Efficiency of Machine Learning Techniques for Predicting Vapor Pressure Deficit in Arid and Semi-Arid Regions (Case Study: South Khorasan Province)

Ghochanian Haghverdi, Elham; Khozeymeh Nezhad, Hossein; Moghri Friz, Alireza; Khorashadizadeh, Omid

doi:10.22077/jdcr.2024.8327.1082

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	Efficiency of Machine Learning Techniques for Predicting Vapor Pressure Deficit in Arid and Semi-Arid Regions (Case Study: South Khorasan Province)
مجله پژوهش های خشکسالی و تغییراقلیم
دوره 2، شماره 4 - شماره پیاپی 8، اسفند 1403، صفحه 85-102 اصل مقاله (1.3 M)
نوع مقاله: مقاله پژوهشی
شناسه دیجیتال (DOI): 10.22077/jdcr.2024.8327.1082
نویسندگان
Elham Ghochanian Haghverdi^* ¹؛ Hossein Khozeymeh Nezhad¹؛ Alireza Moghri Friz²؛ Omid Khorashadizadeh³
¹Department of Water Science and Engineering, Faculty of Agriculture, University of Birjand, Birjand, Iran.
²Department of Soil and Water Research Department, South Khorasan Agriculture and Natural Resources Research and Education Center, AREEO, Birjand, Iran.
³Regional Water Company of south Khorasan, Water Resources Management Company, Birjand, Iran.
چکیده
Climate change, as one of the global challenges of the present century, has profound impacts on water resources and agriculture. The increase in temperature and decrease in rainfall in arid and semi-arid regions have made optimal water resource management a top priority.In countries facing climate change and drought, accurate estimation of evapotranspiration plays a vital role in water resource management and ensuring food security.One of the key factors affecting evapotranspiration is the vapor pressure deficit (VPD), which significantly impacts the accuracy of related calculations. This study focuses on predicting the vapor pressure deficit using advanced machine learning techniques. The methods employed include linear regression (LR), generalized additive model (GAM), random subspace (RSS), random forest (RF), and M5 Purned model(M5P). In this study, monthly average data, including temperature, humidity, precipitation, and vapor pressure deficit, were extracted from the JRA-55 database for the period from 1958 to 2023. The analysis of vapor pressure deficit data in the study areas of Birjand, Sarayan, Qaen, and Tabas showed that the annual average vapor pressure deficit increased by 6 Pascals, 10 Pascals, 4 Pascals, and 5 Pascals, respectively.In the next step, the extracted data for temperature, precipitation, and humidity were used as input variables, and vapor pressure deficit was used as the target variable in machine learning algorithms. Model performance was evaluated using root mean square error (RMSE), mean absolute error (MAE), Pearson correlation (CC), and Kling-Gupta efficiency (KGE) as evaluation indices.The results showed that the GAM model outperformed other models in all study areas. The evaluation values for each region were as follows: Birjand [ RMSE=0.308, MAE=0.247, KGE=0.914, CC=0.920], SAarayan [RMSE=0.401, MAE=0.303, KGE=0.937, CC=0.951], Qaen [RMSE=0.072, MAE=0.055, KGE=0.987, CC=0.997] and Tabas[RMSE=0.230, MAE=0.184, KGE=0.920, CC=0.942] The predictions made by the model indicated that, over the next 10 years, the annual average vapor pressure deficit in the studied regions will significantly increase: Birjand: 9 Pascals, Sarayan: 10 Pascals, Qaen: 7 Pascals and Tabas: 5 PascalsThis increase signifies serious challenges for water resources and an increase in water consumption in the region’s hot and dry climatic conditions. Finally, this study recommends the GAM model as an effective tool for future research, especially for use in the development of smart irrigation systems, which play a crucial role in sustainable water resource management.
کلیدواژه‌ها
Vapor pressure deficit؛ GAM؛ Machine learning؛ Drought

مراجع
Ahmar, S., Gill, R.A., Jung, K.H., Faheem, A., Qasim, M.U., Mubeen, M. & Zhou, W. (2020). Conventional and molecular techniques from simple breeding to speed breeding in crop plants: recent advances and future outlook. International Journal of Molecular Sciences. 21(7), 2590. https://doi.org/10.3390/ijms21072590. Azarm, k., mohebolhojeh, A. & Mirzaie, M. (2022). Climatological Study of Wintertime Blocking Events in the Northern Hemisphere. 20th Iranian Geophysical Conference, Iran. Blaifi, S.A., Moulahoum, S., Benkercha, R., Taghezouit, B.& Saim, A. (2018). M5P model tree-based fast fuzzy maximum power point tracker. Solar Energy 163, 405–424. https:// doi.org/10.1016/j.solener.2018.01.071 Bolton, D. (1980). The computation of equivalent potential temperature. Monthly Weather Review. 108, 1046–1053. https://doi. org/10.1175/1520-0493(1980)108 Carnicer, J., Barbeta, A., Sperlich, D., Coll, M. & Pe˜nuelas, J. (2013). Contrasting trait syndromes in, angiosperms and conifers are associated with different responses of tree growth to, temperature on a large scale. Frontiers in Plant Science. 4, 409. http://doi. org/10.3389/fpls.2013.00409 Dai, G., 2006. Recent climatology, variability, and trends in global surface humidity. Journal of Climate. 19(15), 3589–3606. Dai, A., Zhao, T. & Chen, J. (2018). Climate change and drought: a precipitation and evaporation perspective. Current Climate Change Reports, 4 (3), 301–312. http://doi. org/10.1007/s40641-018-0101-6 Devi, M.J., Reddy, V.R. (2018). Effect of temperature under different evaporative demand conditions on maize leaf expansion. Environmental and Experimental Botany. 155, 509–517. https://doi.org/10.1016/j. envexpbot.2018.07.024 Ding, J., Yang, T., Zhao, Y., Liu, D., Wang, X., Yao, Y., Peng, S., Wang, T. & Piao, S. (2018). Increasingly important role of atmospheric aridity on Tibetan alpine grasslands. Geophysical Research Letters, 45(6), 2852–2859. https://doi.org/10.1002/2017GL076803 Emami, M., Ahmadi, A., Daccache, A., Nazif, S., Mousavi, S.F. & Karami, H. (2022). Countylevel irrigation water demand estimation using machine learning: case study of California. Water, 14 (12), 1937. https://doi.org/10.3390/w14121937. Feng, Y., Cui, N., Hao, W., Gao, L.& Gong, D. (2019). Estimation of soil temperature from meteorological data using different machine learning models. Geoderma. 338, 67–77. https://doi.org/10.1016/j.geoderma.2018.11.044 Ficklin, D.L. & Novick, K.A. (2017). Historical and projected changes in vapor pressure deficit suggest a continental-scale drying of the United States atmosphere. Journal of Geophysical Research Atmospheres, 122 (4), 2061–2079. https://doi.org/10.1002/2016JD025855 Fletcher, L., Sinclair, T.R. & Allen Jr., L.H. (2007). Transpiration responses to vapor pressure deficit in well watered ‘slow-wilting’and commercial soybean. Environmental and Experimental Botany, 61(2), 145–151. https://doi.org/10.1016/j.envexpbot.2007.05.004 Gong, X.W., Qiu, R.J., Sun, J.S., Ge, J.K., Li, Y.B. & Wang, S.S. (2020). Evapotranspiration and crop coefficient of tomato grown in a solar greenhouse under full and deficit, irrigation. Agricultural Water Management 235, 106154. https://doi.org/10.1016/j.agwat.2020.106154 Grossiord, C., Buckley, T.N., Cernusak, L.A., Novick, K.A., Poulter, B., Siegwolf, R.T.W., Sperry, J.S. & McDowell, N.G. (2020). Plant responses to rising vapor pressure deficit. Wiley New Phytologist. 226 (6), 1550–1566. https://doi.org/10.1111/nph.16485. Huntington, T., Cui, X., Mishra, U. & Scown, C.D. (2020). Machine learning to predict biomass sorghum yields under future climate scenarios. Biofuels Bioprod. Bioref. 14 (3), 566–577. https://doi.org/10.1002/bbb.2087 Iribarne, J.V., and W.L. Godson. 1981. Atmospheric Thermodynamics. D. Reidel, p. 65. Islam, A.R.M., Talukdar, S., Akhter, S., Eibek, K.U., Rahman, M., Pal, S. & Mosavi, A. (2022). Assessing the impact of the farakka barrage on hydrological alteration in the Padma River with future insight. Sustainability 14 (9), 5233. https://doi.org/10.3390/su14095233 Khosravi, K., Daggupati, P., Alami, M.T., Awadh, S.M., Ghareb, M.I. & Panahi, M. (2019). Meteorological data mining and hybrid data-intelligence models for reference evaporation simulation: a case study in Iraq. Computers and Electronics in Agriculture 167. https://doi.org/10.1016/j.compag.2019.105041 Khosravi, A., Zucchini, M., Giorgi, V., Mancini, A. & Neri, D. (2021). Continuous monitoring of olive fruit growth by automatic extensimeter in response to vapor pressure deficit from pit hardening to harvest. Horticulturae 7 (10), 349. https://doi.org/10.3390/horticulturae7100349 Kimball, J.S., Running, S.W. & Nemani, R. (1997). An improved method for estimating surface humidity from daily minimum temperature. Agricultural and Forest Meteorology. 85 (1–2), 87–98. http://doi. org/10.1016/S0168-1923(96)02366-0 Konings, A.G., Williams, A.P. & Gentine, P. (2017). Sensitivity of grassland productivity to aridity controlled by stomatal and xylem regulation. Nature Geoscience, 10 (4), 284–288. https://doi.org/10.1038/ngeo2903 Kobayashi, S., Ota, Y., Harda, Y., Ebita, A., Moriya, M., Onoda, H., Onogi, K., Kamahori, H., Kobayashi, C., Endo, H., Miyaoka, K. & Gentine, P. (2015). The JRA-55 reanalysis: general specifications and basic characteristics. Journal of the Meteorological Society of Japan Ser II.5-48. https://doi.org/10.2151/jmsj.2015-001 Li, Y., Wang, W., Wang, G. & Tan, Q. (2022). Actual evapotranspiration estimation over the Tuojiang River Basin based on a hybrid CNNRF model. Journal of Hydrology, 610, 127788. https://doi.org/10.1016/j.jhydrol.2022.127788 Liu, W. & Sun, F. (2017). Projecting and attributing future changes of evaporative demand over China in CMIP5 climate models. Journal of Hydrometeorology. 18 (4), 977–991. https://doi.org/10.1175/JHM-D-16-0204.1 Maulud, D. & Abdulazeez, A.M. (2020). A review on linear regression comprehensive in machine learning. Journal of Applied Science and Technology Trends 1 (4), 140–147. https:// doi.org/10.38094/jastt1457 Mokhtar, A., Elbeltagi, A., Maroufpoor, S., Azad, N., He, H. & Alsafadi, K. (2021). Estimation of the rice water footprint based on machine learning algorithms. Computers and Electronics in Agriculture. 191, 106501. https://doi.org/10.1016/j.compag.2021.106501 Mollasharifi, A., Mohebalhojeh, A. R. & Ahmadi-Givi, F. (2019). A study of the impacts of the NAO on the relation between the North Atlantic and Mediterranean storm tracks using the NCEP/NCAR and JRA-55 reanalysis data. Journal of the Earth and Space Physics. 45(2),423-440. https://doi.org/ 10.22059/JESPHYS.2019.267521.1007050 Otieno, D., Lindner, S., Muhr, J. & Borken, W. (2012). Sensitivity of peat land herbaceous vegetation to vapor pressure deficit influences net ecosystem CO2 exchange. Wetlands, 32 (5), 895–905. https://doi.org/10.1007/s13157-012-0322-8. Pierce, W., Westerling, A.L. & Oyler, J. (2013). Future humidity trends over the western United States in the CMIP5 global climate models and variable infiltration capacity hydrological modeling system. Hydrology and Earth System Sciences Discussions, 9(12), 13651-13691. https://doi.org/10.5194/hessd-17-18332013 Qiu, R.J., Liu, C.W., Cui, N.B., Wu, Y.J., Wang, Z.C. & Li, G. (2019). Evapotranspiration estimation using a modified Priestley-Taylor model in a rice-wheat rotation system. Agricultural Water Management 224, 105755. http://doi.org/10.1016/j.agwat.2019.105755. Qiu, R.J., Katul, G.G. (2020). Maximizing leaf carbon gain in varying saline conditions: an optimization model with dynamic mesophyll conductance. The Plant Journal. 101 (3), 543–554. https://doi.org/10.1111/tpj.14553 Rawson, H.M., Begg, J.E., Woodward, R.G. (1977). The effect of atmospheric humidity on photosynthesis, transpiration and water use efficiency of leaves of several plant species. Planta 134, 5–10. https://doi.org/10.1007/BF00390086. Restaino, M., Peterson, D.L. & Littell, J. (2016). Increased water deficit decreases Douglas firgrowth throughout western US forests. Proceedings of the National Academy of Sciences. 113(34), 9557–9562. https://doi. org/10.1073/pnas.1602384113 Sangin´es de C´arcer, P., Vitasse, Y., Pe˜nuelas, J., Jassey, V.E.J., Buttler, A. & Signarbieux, C. (2018). Vapor–pressure deficit and extremeclimatic variables limit tree growth. Global Change Biology. 24 (3), 1108–1122. https://doi.org/10.1111/gcb.13973. Sellin, Taneda, H. & Alber, M. (2019). Leaf structural and hydraulic adjustment with respect to air humidity and canopy position in silver birch (Betula pendula). Journal of Plant Research, 132, 369–381. https://doi. org/10.1007/s10265-019-01106-w. Shamshirband, S., Hashemi, S., Salimi, H., Samadianfard, S., Asadi, E., Shadkani, S. & Chau, K.W.(2020). Predicting standardized stream flow index for hydrological drought using machine learning models. Engineering Applications of Computational Fluid Mechanics. 14 (1), 339–350. https://doi.org/10.1080/19942060.2020.1715844 Simmons, J., Willett, K.M., Jones, P.D., Thorne, P.W. & Dee, D.P. (2010). Lowfrequency variations in surface atmospheric humidity, temperature, and precipitation: Inferences fromreanalyses and monthly gridded observational data sets. Journal of Geophysical Research Atmospheres. 115(1), https://doi.org/10.1029/2009JD012442 Skurichina, M. & Duin, R.P. (2002). Bagging, boosting and the random subspace method for linear classifiers. Pattern Analysis &Applications. 5 (2), 121–135. https://doi. org/10.1007/s100440200011 Smidte, S.J., Haacker, E.M.K., Kendall, A.D., Deines, J.M., Pei, L., Cotterman, K.A., Li, H., Liu, X., Basso, B. & Hyndman, D.W. (2016). Complex water management in modern agriculture: Trends in the water–energy–food nexus over the High Plains aquifer. Science of the Total Environment. 566–567, 988–1001. https://doi.org/10.1016/j.scitotenv.2016.05.127 Sun, X., Lai, P., Wang, S., Song, L., Ma, M. & Han, X. (2022). Monitoring of extreme agricultural drought of the past 20 years in southwest china using gldas soil moisture. remote sensing. 14, 13-23. https://doi. org/10.3390/rs14061323. Wada, Y., Bierkens, M.F.P. (2014). Sustainability of global water use: past reconstruction and future projections. Environmental Research Letters, 9(10), 104003. https://doi. org/10.1088/1748-9326/9/10/104003. Wang, K., Dickinson, R.E. & Liang, S. (2012). Global atmospheric evaporative demand over land from 1973 to 2008. Journal of Climate. 25 (23), 8353–8361. https://doi.org/10.1175/JCLI-D-11-00492.1 Willett, K.M., Jones, P.D., Gillett, N.P. & Thorne, P.W. (2008). Recent changes in surface humidity: development of the HadCRUH dataset. Journal of Climate, 21(20), 5364–5383. https://doi.org/10.1175/2008jcli2274.1. Willett, K.M., Dunn, R.J.H., Thorne, P.W., Bell, S., de Podesta, M., Parker, D.E., Jones, P. D. & Williams Jr., C.N. (2014). HadISDH land surface multi-variable humidity and temperature record for climate monitoring. Climate of the Past, 10(6), 1983–2006. https:// doi.org/10.5194/cp-10-1983-2014. Yuan, W., Zheng, Y., Piao, S., Ciais, P., Lombardozzi, D., Wang, Y. & Yang, S. (2019). Increased atmospheric vapor pressure deficit reduces global vegetation growth. Science Advances, 5(8), eaax1396. https://doi. org/10.1126/sciadv.aax1396. Zhang, D., Liu, Y., Li, Y., Qin, L., Li, J. & Xu, F. (2018). Reducing the excessive evaporative demand improved the water-use efficiency of greenhouse cucumber by regulating the trade-off between irrigation demand and plant productivity. HortScience, 53(12), 1784–1790. https://doi.org/10.21273/HORTSCI13129-18.
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Efficiency of Machine Learning Techniques for Predicting Vapor Pressure Deficit in Arid and Semi-Arid Regions (Case Study: South Khorasan Province)