Controlling factors of errors in the predicted annual and monthly evaporation from the Budyko framework. (November 2018)

• Record Type: Journal Article
• Authors: Wu, Chuanhao; Yeh, Pat J.-F.; Hu, Bill X.; Huang, Guoru
• Is Part Of: Advances in water resources. Volume 121(2018)
• Page Start: 432
• Page End: 445
• Publication Date: 2018-11
• Subjects: Evaporation -- Budyko framework -- Prediction error -- Controlling factors; Hydrology -- Periodicals; Hydrodynamics -- Periodicals; Hydraulic engineering -- Periodicals; 551.48
• DOI: 10.1016/j.advwatres.2018.09.013
• Languages: English
• ISSNs: 0309-1708

Abstract

Highlights: An error-decomposition framework is used to assess the errors in BF-predicted E. Dominant factors in prediction errors of E are identified for 14 basins over China. P, PET, R and Δ S contribute comparably to prediction errors of E in humid climate. Δ S dominates prediction errors of E in arid climate. Abstract: The Budyko framework (BF) has been used to predict evaporation ( E ) at annual or monthly time scales, but few studies have analyzed the errors in the predicted E in a systematic manner. This study develops an error-decomposition framework which expresses the errors in the BF-predicted annual and monthly E as a function of (1) the anomalies (i.e. deviations from the long-term mean) of precipitation ( P ), potential evapotranspiration ( PET ), runoff ( R ) and catchment water storage change (Δ S ), (2) the (long-term) mean water storage change, and (3) the mean difference between the predicted and actual E . The error variance of BF-predicted E can be decomposed into the variance and covariance terms of P, PET, R and Δ S . The relative contribution of each of these controlling factors to the total error variance of E are evaluated at 14 major river basins in China with the mean annual aridity index ranging between 0.55 and 11.78. It is found that climatic factors ( P and PET ) and catchment responses ( R and Δ S ) play different roles in the errors of predicted E among diverse climates of 14 basins. Under the humid (energy-limited) condition, the variance and covariance terms of P, PET, R and Δ S are comparably important in the contribution to the prediction error variance of E . In contrast, under the arid (water-limited) condition the error variance of predicted E is dominated by the magnitude of Δ S anomalies. Results of this study suggest that the incorporation of Δ S into BF can improve the predictability of annual and monthly E more under the arid climates than humid climates.