federal Drought Indicators. Ph.D. thesis, University of South Carolina.
Nasrollahi, M., 2015. Assessment of drought hazard, vulnerability and risk (case study: Semnan province). M.Sc. Thesis, Faculty of Natural Resources, University of Tehran, (In Persian with English summary).
Nezlin, N., Kostianoy, A. and Li, B.-L., 2005. Inter-annual variability and interaction of remote-sensed vegetation index and atmospheric precipitation in the Aral Sea region. Journal of Arid Environment. 62 (4): 677–700.
Park, S., Im, J., Jang, E. and Rhee, J., 2016. Drought assessment and monitoring through blending of multi-sensor indices using machine learning approaches for different climate regions. Agricultural and forest meteorology. 216: 157-169. Doi:https://doi.org/10.1016/j.agrformet.2015.10.011.
Paulo, A. A., Rosa, R. D. and Pereira, L. S., 2012. Climate trends and behaviour of drought indices based on precipitation and evapotranspiration in Portugal. Natural Hazards and Earth System Sciences. 12(5): 1481-1491. doi:10.5194/nhess-12-1481-2012.
Piao, S., Fang, J., Zhou, L., Guo, Q., Henderson, M., Ji, W., and Tao, S., 2003. Interannual variations of monthly and seasonal normalized difference vegetation index (NDVI) in China from 1982 to 1999. Journal of Geophysical Research: Atmospheres. 108(D14). Doi:https://doi.org/10.1029/2002JD002848
Poortaheri, M., Eftekhari, A. and Kazemi, N., 2013. The role of drought risk management approach in reducing social—economic vulnerability of farmers and rural regions case study: Sulduz Rural District, Azerbaijan Gharbi. JOURNAL OF RURAL RESEARCH. 4(1):1–12. (In Persian with English summary).
Proodhan, F.A., Jiahua, Z., Fengmei, Y., Lamei, Sh., Til, P., Pangali, Sh., Da, Zh., Dan, C., Minxuan, Zhe., Naveed, A. and Hasiba, P. M. 2021. Deep Learning for Monitoring Agricultural Drought in South Asia Using Remote Sensing Data. Remote Sensing. 13(9): 1715. https://doi.org/10.3390/rs13091715. (In Persian with English summary).
Prasad, A.M., Iverson, L.R. and Liaw, A., 2006. Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems. 9 (2): 181–199.
R Core Team., 2021. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.URL https://www.R-project.org/.
Rajsekhar, D., Singh, V. P. and Mishra, A. K., 2015. Integrated drought causality, hazard, and vulnerability assessment 20 for future socioeconomic scenarios: An information theory perspective. Journal of Geophysical Research: Atmospheres. 120: 6346–6378. http://doi.org/10.1002/2014JD022670.
Rhee, J., Im, J. and Carbone, G.J., 2010. Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data. 114 (12): 2875–2887.
Rouse, J.W., 1974. Monitoring the vernal advancement of retro gradation of natural vegetation. NASA/GSFC, Type III, Final Report, Greenbelt, MD. 371.
Sahana, V., Mondal, A. and Sreekumar, P., 2021, Drought vulnerability and risk assessment in India: Sensitivity analysis and comparison of aggregation techniques. Journal of Environmental Management. 299(113689): 1-10.https://doi.org/10.1016/j.jenvman.2021.113689
Shahid, S. and Behrawan, H., 2008, Drought risk assessment in the western part of Bangladesh. Natural. Hazards 46: 391-413. (In Persian with English summary).
Sebagti, M., Ahmadi, H. and Moghadam, A. 2016. Calculating the duration and severity of drought by means of modified SPEI index (case study: Tabriz and Urmia cities). Environment and Water Engineering. 2(2): 188-195. (In Persian with English summary).
Shen, R, Huang, A, Li, B, Guo, J., 2019. Construction of a drought monitoring model using deep learning based on multi-source remote sensing data. International Journal of Applied Earth Observation and Geoinformation. 79: 48-57.
Svoboda, M., 2000. An introduction to the drought monitor. Drought Network News. 80: 1994-2001.
United Nation Development Program., 2004. Reducing disaster risk, A challenge for development. United Nation Development Program/ Bureau for Crisis Prevention and Recovery, New York: oxford university press at http://www. undp.org/bcpr/disred/rdr.htm.
Vicente-Serrano, S.M., Beguería, S. and López-Moreno, J.I., 2010. A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. Journal of climate. 23 (7): 1696–1718.
Vicente-Serrano, S. M., Beguería, S., Gimeno, L., Eklundh, L., Giuliani, G., Weston, D. and Pegram, G. G. 2012. Challenges for drought mitigation in Africa: The potential use of geospatial data and drought information systems. Applied Geography. 34: 471-486.
Wang, L. and Qu, J.J., 2007. NMDI: A normalized multi-band drought index for monitoring soil and vegetation moisture with satellite remote sensing. Geophysical Research Letters. 34 (20).
Wardlow, B. D., Anderson, M. C. and Verdin, J. P., 2012. Remote sensing of drought: Innovative monitoring approaches. CRC Press.
Waseem, M., Ajmal, M. and Kim, T. W., 2015. Development of a new composite drought index for multivariate drought assessment. Journal of Hydrology. 527, 30-37. Doi:https://doi.org/10.1016/j.jhydrol.2015.04.044.
Yin, J., Zhan, X., Hain, C.R., Liu, J. and Anderson, M.C., 2018. A method for objectively integrating soil moisture satellite observations and model simulations toward a blended drought index. Water Resources Research. 54 (9): 6772-6791. https://doi.org/10.1029/ 2017WR021959.
Zhang, A. and Jia, G., 2013. Monitoring meteorological drought in semiarid regions using multi-sensor microwave remote sensing data. Remote Sens. Environ. 134: 12–23. Doi:https://doi.org/10.1016/j.rse.2013.02.023.
| ||
|
آمار تعداد مشاهده مقاله: 703 تعداد دریافت فایل اصل مقاله: 479 |
||