Advances in evapotranspiration prediction using gross primary productivity based on eco‐physiological constraints. Issue 6 (20th June 2022)

• Record Type: Journal Article
• Authors: Nguyen, My Ngoc; Choi, Minha
• Is Part Of: Hydrological processes. Volume 36:Issue 6(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-06-20
• Subjects: diurnal temperature -- eco‐physiological constraints -- evapotranspiration -- gross primary productivity -- Priestley–Taylor -- soil water index; Hydrology -- Periodicals; Hydrology -- Research -- Periodicals; Hydrologic models -- Periodicals; Hydrological forecasting -- Periodicals; 631.432
• DOI: 10.1002/hyp.14628
• Languages: English
• ISSNs: 0885-6087

Abstract

Abstract: Accurate evapotranspiration (ET) estimation plays a central role in better understanding the allocation of water resources in a time of increasing scarcity; however, ET estimation remains many challenges. To enhance the accuracy of ET prediction, this study proposes an enhanced gross primary productivity (GPP)‐based Priestley–Taylor algorithm (GPP‐PT) that uses GPP to compute fractional vegetation cover ( f V ). In terms of soil moisture fraction ( f SM ), it is described by using either diurnal temperature (DT) or soil moisture index (SWI), conducting two variations of the proposed model (i.e., hereafter called GPP‐DT and GPP‐SWI, respectively). These two improved algorithms were compared with their previous models, the GPP‐DT with a modified satellite‐based Priestley–Taylor model (MS‐PT), and the GPP‐SWI with a soil water index (SWI)‐based Priestley–Taylor model (SWI‐PT). Datasets from 42 flux towers covering different land cover types were used to investigate the performance of these algorithms. The GPP‐DT algorithm was found to be superior to the MS‐PT model, with 12.60% and 10.42% reductions in the root mean square error (RMSE) and mean absolute error (MAE), respectively, and with 9.05% and 2.19% increases in the determination coefficient ( R 2 ) and index of agreement (IOA), respectively. Similarly, the GPP‐SWI model yielded RMSE and MAE reductions of 10.95% and 10.67%, respectively, and R 2 and IOA increases of 8.88% and 3.72%, respectively, compared to the SWI‐PT model. In the direct comparison between the two newly proposed models, the GPP‐DT model performed better in shrubland and forest, whereas the GPP‐SWI model performed more efficiently in grassland. Sensitivity analysis found that soil moisture was more sensitive to both evaporation and transpiration than DT in most land cover types, and the GPP had a stable relationship with transpiration in different biomes. The newly improved GPP‐PT models were robust and effective for estimating ET and might thus be used as a reliable input for hydrological models. Abstract : Graphical abstract summarizing concept of the proposed models, study areas, and key findings in this research.