Quantile Mapping for Bias Adjustment of Gridded Monthly Reference Crop Evapotranspiration (ETo) in Northern Thailand
Keywords:
bias adjustment , reference crop evapotranspiration , Quantile Mapping , CHELSAAbstract
Background and Objectives : Reference crop evapotranspiration (ETo) is essential for water management, especially for determining irrigation requirements and planning water allocation. Due to the limited number of ETo monitoring stations in Thailand, high-resolution gridded ETo datasets, such as CHELSA and TerraClimate, serve as alternative data sources. However, these datasets may contain biases. This research aims to correct the bias in gridded ETo data in northern Thailand using the Quantile Mapping method.
Methodology : The probability distribution of the ETo data was tested. Monthly ETo data from seven meteorological stations in northern Thailand were used as reference data for the bias correction of the CHELSA and TerraClimate gridded ETo datasets via the Quantile Mapping method. The reliability of the gridded ETo datasets before and after correction was evaluated using percent bias (PBIAS) and correlation coefficient (r).
Main Results : Results showed that all ETo data followed a Gumbel distribution. The strong positive correlation between station and gridded data (r > 0.8) indicated high precision. Before correction, both CHELSA and TerraClimate overestimated ETo compared to observations. Bias correction successfully reduced the PBIAS to within ±10%, indicating good accuracy.
Conclusions : The study concluded that Quantile Mapping effectively corrects bias in gridded ETo data, enhancing their reliability for applications such as irrigation planning, drought monitoring, and related studies.
References
A, Y., Wang, G., Liu, T., Xue, B., & Kuczera, G. (2019). Spatial variation of correlations between vertical soil water and evapotranspiration and their controlling factors in a semi-arid region. Journal of Hydrology, 574, 53–63. https://doi.org/10.1016/j.jhydrol.2019.04.023
Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. , & Hegewisch, K. C. (2018). TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015. Scientific Data, 5, 1–12. https://doi.org/10.1038/sdata.2017.191
Alam, M. A., Emura, K., Farnham, C., & Yuan, J. (2018). Best-fit probability distributions and return periods for maximum monthly rainfall in Bangladesh. Climate, 6(1), 9. http://doi.org/10.3390/cli6010009
Allen, R. G., Pereira, L. S., Raes, D.,& Smith, M. (1998). FAO Irrigation and Drainage Paper No. 56: Crop Evapotranspiration (guidelines for computing crop water requirements). Food and Agriculture Organization of the United Nations.
Ansari, R., Usman Liaqat, M., & Grossi, G. (2022). Evaluation of gridded datasets for terrestrial water budget assessment in the Upper Jhelum River Basin-South Asia. Journal of Hydrology, 613, 128294. https://doi.org/10.1016/j.jhydrol.2022.128294
Bennett, J. C., Grose, M. R., Corney, S. P., White, C. J., Holz, G. K., Katzfey, J. J., Post, D. A.,& Bindoff, N. L. (2013). Performance of an empirical bias-correction of a high-resolution climate dataset. International Journal of Climatology, 34(7), 2189–2204. https://doi.org/10.1002/joc.3830
Blankenau, P. A., Kilic, A., & Allen, R. (2020). An evaluation of gridded weather data sets for the purpose of estimating reference evapotranspiration in the United States. Agricultural Water Management, 242, 106376. https://doi.org/10.1016/j.agwat.2020.106376
Cruz-Blanco, M., Lorite, I. J., & Santos, C. (2014). An innovative remote sensing based reference evapotranspiration method to support irrigation water management under semi-arid conditions. Agricultural Water Management, 131, 135–145. https://doi.org/10.1016/j.agwat.2013.09.017
Davis, S. L., & Dukes, M. D. (2010). Irrigation scheduling performance by evapotranspiration-based controllers. Agricultural Water Management, 98(1), 19–28. https://doi.org/10.1016/j.agwat.2010.07.006
De Keyser, J., Hayes, D. S., Marti, B., Siegfried, T., Seliger, C., Schwedhelm, H., Anarbekov, O., Gafurov, Z., López Fernández, R. M., Ramos Diez, I., Alapfy, B., Carey, J., Karimov, B., Karimov, E., Wagner, B., & Habersack, H. (2023). Integrating Open-Source Datasets to Analyze the Transboundary Water–Food–Energy–Climate Nexus in Central Asia. Water, 15(19). https://doi.org/10.3390/w15193482
de Oliveira-Júnior, J. F., Correia Filho, W. L. F., de Barros Santiago, D., de Gois, G., da Silva Costa, M., da Silva Junior, C. A., Teodoro, P. E. , & Freire, F. M. (2021). Rainfall in Brazilian Northeast via in situ data and CHELSA product: mapping, trends, and socio-environmental implications. Environmental Monitoring and Assessment, 193(5), 1–19. https://doi.org/10.1007/s10661-021-09043-9
Dewes, C. F., Rangwala, I., Barsugli, J. J., Hobbins, M. T., & Kumar, S. (2017). Drought risk assessment under climate change is sensitive to methodological choices for the estimation of evaporative demand. PLoS ONE, 12(3), 1–22. https://doi.org/10.1371/journal.pone.0174045
Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., & Kessler, M. (2017). Climatologies at high resolution for the earth’s land surface areas. Scientific Data, 4, 1–20. https://doi.org/10.1038/sdata.2017.122
Li, X., Wang, L., Chen, D., Yang, K. , & Wang, A. (2014). Seasonal evapotranspiration changes (1983–2006) of four large basins on the tibetan plateau. Journal of Geophysical Research, 119(23), 13,079-13,095. https://doi.org/10.1002/2014JD022380
Masaki, Y., Hanasaki, N., Takahashi, K., & Hijioka, Y. (2015). Propagation of biases in humidity in the estimation of global irrigation water. Earth System Dynamics, 6(2), 461–484. https://doi.org/10.5194/esd-6-461-2015
Mo, X., Liu, S., Lin, Z., & Zhao, W. (2004). Simulating temporal and spatial variation of evapotranspiration over the Lushi basin. Journal of Hydrology, 285(1–4), 125–142. https://doi.org/10.1016/j.jhydrol.2003.08.013
Moorhead, J., Gowda, P., Hobbins, M., Senay, G., Paul, G., Marek, T., & Porter, D. (2015). Accuracy Assessment of NOAA Gridded Daily Reference Evapotranspiration for the Texas High Plains. Journal of the American Water Resources Association, 51(5), 1262–1271. https://doi.org/10.1111/1752-1688.12303
Pereira, L. S., Allen, R. G., Smith, M. , & Raes, D. (2015). Crop evapotranspiration estimation with FAO56: Past and future. Agricultural Water Management, 147, 4–20. https://doi.org/10.1016/j.agwat.2014.07.031
Ruhoff, A., de Andrade, B. C., Laipelt, L., Fleischmann, A. S., Siqueira, V. A., Moreira, A. A., Barbedo, R., Cyganski, G. L., Fernandez, G. M. R., Brêda, J. P. L. F., de Paiva, R. C. D., Meller, A., Teixeira, A. de A., Araújo, A. A., Fuckner, M. A. , & Biggs, T. (2022). Global Evapotranspiration Datasets Assessment Using Water Balance in South America. Remote Sensing, 14(11). https://doi.org/10.3390/rs14112526
Salgado, R. , & Mateos, L. (2021). Evaluation of different methods of estimating ET for the performance assessment of irrigation schemes. Agricultural Water Management, 243, 106450. https://doi.org/10.1016/j.agwat.2020.106450
Santos, C., Lorite, I. J., Tasumi, M., Allen, R. G. , & Fereres, E. (2010). Performance assessment of an irrigation scheme using indicators determined with remote sensing techniques. Irrigation Science, 28(6), 461–477. https://doi.org/10.1007/s00271-010-0207-7
Switanek, B. M., Troch, A. P., Castro, L. C., Leuprecht, A., Chang, H. I., Mukherjee, R., & Demaria, M. C. E. (2017). Scaled distribution mapping: A bias correction method that preserves raw climate model projected changes. Hydrology and Earth System Sciences, 21(6), 2649–2666. https://doi.org/10.5194/hess-21-2649-2017
Temeepattanapongsa, S., & Thepprasit, C. (2015). Comparison and recalibration of equations for estimating reference crop evapotranspiration in Thailand. Kasetsart Journal - Natural Science, 49(5), 772–784.
Teutschbein, C., & Seibert, J. (2012). Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods. Journal of Hydrology, 456–457, 12–29. https://doi.org/10.1016/j.jhydrol.2012.05.052
Trisurat, Y., Alkemade, R. ,& Verburg, P. H. (2010). Projecting land-use change and its consequences for biodiversity in northern thailand. Environmental Management, 45(3), 626–639. https://doi.org/10.1007/s00267-010-9438-x
Ueangsawat, K., Nilsamranchit, S., & Jintrawet, A. (2016). Comparison of Estimation Methods for Daily Reference Evapotranspiration Under Limited Climate Data in Upper Northern Thailand. Environment and Natural Resources, 14(2), 10–23. https://doi.org/10.14456/ennrj.2016.9
Velasquez, P., Messmer, M. ,& Raible, C. C. (2020). A new bias-correction method for precipitation over complex terrain suitable for different climate states: A case study using WRF (version 3.8.1). Geoscientific Model Development, 13(10), 5007–5027. https://doi.org/10.5194/gmd-13-5007-2020
Waller, P. , & Yitayew, M. (2016). Crop Evapotranspiration. In P. Waller & M. Yitayew (Eds.), Irrigation and Drainage Engineering (pp. 89–104). Springer International Publishing. https://doi.org/10.1007/978-3-319-05699-9_6
Wan Zin, W. Z., Jemain, A. A. , & Ibrahim, K. (2009). The best fitting distribution of annual maximum rainfall in Peninsular Malaysia based on methods of L-moment and LQ-moment. Theoretical and Applied Climatology, 96(3–4), 337–344. https://doi.org/10.1007/s00704-008-0044-2
Wu, B., Jiang, L., Yan, N., Perry, C. ,& Zeng, H. (2014). Basin-wide evapotranspiration management: Concept and practical application in Hai Basin, China. Agricultural Water Management, 145, 145–153. https://doi.org/10.1016/j.agwat.2013.09.021
Wu, J., Lakshmi, V., Wang, D., Lin, P., Pan, M., Cai, X., Wood, E. F. , & Zeng, Z. (2020). The reliability of global remote sensing evapotranspiration products over Amazon. Remote Sensing, 12(14). https://doi.org/10.3390/rs12142211
Xu, C., Wang, W., Hu, Y. , & Liu, Y. (2024). Evaluation of ERA5, ERA5-Land, GLDAS-2.1, and GLEAM potential evapotranspiration data over mainland China. Journal of Hydrology: Regional Studies, 51, 101651. https://doi.org/10.1016/j.ejrh.2023.101651
Zhong, L., Chen, B., Wu, C., Yeh, P. J. F., Li, J., Lv, W., Zhao, J. , & Zhou, J. (2022). Identification and risk assessment of flash drought in the Pearl River basin based on the Standardized Evaporative Stress Ratio. Theoretical and Applied Climatology, 150(3–4), 1513–1529. https://doi.org/10.1007/s00704-022-04228-9
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