Abstract
Abstract. 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 (−43.2 % to 21.0 %). Meanwhile, the potential evapotranspiration is unrealistically high over the upper Yarkand and the upper Hotan River (1240.4 and 1153.7 mm yr−1, 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 213.50×108 to 300.58×108 m3 yr−1 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.
Highlights
A failure of discharge estimation can happen to a researcher especially when exploring a new region
We proposed an ORCHIDEE-Budyko framework which is used to attribute the modeled discharge bias to different sources as the forcing and model structure
Bias in the precipitation (P ) and any processes related to the potential evapotranspiration (PET) is considered as bias from forcing and bias in any processes affecting the actual evapotranspiration (ET) estimation is considered as bias from model structures
Summary
A failure of discharge estimation can happen to a researcher especially when exploring a new region. It is often attributed to model inapplicability to the region, and tuning the model parameters is a common way to eliminate the discharge bias (Refsgaard, 1997; Westerberg et al, 2011). A hidden assumption is often ignored that the atmospheric variables (or named here forcing) are essentially correct, while it may fail in some regions (Fekete et al, 2004; Adam et al, 2006). Without knowing the bias in forcing, the calibration becomes meaningless if the model parameters are tuned to values that are far from their physical meaning (Hernández and Francés, 2014; Qin et al, 2018a, b). An important step before applying a model to a new region is to understand where the bias sources lie and their relative relations (Renard et al, 2010).
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