ارزیابی رابطۀ میان دمای سطح زمین و نسبت سطح برف با استفاده از داده‌های سنجش از دور (مطالعۀ موردی: حوضۀ آبخیز کسیلیان)

نوع مقاله : پژوهشی

نویسندگان

1 دانشجوی دکتری آبخیزداری، دانشکدۀ منابع طبیعی و علوم زمین، دانشگاه شهرکرد، شهرکرد

2 استادیار، دانشکدۀ منابع طبیعی و علوم زمین، دانشگاه شهرکرد، شهرکرد

3 دانشیار، دانشکدۀ منابع طبیعی و علوم زمین، دانشگاه شهرکرد، شهرکرد

چکیده

دمای سطح زمین بر میزان تغییرات مکانی و زمانی برف پشته‏ها به عنوان یکی از اجزای مهم بیلان آبی اثر می‏گذارد... به‌رغم سهولت دسترسی به داده‏های سنجش از دور، نظیر محصولات ماهوارۀ مودیس در پایش و ارزیابی اثر دمای سطح زمین بر مساحت پوشش برفی گاهی به دلایلی سری پیوستۀ آن موجود نیست. به منظور غلبه بر این محدودیت، با استفاده از روابط رگرسیونی، ارتباط دمای سطح زمین و گسترۀ پوشش سطح برف در هر 8 روز و به صورت میانگین ماهانه طی سال‏های 1382 تا 1394 ارزیابی شد. نتایج به‌دست‌آمده نشان داد حداقل و حداکثر میانگین سالانۀ درصد پوشش سطح برف در حوضۀ کسیلیان به‏ترتیب در سال‏های 1388 و 1390 برابر با ‌86/5 و ‌32/20 درصد و همچنین، میزان میانگین سالانۀ دمای حداقل و حداکثر سطح زمین در سال‏های 1383 و 1389 برابر با 65/17 و 1/21 درجۀ سانتی‏گراد است. بررسی الگوی تغییرات دو متغیر طی دورۀ مطالعاتی نشان داد تغییرات سطح پوشش برف نسبت به دمای سطح زمین به‏صورت معکوس و به‌‌تدریج در حال افزایش است. همچنین، نتایج نشان داد در رگرسیون توانی، مقادیر NSE برای شبیه‏سازی میزان درصد پوشش سطوح برفی، ، RMSE و Bias به‌ترتیب برابر 6/0، 64/0، 88/9 و 14/2- است. این ضرایب در رگرسیون خطی به‌ترتیب برابر با 16/0، 47/0، 37/14 و 32/86 به دست آمد. بنابراین، مطالعۀ حاضر می‏تواند برای برآورد پوشش سطح برف و بازسازی نواقص آماری موجود در تصاویر ماهواره‏‏ای مؤثر باشد.

کلیدواژه‌ها


عنوان مقاله [English]

Evaluation of land surface temperature and snow cover ratio by using remote sensing data (case study: Kasilian watershed)

نویسندگان [English]

  • Hamed Mahmoudi 1
  • Mehdi Pajoohesh 2
  • Khodayar Abdollahi 2
  • afshin honarbakhsh 3
1 Ph.D. Candidate of watershed management, Faculty of Natural Resources and Earth Science, Shahrekord University, Shahrekord Iran
2 Nature engineering, Faculty of Natural Resources and Earth Science, Shahrekord University, Shahrekord, Iran
3 watershed engineering depatment,faculty of natural resources and earth sciences, shahrekord university, shahrekord , islamic republic of iran.
چکیده [English]

Land Surface Temperature (LST) affects snowpack spatiotemporal changes as one of crucial components of water balance. . Despite of ease of access to Remote Sensing (RS) data, such as Moderate Resolution Imaging Spectroradiometer (MODIS) products that used for monitoring and evaluating LST effect on snow cover area SCA which are sometimes not available for some reason. In order to overcome limitation, the monthly average values of SCA and LST in each 8 days for 13 years (2003-2016) were evaluated by regression relations.The results showed, minimum and maximum values of annual mean of snow cover area percent occurred in 20009and 2011 equal to 5.86%, 20.32, , respectively. In addition to, the annual mean of minimum and maximum values of LST related to 2004 and 2010 were 17.65 and 21.1 , respectively. The pattern of two variables changes illustrated that the SCA changes to LST are reverse and gradually increasing during the study period. Also, the results revealed that in power regression, the Nash-Sutcliff Efficiency (NSE) coefficient for SCA percent simulation, , RMSE and Bias, are 0.6, 0.64, 9.88 and -2.14, respectively. These coefficients are 0.16%, 47%, 14.37% and 86.32% in linear regression method, respectively. Thus, this study may be helpful to estimate SCA and reconstruct missing data in satellite images.

کلیدواژه‌ها [English]

  • simulation
  • MODIS Satellite
  • snow melt
  • Temperature Gradient
  • Spatiotemporal Scale
[1].  Doesken NJ, Judson A. The snow booklet: A guide to the science, climatology, and measurement of snow in the United States. Colorado State University Publications & Printing; 1997.
 
[2].   Fassnacht SR, López-Moreno JI, Ma C, Weber AN, Pfohl AK, Kampf SK, et al. Spatio-temporal snowmelt variability across the headwaters of the Southern Rocky Mountains. Frontiers of Earth Science. 2017 Sep 1; 11(3):505-14.
[3].   Rittger K, Painter TH, Dozier J. Assessment of methods for mapping snow cover from MODIS. Advances in Water Resources. 2013 Jan 1; 51:367-80.
[4].  Barnett TP, Adam JC, Lettenmaier DP. Potential impacts of a warming climate on water availability in snow-dominated regions. Nature. 2005 Nov; 438(7066):303-9.
[5].  Najafi A, Ghodoosi J, Saqafian B, Porhemat J. Snowmelt runoff estimation by using RS & GIS (A case study in Shahar-chi watershed- Orumiyeh). Pajouhesh-va-Sazandegi. 2007; 3(76):177-185. [Persian].
[6].  Martin MA, Ghent D, Pires AC, Göttsche FM, Cermak J, Remedios JJ. Comprehensive in situ validation of five satellite land surface temperature data sets over multiple stations and years. Remote Sensing. 2019 Jan; 11(5):479.
[7].  Sekertekin A, Bonafoni S. Land surface temperature retrieval from Landsat 5, 7, and 8 over rural areas: assessment of different retrieval algorithms and emissivity models and toolbox implementation. Remote Sensing. 2020 Jan; 12(2):294.
[8].  Karnieli A, Agam N, Pinker RT, Anderson M, Imhoff ML, Gutman GG, et al. Use of NDVI and land surface temperature for drought assessment: Merits and limitations. Journal of climate. 2010 Feb; 23(3):618-33.
[9].  Geiger R. The Climate Near the Ground Harvard University Press. Massachusetts, Cambridge. 1965.
[10].            Prihodko L, Goward SN. Estimation of air temperature from remotely sensed surface observations. Remote Sensing of Environment. 1997 Jun 1; 60(3):335-46.
[11].            Zhu W, Lű A, Jia S. Estimation of daily maximum and minimum air temperature using MODIS land surface temperature products. Remote Sensing of Environment. 2013 Mar 15; 130:62-73.
[12].            Manzo-Delgado L, Sánchez-Colón S, Álvarez R. Assessment of seasonal forest fire risk using NOAA-AVHRR: a case study in central Mexico. International Journal of Remote Sensing. 2009 Sep 22; 30(19):4991-5013.
[13].            Bhattarai N, Mallick K, Stuart J, Vishwakarma BD, Niraula R, Sen S, et al. An automated multi-model evapotranspiration mapping framework using remotely sensed and reanalysis data. Remote Sensing of Environment. 2019 Aug 1; 229:69-92.
[14].            Lu N, Liang S, Huang G, Qin J, Yao L, Wang D, et al. Hierarchical Bayesian space-time estimation of monthly maximum and minimum surface air temperature. Remote Sensing of Environment. 2018 Jun 15; 211:48-58.
[15].            Li F, Sun W, Yang G, Weng Q. Investigating spatiotemporal patterns of surface urban heat islands in the Hangzhou Metropolitan Area, China, 2000–2015. Remote Sensing. 2019 Jan; 11(13):1553.
[16].            McCarthy JJ, Canziani OF, Leary NA, Dokken DJ, White KS, editors. Climate change 2001: impacts, adaptation, and vulnerability: contribution of Working Group II to the third assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press; 2001 Jul 2.
[17].            Liu Y, Hiyama T, Yamaguchi Y. Scaling of land surface temperature using satellite data: A case examination on ASTER and MODIS products over a heterogeneous terrain area. Remote Sensing of Environment. 2006 Nov 30; 105(2):115-28.
[18].            Pouteau R, Rambal S, Ratte JP, Gogé F, Joffre R, Winkel T. Downscaling MODIS-derived maps using GIS and boosted regression trees: the case of frost occurrence over the arid Andean highlands of Bolivia. Remote Sensing of Environment. 2011 Jan 17; 115(1):117-29.
[19].            Van De Kerchove R, Lhermitte S, Veraverbeke S, Goossens R. Spatio-temporal variability in remotely sensed land surface temperature, and its relationship with physiographic variables in the Russian Altay Mountains. International Journal of Applied Earth Observation and Geoinformation. 2013 Feb 1; 20:4-19.
[20].             Khosravi M, Tavousi T, Raeespour K, Omidi Ghaleh mohammadi, M. A Survey on Snow Cover Variation in Mount Zardkooh-Bakhtyare Using Remote Sensing (R.S). Hydrogeomorphology. 2017; 3(12): 25-44. [Persian].
[21].            Carroll T, Cline D, Fall G, Nilsson A, Li L, Rost A. NOHRSC operations and the simulation of snow cover properties for the coterminous US. InProc. 69th Annual Meeting of the Western Snow Conf 2001 Apr (pp. 1-14).
[22].            Li ZL, Tang BH, Wu H, Ren H, Yan G, Wan Z, et al. Satellite-derived land surface temperature: Current status and perspectives. Remote sensing of environment. 2013 Apr 15; 131:14-37.
[23].            Isaya Ndossi M, Avdan U. Application of open source coding technologies in the production of land surface temperature (LST) maps from Landsat: a PyQGIS plugin. Remote sensing. 2016 May; 8(5):413.
[24].            Hall DK, Riggs GA, Salomonson VV, DiGirolamo NE, Bayr KJ. MODIS snow-cover products. Remote sensing of Environment. 2002 Nov 1; 83(1-2):181-94.
[25].            Lee S, Klein AG, Over TM. A comparison of MODIS and NOHRSC snow‐cover products for simulating streamflow using the Snowmelt Runoff Model. Hydrological Processes: An International Journal. 2005 Oct 15; 19(15):2951-72.
[26].            Sahu R, Gupta RD. Snow cover area analysis and its relation with climate variability in Chandra basin, Western Himalaya, during 2001–2017 using MODIS and ERA5 data. Environmental Monitoring and Assessment. 2020 Aug; 192(8):1-26.
[27].            Rayegani B, Khajeddin S J, Soltani S, Barati S. Analysis of MODIS Snow-Cover Map Changes During Missing Data Period. JWSS. 2008; 12 (44):315-332. [Persian].
[28].            Wang X, Xie H, Liang T. Evaluation of MODIS snow cover and cloud mask and its application in Northern Xinjiang, China. Remote Sensing of Environment. 2008 Apr 15; 112(4):1497-513.
[29].            Georgievsky MV. Application of the Snowmelt Runoff model in the Kuban river basin using MODIS satellite images. Environmental Research Letters. 2009 Oct 21; 4(4):045017.
[30].            Mu Q, Zhao M, Running SW. Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sensing of Environment. 2011 Aug 15; 115(8):1781-800.
[31].            Hall DK, Riggs GA, DiGirolamo NE, Román MO. MODIS cloud-gap filled snow-cover products: Advantages and uncertainties. Hydrol. Earth Syst. Sci. Discuss. 2019 Apr 23:1-23.
[32].            Sahu R, Gupta RD. Snow Cover Analysis in Chandra Basin of Western Himalaya from 2001 to 2016. InApplications of Geomatics in Civil Engineering 2020 (pp. 557-566). Springer, Singapore.
[33].            Halabian A, Solhi S. Spatiotemporal Changes in Snow-Cover related to the Land Surface Temperature over Central Alborz. Physical Geography 2020; 13(47): 53-75.
[34].            Jabbar A, Othman AA, Merkel B, Hasan SE. Change detection of glaciers and snow cover and temperature using remote sensing and GIS: A case study of the Upper Indus Basin, Pakistan. Remote Sensing Applications: Society and Environment. 2020 Mar 24:100308.
[35].            Motevalli A, Vafakhah M. Flood hazard mapping using synthesis hydraulic and geomorphic properties at watershed scale. Stochastic Environmental Research and Risk Assessment. 2016 Oct 1; 30(7):1889-900.
[36].            Maurer EP, Rhoads JD, Dubayah RO, Lettenmaier DP. Evaluation of the snow‐covered area data product from MODIS. Hydrological Processes. 2003 Jan; 17(1):59-71.
[37].            Mir RA, Jain SK, Saraf AK, Goswami A. Accuracy assessment and trend analysis of MODIS-derived data on snow-covered areas in the Sutlej basin, Western Himalayas. International Journal of Remote Sensing. 2015 Aug 3; 36(15):3837-58.
[38].            Krajčí P, Holko L, Parajka J. Variability of snow line elevation, snow cover area and depletion in the main Slovak basins in winters 2001–2014. Journal of hydrology and hydromechanics. 2016 Mar 1; 64(1):12-22.
[39].            Wan Z, Hook S, Hulley G. MOD11A2 MODIS/Terra land surface temperature/emissivity 8-day L3 global 1km SIN grid V006. NASA EOSDIS Land Processes DAAC. 2015; 10.
[40].            Fritz P, Suzuki O, Silva C, Salati E. Isotope hydrology of ground waters in the Pampa del Tamarugal, Chile. Journal of Hydrology. 1981 Sep 1; 53(1-2):161-84.
[41].            Moriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD, Veith TL. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE. 2007;50(3):885-900.
[42].            Dong J, Peters-Lidard C. On the relationship between temperature and MODIS snow cover retrieval errors in the Western US. IEEE journal of selected topics in applied earth observations and remote sensing. 2010 Feb 5; 3(1):132-40.
[43].            Forsythe N, Kilsby CG, Fowler HJ, Archer DR. Assessment of runoff sensitivity in the Upper Indus Basin to interannual climate variability and potential change using MODIS satellite data products. Mountain Research and Development. 2012 Feb; 32(1):16-29.
[44].            Forsythe N, Fowler HJ, Kilsby CG, Archer DR. Opportunities from remote sensing for supporting water resources management in village/valley scale catchments in the Upper Indus Basin. Water resources management. 2012 Mar 1; 26(4):845-71.
[45].            Jarchow CJ, Nagler PL, Glenn EP, Ramírez-Hernández J, Rodríguez-Burgueño JE. Evapotranspiration by remote sensing: An analysis of the Colorado River Delta before and after the Minute 319 pulse flow to Mexico. Ecological Engineering. 2017 Sep 1; 106:725-32.
[46].            Hazaymeh K, Hassan QK. Spatiotemporal image-fusion model for enhancing the temporal resolution of Landsat-8 surface reflectance images using MODIS images. Journal of Applied Remote Sensing. 2015 Jan; 9(1):096095.
[47].            Pu Z, Xu L. MODIS/Terra observed snow cover over the Tibet Plateau: distribution, variation and possible connection with the East Asian Summer Monsoon (EASM). Theoretical and Applied Climatology. 2009 Aug 1; 97(3-4):265-78.
[48].            Li C, Su F, Yang D, Tong K, Meng F, Kan B. Spatiotemporal variation of snow cover over the Tibetan Plateau based on MODIS snow product, 2001–2014. International Journal of Climatology. 2018 Feb; 38(2):708-28.
[49].            Chaudhuri G, Mishra NB. Spatio-temporal dynamics of land cover and land surface temperature in Ganges-Brahmaputra delta: A comparative analysis between India and Bangladesh. Applied Geography. 2016 Mar 1; 68:68-83.
[50].            Rahmani N, Shahedi K, Soleymani K, Yaqhoubzadeh, M. Evaluation of the Land use Change Impact on Hydrologic Characteristics (Case Study: Kasilian Watershed). jwmr. 2016; 7 (13):32-23. [Persian].
 
[51].            Shrestha M, Wang L, Koike T, Xue Y, Hirabayashi Y. Modeling the spatial distribution of snow cover in the Dudhkoshi region of the Nepal Himalayas. Journal of Hydrometeorology. 2012 Feb; 13(1):204-22.
[52].            Keikhosrvai Kiany, M., Masoudian, S. Identification of snow reservoirs in Iran. Physical Geography Research Quarterly, 2017; 49(3): 395-408.
[53].            Painter TH, Rittger K, McKenzie C, Slaughter P, Davis RE, Dozier J. Retrieval of subpixel snow covered area, grain size, and albedo from MODIS. Remote Sensing of Environment. 2009 Apr 15; 113(4):868-79.
[54].            Salomonson VV, Appel I. Development of the Aqua MODIS NDSI fractional snow cover algorithm and validation results. IEEE Transactions on geoscience and remote sensing. 2006 Jun 26; 44(7):1747-56.
[55].            Liston GE, Pielke RA, Greene EM. Improving first‐order snow‐related deficiencies in a regional climate model. Journal of Geophysical Research: Atmospheres. 1999 Aug 27; 104(D16):19559-67.
[56].            Rodell M, Houser PR. Updating a land surface model with MODIS-derived snow cover. Journal of hydrometeorology. 2004 Dec; 5(6):1064-75.
[57].            Emami H, Salajegheh A, Moghaddam Nia, A, Khalighi, S. Evaluation of TRMM satellite accuracy and efficiency in estimating monthly rainfall in Gorganroud watershed. ECO Hydrology. 2020; 7(3): 719-729. [Persian].
[58].            O’Leary D, Hall D, Medler M, Flower A. Quantifying the early snowmelt event of 2015 in the Cascade Mountains, USA by developing and validating MODIS-based snowmelt timing maps. Frontiers of Earth Science. 2018 Dec 1; 12(4):693-710.
[59].            Hammond JC, Saavedra FA, Kampf SK. Global snow zone maps and trends in snow persistence 2001–2016. International Journal of Climatology. 2018 Oct; 38(12):4369-83.