Use of Ensemble Methods for Improving Accuracy of Remotely Sensed-derived Actual Evapotranspiration of Global Databases Case Study: (Karkheh Dam Watershed)
Subject Areas : Farm water management with the aim of improving irrigation management indicators
Jalal Yarahmadi
1
(Department of water Engineering and Management , Tarbiat Modares University, Tehran, Iran)
Ali Shamsoddini
2
(Department of remote sensing and GIS, Faculty of Humanities, Tarbiat Modares University, Tehran, Iran)
seyed majid mirlatifi
3
(Department of water Engineering and Management , Tarbiat Modares University, Tehran, Irandares University, Tehran, Iran)
Majid Delavar
4
(Department of water Engineering and Management , Tarbiat Modares University, Tehran, Iran)
Keywords: SVR, Global Databases, GLEAM, SWAT, Evapotranspiration,
Abstract :
Actual Evapotranspiration is one of the effective components in the hydrological cycle. Therefore, accurate estimation of this component at the watershed scale has an important role in the management of available water resources. In this study, the accuracy of actual evapotranspiration product values of five global terrestrial databases including MOD16, MYD16, SSEBOP, GLEAM, GLDAS was compared to the evapotranspiration values predicted from the SWAT model simulation in the Karkheh dam watershed in 2006, 2008 and 2011 which are low/high and medium rainfall respectively on a monthly scale. Then, the feasibility of improving the accuracy of evapotranspiration values obtained from these databases was investigated in eight different scenarios using simple averaging, M5 and SVR models as ensemble methods. The results showed that although actual evapotranspiration products are able to explain the trend of time changes of actual evapotranspiration in catchment, they are significantly different with the output values of the SWAT model as observation values at a significant level of 5%. The results also indicated the use of simple averaging has no effect on improving the results at the Karkheh dam watershed. However, the use of the other two ensemble methods improves the accuracy of actual evapotranspiration and the ensemble method explains 80% of the SWAT-derived actual evapotranspiration variations. Moreover, the ensemble model derived from SVR fed by the attributes of the superior data combination scenario, reduced the estimation error by about 44% compared to that derived from the best global terrestrial product which was GLEAM in this study.
امامیفر، س. 1391. مقایسه روشهای سنجش از دور و زمین آمار برای برآورد منطقهای دما ی هوا ، پایانامه کارشناسی ارشد دانشگاه تهران، 112ص.
دلاور، م.، حاجی حسینی.، ح. و مرید، س. 1396. گزارش مدلسازی سیستم منابع آب و کشاورزی حوضه آبریز کرخه طرح تهیه مدل جامع شبیهسازی هیدرولوژیکی حوضه کرخه جهت ارزیابی اثرات تغییرات محیطی بر آبدهی در بالادست سد کرخه و توسعه سامانه پیش بینی بلند مدت جریان. سازمان آب و برق خوزستان.
رحیمیخوب، ع. و مرادی، م.ا. 1391. برآورد تبخیر و تعرق مرجع با استفاده از تصاویر ماهواره نوا و مدل درختی M5 برای شبکه های آبیاری - مطالعه موردی شبکه آبیاری قزوین. نشریه علوم و فنون کشاورزی و منابع طبیعی.
فرجی، ز.، کاویانی، ع. و شکیبا، ع. 1396. ارزیابی داده های تبخیر– تعرق، بارش و دمای هوای حاصل از مدل سطح زمین (GLDAS) با استفاده از داده های مشاهداتی در استان قزوین. نشریه پژوهش های حفاظت آب و خاک، 24(3).
Abiodun O O, Guan H, Post VE A, and Batelaan O .(2018) Comparison of MODIS and SWAT evapotranspiration over a complex terrain at different spatial scales. Hydrology and Earth System Sciences 22: 2775–2794.
Aguilar AL, Flores H, Crespo G, Marín M I, Campos I and Calera A. (2018) Performance Assessment of MOD16 in Evapotranspiration Evaluation in Northwestern. Mexico. Water 10, 901.
Allen RG, Pereira LS, Howell TA, and Jensen ME. (2013) Evapotranspiration information reporting: I. Factors governing measurement accuracy. Agricultural Water Management 98: 899–920.
Arruda Souza V, Roberti DR, Ruhoff AL, Zimmer T, Adamatti D, Gustavo J. and Osvaldo LL. (2019) Evaluation of MOD16 Algorithm over Irrigated Rice Paddy Using Flux Tower Measurements in Southern Brazil. Water .
Anderson MC, Norman JM, Mecikalski JR, Otkin JA and Kustas WP. (2007) A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 1. Model formulation. Journal of geophysical research 112: 101-117.
Boser BE, Guyon IM and VN Vapnik (1992) A training algorithm for optimal margin classifiers. In D.Haussler, editor, 5th Annual ACM Workshop on COLT, pages 144-152, Pittsburgh, PA.
Diak G, Kustas WP, and Moran MS. (2008) Evapotranspiration: Remote Sensing.
Dibike Y. B, Velickov S, Solomatine D. P. and Abbott M. (2001) Model induction with support vector machines: introduction and applications. Journal of Computing Civil Engineering, 15(3): 208-216.
Fana J, Wua J, Fucang Z, Huanjie C, Wenzhi Z, Xiukang W, (2019) Empirical and machine learning models for predicting daily global solar radiation from sunshine duration: A review and case study in China. Renewable and Sustainable Energy Reviews, 100: 186–212.
Fao (2008) Food and agriculture organization of united nations. Aquastat Database from Water Report 34:220-232.
Funk C, Peterson P, Landsfeld M, Pedreros D, Verdin J, Rowland J, Romero B, Husak G, Michaelsen, J and Verdin P. (2014) A Quasi-Global Precipitation Time Series for Drought Monitoring Data Series 832, 4p. Technical report.
Gao Y, Long D. (2008) Intercomparison of remote sensing-based models for estimation of evapotranspiration and accuracy assessment based on SWAT, Hydrological Processes 22(25): 4850 4869.
Hain CR, Crow WT, Mecikalski JR, Anderson MC and Holmes T. (2011) An intercomparison of available soil moisture estimates from thermal infrared and passive microwave remote sensing and land surface modeling. Journal of Geophysical Research.
He x, Xu T, Xia Y and Bateny s, (2020) A Bayesian Three-Cornered Hat (BTCH) Method: Improving the Terrestrial Evapotranspiration Estimation. Remote Sensing 12: 878.
Hofste RW(2014) Comparative analysis among near-operational evapotranspiration products for the Nile Basin based on earth observations. Master thesis, Delft University of Technology, Delft, The Netherlands.
Jain SK, Tagi J, Singh V. (2010) Simulation of runoff and sediment yield for a Himalayan Watershed using SWAT Model, Journal of Water Resource and Protection 2:267-281.
Jongjina B, Jaehwanb J, Jongminc P And Minhad C. (2019) A study on the analyzing of uncertainty for actual evapotranspiration: flux tower, satellite-based and reanalysis based dataset. Journal of Korea. Water Resources Association 52: 11-19.
Liu S, Xu Z, Zhu Z, Jia Z , and Zhu M. (2013) Measurements of evapotranspiration from eddy-covariance systems and large aperture scintillometers in the Hai River Basin China. Journal of Hydrology 487:24–38.
Liu X, Liu C and Brutsaert W. (2018) Investigation of a generalized nonlinear form of the complementary principle for evaporation estimation. Journal of Geophysical Research: Atmospheres 123: 3933–3942.
Long D, Longuevergne L and Scanlon BR. (2014) Uncertainty in evapotranspiration from land surface modeling, remote sensing, and GRACE satellites. Water Resources Research 50:1131–115.
López O, Houborg R and McCabe MF. (2016) Evaluating the hydrological consistency of evaporation products using satellite-based gravity and rainfall data. Hydrology and Earth System Sciences 21: 323–343.
Mianabad A, Alizadeh A, Sanaeinejad SH, Gahreman B, Davari K. (2016) Evaluation of SEBAL algorithm for estimating actual evapotranspiration of Neishabour-Rokh basin using SWAT model, Journal of Water Research in Agriculture 30(4) 525-54.
Miralles DG, De Jeu RAM, Gash JH, Holmes TRH and Dolman AJ. (2011) An application of GLEAM to estimating global evaporation. Hydrology Earth System Sciences Discussion 8: 1–27.
Moriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD, Veith TL(2017) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations, American Society of Agricultural and Biological Engineers, 50:885–900.
Mu Q, Zhao M, and Running S. W. (2011). Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sensing of Environment 115: 1781-1800.
Musselman, R.C; and Minnick T. J. (2000). Nocturnal stomatal conductance and ambient air quality standards for azone. Atmos. Environ. 34: 719–733.
Neitsch SL, Arnold JG, Kiniry JR, Srinivasan R and Williams JR. (2011) Soil and Water Assessment Tool User Manual, Version 2012, Grassland, Soil and Water Research Laboratory, Temple, Tex, USA.
Noori R, Abdoli M. A, Ameri A. and Jalili Ghazizade M. (2009). Prediction of municipal solid waste generation with combination of support vector machine and Principal component analysis: a case study of Mashhad. Environ. Prog.Sustain. Energy 28: 249-258.
Onuşluel Gül G, Rosbjerg D. (2010) Modeling of hydrologic processes and potential response to climate change through the use of a multisite SWAT, Water and Environment Journal, 24(1): 21–31.
Paca VH, Espinoza-Dávalos GE, Hessels TM, Moreira DM Comair GF and Bastiaanssen WGM (2019) The spatial variability of actual evapotranspiration across the Amazon River Basin based on remote sensing products validated with flux towers. Ecological Processes, 8(6):1-20.
Parinussa RM, Yilmaz MT, Anderson MC, Hain CR and de Jeu RM (2014) An intercomparison of remotely sensed soil moisture products at various spatial scales over the berian Peninsula. Hydrological Processes 28(18):4865–4876.
Pan, S. Pan, N. Tian, H. Friedlingstein, P. Sitch, S. Shi, H. Arora, V. K. Haverd, V. Jain, A.K. and Running, S. W. (2020). Evaluation of global terrestrial evapotranspiration using state-ofthe-art approaches in remote sensing, machine learning and land surface modeling. Hydrology andEarth System Sciences, 24: 1485–1509.
Quinlan JR,(1986) Introduction of decision trees. Machine Learning 1:81-106.
Quinlan JR,(1992) Learning with continuous classes. Proceedings of the Fifth Australian Joint Conference on Artificial Intelligence, Hobart, Australia, 16-18 November, World Scientific, Singapore pp. 343–348.
Ramoelo, A. Majoz. N. Mathieu, R. Jovanovic, N. Nickles, A. and Dzikiti, S. (2014). Validation of Global Evapotranspiration Product (MOD16) using Flux Tower Data in the African Savanna, South Africa. Remote Sensing, 6: 7406-7423
Reitz M, Senay GB, and Sanford WE(2017) Combining remote sensing and water-balance evapotranspiration estimates for the conterminous United States. Remote Sensing 9: 1181-1197.
Rodell M, Beaudoing HK, Lecuyer TS, Olson WS Famiglietti J S, Houser PR, Adler R, Bosilovich, MG, Clayson CA and Chambers D, (2015) The observed state of the water cycle in the early twenty-first century. Journal of Climate 28:82-98.
Sharma DN, and Tare1 V, (2018) Evaporation estimation using SSEBOP method SENTINEL2 and LANDSAT-8 dataset. Remote Sensing and Spatial Information Sciences 5:20-25.
Vapnik VN, (1995) The Nature of Statistical Learning Theory. Springer, New York. 314 pp.
_||_