ارزیابی داده‌های شبکه‌بندی شده AgMERRA در شبیه‌سازی عملکرد و نیاز آبی گندم دیم در استان خراسان رضوی

نوع مقاله : مقالات پژوهشی

نویسندگان

• فاطمه یعقوبی
• محمد بنایان اول
• قربانعلی اسدی

دانشگاه فردوسی مشهد

https://doi.org/10.22067/jsw.v32i2.68948

چکیده

شبیه‌سازی با استفاده از مدل‌های گیاهی نیازمند داده‌های بلند مدت و با کیفیت آب و هوایی است. در بسیاری از مناطق این داده‌ها با کیفیت مطلوب و یا دوره آماری مناسب در دسترس نمی‌باشند. لذا این مطالعه با هدف ارزیابی داده‌های AgMERRA در شبیه‌سازی نیاز آبی و عملکرد گندم دیم در استان خراسان رضوی به اجرا درآمد. در این پژوهش از داده‌های روزانه ایستگاه‌های سینوپتیک تربت ‌جام، تربت‌ حیدریه، سبزوار، سرخس، قوچان، کاشمر، گناباد، نیشابور و مشهد استفاده شد. داده‌های روزانه AgMERRA از پایگاه داده‌های سازمان فضایی امریکا جمع‌آوری و سپس با داده‌های مشاهداتی ایستگاه‌های سینوپتیک مورد مقایسه قرار گرفتند. جهت شبیه‌سازی نیاز آبی و عملکرد گندم از مدل کراپ‌وات و CSM-CERES-Wheat استفاده شد. نتایج نشان داد که تشعشع خورشیدی، دمای حداقل و حداکثر AgMERRA در تمامی مناطق همبستگی (r2>0/7) و توافق خوبی (NRMSE< %30) با داده‌های مشاهداتی نشان داد. اما سرعت باد و بارندگی روزانه AgMERRA در توافق با مقادیر مشاهداتی متناظر نبود، با این وجود استفاده از مجموع بارندگی ۱۵ روز سبب بهبود وضعیت توافق و همبستگی مشاهده شده در تمامی مناطق گردید. ضریب تغییرات نیاز آبی و عملکرد شبیه‌سازی شده با استفاده از داده‌های AgMERRA در تمامی مناطق به جز تربت ‌جام، تربت‌ حیدریه و گناباد برای نیاز آبی و مشهد، کاشمر و قوچان برای عملکرد نزدیک به (بین 5- تا 5+ درصد) ضریب تغییرات نیاز آبی و عملکرد شبیه‌سازی شده با استفاده از داده‌های مشاهداتی بود. با این وجود انحراف میانگین بلند مدت نیاز آبی و عملکرد شبیه‌سازی شده با استفاده از داده‌های AgMERRA در تمامی مناطق به جز تربت‌ حیدریه و گناباد برای نیاز آبی در بازه ۱۰- تا ۱۰+ درصد میانگین بلند مدت نیاز آبی و عملکرد شبیه‌سازی شده با استفاده از داده‌های مشاهداتی قرار داشت. با توجه به نتایج حاصله می‌توان از داده‌های AgMERRA جهت برآورد میانگین بلند مدت نیاز آبی و عملکرد گندم دیم در منطقه مورد مطالعه استفاده نمود. با این وجود این مجموعه داده جهت شبیه‌سازی دقیق نیاز آبی و عملکرد در یک سال خاص زیاد قابل اعتماد نیست.

کلیدواژه‌ها

• بارندگی
• داده‌های آب و هوایی
• رگرسیون
• مدل گیاهی

20.1001.1.20084757.1397.32.2.14.0

عنوان مقاله [English]

Evaluation of Grided AgMERRA Weather Data for Simulation of Water Requirement and Yield of Rainfed Wheat in Khorasan Razavi Province

نویسندگان [English]

• fatemeh yaghoubi
• Mohammad Bannayan Aval
• Ghorban Ali Asadi

Ferdowsi University of Mashhad

چکیده [English]

Introduction: Estimating crop water requirement, crop yield and their temporal and spatial variability using crop simulation models are essential for analysis of food security, assessing impact of current and future climates on crop yield and yield gap analysis, however it requires long-term historical daily weather data to obtain robust predictions. Depending on the degree of weather variability among years, at least 10–20 years of daily weather data are necessary for reliable estimates of crop yield and its inter-annual variability. In many regions where crops are grown, daily weather data of sufficient quality and duration are not available. In this way, gridded weather databases with complete terrestrial coverage are available which require comprehensive validation before any application. These weather databases typically derived from global circulation computer models, interpolated weather station data or remotely sensed surface data from satellites. The aims of this study were to evaluate differences between grided AgMERRA weather data and ground observed data and quantify the impact of such differences on simulated water requirement and yield of rainfed wheat at 9 different locations in Khorasan Razavi province.
Materials and Methods: AgMERRA dataset (NASA’s Modern-Era Retrospective analysis for Research and Applications) was selected as the girded weather data source for use in this study because it is publically accessible. We evaluated AgMERRA weather data against observed weather data (OWD) from 9 meteorological stations (Torbat Jam, Torbat Heydarieh, Sabzevar, Sarakhs, Ghoochan, Kashmar, Gonabad, Mashhad, and Neyshabour) in Khorasan Razavi province. For each weather variable (solar radiation, maximum temperature, minimum temperature, precipitation, and wind speed), the degree of correlation and agreement between OWD and AgMERRA data for the grid cell in which weather stations were located were evaluated. The intercept (b), slope (m), and coefficient of determination (r2) of the linear regression were calculated to determine the strength and bias of the relationship, while the root mean square error (RMSE) and normalized root mean square error (NRMSE) were computed to measure the degree of agreement between data sources. Crop water requirement or actual crop evapotranspiration (ETc) under standard condition was computed using CROPWAT 8.0. The CSM-CERES-Wheat (Cropping System Model-Crop Environment Resource Synthesis-Wheat) model, included in the Decision Support System for Agrotechnology Transfer (DSSAT v4.6) software package was used to calculate rainfed wheat yield. For each location in this study, rainfed wheat grain yield and water requirement were simulated using ground-observed and AgMERRA weather data and outputs were compared with each other.
Results and Discussion: The results of this study showed that AgMERRA daily maximum and minimum temperature and solar radiation showed strong correlation and good agreement with data from ground weather stations. AgMERRA daily precipitation had low correlation and good agreement (mean r2= 0.34, RMSE= 2.25 mm and NRMSE= 4.94% across the 9 locations) with OWD daily values, but correlation with 15-day precipitation totals were much better (mean r2 >0.7 across the 9 locations). There was reasonable agreement between a number of observed dry and wet days with AgMERRA compared to OWD. Results indicated that coefficient of variation of simulated water requirement and yield using AgMERRA weather data was remarkably similar to the degree of variation observed in simulated water requirement and yield using OWD at all locations (distribution of CVs in simulated water requirement and yield using AgMERRA weather data were within ±5% of the CV calculated for simulated water requirement and yield using observed weather data) except Torbat Jam, Torbat Heydarieh and Gonabad for water requirement and Mashhad, Kashmar and Ghoochan for yield. There was good agreement between long-term average yield simulated with AgMERRA weather data and long-term average yield simulated using observed weather data. For example, the distribution of simulated yields using AgMERRA data was within 10% of the simulated yields using observed data at all locations. Using AgMERRA weather data resulted in simulated crop water requirement that were not in close agreement with crop water requirement simulated with ground station data at two location including Gonabad and Torbat Heydarieh.
Conclusions: These results supported the use of uncorrected AgMERRA daily maximum and minimum temperature and solar radiation in areas that their weather stations only have a few years of daily weather records available or areas without weather station. Considering the advantage of continuous coverage and availability, use of AgMERRA dataset appears to be a promising option for simulation of long-term average yield and water requirement, as well as for assessing impact of climate change on crop production and also estimating the magnitude of existing gaps between yield potential and current average farm yield in Khorasan Razavi province. But they are not very reliable for accurate simulation of water requirement and yield in a specific year and estimate their inter-annual variation.

کلیدواژه‌ها [English]

• Crop Model
• Precipitation
• Regression
• Weather data

مراجع

1- Allen R.G., Pereira L.S., Raes D., and Smith D. 1998. Crop Evapotranspiration. FAO irrigation and drainage paper No.56. FAO, Rome, Italy.

2- 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: 9–16.

3- Bannayan M., Mansoori H., and Rezaei E.E. 2014. Estimating climate change, CO2 and technology development effects on wheat yield in northeast Iran. Biometeorology, 58: 395-405.

4- Bannayan M., Paymard P., and Ashraf B. 2016. Vulnerability of maize production under future climate change: possible adaptation strategies. Journal of Science of Food and Agriculture, 96: 4465-4474.

5- Battisti D.S., and Naylor R.L. 2009. Historical warnings of future food insecurity with unprecedented seasonal heat. Science, 323: 240–244.

6- Bondeau A., Smith P.C., Zaehle S., Schaphoff S., Lucht W., Cramer W., Gerten D., Lotze-Campen H., Müller C., Reichstein M., and Smith B. 2007. Modelling the role of agriculture for the 20th century global terrestrial carbon balance. Global Change Biology, 13: 679–706.

7- Boote K.J., Jones J.W., and Pickering N.B. 1996. Potential uses and limitations of crop models. Agronomy Journal, 88: 704–716.

8- Bosilovich M.G., Chen J., Robertson F.R., and Adler R.F .2008. Evaluation of global precipitation in reanalysis. Journal of Applied Meteorological Climatology, 47: 2279-2299.

9- Ceglar A., Toreti A., Balsamo G., and Kobayashi S. 2017. Precipitation over Monsoon Asia: a comparison of reanalyses and observations. Journal of Climate, 30(2): 465–476.

10- Daly C., Neilson R.P., and Phillips D.L. 1994. A statistical-topographic model for mapping climatological precipitation over mountainous terrain. Journal of Applied Meteorology, 33: 140-158.

11- Dehghan H., and Alizadeh A. 2012. Evaluation and calibration of different methods to estimate reference crop evapotranspiration under climatic data limitations (Case study: Khorasan Razavi province). Iranian Journal of Water and Soil, 26(1): 236-250. (in Persian with English abstract)

12- Dinku T., Connor S.J., Ceccato P., and Ropelewski C.F. 2008. Comparison of global gridded precipitation products over a mountainous region of Africa. International Journal of Climatology, 28: 1627–1638.

13- Eyshi Rezaie E., and Bannayan M. 2012. Rainfed wheat yields under climate change in northeastern Iran. Meteorological Applications, 19(3): 346-354.

14- FAO, 1992. CROPWAT; a Computer Program for Irrigation Planing and Management, FAO Irrigation and Drainage Paper No. 46 Food and Agriculture Organization, Rome.

15- Folberth C., Gaiser T., Abbaspour K.C., Schulin R., and Yang H. 2012. Regionalizationof a large-scale crop growth model for Sub-Saharan Africa Model setup,evaluation, and estimation of maize yields. Agriculture, Ecosystems and Environment, 151: 21–33.

16- Folberth C., Yang H., Gaiser T., Abbaspour K.C., and Schulin R. 2013. Modeling maizeyield responses to improvement in nutrient, water and cultivar inputs inSub-Saharan Africa. Agricultural Systems, 119: 22–34.

17- Foley J.A., Defries R., Asner G.P., Barford C., Bonan G., Carpenter S.R., Chapin F.S., Coe M.C., Daily G.C., Gibbs H.K., Helkowski J.H., Holloway T., Howard E.A., Kucharik C.J., Monfreda C., Patz J.A., Prentice I.C., Ramankutty N., and Synder P.K. 2005. Global consequences of land use. Science, 309: 570–574.

18- Grassini P., Van Bussel L.G.J., Van Wart J., Wolf J., Claessens L., Yang H., Boogaard H., de Groot H., Van Ittersum M.K., and Cassman K.G. 2015. How goodis good enough? Data requirements for reliable crop yield simulations andyield-gap analysis. Field Crops Research, 177: 49–63.

19- Hoogenboom G., Jones J.W., Wilkens P.W., Porter C.H., Boote K.J., Hunt L.A., Singh U., Lizaso J.I., White J.W., Uryasev O., Ogoshi R., Koo J., Shelia V., and Tsuji G.Y. 2014. Decision Support System for Agrotechnology Transfer (DSSAT) Version 4.6 (www.DSSAT.net). DSSAT foundation prosser, Washington.

20- Jensen M.E., and Allen R.G. 2016. Evaporation, evapotranspiration, and irrigation water requirements. ASCE manuals and reports on engineering practice No. 70, 2nd edn. American society of civil engineers, Reston.

21- Jones P.G., and Thornton P.K. 2013. Generating downscaled weather data from a suite of climate models for agricultural modelling applications. Agricultural Systems, 114: 1-5.

22- Joyce R.J., Janowiak J.E., Arkin P.A., and Xie P. 2004. CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. Journal of Hydrometeorology, 5: 487- 503

23- Kanamitsu M., Ebisuzaki W., Woolen J., Yang S., Hnilo J.J., Fiorino M., and Potter G.L. 2002. NCEP–DOE AMIP-II reanalysis (R-2). Bulletin of the American Meteorological Society. 83: 1631–1643.

24- Lashkari A., Bannayan M., Koocheki A., Alizadeh A., Choi Y.S., and Park S.K. 2016. Applicability of AgMERRA forcing dataset for gap-filling of in-situ meteorological observation, case study: Mashhad Plain. Journal of Water and Soil, 29(6): 1749-1758. (In Persian with English abstract)

25- Licker R., Johnston M., Foley J.A., Barford C., Kucharik C.J., Monfreda C., and Ramankutty N. 2010. Mind the gap: how do climate and agriculturalmanagement explain the ‘yield gap’ of croplands around the world? Global Ecology and Biogeography, 19: 769–782.

26- Lobell D. 2007. Changes in diurnal temperature range and national cereal yields. Agricultural and Forest Meteorology, 145: 229–238.

27- Lobell D.B., Burke M.B., Tebaldi C., Mastrandrea M.D., Falcon W.P., and Naylor R.L. 2008. Prioritizing climate change adaptation needs for food security in 2030. Science, 319: 607–610.

28- Miri M., Azizi G., Khoshakhlagh F., and Rahimi M. 2017. Evaluation statistically of temperature and precipitation datasets with observed data in Iran. Iran-Watershed Management Science & Engineering, 10(35): 39-50. (In Persian with English abstract)

29- Mohanty M., Sinha N.K., and Patra A.K. 2015. Crop Growth Simulation Models in Agricultural Crop Production. Pages 1-27 in Crop Growth Simulation Modelling and Climate Change. Mohanty, M., Sinha, N. K., Hati, K. M., Chaudhary, R. S., Patra, A.K. ed., Scientific Publishers, India.

30- New M., Lister D., Hulme M., and Makin I. 2002. A high-resolution data set of surfaceclimate over global land areas. Climate Research, 21: 1–25.

31- Quaye-Ballard J.A., An R., Ruan R., Adjei K.A., and Akorful-Andam S. 2013. Validation of climate research unit high resolution time-series rainfall data over three source region: results of 52 years. Advanced Materials Research, 26(73): 3542-3546.

32- Pilgrim D.H., Chapman T.G., and Doran D.G. 1998. Problems of rainfall-runoff modeling in arid and semiarid regions. Hydrological Sciences Journal, 33(4): 379-400.

33- Priestley C.H.B., and Taylor R.J. 1972. On the assessment of surface heat-flux and evaporation using large-scale parameters. Monthly Weather Review, 100: 81– 92.

34- Richardson C.W. 1981. Stochastic simulation of daily precipitation, temperature, and solar radiation. Water Resources Research, 17: 182-190.

35- 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.

36- Sadras V.O. 2003. Influence of size of rainfall events on water-driven processes. I.Water budget of wheat crops in south-eastern Australia. Australian Journal of Agricultural Research, 54: 341–351.

37- 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, 1-13.

38- Van Bussel L.G.J., Müller C., Van Keulen H., Ewert F., and Leffelaar P.A. 2011. The effect of temporal aggregation of weather input data on crop growth models’results. Agricultural and Forest Meteorology, 151: 607–619.

39- Van Ittersum M.K., Cassman K.G., Grassini P.G., Wolf J., and Tittonell P. 2013. Yield gap analysis with local to global relevance-a review. Field Crops Research, 143: 4–17.

40- Van Wart J., Kersebaum K.C., Peng S., Milner M., and Cassman K.G. 2013. A protocol for estimating crop yield potential at regional to national scales. Field Crops Research, 143: 34–43.

41- Van Wart J., Grassini P., Yang H., Claessens L., Jarvis A., and Cassman K.G. 2015. Creating long-term weather data from thin air for crop simulation modeling. Agricultural and Forest Meteorology, 209: 49-58.

42- White J.W., Hoogenboom G., Hoell P.W., and Stackhouse Jr P.W. 2008. Evaluation of NASA satellite- and assimilation model-derived long-term daily temperature data over the continental US. Agricultural and Forest Meteorology, 148: 1574–1584.

43- White J.W., Hoogenboom G., Stackhouse Jr P.W., and Hoell J.M. 2008. Evaluation of satellite-based, modeled-derived daily solar radiation data for the continental United States. Agronomy Journal, 103(4): 1242–1251.

44- Zhiming F., Dengwei L., and Yuehong Z. 2007. Water requirements and irrigation scheduling of spring maize using GIS and CropWat model in Beijing-Tianjin-Hebei region. Chinese Geographical Science, 7(1): 56-63.