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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/hess-2020-87
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2020-87
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 10 Mar 2020

Submitted as: research article | 10 Mar 2020

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This preprint is currently under review for the journal HESS.

Hierarchical Sensitivity Analysis for Large-scale Process-based Hydrological Modeling with Application in an Amazonian Watershed

Haifan Liu1, Heng Dai2, Jie Niu2, Bill X. Hu2, Han Qiu3, Dongwei Gui4, Ming Ye5, Xingyuan Chen6, Chuanhao Wu2, Jin Zhang2, and William Riley7 Haifan Liu et al.
  • 1School of Water Resources and Environment, China University of Geosciences, Beijing, 100083, China
  • 2Institute of Groundwater and Earth Sciences, Jinan University, Guangzhou 510632, China
  • 3State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
  • 4Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI. USA
  • 5Department of Earth, Ocean, and Atmospheric Science, Florida State University, Tallahassee, FL 32306, USA
  • 6Pacific Northwest National Laboratory, Richland, WA 99352, USA
  • 7Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA

Abstract. Sensitivity analysis is an effective tool for identifying important uncertainty sources and improving model calibration and predictions, especially for integrated systems with heterogeneous parameter inputs and complex process coevolution. In this work, an advanced hierarchical global sensitivity analysis framework, which integrates the concept of variance-based global sensitivity analysis with a hierarchical uncertainty framework, was implemented to quantitatively analyse several uncertainties associated with a three-dimensional, process-based hydrologic model (PAWS). The uncertainty sources considered include model parameters (three vadose zone parameters, two groundwater parameters, and one overland flow parameter), model structure (different thicknesses to represent unconfined and confined aquifer layers) and climate scenarios. We apply the approach to an ~ 9,000 km2 Amazon catchment modeled at 1 km resolution to provide a demonstration of multiple uncertainty source quantification using a large-scale process-based hydrologic model. The sensitivity indices are assessed based on two important hydrologic outputs: evapotranspiration (ET) and groundwater contribution to streamflow (QG). It was found that, in general, parameters, especially the vadose zone parameters, are the most important uncertainty contributors for all sensitivity indices. In addition, the influence of climate scenarios on ET predictions is also nonignorable. Furthermore, the thickness of the aquifers along the river grid cells is important for both ET and QG. These results can assist in model calibration and provide modelers with a better understanding of the general sources of uncertainty in predictions associated with complex hydrological systems in Amazonia. We demonstrated a pilot example of comprehensive global sensitivity analysis of large-scale, complex hydrological and environmental models in this study. The hierarchical sensitivity analysis methodology used is mathematically rigorous and capable of being implemented in a variety of large-scale hydrological models with various sources of uncertainty.

Haifan Liu et al.

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