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

Abstract

Abstract. The bias in atmospheric variables as well as that in model computation are two major causes of failures in discharge estimation. Attributing the bias in discharge estimation becomes difficult if there is a lack of qualified meteorological observations. The problem is more complicated over the mountainous area where strong orographic effects exist and with severe heterogenous geography (e.g. the Upper Tarim basin). In this study, we in the first step improved the forcing inputs using local refined precipitation dataset and glacier simulations over the upper Tarim basin. The discharge bias was then obtained by comparing the estimated discharge from an advanced land surface model (ORCHIDEE) with in-situ discharge observations. We then introduced a framework with Budyko approach which succeeded in attributing the estimated bias to possible biases from major forcing variables and model structure. The possibility of these biases was discussed by referring to many other studies with similar climatic or land surface characteristics. Results show that the water inputs (rainfall, snowfall or glacier melt) are very likely underestimated especially for the headwater catchments of the upper Yarkand and the upper Aksu, with the largest range of 43.2 % and 33.9 % respectively. Meanwhile, the potential evapotranspiration is unrealistically high over the upper Yarkand and the upper Hotan (1240.4 mm/yr and 1153.7 mm/yr respectively). The bias in actual evapotranspiration which is determined by the model strcuture is possible but not the only contributor to the discharge underestimation (overestimated by 76.1 %, 19.1 % and 105.8 % for its three headwater catchments). Based on the discharge simulation and bias analysis, we estimated the water consumption by human intervention ranging from 213.50 × 108 m/yr to 300.58 × 108 m3/yr, which is another bias source in 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, although the framework needs further argumentations about its robustness, it provides a unique method for evaluating the regional water cycle and its biases with our current knowledge of observational uncertainties.

Assessing water distribution and agricultural expansion in the Cele Oasis, China

“…Governing hydrologic processes in the mountains and desert can be described as precipitation-runoff and runoff-evaporation, respectively (Han et al 2009). For the majority of the years from 1960 to 2010 in the Cele Oasis, the annual precipitation is < 50 mm (Cheng 1997;Li et al 2011;Liu et al 2018;Zhou et al 2018) and pan evaporation is in the range of 2100-3400 mm (Fig. 2).…”

Evaluation of ORCHIDEE-MICT-simulated soil moisture over China and impacts of different atmospheric forcing data

“…-atmosphere transfer scheme) module calculates the exchange of water and energy between land and the atmosphere with a highfrequency time interval (half an hour), while the STOMATE (Saclay Toulouse Orsay Model for the Analysis of Terrestrial Ecosystems) module estimates the carbon cycle at the daily timescale. The ORCHIDEE-MICT (aMeliorated Interactions between Carbon and Temperature,SVN version 3952;Guimberteau et al, 2018;Zhu et al, 2015) is a recent version of ORCHIDEE including new processes such as the interactions among frozen soil, snow, plants, and soil carbon pools.…”

Reply to Referee #1

“…Saclay Toulouse Orsay Model for the Analysis of Terrestrial Ecosystems) module estimates the carbon cycle at the daily timescale. The ORCHIDEE-MICT (aMeliorated Interactions between Carbon and Temperature, SVN version 3952;Guimberteau et al, 2018;Zhu et al, 2015) is a recent version of ORCHIDEE including new processes such as the interactions among frozen soil, snow, plants, and soil carbon pools. It accounts for soil freezing, soil carbon discretization, snow processes, and lateral water flows to improve the simulation of the main biogeochemical cycles in permafrost regions.…”