J
Jing Yang
Researcher at Chinese Academy of Sciences
Publications - 31
Citations - 4083
Jing Yang is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Precipitation & Uncertainty analysis. The author has an hindex of 19, co-authored 30 publications receiving 3272 citations. Previous affiliations of Jing Yang include Swiss Federal Institute of Aquatic Science and Technology & University of Guelph.
Papers
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Modelling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT
Karim C. Abbaspour,Jing Yang,Ivan Maximov,Rosi Siber,Konrad Bogner,Johanna Mieleitner,Juerg Zobrist,Raghavan Srinivasan +7 more
TL;DR: In this paper, the authors used the SWAT (Soil and Water Assessment Tool) to simulate all related processes affecting water quantity, sediment, and nutrient loads in the Thur River basin, which is a direct tributary to the Rhine.
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Comparing uncertainty analysis techniques for a SWAT application to the Chaohe Basin in China
TL;DR: Five uncertainty analysis procedures for watershed models are compared and if computationally feasible, Bayesian-based approaches are most recommendable because of their solid conceptual basis, but construction and test of the likelihood function requires critical attention.
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Comparing bias correction methods in downscaling meteorological variables for a hydrologic impact study in an arid area in China
TL;DR: In this paper, the authors compared five precipitation correction methods and three temperature correction methods in downscaling RCM simulations applied over the Kaidu River basin, one of the headwaters of the Tarim River basin.
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Convergence and uncertainty analyses in Monte-Carlo based sensitivity analysis
TL;DR: Two methods to monitor the convergence and estimate the uncertainty of sensitivity analysis techniques are proposed based on the central limit theorem and the bootstrap technique to assess five differentensitivity analysis techniques applied to an environmental model.
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Hydrological modelling of the Chaohe Basin in China: Statistical model formulation and Bayesian inference
TL;DR: In this paper, the authors developed a procedure to overcome the problem of non-identifiability of distributed parameters by introducing aggregate parameters and using Bayesian inference, and they demonstrated the good performance of this approach to uncertainty analysis, particularly with respect to the fulfilment of statistical assumptions of the error model.