Evaluation of Evapotranspiration Estimates in the Yellow River Basin against the Water Balance Method

Abstract

Evapotranspiration (ET), a critical process in global climate change, is very difficult to estimate at regional and basin scales. In this study, we evaluated five ET products: the Global Land Surface Evaporation with the Amsterdam Methodology (GLEAM, the EartH2Observe ensemble (E2O)), the Global Land Data Assimilation System with Noah Land Surface Model-2 (GLDAS), a global ET product at 8 km resolution from Zhang (ZHANG) and a supplemental land surface product of the Modern-ERA Retrospective analysis for Research and Applications (MERRA_land), using the water balance method in the Yellow River Basin, China, including twelve catchments, during the period of 1982–2000. The results showed that these ET products have obvious different performances, in terms of either their magnitude or temporal variations. From the viewpoint of multiple-year averages, the MERRA_land product shows a fairly similar magnitude to the ETw derived from the water balance method, while the E2O product shows significant underestimations. The GLEAM product shows the highest correlation coefficient. From the viewpoint of interannual variations, the ZHANG product performs best in terms of magnitude, while the E2O still shows significant underestimations. However, the E2O product best describes the interannual variations among the five ET products. Further study has indicated that the discrepancies between the ET products in the Yellow River Basin are mainly due to the quality of precipitation forcing data. In addition, most ET products seem to not be sensitive to the downward shortwave radiation.

Impact of amplified evaporation due to lake expansion on the water budget across the inner Tibetan Plateau

“…Among the three ET data sets, the GLEAM and GLDAS/ NOAH data include potential ET estimates for the study period, which have been evaluated to be among the most reliable estimates of global evapotranspiration (Lorenz et al, 2014;McCabe et al, 2016;Wei et al, 2017). However, restricted by the relatively sparse stations and complex terrain conditions of the TP, different data sets generally have inconsistent performance among different basins or/and land cover types (Yang et al, 2017c;Liu, 2018;Liu et al, 2018;Wang et al, 2018a). Since there are a limited number of meteorological stations distributed within or around the studied lake basins (see Figure 3), it is more practical to estimate the entire ITP lake evaporations based on the spatial interpolation (e.g., by the Spline method) of pan evaporation measurements (ETpan).…”

Deterministic and probabilistic projections and their credibility in analyzing future precipitation variations in the Yellow River Basin, China

“…Both in terms of time and space, the precipitation in the YRB is remarkably uneven, with June to October accounting for about 70% of annual total precipitation and the precipitation in the lower and middle reaches two times that of the upper reaches (Mu et al 2013). Over the past 50 or 60 years, with the increase of air temperature, the precipitation of the YRB has fallen, and so too has runoff, exacerbating a profound contradiction between water demand and water supply and worsening the drought situation (Wu et al 2017;Wang et al 2018Wang et al , 2019a.…”

Attributing of global evapotranspiration trends based on the Budyko framework

“…To detect the significance level of these data, we used the non-parametric Mann-Kendall test to determine the significance level of the linear trends (Mann, 1945 andKendall, 1975). Both methods have 20 been widely used in climate change studies (Su et al, 2015;Wang et al, 2018a;Shan et al, 2015;Shi et al, 2016).…”