Stand-alone uncertainty characterization of GLEAM, GLDAS and MOD16 evapotranspiration products using an extended triple collocation approach

다국어 초록 (Multilingual Abstract)

<P><B>Abstract</B></P> <P>An optimal use of the global scale actual evapotranspiration (AET) products for various hydro-meteorological applications requires a systematic characterization of their uncertainties. This study presents the first application of an extended triple collocation (TC) approach to provide mutually uncorrelated absolute and relative error structure among three readily available AET (MOD16, GLEAM, and GLDAS) products on the point and spatial scale within the extent of Asia. The physical evaluation results of GLEAM, GLDAS and MOD16 exhibited reasonable accuracy compared to the in-situ AET with mean Index of Agreement >0.71, 0.59 and 0.58, respectively, thereby yielding Root Mean Square Error between ∼4–13 mm/8 day over nine AsiaFlux sites representing forest, rice paddy, and grassland biomes. Theoretical uncertainty assessment of four AET dataset combinations revealed that an average ∼1.5–5.5 mm/8 day random error was contributed from in-situ AET, thereby reducing the accuracy of other datasets. GLEAM performed consistently better with least absolute and relative uncertainties over forest compared with rice paddy and grassland surfaces where GLDAS had almost similar errors as those obtained from GLEAM, while MOD16 showed high uncertainties over all vegetation conditions. Interestingly, all four datasets had large relative uncertainties (>25%) for low vegetation compared to the errors of tall canopies. A spatially merged product generated from the least uncertainties showed better agreement in order of GLDAS > GLEAM > MOD16 over 47%, 42% and 11% of the study area. Overall, the application of extended TC approach on the quality of three AET products is a step forward to develop the merged near real-time accurate AET dataset by processing of theoretical and systematic uncertainties in the current AET algorithms.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Evaluated uncertainty of AET products through an extended triple collocation approach. </LI> <LI> GLEAM and GLDAS showed least absolute uncertainties compared to MOD16 dataset. </LI> <LI> GLEAM revealed low errors in forest but performed similarly as to GLDAS in rice paddy. </LI> <LI> A merged AET map was developed with least uncertainties from three selected products. </LI> </UL> </P>