Comparison Pan Evaporation Data with Global Land-surface Evaporation GLEAM in Java and Bali Island Indonesia

https://doi.org/10.22146/ijg.30926

Trinah Wati(1*), Ardhasena Sopaheluwakan(2), fatkhuroyan fatkhuroyan(3)

(1) Indonesia Agency for Meteorology Climatology and Geophysics (BMKG)
(2) Indonesia Agency for Meteorology Climatology and Geophysics (BMKG)
(3) Indonesia Agency for Meteorology Climatology and Geophysics (BMKG)
(*) Corresponding Author

Abstract


This paper evaluates the variability of pan evaporation (Epan) data in Java and Bali during 2003-2012 and compares to GLEAM (Global Land-surface Evaporation: the Amsterdam Methodology) data version v3.b namely actual evaporation (E) and potential evaporation (Ep) in the same period with statistical method. Gleam combines a wide range of remotely sensed observations to the estimation of terrestrial evaporation and root-zone soil moisture at a global scale (0.25-degree). The aim is to assess the accuracy of Gleam data by examining correlation, mean absolute error, Root mean square error and mean error between Epan and Gleam data in Java and Bali Island. The result shows the correlation between Epan with Ep Gleam is higher than Epan with E Gleam. Generally, the accuracy of Gleam data is a good performance to estimate the land evaporation in Java and Bali at annual and monthly scale. In daily scale, the correlation is less than 0.50 both between Epan with E Gleam and between Epan with Ep Gleam. In daily scale, the average errors ranging from 0.15 to 3.09 mm according to RMSE, MAE, and ME.The result of this study is essential in providing valuable recommendation for choosing alternative evaporation data in regional or local scale from satellite data.



Keywords


Pan evaporation; GLEAM evaporation; Java and Bali

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References

Abtew, W., Obeysekera, J., & Iricanin, N. (2011). Pan evaporation and potential evapotranspiration trends in South Florida. Hydrological Processes, 25(6), 958–969. https://doi.org/10.1002/hyp.7887

Aldrian, E., & Dwi Susanto, R. (2003). Identification of three dominant rainfall regions within Indonesia and their relationship to sea surface temperature. International Journal of Climatology, 23(12), 1435–1452. https://doi.org/10.1002/joc.950

Alexandridis, T. K., Cherif, I., Chemin, Y., Silleos, G. N., Stavrinos, E., & Zalidis, G. C. (2009). Integrated methodology for estimating water use in mediterranean agricultural areas. Remote Sensing, 1(3), 445–465. https://doi.org/10.3390/rs1030445

Allen, R. G., Tasumi, M., Morse, A., Trezza, R., Wright, J. L., Bastiaanssen, W., … Robison, C. W. (2007). Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC) - Applications. Journal of Irrigation and Drainage Engineering, 133(4), 395–406. https://doi.org/10.1061/(ASCE)0733-9437(2007)133:4(395)

Brutsaert, W., & Parlange, M. B. (1998). Hydrologic cycle explains the evaporation paradox [8]. Nature, 396(6706), 30. https://doi.org/10.1038/23845

Cleugh, H. A., Leuning, R., Mu, Q., & Running, S. W. (2007). Regional evaporation estimates from flux tower and MODIS satellite data. Remote Sensing of Environment, 106(3), 285–304. https://doi.org/10.1016/j.rse.2006.07.007

Courault, D., Seguin, B., & Olioso, A. (2005). Review on estimation of evapotranspiration from remote sensing data: From empirical to numerical modeling approaches. Irrigation and Drainage Systems, 19(3), 223–249.

Fisher, J. B., Whittaker, R. J., & Malhi, Y. (2011). ET come home: Potential evapotranspiration in geographical ecology. Global Ecology and Biogeography, 20(1), 1–18. https://doi.org/10.1111/j.1466-8238.2010.00578.x

Gibson, L. A., Jarmain, C., Su, Z., & Eckardt, F. E. (2013). Estimating evapotranspiration using remote sensing and the surface energy balance system - A South African perspective. Water SA, 39(4), 477–484. https://doi.org/Doi 10.4314/Wsa.V39i4.5

Glenn, E. P., Huete, A. R., Nagler, P. L., Hirschboeck, K. K., & Brown, P. (2007). Integrating Remote Sensing and Ground Methods to Estimate Evapotranspiration. Critical Reviews in Plant Sciences, 26(3), 139–168. https://doi.org/10.1080/07352680701402503

Jia, Z., Liu, S., Xu, Z., Chen, Y., & Zhu, M. (2012). Validation of remotely sensed evapotranspiration over the Hai River Basin, China. Journal of Geophysical Research Atmospheres, 117(13), 1–21. https://doi.org/10.1029/2011JD017037

Jin, X., Guo, R., & Xia, W. (2013). Distribution of actual evapotranspiration over Qaidam basin, an Arid area in China. Remote Sensing, 5(12), 6976–6996. https://doi.org/10.3390/rs5126976

Kalma, J. D., McVicar, T. R., & McCabe, M. F. (2008). Estimating land surface evaporation: A review of methods using remotely sensed surface temperature data. Surveys in Geophysics, 29(4–5), 421–469. https://doi.org/10.1007/s10712-008-9037-z

Li, Z. L., Tang, R., Wan, Z., Bi, Y., Zhou, C., Tang, B., … Zhang, X. (2009). A review of current methodologies for regional Evapotranspiration estimation from remotely sensed data. Sensors, 9(5), 3801–3853. https://doi.org/10.3390/s90503801

Lim, W. H., & Roderick, M. L. (2009). An Atlas of the Global Water Cycle.

Liu, B., Ma, Z., Xu, J., & Xiao, Z. (2009). Comparison of pan evaporation and actual evaporation estimated by land surface model in Xinjiang from 1960 to 2005. Journal of Geographical Sciences, 19(4), 502–512. https://doi.org/10.1007/s11442-009-0502-5

Martens, B., Miralles, D. G., Lievens, H., Schalie, R. Van Der, Jeu, R. A. M. De, Fernández-prieto, D., … Verhoest, N. E. C. (2016). GLEAM v3 : satellite-based land evaporation and root-zone soil moisture, (August), 1–36. https://doi.org/10.5194/gmd-2016-162

Mayer, D., Steiner, A., & Steinacker, R. (2012). Innovations and applications of the VERA quality control. Geoscientific Instrumentation, Methods and Data Systems, 1(2), 135–149. https://doi.org/10.5194/gi-1-135-2012

Mehta, V. M., DeCandis, A. J., & Mehta, A. V. (2005). Remote-sensing-based estimates of the fundamental global water cycle: Annual cycle. Journal of Geophysical Research Atmospheres, 110(22), 1–14. https://doi.org/10.1029/2004JD005672

Miralles, D. G., De Jeu, R. A. M., Gash, J. H., Holmes, T. R. H., & Dolman, A. J. (2011). Magnitude and variability of land evaporation and its components at the global scale. Hydrology and Earth System Sciences, 15(3), 967–981. https://doi.org/10.5194/hess-15-967-2011

Miralles, D. G., Holmes, T. R. H., Jeu, R. A. M. De, Gash, J. H., Meesters, A. G. C. A., & Dolman, A. J. (2011). Global land-surface evaporation estimated from satellite-based observations. Hydrology and Earth System Sciences, (2), 453–469. https://doi.org/10.5194/hess-15-453-2011

Miralles, D. G., Jeu, R. A. M. De, Gash, J. ., Holmes, T. R. ., & Dolman, A. . (2011). An application of GLEAM to estimating global evaporation, 1–27. https://doi.org/10.5194/hessd-8-1-2011

Miralles, D. G., Jiménez, C., Jung, M., Michel, D., Ershadi, A., Mccabe, M. F., … Fernández-Prieto, D. (2016). The WACMOS-ET project - Part 2: Evaluation of global terrestrial evaporation data sets. Hydrology and Earth System Sciences, 20(2), 823–842. https://doi.org/10.5194/hess-20-823-2016

Mu, Q., Zhao, M., & Running, S. W. (2011). Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sensing of Environment, 115(8), 1781–1800. https://doi.org/10.1016/j.rse.2011.02.019

Murray, R. S., Nagler, P. L., Morino, K., & Glenn, E. P. (2009). An Empirical Algorithm for Estimating Agricultural and Riparian Evapotranspiration Using MODIS Enhanced Vegetation Index and Ground Measurements of ET. II. Application to the Lower Colorado River, U.S. Remote Sensing, 1(4), 1125–1138. https://doi.org/10.3390/rs1041125

Nagler, P. L., Cleverly, J., Glenn, E., Lampkin, D., Huete, A., & Wan, Z. (2005). Predicting riparian evapotranspiration from MODIS vegetation indices and meteorological data. Remote Sensing of Environment, 94(1), 17–30. https://doi.org/10.1016/j.rse.2004.08.009

Roderick, M. L., & Farquhar, G. D. (2004). Changes in Australian pan evaporation from 1970 to 2002. International Journal of Climatology, 24(9), 1077–1090. https://doi.org/10.1002/joc.1061

Ruhoff, A. L., Paz, A. R., Collischonn, W., Aragao, L. E. O. C., Rocha, H. R., & Malhi, Y. S. (2012). A MODIS-based energy balance to estimate Evapotranspiration for clear-sky days in Brazilian tropical savannas. Remote Sensing, 4(3), 703–725. https://doi.org/10.3390/rs4030703

Schönwiese, C.-D., & Rapp, J. (1997). Climate Trend Atlas of Europe Based on Observations 1891-1990. SPRIGER-SCIENCE+Business MEDIA, B.V.

Stanhill, G. (2002). Is the class A evaporation pan still the most practical and accurate meteorological method for determining irrigation water requirements? Agricultural and Forest Meteorology, 112(3–4), 233–236. https://doi.org/10.1016/S0168-1923(02)00132-6

Supari, Sudibyakto, Ettema, J., & Aldrian, E. (2012). SPATIOTEMPORAL CHARACTERISTICS OF EXTREME RAINFALL EVENTS OVER JAVA ISLAND, INDONESIA. Case: East Java Province, 44(1). Retrieved from http://itc.nl/library/papers_2012/msc/aes/supari.pdf

Teuling, A. J., Hirschi, M., Ohmura, A., Wild, M., Reichstein, M., Ciais, P., … Seneviratne, S. I. (2009). A regional perspective on trends in continental evaporation. Geophysical Research Letters, 36(2), 1–5. https://doi.org/10.1029/2008GL036584

Tian, J., Su, H., Sun, X., Chen, S., He, H., & Zhao, L. (2013). Impact of the spatial domain size on the performance of the ts-vi triangle method in terrestrial evapotranspiration estimation. Remote Sensing, 5(4), 1998–2013. https://doi.org/10.3390/rs5041998

Wang, K., & Dickinson, R. E. (2012). A review of global terrestrial evapotranspiration: Observation, modeling, climatology, and climatic variability. Reviews of Geophysics, 50(2). https://doi.org/10.1029/2011RG000373

Wati, T. (2015). Kajian Evaporasi Pulau Jawa dan Bali Berdasarkan Data Pengamatan 1975-2013. IPB Bogor University of Agriculture. Retrieved from http://repository.ipb.ac.id/handle/123456789/79057

Wijngaard, J. B., Klein Tank, A. M. G., & Können, G. P. (2003). Homogeneity of 20th century European daily temperature and precipitation series. International Journal of Climatology, 23(6), 679–692. https://doi.org/10.1002/joc.906

Zhang, K., Kimball, J. S., Nemani, R. R., & Running, S. W. (2010). A continuous satellite-derived global record of land surface evapotranspiration from 1983 to 2006. Water Resources Research, 46(9), 1–21. https://doi.org/10.1029/2009WR008800

Zhou, H., Liu, S., Sun, R., Li, M., & Lu, L. (2005). Estimation of regional evapotranspiration in the Mu Us Sandland. International Geoscience and Remote Sensing Symposium (IGARSS), 6(1963), 4419–4421. https://doi.org/10.1109/IGARSS.2005.1525899



DOI: https://doi.org/10.22146/ijg.30926

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