بررسی عملکرد دو پایگاه داده هواشناسی در تخمین ردپای آب گیاه ذرت، مطالعه موردی: دشت قزوین

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی آب دانشکده کشاورزی و منابع طبیعی دانشگاه بین المللی امام خمینی(ره)

2 دانشجوی دکتری سازه های آبی، دانشگاه تهران، تهران، ایران

3 دانشجوی دکتری آبیاری و زهکشی دانشگاه تهران، تهران، ایران

چکیده

از آنجایی که دسترسی به ایستگاه‌های سینوپتیک در همه مناطق وجود ندارد، یا ساخت و تجهیز آن‌ها به تعداد بالا ممکن است مقرون‌به‌صرفه نباشد، و یا احتمال غلط بودن داده‌های آن‌ها به‌علت خطای اندازه‌گیری یا واسنجی‌نشدن ابزارها وجود دارد، بهتر است به دنبال یافتن ابزارهای جایگزین بود که پایگاه‌های داده هواشناسی یکی از این روش‌های مناسب می‌باشند. در این مطالعه، به ارزیابی دو پایگاه داده GPCC و AgMERRA پرداخته شد. هدف از این مطالعه، بررسی دقت این دو پایگاه در محاسبه ردپای آب بود که برای یک محصول و در یک منطقه خاص به‌عنوان مطالعه موردی مورد ارزیابی قرار گرفت. به-منظور مقایسه بهتر تخمین‌ها، از میانگین تخمین، R2، RMSE و ME (حداکثر خطا) استفاده شد. نتایج نشان داد که پایگاه داده GPCC عملکرد بسیار بالاتری نسبت به پایگاه داده AgMERRA دارد. میانگین ردپای آب آبی، سبز و ردپای کل آب برای گیاه ذرت در این استان به ترتیب برابر با 58/242، 47/149 و 05/392 مترمکعب بر تند بود که این مقادیر در پایگاه GPCC برابر بود با 58/207، 78/143 و 35/351 مترمکعب بر تن و برای پایگاه AgMERRA برابر بود با 06/149، 58/110 و 64/259 مترمکعب بر تن. بر اساس نتایج، هر دو پایگاه در تخمین ردپای آب سبز، عملکرد بهتری نسبت به ردپای آب آبی داشتند. این مطالعه نشان می‌دهد که پایگاه‌های داده، می‌توانند ابزارهای مناسبی در مطالعات هواشناسی در کشاورزی باشند و در صورت صحت‌سنجی و واسنجی آن‌ها می‌توانند در مطالعات مختلف مدیریت آبی، نظیر مدیریت آبیاری، مدیریت منابع آب و مدیریت کشاورزی مورد استفاده قرار گیرند.

کلیدواژه‌ها


عنوان مقاله [English]

Study of the performance of two meteorological datasets in estimating the maize water footprint, a case study: Qazvin Plain

نویسندگان [English]

  • Hadi Ramezani Etedali 1
  • Faraz Gorgin 2
  • Parisa Kakvand 3
1 Dept. of Water Sciences and Engineering, Imam Khomeini International University
2 Ph. D Student, Department of Water Structures, University of Tehran, Tehran, Iran
3 Ph. D Student, Department of Irrigation and Drainage Engineering, University of Tehran, Tehran, Iran
چکیده [English]

Due to the lack of availability of synoptic stations, the high costs of their construction, or the possibility of the inaccuracy of their data or inaccurate calibration, it is better to find alternative tools, which meteorological datasets are one of these suitable devices. In this study, two datasets, GPCC and AgMERRA, were evaluated. The purpose of this study is to investigate the accuracy of these two datasets in calculating the water footprint of maize for a specific crop in a specific region as case studies. For comparison of the estimations, the average estimation, R2, RMSE, and maximum error (ME) were used. The results showed that GPCC is more efficient than AgMERRA in estimating the water footprint of maize. The average blue, green, and the total water footprint of maize in this province were 242/58, 149/47, and 392/05 m3/ton which was 207/58, 143/78, and 351/35 m3/ton for GPCC and 149/06, 110.58, and 259/64 m3/ton, respectively. According to the results, both datasets were more efficient in estimating the green water footprint than the blue water footprint. This study shows that datasets can be suitable tools in meteorological studies in agriculture, and if they are validated and calibrated, they can be used in various water management, such as irrigation management, water resources management, and agriculture management.

کلیدواژه‌ها [English]

  • Green Water footprint
  • Blue Water Footprint
  • Synoptic Station
  • GPCC
  • AgMERRA
Ababaei, B. 2020. SPATIO-TEMPORAL VARIATIONS OF SEVEN WEATHER VARIABLES IN IRAN: APPLICATION OF CRU TS AND GPCC DATA SETS†. Irrigation and Drainage. 69(1): 164-185 https://doi.org/10.1002/ird.2399
Ababaei, B. and Ramezani Etedali, H. 2017. Water footprint assessment of main cereals in Iran. Agricultural Water Management. 179: 401-411 https://doi.org/10.1016/j.agwat.2016.07.016
Abi Saab, M. T., El Alam, R., Jomaa, I., Skaf, S., Fahed, S., Albrizio, R. and Todorovic, M. 2021. Coupling remote sensing data and aquacrop model for simulation of winter wheat growth under rainfed and irrigated conditions in a mediterranean environment. Agronomy. 11(11) 2265 https://doi.org/10.3390/agronomy11112265
Adeboye, O. B., Schultz, B., Adeboye, A. P., Adekalu, K. O. and Osunbitan, J. A. 2021. Application of the AquaCrop model in decision support for optimization of nitrogen fertilizer and water productivity of soybeans. Information Processing in Agriculture. 8(3): 419-436 https://doi.org/10.1016/j.inpa.2020.10.002
Ahmed, K., Shahid, S., Wang, X., Nawaz, N. and Najeebullah, K. 2019. Evaluation of gridded precipitation datasets over arid regions of Pakistan. Water (Switzerland). 11(2): 210 https://doi.org/10.3390/w11020210
Bahroloum, R., Ramezani Etedali, H., Azizian, A. and Ababaei, B. 2020. Use of Gridded Weather Datasets in Simulation of Wheat Yield and Water Requirement (Case Study: Iran’s Qazvin Plain). Iranian Journal of Ecohydrology, 7(3): 691–706. https://scholar.google.com/citations?view_op=view_citation&hl=en&user=7qhNtjMAAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=7qhNtjMAAAAJ:9ZlFYXVOiuMC
Bai, J., Chen, X., Dobermann, A., Yang, H., Cassman, K. G. and Zhang, F. 2010. Evaluation of nasa satellite-and model-derived weather data for simulation of maize yield potential in China. Agronomy Journal. 102(1): 9-16 https://doi.org/10.2134/agronj2009.0085
Bazrafshan, O., Zamani, H., Ramezanietedli, H., Gerkaninezhad Moshizi, Z., Shamili, M., Ismaelpour, Y. and  Gholami, H. 2020. Improving water management in date palms using economic value of water footprint and virtual water trade concepts in Iran. Agricultural Water Management. 229-105941 https://doi.org/10.1016/j.agwat.2019.105941
Becker, A., Finger, P., Meyer-Christoffer, A., Rudolf, B., Schamm, K., Schneider, U. and Ziese, M. 2013. A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sample applications including centennial (trend) analysis from 1901-present. Earth System Science Data. 5(1): 71-99, https://doi.org/10.5194/essd-5-71-2013
Bosilovich, M. G., Chen, J., Robertson, F. R. and Adler, R. F. 2008. Evaluation of global precipitation in reanalyses. 47(9), 2279-2299 Journal of Applied Meteorology and Climatology. https://doi.org/10.1175/2008JAMC1921.1
Capolongo, D., Refice, A., Bocchiola, D., D’Addabbo, A., Vouvalidis, K., Soncini, A., Zingaro, M., Bovenga, F. and Stamatopoulos, L. 2019. Coupling multitemporal remote sensing with geomorphology and hydrological modeling for post flood recovery in the Strymonas dammed river basin (Greece). Science of the Total Environment, 651: 1958-1968, https://doi.org/10.1016/j.scitotenv.2018.10.114
Doorenbos, J., Kassam, A. H., Bentvelsen, C. and Uittenbogaard, G. 1980. Yield Response to Water. In Irrigation and Agricultural Development. 33: 257. https://doi.org/10.1016/b978-0-08-025675-7.50021-2
Elbeltagi, A., Zhang, L., Deng, J., Juma, A. and Wang, K. 2020. Modeling monthly crop coefficients of maize based on limited meteorological data : A case study in Nile Delta , Egypt. Computers and Electronics in Agriculture, 173(August 2019), 105368. https://doi.org/10.1016/j.compag.2020.105368
Fathololoumi, S., Vaezi, A. R., Alavipanah, S. K., Ghorbani, A. and Biswas, A. 2020. Comparison of spectral and spatial-based approaches for mapping the local variation of soil moisture in a semi-arid mountainous area. Science of the Total Environment. 724, 138319. https://doi.org/10.1016/j.scitotenv.2020.138319
García-Vila, M., Fereres, E., Mateos, L., Orgaz, F. and Steduto, P. 2009. Deficit irrigation optimization of cotton with aquacrop. Agronomy Journal. 101(3): 477-487, https://doi.org/10.2134/agronj2008.0179s
Golabi, M. and Naseri, A. A. 2015. Assessment Aquacrop Model to Predict the Sugarcane Yield and Soil Salinity Profiles under Salinity Stress. Iranian Journal of Soil and Water Research, 4(46), 685–694.
Hellal, F., Mansour, H., Abdel-Hady, M., El-Sayed, S. and Abdelly, C. 2019. Assessment water productivity of barley varieties under water stress by AquaCrop model. AIMS Agriculture and Food. 4(3): 501-517. https://doi.org/10.3934/agrfood.2019.3.501
Hoekstra, A. Y. 2003. Virtual Water Trade. Proceedings of the internacional expert meeting on virtual water trade. International Expert Meeting on Virtual Water Trade.
Khosravi, F., Taylor, A. and Siu, Y. L. 2021. Chinese water managers’ long-term climate information needs. Science of the Total Environment. 750: 141637. https://doi.org/10.1016/j.scitotenv.2020.141637
Lashkari, A., Salehnia, N., Asadi, S., Paymard, P., Zare, H. and Bannayan, M. 2018. Evaluation of different gridded rainfall datasets for rainfed wheat yield prediction in an arid environment. International Journal of Biometeorology. 62(8), 1543-1556. https://doi.org/10.1007/s00484-018-1555-x
Li, J., Tian, L., Wang, Y., Jin, S., Li, T. and Hou, X. 2021. Optimal sampling strategy of water quality monitoring at high dynamic lakes: A remote sensing and spatial simulated annealing integrated approach. Science of the Total Environment. 777: 146113. https://doi.org/10.1016/j.scitotenv.2021.146113
Marano, R. P. and Filippi, R. A. 2015. Water Footprint in paddy rice systems. Its determination in the provinces of Santa Fe and Entre Ríos, Argentina. Ecological Indicators. 56: 229-236. https://doi.org/10.1016/j.ecolind.2015.03.027
Martínez-Romero, A., López-Urrea, R., Montoya, F., Pardo, J. J. and Domínguez, A. 2021. Optimization of irrigation scheduling for barley crop, combining AquaCrop and MOPECO models to simulate various water-deficit regimes. Agricultural Water Management. 258: 107219. https://doi.org/10.1016/j.agwat.2021.107219
Nazari, R., Ramezani Etedali, H., Nazari, B. and Collins, B. 2020. The impact of climate variability on water footprint components of rainfed wheat and barley in the Qazvin province of Iran. Irrigation and Drainage. 69(4), 826-843, https://doi.org/10.1002/ird.2487
Nunes, H. G. G. C., Farias, V. D. S., Sousa, D. P., Costa, D. L. P., Pinto, J. V. N., Moura, V. B., Teixeira, E. O., Lima, M. J. A., Ortega-Farias, S. and Souza, P. J. O. P. 2021. Parameterization of the AquaCrop model for cowpea and assessing the impact of sowing dates normally used on yield. Agricultural Water Management. 252: 106880. https://doi.org/10.1016/j.agwat.2021.106880
Ollivier, C., Olioso, A., Carrière, S. D., Boulet, G., Chalikakis, K., Chanzy, A., Charlier, J. B., Combemale, D., Davi, H., Emblanch, C., Marloie, O., Martin-StPaul, N., Mazzilli, N., Simioni, G. and Weiss, M. 2021. An evapotranspiration model driven by remote sensing data for assessing groundwater resource in karst watershed. Science of the Total Environment. 781: 146706. https://doi.org/10.1016/j.scitotenv.2021.146706
Queyrel, W., Habets, F., Blanchoud, H., Ripoche, D. and Launay, M. 2016. Pesticide fate modeling in soils with the crop model STICS: Feasibility for assessment of agricultural practices. 542, 787-802, Science of the Total Environment. https://doi.org/10.1016/j.scitotenv.2015.10.066
Raes, D., Steduto, P., Hsiao, T. C. and Fereres, E. 2009. Aquacrop-The FAO crop model to simulate yield response to water: II. main algorithms and software description. Agronomy Journal. 101(3): 438-447. https://doi.org/10.2134/agronj2008.0140s
Ramezani Etedali, H., Ahmadaali, K., Gorgin, F. and Ababaei, B. 2019. OPTIMIZATION OF THE CROPPING PATTERN OF MAIN CEREALS AND IMPROVING WATER PRODUCTIVITY: APPLICATION OF THE WATER FOOTPRINT CONCEPT. Irrigation and Drainage. 68(4): 765-777. https://doi.org/10.1002/ird.2362
Rosenzweig, C., Jones, J. W., Hatfield, J. L., Ruane, A. C., Boote, K. J., Thorburn, P., Antle, J. M., Nelson, G. C., Porter, C., Janssen, S., Asseng, S., Basso, B., Ewert, F., Wallach, D., Baigorria, G. and Winter, J. M. 2013. The Agricultural Model Intercomparison and Improvement Project (AgMIP): Protocols and pilot studies. Agricultural and Forest Meteorology. 170: 166-182. https://doi.org/10.1016/j.agrformet.2012.09.011
Ruane, A. C., Goldberg, R. and Chryssanthacopoulos, J. 2015. Climate forcing datasets for agricultural modeling: Merged products for gap-filling and historical climate series estimation. Agricultural and Forest Meteorology. 200: 233-248. https://doi.org/10.1016/j.agrformet.2014.09.016
Saeidi, R., Ramezani Etedali, H., Sotoodehnia, A., Kaviani, A. and Nazari, B. 2021. Salinity and fertility stresses modify K s and readily available water coefficients in maize (case study: Qazvin region). Irrigation Science. 39(3): 299-313.  https://doi.org/10.1007/s00271-020-00711-1
Salehnia, N., Alizadeh, A., Sanaeinejad, H., Bannayan, M., Zarrin, A. and Hoogenboom, G. 2017. Estimation of meteorological drought indices based on AgMERRA precipitation data and station-observed precipitation data. Journal of Arid Land. 9(6): 797-809. https://doi.org/10.1007/s40333-017-0070-y
Schneider, U., Becker, A., Finger, P., Meyer-Christoffer, A., Ziese, M. and Rudolf, B. 2014. GPCC’s new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle. Theoretical and Applied Climatology. 115(1): 15-40.  https://doi.org/10.1007/s00704-013-0860-x
Shi, H., Li, T. and Wei, J. 2017. Evaluation of the gridded CRU TS precipitation dataset with the point raingauge records over the Three-River Headwaters Region. Journal of Hydrology. 548: 322-332. https://doi.org/10.1016/j.jhydrol.2017.03.017
Vulova, S., Meier, F., Rocha, A. D., Quanz, J., Nouri, H. and Kleinschmit, B. 2021. Modeling urban evapotranspiration using remote sensing, flux footprints, and artificial intelligence. Science of the Total Environment. 786: 147293. https://doi.org/10.1016/j.scitotenv.2021.147293
Yaghoubi, F., Bannayan, M. and Asadi, G. A. 2020. Performance of predicted evapotranspiration and yield of rainfed wheat in the northeast Iran using gridded AgMERRA weather data. International Journal of Biometeorology. 64(9): 1519-1537.  https://doi.org/10.1007/s00484-020-01931-y
Zhu, X., Zhang, M., Wang, S., Qiang, F., Zeng, T., Ren, Z. and Dong, L. 2015. Comparison of monthly precipitation derived from high-resolution gridded datasets in arid Xinjiang, central Asia. Quaternary International. 358: 160-170. https://doi.org/10.1016/j.quaint.2014.12.027