Measuring and modelling evapotranspiration in a South African grassland : comparison of two improved Penman-Monteith formulations

Gwate, Onalenna ; Mantel, Sukhmani K. ; Palmer, Anthony R. ; Gibson, Lesley A. ; Munch, Zahn (2018)

CITATION: Gwate, O., et al. 2018. Measuring and modelling evapotranspiration in a South African grassland : comparison of two improved Penman-Monteith formulations. Water SA, 44(3):482-492, doi:10.4314/wsa.v44i3.16.

The original publication is available at http://www.wrc.org.za

Article

Accurately measuring evapotranspiration (ET) is important in the context of global atmospheric changes and for use with climate models. Direct ET measurement is costly to apply widely and local calibration and validation of ET models developed elsewhere improves confidence in ET derived from such models. This study sought to compare the performance of the Penman-Monteith-Leuning (PML) and Penman-Monteith-Palmer (PMP) ET models, over mesic grasslands in two study sites in South Africa. The study used routine meteorological data from a scientific-grade automatic weather station (AWS) to apply the PML and PMP models. The PML model was calibrated at one site and validated in both sites. On the other hand, the PMP model does not require calibration and hence it was validated in both sites. The models were validated using ET derived from a large aperture scintillometer (LAS). The PML model performed well at both sites with root mean square error (RMSE) within 20% of the mean daily observed ET (R2 of 0.83 to 0.91). Routine meteorological data were able to reproduce fluxes calculated using micrometeorological techniques and this increased the confidence in the use of data from sparsely distributed AWSs to derive reasonable ET values. The PML model was better able to simulate observed ET compared to the PMP model, since the former models both transpiration and soil evaporation (ES), while the latter only models transpiration. Hence, the PMP model systematically underestimated ET in a context where the leaf area index (LAI) was < 2.5. Model predictions in the grasslands could be improved by incorporating the ES component in the PMP model while the PML model could be improved by careful choice of the number of days to be used in the determination of the fraction of ES.

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