Articles | Volume 22, issue 11
Cutting-edge case studies
28 Nov 2018
Cutting-edge case studies |  | 28 Nov 2018

Understanding the water cycle over the upper Tarim Basin: retrospecting the estimated discharge bias to atmospheric variables and model structure

Xudong Zhou, Jan Polcher, Tao Yang, Yukiko Hirabayashi, and Trung Nguyen-Quang

Data sets

ORCHIDEE_gmd-2018-57 T. Nguyen-Quang, J. Polcher, A. Ducharne, T. Arsouze, X. Zhou, A. Schneider, and L. Fita

The WATCH Forcing Data 1958-2001: A meteorological forcing dataset for land surface- and hydrological-models G. Weedon, S. Gomes, P. Viterbo, H. Österle, J. Adam, N. Bellouin, O. Boucher, and M. Best

The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA-Interim reanalysis data G. P. Weedon, G. Balsamo, N. Bellouin, S. Gomes, M. J. Best, and P. Viterbo

CRU TS 3.10: Climatic Research Unit (CRU) Time-Series (TS) Version 3.10 of High Resolution Gridded Data of Month-by-month Variation in Climate (Jan. 1901-Dec. 2009) P. D. Jones and I. C. Harris

Monthly gridded surface precipitation at 0.5° in China (V2.0), National Meteorological Information Center, China Y. Zhao, J. Zhu, Y. Xu, and N. Liu

Short summary
Model bias is commonly seen in discharge simulation by hydrological or land surface models. This study tested an approach with the Budyko hypothesis to retrospect the estimated discharge bias to different bias sources including the atmospheric variables and model structure. Results indicate that the bias is most likely caused by the forcing variables, and the forcing bias should firstly be assessed and reduced in order to perform pertinent analysis of the regional water cycle.