The bias in atmospheric variables and that in model computation are two major causes of failures in discharge estimation. Attributing the bias in discharge estimation becomes difficult if the forcing bias cannot be evaluated and excluded in advance in places lacking qualified meteorological observations, especially in cold and mountainous areas (e.g., the upper Tarim Basin). In this study, we proposed an Organizing Carbon and Hydrology In Dynamic EcosystEms (ORCHIDEE)-Budyko framework which helps identify the bias range from the two sources (i.e., forcing and model structure) with a set of analytical approaches. The latest version of the land surface model ORCHIDEE was used to provide reliable discharge simulations based on the most improved forcing inputs. The Budyko approach was then introduced to attribute the discharge bias to two sources with prescribed assumptions. Results show that, as the forcing biases, the water inputs (rainfall, snowfall or glacier melt) are very likely underestimated for the Tarim headwater catchments span classCombining double low lineinline-formula 43.2 span % to 21.0%). Meanwhile, the potential evapotranspiration is unrealistically high over the upper Yarkand and the upper Hotan River (1240.4 and 1153.7mmyrspan classCombining double low lineinline-formula g'1/span , respectively). Determined by the model structure, the bias in actual evapotranspiration is possible but not the only contributor to the discharge underestimation (overestimated by up to 105.8% for the upper Aksu River). Based on a simple scaling approach, we estimated the water consumption by human intervention ranging from span classCombining double low lineinline-formula 213.50×108/span to span classCombining double low lineinline-formula 300.58×108/span mspan classCombining double low lineinline-formula/span yrspan classCombining double low lineinline-formula g'1/span at the Alar gauge station, which is another bias source in the current version of ORCHIDEE. This study succeeded in retrospecting the bias from the discharge estimation to multiple bias sources of the atmospheric variables and the model structure. The framework provides a unique method for evaluating the regional water cycle and its biases with our current knowledge of observational uncertainties.
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