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The Impact of Climate Change and Human Management on the Water Cycle of China: Dealing with Uncertainties

 4 Décembre 2018

Jury members:

Aaron BOONE, Directeur de recherche, Centre National de Recherches
Météorologiques, Rapporteur
Eleanor BLYTH, Directeur de recherche, Centre for Ecology & Hydrology,
Laurent Li, Directeur de recherche, Laboratoire de Météorologie Dynamique,
Philippe CIAIS, Directeur de recherche, Laboratoire des Sciences du Climat
et de l’Environnement, Examinateur
Jan POLCHER, Directeur de recherche, Laboratoire de Météorologie
Dynamique, Directeur de thèse
Tao YANG, Perfesseur, Hohai University, Co-directeur de thèse

Abstract: "Focusing on different uncertainty sources that can affect the
model accuracy of hydrological modeling and impact analysis, this thesis
reviews the past studies and provides new approaches for estimating and
comparing the uncertainties with their applications concentrated over
China. This thesis first proposes a three-dimensional variance partitioning
approach that estimates the uncertainty among multiple precipitation
products with different types. The new estimation uses full information in
temporal and spatial dimensions and thus is a more comprehensive metric for
uncertainty assessment especially for multiple datasets. This thesis then
proposes a ORCHIDEE-Budyko framework that helps attribute the discharge
bias between model simulation (provided by land surface model ORCHIDEE) and
observations to uncertainty sources of atmospheric variables and model
structure. The framework qualifies the possibility of different
uncertainties with physical-based Budyko hypothesis and support of related
literatures. This thesis finally reviews the human activities and their
impact on river discharge over China regions as well as the related
approaches that used for the quantification. The human impact that
quantified as the difference between observed river discharge and the
naturalized ones is then compared with multi-model simulations driven by
different forcing inputs. Results show that the uncertainty in atmospheric
variables (e.g., precipitation) is large especially for General Circulation
Models (GCMs). Precipitation uncertainty is very likely larger than that of
the model uncertainty. The uncertainty in the modeled discharge with
different forcing is larger than the magnitude of human impact for most of
the regions especially in south China, which impedes the credibility of
human impact quantification for those regions. This understanding of
uncertainties in the natural water cycle and the management humans impose
on it is a prerequisite before attempting to model the anthropogenic