ارزیابی میزان تبخیر-تعرق و بهره‌وری آب بر اساس پایگاه داده WaPOR فائو در دشت قزوین

نوع مقاله : مقالات پژوهشی

نویسندگان

1 گروه مهندسی آب، دانشکده کشاورزی و منابع طبیعی، دانشگاه بین‌المللی امام خمینی (ره)، قزوین، ایران

2 گروه علوم و مهندسی آب، دانشکده کشاورزی و منابع طبیعی، دانشگاه بین المللی امام خمینی(ره)، قزوین، ایران

چکیده

دستیابی به امنیت غذایی در آینده با استفاده پایدار از منابع آب، چالشی بزرگ برای نسل فعلی و آینده خواهد بود. افزایش جمعیت، رشد اقتصادی و تغییرات آب ‌و هوا، همگی بر افزایش فشار بر منابع موجود می­افزایند. کشاورزی یک مصرف‌کننده کلیدی آب است و نظارت دقیق بر بهره­وری آب در کشاورزی و بررسی فرصت‌ها برای افزایش بهره­وری آن ضروری است. پایش سیستماتیک بهره­وری آب از طریق استفاده از تکنیک­های سنجش‌ازدور می­تواند به شناسایی شکاف­های بهره­وری آب و ارزیابی راه‌حل‌های مناسب برای رفع این شکاف­ها کمک کند. دشت قزوین با تأمین حدود ۵ درصد محصولات کشاورزی مورد نیاز کشور به‌عنوان قطب کشاورزی مدرن شناخته ‌شده است. در این پژوهش با استفاده از پایگاه داده WaPOR به ارزیابی مقادیر تبخیر-تعرق، میزان زیست‌توده و بهره­وری حجم آب ناخالص و خالص زیست­توده بر اساس نقشه کاربری اراضی در بازه زمانی سال­های 2009 تا 2021 پرداخته شد. نتایج نشان داد مقادیر تبخیر- تعرق گیاهان تحت پوشش شبکه آبیاری در بازه زمانی سال­های 2009 تا 2016 با روند نسبتاً پایداری همراه بوده اما این روند پایدار در سال 2017 به بعد با کاهش روبه‌رو شده است که ازجمله دلایل کاهش میزان تبخیر -تعرق در این بازه زمانی می­توان به کمبود آب در دسترس گیاه با توجه به منابع محدود آب در طی سال­های اخیر اشاره کرد. بررسی روند میزان کل زیست‌توده در اراضی مختلف نشان می­دهد در طی سال­های موردمطالعه این شاخص در تمامی کاربری­ها با افزایش تدریجی همراه شده است. به‌طوری‌که میزان شاخص کل تولید بیومس (TBP) در سال 2020 به میزان 17 درصد بیشتر از سال 2009 است. میزان ناخالص حجم آب زیست‌توده از ابتدای سال 2009 تا سال 2016 در اراضی تحت پوشش شبکه آبیاری با میزان افزایشی همراه بوده است اما از سال 2017 روند تغییرات دمایی و افت شدید تراز آب زیرزمینی باعث کاهش سطح اراضی تحت پوشش شبکه شده و بسیاری از این اراضی به اراضی آیش و دیم تبدیل‌شده‌اند. بررسی شاخص بهره­وری خالص آب زیست­توده (NBWP) نیز نشان داد میزان بهره­وری خالص در اراضی دیم به‌شدت به میزان بارش سالانه وابسته است و بخش زیادی از عملکرد محصول در اراضی دیم وابسته به میزان بارش دریافتی است. ازجمله پارامترهای تأثیرگذار در برآورد مقدار کل زیست­توده می­توان به مقدار، تبخیر، تعرق و برگاب اشاره کرد که افزایشی یا کاهشی بودن هر یک از این پارامترها تأثیر به سزایی در مقدار زیست­توده برآورد شده خواهد داشت. به‌طور کلی پایگاه داده WaPOR می­تواند به‌عنوان یک راهنما در تعیین قابل‌اطمینان مقادیر تبخیر-تعرق و برنامه­ریزی مرتبط با منابع آب در بخش کشاورزی، مورد استفاده قرار گیرد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Evaluation of Evapotranspiration Rate and Water Productivity Based on FAO WaPOR Database in Qazvin Plain

نویسندگان [English]

  • M.S. Fakhar 1
  • A. Kaviani 2
1 Department of Water Engineering, Faculty of Agricultural and Natural Resources, Imam Khomeini International University, Qazvin, Iran
2 Water Science and Engineering Department, Faculty of Agricultural and Natural Resources, Imam Khomeini International University, Qazvin, Iran
چکیده [English]

Introduction
Achieving food security in the future with sustainable use of water resources will be a big challenge for the current and future generations. Population increase, economic growth and climate change intensifythe pressure on existing resources. Agriculture is a key consumer of water, and it is necessary to closely monitor water productivity for it and explore opportunities to increase its productivity. Systematic monitoring of water productivity through the use of remote sensing techniques can help identifying the gaps in water productivity and evaluate appropriate solutions to address these gaps.
 
Materials and Methods
Qazvin plain is known as a hub of modern agriculture by providing about 5% of the country's agricultural products. Therefore, estimating water demand and water productivity in agricultural management in the region is considered important and necessary. In order to monitor water productivity through access to various data across Africa and the Middle East, the WaPOR database provides the possibility to examine the rate of evapotranspiration, biomass and gross and net biomass volume productivity based on the land use map in the period of years 2009 to 2021. In this database, it is possible to check the mentioned items at three levels with different spatial resolution, which according to the scope of the study, it is possible to check values with a spatial resolution of 250(m). In order to determine the efficiency and accuracy of the land cover classification map of the WaPOR database, the results obtained are examined and compared with the Dynamic World model, which represents a global model with high accuracy. For this purpose, the latest land use map related to 2021 Using the WaPOR database and Dynamic World in the GEE system, it was prepared and based on the classification of the region in order to check the accuracy of the user map of the WaPOR database and to determine the percentage of each class compared to each other. Finally, all estimable indicators were calculated and checked by the WaPOR database during the years 2009 to 2022.
 
Results and Discussion
The amount of evapotranspiration of the plants covered by the irrigation network in the period of 2009 to 2016 has been associated with a relatively stable trend, but this trend has decreased in 2017 onwards, which is one of the reasons for the decrease in the amount of evapotranspiration in this the period of time and can refer to the lack of water available to the plant due to the limited water resources in recent years. The investigation of the total amount of biomass in different lands shows that during the years 2009 to 2022, this index has been accompanied by a gradual increase in all uses, so that the amount of TBP index in 2020 was 17% more than in 2009. It shows the amount of biomass in different lands. The amount of biomass in the lands covered by the water network is 5 to 6 times higher than that of the rainfed lands. Among the influential parameters in estimating the TBP index, we can mention the amount of evaporation, transpiration, and transpiration, the increase or decrease of each of these parameters will have a significant impact on the estimated amount of biomass. The results showed that the amount of biomass production in the areas covered by the irrigation network largely depends on the high transpiration rate in these areas. From the beginning of 2009 to 2016, the gross amount of biomass water in the lands covered by the irrigation network has been accompanied by an increase, but in 2017, drastic changes in the process of underground changes will decrease the area of the lands covered by the network and many of these lands. It has been turned into fallow and rainfed lands. The analysis of NBWP index also showed that the amount of net productivity in rainfed lands is strongly dependent on the annual increase rate, and much of the crop yield in rainfed lands is dependent on the amount received. Among the influential parameters in estimating the total amount of biomass, we can mention the amount of evaporation, transpiration and transpiration, the increase or decrease of each of these parameters will have a significant impact on the amount of estimated biomass.
 
Conclusion
WaPOR database data can play an important role in estimating the rate of delayed transpiration and parameters related to water productivity in the region due to its ten-day spatial resolution and the absence of data gaps. In general, the WaPOR database can be used as a guide in the reliable determination of evapotranspiration values and planning related to water resources in the agricultural sector.
 

کلیدواژه‌ها [English]

  • Agricultural water consumption
  • Biomass
  • GEE
  • Remote sensing
  • Water management

مراجع

  1. Ahmadi, A. (2022). The effect of increasing water use efficiency on improving the status of groundwater resources using WEAP model in Qazvin Plain. Water and Soil Management and Modelling, 2(1), 53–62. (In Persian). http://doi.org/10.22098/MMWS.2022.9333.1034
  2. Allen, R.G., Pereira, L.S., Raes, D., & Smith, M. (1998). Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. Fao, Rome, 300(9), D05109.
  3. Allen, R.G., Tasumi, M., & Trezza, R. (2007). Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)—Model. Journal of Irrigation and Drainage Engineering, 133(4), 380–394. https://doi.org/10.1061/(ASCE)0733-9437(2007)133:4(380)
  4. Baastiansen, W.G.M., Menenti, M., Feddes, R.A., & Holtslag, A. (1998). AM: A remote sensing surface balance algorithm for land (SEBAL). Journal of Hydrology, 212–213. https://doi.org/10.1016/S0022-1694(98)00253-4
  5. Barideh, R., Veysi, S., Ebrahimipak, N., & Davatgar, N. (2022). The challenge of reference evapotranspiration between the WaPOR data set and geostatistical methods. Irrigation and Drainage, 71(5), 1268–1279. https://doi.org/10.1002/ird.2738
  6. Bastiaanssen, W.G.M., Cheema, M.J.M., Immerzeel, W.W., Miltenburg, I.J., & Pelgrum, H. (2012). Surface energy balance and actual evapotranspiration of the transboundary Indus Basin estimated from satellite measurements and the ETLook model. Water Resources Research, 48(11).  https://doi.org/10.1029/2011WR010482
  7. Blatchford, M.L., Mannaerts, C.M., Njuki, S.M., Nouri, H., Zeng, Y., Pelgrum, H., Wonink, S., & Karimi, P. (2020). Evaluation of WaPOR V2 evapotranspiration products across Africa. Hydrological Processes, 34(15), 3200–3221. https://doi.org/10.1002/hyp.13791
  8. Brown, C.F., Brumby, S.P., Guzder-Williams, B., Birch, T., Hyde, S.B., Mazzariello, J., Czerwinski, W., Pasquarella, V.J., Haertel, R., & Ilyushchenko, S. (2022). Dynamic World, Near real-time global 10 m land use land cover mapping. Scientific Data, 9(1), 251. https://doi.org/10.1038/s41597-022-01307-4
  9. Carlson, T.N., & Ripley, D.A. (1997). On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sensing of Environment, 62(3), 241–252. https://doi.org/10.1016/S0034-4257(97)00104-1
  10. Chai, Y., Chai, Q., Yang, C., Chen, Y., Li, R., Li, Y., Chang, L., Lan, X., Cheng, H., & Chai, S. (2022). Plastic film mulching increases yield, water productivity, and net income of rain-fed winter wheat compared with no mulching in semiarid Northwest China. Agricultural Water Management, 262, 107420. https://doi.org/10.1016/j.agwat. 2021.107420
  11. Coenders-Gerrits, A.M.J., Van der Ent, R.J., Bogaard, T.A., Wang-Erlandsson, L., Hrachowitz, M., & Savenije, H.H.G. (2014). Uncertainties in transpiration estimates. Nature, 506(7487), E1–E2. https://doi.org/10.1038/ nature12925
  12. Cuxart, J., & Boone, A.A. (2020). Evapotranspiration over land from a boundary-layer meteorology perspective. Boundary-Layer Meteorology, 177(2–3), 427–459. https://doi.org/10.1007/s10546-020-00550-9
  13. Duchemin, B., Hadria, R., Erraki, S., Boulet, G., Maisongrande, P., Chehbouni, A., Escadafal, R., Ezzahar, J., Hoedjes, J.C.B., & Kharrou, M.H. (2006). Monitoring wheat phenology and irrigation in Central Morocco: On the use of relationships between evapotranspiration, crops coefficients, leaf area index and remotely-sensed vegetation indices. Agricultural Water Management, 79(1), 1–27. https://doi.org/10.1016/j.agwat.2005.02.013
  14. Engman, E.T., & Gurney, R.J. (1991). Remote sensing in hydrology. Chapman and Hall Ltd.
  15. Fakhar, M.S., & Kaviani, A. (2022). Evaluation of FAO WaPOR product and PYSEBAL algorithm in estimating The amount of water consumed. Iranian Journal of Soil and Water Research ISNN, 2423, 7833. (In Persian). http//doi.org/10.22059/ijswr.2022.341474.669242
  16. (2021). The State of the World’s Land and Water Resources for Food and Agriculture—Systems at Breaking Point. In Synthesis Report. Food and Agriculture Organization of the United Nations Rome, Italy.
  17. FAO, 2020. WaPOR database methodology. http://www.fao.org/in-action/remote-sensing-for-water-productivity/ resources/publications/wapor-publications/en/.
  18. FAO (2016). Water Accounting and Auditing: A Sourcebook. FAO Water Reports, no. 43.
  19. Golian, S., Javadian, M., & Behrangi, A. (2019). On the use of satellite, gauge, and reanalysis precipitation products for drought studies. Environmental Research Letters, 14(7), 75005.
  20. Gutezeit, B. (2006). Storage of intercepted water on vegetable plants measured by gamma scanning technique. European Journal of Horticultural Science, 71(1), 30.
  21. Hallett, P.D. (2008). A brief overview of the causes, impacts and amelioration of soil water repellency–a review. Soil and Water Research, 3(1), 521–528.
  22. Hedayati, A., & Kakavand, R. (2012). Climatic zoning of Qazvin Province. Nivar, 36(77–76), 59–66. (In Persian)
  23. Hessels, T., van Opstal, J., Trambauer, P., Bastiaanssen, W., Faouzi, M., Mohamed, Y., & ErRaji, A. (2017). pySEBAL Version 3.3. 7.
  24. Idso, S.B., Jackson, R.D., & Reginato, R.J. (1975). Estimating evaporation: a technique adaptable to remote sensing. Science, 189(4207), 991–992. http://doi.org/10.1126/science.189.4207.991
  25. Jiménez, C., Prigent, C., Mueller, B., Seneviratne, S.I., McCabe, M.F., Wood, E.F., Rossow, W.B., Balsamo, G., Betts, A.K., & Dirmeyer, P.A. (2011). Global intercomparison of 12 land surface heat flux estimates. Journal of Geophysical Research: Atmospheres, 116(D2). https://doi.org/10.1029/2010JD014545
  26. Karishma, C.G., Kannan, B., Nagarajan, K., Panneerselvam, S., & Pazhanivelan, S. (2022). Spatial and temporal estimation of actual evapotranspiration of lower Bhavani basin, Tamil Nadu using Surface Energy Balance Algorithm for Land Model. Journal of Applied and Natural Science, 14(2), 566–574. https://doi.org/10.31018/ jans.v14i2.3412
  27. Katul, G.G., Oren, R., Manzoni, S., Higgins, C., & Parlange, M.B. (2012). Evapotranspiration: a process driving mass transport and energy exchange in the soil‐plant‐atmosphere‐climate system. Reviews of Geophysics, 50(3). https://doi.org/10.1029/2011RG000366
  28. Liou, Y.-A., & Kar, S.K. (2014). Evapotranspiration estimation with remote sensing and various surface energy balance algorithms—A review. Energies, 7(5), 2821–2849. https://doi.org/10.3390/en7052821
  29. Liu, Y.J., Chen, J., & Pan, T. (2019). Analysis of changes in reference evapotranspiration, pan evaporation, and actual evapotranspiration and their influencing factors in the North China Plain during 1998–2005. Earth and Space Science, 6(8), 1366–1377. https://doi.org/10.1029/2019EA000626
  30. Mohammadi, M., Mohammadi Ghaleney, M., & Ebrahimi, K. (2011). Spatial and temporal variations of groundwater quality of Qazvin plain, Water Research Iran, 5(8), 41-51. (In Persian)
  31. Oberg, J.W., & Melesss, A.M. (2006). Evapotranspiration dynamics at an ecohydrological restoration site: an energy balance and remote sensing approach 1. JAWRA Journal of the American Water Resources Association, 42(3), 565–582. https://doi.org/10.1111/j.1752-1688.2006.tb04476.x
  32. Paredes, P., & Pereira, L.S. (2019). Computing FAO56 reference grass evapotranspiration PM-ETo from temperature with focus on solar radiation. Agricultural Water Management, 215, 86–102. https://doi.org/10.1016/ j.agwat.2018.12.014
  33. Pereira, L.S., Paredes, P., López-Urrea, D.J., & Jovanovic, N. (2021). Updates and advances to the FAO56 crop water requirements method. Agricultural Water Management. https://doi.org/10.1016/j.agwat.2020.106697
  34. Rahimpour, M., Karimi, N., Rouzbahani, R., & Eftekhari, M. (2018). Validation and calibration of FAO WaPOR product (actual evapotranspiration) in Iran using in-situ measurements. Iran-Water Resources Research, 14(2), 254–263. (In Persian)
  35. Su, Z. (2002). The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes. Hydrology and Earth System Sciences, 6(1), 85–100. https://doi.org/10.5194/hess-6-85-2002, 2002
  36. Yang, Y., Anderson, M., Gao, F., Xue, J., Knipper, K., & Hain, C. (2022). Improved daily evapotranspiration estimation using remotely sensed data in a data fusion system. Remote Sensing, 14(8), 1772. https://doi.org/ 10.3390/rs14081772

 

ارسال نظر در مورد این مقاله
CAPTCHA Image
آب و خاک
دوره 37، شماره 3 - شماره پیاپی 89
مرداد و شهریور 1402
صفحه 487-502

فایل ها

سابقه مقاله

  • تاریخ دریافت: 28 اسفند 1401
  • تاریخ بازنگری: 30 اردیبهشت 1402
  • تاریخ پذیرش: 08 خرداد 1402
  • تاریخ اولین انتشار: 16 خرداد 1402

هم رسانی

ارجاع به این مقاله

آمار