Journal Article
Hydrology and Earth System Sciences, vol. 24, iss. 10, pp. 4971-4996, 2020
Authors
Haifan Liu, Heng Dai, Jie Niu, Bill X. Hu, Dongwei Gui, Han Qiu, Ming Ye, Xingyuan Chen, Chuanhao Wu, Jin Zhang, William Riley
Abstract
Abstract. Sensitivity analysis methods have recently received much attention for identifying important uncertainty sources (or uncertain inputs) and improving
model calibrations and predictions for hydrological models. However, it is still challenging to apply the quantitative and comprehensive global
sensitivity analysis method to complex large-scale process-based hydrological models (PBHMs) because of its variant uncertainty sources and high
computational cost. Therefore, a global sensitivity analysis method that is capable of simultaneously analyzing multiple uncertainty sources of
PBHMs and providing quantitative sensitivity analysis results is still lacking. In an effort to develop a new tool for overcoming these weaknesses,
we improved the hierarchical sensitivity analysis method by defining a new set of sensitivity indices for subdivided parameters. A new binning
method and Latin hypercube sampling (LHS) were implemented for estimating these new sensitivity indices. For test and demonstration purposes, this
improved global sensitivity analysis method was implemented to quantify three different uncertainty sources (parameters, models, and climate
scenarios) of a three-dimensional large-scale process-based hydrologic model (Process-based Adaptive Watershed Simulator, PAWS) with an application case in an ∼ 9000 km2
Amazon catchment. The importance of different uncertainty sources was quantified by sensitivity indices for two hydrologic outputs of interest:
evapotranspiration (ET) and groundwater contribution to streamflow (QG). The results show that the parameters, especially the
vadose zone parameters, are the most important uncertainty contributors for both outputs. In addition, the influence of climate scenarios on
ET predictions is also important. Furthermore, the thickness of the aquifers is important for QG predictions, especially in
main stream areas. These sensitivity analysis results provide useful information for modelers, and our method is mathematically rigorous and can be
applied to other large-scale hydrological models.
