دوماه نامه

نوع مقاله : مقالات پژوهشی

نویسندگان

1 دانشگاه تربیت مدرس

2 دانشگاه شهرکرد

3 دانشگاه گیلان

4 دانشگاه هرمزگان

چکیده

بهمنظور تبیین هرچه واقعبینانهتر فرآیندهای حاکم بر بیلان آب و انرژی و نیز فرآیندهای کیفیت آب و بیولوژیکی گیاه، نیاز به اطلاعات هواشناسی در مقیاس‌های زمانی کوچکتر از آنچه که در اغلب مناطق در دسترس است، می‌باشد. در پژوهش حاضر، چارچوبی فیزیکی‌بنیان به‌منظور ریزمقیاس‌سازی داده‌های هواشناسی روزانه مورد نیاز در برآورد تبخیر- تعرق مرجع زیرروزانه توسعه یافت. در این چارچوب، داده‌های هواشناسی روزانه گم شده با بهکارگیری یک الگوریتم مبتنی بر جستجو- بهینهسازی برآورد می‌شوند. همین‌طور، با استفاده از گونه یکپارچه‌سازی شده الگوریتم بهینه‌سازی رفتار جمعی اجزا (UPSO)، مدل‌های مختلف ریزمقیاس‌سازی داده‌های هواشناسی واسنجی می‌گردد. تشعشع خورشیدی روزانه و زیرروزانه از طریق روش عمومی و فیزیکی‌بنیان یانگ و همکاران برآورد می‌شود. به‌منظور ارزیابی عملکرد چارچوب فوق، از داده‌های بلندمدت سه ساعته و روزانه ایستگاه سینوپتیک آبادان (59 سال) و اهواز (50 سال) استفاده شد. نتایج نشان داد در مقایسه با مدل‌های WAVE I، WAVE II، WCALC، ERBS و ESRA، مدل TM با ضریب کارآیی مدل (EF) 9775/0 تا 9924/0 دارای بهترین عملکرد در ریزمقیاس‌سازی دمای هوای روزانه بود. همچنین، آن دسته از مدل‌های ریزمقیاس‌سازی دمای هوای روزانه که در آن‌ها زمان وقوع دمای حداقل و حداکثر به‌عنوان توابعی از زمان طلوع و غروب خورشید بیان می‌شود در مقایسه با مدل‌هایی که یک زمان قراردادی ثابت را برای رخدادهای یاد شده در نظر می‌گیرند از عملکرد بهتری در تبیین تغییرات زمانی دمای هوای زیرروزانه برخوردار بودند. مدل HUM III (که بر اساس ریزمقیاس‌سازی کسینوسی فشار بخار واقعی می‌باشد) با آماره EF در دامنه 7266/0 تا 8896/0 دارای بهترین عملکرد در ریزمقیاس‌سازی دمای نقطه شبنم، فشار بخار واقعی و رطوبت نسبی بود. مقادیر زیرروزانه سرعت باد با آماره EF در دامنه 3357/0 تا 6300/0 برآورد گردید. نتایج حاکی از انطباق بالای مقادیر مجموع 24 ساعته تشعشع خورشیدی زیرروزانه با مقادیر نظیر روزانه (EF بین 9729/0 تا 9801/0) بود. همچنین، برای مناطقی که مقادیر زیرروزانه اندازهگیری شده اطلاعات هواشناسی موجود نیست، استفاده از مدل‌های WAVE II و HUM II (که بر اساس ریزمقیاس‌سازی خطی رطوبت نسبی می‌باشد) قابل توصیه است. نتایج حاکی از لزوم واسنجی مدل گرین و کوزک برای ریزمقیاس‌سازی سرعت باد در مناطق مختلف بود.

کلیدواژه‌ها

عنوان مقاله [English]

Development of a Disaggregation Framework toward the Estimation of Subdaily Reference Evapotranspiration: 1- Performance Comparison of some Daily-to-subdaily Weather Data Disaggregation Models

نویسندگان [English]

  • Farzin Parchami-Araghi 1
  • seyed majid mirlatifi 1
  • Shoja Ghorbani Dashtaki 2
  • Majid Vazifehdoust 3
  • Adnan Sadeghi-Lari 4

1 Tarbiat Modares University, Tehran, Iran

2 Shahrekord University

3 Guilan University,Guilan, Iran

4 Hormozgan University, Bandar Abbas, Iran

چکیده [English]

Introduction: In order to provide more realistic representation of processes governing the water and energy balances as well as water quality and plant physiological processes, weather data are needed at finer timescales than currently are available at most regions. In this study, a physically based framework was developed to disaggregate daily weather data needed for estimation of subdaily reference evapotranspiration, including air temperature, wind speed, dew point, actual vapour pressure, relative humidity, and solar radiation. In this paper, the results of performance comparison of the utilized disaggregation approaches are presented.
Materials and Methods: In developed framework, missing daily weather data are filled by implementation of a search-optimization algorithm. Meanwhile, disaggregation models can be calibrated using Unified Particle Swarm Optimization (UPSO) algorithm. Daily and subdaily solar radiation is estimated, using a general physically based model proposed by Yang et al. (YNG model). Long-term daily and three-hourly weather data obtained from Abadan (59 years) and Ahvaz (50 years) synoptic weather stations were used to evaluate the performance of the developed framework. In order to evaluate the accuracy of the different disaggregation models, the mean error (ME), mean absolute error (MAE), root mean square error (RMSE), and Pearson correlation coefficient (r), and model efficiency coefficient (EF) statistics were calculated. Different contributions to the overall mean square error was decomposed, using a regression-based method.
Results and Discussion: The results indicated that compared to the WAVE I, WAVE II, WCALC, ERBS, and ESRA models, the calibrated TM model had the best performance to disaggregate daily air temperature with a EF of 0.9775 to 0.9924. Compared to air temperature disaggregation models with an arbitrary value for the time of maximum and minimum air temperature, the models in which the above mentioned times are described as a function of sunrise and/or sunset had better performance in describing the diurnal variations of the air temperature. HUM III model (based on cosinusoidal disaggregation of daily actual vapour pressure) had the best performance to disaggregate daily dew point, actual vapour pressure, and relative humidity with an EF of 0.7266 to 0.8896. In addition, subdaily wind speeds were predicted with an EF of 0.3357 to 0.6300. The results showed high agreement between daily and sum-of-subdaily solar radiation (with an EF of 0.9801 to 0.9729). The use of the WAVE II and HUM II (based on linear disaggregation of relative humidity) models can be recommended for the regions with no subdaily weather data needed for calibration of the weather data disaggregation models. The results indicate the need for calibration of Green and Kozek model for disaggregation of the daily wind speed at different regions.

کلیدواژه‌ها [English]

  • Evapotranspiration
  • Physically-base Method
  • Subdaily Solar Radiation
  • Unified Particle Swarm Optimization
1- Allen R.G., Walter I.A., Elliott R.L., Howell T.A., Itenfisu D., Jensen M.E., and Snyder R.L. 2005. The ASCE standardized reference evapotranspiration equation. American Society of Civil Engineers, Reston, Virginia, p 192.
2- Ångström A. 1924. Solar and terrestrial radiation. Quarterly Journal of the Royal Meteorological Society, 50: 121– 125.
3- Baigorria G.A., and Bowen W.T. 2001. A process-based model for spatial interpolation of extreme temperatures and solar radiation. p. 1–9. In: Methodologies for Interdisciplinary Multiple Scale Perspectives. Proceedings of the SAAD III Third International Symposium on Systems Approaches for Agricultural Development, Lima, Peru.
4- Bechini L., Ducco G., Donatelli M., and Stein A. 2000. Modelling, interpolation and stochastic simulation in space and time of global solar radiation. Agriculture, Ecosystems and Environment, 81: 29– 42.
5- Bilbao J., Miguel A., and Kambezidis H.D. 2002. Air temperature model evaluation in the north Mediterranean Belt area. Journal of Applied Meteorology, 41:872–884.
6- Bristow K.L., and Campbell G.S. 1984. On the relationship between incoming solar radiation and daily maximum and minimum temperature. Agricultural and Forest Meteorology, 31: 159– 166.
7- Cesaraccio C., Spano D., Duce P., and Snyder R.L. 2001. An improved model for determining degree-day values from daily temperature data. International Journal of Biometeorology, 45: 161–169.
8- de Wit C.T. 1978 Simulation of assimilation, respiration and transpiration of crops. Wageningen, Pudoc, p 148.
9- de Wit C.T., Goudriaan J., and van Laar H.H. 1978. Simulation of Simulation, Respiration and Transpiration of Crops. Pudoc. Wageningen, The Netherlands, 148 pp.
10- Debele B., Srinivasan R., and Parlange J.Y. 2007. Accuracy evaluation of weather data generation and disaggregation methods at finer timescales. Advances in Water Resources, 30:1286–1300.
11- Ehnberg J.S.G., and Bollen M.H.J. 2005. Simulation of global solar radiation based on cloud observations. Solar Energy, 78: 157– 162.
12- Erbs D.G. 1984. Models and applications for weather statistics related to building heating and cooling loads. Ph.D. thesis, Mechanical Engineering Department, University of Wisconsin-Madison, Wisconsin, Madison, 336 pp.
13- ESRA. 2000. European solar radiation atlas (ESRA). In: Scharmer K, Greif J (eds) Database and exploitation software, vol. 2. Commission of the European Communities, Ecole des Mines de Paris, France.
14- Gauch H.G., Hwang J.T.G., and Fick G.W. 2003. Model evaluation by comparison of model-based predictions and measured values. Agronomy Journal, 95: 1442–1446.
15- Green H.M., and Kozek A.S. 2003. Modelling Weather Data by Approximate Regression Quantiles. Australian and New Zealand Industrial and Applied Mathematics Journal (E), 44: C229-C248.
16- Gutierrez-Magness A.L., and McCuen R.H. 2004. Accuracy evaluation of rainfall disaggregation methods. Journal of Hydrologic Engineering, 9(2):71–78.
17- Hua L.J., Ma Z.G., and Guo W.D. 2008. The impact of urbanization on air temperature across China. Theoretical and Applied Climatology, 93: 179–194.
18- LADSS. 2004. Climate data ‘‘cleaning’’ process. Available at http://www.macaulay.ac.uk /LADSS/documents/data_cleaning_process. pdf (visited 5 September 2013).
19- Medellu C.S., Marsoedi S., and Berhimpon S. 2012. The Influence of Opening on the Gradient and Air Temperature Edge Effects in Mangrove Forests. International Journal of Basic and Applied Sciences IJBAS-IJENS, 12(2): 53-57.
20- Meteotest. 2003. Meteonorm version 5.0. The global meteorological data base for engineers, planners and education. Software and data on CD-ROM. James and James, London.
21- Moriasi D.N., Arnold J.G., Van Liew M.W., Bingner R.L., Harnel R.D., and Veith T.L. 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transaction of the ASAE, 50: 885-900.
22- Nash J.E., and Sutcliffe J.V. 1970. River Flow Forecasting Through ConceptualModels- Part I- A Discussion of Principles. journal of Hydrology, 10 (3):282-290.
23- Parchami-Araghi F., Mirlatifi S.M., Ghorbani Dashtaki Sh., and Mahdian M.H. 2013. Point estimation of soil water infiltration process using Artificial Neural Networks for some calcareous soils. Journal of Hydrology, 481: 35–47.
24- Parsopoulos K.E., and Vrahatis M.N. 2004. UPSO: a unified particle swarm optimization scheme. In Simos, T., and Maroulis G. (Eds.), Lecture Series on Computer and Computational Sciences (Vol. 1, pp. 868-873). Zeist, The Netherlands: VSP International Science Publishers.
25- Parton W.J., and Logan J.A. 1981. A model for diurnal variation in soil and air temperature. Agricultural and Forest Meteorology, 23:205-216.
26- Podesta G.P., Nunez L., Villanueva C. A., and Skansi M. A. 2004. Estimating daily solar radiation in the Argentine Pampas. Agricultural and Forest Meteorology, 123: 41– 53.
27- Pomeroy J.W., Toth B., Granger R.J., Hedstrom N.R., and Essery R.L.H. 2003. Variation in surface energetics during snowmelt in a subarctic mountain catchment. Journal of Hydrometeorology, 4: 702– 719.
28- Prescott J.A. 1940. Evaporation from water surface in relation to solar radiation, Transactions of the Royal Society of South Australia, 64: 114– 125.
29- Reicosky D.C., Winkelman L.J., Baker J.M., and Baker D.G. 1989. Accuracy of hourly air temperatures calculated from daily minima and maxima. Agricultural and Forest Meteorology, 46: 193-209.
30- Revfeim K.J.A. 1997. On the relationship between radiation and mean daily sunshine. Agricultural and Forest Meteorology, 86: 181–191.
31- Safeeq M., and Fares A. 2011. Accuracy evaluation of ClimGen weather generator and daily to hourly disaggregation methods in tropical conditions. Theoretical and Applied Climatology, 106: 321–341.
32- Tang W.J., Yang K., Qin J., Cheng C.C.K., and He J. 2011. Solar radiation trend across China in recent decades: a revisit with quality-controlled data. Atmospheric Chemistry and Physics, 11: 393–406.
33- Teh C.B.S. 2006. Introduction to mathematical modeling of crop growth: How the equations are derived and assembled into a computer program. Boca Raton: Brown Walker Press, 256p.
34- United Nations Educational, Scientific and Cultural Organization (UNESCO). (1979). Map of the world distribution of arid regions: Map at scale 1:25,000,000 with explanatory note. MAB Technical Notes 7, UNESCO, Paris.
35- Waichler S.R., and Wigmosta M.S. 2003. Development of hourly meteorological values from daily data and significance to hydrological modeling at H. J. Andrews Experimental Forest. Journal of Hydrometeorology., 4:251–263.
36- Wilkerson G.G., Jones J.W., Boote K.J., Ingram K.T., and Mishoe J.W. 1983. Modeling soybean growth for crop management. Transactions of ASAE, 26: 63-73.
37- Yang K., He J., Tang W.J., Qin J., and Cheng C.C.K. 2011. On downward shortwave and longwave radiations over high altitude regions: Observation and modeling in the Tibetan Plateau. Agricultural and Forest Meteorology 150: 38–46.
38- Yang K., Koike T., and Ye B. 2006. Improving estimation of hourly, daily, and monthly solar radiation by importing global data sets. Agricultural and Forest Meteorology, 137: 43-55.
CAPTCHA Image